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International Scientific Conference on Electric Power engineering (EPE)

作者： Jan Dudek Roman Hrbac Jan Veleba Ivo Ullman Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava Ostrava Czech Republic Industrial Automation Division ABB s.r.o. Operation Center Czech Republic (CZOPC) Ostrava Czech Republic CEPS a.s. HV Substation Power Equipment and Buildings Czech Republic

ISBN: (纸本)9781509064076

This paper describes the Failure Mode and Effects Analysis (FMEA) focusing on tertiary side of (E)HV/HV/MV transformers and MV self-consumption subsystem of the Czech transmission power system. The goal of this FMEA is a) to identify all possible failure modes, which can occur in the examined system; b) to determine the impact of each failure mode on upstream (E)HV/HV system (referred to as FMEA I.) and on MV self-consumption subsystem (referred to as FMEA II.); c) to evaluate the severity and frequency of each failure mode; and d) to propose suitable actions for mitigation or complete elimination of these failure modes and their impacts.

关键词： Frequency modulation Current transformers Inductors Grounding Maintenance engineering Switching circuits Uninterruptible power systems

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Measurement-level fusion for OTHR network using message passing

arXiv

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arXiv 2020年

作者： Lan, Hua Wang, Zengfu Bai, Xianglong Pan, Quan Lu, Kun School of Automation Northwestern Polytechnical University Key Laboratory of Information Fusion Technology Ministry of Education Xi’anShaanxi710072 China Nanjing Reasearch Insitute of Electronics Technology Nanjing Jiangsu210039 China Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2826 CD Netherlands

Tracking an unknown number of targets based on multipath measurements provided by an over-the-horizon radar (OTHR) network with a statistical ionospheric model is complicated, which requires solving four subproblems: target detection, target tracking, multipath data association and ionospheric height identification. A joint solution is desired since the four subproblems are highly correlated, but suffering from the intractable inference problem of high-dimensional latent variables. In this paper, a unified message passing approach, combining belief propagation (BP) and mean-field (MF) approximation, is developed for simplifying the intractable inference. Based upon the factor graph corresponding to a factorization of the joint probability distribution function (PDF) of the latent variables and a choice for a separation of this factorization into BP region and MF region, the posterior PDFs of continuous latent variables including target kinematic state, target visibility state, and ionospheric height, are approximated by MF due to its simple MP update rules for conjugate-exponential models. With regard to discrete multipath data association which contains one-to-one frame (hard) constraints, its PDF is approximated by loopy BP. Finally, the approximated posterior PDFs are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue among target detection, target tracking, multipath data association, and ionospheric height identification. Meanwhile, the proposed approach has the measurement-level fusion architecture due to the direct processing of the raw multipath measurements from an OTHR network, which is benefit to improving target tracking performance. Its performance is demonstrated on a simulated OTHR network multitarget tracking scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Target tracking

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Improvement of Sun Flare Prediction by SVM Integrated GA

Improvement of Sun Flare Prediction by SVM Integrated GA

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Yukiko Yamamoto Setsuo Tsuruta Takayuki Muranushi Yuko Hada Muranushi Syoji Kobashi Yoshiyuki Mizuno Rainer Knauf School of Information Environment Tokyo Denki University Inzai Japan Kwasan and Hida Observatories Kyoto University Kyoto Japan Kyoto Women's University Kyoto Japan RIKEN Advanced Institute for Computational Science Kobe Japan Graduate School of Engineering University of Hyogo Himeji Japan Faculty of Computer Science and Automation Ilmenau University of Technology Ilenau Germany

Solar activity has various influences on the global environment, in particular on the weather and the likelihood of natural disasters. In particular, it may have serious impacts on Earth such as failure of satellite communication and navigation (GPS), satellite damage, increased radiation exposure to astronauts, geomagnetic storm and aurora, and power plant failures causing more serious disaster. For a precise forecast of larger scale solar flares causing serious disaster, it is important to improve the space weather forecast, which is basically a daily forecast of the solar flare. In our work so far, a machine-learning algorithm called Support Vector Machine (SVM) was used to forecast the space weather. Here, we propose to extend this technology by integrating a Genetic Algorithm (GA) for a more precise forecast and present an evaluation of this approach.

