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This paper describes a taxonomy which allows to assess and compare different implementations of master data objects. A systematic breakdown of core entities provides a framework to tell apart four subdividing categories of master data objects: independent and dependent objects, relational objects, and reference objects that serve to attribute information. This supports the preparation of data migrations from one system to another.
Zeitliche Anpassung führt zu verbesserter Schalllokalisation bei bimodal versorgten CI-/HG-Trägern
(2021)
Bei bimodal versorgten Cochlea-Implantaten (CI) / Hörgerät (HG)-Trägern entsteht durch die unterschiedliche Signalverarbeitung der Geräte eine konstante interaurale Zeitverzögerung in der Größenordnung von mehreren Millisekunden. Für MED-EL CI-Systeme in Kombination mit verschiedenen HG-Typen haben wir den jeweiligen Device-Delay-Mismatch quantifiziert. In der aktuellen Studie untersuchen wir den Einfluss der Device-Delay-Mismatch bei simulierten und tatsächlichen bimodalen Hörern auf die Genauigkeit der Schalllokalisation.
Um den Device-Delay-Mismatch bei bimodal versorgten Patienten zu verringern, haben wir die CI-Stimulation um die gemessene HG-Signallaufzeit und zwei weitere Werte verzögert. Nach einer Angewöhnungsphase war der effektive Winkelfehler bei Verzögerung um die HG-Signallaufzeit hochsignifikant reduziert im Vergleich zu der Testkondition ohne CI-Verzögerung (mittlere Verbesserung: 11 % ; p < .01, Wilcoxon Signed Rank Test). Aber auch mit den beiden weiteren Verzögerungswerten wurden Verbesserungen erreicht. Anhand der Ergebnisse lässt sich der optimale patientenspezifische Verzögerungswert näher eingrenzen.
In bimodal cochlear implant (CI) / hearing aid (HA) users a constant interaural time delay in the order of several milliseconds occurs due to differences in signal processing of the devices. For MED-EL CI systems in combination with different HA types, we have quantified the respective device delay mismatch (Zirn et al. 2015). In the current study, we investigate the effect of the device delay mismatch in simulated and actual bimodal listeners on sound localization accuracy.
To deal with the device delay mismatch in actual bimodal listeners we delayed the CI stimulation according to the measured HA processing delay and two other values. With all delay values highly significant improvements of the rms error in the localization task were observed compared to the test without the delay. The results help to narrow down the optimal patient-specific delay value.
Facial image manipulation is a generation task where the output face is shifted towards an intended target direction in terms of facial attribute and styles. Recent works have achieved great success in various editing techniques such as style transfer and attribute translation. However, current approaches are either focusing on pure style transfer, or on the translation of predefined sets of attributes with restricted interactivity. To address this issue, we propose FacialGAN, a novel framework enabling simultaneous rich style transfers and interactive facial attributes manipulation. While preserving the identity of a source image, we transfer the diverse styles of a target image to the source image. We then incorporate the geometry information of a segmentation mask to provide a fine-grained manipulation of facial attributes. Finally, a multi-objective learning strategy is introduced to optimize the loss of each specific tasks. Experiments on the CelebA-HQ dataset, with CelebAMask-HQ as semantic mask labels, show our model’s capacity in producing visually compelling results in style transfer, attribute manipulation, diversity and face verification. For reproducibility, we provide an interactive open-source tool to perform facial manipulations, and the Pytorch implementation of the model.
Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks
(2021)
Significant progress has been made in the field of deep learning through intensive research over the last decade. So-called convolutional neural networks are an essential component of this research. In this type of neural network, the mathematical convolution operator is used to extract characteristics or anomalies. The purpose of this work is to investigate the extent to which it is possible in certain initial settings to input aerial recordings and flight data of Unmanned Aerial Vehicles (UAVs) in the architecture of a neural network and to detect and map an object. Using the calculated contours or dimensions of the so-called bounding boxes, the position of the objects can be determined relative to the current UAV location.
The applicability of characteristics of local magnetic fields for more precise determination of localization of subjects and/or objects in indoor environments, such as railway stations, airports, exhibition halls, showrooms, or shopping centers, is considered. An investigation has been carried out to find out whether and how low-cost magnetic field sensors and mobile robot platforms can be used to create maps that improve the accuracy and robustness of later navigation with smartphones or other devices.
