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An investigation is underway regarding the usefulness of altazimuth-mounting telescopes' incorporation of laser gyros for pointing and fiber gyros with extremely small random-walk coefficient for telescope inertial stabilization during tracking. A star tracker is expected to help stabilize long-term gyro bias. Gyro and telescope specifications have been derived by means of computer simulations and systems analyses.
Artificial intelligence (AI), and in particular machine learning algorithms, are of increasing importance in many application areas but interpretability and understandability as well as responsibility, accountability, and fairness of the algorithms' results, all crucial for increasing the humans' trust into the systems, are still largely missing. Big industrial players, including Google, Microsoft, and Apple, have become aware of this gap and recently published their own guidelines for the use of AI in order to promote fairness, trust, interpretability, and other goals. Interactive visualization is one of the technologies that may help to increase trust in AI systems. During the seminar, we discussed the requirements for trustworthy AI systems as well as the technological possibilities provided by interactive visualizations to increase human trust in AI.
Der True-Crime-Podcast erfreut sich in Deutschland einer großen Hörerschaft, die ganz unterschiedliche Nutzungsmotivationen mitbringt. Die Faszination für wahre Kriminalfälle kommt nicht von ungefähr – das Phänomen, welches dieser zugrunde liegt, ist die Morbid Curiosity. Da bislang noch nicht allzu viel Forschung in diesem Gebiet betrieben wurde, gibt diese Arbeit durch einen Vergleich verschiedener Untersuchungen Aufschluss darüber, wo diese Neugier für morbide Ereignisse herkommt. Gleichzeitig soll ein Überblick über das Genre True Crime und das Medium Podcast gegeben werden. Das Ziel ist es, eine Verbindung zwischen dem Phänomen Morbid Curiosity und dem Format True-Crime-Podcast herzustellen. Die Forschungsfrage lautet: Welche Rolle spielt Morbid Curiosity bei der Nutzungsmotivation der Konsumenten von True-Crime-Podcasts? Die Ergebnisse zeigen, dass Frauen andere Nutzungsmotivationen mitbringen als Männer und gleichzeitig die primären Konsumentinnen sind. Die Neugier für morbide Ereignisse spielt dabei durchaus eine Rolle, wenn auch scheinbar eine untergeordnete. Es werden Ansätze für weitere Forschung gegeben.
Digitalisierung ist heute allgegenwärtig. Wo im Privaten bereitwillig neue digitale Tools, Apps und Funktionen genutzt werden, tun sich Unternehmen in der Umsetzung von Digitalisierungsprojekten oft schwer. Dieser Beitrag beleuchtet die Motive für Digitalisierungsvorhaben, ihre Hürden sowie die Auswirkung auf die Arbeitsbelastung von Mitarbeitenden und versucht in der Verknüpfung mit den Grundprinzipien des Kontinuierlichen Verbesserungsprozesses, Handlungsempfehlungen für eine erfolgreiche Umsetzung dieser abzuleiten.
Projektmanagement und mit ihm die PM-Prozesse, Methoden und Werkzeuge entwickeln sich stetig weiter, in kleinen, kaum spürbaren Schritten oder in großen unübersehbaren Veränderungen. In den letzten Jahren war der Diskurs über das Pro & Contra agiler Vorgehensweisen so allgegenwärtig, dass andere Aspekte nicht immer die notwendige Aufmerksamkeit bekamen. Erkannte Notwendigkeiten der PM-Entwicklung konnten noch nicht in spürbare Fortschritte umgewandelt werden. Einflüsse der Globalisierung und der IT, aber auch die aus der zunehmenden Forderung nach Nachhaltigkeit resultierenden Veränderungen in der Projektarbeit sollen daher genauer betrachtet werden. Ist erst einmal die Sensibilität für relevante Trends beim Projektpersonal geschaffen, rücken ein aktualisiertes Kompetenzprofil und ein erweiterter Methodenkanon in greifbare Nähe.
Das Zeitalter der Digitalisierung ist geprägt durch einen erhöhten Wettbewerb. Eine Chance, bei steigendem Wettbewerb erfolgreich zu bestehen, liegt daher nur in der durchgängigen Digitalisierung von Produktionsunternehmen. Dieser Beitrag stellt eine dreistufige generische Unternehmensmodellplattform Industrie 4.0 vor, die die Durchgängigkeit von Prozessen vom Kunden bis zum Lieferanten auf allen Unternehmensebenen in den Mittelpunkt stellt. Die Schritte zur Bewertung und Gestaltung des Fortschritts auf dem Weg zum digitalisierten Produktionsunternehmen werden aufgezeigt.
Die fortschreitende Digitalisierung der Schulen macht es möglich, die Lerndaten der Schülerinnen und Schüler in einer zentralen Cloud zu speichern. Die Befürworter versprechen sich davon eine bessere individuelle Förderung und fordern eine bundesweite Lösung, um möglichst viele Daten auswerten zu können. Die Gegner befürchten eine automatisierte Steuerung des Lernens.
Social-Media-Content - Auswirkungen auf Fear of Missing Out und den Selbstwert junger Nutzer*innen
(2023)
Social-Media-Marketing ist ein wichtiger Baustein einer erfolgreichen Content-Strategie. Insbesondere jüngere Zielgruppen sind auf Social Media anzutreffen – und das oftmals über viele Stunden täglich. Neben den Vorteilen, die Social Media den Nutzer*innen bietet, gibt es aber auch Schattenseiten. Zwei negative Aspekte, die sogenannte Fear of Missing Out und ein verminderter Selbstwert, wurden im Frühjahr 2022 in einer empirischen Befragung von 1338 Personen zwischen 14 und 30 Jahren untersucht. Daneben wurden auch Daten zum grundsätzlichen Social-Media-Nutzungsverhalten erhoben. Die zentralen Erkenntnisse, die sich aus der Studie ableiten, werden in diesem Kapitel vorgestellt und mit Bezug auf ihre Relevanz für das Content-Marketing hin eingeordnet.
Die Verwendung von markenbezogenen nutzer-generierten Inhalten auf den unternehmenseigenen Social-Media-Kanälen ist ein äußerst vielversprechender Ansatz im Content-Marketing. Dabei können durch die authentischen, vom Nutzer bereitgestellten Inhalte zahlreiche Kommunikationsziele erreicht werden. Hierzu gehören etwa die Verstärkung des Nutzerengagements oder aber auch die Förderung von Verkäufen. Daneben müssen allerdings auch Risiken, wie etwa rechtliche Aspekte, beachtet werden. Damit Unternehmen die Potentiale von markenbezogenen nutzer-generierten Inhalten für sich nutzen können, wird im nachstehenden Beitrag ein Strukturierungsrahmen vorgestellt. Dieser fasst die wesentlichen Aspekte dieser durchaus komplexen Thematik strukturiert zusammen. Der hier entwickelte Strukturierungsrahmen wurde durch Experteninterviews überprüft.
In diesem Beitrag werden die psychologischen Hintergründe und Wirkungsweisen des Content-Marketing betrachtet. Nach einer kurzen Einführung in die Thematik wird zuerst das für das weitere Verständnis notwendige psychologische Basiswissen vermittelt. Darauf bezugnehmend wird die allgemeine Wirkungsweise von Content-Marketing beleuchtet. Die Sichtweise wird dann für die letzten beiden Kapitel umgedreht und die beschriebenen psychologischen Faktoren dazu genutzt, um Anwender bei der Wahl der Content-Marketing-Inhalte und zuletzt bei der konkreten Ausgestaltung zu unterstützen.
Die meisten Effekte, die durch Content-Marketing hervorgerufen werden, funktionieren im B2C- oder B2B-Bereich durch das Ansprechen von Bedürfnissen, Interessen und Emotionen sowie die recht freien Entscheidungsmöglichkeiten der Adressaten. Im B2B‑Bereich werden ebenfalls Menschen mit Bedürfnissen, Interessen und Emotionen angesprochen, jedoch vorrangig beruflicher Natur, sodass in der Ausgestaltung geringfügige Unterschiede gemacht werden müssen.
Verfassen guter Texte
(2023)
Wer Texte für seinen Internetauftritt schreibt, möchte, dass diese auch gelesen werden. Doch Lesende sind ungeduldig, insbesondere am Monitor. Fasziniert man sie nicht in den ersten Sekunden, springen sie ab. Erfahren Sie hier, welche stilistischen Regeln Journalistinnen und Journalisten nutzen, um die Aufmerksamkeit ihrer Leser- oder Hörerschaft zu gewinnen und Texte mit wenig Aufwand zu perfektionieren. Ein paar Besonderheiten gelten auch für den Aufbau. Ein Schwerpunkt des Kapitels liegt auf dem Teaser, den ersten Zeilen, die in den Text locken sollen, sowie der Headline. Häufig ist es jedoch nicht der Text, der die Aufmerksamkeit der User fesselt, sondern ein Foto, idealerweise mit einer informativen Bildunterschrift. Zahlreiche Beispiele aus dem journalistischen Alltag machen das Beschriebene anschaulich. Als Zugabe informiert die Autorin Sie über die Bedeutung des Nutzwerts und attraktive Anlässe für eine Veröffentlichung.
Vor dem Hintergrund einer zunehmenden Informations- und Reizüberlastung der Konsumenten werden aus Unternehmenssicht zielgruppenadäquate Inhalte, insbesondere zur Erreichung von kommunikationspolitischen Zielsetzungen, immer wichtiger. Um diese zu gewährleisten, bedarf es einer sinnvollen Planung, Produktion und Distribution von Inhalten. Der vorliegende Beitrag gibt einen Überblick über einen solchen Prozess und veranschaulicht die notwendigen Schritte für ein erfolgreiches Content-Marketing.
