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Fuzzy-Model-Based Fault-Tolerant Control for Stochastic Re-Entrant Manufacturing systems

IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3542-3556页

作者： Zhang, Kexin Gao, Qing Ding, Steven X. Lu, Jinhu Qiu, Jianbin Guo, Yige Beihang University School of Cyber Science and Technology Beijing100191 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China Zhongguancun Laboratory Beijing100095 China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg47057 Germany Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China Zhongguancun Laboratory Beijing China

This study addresses the problem of guaranteed cost fault-tolerant fuzzy control for multiline re-entrant manufacturing systems (RMSs) against stochastic disturbances and workstation faults. Initially, a nonlinear hyperbolic impulsive partial differential equation model is employed to describe the complex and hybrid dynamics of RMSs suffering from unexpected faults within the working stations, and then the corresponding approximation T-S fuzzy model is constructed. In what follows, with the aid of the parallel distributed compensation fuzzy control scheme, the main results of stability analysis and controller synthesis for the closed-loop re-entrant manufacturing control system are derived using a timer-dependent Lyapunov functional with spatio-temporal auxiliary variables. It is found that by means of the proposed fault-tolerant control approach, the RMS can be effectively and robustly driven to a desired production mode with steady feeding and production rates while the upper bound of a quadratic cost function is minimized. Finally, the effectiveness of the proposed control approach is validated through numerical simulations. © 2013 IEEE.

关键词： Fuzzy control

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Lightweight and Roubust Key Agreement for Securing IIoT-Driven Flexible Manufacturing systems

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IEEE Internet of Things Journal 2025年第11期12卷 17197-17209页

作者： Hammad, Muhammad Badshah, Akhtar Almeer, Mohammed A. Waqas, Muhammad Song, Houbing Chen, Sheng Han, Zhu GIK Institute of Engineering Sciences and Technology Faculty of Mechanical Engineering Topi23640 Pakistan University of Malakand Department of Software Engineering Dir Lower18800 Pakistan University of Bahrain Computer Engineering Department College of Information Technology 32038 Bahrain University of Greenwich Faculty of Engineering and Science Centre for Sustainable Cybersecurity United Kingdom Edith Cowan University School of Engineering PerthWA6027 Australia Department of Information Systems BaltimoreMD United States University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-Or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrate that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages. © 2025 IEEE.

关键词： Flexible manufacturing systems

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Application scenarios for simultaneous optical power and data transfer

Application scenarios for simultaneous optical power and dat...

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Optical Power Delivery 2025

作者： Nacke, Richard Pellegrino, Carmine Lackner, David Siefer, Gerald Majlesein, Behnaz Younus, Othman Osahon, Isaac N.O. Tavakkolnia, Iman Bett, Andreas W. Haas, Harald Helmers, Henning Fraunhofer Institute for Solar Energy Systems ISE Freiburg Germany LiFi Research and Development Centre Electrical Engineering Division University of Cambridge Cambridge United Kingdom Albert Ludwigs University Institute of Physics Freiburg Germany

ISBN: (纸本)9781510684669

Optical power transfer and optical data communication are versatile technologies with numerous applications, utilizing both fiber-coupled systems and free-space optical links. The integration of these technologies into a single link simplifies system design while combining the benefits of simultaneous power delivery and data communication for receiving systems. This study evaluates various applications by examining their power demands, data rate requirements, and external factors such as link medium and operational distance. A graphical representation is provided to facilitate application targeting for specific groups, dictating the needed system requirements. Three key application clusters are identified: A) Internet of Things connectivity (free-space, ~10 m, 0.2-10 W), B) network infrastructure (fiber/free-space/underwater, 10 m – 1 km, 10-100 W), and C) moving objects (free-space, 100 m – 500 km, 1-10 kW). © 2025 SPIE.

关键词： Optical links

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Rapid Detection of Microbial Colony in Biopharmaceutical Cleanroom Using Hyperspectral Imaging Technology

Rapid Detection of Microbial Colony in Biopharmaceutical Cle...

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Label-Free Biomedical Imaging and Sensing, LBIS 2025

作者： Chang, Wei-Hong Cheng, Yu-Cheng Yan, Yung-Jhe Ou-Yang, Mang Biomedical Technology and Device Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Institute of Intelligent and Systems National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781510684102

Microbial detection in biopharmaceutical cleanrooms typically requires more than a week, primarily due to the need for microbial culturing, which takes 3 to 7 days for colonies to form and be detectable. This prolonged detection time may lead to downtime during production line transitions, thereby reducing overall production efficiency. This study aims to develop a rapid microbial detection method (RMM) based on hyperspectral imaging (HSI) technology. HSI, with its high spectral resolution, is capable of effectively detecting subpixel-level targets. In this study, samples were collected from biopharmaceutical cleanroom environments for microbial identification, isolation, and culturing. Four representative microbial strains were selected: Staphylococcus spp., Bacillus spp., Pseudomonas spp., and Micrococcus spp. These strains were used to establish the optimal dilution ratio for obtaining an appropriate number of colony-forming units (CFUs) to simulate the culturing of environmental microbial samples on TSA agar plates under controlled conditions. Colony formation was monitored using a coaxial heterogeneous hyperspectral imaging system (CHHIS) after shorter cult ivation periods (0, 12, 24, and 36 hours). The shortest cultivation time required for detectable colony formation was established to validate the applicability of this technology in rapid microbial detection methods (RMM). © 2025 SPIE.