The aim of this work is the application and evaluation of a method to visually detect markers at a distance of up to five meters and determine their real-world position. Combinations of cameras and lenses with different parameters were studied to determine the optimal configuration. Based on this configuration, camera images were taken after proper calibration. These images are then transformed into a bird's eye view using a homography matrix. The homography matrix is calculated with four-point pairs as well as with coordinate transformations. The obtained images show the ground plane un distorted, making it possible to convert a pixel position into a real-world position with a conversion factor. The proposed approach helps to effectively create data sets for training neural networks for navigation purposes.
For some years now, additive manufacturing (AM) has offered an alternative to conventional manufacturing processes. The strengths of AM are primarily the rapid implementation of ideas into a usable product and the ability to produce geometrically complex shapes. It has also significantly advanced the lightweight design of products made of plastic. So far, the strength of printed components made of polymers is previously very limited.
Recently, new AM processes have become available that allow the embedding of short and also long fibers in polymer matrix. Thus, the manufacturing of components that provide a significant increase in strength becomes possible. In this way, both complex geometries and sophisticated applications can be implemented. This paper therefore investigates how this new technology can be implemented in product development, focusing on sports equipment. An extensive literature research shows that lightweight design plays a decisive role in sports equipment. In addition, the advantages of AM in terms of individualized products and low quantities can be fully exploited.
An example of this approach is the steering system for a seat sled used by paraplegic athletes in the Olympic discipline of Nordic paraskiing. A particular challenge here is the placement and alignment of the long carbon fibers within the polymer matrix and the verification of the strength by means of Finite-Element-Analysis (FEA). In addition, findings from bionics are used to optimize the lightweight design of the steering system. Using this example, it can be shown that the weight of the steering system can be drastically reduced compared to conventional manufacturing. At the same time, a number of parts can be saved through function integration and thus the manufacturing and assembly effort can be reduced significantly.
Today, Additive Manufacturing (AM) is an important part of teaching for the education of future engineers. Therefore, a variety of approaches have been developed in recent years on how to bring the design for additive manufacturing (DfAM) into university teaching. In a detailed literature review, the advantages and disadvantages of the previous approaches are considered and analysed. Based on this, an extended approach is presented in which students analyse and optimize a given product with respect to additive manufacturing. In doing so, the students have to solve challenging tasks in optimization in product development with the help of methodical approaches and practically implement their developed solutions with state-of-the-art additive processes. To work on this task, the students have two different 3D printers at their disposal, which work with different processes and materials. Thus, the students learn to adapt the design to different manufacturing processes and to consider the restrictions of different materials. The assessment of the results from this course is done through feedback and a written survey.
Systematische Erfassung von Einflussfaktoren für das Additive Tooling von Spritzgusswerkzeugen
(2021)
Additive tooling is a quick and cost-effective way of producing injection molded products and high fidelity prototypes using the injection molding process. As part of product development, additive tooling is integrated into a complex process. A lack of design and application knowledge represents a barrier in its use. The present work shows how a Design-Structure-Matrix (DSM) can be used to systematically record and analyze influencing factors and their interrelationships. A systematic literature search is carried out to identify the factors and relationships.
As a reaction to the increasing market dynamics and complex requirements, today’s products need to be developed quickly and customized to the customer’s individual needs. In the past, CAD systems are mainly used to visualize the model that the product designer creates. Generative Design shifts the task of the CAD program by actively participating in the shaping process. This results in more design options and the complexity of the shapes and geometries increases significantly. This potential can be optimally exploited through the combination of Generative Design with Additive Manufacturing (AM). Artificial intelligence and the input of target parameters generate geometries, for example, by creating material for stressed areas, which in turn develops biomorphic shapes and thus significantly reduces the consumption of resources. This contribution aims at the evaluation of existing applications in CAD systems for generative design. Special attention is paid to the requirements in design education and easy access for students. For this purpose, three representative CAD systems are selected and analyzed with the help of a comprehensive example of mass reduction. The aim is to perform an individual result analysis in order to assess the application based on various criteria. By using different materials, the influence of the material for the generation is investigated by comparing the material distribution. By comparing the generated models, differences of the CAD systems can be identified and possible fields of application can be presented. By specifying the manufacturing parameters for the generation of the models, the feasibility of AM can be guaranteed without having to modify the results. The physical implementation of the example by means of Fused Deposition Modeling demonstrates this in an exemplary way and examines the interface of the Generative Design and AM. The results of this contribution will enable an evaluation of the different CAD systems for Generative Design according to technical, visual and economic aspects.
