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arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

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Comparing of transfer process data in PLC and mcu based on IoT 5th

Comparing of transfer process data in PLC and mcu based on I...

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5th International Conference on Advanced engineering Theory and Applications, AETA 2018

作者： Gavlas, Antonin Koziorek, Jiri Rakay, Robert Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 15 Ostrava70833 Czech Republic Department of Automation Control and Human Machine Interactions Faculty of Mechanical Engineering Technical University of Košice Park Komenského 8 Košice042 00 Slovakia

ISBN: (纸本)9783030149062

An article is based on comparing of data processing within PLC and MCU with using Cloud platforms. The last advances in computation and communication technologies are taking shape in the form of IoT (Internet of Things), M2M (Machine to Machine) technology, Industry 4.0 and Cyber Physical Systems. Internet of Things connects an enormous number of devices. Devices within IoT need small number of sensors and control data, but with numerous messages. Today Cloud services work with the volume of these data and support large scale IoT systems. In this paper we would like to represent a comparison of solutions Industrial and Commercial IoT. The proposed systems use one of the newest communication protocols MQTT. © Springer Nature Switzerland AG 2020.

关键词： Internet of things

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Defect engineering approaches for metal oxide semiconductor-based chemiresistive gas sensing

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Coordination Chemistry Reviews 2025年 541卷

作者： Kumar, Amit Mazumder, Julaiba Tahsina Joyen, Kenza Favier, Frédéric Mirzaei, Ali Kim, Jin-Young Kwoka, Monika Bechelany, Mikhael Jha, Ravindra Kumar Kumar, Mahesh Kim, Hyoun Woo Kim, Sang Sub Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur 342030 India Department of Electronics Assam Skill University Assam Mangaldai 784125 India European Institut of Membranes (IEM) – UMR 5635 University of Montpellier ENSCM CNRS Montpellier 34090 France Institut Charles Gerhardt Montpellier (ICGM) – UMR 5253 University of Montpellier ENSCM CNRS Montpellier 34090 France Department of Materials Science and Engineering Shiraz University of Technology Shiraz 715557-13876 Iran Customized manufacturing R&D department Korea Institute of Industrial Technology Gyeonggi-do 15014 South Korea Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland Functional Materials Group Gulf University for Science and Technology (GUST) Mubarak Al-Abdullah 32093 Kuwait Department of Electronics and Electrical Engineering & Centre for Intelligent Cyber-Physical Systems Indian Institute of Technology Guwahati Guwahati 781039 India Division of Materials Science and Engineering Hanyang University Seoul 04763 South Korea Department of Materials Science and Engineering Inha University Incheon 22212 South Korea

Defect engineering in metal oxides presents a promising approach for tailoring material properties. This strategy enhances gas sorption, catalysis, and control over key physical characteristics such as bandgap, magnetic behavior, and electrical conductivity. Despite its potential, the role of defect engineering in advancing metal oxide semiconductor (MOS) gas-sensing performance remains underexplored. This review introduces defect engineering strategies, emphasizing their applications in gas sensing. Gas sensors play a vital role in environmental monitoring, industrial safety, healthcare, and in improving energy efficiency. The demand for advanced gas sensors has never been more critical, given the need for real-time, accurate, and cost-effective detection of pollutant gases. MOS-based gas sensors are widely used in air quality monitoring, industrial safety, and health diagnostics. Vacancies and defect architectures in MOS have been widely studied for their role in sensing performance, as they fundamentally influence sensor efficiency. The effectiveness of MOS sensors largely depends on the type and concentration of defects. This review introduces vacancies and defects in MOS, followed by an in-depth discussion of defect types, factors influencing defect formation, and their role in charge transport. Additionally, it examines the correlation between elemental chemical properties and defect chemistry. Special attention is given to the defect chemistry of metal oxides, including TiO₂, ZnO, Co₃O₄, ZrO₂, WO₃, and CeO₂. It concludes with an examination of surface engineering methods for defect control to improve gas-sensing capabilities. © 2024

关键词： Chemiresistive gas sensing Defect engineering Metal-oxide semiconductor

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Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation

Enhanced Subspace Distribution Matching for Fast Visual Doma...

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作者： Kang, Qi Yao, Siya Zhou, Mengchu Zhang, Kai Abusorrah, Abdullah Department of Control Science and Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Department of Electrical and Computer Engineering Faculty of Engineering and Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah21481 Saudi Arabia Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace. This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace. It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching. We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations. Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time. The results can be used in social cognition, person reidentification, and human-machine interactions. © 2014 IEEE.

