Complete list from the university bibliography

Anzahl der Treffer: 464
Erstellt: Wed, 27 Mar 2024 23:38:05 +0100 in 0.0714 sec


Janke, Mario; Mäder, Patrick
7 dimensions of software change patterns. - In: Scientific reports, ISSN 2045-2322, Bd. 14 (2024), 6141, S. 1-17

Evolving software is a highly complex and creative problem in which a number of different strategies are used to solve the tasks at hand. These strategies and reoccurring coding patterns can offer insights into the process. However, they can be highly project or even task-specific. We aim to identify code change patterns in order to draw conclusions about the software development process. For this, we propose a novel way to calculate high-level file overarching diffs, and a novel way to parallelize pattern mining. In a study of 1000 Java projects, we mined and analyzed a total of 45,000 patterns. We present 13 patterns, showing extreme points of the 7 pattern categories we identified. We found that a large number of high-level change patterns exist and occur frequently. The majority of mined patterns were associated with a specific project and contributor, where and by whom it was more likely to be used. While a large number of different code change patterns are used, only a few, mostly unsurprising ones, are common under all circumstances. The majority of code change patterns are highly specific to different context factors that we further explore.



https://doi.org/10.1038/s41598-024-54894-0
Jaziri, Nesrine; Schulz, Alexander; Bartsch, Heike; Müller, Jens; Tounsi, Fares
A novel 2-in-1 heat management and recovery system for sustainable electronics. - In: Energy conversion and management, ISSN 0196-8904, Bd. 303 (2024), 118171, S. 1-12

Overheating poses major challenges in miniaturized electronics, especially as their power consumption increases. For this reason, thermal management is a necessity for efficient electronics, and its optimization is a central task in the design especially for miniaturized compact electronics. On the other hand, recovering this waste energy could be beneficial for battery-free electronics such as wireless sensors and devices located in remote environments, where the charging or changing of batteries are challenging and delicate tasks. Furthermore, batteries are known for their storage capacity degradation over time and environmental pollution. This paper presents the design, development, demonstration, and validation of an innovative 2-in-1 heat management and recovery system for autonomous electronic devices. The design incorporates the use of thermal vias as in-package heat management and vertical thermocouples, enabling simultaneously management and recovery of the heat emitted from a Si-chip. The proposed design is fabricated in Low Temperature Co-fired Ceramic (LTCC) technology, allowing the creation of a monolithic package containing miniaturized multilayer microvias in the range of 90 µm using different materials to act as embedded thermal management and vertical thermocouples, simultaneously. The design consists of 20 lateral (Ag/Co) and 21 vertical (Ag/AgPd) micro-TEGs connected electrically in series in the system. The hybrid TEG is made by combining thick- and thin-film technologies, favoring the use of different materials and technologies with high power factors for further improvements in the field of thermal energy harvesting. The proposed design allows the management of 67 % of the IC temperature by reducing it from 246 ˚C to 80 ˚C using Ag and AgPd thermal vias. At the same time, the system recovers the lost thermal energy to generate 37.5 µW of electrical power at a temperature difference of 58 ˚C. The proposed approach allows simultaneously transitioning into green and sustainable battery-free electronics and enhances the devicés reliability by maintaining thermal stabilization in a miniaturized devices using a monolithic package.



https://doi.org/10.1016/j.enconman.2024.118171
Oppermann, Hannes; Thelen, Antonia; Haueisen, Jens
Single-trial EEG analysis reveals burst structure during photic driving. - In: Clinical neurophysiology, ISSN 1872-8952, Bd. 159 (2024), S. 66-74

Objective: Photic driving in the human visual cortex evoked by intermittent photic stimulation is usually characterized in averaged data by an ongoing oscillation showing frequency entrainment and resonance phenomena during the course of stimulation. We challenge this view of an ongoing oscillation by analyzing unaveraged data. Methods: 64-channel EEGs were recorded during visual stimulation with light flashes at eight stimulation frequencies between 7.8 and 23 Hz for fourteen healthy volunteers. Time-frequency analyses were performed in averaged and unaveraged data. Results: While we find ongoing oscillations in the averaged data during intermittent photic stimulation, we find transient events (bursts) of activity in the unaveraged data. Both resonance and entrainment occur for the ongoing oscillations in the averaged data and the bursts in the unaveraged data. Conclusions: We argue that the continuous oscillations in the averaged signal may be composed of brief, transient bursts in single trials. Our results can also explain previously observed amplitude fluctuations in averaged photic driving data. Significance: Single-trial analyses might consequently improve our understanding of resonance and entrainment phenomena in the brain.