Content-Marketing
(2023)
Content-Marketing, also die Planung, Produktion und Distribution von zielgruppen-adäquaten Inhalten, hat insbesondere durch Social Media nochmals an Bedeutung gewonnen. Im Hinblick auf die enorme Menge an Inhalten, die auf Nutzer konstant einwirken, ist es für Unternehmen immer schwieriger, die Aufmerksamkeit der Nutzer zu gewinnen. Nur Inhalte, die den Wünschen der Nutzer entsprechen und diesen in irgendeiner Form einen Mehrwert bieten, haben die Chance, zur Erfüllung von Kommunikationszielen von Unternehmen beizutragen. Die Bereitstellung derartiger Inhalte setzt einen sinnvollen (Planungs-)Prozess voraus. Das vorliegende Buch bietet Praktikern und Studierenden einen Überblick über die verschiedenen Bereiche eines Content-Marketing.
The invention concerns a method for spectrum monitoring a given frequency band, in which the spectral power density (S(f)) within the given frequency band is determined for all noise and signal components in the frequency band and, in order to detect the presence of one or more signals within the given frequency band, it is evaluated whether the spectral power density (S(f)) exceeds a threshold value (&lgr;). According to the invention, the threshold value (&lgr;) is calculated in accordance with an estimation of a distribution density (hR(S)) for the noise component of the spectral power density (S(f)) within the given frequency band and in accordance with a predefined value for the false-alarm probability (Pfa).
The invention relates to a container (1) for a liquid medium (3), in particular a blood bag, comprising a flexible outer wall (5) and a device (13) connected to the container (1) for acquiring and/or storing data. According to the invention, the device (13) for acquiring and/or storing data is arranged within the flexible outer wall (5), wherein positioning means (15) are provided which hold the device (13) for acquiring and/or storing data in a floating manner in the liquid medium when the container (1) is filled with said liquid medium (3), and wherein the device (13) or the device (13) and the positioning means (15) are designed such that the mass of liquid medium (3) which is displaced in each case is essentially equal to the mass of the device (13) or to the mass of the device (13) and the positioning means (15).
A wet-chemical treatment system for electrochemically coating flat substrates with coating material, has having a basin for receiving an electrolyte, a transporting means, by means of which the flat substrates can be transported through the electrolyte horizontally, and at least one contact element which comprises a shaft having an axis of rotation and a cylindrical circumferential surface suitable for rolling on the substrate, wherein the circumferential surface comprises at least one electrically insulated segment and at least one electrically conductive segment which can be connected to a current source in such a way that the polarity can be reversed, wherein the axis of rotation of the contact element is positioned above the surface of the electrolyte, and wherein the contact element is designed as a consumable electrode.
본 발명은 기판들을 금속화하기 위한 디바이스에 관한 것이다. 특별히, 본 발명은 습식-화학물질 연속 (인-라인) 처리 시스템 환경에서 솔라 셀들을 전기도금하기 위해 사용되는 컨택 엘리먼트(contact element)들의 분야에 관한 것이다. 코팅 재료로 평평한 기판들(1)들을 전기화학적으로 코팅하기 위한 본 발명에 따른 습식-화학물질 처리 시스템은 전해질 용액, 이송 수단들 - 이송 수단들을 이용하여 평평한 기판(1)이 전해질 용액을 통과하여 수평으로 이송될 수 있는 - 을 수용하는 용기(basin), 및 회전축(5)을 갖는 샤프트(4) 및 기판(1) 위에서 구르기에 적합한 원통형의 원주 표면을 포함하는 적어도 하나의 컨택 엘리먼트(2)를 가지며, 원주 표면은 극성이 역전될 수 있는 방식으로 전류 소스 (6)에 연결될 수 있는 적어도 하나의 전기적으로 전도성인 세그먼트 (3A) 및 적어도 하나의 전기적으로 절연된 세그먼트 (3B)를 포함하고, 컨택 엘리먼트 (2)의 회전축 (5)은 전해질 용액의 표면 위에 위치되고, 컨택 엘리먼트 (2)는 소모 전극(consumable electrode)으로 디자인된다.
The invention relates to a device for metalising substrates. In particular, the invention relates to the field of contact elements used to electroplate solar cells within the context of a wet-chemical continuous treatment system. A wet-chemical treatment system according to the invention, for electrochemically coating flat substrates (1) with coating material, has a tank for accommodating an electrolyte, transporting means, by means of which the flat substrates (1) can be transported through the electrolyte horizontally, and at least one contact element (2), which comprises a shaft (4) having an axis of rotation (5) and a cylindrical circumferential surface suitable for rolling on the substrate (1), wherein the circumferential surface comprises at least one electrically insulating segment (3B) and at least one electrically conductive segment (3A), which can be connected to a current source (6) in such a way that the polarity can be reversed, wherein the axis of rotation (5) of the contact element (2) is positioned above the surface of the electrolyte, and wherein the contact element (2) is designed as a consumable electrode.
Nasschemische Behandlungsanlage zum elektrochemischen Beschichten von flachen Substraten (1) mit Beschichtungsmaterial, mit einem Becken zur Aufnahme eines Elektrolyten, und mit Transportmitteln, mit welchen die flachen Substrate (1) horizontal durch den Elektrolyten transportierbar sind, sowie mit mindestens einem Kontaktelement (2), welches eine Welle (4) mit Drehachse (5) und eine zum Abrollen auf dem Substrat (1) geeignete zylindrische Umfangsfläche aufweist, wobei die Umfangsfläche mindestens ein elektrisch isoliertes Segment (3B) und mindestens ein elektrisch leitendes Segment (3A) umfasst, das mit einer Stromquelle (6) umpolbar verbindbar ist, wobei die Drehachse (5) des Kontaktelements (2) oberhalb der Oberfläche des Elektrolyten positioniert ist, und wobei das Kontaktelement (2) als Verbrauchselektrode ausgestaltet ist.
Die Erfindung betrifft eine Vorrichtung zum Metallisieren von Substraten. Insbesondere betrifft die Erfindung das Gebiet der zur Galvanisierung von Solarzellen verwendeten Kontaktelemente im Rahmen einer nasschemischen Durchlauf-Behandlungsanlage. Eine erfindungsgemäße nasschemische Behandlungsanlage zum elektrochemischen Beschichten von flachen Substraten (1) mit Beschichtungsmaterial hat ein Becken zur Aufnahme eines Elektrolyten, sowie Transportmittel, mit welchen die flachen Substrate (1) horizontal durch den Elektrolyten transportierbar sind, und mindestens ein Kontaktelement (2), welches eine Welle (4) mit Drehachse (5) und eine zum Abrollen auf dem Substrat (1) geeignete zylindrische Umfangsfläche aufweist, wobei die Umfangsfläche mindestens ein elektrisch isoliertes Segment (3B) und mindestens ein elektrisch leitendes Segment (3A) umfasst, das mit einer Stromquelle (6) umpolbar verbindbar ist, wobei die Drehachse (5) des Kontaktelements (2) oberhalb der Oberfläche des Elektrolyten positioniert ist.
Die Erfindung betrifft das Gebiet des Transports flacher Substrate wie beispielsweise Siliziumsubstrate. Insbesondere betrifft die Erfindung den besonders schonenden und kontinuierlichen Transport solcher Substrate. Das erfindungsgemäße Verfahren dient dem Transport eines vertikal ausgerichteten flachen Substrats (1) in eine Transportrichtung innerhalb eines mit einem flüssigen Medium (F) gefüllten Transportkanals (2), wobei das flüssige Medium (F) gegen mindestens eine der Flachseiten des Substrats (1) strömt und eine die Summe aus Gewichts- und Auftriebskraft des Substrats (1) aufhebende Tragekomponente, sowie eine in Transportrichtung gerichtete Vorschubkomponente aufweist, so dass das Substrat (1) ohne mechanische Hilfsmittel getragen und transportiert wird. Die erfindungsgemäße Vorrichtung umfasst einen Transportkanal (2) zur Aufnahme eines flüssiges Mediums (F) sowie eines innerhalb dieses Mediums (F) in vertikaler Ausrichtung zu führenden Substrats (1), wobei der Transportkanal (2) in seinem Wandbereich (3, 4) Einströmöffnungen (5) aufweist.
Die Erfindung betrifft das Gebiet des Transports flacher Substrate wie beispielsweise Siliziumsubstrate. Insbesondere betrifft die Erfindung den besonders schonenden und kontinuierlichen Transport solcher Substrate. Das erfindungsgemäße Verfahren dient dem Transport eines vertikal ausgerichteten flachen Substrats (1) in eine Transportrichtung innerhalb eines mit einem flüssigen Medium (F) gefüllten Transportkanals (2), wobei das flüssige Medium (F) gegen mindestens eine der Flachseiten des Substrats (1) strömt und eine die Summe aus Gewichts- und Auftriebskraft des Substrats (1) aufhebende Tragekomponente, sowie eine in Transportrichtung gerichtete Vorschubkomponente aufweist, so dass das Substrat (1) ohne mechanische Hilfsmittel getragen und transportiert wird. Die erfindungsgemäße Vorrichtung umfasst einen Transportkanal (2) zur Aufnahme eines flüssiges Mediums (F) sowie eines innerhalb dieses Mediums (F) in vertikaler Ausrichtung zu führenden Substrats (1), wobei der Transportkanal (2) in seinem Wandbereich (3, 4) Einströmöffnungen (5) aufweist.
The invention relates to a method for determining properties of a pipeline, more particularly the position of a branch in a waste water pipeline, in which: a sound wave transmission signal (S, S') is fed into the pipeline (1) at a predetermined infeed point and propagates in the axial direction of the pipeline (1), wherein the frequency spectrum of the sound wave transmission signal (S, S') has a frequency component or a spectral range, the maximum frequency of which is lower than the lower limit frequency (fc) for the first upper mode; in which method components (Sr1, Sr2, Sr3, S'r1, S'r2, S'r3) of the sound wave transmission signal (S, S') reflected inside the pipeline (1) are detected as a sound wave reception signal (E, E'); and in which method, by evaluating the sound wave reception signal (E, E') in relation to the sound wave transmission signal (S, S'), the pipeline (1) is examined for the presence of reflection sites along the pipeline (1) that cause sound wave reflections (Sr1, Sr2, Sr3, S'r1, S'r2, S'r3), wherein at least the distance (I) of a reflection site from the infeed point is determined by evaluating the respective sound wave reception signal (E, E'). The invention further relates to a device for implementing said method.