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.
Engineering, construction and operation of complex machines involves a wide range of complicated, simultaneous tasks, which potentially could be automated. In this work, we focus on perception tasks in such systems, investigating deep learning approaches for multi-task transfer learning with limited training data. We show an approach that takes advantage of a technical systems’ focus on selected objects and their properties. We create focused representations and simultaneously solve joint objectives in a system through multi-task learning with convolutional autoencoders. The focused representations are used as a starting point for the data-saving solution of the additional tasks. The efficiency of this approach is demonstrated using images and tasks of an autonomous circular crane with a grapple.
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters
(2021)
We present first empirical results from our ongoing investigation of distribution shifts in image data used for various computer vision tasks. Instead of analyzing the original training and test data, we propose to study shifts in the learned weights of trained models. In this work, we focus on the properties of the distributions of dominantly used 3x3 convolution filter kernels. We collected and publicly provide a data set with over half a billion filters from hundreds of trained CNNs, using a wide range of data sets, architectures, and vision tasks. Our analysis shows interesting distribution shifts (or the lack thereof) between trained filters along different axes of meta-parameters, like data type, task, architecture, or layer depth. We argue, that the observed properties are a valuable source for further investigation into a better understanding of the impact of shifts in the input data to the generalization abilities of CNN models and novel methods for more robust transfer-learning in this domain.
A fundamental and still largely unsolved question in the context of Generative Adversarial Networks is whether they are truly able to capture the real data distribution and, consequently, to sample from it. In particular, the multidimensional nature of image distributions leads to a complex evaluation of the diversity of GAN distributions. Existing approaches provide only a partial understanding of this issue, leaving the question unanswered. In this work, we introduce a loop-training scheme for the systematic investigation of observable shifts between the distributions of real training data and GAN generated data. Additionally, we introduce several bounded measures for distribution shifts, which are both easy to compute and to interpret. Overall, the combination of these methods allows an explorative investigation of innate limitations of current GAN algorithms. Our experiments on different data-sets and multiple state-of-the-art GAN architectures show large shifts between input and output distributions, showing that existing theoretical guarantees towards the convergence of output distributions appear not to be holding in practice.
Correlation Clustering, also called the minimum cost Multicut problem, is the process of grouping data by pairwise similarities. It has proven to be effective on clustering problems, where the number of classes is unknown. However, not only is the Multicut problem NP-hard, an undirected graph G with n vertices representing single images has at most edges, thus making it challenging to implement correlation clustering for large datasets. In this work, we propose Multi-Stage Multicuts (MSM) as a scalable approach for image clustering. Specifically, we solve minimum cost Multicut problems across multiple distributed compute units. Our approach not only allows to solve problem instances which are too large to fit into the shared memory of a single compute node, but it also achieves significant speedups while preserving the clustering accuracy at the same time. We evaluate our proposed method on the CIFAR10 …
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail. In order to achieve higher accuracy, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time. We aim to use machine learning to approximate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network on it. By using a special logarithmic transform we are able to learn the variables tendencies achieving an average score of . On a GPU we achieve a speed-up of 120 compared to the original model.
Recently, adversarial attacks on image classification networks by the AutoAttack (Croce and Hein, 2020b) framework have drawn a lot of attention. While AutoAttack has shown a very high attack success rate, most defense approaches are focusing on network hardening and robustness enhancements, like adversarial training. This way, the currently best-reported method can withstand about 66% of adversarial examples on CIFAR10. In this paper, we investigate the spatial and frequency domain properties of AutoAttack and propose an alternative defense. Instead of hardening a network, we detect adversarial attacks during inference, rejecting manipulated inputs. Based on a rather simple and fast analysis in the frequency domain, we introduce two different detection algorithms. First, a black box detector that only operates on the input images and achieves a detection accuracy of 100% on the AutoAttack CIFAR10 benchmark and 99.3% on ImageNet, for epsilon = 8/255 in both cases. Second, a whitebox detector using an analysis of CNN feature maps, leading to a detection rate of also 100% and 98.7% on the same benchmarks.
