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SSRN 2023年

作者： Halgas, Stanislaw Department of Electrical Electronic Computer and Control Engineering Lodz University of Technology Stefanowskiego 18 Lódź90-537 Poland

The paper deals with the diagnosis of soft faults in linear electronic circuits. Circuits containing operational amplifiers operating at low frequencies are considered. A method for identifying single and double faults in the AC domain is proposed. The method is non-iterative and relies on certain relations derived from the modified nodal description of circuit. An essential part of the paper is a procedure enabling the selection of measurement nodes and test frequencies. The algorithm considers the AC sensitivities, the measurement accuracy, and the diagnostic method applied. The effectiveness of both methods was verified on several electronic circuits. The simulation and laboratory tests were done. Considering parameter tolerances and measurement uncertainties leads to determining an interval of parameter values covering the actual fault. © 2023, The Authors. All rights reserved.

关键词： Failure analysis

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Comparison and AI-based prediction of graph comprehension skills based on the visual strategies of first-year physics and medicine students

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Physical Review Physics Education Research 2024年第2期20卷 020138页

作者： Verena Ruf Yavuz Dinc Stefan Küchemann Markus Berndt Steffen Steinert Daniela Kugelmann Jonathan Bortfeldt Jörg Schreiber Martin R. Fischer Jochen Kuhn Chair of Physics Education Research Faculty of Physics Institute of Medical Education Ludwig-Maximilians-Universität University Hospital Department of Electrical and Computer Engineering Institue of Anatomy Faculty of Medicine Chair of Medical Physics Faculty of Physics

Graphical representations of data are common in many disciplines. Previous research has found that physics students appear to have better graph comprehension skills than students from social science disciplines, regardless of the task context. However, the graph comprehension skills of physics students have not yet been compared with (veterinary) medicine students, both of which are disciplines that require multiple science, technology, engineering, and mathematics (STEM) courses. This study extends previous research on this subject by exploring whether physics majors possess superior graph comprehension skills due to their study discipline. Here, participants solved 24 graph comprehension tasks across various subjects, including mathematics, physics, and medicine; these tests were conducted at the beginning and end of their first semester. Graph comprehension gain was calculated based on the percentage of correct and incorrect answers in the pretest and the post-test. In addition to these comparisons, we replicated previous research that successfully distinguished correct and incorrect solvers based on their visual behavior by using a novel machine-learning method tailored to small datasets. Through this replication of statistical analyses, we aim to ensure the reliability of adaptive learning systems in the future, regardless of data size, using the same machine-learning method. Physics and medical students were found to exhibit relatively similar graph comprehension gain; this is in contrast to previous research comparing physics and non-STEM students. Our results also revealed that both physics and medical students use similar visual strategies to solve these tasks. However, correct and incorrect solvers could be distinguished via machine-learning methods regardless of their discipline. Our research suggests that visual behavior is a good predictor of graph comprehension skills.

关键词： Scientific reasoning & problem solving Undergraduate research

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On the Issue of Determining the Design Parameters of Magnetic Systems with Concentrators for Extracting Ferromagnetic Particles from Dispersed Materials

On the Issue of Determining the Design Parameters of Magneti...

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International Conference on Modern electrical and Energy Systems (MEES)

作者： Vadym Hulevskyi Yulia Postol Department of Power Engineering and Electrical Technologies Faculty of Energy and Computer Technology Dmytro Motornyi Tavria State Agrotechnologica University Melitopol Ukraine Department of Power Engineering and Electrical Technologies Faculty of Energy and Computer Technology Dmytro Motornyi Tavria State Agrotechnological University Melitopol Ukraine

The article discusses the problem of improving calculations when developing technical means for magnetic purification of dispersed agricultural raw materials. To solve this problem, the authors offer a scientifically based analysis of the design of a magnetic system with concentrators in order to develop mathematical methods for calculating working areas with spherical (or other shaped) bodies of certain sizes placed in them. Theoretical studies of particle dynamics in the working zone of field concentrators have been carried out. A design has been proposed for a separator for purifying dispersed materials from ferromagnetic impurities

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Development of a digital twin environment for smart collision avoidance algorithms for mobile robots using reinforcement learning

Development of a digital twin environment for smart collisio...