关键词： Support vector machines Genetic algorithms Sociology Statistics Weather forecasting Electronic mail

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Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search

arXiv

引用

arXiv 2021年

作者： Ma, Lianbo Li, Nan Yu, Guo Geng, Xiaoyu Cheng, Shi Wang, Xingwei Huang, Min Jin, Yaochu College of Software Northeastern University Shenyang110819 China The Institute of Intelligent Manufacturing Nanjing Tech University Nanjing211816 China The School of Computer Science Shaanxi Normal University Xi’an710062 China The College of Computer Science Northeastern University Shenyang110819 China The College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Faculty of Technology Bielefeld University Bielefeld33619 Germany The Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

In multi-objective evolutionary neural architecture search (NAS), existing predictor-based methods commonly suffer from the rank disorder issue that a candidate high-performance architecture may have a poor ranking compared with the worse architecture in terms of the trained *** alleviate the above issue, we aim to train a Pareto-wise end-to-end ranking classifier to simplify the architecture search process by transforming the complex multi-objective NAS task into a simple classification task. To this end, a classifier-based Pareto evolution approach is proposed, where an online classifier is trained to directly predict the dominance relationship between the candidate and reference architectures. Besides, an adaptive clustering method is designed to select reference architectures for the classifier, and an α-domination assisted approach is developed to address the imbalance issue of positive and negative samples. The proposed approach is compared with a number of state-of-the-art NAS methods on widely-used test datasets, and computation results show that the proposed approach is able to alleviate the rank disorder issue and outperforms other methods. Especially, the proposed method is able to find a set of promising network architectures with different model sizes ranging from 2M to 5M under diverse objectives and constraints. © 2021, CC BY.

关键词： Network architecture

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Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation

Introducing Shape Prior Module in Diffusion Model for Medica...

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IEEE International Conference on Mechatronics, Robotics and automation (ICMRA)

作者： Zhiqing Zhang Guojia Fan Tianyong Liu Nan Li Yuyang Liu Ziyu Liu Canwei Dong Shoujun Zhou Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Shenzhen China College Of Information Science and Engineering Northeastern University (Of Affiliation) Shenzhen China College Of Geosciences Northeast Petroleum University Shenzhen China Department of Computer Science City University of Hong Kong Shenzhen China Shenyang Institute of Automation University of Chinese Academy of Sciences Shenzhen China Faculty of Science and Engineering Chinese Academy of Sciences University of Nottingham Ningbo China Ningbo China College Of Information Science And Engineering Northeastern University (Of Affiliation) Shenzhen China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

Medical image segmentation is critical for diagnosing and treating spinal disorders. However, the presence of high noise, ambiguity, and uncertainty makes this task highly challenging. Factors such as unclear anatomical boundaries, inter-class similarities, and irrational annotations contribute to this challenge. Achieving both accurate and diverse segmentation templates is essential to support radiologists in clinical practice. In recent years, denoising diffusion probabilistic modeling (DDPM) has emerged as a prominent research topic in computer vision. It has demonstrated effectiveness in various vision tasks, including image deblurring, super-resolution, anomaly detection, and even semantic representation generation at the pixel level. Despite the robustness of existing diffusion models in visual generation tasks, they still struggle with discrete masks and their various effects. To address the need for accurate and diverse spine medical image segmentation templates, we propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM). Our approach integrates the diffusion model into a standard U-shaped architecture. At each step, we combine the noise-added image with the labeled mask to guide the diffusion direction accurately towards the target region. Furthermore, to capture specific anatomical a priori information in medical images, we incorporate a shape a priori module. This module efficiently extracts structural semantic information from the input spine images. We evaluate our method on a single dataset of spine images acquired through X-ray imaging. Our results demonstrate that VerseDiff-UNet significantly outperforms other state-of-the-art methods in terms of accuracy while preserving the natural features and variations of anatomy. The generality and validity of our proposed model are confirmed through extensive experiments. By enabling more precise segmentation of anatomical structures, our framew

关键词： Image segmentation Visualization Accuracy Shape Semantics Noise reduction Anatomical structure Diffusion models Probabilistic logic X-ray imaging

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Drone-Based Predictive Structure Analysis: A Review

Drone-Based Predictive Structure Analysis: A Review

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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Trushant Patil Durgesh Nandan Kanhaiya Sharma Deepak Parashar Department of Robotics and Automation SIT Symbiosis International (Deemed University) Pune India Department of Electronics and Telecommunication Engg. SIT Symbiosis International (Deemed University) Pune India Department of Computer Science & Engineering SIT Symbiosis International (Deemed University) Pune India Faculty of Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

Drone-based predictive structure analysis is a rapidly growing field that has the potential to revolutionize infrastructure and building inspection. In this review of the research, we give a general overview of how drones could increase the effectiveness and precision of structural analysis, particularly for keeping an eye on difficult-to-reach locations. The usage of drones for 3D mapping, inspection, and monitoring of diverse constructions, such as concrete buildings, bridges, tunnels, and dams. The investigations show that drone-based inspection can produce 3D models and high-resolution photos, allowing for remote monitoring and the early identification of possible problems. The findings show that drones may greatly boost infrastructure management's safety and effectiveness, making them an important tool for predictive structural research.