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks. At the same time, image synthesis using generative adversarial networks (GANs) has drastically improved over the last few years. The recently proposed TransGAN is the first GAN using only transformer-based architectures and achieves competitive results when compared to convolutional GANs. However, since transformers are data-hungry architectures, TransGAN requires data augmentation, an auxiliary super-resolution task during training, and a masking prior to guide the self-attention mechanism. In this paper, we study the combination of a transformer-based generator and convolutional discriminator and successfully remove the need of the aforementioned required design choices. We evaluate our approach by conducting a benchmark of well-known CNN discriminators, ablate the size of the transformer-based generator, and show that combining both architectural elements into a hybrid model leads to better results. Furthermore, we investigate the frequency spectrum properties of generated images and observe that our model retains the benefits of an attention based generator.
Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These supervised methods allow a much finer-grained control of the output image, offering more flexibility and stability. Nevertheless, the main drawback of such models is the necessity of annotated data. In this work, we introduce an novel framework that benefits from two popular learning techniques, adversarial training and representation learning, and takes a step towards unsupervised conditional GANs. In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model. In this way, we break the traditional dependency between condition and label, substituting the latter by unsupervised features coming from the latent space. Finally, we show that this new technique is able to produce samples on demand keeping the quality of its supervised counterpart.
Generative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This is in particular caused by their highly non-convex optimization space leading to a number of instabilities. Among them, mode collapse stands out as one of the most daunting ones. This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them. In this work, we combat mode collapse using second-order gradient information. To do so, we analyse the loss surface through its Hessian eigenvalues, and show that mode collapse is related to the convergence towards sharp minima. In particular, we observe how the eigenvalues of the are directly correlated with the occurrence of mode collapse. Finally, motivated by these findings, we design a new optimization algorithm called nudged-Adam (NuGAN) that uses spectral information to overcome mode collapse, leading to empirically more stable convergence properties.
In this preliminary report, we present a simple but very effective technique to stabilize the training of CNN based GANs. Motivated by recently published methods using frequency decomposition of convolutions (eg Octave Convolutions), we propose a novel convolution scheme to stabilize the training and reduce the likelihood of a mode collapse. The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training. Intuitively, this method forces GANs to learn low frequency coarse image structures before descending into fine (high frequency) details. Our approach is orthogonal and complementary to existing stabilization methods and can simply plugged into any CNN based GAN architecture. First experiments on the CelebA dataset show the effectiveness of the proposed method.
We demonstrate how to exploit group sparsity in order to bridge the areas of network pruning and neural architecture search (NAS). This results in a new one-shot NAS optimizer that casts the problem as a single-level optimization problem and does not suffer any performance degradation from discretizating the architecture.
Despite the success of convolutional neural networks (CNNs) in many computer vision and image analysis tasks, they remain vulnerable against so-called adversarial attacks: Small, crafted perturbations in the input images can lead to false predictions. A possible defense is to detect adversarial examples. In this work, we show how analysis in the Fourier domain of input images and feature maps can be used to distinguish benign test samples from adversarial images. We propose two novel detection methods: Our first method employs the magnitude spectrum of the input images to detect an adversarial attack. This simple and robust classifier can successfully detect adversarial perturbations of three commonly used attack methods. The second method builds upon the first and additionally extracts the phase of Fourier coefficients of feature-maps at different layers of the network. With this extension, we are able to improve adversarial detection rates compared to state-of-the-art detectors on five different attack methods. The code for the methods proposed in the paper is available at github.com/paulaharder/SpectralAdversarialDefense
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.
The term “attribute transfer” refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator. In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our “Attribute Transfer Inpainting Generative Adversarial Network” (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.
The Go programming language is an increasingly popular language but some of its features lack a formal investigation. This article explains Go's resolution mechanism for overloaded methods and its support for structural subtyping by means of translation from Featherweight Go to a simple target language. The translation employs a form of dictionary passing known from type classes in Haskell and preserves the dynamic behavior of Featherweight Go programs.
The internal crowdsourcing-based ideation within a company can be defined as an involvement of its staff, specialists, managers, and other employees, to propose solution ideas for a pre-defined problem. This paper addresses a question, how many participants of the company-internal ideation process are required to nearly reach the ideation limit for the problems with a finite number of workable solutions. To answer the research question, the author proposes a set of metrics and a non-linear ideation performance function with a positive decreasing slope and ideation limit for the closed-ended problems. Three series of experiments helped to explore relationships between the metric attributes and resulted in a mathematical model which allows companies to predict the productivity metrics of their crowdsourcing ideation activities such as quantity of different ideas and ideation limit as a function of the number of contributors, their average personal creativity and ideation efficiency of a contributors’ group.