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SICE Annual Conference

作者： Natsumi Matsumoto Kazuyuki Kobayashi Tomoyuki Ohkubo Kaiqiao Tian Nashwan J Sebi Ka C Cheok Cai Changqing Graduate School of Science and Engineering Hosei University Tokyo Japan Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan Electrical & Computer Engineering Department School of Engineering & Computer Science Oakland University Rochester USA School of Electrical Engineering and Information Technology Changchun Institute of Technology China

This paper describes a study on building a digital twin environment using point cloud data for mobile robots. A digital twin environment for mobile robots is a method to reproduce almost the same environment in a simulation space as if it were a twin, using data from an actual natural environment. In fact, in the development of mobile robots, experiments on public urban area are conducted, however, they are incredibly time-consuming and labor-intensive, so it would be helpful if a realistic simulation environment could be created. This study focuses on building a digital twin environment using point-cloud-data for mobile robots. In order to verify the effectiveness of this approach, we constructed the environment using data from a real mobile robot that has been driven. The navigation of the mobile robot was performed in the constructed simulation environment, and it was possible to realize a navigation run in the driving environment that was almost the same as the run of the real mobile robot.

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The Effect of Mismatch between SPECT and CT Images on Deep Learning-based Attenuation Correction for Myocardial Perfusion SPECT

The Effect of Mismatch between SPECT and CT Images on Deep L...

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2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Du, Yu Shang, Jingjie Sun, Jingzhang Wang, Lu Xu, Hao Mok, Greta S. P. University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China The First Affiliated Hospital of Jinan University Department of Nuclear Medicine PET/CT-MRI Centre Guangdong Guangzhou China

ISBN: (纸本)9781665488723

Attenuation correction (AC) using deep learning (DL)-based methods improves image quality and quantitative accuracy in myocardial perfusion (MP) SPECT when CT-based AC (CT-AC) is not feasible. However, potential misalignments between SPECT and CT may affect the DL-based AC performance. This study aims to evaluate this effect based on real patient data. Fifity-eight stress/rest99mTc-sestamibi scans from 34 patients were retrospectively analyzed. Thirty-six projections, acquired from RAO to LPO in 11 min, were reconstructed by OS-EM algorithm, with and without CT-AC. A 3D conditional generative adversarial network was implemented and optimized for the DL-ACµ and DL-AC, while 43, 5 and 10 datasets were used for training, validation and testing with a 6-fold cross-validation. SPECT and CT mismatch was modeled by randomly shifting the CT images in 3 dimensions (8.6±3.8 mm) with respective to their corresponding SPECT images based on a clinical reference. Mismatched SPECT and CT images were also used to train the network separately, i.e., mDL-AC and mDL-ACµ. NMSE, SSIM, joint correlation histogram and linear regression on a 3D cardiac VOI were compared for all cases, with matched CT-AC as the reference. NMSE and SSIM show significantly degraded performance on mDL-ACµ and mDL-AC as compared to DL-ACµ and DL-AC, respectively. mDL-ACµ is consistently better than mDL-AC. The joint correlation histogram and linear regression result is consistent. We conclude that though SPECT and CT mismatch significantly degrades DL-based AC performance, mDL-ACµ is more robust to mDL-AC for MP SPECT and shows improved results as compared to NAC. © 2022 IEEE.

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Universal dual-port grid-forming control: bridging the gap between grid-forming and grid-following control

arXiv

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arXiv 2023年

作者： Subotić, Irina Groß, Dominic The Automatic Control Laboratory ETH Zürich Switzerland The Department of Electrical and Computer Engineering the University of Wisconsin-Madison United States

We analyze a dual-port grid-forming (GFM) control for power systems containing ac and dc transmission, converter-interfaced generation and energy storage, and legacy generation. To operate such a system and provide standard services, state-of-the-art control architectures i) require assigning grid-following (GFL) and GFM controls to different converters, and ii) result in highly complex system dynamics. In contrast, dual-port GFM control (i) subsumes common functions of GFM and GFL controls in a simple controller, ii) can be applied to a wide range of emerging technologies independently of the network configuration, and iii) significantly reduces system complexity. In this work, we provide i) an end-to-end modeling framework that allows to model complex topologies through composition of reduced-order device models, ii) an in-depth discussion of universal dual-port GFM control for emerging power systems, and iii) end-to-end stability conditions that cover a wide range of network topologies, emerging technologies, and legacy technologies. Finally, we validate our findings in detailed case studies. © 2023, CC BY-NC-ND.