https://doi.org/10.1016/j.clinph.2024.01.005
Bohm, Sebastian; Runge, Erich
Efficient analytical evaluation of the singular BEM integrals for the three-dimensional Laplace and Stokes equations over polygonal elements. - In: Engineering analysis with boundary elements, ISSN 0955-7997, Bd. 161 (2024), S. 70-77

Singularities in the fundamental solutions pose a mathematical challenge for all applications of the boundary element method, if the source and field point lie on the same element. To avoid complex and error-prone numerical procedures, analytical solutions for the integrals that arise are desirable. In this work, easy and efficiently to implement analytical solutions are presented for the fundamental solutions of the three-dimensional Stokes equation as well as Laplace’s equation. Explicit expressions are derived for general triangular elements using constant shape functions. In addition, options for extending to arbitrary polygonal elements are shown. In particular, the three cases that the incenter, the centroid or the vertices of the triangles are used as source points for the calculation are addressed. The impressive numerical efficiency of the method is demonstrated by explicit examples.



https://doi.org/10.1016/j.enganabound.2024.01.013
Ikegami, Yukino; Tsuruta, Setsuo; Kutics, Andrea; Damiani, Ernesto; Knauf, Rainer
Fast ML-based next-word prediction for hybrid languages. - In: Internet of things and cyber-physical systems, ISSN 2667-3452, Bd. 25 (2024), 101064, S. 1-15

Smartphone users are beyond two billion worldwide. Heavy users of the texting application rely on input prediction to reduce typing effort. In languages based on the Roman alphabet, many techniques are available. However, Japanese text is based on multiple character sets such as Kanji (Chinese-like word symbols), Hiragana and Katakana syllable sets. For its time/labor intensive input, next word prediction is crucial. It is still an open challenge. To tackle this, a hybrid language model is proposed. It integrates a Recurrent Neural Network (RNN) with an n-gram model. RNNs are powerful models for learning long sequences for next word prediction. N-gram models are best at current word completion. Our RNN language model (RNN-LM) predicts the next words. According the “price” of the performance gain paid by a higher time complexity, our model best deploys on a client-server architecture. Heavily-loaded RNN-LM deploys on the server while the n-gram model on the client. Our RNN-LM consists of an input layer equipped with word embedding, an output layer, and hidden layers connected with LSTMs (Long Short-Term Memories). Training is done via BPTT (Back Propagation Through Time). For robust training, BPTT is elaborated by learning rate refinement and gradient norm scaling. To avoid overfitting, the dropout technique is applied except for LSTM. Our novel model is compact (2 LSTMs, 650 units per layer), indeed. Due to synergetic elaboration, it shows 10 % lower perplexity than Zaremba's excellent conventional models in our Japanese text prediction experiment. Our model has been incorporated into IME (Input Method Editor) we call Flick. On the Japanese text input experiment, Flick outperforms Mozc (Google Japanese Input) by 16 % in time and 34 % in the number of keystrokes.



https://doi.org/10.1016/j.iot.2024.101064
Walther, Dominik; Junger, Christina; Schmidt, Leander; Schricker, Klaus; Notni, Gunther; Bergmann, Jean Pierre; Mäder, Patrick
Recurrent autoencoder for weld discontinuity prediction. - In: Journal of advanced joining processes, ISSN 2666-3309, Bd. 9 (2024), 100203, S. 1-12

Laser beam butt welding is often the technique of choice for a wide range of industrial tasks. To achieve high quality welds, manufacturers often rely on heavy and expensive clamping systems to limit the sheet movement during the welding process, which can affect quality. Jiggless welding offers a cost-effective and highly flexible alternative to common clamping systems. In laser butt welding, the process-induced joint gap has to be monitored in order to counteract the effect by means of an active position control of the sheet metal. Various studies have shown that sheet metal displacement can be detected using inductive probes, allowing the prediction of weld quality by ML-based data analysis. The probes are dependent on the sheet metal geometry and are limited in their applicability to complex geometric structures. Camera systems such as long-wave infrared (LWIR) cameras can instead be mounted directly behind the laser to overcome a geometry dependent limitation of the jiggles system. In this study we will propose a deep learning approach that utilizes LWIR camera recordings to predict the remaining welding process to enable an early detection of weld interruptions. Our approach reaches 93.33% accuracy for time-wise prediction of the point of failure during the weld.



https://doi.org/10.1016/j.jajp.2024.100203
Machts, René; Hunold, Alexander; Drebenstedt, Christian; Rock, Michael; Leu, Carsten; Haueisen, Jens
Rain may improve survival from direct lightning strikes to the human head. - In: Scientific reports, ISSN 2045-2322, Bd. 14 (2024), 1695, S. 1-9

