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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Wei Zhang, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350337488

Clustering individual cells or spots based on their gene expression profiles in a spatial context is a powerful approach to uncovering the underlying biological diversity and relationships among cells. The intricate information within spatial transcriptomics data demands sophisticated algorithms that effectively integrate gene expression, cell position, and tissue image data for accurate cell or spot clustering. This work proposes a Multi-View Comparative Learning method for clustering Spatial Transcriptomics data (MVCLST). MVCLST first builds on two data views using gene expression profiles, cell space coordinates, and image features. Then it employs four different encoders to capture the common and private features of the two views. The model employs a contrastive learning loss to encourage effective interaction between the two views and ensure feature consistency. The shared and private features from both views are fused using corresponding decoders. Finally, the model employs the Leiden algorithm for downstream clustering of the learned features. We test the MVCLST method on a human dorsolateral prefrontal cortex dataset. The results show that MVCLST outperforms other state-of-the-art methods in most cases. Additionally, the clusters identified by MVCLST align closely with manual annotations and established neuroscience definitions. © 2023 IEEE.

关键词： contrast learning multiview learning spatial transcriptome data clustering

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Parks-McClellan Algorithm-Based Active Damping Strategy Applied to Grid-Connected Inverters

Parks-McClellan Algorithm-Based Active Damping Strategy Appl...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： José de Arimatéia O. Filho Gabriel Lucas Bressanini Tiago Davi Curi Busarello Luis De Oro Arenas Paulo Fernando Silva Helmo Kelis Morales Paredes Group of Automation and Integratd Systems (GASI) São Paulo State University (Unesp) Sorocaba Brazil Department of Control Automation and Computing Engineering Federal University of Santa Catarina (UFSC) Blumenau Brazil

In recent years, grid-connected inverters have gained wide prominence, primarily attributed to the surge in distributed energy resources. To ensure the reliable operation of these inverters, it is crucial to adequately damp the resonance peak of the LCL filter at the inverter output. By achieving this, the grid-connected inverter guarantees the system's stability while minimizing the harmonic content in the current injected into the distribution grid. To address this issue, this paper proposes the utilization of digital differentiators in conjunction with optimized wideband differentiators, which are designed using the Parks-McClellan (PM) algorithm. These differentiators are applied specifically to the LCL filter capacitor voltage to dampen the resonance peak effectively. Simulation results univocally demonstrate the effectiveness of the proposed approach.

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A reinforcement learning and predictive analytics approach for enhancing credit assessment in manufacturing

Decision Analytics Journal

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Decision Analytics Journal 2025年 15卷

作者： Razaque, Abdul Beishenaly, Aliya Kalpeyeva, Zhuldyz Uskenbayeva, Raisa Nikolaevna, Moldagulova Aiman Department of Software Engineering & AI School of Computing Gachon University Seongnam-si South Korea Department of Software Engineering Institute of Automation and Information Technologies Satbayev University Kazakhstan Department of Computer Engineering International Information Technology University (IITU) Almaty Kazakhstan

The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities, and less favorable client conditions. To overcome this issue, a comprehensive credit assessment scoring system should be implemented to increase importers’ confidence. The article proposes a predictive-based reinforcement learning (PRL) model to help manufacturers and importers acquire more accurate and dependable credit scores while avoiding default risk. Furthermore, the proposed PRL model enhances decision-making, system efficiency, and risk-tolerant financial conditions. To attain these cutting-edge objectives, the proposed PRL model combines three algorithms. Algorithm 1 collects and aggregates data to indicate areas for improvement if credit scoring is poor. Algorithm 2 uses reinforcement learning to validate and enhance bank scores. Algorithm 3 focuses on predictive modeling for bank scoring, ensuring that the credit decision-making system is operational and constantly improving. Furthermore, reinforcement learning leverages the features from local interpretable model-agnostic explanations (LIME) and shapely additive explanations (SHAP) to generate locally reliable explanations and attribute the contribution of each feature for determining the output of the model. The Python platform tests the proposed PRL to achieve the objectives. Based on the results, The PRL model markedly enhances credit assessment precision, achieving an accuracy of over 99.5%, which outstrips current methodologies such OCLA (96.12%), PSML (84.12%), and EMPCC (91.67%). Furthermore, the PRL model augments leverage ratios, rising from 2.75% in 2015 to 3.36% in 2024.5, and increases accounts receivable turnover from 4.38% in 2015 to 7.4% in 2024.5, surpassing alternative credit evaluation methodologi

关键词： Financial analytics Intelligent credit systems Machine learning Manufacturing Predictive analytics Reinforcement learning

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Comparative Analysis of Electric Motor Designs: Traditional Steel Laminations vs. Soft Magnetic Composite Materials

Comparative Analysis of Electric Motor Designs: Traditional ...