Verfahren zur Bestimmung von Eigenschaften einer Rohrleitung, insbesondere der Position eines Abzweigs einer Abwasserrohrleitung,(a) bei dem ein Schallwellensendesignal (S, S') an einem vorgegebenen Einspeisepunkt in die Rohrleitung (1) eingespeist wird und sich in axialer Richtung der Rohrleitung (1) ausbreitet,(b) wobei das Frequenzspektrum des Schallwellensendesignals (S, S') eine Frequenzkomponente oder einen Spektralbereich aufweist, dessen maximale Frequenz kleiner ist als die untere Grenzfrequenz (f) für die erste Obermode,(c) bei dem innerhalb der Rohrleitung (1) reflektierte Anteile (S, S, S, S', S', S') des Schallwellensendesignals (S, S') als Schallwellenempfangssignal (E, E') detektiert werden, und(d) bei dem die Rohrleitung (1) durch eine Auswertung des Schallwellenempfangssignal (E, E') in Bezug auf das Schallwellensendesignal (S, S') hinsichtlich des Vorhandenseins von Schallwellenreflexionen (S, S, S, S', S', S') verursachenden Reflexionsorten entlang der Rohrleitung (1) untersucht wird,(e) wobei mittels der Auswertung des Schallwellenempfangssignals (E, E') zumindest jeweils der Abstand (I) eines Reflexionsortes von dem Einspeisepunkt bestimmt wird, dadurch gekennzeichnet,(f) dass die Schallgeschwindigkeit (c) der Grundmode bei der aktuellen Temperatur innerhalb der Rohrleitung (1) unter Verwendung eines Schallwellenmesssignals ermittelt wird, welches eine Frequenz oder ein Frequenzspektrum aufweist, bei dem das Schallwellenmesssignal innerhalb der Rohrleitung (1) mit ausreichender Genauigkeit als ebene Schallwelle behandelt werden kann, wobei hierzu die Laufzeiten des Schallwellenmesssignals über eine vorbekannte Strecke (L) in beiden Richtungen gemessen wird,(g) dass die so ermittelte Schallgeschwindigkeit (c) einer ebenen Schallwelle gleich der tatsächlichen Schallgeschwindigkeit der Grundmode bei der aktuellen Temperatur innerhalb der Rohrleitung (1) gesetzt wird, und(h) dass die so bestimmte Schallgeschwindigkeit zur Bestimmung des Abstand (I) eines Reflexionsortes von dem Einspeisepunkt verwendet wird.
Verfahren zur Bestimmung von Eigenschaften einer Rohrleitung, insbesondere der Position eines Abzweigs einer Abwasserrohrleitung, (a) bei dem ein Schallwellensendesignal (S, S') an einem vorgegebenen Einspeisepunkt in die Rohrleitung (1) eingespeist wird und sich in axialer Richtung der Rohrleitung (1) ausbreitet, (b) wobei das Frequenzspektrum des Schallwellensendesignals (S, S') eine Frequenzkomponente oder einen Spektralbereich aufweist, dessen maximale Frequenz kleiner ist als die untere Grenzfrequenz (fc) für die erste Obermode, (c) bei dem innerhalb der Rohrleitung (1) reflektierte Anteile (Sr1, Sr2, Sr3, S'r1, S'r2, S'r3) des Schallwellensendesignals (S, S') als Schallwellenempfangssignal (E, E') detektiert werden, und (d) bei dem die Rohrleitung (1) durch eine Auswertung des Schallwellenempfangssignal (E, E') in Bezug auf das Schallwellensendesignal (S, S') hinsichtlich des Vorhandenseins von Schallwellenreflexionen (Sr1, Sr2, Sr3, S'r1, S'r2, S'r3) verursachenden Reflexionsorten entlang der Rohrleitung (1) untersucht wird, (e) wobei mittels der Auswertung des Schallwellenempfangssignals (E, E') zumindest jeweils der Abstand (I) eines Reflexionsortes von dem Einspeisepunkt bestimmt wird.
Die Erfindung betrifft ein Verfahren zur Bestimmung von Eigenschaften einer Rohrleitung, insbesondere der Position eines Abzweigs einer Abwasserrohrleitung, bei dem ein Schallwellensendesignal (S, S‘) an einem vorgegebenen Einspeisepunkt in die Rohrleitung (1) eingespeist wird und sich in axialer Richtung der Rohrleitung (1) ausbreitet, wobei das Frequenzspektrum des Schallwellensendesignals (S, S‘) eine Frequenzkomponente oder einen Spektralbereich aufweist, dessen maximale Frequenz kleiner ist als die untere Grenzfrequenz (fc) für die erste Obermode, bei dem innerhalb der Rohrleitung (1) reflektierte Anteile (Sr1, Sr2, Sr3, S’r1, S’r2, S’r3) des Schallwellensendesignals (S, S‘) als Schallwellenempfangssignal (E, E‘) detektiert werden, und bei dem die Rohrleitung (1) durch eine Auswertung des Schallwellenempfangssignals (E, E‘) in Bezug auf das Schallwellensendesignal (S, S‘) hinsichtlich des Vorhandenseins von Schallwellenreflexionen (Sr1, Sr2, Sr3, S’r1, S’r2, S’r3) verursachenden Reflexionsorten entlang der Rohrleitung (1) untersucht wird, wobei mittels der Auswertung des Schallwellenempfangssignals (E, E‘) zumindest jeweils der Abstand (l) eines Reflexionsortes von dem Einspeisepunkt bestimmt wird. Des Weiteren betrifft die Erfindung eine Vorrichtung zur Realisierung des Verfahrens.
Die Erfindung betrifft einen elektromotorischen Aktor, insbesondere für einen mobilen Roboter, mit einem geschalteten, zylindrischen Linearreluktanzmotor-Antrieb (21), bestehend aus einem hohlzylindrischen Stator (23), welcher am Innenumfang zwischen umlaufenden Statorzähnen (23a) vorgesehene umlaufende Nuten (23b) aufweist, in welchen Motorwicklungen (27a, 27b, 27c, 27’a, 27’b, 27’c) angeordnet sind, und einem innerhalb des Stators (23) koaxial vorgesehenen zylindrischen oder hohlzylindrischen Translator (25) mit am Außenumfang vorgesehenen, umlaufenden Translatorzähnen (25a), wobei der Stator (23) im Bereich der Statorzähne (23a) und der Translator (25) im Bereich der Translatorzähne (25a) aus einem ferromagnetischen Material bestehen. Erfindungsgemäß ist der Translator (25) über eine mechanische Vorrichtung zur Speicherung und Abgabe von kinetischer Energie (31) fest oder nur für einen vorbestimmten Bereich eines Bewegungsbereichs des Translators (25) mit dem Stator (25) gekoppelt.
Die Erfindung betrifft ein Behältnis (1) für ein flüssiges Medium (3), insbesondere Blutbeutel, mit einer flexiblen Außenwandung (5) und einer mit dem Behältnis (1) verbundenen Vorrichtung (13) zur Erfassung und/oder Speicherung von Daten. Erfindungsgemäß ist die Vorrichtung (13) zur Erfassung und/oder Speicherung von Daten innerhalb der flexiblen Außenwandung (5) angeordnet, wobei Positionierungsmittel (15) vorgesehen sind, welche die Vorrichtung (13) zur Erfassung und/oder Speicherung von Daten bei mit dem flüssigen Medium (3) gefüllten Behältnis (1) schwimmend im flüssigen Medium (3) halten, und wobei die Vorrichtung (13) oder die Vorrichtung (13) und die Positionierungsmittel (15) so ausgebildet sind, dass die jeweils verdrängte Masse von flüssigem Medium (3) im Wesentlichen gleich der Masse der Vorrichtung (13) oder der Masse der Vorrichtung (13) und der Positionierungsmittel (15) ist.
Die Erfindung betrifft ein Behältnis (1) für ein flüssiges Medium (3), insbesondere Blutbeutel, mit einer flexiblen Außenwandung (5) und einer mit dem Behältnis (1) verbundenen Vorrichtung (13) zur Erfassung und/oder Speicherung von Daten. Erfindungsgemäß ist die Vorrichtung (13) zur Erfassung und/oder Speicherung von Daten innerhalb der flexiblen Außenwandung (5) angeordnet, wobei Positionierungsmittel (15) vorgesehen sind, welche die Vorrichtung (13) zur Erfassung und/oder Speicherung von Daten bei mit dem flüssigen Medium (3) gefüllten Behältnis (1) schwimmend im flüssigen Medium (3) halten, und wobei die Vorrichtung (13) oder die Vorrichtung (13) und die Positionierungsmittel (15) so ausgebildet sind, dass die jeweils verdrängte Masse von flüssigem Medium (3) im Wesentlichen gleich der Masse der Vorrichtung (13) oder der Masse der Vorrichtung (13) und der Positionierungsmittel (15) ist.
Messschraube zur Ermittlung von Schraubenbelastungen, die einen Schraubenkopf (8) und einen Schraubenschaft (9) mit einem oberen Schaftbereich (7) aufweist, der bei bestimmungsgemäßer Benutzung der Schraube nicht an einem Objekt anliegt, wobei mindestens zwei Dehnungsmesssensoren so im Schraubenschaft (9) angeordnet und dehnungskinematisch mit dem Schraubenschaft (9) gekoppelt sind, dass sie Dehnungswerte im Schraubenschaft (9) erfassen, aus denen über konstitutive Materialgesetze Belastungen in mehr als einer Achse im oberen Schaftbereich (7) bestimmt werden können, wobei die Dehnungsmesssensoren als faseroptische Sensoren an mehreren Stellen entlang mindestens einer optischen Faser (1) ausgebildet sind.
