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31st International Conference on Electrical engineering, ICEE 2023

作者： Iranfar, Arash Soleymannejad, Mohammad Moshiri, Behzad Taghirad, Hamid D. School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada Industrial Control Center of Excellence Faculty of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350312560

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is. © 2023 IEEE.

关键词： Classification (of information)

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A Robust Guaranteed Cost Formation control for a Class of Uncertain Multi-Agent Systems for Event-Trigger-Based Variable Gain controllers 6

A Robust Guaranteed Cost Formation Control for a Class of Un...

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6th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2024

作者： Asada, Daiki Oya, Hidetoshi Nagai, Shunya Hoshi, Yoshikatsu Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Tokyo City University The Department of Computer Science Tokyo Japan Niihama College Department of Electronics and Control Engineering Ehime Japan

ISBN: (纸本)9798350380040

In this paper, we propose a design method for a new event-trigger-based variable gain robust controller which achieves both consensus and guaranteed cost performance for a class of uncertain multi-agent systems (MASs) with the leader-follower structure. The proposed variable gain robust controller is composed of stabilizing state feedback laws, state feedback inputs for consensus, and compensation inputs with adjustable parameters, and is designed for giving consideration to relative positions explicitly. In this paper, we show that sufficient conditions for the existence of the proposed robust guaranteed cost formation controller are given in terms of Linear Matrix Inequalities (LMIs). Finally, a simple example is included. © 2024 IEEE.

关键词： State feedback

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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Evaluating the Performance of the OWC Satellite System Under Different Parameters Using the Optisystem Program

Evaluating the Performance of the OWC Satellite System Under...

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Conference of Recent Trends and Applications of Soft Computing in engineering, RTASCE 2024

作者： Abood, Layla H. Al-Hilali, Aqeel Ali Galeb, Basim Saber, Ali Ali Bashar, Mustafa Department of Control and Systems Engineering University of Technology Baghdad Iraq Medical Instrumentation Engineering Department Al-Farahidi University Baghdad Iraq Computer Technician Engineering Al Hikma University College Baghdad Iraq Cybersecurity Technology Engineering Department Al-Kitab University Kirkuk Iraq Medical Instrumentation Engineering Department Uruk University Baghdad Iraq

ISBN: (纸本)9783031828805

The potential of optical wireless communication (OWC) systems for high-speed data transfer, especially over extended distances, is being investigated more and more. However, problems including signal attenuation, dispersion, and noise pose a serious challenge to these systems’ effectiveness as transmission lengths increase. This research examines how an OWC system built using OptiSystem software is affected by different transmission lengths, ranging from 1000 to 3000 km. The results of the investigation show that the Quality Factor (QF) and the Bit Error Rate (BER) have a significant negative connection, with more signal distortion and attenuation occurring at greater distances. While rising noise power—due to attenuation and compensatory noise from optical amplifiers—further degrades signal quality by reducing the Signal-To-Noise Ratio (SNR) and Optical Signal-To-Noise Ratio (OSNR), the eye diagram's narrowing indicates increased pulse broadening and intersymbol interference. Furthermore, timing irregularities, as measured by RMS jitter and peak-to-peak jitter, became larger at greater distances, indicating the detrimental effects of attenuation on signal accuracy. The exponential increase in BER logarithm with distance highlights the increasing error rates. These results highlight the serious problems with long-distance transmission and the need for creative solutions. To further enhance the performance and dependability of high-speed OWC systems over long distances, future research should concentrate on creating sophisticated dispersion compensation algorithms, improving noise control tactics, and investigating adaptive modulation schemes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Timing jitter

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Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

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control Theory and Technology 2024年第3期22卷 468-478页

作者： Zhiyuan Zhu Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaP.0.Box 400743CharlottesvilleVA 22904-4743USA

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output *** imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead *** of the presence of output dead zone and the additive disturbance,the states cannot be expected to driven into an arbitrarily small neighborhood of the *** solve the global practical stabilization problem,we proposes a low gain-based linear dynamic output feedback law,under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set,both in finite *** results illustrate the effectiveness of our proposed control method.

