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Durch den verstärkten Einsatz von EDV-Geräten an den Arbeitsplätzen in Büroräumen stiegen die flächenbezogenen inneren Kühllasten deutlich an. Für die Arbeitsplätze in Fensternähe müssen EDV-gerechte Lichtverhältnisse vorhanden sein. Es werden Versuchsergebnisse eines neu entwickelten Kühlkonvektors im Fensterbereich im Zusammenhang mit Innenjalousien vorgestellt. Dieser Kühlkonvektor eignet sich auch für den kombinierten Einsatz mit Kühldecken und raumlufttechnischen (RLT)-Anlagen, wie weitere Versuche bestätigen. Der Kühlkonvektor kann auch beim Ersatz von Induktionsgeräten angewandt werden, die z.B. nach dem 4-Leiter-Prinzip angeschlossen sind. Die Untersuchungen haben gezeigt, daß mit der Kombination von Kühldecke, Kühlkonvektor und RLT-Anlage große flächenbezogene Kühllasten abgeführt werden können, ohne daß dadurch die thermische Behaglichkeit negativ beeinflußt wird. Der Luftstrom läßt sich auf das hygienisch bzw. auf das zur Entfeuchtung notwendige Maß reduzieren, wodurch sich die Betriebskosten senken. Der Anteil der drei Komponenten an der gesamten Kühllast beträgt im Mittel: 15 % RLT-Anlage, 15 % Kühlkonvektor und 70 % Kühldecke.
Duplikaterkennung, -suche und -konsolidierung für Kunden- und Geschäftspartnerdaten, sog. „Identity Resolution“, ist die Voraussetzung für erfolgreiches Customer Relationship Management und Customer Experience Management, aber auch für das Risikomanagement zur Minimierung von Betrugsrisiken und Einhaltung regulatorischer Vorschriften und viele weitere Anwendungsfälle. Diese Systeme sind jedoch hochkomplex und müssen individuell an die kundenspezifischen Anforderungen angepasst werden. Der Einsatz lernbasierter Verfahren bietet großes Potenzial zur automatisierten Anpassung. In diesem Beitrag präsentieren wir für ein KMU praxisfähige, lernbasierte Verfahren zur automatischen Konfiguration von Business-Regeln in Duplikaterkennungssystemen. Dabei wurden für Fachanwender Möglichkeiten entwickelt, um beispielgetrieben das Match-System an individuelle Business-Regeln (u.a. Umzugserkennung, Sperrlistenabgleich) anzupassen und zu konfigurieren. Die entwickelten Verfahren wurden evaluiert und in einer prototypischen Lösung integriert. Wir konnten zeigen, dass unser Machine-Learning-Verfahren, die von einem Domainexperten erstellten Business-Regeln für das Duplikaterkennungssystem „identity“ verbessern konnte. Zudem konnte der hierzu erforderliche Zeitaufwand verkürzt werden.
The purpose of this study was to 1) compare knee joint kinematics and kinetics of fake-and-cut tasks of varying complexity in 51 female handball players and 2) present a case study of one athlete who ruptured her ACL three weeks post data collection. External knee joint moments and knee joint angles in all planes at the instance of the peak external knee abduction moment (KAM) as well as moment and angle time curves were analyzed. Peak KAMs and knee internal rotation moments were substantially higher than published values obtained during simple change-of-direction tasks and, along with flexion angles, differed significantly between the tasks. Introducing a ball reception and a static defender increased joint loads while they partially decreased again when anticipation was lacking. Our results suggest to use game-specific assessments of injury risk while complexity levels do not directly increase knee loading. Extreme values of several risk factors for a post-test injured athlete highlight the need and usefulness of appropriate screenings.
This paper presents the development of an energy harvesting solution for a driven tool holder. The tool holder environment was analysed, a test stand built and the designed electromagnetic rotation harvester was evaluated. The reported harvester is based on low cost off-the-shelf components and 3D printed parts. The utilisation of SMD coils allows easy adaptation to changing parameters of the integration area. Energy harvesting in tool holders enables predictive maintenance or condition monitoring in the industrial production. These capabilities are mandatory nowadays in regards of IIoT. A reliable energy source is key for continuous monitoring. Changing batteries becomes obsolete. The results provide useful insight for future harvesters.
Deafblindness, a form of dual sensory impairment, signifcantly impacts communication, access to information and mobility. Inde- pendent navigation and wayfnding are main challenges faced by individuals living with combined hearing and visual impairments. We developed a haptic wearable that provides sensory substitution and navigational cues for users with deafblindness by conveying vibrotactile signals onto the body. Vibrotactile signals on the waist area convey directional and proximity information collected via a fisheye camera attached to the garment, while semantic informa- tion is provided with a tapping system on the shoulders. A playful scenario called “Keep Your Distance” was designed to test the navigation system: individuals with deafblindness were “secret agents” that needed to follow a “suspect”, but they should keep an opti- mal distance of 1.5 meters from the other person to win the game. Preliminary fndings suggest that individuals with deafblindness enjoyed the experience and were generally able to follow the directional cues.
The collection of selected papers of the TRIZ Future Conference 2017 is in open access and is included to the Innovator, the journal of the European TRIZ Assocation.
The authors set the focus in this paper on the description of polarization with the help of the Jones calculus and the application of polarization in photography. Furthermore, the effect of the circular polarization filter is described by using the Jones calculus. Also, an enhancement of artistic and creative possibilities in photography through quantization or parametrization by the Jones matrices is presented.
Recently, RobustBench (Croce et al. 2020) has become a widely recognized benchmark for the adversarial robustness of image
classification networks. In it’s most commonly reported sub-task, RobustBench evaluates and ranks the adversarial robustness of trained neural networks on CIFAR10 under AutoAttack (Croce and Hein 2020b) with l∞ perturbations limited to ϵ = 8/255. With leading scores of the currently best performing models of around 60% of the baseline, it is fair to characterize this benchmark to be quite challenging. Despite it’s general acceptance in recent literature, we aim to foster discussion about the suitability of RobustBench as a key indicator for robustness which could be generalized to practical applications. Our line of argumentation against this is two-fold and supported by excessive experiments presented in this paper: We argue that I) the alternation of data by AutoAttack with l∞, ϵ = 8/255 is unrealistically strong, resulting in close to perfect detection rates of adversarial samples even by simple detection algorithms and human observers.
We also show that other attack methods are much harder to detect while achieving similar success rates. II) That results on low resolution data sets like CIFAR10 do not generalize well to higher resolution images as gradient based attacks appear to become even more detectable with increasing resolutions.