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arXiv 2025年

作者： Elvander, Filip Haasler, Isabel Department of Information and Communications Engineering Aalto University Finland Division of Systems and Control Department of Information Technology Uppsala University Sweden

Crowd dynamics and many large biological systems can be described as populations of agents or particles, which can only be observed on aggregate population level. Identifying the dynamics of agents is crucial for understanding these large systems. However, the population of agents is typically not homogeneous, and thus the aggregate observations consist of the superposition of multiple ensembles each governed by individual dynamics. In this work, we propose an optimal transport framework to jointly separate the population into several ensembles and identify each ensemble’s dynamical system, based on aggregate observations of the population. We propose a bi-convex optimization problem, which we solve using a block coordinate descent with convergence guarantees. In numerical experiments, we demonstrate that the proposed approach exhibits close-to-oracle performance also in noisy settings, yielding accurate estimates of both the ensembles and the parameters governing their dynamics. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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Diagnostics of the Safety PLC Output Circuit and its Impact on Safety

Diagnostics of the Safety PLC Output Circuit and its Impact ...

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International Carpathian control Conference (ICCC)

作者： Juraj Ždánsky Karol Rástočný Jozef Hrbček Jozef Brtiš Rastislav Pirník Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Žilina Slovakia

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Safety PLCs are commonly used in industrial applications to implement safety functions. However, they almost always require output circuits for power adaptation between the safety PLC and the controlled technology. This power adaptation also affects safety. This paper focuses on the design of output circuit architecture that allows the use of diagnostic testing to improve safety, even during continuous operation where diagnostic testing is not usually possible.

关键词： Circuits Safety Testing

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Design and Implementation of Safety Functions for the CNC Milling Machine

Design and Implementation of Safety Functions for the CNC Mi...

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ELEKTRO

作者： Jozef Brtiš Juraj Ždansky Rastislav Pirník Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Žilina Slovakia

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

The paper deals with the design and implementation of safety functions for a CNC milling machine. It is a relatively small CNC milling machine that does not pose a great danger to trained persons. Because it is used in areas where even untrained persons can come into contact with it, it was necessary to implement a safety system that implements safety functions preventing accidents when entering the dangerous zone of the CNC milling machine. The safety system is implemented based on a safety PLC and the sensors are designed using a two-channel architecture to meet the required SIL (safety integrity level).

关键词： Economics Education Milling Sensor systems Safety Sensors Standards

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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Computer Systems Science & engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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Tor network anonymity evaluation based on node anonymity

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Cybersecurity 2024年第4期7卷 73-88页

作者： Jun Cui Changqi Huang Huan Meng Ran Wei School of Life Sciences Tiangong University399 Binshui West RoadXiqing DistrictTianjin300387China Tianjin Engineering Research Center of Biomedical Electronic Technology Tianjin 300387China Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices Tianjin 300387China School of Electronics and Information Engineering Tiangong UniversityTianjin 300387China Department of Rehabilitation Engineering Bejing College of Social Administration(Training Center of the Ministry of Civil Affairs)Beijing 102600China

In order to address the shortcomings of traditional anonymity network anonymity evaluation methods,which only analyze from the perspective of the overall network and ignore the attributes of individual nodes,we proposes a dynamic anonymity model based on a self-built anonymous system that combines node attributes,network behavior,and program security *** anonymity of evaluation nodes is assessed based on stable intervals and behavior baselines defined according to their normal operating *** anonymity of the network is evaluated using an improved normalized information entropy method that refines anonymity evaluation to the anonymity of each node and expands the dimensionality of evaluation *** paper compares the effectiveness of our proposed method with static framework information entropy and single indicator methods by evaluating the degree of anonymity provided by a self-built Tor anonymous network under multiple operating scenarios including normal and under *** approach utilizes dynamically changing network anonymity based on multiple anonymous attributes and better reflects the degree of anonymity in anonymous systems.

关键词： Node anonymity Behavioral baseline Network anonymity Self-built Tor network Dynamic evaluation

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Fault Detection Filtering for Unmanned Surface Vehicles with Markov Switching and Random Packet Losses

Fault Detection Filtering for Unmanned Surface Vehicles with...

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

作者： Hu, Meng-Jie Park, Ju H. Cheng, Jun School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China Yeungnam University Department of Electrical Engineering Gyeongsan38541 Korea Republic of School of Mathematics and Statistics Guangxi Normal University Guilin541006 China

ISBN: (纸本)9781665410205

This paper explores the dissipative fault detection filtering (FDF) issue for unmanned surface vehicles (USVs) within a network environment, considering switching channels and random occurrence of packet losses. Specifically, a multi-channel transmission mechanism is adopted to enhance the reliability of the system, and the switching channel is orchestrated by a Markov chain. The random intermittent data dropouts in each channel between the system and FDF modeled as a Bernoulli process are considered. Leveraging the Lyapunov theory and slack matrix technique, switching-channel-dependent FDF is crafted to detect faults for USVs and ensure that the augmented system keeps stochastic stability while attaining a strictly dissipative performance. Finally, an illustrative example is presented to validate the effectiveness of the proposed method for USVs. © 2024 IEEE.