关键词： Image classification

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On ℓ2–ℓ∞ output-feedback control scheduled by stochastic communication protocol for two-dimensional switched systems

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International Journal of Systems science 2021年第14期52卷 2961-2976页

作者： Kaiqun Zhu Jun Hu Yurong Liu Naif D. Alotaibi Fuad E. Alsaadi a Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai People's Republic of China b Department of Mathematics Harbin University of Science and Technology Harbin People's Republic of China c Department of Mathematics Yangzhou University Yangzhou People's Republic of Chinad School of Mathematics and Physics Yancheng Institute of Technology Yancheng People's Republic of China e Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

This paper is concerned with theℓ2–ℓ∞output-feedback control problem for a class of two-dimensional (2-D) switched systems described by the general Fornasini–Marchesini second model under the stochastic communication protocol (SCP). In a networked 2-D switched control system, the sensors and the controller are typically connected through a shared communication network of limited bandwidth, which is likely to result in data collision problem. To circumvent this network-induced problem, a new SCP is dedicatedly put forward for the 2-D switched system in order to orchestrate the transmission order of sensor nodes, under which only one sensor node obtains the privilege to access the network at each transmission instant. Then, based on the nominal system information compensation, a set of new mode-dependent output-feedback controllers is designed for 2-D switched systems subject to SCP and external disturbances. Moreover, using the average dwell-time approach, sufficient conditions are established to ensure the existence of the desired controllers that guarantee both the exponential mean-square stability and theℓ2–ℓ∞performance. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed controller design method.

关键词： Two-dimensional switched systems Fornasini–Marchesini second model average dwell-time ℓ2– ℓ∞ control stochastic communication protocol

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Investigation on application of egg shell micro powder based epoxy composite for vibration isolation of MEMS sensor

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AIP Conference Proceedings 2023年第1期2976卷

作者： Nitin Satpute Atharva Joshi Kaustubh Utpat Nishant Kulkarni Siddharth Jabade Ludwin Molina Arias Ramesh Narina Marek Iwaniec Faculty of Science and Technology Vishwakarma University Pune India Department of Mechanical Engineering Vishwakarma Institute of Technology Pune India Department of Process Control AGH University of Science and Technology Krakow Poland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

In the presented work, the stiffness and damping properties of eggshell-based epoxy composites are investigated. The ingredients used for the preparation of the composite include 25 - 45 µm sized eggshell powder, polydimethylsiloxane (PDMS), hardener, and silane as a coupling agent. Experimentation on prototype samples has been conducted using a vibration shaker, accelerometer, and fast Fourier transform (FFT) analyzer, to investigate the influence of the addition of micro-powder on the damping factor and stiffness of the composite. The study reveals that there is an improvement of up to 201% in damping factor with the 2.5% addition of micro powder by weight. The experimental study indicates that the stiffness properties of the composite are not influenced by the addition of eggshell micro powder. Additionally, a case study is demonstrated with finite element simulation to validate the advantages of using the proposed epoxy composite for Microelectromechanical System (MEMS) sensor packaging. In case of a harmonic force of 0.2 N acting on the sensor within the frequency range of 40-60 Hz, the maximum acceleration of the MEMS sensor increases by 48%, whereas the force transmissibility decreases by 44%.

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The Resistive Ground Fault of PWM Voltage Inverter In The EV Charging Station

Research Square

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Research Square 2021年

作者： Zurek-Mortka, Marta Szymanski, Jerzy Ryszard Lukasiewicz Research Network – Institute for Sustainable Technologies Department of Control Systems Radom26-600 Poland Kazimierz Pulaski University of Technology and Humanities Faculty of Transport Electrical Engineering and Computer Science Radom26-600 Poland

During the direct touch of the inverter output voltage, the nonsinusoidal ground currents with a basic harmonic frequency between 1.5 kHz and 16 kHz, flow via a human’s body. Here was proved that Residual Current Device (RCD) type AC/I∆n = 30 mA does not switch off the power supply when a ground current with a value of about some hundred milliamps occurs. Because RCDs do not disconnect the power supply, the touch on the inverter’s voltage is dangerous to the health and life. For the authors, RCD usage in Voltage Frequency Converters is not a good engineer practice. The article presents tests of RCD operation in the event of a ground fault during EV battery charging. © 2021, CC BY.