关键词： Hyperspectral imaging

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Monocular vision navigation system for UAV autonomous mission: a real-time window-based obstacle avoidance approach

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Neural Computing and Applications 2025年 1-18页

作者： Javaid, Abdulrahman Alnaser, Mustafa Baroudi, Uthman Alfaraj, Amjad Research and Development Department Yokogawa Saudi Arabia Company Al Khobar Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Center for Intelligent Secure Systems King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia

The need for autonomous unmanned aerial vehicles (UAVs) is rapidly increasing for various industrial applications today. However, the realization of UAV autonomy requires a careful mix of Artificial Intelligence (AI) and computer vision (CV) techniques to be implemented to address end-user challenges and satisfy field requirements. This study aims to develop an intelligent system for UAV to achieve autonomy with standard low-cost hardware components. We have infused state-of-the-art AI and CV technologies into our developed system architecture to improve autonomous navigational capabilities of UAVs. The proposed architecture has been implemented and tested on the DJI Tello UAV equipped only with a monocular camera on-board and an inertial measurement unit (IMU). We focused on solving multiple practical technical problems that have arisen during this study due to the hardware limitations of the UAV. The study investigated methods for representing obstacles within an environmental map, which are crucial for effective avoidance and path planning, such as their edges, width, and shape, to ensure accurate detection and navigation. The experiments were carried out in an environment that included dynamic obstacles that were not considered in the map. The experimental results showed outstanding performance in terms of successful autonomous navigation for a wide range of light intensity. These results show that the proposed approach has a high potential to achieve autonomous UAV operation in the field with low hardware and energy requirements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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Real-time Computation of E-field in Transcranial Magnetic Stimulation for Neuronavigation and Optimization

Real-time Computation of E-field in Transcranial Magnetic St...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Hasan, Nahian I. Dannhauer, Moritz Wang, Dezhi Deng, Zhi-De Gomez, Luis J. Elmore Family School of Electrical and Computer Engineering Purdue University WLIN United States National Institute of Mental Health Intramural Research Program National Institutes of Health United States

ISBN: (纸本)9781946815200

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique used for the treatment of depression, as well as various neurological and psychiatric disorders. There has been ongoing interest in developing fast E-field solvers that can be easily incorporated into neuronavigation systems to facilitate real-time E-field in targeted brain regions, optimizing dosing parameters. © 2025 USNC-URSI.

关键词： Neurons

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Noise-Robust Speech Emotion Recognition Using Shared Self-Supervised Representations with Integrated Speech Enhancement

Noise-Robust Speech Emotion Recognition Using Shared Self-Su...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tzeng, Jing-Tong Leem, Seong-Gyun Salman, Ali N. Lee, Chi-Chun Busso, Carlos College of Semiconductor Research National Tsing Hua University Taiwan Department of Electrical and Computer Engineering The University of Texas Dallas United States Department of Electrical Engineering National Tsing Hua University Taiwan Language Technologies Institute Carnegie Mellon University United States

ISBN: (纸本)9798350368741

Recent studies have demonstrated the effectiveness of fine-tuning self-supervised speech representation models for speech emotion recognition (SER). However, applying SER in real-world environments remains challenging due to pervasive noise. Relying on low-accuracy predictions due to noisy speech can undermine the user's trust. This paper proposes a unified self-supervised speech representation framework for enhanced speech emotion recognition designed to increase noise robustness in SER while generating enhanced speech. Our framework integrates speech enhancement (SE) and SER tasks, leveraging shared self-supervised learning (SSL)-derived features to improve emotion classification performance in noisy environments. This strategy encourages the SE module to enhance discriminative information for SER tasks. Additionally, we introduce a cascade unfrozen training strategy, where the SSL model is gradually unfrozen and fine-tuned alongside the SE and SER heads, ensuring training stability and preserving the generalizability of SSL representations. This approach demonstrates improvements in SER performance under unseen noisy conditions without compromising SE quality. When tested at a 0 dB signal-to-noise ratio (SNR) level, our proposed method outperforms the original baseline by 3.7% in F1-Macro and 2.7% in F1-Micro scores, where the differences are statistically significant. © 2025 IEEE.

关键词： multitask learning noisy speech Speech emotion recognition speech enhancement

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A Progressive Deep Learning Network for Ptychographic Imaging 4

A Progressive Deep Learning Network for Ptychographic Imagin...

