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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Gaikwad, Pranav Pandurang Alhomaidi, Esam Gupta, Shishir Perada, Anvesh Dande, Mangesh Purushottam Muthukumar, E. University of Cumberlands Department of Information Technology WilliamsburgKY40769 United States King Fahd University of Petroleum and Minerals Department of Industrial & Systems Engineering Dhahran31261 Saudi Arabia Allenhouse Business School Department of Business Administration Uttar Pradesh Kanpur208008 India Drexel University Department of Electrical and Computer Engineering PhiladelphiaPA19104 United States Indira School of Business Studies Pgdm Department of Operations Management Pune411033 India Nehru Institute of Engineering and Technology Department of Mba Coimbatore641105 India

ISBN: (纸本)9798350364699

Machine learning algorithms are implemented in predictive logistics management to optimize the supply chain for vehicle sales. These algorithms are capable of predicting future demand, managing inventory levels, and optimizing distribution procedures by analyzing historical data, prevailing market patterns, and additional external factors. Organizations may enhance customer satisfaction, minimize lead times, and maintain appropriate inventory levels by implementing predictive models. This approach not only improves the overall efficacy of the supply chain, but it also decreases the costs associated with inventory shortages and excess inventory. The integration of machine learning (ML) into logistics management (LM) offers a data-driven solution to the complex and ever-changing supply chain scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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基于双光子聚合制造的量子点光纤温度传感器（英文）

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激光与光电子学进展 2024年

作者：王超孙盛芝钱滨彭思达冯泽祺邱建荣 Laboratory of Infrared Materials and Devices The Research Institute of Advanced Technologies Ningbo University Institute of Light+X Science and Technology Faculty of Electrical Engineering and Computer Science Ningbo University State key laboratory of fluid power and mechatronic systems Zhejiang University Hangzhou State Key Laboratory of Modern Optical Instrumentation Zhejiang University

Temperature is a common physical quantity with great significance, especially under harsh conditions, such as high voltage, and strong electric/magnetic fields. Here, we demonstrate the development of an all-fiber-optic temperature sensor based on a fiber-end microstructure doped with quantum dots(QDs). The microstructure was fabricated on a fiber tip by two-photon polymerization technology. The results show that the fluorescence intensity of the QDs microstructure reduced with the increase of temperature while the peak position of photoluminescence exhibits a linear change within the temperature range of 26-70 ℃. The developed sensor exhibits a high-temperature sensitivity of 135 pm/℃. The methods developed in our work could have enormous potential for the development of fiber-end temperature sensors.

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Human–AI interactive optimized shared control

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Journal of Automation and Intelligence 2025年

作者： Tan, Junkai Xue, Shuangsi Cao, Hui Ge, Shuzhi Sam School of Electrical Engineering Xi'an Jiaotong University Xi'an 710049 China State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University Xi'an 710049 China The Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore

This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven digital system functions as the virtual agent. In this digital twin architecture, the real agent acquires an optimal control strategy through observed actions, while the AI virtual agent mirrors the real agent to establish a digital replica system and corresponding control policy. Both the real and virtual optimal controllers are approximated using reinforcement learning (RL) techniques. Specifically, critic neural networks (NNs) are employed to learn the virtual and real optimal value functions, while actor NNs are trained to derive their respective optimal controllers. A novel shared mechanism is introduced to integrate both virtual and real value functions into a unified learning framework, yielding an optimal shared controller. This controller adaptively adjusts the confidence ratio between virtual and real agents, enhancing the system's efficiency and flexibility in handling complex control tasks. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed AI–human interactive system is validated through two numerical examples: a representative nonlinear system and an unmanned aerial vehicle (UAV) control system. © 2025 The Authors

关键词： Adaptive dynamic programming (ADP) Data-driven Digital-twin system Human–AI interaction Optimal shared control

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Sub-Loc:predicting protein sub-mitochondrial localization based on sequence embedding

Sub-Loc:predicting protein sub-mitochondrial localization ba...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Xiaoting Wang, Juan Bian, Haodong Guo, Maozu Inner Mongolia University Ecological Big Data Engineering Research Center of the Ministry of Education School of Computer Science Hohhot China Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

ISBN: (纸本)9781665468190

Mitochondria are subcellular organelles existing in most eukaryotic organisms. They have a pivotal role in lots of bio-chemical processes for cells. Proteins in different compartments of mitochondria have their transport routes. Locating proteins in mitochondria can provide a solid foundation for mitochondrial pathologies. So far, there have been several computational methods for solving the issue. However, their accuracy is so low that they do not identify the localization precisely. We develop an unsupervised learning model to represent mitochondrial proteins as n-dimensional vectors, called sequence embedding, which can learn the global and context information from mitochondrial proteins. We design a new model, called Sub-Loc, to predict the sub-mitochondrial localization of proteins using the SVM classifier and the sequence embedding method. The sequence embedding method and the Sub-Loc are tested by experiments. Experimental results show the sequence embedding method remarkably enhances the performance of prediction for sub-mitochondrial localization of proteins compared with other feature representations. The Sub-Loc outperforms other approaches for predicting sub-mitochondrial localization of proteins. © 2022 IEEE.