关键词： Complex networks

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Explainable AI for Epileptic Seizure Prediction: A SHAPE and LIME Approach

Explainable AI for Epileptic Seizure Prediction: A SHAPE and...

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Information Technology (ACIT)

作者： Md. Simul Hasan Talukder Sohag Kumar Mondal Oyshi Tabassum Aditi Mohammad Aljaidi Rejwan Bin Sulaiman Ahmad Al-Qerem Electrical and Electronic Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh Electrical and Electronic Engineering Khulna University of Engineering and Technology Khulna Bangladesh Khulna Bangladesh Department of Computer Science and Engineering Daffodil International University Savar Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia School of Computer Science & Technology Northumbria University Newcastle Tayne United Kingdom

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Epilepsy is a neurological chaos typified by frequent, spontaneous seizures. Early recognition of epilepsy by utilising AI techniques to scan EEG signals can enhance the prevention and management of it. This study aims to improve the classification of epileptic seizures by using the preprocessed version of the Epileptic Seizure (ERS) dataset. The preprocessed dataset is subjected to multiple stages in order to improve its quality and achieve balanced class representation. Data cleaning, outlier management, merging, and oversampling are those. After that, an 80:20 split of the dataset is made into subsets for testing and training. Next, training, testing, and evaluation are performed on the models of Gradient Boosting Classifier (GBC), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), CatBoost Classifier (CBC), and ExtraTree Classifier (ETC). The ExtraTree Classifier is remarkably accurate, with a 99.51% accuracy rate, which is the highest accuracy. Finally, explainable artificial intelligence (SHAP and LIME) is used to clarify the ExtraTree Classifier's decision-making procedure. By providing a visual representation of significant variables and their influence on the model's predictions, this interpretability tool improves comprehension of the classification results.

关键词： Training Visualization Accuracy Shape Prevention and mitigation Epilepsy Predictive models Brain modeling Random forests Testing

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Adaptive Active Fault-Tolerant Attitude control for Spacecraft with Adaptive Prescribed Performance

Adaptive Active Fault-Tolerant Attitude Control for Spacecra...

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International Symposium on Autonomous Systems (ISAS)

作者： Ze Yang Baoqing Yang Ruihang Ji Jie Ma Control and Simulation Center Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering National University of Singapore Singapore

For spacecraft attitude control affected by environmental disturbance, parameter uncertainty and actuator fault, a novel composite active fault-tolerant scheme, combining a strong tracking Cubature Kalman filter (STCKF) with adaptive prescribed performance control (APPC), is investigated in this paper. The proposed STCKF is capable of estimating lumped fault rapidly but accurately, and it is robust to model uncertainty. An adaptive finite-time prescribed performance function (APPF) whose boundaries can be flexibly adjusted in the case of actuator faults is proposed. Then an active fault tolerant controller is designed using nonsingular terminal sliding mode control (NTSMC) in conjunction with APPF. Simulation experiments and comparisons show that the proposed strategy exhibits better fault tolerance, lower conservatism and better steady-state performance.

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A powerful notch filter for PLI cancelation 7

A powerful notch filter for PLI cancelation

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7th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2021

作者： Mobaien, Ali Roonizi, Arman Kheirati Boostani, Reza Shiraz University Department of Electrical and Computer Engineering Shiraz Iran Fasa University Faculty of Science Department of Computer Science Fasa Iran

ISBN: (纸本)9781665409384

In this work, we present a powerful notch filter for power-line interference (PLI) cancelation from biomedical signals. This filter has a unit gain and a zero-phase response. Moreover, the filter can be implemented adaptively to adjust its bandwidth based on the signal-To-noise ratio. To realize this filter, a dynamic model is defined for PLI based on its sinusoid property. Then, a constrained least square error estimation is used to emerge the PLI based on the observations while the constraint is the PLI dynamic. At last, the estimated PLI is subtracted from recordings. The proposed filter is assessed using synthetic data and real biomedical recordings in different noise levels. The results demonstrate this filter as a very powerful and effective means for canceling the PLI out. © 2021 IEEE.

关键词： Notch filters

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