Die vorliegende Erfindung betrifft eine Messchraube zur Ermittlung von Schraubenbelastungen sowie ein Verfahren zu deren Herstellung. Die Messschraube umfasst mindestens zwei Dehnungsmesssensoren, die so im Schraubenschaft angeordnet und dehnungskinematisch mit dem Schraubenschaft gekoppelt sind, dass sie Dehnungswerte im Schraubenschaft erfassen, aus denenüber konstitutive Materialgesetze Belastungen in mehr als einer Achse im oberen freien Schaftbereich bestimmt werden können. Durch Möglichkeit der Bestimmung mehrachsiger Belastungen lassen sich Schrauben sachgerechter auslegen, so dass die Gefahr von Schraubenbrüchen reduziert wird.
Elektronische Pille zur steuerbaren Abgabe einer Substanz in einem menschlichen oder tierischen Körper, (a) mit einem Gehäuse (3), in welchem die abzugebende Substanz (17) aufgenommen und in welchem eine Abgabeöffnung (47) zur Abgabe der Substanz (17) vorgesehen ist, wobei die Substanz (17) zur Abgabe aus dem Gehäuse (3) mit einem vorbestimmten Druck beaufschlagt ist, (b) mit einer elektronischen Steuereinheit (53, 59, 61, 63), und (c) mit einer im Verlauf eines Abgabepfades angeordneten Ventileinheit (33), welche von der Steuereinheit (53, 59, 61, 63) von einer Geschlossen- Stellung in eine Geöffnet-Stellung steuerbar ist und umgekehrt, (d) dadurch gekennzeichnet, (e) dass die Ventileinheit (33) eine von der elektronischen Steuereinheit (53, 59, 61, 63) ansteuerbare Heizeinheit (49) umfasst, (f) dass die Ventileinheit (33) ein Schaltelement (39) umfasst, welches in der Geschlossen-Stellung zumindest mit einem Teilbereich dichtend an einem Ventilsitz (37) der Ventileinheit (33) anliegt und welches von der Heizeinheit (49) beheizbar ist, (g) dass das Schaltelement (39) zwei temperaturabhängige stabile Schaltstellungen aufweist, welche durch eine Formänderung des Schaltelements (39) definiert sind, und (h) die Heizeinheit (49) an der dem Ventilsitz (37) abgewandten Seite des Schaltelements (39) angeordnet und integriert mit dem Schaltelement (39) ausgebildet ist (i) wobei die Steuereinheit (53, 59, 61, 63) zum Öffnen der Ventileinheit (33) die Heizeinheit (49) aktiviert, so dass das Schaltelement (39) bei Überschreiten einer Schwellentemperatur aus der Geschlossen-Stellung in die Geöffnet-Stellung geschaltet wird und (j) wobei die Steuereinheit (53, 59, 61, 63) zum Schließen der Ventileinheit (33) die Heizeinheit (49) deaktiviert, so dass das Schaltelement bei Unterschreiten einer Schwellentemperatur aus der Geöffnet-Stellung in die Geschlossen-Stellung schaltet.
Die Erfindung betrifft eine elektronische Pille zur steuerbaren Abgabe einer Substanz, insbesondere eines Medikaments, in einem menschlichen oder tierischen Körper, mit einem Gehäuse (3), in welchem die abzugebende Substanz (17) aufgenommen und in welchem eine Abgabeöffnung (47) zur Abgabe der Substanz (17) vorgesehen ist, wobei die Substanz (17) zur Abgabe aus dem Gehäuse (3) mit einem vorbestimmten Druck beaufschlagbar ist, mit einer elektronischen Steuereinheit (53, 59, 61, 63) und mit einer im Verlauf eines Abgabepfades angeordneten Ventileinheit (33), welche von der Steuereinheit (53, 59, 61, 63) von einer Geöffnet-Stellung in eine Geschlossen-Stellung steuerbar ist. Erfindungsgemäß ist im Gehäuse (3) im Verlauf des Abgabepfades für die abzugebende Substanz (17) eine Drosselstrecke (45) vorgesehen.
The application relates to an electronic pill for dispensing a substance, in particular a drug, in a human or animal body in a controllable manner, said electronic pill having a housing (3) in which the substance (17) to be dispensed is accommodated and in which a dispensing opening (47) for dispensing the substance (17) is provided, wherein the substance (17) can be subjected to a predetermined pressure in order to be dispensed from the housing (3), having an electronic control unit (53, 59, 61, 63), and having a valve unit (33) which is arranged in the course of a dispensing path and can be moved from an open position to a closed position by the control unit (53, 59, 61, 63). In the housing (3), a throttle section (45) is provided in the course of the dispensing path for the substance (17) to be dispensed.
Die Erfindung betrifft eine elektronische Pille zur steuerbaren Abgabe einer Substanz, insbesondere eines Medikaments, in einem menschlichen oder tierischen Körper, mit einem Gehäuse (3), in welchem die abzugebende Substanz (17) aufgenommen und in welchem eine Abgabeöffnung (47) zur Abgabe der Substanz (17) vorgesehen ist, wobei die Substanz (17) zur Abgabe aus dem Gehäuse (3) mit einem vorbestimmten Druck beaufschlagbar ist, mit einer elektronischen Steuereinheit (53, 59, 61, 63) und mit einer im Verlauf eines Abgabepfades angeordneten Ventileinheit (33), welche von der Steuereinheit (53, 59, 61, 63) von einer Geöffnet-Stellung in eine Geschlossen-Stellung steuerbar ist. Erfindungsgemäß ist im Gehäuse (3) im Verlauf des Abgabepfades für die abzugebende Substanz (17) eine Drosselstrecke (45) vorgesehen.
The invention relates to a multi railed track vehicle, designed with a conducting connection of pairs of rails with a connection resistance reducing agent for reducing the connection resistance to the rail. According to the invention, the connection resistance reducing agent is designed to generate arcs between at least one rail and the track vehicle.
Verfahren und Vorrichtung zur Gleisnebenschlusserzeugung durch Bahnfahrzeuge (DE102008038494A1)
(2010)
Die Erfindung betrifft ein mehrgleisiges Schienenfahrzeug, das zur elektrisch leitenden Verbindung von Gleispaaren und mit einem Übergangswiderstandsverringerungsmittel zur Verringerung des Übergangswiderstandes zum Gleis ausgebildet ist. Hierbei ist vorgesehen, dass das Übergangswiderstandsverringerungsmittel zum Generieren von Funken zwischen zumindet einem Gleis und dem Schienenfahrzeug ausgebildet ist.
The device (1) has a detection unit (38) that is provided for detecting two electromagnetic radiations that are radiated by a light source (10) after partial radiography of a medium with applied magnetic field (44). A determination unit (40) is provided for determining a phase relation between the two detected electromagnetic radiations. An evaluation unit (46) is provided for determining a concentration value of a magneto-optic substance in the medium on the basis of the determined phase relation. Independent claims are also included for the following: (1) a method for determining concentration value of a magneto-optic substance in a medium (2) a computer program product for performing a method for determining concentration value of a magneto-optic substance in a medium.
Vorrichtung und Verfahren zur optischen Messung der Entfernung eines Objekts (DE102007060966A1)
(2009)
Eine Vorrichtung zur optischen Messung der Entfernung eines Objekts weist eine Quelle zur Aussendung optischer Strahlung, die eine eine optische Strahlung transportierende Lichtleitfaser aufweist, ein optisches Element, welches die optische Strahlung fokussiert und auf das Objekt abbildet, und einen Empfänger auf, der die optische Strahlung empfängt und in ein elektrisches Signal zur Weitergabe an eine Steuereinrichtung umwandelt. Die Lichtleitfaser ist mittels einer Verschiebeeinrichtung relativ zu dem optischen Element verschieblich. Ein Abschnitt der Lichtleitfaser führt zu dem Empfänger und ist mittels einer Kopplungseinrichtung mit einem von der Quelle zur Aussendung optischer Strahlung ausgehenden Abschnitt der Lichtleitfaser zu einer einzigen, mittels der Verschiebeeinrichtung verschieblichen Lichtleitfaser gekoppelt.
Die vorliegende Erfindung betrifft ein Verfahren zur Laufzeitmessung mittels Ultraschall, bei dem ein komplexes Sendesignal erzeugt wird, mit dem zumindest ein Ultraschallsender durch Aussenden eines Ultraschallpulses angesteuert wird. Mit zumindest einem Ultraschallempfänger wird der Ultraschallpuls nach Durchlaufen einer Übertragungsstrecke empfangen und in ein komplexes Empfangssignal gewandelt. Das komplexe Empfangssignal wird mit dem komplexen Sendesignal korreliert, um ein komplexes Korrelationssignal zu erhalten. Das Korrelationssignal wird nicht nur nach Betrag, sondern auch nach Phase ausgewertet, um eine Laufzeit des Ultraschalls auf der Übertragungsstrecke zu bestimmen. Auf diese Weise wird zum einen eine höhere Genauigkeit der Laufzeitmessung erreicht, zum anderen beeinflussen andere akustische Laufwege des Ultraschallpulses die Messgenauigkeit nicht, so dass nur geringe Anforderungen an die Qualität der akustischen Übertragungsstrecke gestellt werden müssen.
Die Erfindung betrifft eine Anordnung zur Messung von Temperatur und Luftdruck sowie der Überwachung des Verschleißes von Fahrzeugreifen, wobei hierfür eine Drahtschleife in das Profil eingebettet wird, die bei verschlissenem Reifen unterbrochen wird, die Messung von Temperatur und Druck in einem sehr kleinen, in die Reifenwange einvulkanisierten elektronischen Transponder erfolgt (Bild 2), der die Meßwerte auf Anforderung des Tranceivers induktiv mit einem digitalen Trägerfrequenzverfahren über eine radial in der Reifenwange integrierte Flachspule auf einen am Fahrzeug montierten Transceiver überträgt. Der Transponder besteht erfindungsgemäß aus einem/wenigen Siliziumchips, auf denen Temperatursensor und mikromechanischer Drucksensor zusammen mit einem Mikroprozessor und zugehöriger Auswerte- und Übertragungselektronik integriert sind, sowie wenigen externen Komponenten, alle in einem Kunststoffgehäuse aus einem Material, das aus der gleichen Stoffgruppe kommt wie das Reifenmaterial oder mit diesem sich sehr innig verbinden läßt, zusammengefaßt. Die Kommunikation erfolgt erfindungsgemäß zwischen Transceiver und Transponder in geträgerter digitaler Form, wobei der Transceiver ein Kommando an den Transponder ausstrahlt, der dieses z. B. durch Durchführung der Messung, Kompensation- und Linearisierung der Meßwerte und Übertragung der Meßdaten und/oder weiterer im Transponder gespeicherter Daten beantwortet.
