Offenburg University of Applied Sciences
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Unanticipated fake-and-cut maneuvers do not increase knee abduction moments in sport-specific tasks: Implication for ACL injury prevention and risk screening
Get PDFNon-contact anterior cruciate ligament injuries typically occur during cutting maneuvers and are associated with high peak knee abduction moments (KAM) within early stance. To screen athletes for injury risk or quantify the efficacy of prevention programs, it may be necessary to design tasks that mimic game situations. Thus, this study compared KAMs and ranking consistency of female handball players in three sport-specific fake-and-cut tasks of increasing complexity. The biomechanics of female handball players (n = 51, mean ± SD: 66.9 ± 7.8 kg, 1.74 ± 0.06 m, 19.2 ± 3.4 years) were recorded with a 3D motion capture system and force plates during three standardized fake-and-cut tasks. Task 1 was designed as a simple pre-planned cut, task 2 included catching a ball before a pre-planned cut in front of a static defender, and task 3 was designed as an unanticipated cut with three dynamic defenders involved. Inverse dynamics were used to calculate peak KAM within the first 100 ms of stance. KAM was decomposed into the frontal plane knee joint moment arm and resultant ground reaction force. RANOVAs (α ≤ 0.05) were used to reveal differences in the KAM magnitudes, moment arm, and resultant ground reaction force for the three tasks. Spearman's rank correlations were calculated to test the ranking consistency of the athletes' KAMs. There was a significant task main effect on KAM (p = 0.02; ηp2 = 0.13). The KAM in the two complex tasks was significantly higher (task 2: 1.73 Nm/kg; task 3: 1.64 Nm/kg) than the KAM in the simplest task (task 1: 1.52 Nm/kg). The ranking of the peak KAM was consistent regardless of the task complexity. Comparing tasks 1 and 2, an increase in KAM resulted from an increased frontal plane moment arm. Comparing tasks 1 and 3, higher KAM in task 3 resulted from an interplay between both moment arm and the resultant ground reaction force. In contrast to previous studies, unanticipated cutting maneuvers did not produce the highest KAMs. These findings indicate that the players have developed an automated sport-specific cutting technique that is utilized in both pre-planned and unanticipated fake-and-cut tasks
Generation of Industrial Electricity and Heat Demand Profiles for Energy System Analysis
Get PDFTo achieve Germany's climate targets, the industrial sector, among others, must be transformed. The decarbonization of industry through the electrification of heating processes is a promising option. In order to investigate this transformation in energy system models, high-resolution temporal demand profiles of the heat and electricity applications for different industries are required. This paper presents a method for generating synthetic electricity and heat load profiles for 14 industry types. Using this methodology, annual profiles with a 15-minute resolution can be generated for both energy demands. First, daily profiles for the electricity demand were generated for 4 different production days. These daily profiles are additionally subdivided into eight end-use application categories. Finally, white noise is applied to the profile of the mechanical drives. The heat profile is similar to the electrical but is subdivided into four temperature ranges and the two applications hot water and space heating. The space heating application is additionally adjusted to the average monthly outdoor temperature. Both time series were generated for the analysis of an electrification of industrial heat application in energy system modelling
FrequencyLowCut Pooling – Plug & Play against Catastrophic Overfitting
No full textOver the last years, Convolutional Neural Networks (CNNs) have been the dominating neural architecture in a wide range of computer vision tasks. From an image and signal processing point of view, this success might be a bit surprising as the inherent spatial pyramid design of most CNNs is apparently violating basic signal processing laws, i.e. Sampling Theorem in their down-sampling operations. However, since poor sampling appeared not to affect model accuracy, this issue has been broadly neglected until model robustness started to receive more attention. Recent work in the context of adversarial attacks and distribution shifts, showed after all, that there is a strong correlation between the vulnerability of CNNs and aliasing artifacts induced by poor down-sampling operations. This paper builds on these findings and introduces an aliasing free down-sampling operation which can easily be plugged into any CNN architecture: FrequencyLowCut pooling. Our experiments show, that in combination with simple and Fast Gradient Sign Method (FGSM) adversarial training, our hyper-parameter free operator substantially improves model robustness and avoids catastrophic overfitting. Our code is available at https://github.com/GeJulia/flc_poolin
Robust Models are less Over-Confident
No full textDespite the success of convolutional neural networks (CNNs) in many academic benchmarks for computer vision tasks, their application in the real-world is still facing fundamental challenges. One of these open problems is the inherent lack of robustness, unveiled by the striking effectiveness of adversarial attacks. Current attack methods are able to manipulate the network's prediction by adding specific but small amounts of noise to the input. In turn, adversarial training (AT) aims to achieve robustness against such attacks and ideally a better model generalization ability by including adversarial samples in the trainingset. However, an in-depth analysis of the resulting robust models beyond adversarial robustness is still pending. In this paper, we empirically analyze a variety of adversarially trained models that achieve high robust accuracies when facing state-of-the-art attacks and we show that AT has an interesting side-effect: it leads to models that are significantly less overconfident with their decisions, even on clean data than non-robust models. Further, our analysis of robust models shows that not only AT but also the model's building blocks (like activation functions and pooling) have a strong influence on the models' prediction confidences. Data & Project website: https://github.com/GeJulia/robustness_confidences_evaluatio
Does Medical Imaging learn different Convolution Filters?