关键词： Support vector machines

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VECOM: Variation Resilient Encoding and Offset Compensation Schemes for Reliable ReRAM Based DNN Accelerator

arXiv

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arXiv 2023年

作者： Jang, Je-Woo Nguyen, Thai-Hoang Yang, Joon-Sung School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Dept. of Semiconductor Systems Engineering Yonsei University Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

Resistive Random-Access Memory (ReRAM)-based Processing In-Memory (PIM) Accelerator has emerged as a promising computing architecture for memory-intensive applications, such as Deep Neural Networks (DNNs). However, due to its immaturity, ReRAM devices often suffer from various reliability issues, which hinder the practicality of the PIM architecture and lead to a severe degradation in DNN accuracy. Among various reliability issues, device variation and offset current from High Resistance State (HRS) cell have been considered as major problems in a ReRAM-based PIM architecture. Due to these problems, the throughput of the ReRAM-based PIM is reduced as fewer wordlines are activated. In this paper, we propose VECOM, a novel approach that includes a variation-resilient encoding technique and an offset compensation scheme for a robust ReRAM-based PIM architecture. The first technique (i.e., VECOM encoding) is built based on the analysis of the weight pattern distribution of DNN models, along with the insight into the ReRAM's variation property. The second technique, VECOM offset compensation, tolerates offset current in PIM by mapping the conductance of each Multi-level Cell (MLC) level added with a specific offset conductance. Experimental results in various DNN models and datasets show that the proposed techniques can increase the throughput of the PIM architecture by up to 9.1 times while saving 50% of energy consumption without any software overhead. Additionally, VECOM is also found to endure low R-ratio ReRAM cell (up to 7) with a negligible accuracy drop. © 2023, CC BY.

关键词： Deep neural networks

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Microwave photonics

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Science China(Information Sciences) 2022年第12期65卷 49-112页

作者： Jianping YAO Jose CAPMANY Microwave Photonics Research Laboratory School of Electrical Engineering and Computer Science University of Ottawa ITEAM Research Institute Universitat Politècnica de València iPronics Programmable Photonics

Microwave photonics, an interdisciplinary field that combines microwave engineering and photonic technology for the generation, transmission, processing, and control of microwave signals, to take advantage of the broad bandwidth, high frequency, and low loss offered by modern photonics, has been intensively researched for the last few decades, and numerous solutions have been proposed and demonstrated. In this article, an overview about microwave photonics is provided which covers the basic concepts of microwave photonics, photonic-assisted microwave signal generation, photonic-assisted microwave signal processing, and true time delay beamforming. The implementation of microwave photonic systems based on photonic integrated circuits is also reviewed, including the design, fabrication, and material platforms, application-specific photonic integrated circuits for microwave photonics, and programmable integrated microwave *** challenges and future applications of microwave photonics are also discussed.

关键词： microwave photonics photonic integrated circuits microwave photonic link microwave photonic signal processor optoelectronic oscillator radio over fiber

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Integrated Precoder and Trajectory Design for MIMO UAV-Assisted Relay System With Finite-Alphabet Inputs

arXiv

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arXiv 2024年

作者： Di, Haoyang Zhu, Xiaodong Shao, Yulin School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau China Department of Electrical and Electronic Engineering Imperial College London United Kingdom

Unmanned aerial vehicles (UAVs) are gaining widespread use in wireless relay systems due to their exceptional flexibility and cost-effectiveness. This paper focuses on the integrated design of UAV trajectories and the precoders at both the transmitter and UAV in a UAV-assisted relay communication system, accounting for transmit power constraints and UAV flight limitations. Unlike previous works that primarily address multiple-input single-output (MISO) systems with Gaussian inputs, we investigate a more realistic scenario involving multipleinput multiple-output (MIMO) systems with finite-alphabet inputs. To tackle the challenging and inherently non-convex problem, we propose an efficient solution algorithm that leverages successive convex approximation and alternating optimization techniques. Simulation results validate the effectiveness of the proposed algorithm, demonstrating its capability to optimize system performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Neural Network Security Control of Markov Switching systems: Finite-Time Performance Implementation 8th

Neural Network Security Control of Markov Switching Systems...

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Cao, Zhiru Peng, Chen Wu, Jiancun Zhu, Kaiqun School of Mechatronic Engineering Shanghai University Shanghai200444 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China The State Key Laboratory of Internet of Things for Smart City University of Macau Macau999078 China

ISBN: (纸本)9789819602933

This paper is concerned with the security sliding mode control problem for Markov switching systems suffering from both physical and communication constraints, that is, the actuator saturation and injection attack. With the help of the learning capability of neural networks, the nonlinear upper bound of injection attacks is analyzed and estimated, and the security control strategy based on neural networks is proposed to improve the reliability of control. Considering the transient performance implementation, a time-dependent sliding mode controller is proposed to guarantee the reachability of the specified sliding surface within a given finite time, in which the adaptive rules of neural network parameters are designed to reduce the effects of the considered attack. The arrival and sliding stages are, respectively, analyzed and sufficient conditions are achieved to guarantee the random finite-time boundedness of the closed-loop system. Eventually, a simulation example is given, combined with a genetic algorithm, to illustrate the validity of the proposed adaptive security control method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Markov processes

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computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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Exploring Multi-Perspective Document Structures for News Discourse Profiling

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2349-2359页

作者： Hang Yang Dianbo Sui Yubo Chen Kang Liu Jun Zhao School of Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Computer Science and Software Engineering Harbin Institute of Technology Weihai China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Learning the discourse structures of news articles is vital for obtaining valuable structural information around main events, which has been shown useful for many natural language processing applications. This paper proposes the extraction of graphs explicitly representing the discourse structure, coreference structure, and semantic similarities within a document. Concept embeddings are then updated using a weighted combination of feature embeddings, modeled after capsule networks, and resulting in improved context-dependent representation of the content of each news article. Experimental tests on the NewsDiscourse dataset demonstrate the effectiveness of our proposed method.

关键词： Speech processing Routing Computational modeling Weapons Encoding Transformers Representation learning Natural language processing Context modeling Artificial intelligence

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