Die Erfindung betrifft eine mobile Vorrichtung zur Messung und Aufzeichnung von Temperaturzeitreihen, bei der die Temperatur in regelmäßigen, vorbestimmten Intervallen erfaßt wird und in einem Halbleiterspeicher abgelegt wird. Erfindungsgemäß werden alle erforderlichen Funktionen einschließlich des Sensors und des Speichers in einer integrierten Schaltung zusammengefügt, welche zusammen mit einer Batterie und einem zeitbestimmenden Element (Quarz) in Form einer Chip-Karte integriert werden. Die Chip-Karte kann erfindungsgemäß durch den Hersteller und den Anwender konfiguriert werden, wobei Daten über den Meßvorgang sowie die Meßintervalle auf der Karte gespeichert werden. Die Karte verfügt ferner erfindungsgemäß über mehrere Betriebszustände, wobei im Zustand "passiv" nahezu kein Strom verbraucht wird (Lagerung), im Zustand "aktiv" eine Meßwerterfassung stattfindet, im Zustand "ruhen" alle Funktionen bis auf eine Zeitgeberfunktion inaktiviert sind. Das Auslesen der Daten ist erfindungsgemäß über Paßworte in mehreren Zugangsebenen abgesichert, eine Manipulation wird ebenso verhindert. Die Anzahl der speicherbaren Meßwerte wird erfindungsgemäß durch ein digitales, blockorientiertes Kompressionsverfahren erhöht. Die Auswertung und Darstellung der Daten erfolgt erfindungsgemäß durch ein externes Datenverarbeitungssystem, wobei die Schnittstelle durch Formgebung und elektrische Ausführung kompatibel mit weitverbreiteten Standards ausgeführt ist.
Introduction: Subjects with mild to moderate hearing loss today often receive hearing aids (HA) with open-fitting (OF). In OF, direct sound reaches the eardrums with minimal damping. Due to the required processing delay in digital HA, the amplified HA sound follows some milliseconds later. This process occurs in both ears symmetrically in bilateral HA provision and is likely to have no or minor detrimental effect on binaural hearing. However, the delayed and amplified sound are only present in one ear in cases of unilateral hearing loss provided with one HA. This processing alters interaural timing differences in the resulting ear signals.
Methods: In the present study, an experiment with normal-hearing subjects to investigate speech intelligibility in noise with direct and delayed sound was performed to mimic unilateral and bilateral HA provision with OF.
Results: The outcomes reveal that these delays affect speech reception thresholds (SRT) in the unilateral OF simulation when presenting speech and noise from different spatial directions. A significant decrease in the median SRT from –18.1 to –14.7 dB SNR is observed when typical HA processing delays are applied. On the other hand, SRT was independent of the delay between direct and delayed sound in the bilateral OF simulation.
Discussion: The significant effect emphasizes the development of rapid processing algorithms for unilateral HA provision.
CNN-based deep learning models for disease detection have become popular recently. We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary classification performance on combined Pulmonary Chest Xrays dataset. Despite the widespread application in different fields in medical images, there remains a knowledge gap in determining their relative performance when applied to the same dataset, a gap this study aimed to address. The dataset combined Shenzhen, China (CH) and Montgomery, USA (MC) data. We trained our model for binary classification, calculated different parameters of the mentioned models, and compared them. The models were trained to keep in mind all following the same training parameters to maintain a controlled comparison environment. End of the study, we found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset, where DenseNet169 performed with 89.38 percent and MobileNet with 92.2 percent precision.
The COVID19 pandemic, a unique and devastating respiratory disease outbreak, has affected global populations as the disease spreads rapidly. Recent Deep Learning breakthroughs may improve COVID19 prediction and forecasting as a tool of precise and fast detection, however, current methods are still being examined to achieve higher accuracy and precision. This study analyzed the collection contained 8055 CT image samples, 5427 of which were COVID cases and 2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 non COVID cases. The most accurate models are MobileNet V3 (97.872 percent), DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). High accuracy indicates that these models can make many accurate predictions, as well as others, are also high for MobileNetV3 and DenseNet201. An extensive evaluation using accuracy, precision, and recall allows a comprehensive comparison to improve predictive models by combining loss optimization with scalable batch normalization in this study. Our analysis shows that these tactics improve model performance and resilience for advancing COVID19 prediction and detection and shows how Deep Learning can improve disease handling. The methods we suggest would strengthen healthcare systems, policymakers, and researchers to make educated decisions to reduce COVID19 and other contagious diseases.
Virtual-Reality
(2023)
Die Virtual-Reality (VR) Technologie ermöglicht Unternehmen eine Produktpräsentation, die weit über traditionelle Darstellungsmethoden hinausgeht. Obgleich die Integration der VR-Technologie für Unternehmen viele Chancen eröffnet, ist deren Einsatz auch mit Risiken verbunden. Insbesondere der Mangel an empirisch gesicherten Erkenntnissen zur Kundenakzeptanz, zu den Auswirkungen der Nutzung sowie zu Kannibalisierungseffekten ist ein wesentlicher Grund, der die Verbreitung von VR in der Kundenkommunikation noch hemmt. Das Buch adressiert diese Forschungslücken und identifiziert mittels eines nutzerzentrierten, quantitativen Forschungsdesigns konkrete Chancen und Risiken, die mit dem Einsatz von VR-Produktpräsentationen verbunden sind.
This paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to well-known frameworks like Stable-Baselines3.
The use of artificial intelligence continues to impact a broad variety of domains, application areas, and people. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for increasing humans' trust into the systems - are still largely missing. The purpose of this seminar is to understand how these components factor into the holistic view of trust. Further, this seminar seeks to identify design guidelines and best practices for how to build interactive visualization systems to calibrate trust.
With the rising necessity of explainable artificial intelligence (XAI), we see an increase in task-dependent XAI methods on varying abstraction levels. XAI techniques on a global level explain model behavior and on a local level explain sample predictions. We propose a visual analytics workflow to support seamless transitions between global and local explanations, focusing on attributions and counterfactuals on time series classification. In particular, we adapt local XAI techniques (attributions) that are developed for traditional datasets (images, text) to analyze time series classification, a data type that is typically less intelligible to humans. To generate a global overview, we apply local attribution methods to the data, creating explanations for the whole dataset. These explanations are projected onto two dimensions, depicting model behavior trends, strategies, and decision boundaries. To further inspect the model decision-making as well as potential data errors, a what-if analysis facilitates hypothesis generation and verification on both the global and local levels. We constantly collected and incorporated expert user feedback, as well as insights based on their domain knowledge, resulting in a tailored analysis workflow and system that tightly integrates time series transformations into explanations. Lastly, we present three use cases, verifying that our technique enables users to (1)~explore data transformations and feature relevance, (2)~identify model behavior and decision boundaries, as well as, (3)~the reason for misclassifications.
There is an ongoing debate about the use and scope of Clayton M. Christensen´s idea of disruptive innovation, including the question of whether it is a management buzz phrase or a valuable theory. This discussion considers the general question of how innovation in the field of management theories and concepts finds its way to the different target groups. This conceptual paper combines the different concepts of the creation and dissemination of management trends in a basic framework based on a short review of models for the dissemination of management ideas. This framework allows an analysis of the character of new management ideas like disruptive innovation. By measuring the impact of the theory on the academic sphere using a bibliometric statistic of the number of academic publications on Google scholar and Scopus and a meta-analysis of research papers, we show the significant influence of disruptive innovation beyond pure management fads.
Variable refrigerant flow (VRF) and variable air volume (VAV) systems are considered among the best heating, ventilation, and air conditioning systems (HVAC) thanks to their ability to provide cooling and heating in different thermal zones of the same building. As well as their ability to recover the heat rejected from spaces requiring cooling and reuse it to heat another space. Nevertheless, at the same time, these systems are considered one of the most energy-consuming systems in the building. So, it is crucial to well size the system according to the building’s cooling and heating needs and the indoor temperature fluctuations. This study aims to compare these two energy systems by conducting an energy model simulation of a real building under a semi-arid climate for cooling and heating periods. The developed building energy model (BEM) was validated and calibrated using measured and simulated indoor air temperature and energy consumption data. The study aims to evaluate the effect of these HVAC systems on energy consumption and the indoor thermal comfort of the building. The numerical model was based on the Energy Plus simulation engine. The approach used in this paper has allowed us to reach significant quantitative energy saving along with a high level of indoor thermal comfort by using the VRF system compared to the VAV system. The findings prove that the VRF system provides 46.18% of the annual total heating energy savings and 6.14% of the annual cooling and ventilation energy savings compared to the VAV system.
Modern CNNs are learning the weights of vast numbers of convolutional operators. In this paper, we raise the fundamental question if this is actually necessary. We show that even in the extreme case of only randomly initializing and never updating spatial filters, certain CNN architectures can be trained to surpass the accuracy of standard training. By reinterpreting the notion of pointwise ($1\times 1$) convolutions as an operator to learn linear combinations (LC) of frozen (random) spatial filters, we are able to analyze these effects and propose a generic LC convolution block that allows tuning of the linear combination rate. Empirically, we show that this approach not only allows us to reach high test accuracies on CIFAR and ImageNet but also has favorable properties regarding model robustness, generalization, sparsity, and the total number of necessary weights. Additionally, we propose a novel weight sharing mechanism, which allows sharing of a single weight tensor between all spatial convolution layers to massively reduce the number of weights.
