Risky Play: A Risk-based Case Study for Common Mode Current Assessment of a Medical Plasma Device (Dataset)

This dataset contains raw- and de-embedding data which are the source of the referenced publication's plots. Uploaded are raw common mode current measurements in dBm as a .mat file, touchstone files for the cable used and characteristics of the Fischer Customs Communication F-75 and F-2000 probes as .csv, both used for de-embedding cable and current probe from the raw data. In addition, touchstone files of the input impedance measurements are contained

SySCoRe Repeatability Package (ARCH 2023 Category Report Case Study)

Synthesis via Stochastic Coupling Relations (SySCoRe) for stochastic continuous-state systems

Assessment of the Effect of a Test Setup on the Input Impedance Measurement of Cables (Dataset)

The dataset includes the source of all measured and simulated data utilized in the paper titled "Assessment of the Effect of a Test Setup on the Input Impedance Measurement of Cables". Simulation data is available in ".xlsx" format, whereas measurement data is provided in ".s1p" format

SySCoRe: Synthesis via Stochastic Coupling Relations

SySCoRe is a MATLAB toolbox that synthesizes controllers for stochastic continuous-state systems to satisfy temporal logic specifications. Starting from a system description and a co-safe temporal logic specification, SySCoRe provides all necessary functions for synthesizing a robust controller and quantifying the associated formal robustness guarantees. It distinguishes itself from other available tools by supporting nonlinear dynamics, complex co-safe temporal logic specifications over infinite horizons and model-order reduction. To achieve this, SySCoRe first generates a finite-state abstraction of the provided model and performs probabilistic model checking. Then, it establishes a probabilistic coupling to the original stochastic system encoded in an approximate simulation relation, based on which a lower bound on the satisfaction probability is computed. SySCoRe provides non-trivial lower bounds for infinite-horizon properties and unbounded disturbances since its computed error does not grow linear in the horizon of the specification. It exploits a tensor representation to facilitate the efficient computation of transition probabilities. We showcase these features on several benchmarks

Steady-state Temperature Calculation of Transistors using Harmonic Balance

Accurate calculation of semiconductor losses and temperature is the foundation of any design methodology for a power electronic converter. Computation accuracy and speed play a vital role if a large set of parameters needs to be considered. Averaged loss models often neglect the temperature dependence of transistors, leading to fast, but inaccurate results. In contrast, iterative methods and simulation tools, which can include temperature dependence, take significantly longer to compute, but yield more precise results. This paper presents a best of both worlds approach, by using the harmonic balance method to obtain the steady-state solution for any inverter topology including temperature dependent conduction and switching losses. The proposed method solves for the discrete Fourier series of the device temperature, by expressing the temperature dependence and operating parameters in the frequency domain. The set of equations for each coefficient is solved by a single matrix inversion, resulting in very fast computation for steady-state temperature cycles. The steady-state operation of over one thousand possible inverter designs is calculated within less than one minute, matching iterative simulation in device temperature, conduction and switching losses, at a fraction of the computation time. In addition, the method shows good agreement with temperature measurements of a three-phase silicon-carbide inverter

Aircraft Marshaling Signals Dataset of FMCW Radar and Event-Based Camera for Sensor Fusion

The advent of neural networks capable of learning salient features from variance in the radar data has expanded the breadth of radar applications, often as an alternative sensor or a complementary modality to camera vision. Gesture recognition for command control is the most commonly explored application. Nevertheless, more suitable benchmarking datasets are needed to assess and compare the merits of the different proposed solutions. Furthermore, most current publicly available radar datasets used in gesture recognition provide little diversity, do not provide access to raw ADC data, and are not significantly challenging. To address these shortcomings, we created and made available a new dataset that combines two synchronized modalities: radar and dynamic vision camera of 10 aircraft marshalling signals at several distances and angles, recorded from 13 people. Moreover, we propose a sparse encoding of the time domain (ADC) signals that achieve a dramatic data rate reduction (>76%) while retaining the efficacy of the downstream FFT processing (<2% accuracy loss on recognition tasks). Finally, we demonstrate early sensor fusion results based on compressed radar data encoding in range-Doppler maps with dynamic vision data. This approach achieves higher accuracy than either modality alone

Nanosecond Repetitively Pulsed Plasmas with MHz Bursts for CO2 Dissociation

Dataset belonging to the above mentioned submission to Journal of Physics

Nanosecond Repetitively Pulsed Plasmas with MHz Bursts for CO2 Dissociation

Dataset belonging to the above mentioned submission to Journal of Physics

Cracks in Steel Bridges (CSB) dataset: data underlying the publication: Loss function inversion for improved crack segmentation in steel bridges using a CNN framework

The presented dataset used for the experiments is described in the article "Loss function inversion for improved crack segmentation in steel bridges using a CNN framework" (doi:https://doi.org/10.1016/j.autcon.2024.105896). The dataset consists of images of steel bridge structures and pixel-wise fatigue crack annotations. Some of the images contain bridge structures with cracks or corrosion, while others capture structures without any defect. The images are provided by bridge infrastructure owners "Rijkswatersaat" and "ProRail" and by "Nebest" engineering company. The annotation of images was made using a semi-automatic annotation tool described in the article "Segmentation Tool for Images of Cracks" (doi:https://doi.org/10.1007/978-3-031-35399-4_8) and which implementation is available at https://github.com/akomp22/crack-segmentation-tool. The dataset consists of high-resolution images and is stored in the folder "entire images". The images are divided into test and train sets. Images that capture cracks are stored in the folder "crack_train" and "crack_test". Images capturing structure without a crack are stored in folders "nocrack_train" and "nocrack_test". For each image, a .json file is stored in the same folder and under the same name as the corresponding image. The .json file stores the position (x,y) of pixels on the image, which lie in a crack region. An example of a code to generate a binary segmentation map from the .json files is given in the "read_json_annotation.py" file. Additional patch datasets were generated from the entire images. The patch datasets are stored in the “patch dataset” folder. The multiple patch datasets differ by the patch size, number of patches, and fraction of patches that do not contain cracks among all patches of the particular dataset. Furthermore, we provide segmentation maps in file "predictions.rar" for entire test images which are given by the method proposed in our research article. For more explanations, please refer to the article: https://doi.org/10.1016/j.autcon.2024.10589