There is evidence that humans can survive a direct lightning strike to the head. Our question is: could water (rain) on the skin contribute to an increase in the survival rate? We measure the influence of rain during high-energy direct lightning strikes on a realistic three-compartment human head phantom. We find a lower number of perforations and eroded areas near the lightning strike impact points on the head phantom when rain was applied compared to no rain. Current amplitudes in the brain were lower with rain compared to no rain before a fully formed flashover. We conclude that rain on the scalp potentially contributes to the survival rate of 70-90% due to: (1) lower current exposition in the brain before a fully formed flashover, and (2) reduced mechanical and thermal damage.



https://doi.org/10.1038/s41598-023-50563-w
Mühlenhoff, Julian; Radler, Oliver; Sattel, Thomas
Development of a hydraulic actuator for MRI- and radiation-compatible medical applications. - In: Actuators, ISSN 2076-0825, Bd. 13 (2024), 3, 90, S. 1-17

This paper presents methods for the actuation, measurement, and control of a magnetic resonance imaging- and radiation-compatible single-axis translatory actuation system. As an exemplary demanding use case, the axis is developed for a robotic phantom for evaluating emitted radiation doses of radiotherapy devices. For this, the robot has to follow given three-dimensional trajectories of patients’ movements with an accuracy of 200 µm. For enabling use of magnetic resonance imaging, actuation of the robot is realized by hydraulic transmission without any metal parts or electrical components at the imaging side. The hydraulic axis is developed, built-up, and tested. In order to compensate for deviations from the targeted actuation trajectory resulting from tolerances, friction, and non-linearities in the system, a combination of photogrammetric measurement and iterative learning control is applied. The developed photogrammetric system is capable of determining the robot’s position with systematic errors of 35 µm and stochastic errors of 0.3 µm. Different types of iterative learning control methods are applied, parameterized, and tested. With this, the hydraulically actuated axis is able to follow given trajectories with maximum errors below 130 µm.



https://doi.org/10.3390/act13030090
Chu, Xu; Pandey, Sandeep
Non-intrusive, transferable model for coupled turbulent channel-porous media flow based upon neural networks. - In: Physics of fluids, ISSN 1089-7666, Bd. 36 (2024), 2, 025112, S. 025112-1-025112-13

Turbulent flow over permeable interfaces is omnipresent featuring complex flow topology. In this work, a data-driven, end-to-end machine learning model has been developed to model the turbulent flow in porous media. For the same, we have derived a non-linear reduced order model (ROM) with a deep convolution autoencoder. This model can reduce highly resolved spatial dimensions, which is a prerequisite for direct numerical simulation, by 99%. A downstream recurrent neural network has been trained to capture the temporal trend of reduced modes; thus, it is able to provide future evolution of modes. We further evaluate the trained model's capability on a newer dataset with a different porosity. In such cases, fine-tuning could reduce the efforts (up to two-order of magnitude) to train a model with limited dataset (10%) and knowledge and still show a good agreement on the mean velocity profile. Especially, the fine-tuned model shows a better agreement in the porous domain than the channel and interface areas indicating the topological feature is less challenging for training than the multi-scale nature of the turbulent flows. Leveraging the current model, we find that even quick fine-tuning achieves an impressive order-of-magnitude reduction in training time by approximately O(102) and still results in effective flow predictions. This promising discovery encourages the fast development of a substantial amount of data-driven models tailored for various types of porous media. The diminished training time substantially lowers the computational cost when dealing with changing porous topologies, making it feasible to systematically explore interface engineering with different types of porous media.



https://doi.org/10.1063/5.0189632
Schwarz, Andreas; Sellnow, Timothy L.; Geppert, Johanna; Sellnow, Deanna D.
Protective action as an enduring keystone of risk communication: effective form, function and process of risk messaging as advocated by global higher education practitioners during a pandemic. - In: Journal of contingencies and crisis management, ISSN 1468-5973, Bd. 32 (2024), 1, e12545, S. 1-6

Risk communication is a keystone in crisis prevention and mitigation. For that purpose, many institutions worldwide have the task of translating scientific risk information into actionable messages for public safety. As a collaboration among international risk and crisis communication scholars and practitioners, we sought to identify what risk communication practitioners at higher education organizations in the Global South and North identify as essential elements of effective risk communication, based on 32 interviews in 16 countries during the first wave of the COVID-19 pandemic (June-August, 2020). Results exemplify a shared vision for addressing the stickiest, most wicked challenges to effective risk communication globally. The interviews revealed globally shared best practices related to form, function, and process leading directly to what we consider the keystone of effective risk communication: saving lives (outcome).



https://doi.org/10.1111/1468-5973.12545