Learning programming fundamentals is considered as one of the most challenging and complex learning activities. Some authors have proposed visual programming language (VPL) approaches to address part of the inherent complexity [1]. A visual programming language lets users develop programs by combining program elements, like loops graphically rather than by specifying them textually. Visual expressions, spatial arrangements of text and graphic symbols are used either as syntax elements or secondary notation. VPLs are normally used for educational multimedia, video games, system development, and data warehousing/business analytics purposes. For example, Scratch, a platform of Massachusetts Institute of Technology, is designed for kids and after school programs.
Design of mobile software applications is considered as one of the most challenging application domains due to the build in sensors as part of a mobile device, like GPS, camera or Near Field Communication (NFC). Sensors enable creation of context-aware mobile applications in which applications can discover and take advantage of contextual information, such as user location, nearby people and objects, and the current user activity. As a consequence, context-aware mobile applications can sense clues about the situational environment making mobile devices more intelligent, adaptive, and personalized. Such context aware mobile applications seem to be motivating and attractive case studies, especially for programming beginners (“my own first app”).
In this work, we introduce a use-case centered approach as well as clear separation of user interface design and sensor-based program development. We provide an in-depth discussion of a new VPL based teaching method, a step by step development process to enable programming beginners the creation of context aware mobile applications. Finally, we argue that addressing challenges for programming beginners by our teaching approach could make programming teaching more motivating, with an additional impact on the final software quality and scalability.
The key contributions of our study are the following:
- An overview of existing attempts to use VPL approaches for mobile applications
- A use case centered teaching approach based on a clear separation of user interface design and sensor-based program development
- A teaching case study enabling beginners a step by step creation of context-aware mobile applications based on the MIT App Inventor (a platform of Massachusetts Institute of Technology)
- Open research challenges and perspectives for further development of our teaching approach
References:
[1] Idrees, M., Aslam, F. (2022). A Comprehensive Survey and Analysis of Diverse Visual Programming Languages, VFAST Transactions on Software Engineering, 2022, Volume 10, Number 2, pp 47-60.
During pyrolysis, biomass is carbonised in the absence of oxygen to produce biochar with heat and/or electricity as co-products making pyrolysis one of the promising negative emission technologies to reach climate goals worldwide. This paper presents a simplified representation of pyrolysis and analyses the impact of this technology on the energy system. Results show that the use of pyrolysis can allow getting zero emissions with lower costs by making changes in the unit commitment of the power plants, e.g. conventional power plants are used differently, as the emissions will be compensated by biochar. Additionally, the process of pyrolysis can enhance the flexibility of energy systems, as it shows a correlation between the electricity generated by pyrolysis and the hydrogen installation capacity, being hydrogen used less when pyrolysis appears. The results indicate that pyrolysis, which is available on the market, integrates well into the energy system with a promising potential to sequester carbon.
TRIZ Innovationstechnologie
(2023)
3D Bin Picking with an innovative powder filled gripper and a torque controlled collaborative robot
(2023)
A new and innovative powder filled gripper concept will be introduced to a process to pick parts out of a box without the use of a camera system which guides the robot to the part. The gripper is a combination of an inflatable skin, and a powder inside. In the unjammed condition, the powder is soft and can adjust to the geometry of the part which will be handled. By applying a vacuum to the inflatable skin, the powder gets jammed and transforms to a solid shaped form in which the gripper was brought before applying the vacuum. This physical principle is used to pick parts. The flexible skin of the gripper adjusts to all kinds of shapes, and therefore, can be used to realize 3D bin picking. With the help of a force controlled robot, the gripper can be pushed with a consistent force on flexible positions depending of the filling level of the box. A Kuka LBR iiwa with joint torque sensors in all of its seven axis’ was used to achieve a constant contact pressure. This is the basic criteria to achieve a robust picking process.
Socially assistive robots (SARs) are becoming more prevalent in everyday life, emphasizing the need to make them socially acceptable and aligned with users' expectations. Robots' appearance impacts users' behaviors and attitudes towards them. Therefore, product designers choose visual qualities to give the robot a character and to imply its functionality and personality. In this work, we sought to investigate the effect of cultural differences on Israeli and German designers' perceptions and preferences regarding the suitable visual qualities of SARs in four different contexts: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. Our results indicate that Israeli and German designers share similar perceptions of visual qualities and most of the robotics roles. However, we found differences in the perception of the COVID-19 officer robot's role and, by that, its most suitable visual design. This work indicates that context and culture play a role in users' perceptions and expectations; therefore, they should be taken into account when designing new SARs for diverse contexts.
Recent advances in spiked shoe design, characterized by increased longitudinal stiffness, thicker midsole foams, and reconfigured geometry are considered to improve sprint performance. However, so far there is no empirical data on the effects of advanced spikes technology on maximal sprinting speed (MSS) published yet. Consequently, we assessed MSS via ‘flying 30m’ sprints of 44 trained male (PR: 10.32 s - 12.08 s) and female (PR: 11.56 s - 14.18 s) athletes, wearing both traditional and advanced spikes in a randomized, repeated measures design. The results revealed a statistically significant increase in MSS by 1.21% on average when using advanced spikes technology. Notably, 87% of participants showed improved MSS with the use of advanced spikes. A cluster analysis unveiled that athletes with higher MSS may benefit to a greater extent. However, individual responses varied widely, suggesting the influence of multiple factors that need detailed exploration. Therefore, coaches and athletes are advised to interpret the promising performance enhancements cautiously and evaluate the appropriateness of the advanced spike technology for their athletes critically.
High-tech running shoes and spikes ("super-footwear") are currently being debated in sports. There is direct evidence that distance running super shoes improve running economy; however, it is not well established to which extent world-class performances are affected over the range of track and road running events.
This study examined publicly available performance datasets of annual best track and road performances for evidence of potential systematic performance effects following the introduction of super footwear. The analysis was based on the 100 best performances per year for men and women in outdoor events from 2010 to 2022, provided by the world governing body of athletics (World Athletics).
We found evidence of progressing improvements in track and road running performances after the introduction of super distance running shoes in 2016 and super spike technology in 2019. This evidence is more pronounced for distances longer than 1500 m in women and longer than 5000 m in men. Women seem to benefit more from super footwear in distance running events than men.
While the observational study design limits causal inference, this study provides a database on potential systematic performance effects following the introduction of super shoes/spikes in track and road running events in world-class athletes. Further research is needed to examine the underlying mechanisms and, in particular, potential sex differences in the performance effects of super footwear.
We revisit the quantitative analysis of the ultrafast magnetoacoustic experiment in a freestanding nickel thin film by Kim and Bigot [J.-W. Kim and J.-Y. Bigot, Phys. Rev. B 95, 144422 (2017)] by applying our recently proposed approach of magnetic and acoustic eigenmode decomposition. We show that the application of our modeling to the analysis of time-resolved reflectivity measurements allows for the determination of amplitudes and lifetimes of standing perpendicular acoustic phonon resonances with unprecedented accuracy. The acoustic damping is found to scale as ∝ω2 for frequencies up to 80 GHz, and the peak amplitudes reach 10−3. The experimentally measured magnetization dynamics for different orientations of an external magnetic field agrees well with numerical solutions of magnetoelastically driven magnon harmonic oscillators. Symmetry-based selection rules for magnon-phonon interactions predicted by our modeling approach allow for the unambiguous discrimination between spatially uniform and nonuniform modes, as confirmed by comparing the resonantly enhanced magnetoelastic dynamics simultaneously measured on opposite sides of the film. Moreover, the separation of timescales for (early) rising and (late) decreasing precession amplitudes provide access to magnetic (Gilbert) and acoustic damping parameters in a single measurement.
While most ultrafast time-resolved optical pump-probe experiments in magnetic materials reveal the spatially homogeneous magnetization dynamics of ferromagnetic resonance (FMR), here we explore the magneto-elastic generation of GHz-to-THz frequency spin waves (exchange magnons). Using analytical magnon oscillator equations, we apply time-domain and frequency-domain approaches to quantify the results of ultrafast time-resolved optical pump-probe experiments in free-standing ferromagnetic thin films. Simulations show excellent agreement with the experiment, provide acoustic and magnetic (Gilbert) damping constants and highlight the role of symmetry-based selection rules in phonon-magnon interactions. The analysis is extended to hybrid multilayer structures to explore the limits of resonant phonon-magnon interactions up to THz frequencies.
The technique of laser ultrasonics perfectly meets the need for noncontact, noninvasive, nondestructive mechanical probing of nanometer- to millimeter-size samples. However, this technique is limited to the excitation of low-amplitude strains, below the threshold for optical damage of the sample. In the context of strain engineering of materials, alternative optical techniques enabling the excitation of high-amplitude strains in a nondestructive optical regime are needed. We introduce here a nondestructive method for laser-shock wave generation based on additive superposition of multiple laser-excited strain waves. This technique enables strain generation up to mechanical failure of a sample at pump laser fluences below optical ablation or melting thresholds. We demonstrate the ability to generate nonlinear surface acoustic waves (SAWs) in Nb-SrTiO3 substrates, with associated strains in the percent range and pressures up to 3 GPa at 1 kHz repetition rate and close to 10 GPa for several hundred shocks. This study paves the way for the investigation of a host of high-strain SAW-induced phenomena, including phase transitions in conventional and quantum materials, plasticity and a myriad of material failure modes, chemistry and other effects in bulk samples, thin layers, and two-dimensional materials.
The utilisation of artificial intelligence (AI) is progressively emerging as a significant mechanism for innovation in human resource management (HRM). The capacity to facilitate the transformation of employee performance across numerous responsibilities. AI development, there remains a dearth of comprehensive exploration into the potential opportunities it presents for enhancing workplace performance among employees. To bridge this gap in knowledge, the present work carried out a survey with 300 participants, utilises a fuzzy set-theoretic method that is grounded on the conceptualisation of AI, KS, and HRM. The findings of our study indicate that the exclusive adoption of AI technologies does not adequately enhance HRM engagements. In contrast, the integration of AI and KS offers a more viable HRM approach for achieving optimal performance in a dynamic digital society. This approach has the potential to enhance employees’ proficiency in executing their responsibilities and cultivate a culture of creativity inside the firm.