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4th International Computational Imaging Conference, CITA 2024

作者： Yue, Han Ren, Yu-Xuan Shu, Steve Feng Academy for Engineering & Technology Fudan University Shanghai200433 China Institute for Translational Brain Research Fudan University Shanghai200032 China School of Electrical and Computer Engineering The University of Sydney CamperdownNSW2006 Australia

ISBN: (纸本)9781510688834

We introduce a progressive deep learning network designed for ptychographic imaging. We present an architecture incorporating progressive receptive field expansion and late-stage bifurcation to balance computational efficiency and reconstruction quality. Experimental results show that the proposed model reduces parameter count by 59.1% compared to PtychoNN while achieving comparable reconstruction quality. Furthermore, our model achieves statistically significant improvements in reconstruction quality, with phase PSNR increasing by 0.47 dB and phase SSIM improving by 4.53% compared to PtychoNN, while maintaining comparable amplitude reconstruction performance. © 2025 SPIE.

关键词： Deep reinforcement learning

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Role of Power Electronics in Power Quality Management of Smart Grid with AI Interfacing

Role of Power Electronics in Power Quality Management of Sma...

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International Conference on Computational Intelligence and Smart Technologies in electrical engineering, CISTEE 2023

作者： Rai, Shiwani Shukla, Hiramani Pathre, Ayonija Kumar, Alok Jaiswal, Pooja Ansari, Aftab Ahmed Department of Electrical and Electronic Engineering IES Institute of Technology and Management IES University Bhopal MP Bhopal India Department of Electrical Engineering U.V. Patel college of Engineering Ganpat University Gujarat Mehsana-Gozaria Highway Ganpat Vidyanagar India Department of Computer Science and Engineering JNCT MP Bhopal India Department of Electrical and Electronics Engineering Trinity Institute of Technology and Research Bhopal India Aspen Enggicon Pvt. Ltd. Jharkand Ranchi India Vidya Pratishthans Kamalnayan Baja Institute of Engineering and Technology Maharashtra Baramati India Department of Electrical and Electronics Engineering Bansal Institute of Research Technology and Science MP Bhopal India

ISBN: (纸本)9789819604753

With continual evolution in power sector and Power Electronics (PE) and the need for better performance with less human interfacing has raised the necessity for AI interfacing in power sector. AI, i.e., Artificial Intelligence is a computer software or machine which is capable of replicating human expertise without any human interference. PE, plays a dominant role in transmitting, converting, and controlling power from kilowatts to gigawatts, efficiently and flexibly between the power grids with 95% efficiency. Its interference with AI can take modern smart grid system into the next level of techno-automation without any human interference. The application of PE is at its peak for enhancing the Power Quality (PQ) of the system. This paper presents a review of transformation of the existing system into hi-tech PE integration to obtain high power quality with AI interfacing to reduce human supervision and to reduce the PQ events to take place in power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric power transmission networks

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A Web-Based Interface that Leverages Machine Learning to Assess an Individual’s Vulnerability to Brain Stroke

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IEEE Access 2025年 13卷 87950-87964页

作者： Bhandari, Divyansh Agarwal, Arnav Reena Roy, R. Priyatharshini, Rajaram Cristian, Rodriguez Rivero Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Cardiff Metropolitan University Cardiff School of Technologies Centre for Engineering Research in Intelligent Sensors and SystemsComputer Science Wales United Kingdom Cardiff Metropolitan University Academic Associate Cardiff School of Technologies Cardiff United Kingdom Universitat Politècnica de Catalunya Industrial and ICT Systems Engineering Dept. of Mining Spain

Cerebral stroke is a major global health issue, contributing to high mortality and long-term disability. Early identification of individuals at high risk of stroke can significantly improve preventive care outcomes. We present a web-based stroke risk assessment tool that uniquely combines an accessible user interface with robust machine learning modeling. The proposed platform leverages a novel combination of SMOTE oversampling and logistic regression to address class imbalance in patient health records, improving the detection of stroke risk factors over existing methods. We compare a range of algorithms – including traditional classifiers and deep learning models – and report comprehensive performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) for each. Our best model (logistic regression with SMOTE and standard scaling) achieves 93.2% accuracy with a substantially higher F1-score for the stroke-positive class than other models, indicating improved sensitivity to stroke cases. To bridge the gap between complex predictive models and end-users, we deploy this model in an intuitive web interface (built with Streamlit) that non-technical individuals and healthcare providers can easily use. This interface requires no specialized knowledge, preserves user privacy by avoiding any data storage, and provides clear explanations of results. By offering a practical and transparent tool for stroke risk screening, our work advances health informatics with an emphasis on accessibility, interpretability, and early intervention. Potential applications range from personal health self-assessment to integration in clinical workflows for preventive care, ultimately aiming to improve public health outcomes through early detection and intervention in stroke. © 2013 IEEE.

关键词： Health insurance

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