A coordinated operation of decentralised micro-scale hybrid energy systems within a locally managed network such as a district or neighbourhood will play a significant role in the sector-coupled energy grid of the future. A quantitative analysis of the effects of the primary energy factors, energy conversion efficiencies, load profiles, and control strategies on their energy-economic balance can aid in identifying important trends concerning their deployment within such a network. In this contribution, an analysis of the operational data from five energy laboratories in the trinational Upper-Rhine region is evaluated and a comparison to a conventional reference system is presented. Ten exemplary data-sets representing typical operation conditions for the laboratories in different seasons and the latest information on their national energy strategies are used to evaluate the primary energy consumption, CO2 emissions, and demand-related costs. Various conclusions on the ecologic and economic feasibility of hybrid building energy systems are drawn to provide a toe-hold to the engineering community in their planning and development.
In the field of network security, the detection of possible intrusions is an important task to prevent and analyse attacks. Machine learning has been adopted as a particular supporting technique over the last years. However, the majority of related published work uses post mortem log files and fails to address the required real-time capabilities of network data feature extraction and machine learning based analysis [1-5]. We introduce the network feature extractor library FEX, which is designed to allow real-time feature extraction of network data. This library incorporates 83 statistical features based on reassembled data flows. The introduced Cython implementation allows processing individual packets within 4.58 microseconds. Based on the features extracted by FEX, existing intrusion detection machine learning models were examined with respect to their real-time capabilities. An identified Decision-Tree Classifier model was thus further optimised by transpiling it into C Code. This reduced the prediction time of a single sample to 3.96 microseconds on average. Based on the feature extractor and the improved machine learning model an IDS system was implemented which supports a data throughput between 63.7 Mbit/s and 2.5 Gbit/s making it a suitable candidate for a real-time, machine-learning based IDS.
The nonlinear behavior of inverters is mainly influenced by the interlocking and switching times of the semiconductors. In the following work, a method is presented that enables the possibility of an online identification of the switching times of the semiconductors. This information allows a compensation of the non-linear behavior, a reduction of the locking time and can be used for diagnostic purposes. First, a theoretical derivation of the method is made by considering different cases when switching of the inverter and deriving identification possibilities. The method is then extended so that the entire module is taken into account. Furthermore, a possible theoretical implementation is shown. After the methodology has been investigated with possible limitations, boundary conditions and with respect to real hardware, an implementation in the FPGA is performed. Finally, the results are presented, discussed
and further improvements are presented in an outlook.
As one result of the digital transformation in the automotive industry, new digital business models comprising software-based solutions are demanded by OEMs. To adequately meet these new requirements, automotive suppliers implement interdisciplinary roles – called Customer Solution Designers. However, due to the novelty, the Customer Solution Design research field is not yet well developed, neither in theory nor in practice. Besides giving an overview of the current state of the Customer Solution Design research field, the core of this paper is two-fold: Based on the conduction of 14 guided expert interviews with selected experts of a large German automotive supplier, we establish a uniform understanding of the Customer Solution Design role by using the Role Model Canvas (I). In addition, a case study strategy comprising two software-based projects, which are executed by a large German automotive supplier, is used to derive a common approach for Customer Solution Design in the context of an agile business framework (II).
Due to the pandemic of 2020, many teaching and research institutions are confronted with extraordinary working conditions. In order to enable empirical data collection under these special circumstances, teachers and scientists need to respond flexibly and new concepts need to be developed. This paper deals with the challenges that arise in day-to-day teaching and provides different approaches to meet these challenges. It covers quantitative surveys, remote UX-testing methods as an alternative to eye tracking studies in the lab, as well as face-to-face user experience testings under strict hygiene measures.
In an experience economy market competition in software branches is becoming more and more intense. Technical innovations, global retail practices and the multidimensional conception of experiences provide both opportunities and challenges for companies worldwide. Retailers strive for an optimized conversion rate, but poor UX still abound. Particularly Germany-based companies are less evolved in an international comparison of industrialized economies. The value of integrating users in the development process is recognized, but methodologies must carefully be incorporated into existing agile workflows. The goal of this study is to bridge the gaps between internal agency and external client and user interests. The contribution is four-fold: an overview of the current status of customer centricity in the E-Commerce branch of trade is provided (I). Based on this corpus, a methodical framework, aiming to incorporate the experience logic in UX practices within an agile project team, is presented (II). The framework is applied by a single case study - the shop relaunch of a motorbike accessory store (III). Finally, all interest groups (UX, development and project management) are incorporated in the qualitative content analysis (IV).