关键词： Output saturation Disturbance rejection Output dead zone Low gain feedback-Linear output feedback Practical stabilization

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Optimizing DNN Inference on Multi-Accelerator SoCs at Training-time

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Risso, Matteo Burrello, Alessio Pagliari, Daniele Jahier Politecnico di Torino Department of Control and Computer Engineering Turin10129 Italy

The demand for executing Deep Neural Networks (DNNs) with low latency and minimal power consumption at the edge has led to the development of advanced heterogeneous Systems-on-Chips (SoCs) that incorporate multiple specialized computing units (CUs), such as accelerators. Offloading DNN computations to a specific CU from the available set often exposes accuracy vs efficiency trade-offs, due to differences in their supported operations (e.g., standard vs. depthwise convolution) or data representations (e.g., more/less aggressively quantized). A challenging yet unresolved issue is how to map a DNN onto these multi-CU systems to maximally exploit the parallelization possibilities while taking accuracy into account. To address this problem, we present ODiMO, a hardware-aware tool that efficiently explores fine-grain mapping of DNNs among various on-chip CUs, during the training phase. ODiMO strategically splits individual layers of the neural network and executes them in parallel on the multiple available CUs, aiming to balance the total inference energy consumption or latency with the resulting accuracy, impacted by the unique features of the different hardware units. We test our approach on CIFAR-10, CIFAR-100, and ImageNet, targeting two open-source heterogeneous SoCs, i.e., DIANA and Darkside. We obtain a rich collection of Paretooptimal networks in the accuracy vs. energy or latency space. We show that ODiMO reduces the latency of a DNN executed on the Darkside SoC by up to 8× at iso-accuracy, compared to manual heuristic mappings. When targeting energy, on the same SoC, ODiMO produced up to 50.8× more efficient mappings, with minimal accuracy drop ( © 1982-2012 IEEE.

关键词： System-on-chip

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Supremal and Robust control Against Actuator Jamming Attacks in Cyber-Physical Systems

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5435-5447页

作者： Duo, Wenli Wang, Shouguang Zhou, MengChu You, Dan Winiewski, Remigiusz Bazydlo, Grzegorz Albeshri, Aiiad Macau University of Science and Technology Institute of Systems Engineering 999078 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou310018 China New Jersey Institute of Technology Helen and John C. Hartmann Department of Electrical and Computer Engineering NewarkNJ07102 United States University of Zielona Góra Institute of Control and Computation Engineering Zielona Góra65-516 Poland King Abdulaziz University Department of Computer Science Jeddah21481 Saudi Arabia

Cyber-physical systems, such as unmanned aerial vehicles and connected and autonomous vehicles, are vulnerable to cyber attacks, which can cause significant damage to society. This paper examines the attack issue in cyber-physical systems within the framework of discrete event systems. Specifically, we consider a scenario where a malicious intruder injects a jamming signal into an actuator channel. It disrupts the transmission of control commands and prevents an actuator from receiving them. This is termed an actuator jamming attack. In the paper, we first analyze the closed-loop system behavior under such an attack. An attack structure is constructed to illustrate how an intruder exploits a jamming attack to drive a system into unsafe states. Then, we study the supervisory control problem for a system exposed to such an attack. The problem is reduced to a basic supervisory control one in discrete event systems by introducing the concept of dynamically controllable language. A solution to this problem is explored, where we establish an existence condition for a supremal and robust supervisor that is capable of defending against actuator jamming attacks, and design an algorithm to derive it. Finally, the effectiveness of our method is illustrated by an intelligent automated guided vehicle system. © 2016 IEEE.