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International Conference on Electrical Machines, ICEM

作者： Branko Ban Anton Kersten Stefan Skoog Lars Sjöberg Stjepan Stipetić Tushar Batra Torquery Consulting Göteborg Sweden Department of Electrical Engineering Univeristy West Trollhättan Sweden Alvier Mechatronics Helsingborg Sweden Department of Electric Machines Drives and Automation Faculty of electrical engineering and computing (FER) University of Zagreb Zagreb Croatia

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

This paper presents a comparative analysis of electric machine designs for vehicle traction using traditional steel laminations versus soft magnetic composite (SMC) materials in four radial flux magnetic topologies: Rare-earth interior permanent magnet synchronous machine (IPM), wound field synchronous machine (WFSM), spoke-ferrite permanent magnet synchronous machine (SP-FER), and induction machine (IM). In total, 32 machine variants are evaluated considering different materials, and they are critically evaluated against a reference IPM traction machine with regards to performance, material cost and environmental impact. Results show that while the IPM design remains superior in performance for a fixed geometry, the WFSM and SP-FER offer significant environmental advantages. The WFSM emerges as the most eco-friendly option, while the SP-FER represents a balanced choice considering environmental impact, durability, production simplicity, and inverter complexity. This study highlights the potential of SMC materials compared to steel laminations in reducing costs (16 % to 29 %) and CO2-eq. emissions 21 % to 38 % in electric motor manufacturing without significantly compromising on performance, making it a viable alternative in automotive applications.

关键词： Permanent magnet machines Magnetic flux Costs Torque Lamination Automotive applications Magnetic analysis Steel Soft magnetic materials Electric motors

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Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Con...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Huarong Shan, Jinjun Li, Xing Yu, Hongnian School of Internet of Things Engineering Jiangnan University Wuxi214122 China York University Department of Earth and Space Science and Engineering TorontoONM3J 1P3 Canada School of Electrical Engineering and Intelligentization Dong-guan University of Technology Dongguan523000 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110819 China School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9781665410205

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches. © 2024 IEEE.

关键词： Sliding mode control

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Examining the Impact of Muscle-Electrode Distance in sEMG Based Hand Motion Recognition 16th

Examining the Impact of Muscle-Electrode Distance in sEMG Ba...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Shi, Jinwei Liu, Mingchun Fang, Yinfeng Yu, Jiahui Gao, Hongwei Ju, Zhaojie School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China School of Telecommunication Engineering Hangzhou Dianzi University Hangzhou310000 China Department of Biomedical Engineering Zhejiang University Hangzhou310027 China Innovation Center for Smart Medical Technologies & Devices Binjiang Institute of Zhejiang University Hangzhou310053 China School of Computing University of Portsmouth PortsmouthPO13HE United Kingdom

ISBN: (纸本)9789819964888

There are several factors that affect the sEMG signal during the process from its generation to its acquisition by sEMG devices. In this study, we tried to explain the physiological functional relationship between sEMG signals and muscles and between muscles and gestures in the human right forearm to increase confidence in the application of artificial intelligence in the medical field. For this purpose, we simulated the muscle and electrode positions with a 3D model to calculate their distance relationship, designed a cuff based on this model, and considered the effect of different distance solving methods on gesture recognition. The results showed that the highest accuracy of 93.95% was achieved for gesture recognition with the center of gravity method to find the electrode-to-muscle distance when the ratio of muscle electrode distance to the number of nerve muscle branches was 1:0.1. It is explained that the distance factor is the main factor affecting the recognition of sEMG signals, and an appropriate increase in the muscle length or neuromuscular branch number factor will play a positive role in the accuracy. The visualization of muscle activation further verifies and explains the relationship between sEMG signals and muscles, which makes the rehabilitation training more scientific and effective. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Muscle

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Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud

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International Journal of Intelligent Networks 2023年第1期4卷 301-309页

作者： Mageshkumar, Nagappan Swapna, J. Pandiaraj, A. Rajakumar, R. Krichen, Moez Ravi, Vinayakumar Department of Computer Science and Technology Madanapalle Institute of Technology and Science Madanapalle India Department of Computer Science & Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur chennai India Al-Baha University Alaqiq 65779-7738 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar34754 Saudi Arabia Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

Data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the Diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the Diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search. © 2023 The Author(s)

关键词： Cryptography

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Temperature distribution measurement of a battery pack’s segment in a controlled testing environment

Temperature distribution measurement of a battery pack’s se...