Offenburg university of Applied Sciences offers pre-study extracurricular preparatory courses for future engineering students in mathematics and physics. Due to pandemic restrictions, the two-week preparatory physics course preceeding winter term 2020/21 was presented as an online -only course.
Students enrolled to the course attended eight online lect ures of approximately 90 minutes duration followed by a group assignment. Both lectures and tutoring to the group assignment used a videoconference system with group sizes of 120 (lecture) and 6 (peer instruction and group assignments). The eight lectures focused on the high school physics curriculum of mechanics, electricity, thermodynamics and optics. Each lecture included four “peer instruction” questions to improve student activation. Student responses were collected using an audience response online tool.
The “peer instruction” questions were discussed by the students in online groups of six students. These groups also received written group assignments consisting of common textbook exercises and additional problems with incomplete information. To solve these problems, groups were encouraged to discuss possible solutions. The on-line course attendance was monitored and showed a characteristic exponential “decay” curve with a half-life of approximately 18 lectures which is comparable to conventional courses: Around 73% of the students enrolled in the preparatory course attended all eight lectures. In addition to the attendance, the progress of the participants was monitored by two online tests: A pre-course online test the first course day and a post -course online test on the last day.
The completion of both tests was highly recommended, but not a formal requirement for the students. The fraction of students completing the pre-course, but not the post-course test was used as an estimate for the drop-out rate of (34±3)%.
The twin concept is increasingly used for optimization tasks in the context of Industry 4.0 and digitization. The twin concept can also help small and medium-sized enterprises (SME) to exploit their energy flexibility potential and to achieve added value by appropriate energy marketing. At the same time, this use of flexibility helps to realize a climate-neutral energy supply with high shares of renewable energies. The digital twin reflects real production, power flows and market influences as a computer model, which makes it possible to simulate and optimize on-site interventions and interactions with the energy market without disturbing the real production processes. This paper describes the development of a generic model library that maps flexibility-relevant components and processes of SME, thus simplifying the creation of a digital twin. The paper also includes the development of an experimental twin consisting of SME hardware components and a PLC-based SCADA system. The experimental twin provides a laboratory environment in which the digital twin can be tested, further developed and demonstrated on a laboratory scale. Concrete implementations of such a digital twin and experimental twin are described as examples.
IoT networks are increasingly used as entry points for cyberattacks, as often they offer low-security levels, as they may allow the control of physical systems and as they potentially also open the access to other IT networks and infrastructures. Existing intrusion detection systems (IDS) and intrusion prevention systems (IPS) mostly concentrate on legacy IT networks. Nowadays, they come with a high degree of complexity and adaptivity, including the use of artificial intelligence. It is only recently that these techniques are also applied to IoT networks. In this paper, we present a survey of machine learning and deep learning methods for intrusion detection, and we investigate how previous works used federated learning for IoT cybersecurity. For this, we present an overview of IoT protocols and potential security risks. We also report the techniques and the datasets used in the studied works, discuss the challenges of using ML, DL and FL for IoT cybersecurity and provide future insights.
Modeling of Random Variations in a Switched Capacitor Circuit based Physically Unclonable Function
(2020)
The Internet of Things (IoT) is expanding to a wide range of fields such as home automation, agriculture, environmental monitoring, industrial applications, and many more. Securing tens of billions of interconnected devices in the near future will be one of the biggest challenges. IoT devices are often constrained in terms of computational performance, area, and power, which demand lightweight security solutions. In this context, hardware-intrinsic security, particularly physically unclonable functions (PUFs), can provide lightweight identification and authentication for such devices. In this paper, random capacitor variations in a switched capacitor PUF circuit are used as a source of entropy to generate unique security keys. Furthermore, a mathematical model based on the ordinary least square method is developed to describe the relationship between random variations in capacitors and the resulting output voltages. The model is used to filter out systematic variations in circuit components to improve the quality of the extracted secrets.