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Towards an inclusive Parkinson's screening system

Towards an inclusive Parkinson's screening system

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Oana Geman Saeid Sanei Iuliana Chiuchisan Adrian Graur Aleš Procházka Oldřich Vyšata Department of Health and Human Development “Stefan eel Mare” University of Suceava Romania Department of Computer Science Electronics and Automation “Stefan cel Mare” University of Suceava Romania Faculty of Engineering and Physical Sciences University of Surrey United Kingdom Department of Computing and Control Engineering Institute of Chemical Technology Czech Republic Department of Neurology Charles University Czech Republic

In this study, brain, and gait dynamic information were combined and used for diagnosis and monitoring of Parkinson's disease (the most important Neurodegenerative Disorder). Analysis of the information corresponding to a prescribed movement involving tremor, and the related changes in brain connectivity is novel and original. Analytically, developing a space-time nonlinear adaptive system which fuses brain and gait information algorithmically is proposed here for the first time. The overall dynamic system will be constrained by the clinical impressions of the patient symptoms embedded in a knowledge-based system. The entire complex constrained problem were solved to enable a powerful model for recognition and monitoring of Parkinson's disease and establishing appropriate rules for its clinical following up.

关键词： Brain modeling Adaptation models Parkinson's disease Chaos Signal processing algorithms Nonlinear dynamical systems

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Output feedback control of linear systems with input and output quantization

Output feedback control of linear systems with input and out...

引用

IEEE Conference on Decision and Control

作者： Daniel Coutinho Minyue Fu Carlos E. de Souza Group of Automation and Control Systems Faculty of Engineering Pontifícia Universidade Católica do Rio Grande do Sul Porto Alegre Brazil School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW Australia Department of Systems and Control Laboratory Nacional de Computação Científica Petropolis Rio de Janeiro Brazil

ISBN: (纸本)9781424431236

A considerable amount of research has been done on the use of logarithmic quantizers for networked feedback control systems. However, most results are developed for the case of a single quantizer (either measurement or control signal quantization). In this paper, we investigate the case of simultaneous input and output quantization for SISO linear output feedback systems. Firstly, we show that the problem of quadratic stabilization via quantized feedback can be addressed with no conservativeness by means of the sector bound approach. Secondly, we provide a bound on the maximal admissible sector bound via a scaled H ¿ optimization problem.

关键词： Output feedback Linear feedback control systems Control systems Linear systems Quantization Communication system control Feedback control Communication channels Digital control Bandwidth

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions

Digital Twin Definition for Detecting Abnormal Perspiration ...

引用

Roedunet International Conference (RoEduNet)

作者： Cristian Vizitiu Ioan Turcin Kevin Alexander Dominey Selver Softic Adrian-Cătălin Dinculescu Vlad I. Bocăneţ Cosmin Dugan Sorin-Aurel Moraru The Space Applications and Technologies Laboratory Institute of Space Science -Subsidiary of INFLPR Magurele Romania Department of Automatics and Information Technology Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania Department Automation Technology CAMPUS 02 University of Applied Sciences Graz Austria Department IT & Business Informatics CAMPUS 02 University of Applied Sciences Graz Austria Department of Manufacturing Engineering Faculty of Industrial Engineering Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation.

关键词： Adaptation models Generative AI Psychology Human factors Machine learning Predictive models Physiology Digital twins Modeling Biomedical monitoring

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Fuzzy Adaptive Output Tracking Control of a Class of Composite Systems

引用

IFAC Proceedings Volumes 2001年第3期34卷 81-84页

作者： Bin Liu Yuanwei Jing Siying Zhang G.M. Dimirovski T.D. Kolemishevska Inst. of Control Science School of Information Science & Engg. Northeastern University Po. Box 321 Shenyang Liaoning 110006 P R. of China Department of Computer Engineering Faculty of Engineering DOGUS University Acibadem Kadikoy TR-81000 Istanbul Rep. of Turkey FAX#: + 90-216-327-9631 Inst.of Automation & Systems Engg. Faculty of EE SS Cyril #x0026 Methodius University Karpos 2 b.b. MK-91000 Skopje Rep. of Macedonia

In this paper the output-tracking problem of a class of composite nonlinear systems is studied. Composite system can be represented solely by models on the grounds of its inputs and measurable outputs. It is assumed that (i) the plant system belongs to aclass of composite non linear systems, and that the fuzzy logic control system applied can efficiently use adjustable parameters to approximate its non linear system function. The new theorem guarantees the synthesized indirect fuzzy adaptive control does ensure stable output tracking and possesses the ability to reduce the number of fuzzy rules.

关键词： Adaptive control composite systems fuzzy control MIMO systems nonlinear systems

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