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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Antun Premec Lovro Magaš Hrvoje Kristek Mario Vraž ić Department of Electric Machines Drives and Automation Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9781665479820

ISBN: (纸本)9781665479837

To ensure operational safety and effective utilization of a battery pack it is important to determine temperature level and temperature distribution across its battery cells. This paper as the first of a series of papers, presents a battery pack segment ls7p testing environment for the purpose of measuring, not only the temperature of each cell, but also the voltage and current of the entire segment. The segment’s cells are thoroughly monitored with three temperature sensors per cell to get the most accurate thermal distribution available. After developing and building the battery pack segment, it is placed in a temperature controlled test chamber, so it can be tested at different temperature levels. Future articles, will describe the segment’s thermal image during charging and discharging. The goal of this paper is to present the accuracy of this testing environment, this must be done without charging or discharging the segment. It is necessary to measure and calculate the temperature sensor’s characteristics, accuracy and offsets, as well as the monitoring system’s sampling time, lag time and any lag time that is generated in the data processing step. With this information, certain measurement compensations can be made (if necessary) for future tests and experiments.

关键词： Temperature measurement Temperature sensors Image segmentation Temperature distribution Voltage measurement Energy measurement Thermal management

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Decentralized Parametric Stability Certificates for Grid-Forming Converter Control

arXiv

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arXiv 2025年

作者： Häberle, Verena He, Xiuqiang Huang, Linbin Dörfler, Florian Low, Steven Automatic Control Laboratory ETH Zurich Zurich8092 Switzerland Department of Automation Tsinghua University Beijing100084 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Department of Computing and Mathematical Sciences The Department of Electrical Engineering Caltech PasadenaCA91125 United States

We propose a decentralized framework for guaranteeing the small-signal stability of future power systems with grid-forming converters. Our approach leverages dynamic loop-shifting techniques to compensate for the lack of passivity in the network dynamics and establishes decentralized parametric stability certificates, depending on the local device-level controls and incorporating the effects of the network dynamics. By following practical tuning rules, we are able to ensure plug-and-play operation without centralized coordination. Unlike prior works, our approach accommodates coupled frequency and voltage dynamics, incorporates network dynamics, and does not rely on specific network configurations or operating points, offering a general and scalable solution for the integration of power-electronics-based devices into future power systems. We validate our theoretical stability results through numerical case studies in a high-fidelity simulation model. Copyright © 2025, The Authors. All rights reserved.

关键词： Decentralized control

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Sizing of Supercapacitor-based Energy Storage System for Elevator Applications

Sizing of Supercapacitor-based Energy Storage System for Ele...

引用

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Damjan Godec Luka Pravica Martina Kutija Martin Makar Department of Electric Machines Faculty of Electrical Engineering and Computing Drives and Automation University of Zagreb Zagreb Croatia

Energy storage systems based on supercapacitors have become attractive solutions for improving elevator efficiency. Electrical energy is stored while the elevator drive is running in generator mode and used when needed. The energy storage system can also be charged in standby mode and used to reduce power peaks during start-up. Therefore, the energy storage system should be appropriately sized, which is not trivial since elevator usage is stochastic in nature. In this paper, the effect of energy storage system size on the efficiency of regenerative energy reuse is analyzed based on a measurement study. Measurements were performed for one week on faculty elevators under real conditions. The data obtained from the measurements were analyzed, and the results of the analysis of energy consumption, elevator starts, and potential energy savings were presented for all three elevators and the group. The elevator energy storage system was emulated in MATLAB with and without energy consumption balancing which is used to reduce peak power. The efficiency of regenerative energy reuse is calculated for a wide range of energy storage sizes, both with and without energy consumption balancing, for all individual elevators and the group.

关键词： Potential energy Industrial electronics Energy consumption Energy measurement Supercapacitors Size measurement Elevators

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