Grey-box modelling combines physical and data-driven models to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling, as differential equations given by physical laws and neural networks can be combined in a single modelling framework. This simplifies the simulation and optimization and allows to consider irregularly-sampled data during training and evaluation of the model. We demonstrate this approach using two levels of model complexity; first, a simple parallel resistor-capacitor circuit; and second, an equivalent circuit model of a lithium-ion battery cell, where the change of the voltage drop over the resistor-capacitor circuit including its dependence on current and State-of-Charge is implemented as NODE. After training, both models show good agreement with analytical solutions respectively with experimental data.
Disturbances of the cardiac conduction system causing reentry mechanisms above the atrioventricular (AV) node are induced by at least one accessory pathway with different conducting properties and refractory periods. This work aims to further develop the already existing and continuously expanding Offenburg heart rhythm model to visualise the most common supraventricular reentry tachycardias to provide a better understanding of the cause of the respective reentry mechanism.
Patients with focal ventricular tachycardia are at risk of hemodynamic failure and if no treatment is provided the mortality rate can exceed 30%. Therefore, medical professionals must be adequately trained in the management of these conditions. To achieve the best treatment, the origin of the abnormality should be known, as well as the course of the disease. This study provides an opportunity to visualize various focal ventricular tachycardias using the Offenburg cardiac rhythm model.
Active participation of industrial enterprises in electricity markets - a generic modeling approach
(2021)
Industrial enterprises represent a significant portion of electricity consumers with the potential of providing demand-side energy flexibility from their production processes and on-site energy assets. Methods are needed for the active and profitable participation of such enterprises in the electricity markets especially with variable prices, where the energy flexibility available in their manufacturing, utility and energy systems can be assessed and quantified. This paper presents a generic model library equipped with optimal control for energy flexibility purposes. The components in the model library represent the different technical units of an industrial enterprise on material, media, and energy flow levels with their process constraints. The paper also presents a case study simulation of a steel-powder manufacturing plant using the model library. Its energy flexibility was assessed when the plant procured its electrical energy at fixed and variable electricity prices. In the simulated case study, flexibility use at dynamic prices resulted in a 6% cost reduction compared to a fixed-price scenario, with battery storage and the manufacturing system making the largest contributions to flexibility.
When shopping online, it is usually not possible to view products in the same way as you are used to when shopping offline. With augmented reality (AR), it is not only possible to view the product in detail, but also to view it at home in the real environment. Such an AR application sets stimuli that can affect the users and their purchase decision and Word-of-mouth intention. In this work, we assume that when viewing a product in AR, not only affective internal states but also cognitive perception processes have an impact on purchase decision and Word-of-mouth intention. While positive affective reactions have already been studied in the context of AR, this paper will also describe inner cognitive perception processes, using the construct of AR authenticity. To test these assumptions, a study was conducted with 155 participants. The results show that both the purchase intention and the Word-of-mouth intention are influenced by the constructs of positive affective reactions and AR authenticity.
Ein tiefgreifendes Verständnis des zyklischen Plastizitätsverhaltens metallischer Werkstoffe ist sowohl für die Optimierung der Materialeigenschaften als auch für die industrielle Auslegung und Fertigung von Bauteilen von hoher Relevanz. Insbesondere moderne Legierungen wie Duplex-Stähle zeigen unter Lastumkehr aufgrund des komplexen mehrphasigen Gefüges sowie der Neigung zu verschiedenen Ausscheidungsreaktionen einen ausgeprägten Bauschinger-Effekt, welcher bei technischen Umformvorgängen berücksichtigt werden muss. Der Bauschinger-Effekt begründet sich maßgeblich in der Entstehung von Rückspannungen, welche aus dem unterschiedlichen Plastizitätsverhalten der austenitischen und ferritischen Phase resultieren. Instrumentierte Mikroindenter-Versuche in ausgewählten Ferrit- und Austenitkörnern haben gezeigt, dass austenitische Gefügebestandteile durch einen deutlich früheren Fließbeginn sowie eine stärkere Rückplastifizierung während der Entlastung charakterisiert sind. Zudem wurde nachgewiesen, dass Ausscheidungen im Rahmen einer 475°C-Versprödung diesen Phasenunterschied verstärken und somit in einem höheren Bauschinger-Effekt resultieren.