Purpose
Although start-ups have gained increasing scholarly attention, we lack sufficient understanding of their entrepreneurial strategic posture (ESP) in emerging economies. The purpose of this study is to examine the processes of ESP of new technology venture start-ups (NTVs) in an emerging market context.
Design/methodology/approach
In line with grounded theory guidelines and the inductive research traditions, the authors adopted a qualitative approach involving 42 in-depth semi-structured interviews with Ghanaian NTV entrepreneurs to gain a comprehensive analysis at the micro-level on the entrepreneurs' strategic posturing. A systematic procedure for data analysis was adopted.
Findings
From the authors' analysis of Ghanaian NTVs, the authors derived a three-stage model to elucidate the nature and process of ESP Phase 1 spotting and exploiting market opportunities, Phase II identifying initial advantages and Phase III ascertaining and responding to change.
Originality/value
The study contributes to advancing research on ESP by explicating the process through which informal ties and networks are utilised by NTVs and NTVs' founders to overcome extreme resource constraints and information vacuums in contexts of institutional voids. The authors depart from past studies in demonstrating how such ties can be harnessed in spotting and exploiting market opportunities by NTVs. On this basis, the paper makes original contributions to ESP theory and practice.
Purpose
Although recent literature has examined diverse measures adopted by SMEs to navigate the COVID-19 turbulence, there is a shortage of evidence on how crisis-time strategy creation behaviour and digitalization activities increase (1) sales and (2) cash flow. Thus, predicated on a novel strategy creation perspective, this inquiry aims to investigate the crisis behaviour, sales and cash flow performance of 528 SMEs in Morocco.
Design/methodology/approach
Novel links between (1) aggregate wage cuts, (2) variable operating hours, (3) deferred payment to suppliers, (4) deferred payment to tax authorities and (5) sales performance are developed and tested. A further link between sales performance and cash flow is also examined and the analysis is conducted using a non-linear structural equation modelling technique.
Findings
While there is a significant association between strategy creation behaviours and sales performance, only variable operating hours have a positive effect. Also, sales performance increases cash flow and this relationship is substantially strengthened by e-commerce digitalization and innovation.
Originality/value
Theoretically, to the best of the authors’ knowledge, this is one of the first inquiries to espouse the strategy creation view to explain SMEs' crisis-time behaviour and digitalization. For practical purposes, to supplement Moroccan SMEs' propensity to seek tax deferrals, it is argued that debt and equity support measures are also needed to boost sales performance and cash flow.
In the past ten years, applications of artificial neural networks have changed dramatically. outperforming earlier predictions in domains like robotics, computer vision, natural language processing, healthcare, and finance. Future research and advancements in CNN architectures, Algorithms and applications are expected to revolutionize various industries and daily life further. Our task is to find current products that resemble the given product image and description. Deep learning-based automatic product identification is a multi-step process that starts with data collection and continues with model training, deployment, and continuous improvement. The caliber and variety of the dataset, the design selected, and ongoing testing and improvement all affect the model's effectiveness. We achieved 81.47% training accuracy and 72.43% validation accuracy for our combined text and image classification model. Additionally, we have discussed the outcomes from the other dataset and numerous methods for creating an appropriate model.
An international study summarizes the threat situation in the OT environment under the heading "Growing security threats" [1]. According to this study, attacks on automation systems are likely to increase in the future. Accordingly, an automation system must be able to protect the integrity of the transmitted information in the future. This requirement is motivated, among other things, by the fact that the network-side isolation of industrial communication systems is no longer considered sufficient as the sole protective measure. This paper uses the example of PROFINET to show how the future requirements for a real-time communication protocol can be met and how they can be derived from the IEC 62443 standard.
As the population grows, so does the amount of biowaste. As demand for energy grows, biogas is a promising solution to the problem. Lignocellulosic materials are challenged of slow degradability due to the presence of polymers such as cellulose, lignin and hemicellulose. There are several pretreatment methods available to enhance the degradability of such materials, including enzymatic pretreatment. In this pretreatment, there are few parameters that can influence the results, the most important being the enzyme to solid ratio and the solid to liquid ratio. During this project, experiments were conducted to determine the optimal conditions for those two factors. It was discovered that a solid to liquid ratio of 31 g of buffer per 1 gram of organic dry matter produced the highest reducing sugar release in flasks when combined with 34 mg of protein per 1 gram of organic dry mass. Additionally, another experiment was carried out to investigate the impact of enzymatic pretreatment on biogas production using artificial biowaste as a substrate. Artificial biowaste produced 577,9 NL/kg oDM, while enzymatically pretreated biowaste produced 639,3 NL/kg oDM. This resulted in a 10,6% rise in cumulative biogas production compared to its use without enzymatic pretreatment. By the conclusion of the investigation, specific cumulative dry methane yields of 364,7 NL/kg oDM and 426,3 NL/kg oDM were obtained from artificial biowaste without and with enzymatic pretreatment, respectively. This resulted in a methane production boost of 16,9%. Additionally in case of the reactors with enzymatically pretreated substrate kinetic constant was lower more than double, where maximum volume of biogas increased, comparing to the reactors without enzymatic pretreatment.
Polyarticulated active prostheses constitute a promising solution for upper limb amputees. The bottleneck for their adoption though, is the lack of intuitive control. In this context, machine learning algorithms based on pattern recognition from electromyographic (EMG) signals represent a great opportunity for naturally operating prosthetic devices, but their performance is strongly affected by the selection of input features. In this study, we investigated different combinations of 13 EMG-derived features obtained from EMG signals of healthy individuals performing upper limb movements and tested their performance for movement classification using an Artificial Neural Network. We found that input data (i.e., the set of input features) can be reduced by more than 50% without any loss in accuracy, while diminishing the computing time required to train the classifier. Our results indicate that input features must be properly selected in order to optimize prosthetic control.
The main focus of this chapter is the theoretical and instrumental processes that underpin densitometric methods widely used in thin-layer chromatography (TLC). Densitometric methods include UV–vis, luminescence and fluorescence optical measurements as well as infrared and Raman spectroscopic measurements. The chapter is divided in two general parts: a theoretical part and a practical part. The systems for direct radioactivity measurements and the combination of TLC with mass spectrometry are also discussed. All these systems allow measuring an intensity distribution directly on a TLC plate. We call this “in situ detection” because no analyte is removed from the plate.
The main focus of this chapter is the theoretical and instrumental processes that underpin densitometric methods widely used in thin-layer chromatography (TLC). Densitometric methods include UV–vis, luminescence, and fluorescence optical measurements as well as infrared and Raman spectroscopic measurements. The chapter is divided in two general parts: a theoretical part and a practical part. The systems for direct radioactivity measurements and the combination of TLC with mass spectrometry are also discussed. All these systems allow measuring an intensity distribution directly on a TLC plate. We call this “in situ detection” because no analyte is removed from the plate.
Recently, photovoltaic (PV) with energy storage systems (ESS) have been widely adopted in buildings to overcome growing power demands and earn financial benefits. The overall energy cost can be optimized by combining a well-sized hybrid PV/ESS system with an efficient energy management system (EMS). Generally, EMS is implemented within the overall functions of the Building Automation System (BAS). However, due to its limited computing resources, BAS cannot handle complex algorithms that aim to optimize energy use in real-time under different operating conditions. Furthermore, islanding the building's local network to maximize the PV energy share represents a challenging task due to the potential technical risks. In this context, this article addresses an improved approach based on upgrading the BAS data analytics capability by means of an edge computing technology. The edge communicates with the BAS low-level controller using a serial communication protocol. Taking advantage of the high computing ability of the edge device, an optimization-based EMS of the PV/ESS hybrid system is implemented. Different testing scenarios have been carried out on a real prototype with different weather conditions, and the results show the implementation feasibility and technical performance of such advanced EMS for the management of building energy resources. It has also been proven to be feasible and advantageous to operate the local energy network in island mode while ensuring system safety. Additionally, an estimated energy saving improvement of 6.23 % has been achieved using optimization-based EMS compared to the classical rule-based EMS, with better ESS constraints fulfillment.
Analysing and predicting the advance rate of a tunnel boring machine (TBM) in hard rock is integral to tunnelling project planning and execution. It has been applied in the industry for several decades with varying success. Most prediction models are based on or designed for large-diameter TBMs, and much research has been conducted on related tunnelling projects. However, only a few models incorporate information from projects with an outer diameter smaller than 5 m and no penetration prediction model for pipe jacking machines exists to date. In contrast to large TBMs, small-diameter TBMs and their projects have been considered little in research. In general, they are characterised by distinctive features, including insufficient geotechnical information, sometimes rather short drive lengths, special machine designs and partially concurring lining methods like pipe jacking and segment lining. A database which covers most of the parameters mentioned above has been compiled to investigate the performance of small-diameter TBMs in hard rock. In order to provide sufficient geological and technical variance, this database contains 37 projects with 70 geotechnically homogeneous areas. Besides the technical parameters, important geotechnical data like lithological information, unconfined compressive strength, tensile strength and point load index is included and evaluated. The analysis shows that segment lining TBMs have considerably higher penetration rates in similar geological and technical settings mostly due to their design parameters. Different methodologies for predicting TBM penetration, including state-of-the-art models from the literature as well as newly derived regression and machine learning models, are discussed and deployed for backward modelling of the projects contained in the database. New ranges of application for small-diameter tunnelling in several industry-standard penetration models are presented, and new approaches for the penetration prediction of pipe jacking machines in hard rock are proposed.