关键词： Charging (batteries)

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Investigation of Conditions for Non-degeneracy and Normality in control Problems with Equality and Inequality State Constraints

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IFAC-PapersOnLine 2020年第2期53卷 6869-6874页

作者： Aram Arutyunov Dmitry Karamzin Fernando L. Pereira Institute of Control Sciences of the Russian Academy of Science Moscow Russia Federal Research Center “Computer Science and Control” of the Russian Academy of Science Moscow Russia Research Center for Systems and Technologies (SYSTEC) Electrical and Computer Engineering Department Faculty of Engineering University of Porto 4200-465 Portugal

This work aims to investigate conditions for normality and non-degeneracy of the maximum principle for general state-constrained optimal control problems in which the state constraints are given by equalities and inequalities. Non-degeneracy is proved under a regularity condition formulated in terms of the limiting normal cone to the feasible control set. The same regularity condition implies normality of the maximum principle if one of the end-points is free.

关键词： Optimal control maximum principle state constraints

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Distributed coordination of deferrable loads: A real-time market with self-fulfilling forecastsI

arXiv

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arXiv 2020年

作者： Abdelghany, Hazem A. Tindemans, Simon H. de Weerdt, Mathijs M. la Poutréa, Han Department of Electrical Sustainable Energy Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Amsterdam Netherlands Electrical and Control Engineering Department Arab Academy for Science Technology and Maritime Transport Cairo Egypt

Increased uptake of variable renewable generation and further electrification of energy demand necessitate efficient coordination of flexible demand resources to make most efficient use of power system assets. Flexible electrical loads are typically small, numerous, heterogeneous and owned by self-interested agents. Considering the multi-temporal nature of flexibility and the uncertainty involved, scheduling them is a complex task. This paper proposes a forecast-mediated real-time market-based control approach (F-MBC) for cost minimizing coordination of uninterruptible time-shiftable (i.e. deferrable) loads. F-MBC is scalable, privacy preserving, and usable by device agents with small computational power. Moreover, F-MBC is proven to overcome the challenge of mutually conflicting decisions from equivalent devices. Simulations in a simplified but challenging case study show that F-MBC produces near-optimal behaviour over multiple time-steps. Copyright © 2020, The Authors. All rights reserved.

关键词： Markov processes

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MAL-XSEL: Enhancing Industrial Web Malware Detection with an Explainable Stacking Ensemble Model

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Processes 2025年第5期13卷 1329-1329页

作者： Ezz El-Din Hemdan Samah Alshathri Haitham Elwahsh Osama A. Ghoneim Amged Sayed Structure and Materials Research Lab Prince Sultan University Riyadh 11586 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh 33511 Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Department of Computer Science Faculty of Computers and Information Tanta University Tanta 31527 Egypt Industrial Electronics and Control Engineering Department Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electrical Energy Engineering College of Engineering & Technology Arab Academy for Science Technology & Maritime Transport Smart Village Campus Giza 12577 Egypt

The escalating global incidence of malware presents critical cybersecurity threats to manufacturing, automation, and industrial process control systems. Given the fast-developing web applications and IoT devices in use by industry operations, securing a transparent and effective malware detection mechanism has become imperative to operational resilience and data integrity. Classical methods of malware detection are conventionally opaque “black boxes” with limited transparency, thus eroding trust and hindering deployment in security-sensitive contexts. In this respect, this research proposes MAL-XSEL—a malware detection framework using an explainable stacking ensemble learning approach for performing high-accuracy classification and interpretable decision-making. MAL-XSEL explicates the model predictions through Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), which enable security analysts to validate how the detection logic works and prioritize the features contributing to the most critical threats. Evaluated on two benchmark datasets, MAL-XSEL outperformed conventional machine learning models, achieving top accuracies of 99.62% (ClaMP dataset) and 99.16% (MalwareDataSet). Notably, it surpassed state-of-the-art algorithms such as LightGBM (99.52%), random forest (99.33%), and decision trees (98.89%) across both datasets while maintaining computational efficiency. A unique interaction of ensemble learning and XAI is employed for detection, not only with improved accuracy but also with interpretable insight into the behavior of malware, thereby allowing trust to be substantiated in an automated system. By closing the divide between performance and interpretability, MAL-XSEL enables cybersecurity practitioners to deploy transparent and auditable defenses against an ever-growing resource of threats. This work demonstrates how there can be no compromise on explainability in security-critical applications and, as such, esta

关键词： malware mitigation industrial web applications classification machine learning explainable artificial intelligence (XAI) SHAP (Shapley additive explanations) (LIME) local interpretable model-agnostic explanations

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