Photonics meet digital art
(2014)
The paper focuses on the work of an interdisciplinary project between photonics and digital art. The result is a poster collection dedicated to the International Year of Light 2015. In addition, an internet platform was created that presents the project. It can be accessed at http://www.magic-of-light.org/iyl2015/index.htm. From the idea to the final realization, milestones with tasks and steps will be presented in the paper. As an interdisciplinary project, students from technological degree programs were involved as well as art program students. The 2015 Anniversaries: Alhazen (1015), De Caus (1615), Fresnel (1815), Maxwell (1865), Einstein (1905), Penzias Wilson, Kao (1965) and their milestone contributions in optics and photonics will be highlighted.
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.
Die angestrebten Klimaschutzziele erfordern, dass Erneuerbare Energien längerfristig zur Hauptenergiequelle der Energieversorgung werden. Um dieses ehrgeizige Ziel zu erreichen, ist es angebracht konventionelle und erneuerbare Energie oder noch besser nachhaltige Einzelprozesse intelligent miteinander zu verknüpfen.
Das Projekt EBIPREP wird von einer interdisziplinären Forschergruppe bestehend aus Chemikern, Prozessingenieuren und Bioprozessingenieuren sowie Physikern, die auf Sensoren und Prozesssteuerung spezialisiert sind durchgeführt. Das Ziel ist es, neue Lösungen für die Nutzungswege von Holzhackschnitzeln und den bei der mechanischen Trocknung anfallenden Holzpresssaft zu entwickeln. Neben der Hackschnitzelvergasung und der katalytischen Reinigung des Holzgases steht die Nutzung des Holzpresssafts in Biogasanlagen und bei der biotechnologischen Wertstofferzeugung, z.B. bei der Enzymherstellung, im Vordergrund.
Was wir tun?
Das EBIPREP-Projekt wird von einer interdisziplinären Forschungsgruppe durchgeführt, die sich aus Chemikern, Prozessingenieuren, Bioprozessingenieuren und Physikern zusammensetzt. Ziel ist es, neue Lösungen für den Einsatz von Hackschnitzeln und Holzpresssaft zu entwickeln, die durch ein innovatives mechanisches Trocknungsverfahren gewonnen werden. Neben der Holzvergasung und katalytischen Reinigung des Holzgases ist der Einsatz von Holzpresssaft in Biogasanlagen und in biotechnologischen Produktionsprozessen von Wertstoffen vorgesehen. Holzhackschnitzel werden thermisch vergast. Es werden Online-Sensoren entwickelt, um die relevanten Parameter der stabilisierten und optimierten Einzelprozesse auszuwerten. Die Verknüpfung von thermischen und biotechnologischer Konversionsprozessen könnte dazu beitragen, die Dimension von Biogasreaktoren erheblich zu reduzieren. Diese Tatsache wird folglich zu einer spürbaren Kostensenkung führen.
Ziele des EBIPREP-Projekts
• die Vorteile der thermischen und biologischen Umwandlung von Biomasse zu kombinieren;
• Entwicklung eines Verfahrens zur Reduzierung von Schadstoffemissionen mit innovativen Sensoren und katalytische Behandlung von Synthesegasen;
• nachhaltige Produktion biotechnologischer wertvoller Produkte
• wirtschaftliche und ökologische Analyse des Gesamtprozesses im Vergleich zu den Einzelprozessen
• Einsatz von Prozessabwässern zur Erzeugung regenerativer Energie oder biotechnologischer Wertstoffe
• Erwerb neuer Kenntnisse auf dem Gebiet der Rückgewinnungstechnik von Rückständen
• und Energieerzeugung;
• Erweiterung neuer Anwendungsfelder für innovative Sensoren und Keramik
• Schäume für Katalysatoren;
• Senkung der Kosten für die Biogasproduktion
Im geplanten Übersichtsvortrag werden die vernetzten Strukturen des Projekts EBIPREP und deren zentralen Ergebnisse vorgestellt.
Investigation of the Angle Dependency of Self-Calibration in Multiple-Input-Multiple-Output Radars
(2021)
Multiple-Input-Multiple-Output (MIMO) is a key technology in improving the angular resolution (spatial resolution) of radars. In MIMO radars the amplitude and phase errors in antenna elements lead to increase in the sidelobe level and a misalignment of the mainlobe. As the result the performance of the antenna channels will be affected. Firstly, this paper presents analysis of effect of the amplitude and phase errors on angular spectrum using Monte-Carlo simulations. Then, the results are compared with performed measurements. Finally, the error correction with a self-calibration method is proposed and its angle dependency is evaluated. It is shown that the values of the errors change with an incident angle, which leads to a required angle-dependent calibration.