Following the traditional paradigm of convolutional neural networks (CNNs), modern CNNs manage to keep pace with more recent, for example transformer-based, models by not only increasing model depth and width but also the kernel size. This results in large amounts of learnable model parameters that need to be handled during training. While following the convolutional paradigm with the according spatial inductive bias, we question the significance of \emph{learned} convolution filters. In fact, our findings demonstrate that many contemporary CNN architectures can achieve high test accuracies without ever updating randomly initialized (spatial) convolution filters. Instead, simple linear combinations (implemented through efficient 1×1 convolutions) suffice to effectively recombine even random filters into expressive network operators. Furthermore, these combinations of random filters can implicitly regularize the resulting operations, mitigating overfitting and enhancing overall performance and robustness. Conversely, retaining the ability to learn filter updates can impair network performance. Lastly, although we only observe relatively small gains from learning 3×3 convolutions, the learning gains increase proportionally with kernel size, owing to the non-idealities of the independent and identically distributed (\textit{i.i.d.}) nature of default initialization techniques.
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.
State-of-the-art models for pixel-wise prediction tasks such as image restoration, image segmentation, or disparity estimation, involve several stages of data resampling, in which the resolution of feature maps is first reduced to aggregate information and then sequentially increased to generate a high-resolution output. Several previous works have investigated the effect of artifacts that are invoked during downsampling and diverse cures have been proposed that facilitate to improve prediction stability and even robustness for image classification. However, equally relevant, artifacts that arise during upsampling have been less discussed. This is significantly relevant as upsampling and downsampling approaches face fundamentally different challenges. While during downsampling, aliases and artifacts can be reduced by blurring feature maps, the emergence of fine details is crucial during upsampling. Blurring is therefore not an option and dedicated operations need to be considered. In this work, we are the first to explore the relevance of context during upsampling by employing convolutional upsampling operations with increasing kernel size while keeping the encoder unchanged. We find that increased kernel sizes can in general improve the prediction stability in tasks such as image restoration or image segmentation, while a block that allows for a combination of small-size kernels for fine details and large-size kernels for artifact removal and increased context yields the best results.
Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling
(2023)
Convolutional neural networks encode images through a sequence of convolutions, normalizations and non-linearities as well as downsampling operations into potentially strong semantic embeddings. Yet, previous work showed that even slight mistakes during sampling, leading to aliasing, can be directly attributed to the networks' lack in robustness. To address such issues and facilitate simpler and faster adversarial training, [12] recently proposed FLC pooling, a method for provably alias-free downsampling - in theory. In this work, we conduct a further analysis through the lens of signal processing and find that such current pooling methods, which address aliasing in the frequency domain, are still prone to spectral leakage artifacts. Hence, we propose aliasing and spectral artifact-free pooling, short ASAP. While only introducing a few modifications to FLC pooling, networks using ASAP as downsampling method exhibit higher native robustness against common corruptions, a property that FLC pooling was missing. ASAP also increases native robustness against adversarial attacks on high and low resolution data while maintaining similar clean accuracy or even outperforming the baseline.
Motivated by the recent trend towards the usage of larger receptive fields for more context-aware neural networks in vision applications, we aim to investigate how large these receptive fields really need to be. To facilitate such study, several challenges need to be addressed, most importantly: (i) We need to provide an effective way for models to learn large filters (potentially as large as the input data) without increasing their memory consumption during training or inference, (ii) the study of filter sizes has to be decoupled from other effects such as the network width or number of learnable parameters, and (iii) the employed convolution operation should be a plug-and-play module that can replace any conventional convolution in a Convolutional Neural Network (CNN) and allow for an efficient implementation in current frameworks. To facilitate such models, we propose to learn not spatial but frequency representations of filter weights as neural implicit functions, such that even infinitely large filters can be parameterized by only a few learnable weights. The resulting neural implicit frequency CNNs are the first models to achieve results on par with the state-of-the-art on large image classification benchmarks while executing convolutions solely in the frequency domain and can be employed within any CNN architecture. They allow us to provide an extensive analysis of the learned receptive fields. Interestingly, our analysis shows that, although the proposed networks could learn very large convolution kernels, the learned filters practically translate into well-localized and relatively small convolution kernels in the spatial domain.
Assessing the robustness of deep neural networks against out-of-distribution inputs is crucial, especially in safety-critical domains like autonomous driving, but also in safety systems where malicious actors can digitally alter inputs to circumvent safety guards. However, designing effective out-of-distribution tests that encompass all possible scenarios while preserving accurate label information is a challenging task. Existing methodologies often entail a compromise between variety and constraint levels for attacks and sometimes even both. In a first step towards a more holistic robustness evaluation of image classification models, we introduce an attack method based on image solarization that is conceptually straightforward yet avoids jeopardizing the global structure of natural images independent of the intensity. Through comprehensive evaluations of multiple ImageNet models, we demonstrate the attack's capacity to degrade accuracy significantly, provided it is not integrated into the training augmentations. Interestingly, even then, no full immunity to accuracy deterioration is achieved. In other settings, the attack can often be simplified into a black-box attack with model-independent parameters. Defenses against other corruptions do not consistently extend to be effective against our specific attack.
Project website: https://github.com/paulgavrikov/adversarial_solarization
Entity Matching (EM) defines the task of learning to group objects by transferring semantic concepts from example groups (=entities) to unseen data. Despite the general availability of image data in the context of many EM-problems, most currently available EM-algorithms solely rely on (textual) meta data. In this paper, we introduce the first publicly available large-scale dataset for "visual entity matching", based on a production level use case in the retail domain. Using scanned advertisement leaflets, collected over several years from different European retailers, we provide a total of ~786k manually annotated, high resolution product images containing ~18k different individual retail products which are grouped into ~3k entities. The annotation of these product entities is based on a price comparison task, where each entity forms an equivalence class of comparable products. Following on a first baseline evaluation, we show that the proposed "visual entity matching" constitutes a novel learning problem which can not sufficiently be solved using standard image based classification and retrieval algorithms. Instead, novel approaches which allow to transfer example based visual equivalent classes to new data are needed to address the proposed problem. The aim of this paper is to provide a benchmark for such algorithms.
Information about the dataset, evaluation code and download instructions are provided under https://www.retail-786k.org/.
For the treatment of bone defects, biodegradable, compressive biomaterials are needed as replacements that degrade as the bone regenerates. The problem with existing materials has either been their insufficient mechanical strength or the excessive differences in their elastic modulus, leading to stress shielding and eventual failure. In this study, the compressive strength of CPC ceramics (with a layer thickness of more than 12 layers) was compared with sintered β-TCP ceramics. It was assumed that as the number of layers increased, the mechanical strength of 3D-printed scaffolds would increase toward the value of sintered ceramics. In addition, the influence of the needle inner diameter on the mechanical strength was investigated. Circular scaffolds with 20, 25, 30, and 45 layers were 3D printed using a 3D bioplotter, solidified in a water-saturated atmosphere for 3 days, and then tested for compressive strength together with a β-TCP sintered ceramic using a Zwick universal testing machine. The 3D-printed scaffolds had a compressive strength of 41.56 ± 7.12 MPa, which was significantly higher than that of the sintered ceramic (24.16 ± 4.44 MPa). The 3D-printed scaffolds with round geometry reached or exceeded the upper limit of the compressive strength of cancellous bone toward substantia compacta. In addition, CPC scaffolds exhibited more bone-like compressibility than the comparable β-TCP sintered ceramic, demonstrating that the mechanical properties of CPC scaffolds are more similar to bone than sintered β-TCP ceramics.
Differentiation between human and non-human objects can increase efficiency of human-robot collaborative applications. This paper proposes to use convolutional neural networks for classifying objects in robotic applications. The body temperature of human beings is used to classify humans and to estimate the distance to the sensor. Using image classification with convolutional neural networks it is possible to detect humans in the surroundings of a robot up to five meters distance with low-cost and low-weight thermal cameras. Using transfer learning technique we trained the GoogLeNet and MobilenetV2. Results show accuracies of 99.48 % and 99.06 % respectively.
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
(2023)
Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification.Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes.The code for our experiments is provided at https://github.com/deepfake-study/deepfake-multiLID.
Following their success in visual recognition tasks, Vision Transformers(ViTs) are being increasingly employed for image restoration. As a few recent works claim that ViTs for image classification also have better robustness properties, we investigate whether the improved adversarial robustness of ViTs extends to image restoration. We consider the recently proposed Restormer model, as well as NAFNet and the "Baseline network" which are both simplified versions of a Restormer. We use Projected Gradient Descent (PGD) and CosPGD for our robustness evaluation. Our experiments are performed on real-world images from the GoPro dataset for image deblurring. Our analysis indicates that contrary to as advocated by ViTs in image classification works, these models are highly susceptible to adversarial attacks. We attempt to find an easy fix and improve their robustness through adversarial training. While this yields a significant increase in robustness for Restormer, results on other networks are less promising. Interestingly, we find that the design choices in NAFNet and Baselines, which were based on iid performance, and not on robust generalization, seem to be at odds with the model robustness.
The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research [1] has shown how such detection can generally be enabled by deep learning methods, but appears to be very limited regarding the overall amount of detected vulnerabilities. We analyse to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardised LLVM Intermediate Representation. Te vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, our proposed technical approach and methodology enables an accurate detection of 23 (compared to 4 [1]) vulnerabilities.
The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.
Grundzüge der Strömungslehre
(2023)
Dieses ausgereifte Lehrbuch stellt in prägnant kurzer und mathematisch verständlicher Darstellung die strömungstechnischen Grundlagen dar. Aufgaben mit Lösungen helfen den Lernstoff richtig anzuwenden und fördern das Verständnis. Das Buch eignet sich zur Begleitung und Vertiefung der Vorlesungen über Strömungslehre sowie zum Selbststudium. Die vorliegende Auflage geht auf die immer größer werdende Rolle des Energiehaushalts ein und trägt damit den aktuellen Entwicklungen Rechnung. Ergänzt wurden aktuelle Übungsaufgaben der Strömungsmechanik, zahlreiche Beispiele veranschaulichen den Energiesatz.