关键词： Discrete event simulation

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Solving non-cyclic dynamic multi-objective optimization problems via GRU prediction and multi-information hybrid exploration

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Journal of Supercomputing 2025年第8期81卷 1-34页

作者： Sun, Hao Bai, Xiaochuang Wang, Cong Yang, He Hu, Ziyu School of Electrical and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Qinhuangdao066004 China Electrical automation department Tianjin Research Institute of Electric Science Co. Ltd. Tianjin China

In non-cyclic dynamic multi-objective optimization problems, the non-cyclic nature of environmental changes may cause the Pareto optimal front (PF) to be different from historical times. In addition, changes may also occur on the Pareto optimal solutions set (PS). However, predictions based solely on a single space show lower accuracy due to insufficient information. Therefore, solving non-cyclic dynamic multi-objective optimization problems via gate recurrent unit (GRU) prediction and multi-information hybrid exploration (GPMHE) is designed to overcome the above challenges. In the objective space, the GRU-based prediction strategy (GP) captures patterns of population change, predicts its distribution in new environments, and subsequently maps the results to the decision space;The multi-information hybrid exploration strategy (MHE) takes the feature individual as the representative individual, adaptively guides the population evolution direction in the decision space, thereby enhancing the algorithm’s adaptability. Compared with five advanced algorithms on the non-cyclic dynamic benchmark test set (NCD), the proposed algorithm GPMHE has shown robust adaptation to dynamic environmental changes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Multiobjective optimization

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The Dark Side of the Language: Pre-trained Transformers in the DarkNet

The Dark Side of the Language: Pre-trained Transformers in t...

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Ranaldi, Leonardo Nourbakhsh, Aria Patrizi, Arianna Ruzzetti, Elena Sofia Onorati, Dario Mastromattei, Michele Fallucchi, Francesca Zanzotto, Fabio Massimo Human-Centric ART Group Department of Enterprise Engineering University of Rome Tor Vergata Italy Department of Computer Control and Management Engineering University of Rome Sapienza Italy

ISBN: (纸本)9789544520922

Pre-trained Transformers are challenging human performances in many NLP tasks. The massive datasets used for pre-training seem to be the key to their success on existing tasks. In this paper, we explore how a range of pretrained Natural Language Understanding models perform on definitely unseen sentences provided by classification tasks over a DarkNet corpus. Surprisingly, results show that syntactic and lexical neural networks perform on par with pre-trained Transformers even after fine-tuning. Only after what we call extreme domain adaptation, that is, retraining with the masked language model task on all the novel corpus, pre-trained Transformers reach their standard high results. This suggests that huge pre-training corpora may give Transformers unexpected help since they are exposed to many of the possible sentences. © 2023 Incoma Ltd. All rights reserved.

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Passivity-Based Distributed Event-Triggered Flocking control of Port-Hamiltonian Systems

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IEEE control Systems Letters 2025年 9卷 342-347页

作者： Aranda-Escolástico, Ernesto Abdelrahim, Mahmoud Fernández-Amorós, David Guinaldo, María Colombo, Leonardo Universidad Nacional de Educación a Distancia Department of Software and Systems Engineering Madrid28040 Spain Prince Sultan University Renewable Energy Laboratory College of Engineering Riyadh11586 Saudi Arabia Universidad Nacional de Educación a Distancia Computer Science and Automatic Control Department Madrid28040 Spain Field and Service Robotics Department Madrid28500 Spain

This letter addresses the problem of flocking motion in the context of port-Hamiltonian systems. The port-Hamiltonian framework offers advantages in modeling networks, dissipation, and heterogeneous agents. In this regard, designing flocking controllers in port-Hamiltonian form significantly increases their practical applications. Additionally, event-triggered control has proven effective in reducing bandwidth consumption. However, challenges remain in combining flocking requirements with energy-dissipating terms. Therefore, we propose a novel event-triggered flocking controller suitable for port-Hamiltonian systems. The method is validated through numerical simulations of a group of hexarotors. © 2017 IEEE.

关键词： Hamiltonians

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