No full textRecent work has investigated the distributions of learned convolution filters through a large-scale study containing hundreds of heterogeneous image models. Surprisingly, on average, the distributions only show minor drifts in comparisons of various studied dimensions including the learned task, image domain, or dataset. However, among the studied image domains, medical imaging models appeared to show significant outliers through "spikey" distributions, and, therefore, learn clusters of highly specific filters different from other domains. Following this observation, we study the collected medical imaging models in more detail. We show that instead of fundamental differences, the outliers are due to specific processing in some architectures. Quite the contrary, for standardized architectures, we find that models trained on medical data do not significantly differ in their filter distributions from similar architectures trained on data from other domains. Our conclusions reinforce previous hypotheses stating that pre-training of imaging models can be done with any kind of diverse image data
A Method for Additive Tooling in Integrated Product Development
No full textThis paper presents a method for supporting the application of Additive Tooling (AT)-based validation environments in integrated product development. Based on a case study, relevant process steps, activities and possible barriers in the realisation of an injection-moulded product are identified and analysed. The aim of the method is to support the target-oriented application of Additive Tooling to obtain physical prototypes at an early stage and to shorten validation cycles
Evaluation of Precision, Accuracy and Threshold for the Design of Vibrotactile Feedback in Eye Tracking Applications
No full textA novelty solution for controls of assistive technology represent the usage of eye tracking devices such as for smart wheelchairs and robotic arms [10, 4]. In this context usage supporting methods like artificial feedback are not well explored. Vibrotactile feedback has shown to be helpful to decrease the cognitive load on the visual and auditive channels and can provide a perception of touch [17]. People with severe limitations of motor functions could benefit from eye tracking controls supported with vibrotactile feedback. In this study fundamental results will be presented in the design of an appropriate vibrotactile feedback system for eye tracking applications. We will show that a perceivable vibrotactile stimulus has no significant effect on the accuracy and precision of a head worn eye tracking device. It is anticipated that the results of this paper will lead to new insights in the design of vibrotactile feedback for eye tracking applications and eye tracking controls
A Survey-Based Study to Identify User Annoyances of German Voice Assistant Users
No full textVoice user interfaces (VUIs) offer an intuitive, fast and convenient way for humans to interact with machines and computers. Yet, whether they’ll be truly successful and find widespread uptake in the near future depends on the user experience (UX) they offer. With this survey-based study (n = 108), we aim to identify the major annoyances German voice assistant users are facing in voice-driven human-computer interactions. The results of our questionnaire show that irritations appear in six categories: privacy issues, unwanted activation, comprehensibility, response quality, conversational design and voice characteristics. Our findings can help identify key areas of work to optimize voice user experience in order to achieve greater adaptation of the technology. In addition, they can provide valuable information for the further development and standardization of voice user experience (VUX) research
Digitale Kundeninteraktionen empirisch erforschen
No full textDigitale Kundeninteraktionen haben in den letzten Jahren enorm an Bedeutung gewonnen: Sowohl im B2B als auch im B2C sind weite Teile der Interaktion digital bzw. hybrid konzipiert. Ob ein bargeldloser Bezahlvorgang an der Kasse, ein Bestellformular beim Onlinekauf oder der Kauf einer Bahnfahrkarte am Automaten – die Kunden und Kundinnen haben digitale Interaktionen in vielfältigen Varianten bereits erlebt und kommen mehr oder weniger gut damit zurecht
Überblick, Energiequellen und Energiebedarf
No full textUnter fossilen Energieträgern verstehen sich energie- und kohlenstoffhaltige Stoffe, die in mehreren Millionen Jahren aus Biomassen unter dafür günstigen Bedingungen (Sauerstoffausschluss) gebildet wurden. Das geologische Alter von Steinkohle beträgt über 250 Millionen Jahre, das von Braunkohle ca. 50 Millionen. Derzeit werden deutlich mehr fossile Energieträger verbraucht als nachgebildet. Während in Deutschland die Stilllegung von Dampfkraftwerken, die mit Steinkohle befeuert werden, begonnen hat, steigt global der Verbrauch dieser Brennstoffe weiterhin an. Die Steinkohleförderung erreichte im Jahr 2019 weltweit den Wert von 7,3 Milliarden Tonnen, mit einer Steigerung von immerhin fast 3 % gegenüber dem Vorjahr 2018 nach [1]. Dieser Anstieg erfolgte hauptsächlich in China und Indonesien – in Deutschland ging der Kohleverbrauch in diesem Zeitraum zurück