关键词： Markov chains

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Image Analysis for E-Healthcare Systems using Multi-Biometric Recognition Model

Image Analysis for E-Healthcare Systems using Multi-Biometri...

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2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023

作者： Bansal, Naman Arora, Pranav Sharma, Deepak Kumar Gupta, Koyel Datta Kuntala, Chandana Netaji Subhas Institute of Technology University of Delhi Department of Instrumentation and Control Engineering New Delhi India Indira Gandhi Delhi Technical University for Women Department of Information Technology New Delhi India Maharaja Surajmal Institute of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350396485

With the dawn of e-Healthcare systems, Medical Record Management has become an important research problem. The storage and organization of medical records have made relatively little progress in a world of constantly emerging new technologies and continuous innovation. Many hospitals keep the records on paper, which raises many challenges, including but not limited to a significant amount of time for searching and retrieving a specific record, high maintenance costs, lack of backup, and limited security. Although the inclusion of technology has made this task far more efficient and secure, there is still much that can be done to improve it. This paper proposes a double index-based approach for mapping medical records directly to a patient's biometrics which would take advantage of the uniqueness of biometrics to identify a patient. The proposed multi-biometric model achieves an accuracy of 97% and an F 1 score of 0.9814. © 2023 IEEE.

关键词： Biometrics

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Adaptive Neuro-Fuzzy Inference Systems for Cognitive Sensor Communication in Smart Cities

Adaptive Neuro-Fuzzy Inference Systems for Cognitive Sensor ...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Pradhan, Rahul Prathiba, Ch. Kholmukhamedov, Timur Patil, B.N. Mary, S. Suma Christal Raj, I Infant GLA University Department of Computer Engineering and Applications Mathura India Vignana Bharathi Institute of Technology Department of CSM Ghatkesar Hyderabad India Turin Polytechnic University Automatic Control and Computer Engineering Department Tashkent Uzbekistan Yeshwantrao Chavan College of Engineering Department of Applied Physics Nagpur India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798331539948

Modern urban development depends on optimizing cognitive sensor communications in smart cities, which is the focus of this investigation. In the era of expanding sensor networks, efficient data transmission is paramount for the success of smart city initiatives. The current state of sensor communication faces challenges like suboptimal adaptability, inefficient energy usage, and limited fault detection. This study proposes a solution through the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS), identifying these challenges as opportunities for improvement. The novelty lies in ANFIS's introduction as an intelligent and adaptive solution. Its ability to learn from sensor data, adapt to changing conditions, and optimize communication parameters sets it apart in enhancing cognitive sensor communication. The research methodology involves a systematic approach, encompassing data collection, pre-processing, and ANFIS model development. The model is rigorously trained, validated, and tested for real-world effectiveness. Integration into the smart city communication system is explored, emphasizing applications in context-aware communication, energy-efficient protocols, and fault detection. Performance is evaluated using metrics like accuracy, reliability, and efficiency, demonstrating the model's efficacy in addressing drawbacks and enhancing overall sensor communication in smart cities. The findings contribute to advancing intelligent systems for urban infrastructure and services. © 2024 IEEE.

关键词： Fuzzy neural networks

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FPGA Implementation of Neural Network Accelerator for PCB Defect Detection 3

FPGA Implementation of Neural Network Accelerator for PCB De...

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3rd International Symposium on Computer engineering and Intelligent Communications, ISCEIC 2022

作者： Zhang, Jinzhou Zhang, Hui Zhao, Bingrui Liu, Jiaxuan Zhou, Xidong College of Electrical and Information Engineering Changsha University of Science & Technology Changsha China School of Robotics Hunan University Changsha China Department of Control Science and Engineering Hunan University Changsha China

ISBN: (纸本)9781510660298

With the rapid development of artificial intelligence, deep neural network (DNN) has been widely used in industrial defect detection, intelligent driving, medical research, etc. However, DNN is still limited in the implementation of edge computing and mobile devices due to its characteristics of high model complexity and high computing resource consumption. Therefore, we designed a neural network hardware accelerator based on Field Programmable Gate Array (FPGA) for printed circuit board (PCB) defect detection. In this paper, firstly, since structure re-parameterization can improve the network's accuracy without increasing the inference model's complexity, we introduce structure reparameterization to improve the YOLOv2 model and propose RepYOLOv2. Secondly, a low-bit quantization method based on integer type is adopted to quantify the model data to 6-bit. Then a specific convolutional computing module and neural network hardware accelerator are designed according to the characteristics of the model. Experimental results on Xilinx ZCU102 FPGA show that the real-time processing speed of the system reaches 2.12 FPS, the throughput is 68.53 GOP/s, and the power consumption is only 1.12 W. Compared with similar work, better performance is obtained. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

关键词： Deep neural networks

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