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2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2022

作者： Mathew, Joseph George, Sudhish N. Control and Instrumentation Group Centre for Development of Advanced Computing Thiruvananthapuram India National Institute of Technology Calicut Department of Electronics and Communication Engineering Kozhikode India

ISBN: (纸本)9781665449403

An infrared thermal imaging system can screen large groups of people quickly, non-intrusively and with a high level of confidence in spotting febrile subjects. A thermal imaging system must identify individuals with a body core temperature above 38°C to effectively do this. Conventionally, this is per-formed using thermal imaging cameras in the Long Wave Infra Red (LWIR) region. But the issue with most of these systems is that it fails to read the temperature at the inner eye canthi, which is the ideal location on the face for fever detection. This is owing to the lack of distinguishing features that are available on a thermal image. Hence, in this paper, an attempt is made to read the temperature at the inner eye canthus by first detecting the eye region by running the Viola - Jones Haar Cascade algorithm over the visible images, in a popular synchronized visible and thermal face dataset. Subsequently, this identified region of interest in the visible image is registered against its corresponding thermal image using the ORB algorithm to locate the eye canthus area on the thermal image. Experimental results analysed in this paper proves that a read out of the temperature at this pixel location can be a sufficiently accurate estimate of the core body temperature of the subject being screened. © 2022 IEEE.

关键词： Image registration

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The Preparation Method, Resistive Switching Mechanism and Application Prospects of Memristor 7

The Preparation Method, Resistive Switching Mechanism and Ap...

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7th International Conference on Electrical, Mechanical and Computer engineering, ICEMCE 2023

作者： Li, Jiao Zhao, Ximei Lei, Tengfei Chai, Lin Wang, Shi Yu, Yuna Collaborative Innovation Center of Memristive and Computing Application Qilu Institute of Technology Jinan250200 China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan250200 China College of Computer and Communication Engineering Qilu Institute of Technology Jinan250200 China

ISBN: (纸本)9798350382877

Memristors have also become one of the most promising research hotspots for solving data storage and computing problems faced in the big data era. As an emerging electronic device, memristor has potentially important applications in information storage and processing. This paper constructs a rod-shaped TiO2-x nanoscale memristor with FTO/TiO2-x/Cu structure, whose reliability is based on the memory behavior and its conversion mechanism. Research progress including materials, preparation methods, resistive switching mechanism and application prospects are described. First, the constituent materials of memristor are introduced, focusing on metal oxides and organic materials such as titanium oxide (TiO2). The choice of these materials is critical to memristor performance and applications. Secondly, the advantages and limitations of the preparation methods are discussed. Then the resistance switching mechanism of memristor was studied in depth, focusing on the generation principle and adjustment mechanism of memristor effect. These include important concepts such as charge migration, Schottky barrier formation and oxygen vacancy drift, which help to understand the electrical properties of memristors. In addition, we explore the prospects of memristors in practical applications. Finally, past and current work in this field is summarized, and the prospects and challenges of memristorrelated research are prospected. © 2023 IEEE.

关键词： Memristors

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Towards Long Lifetime Battery:AI-Based Manufacturing and Management

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1139-1165页

作者： Kailong Liu Zhongbao Wei Chenghui Zhang Yunlong Shang Remus Teodorescu Qing-Long Han Warwick Manufacturing Group University of WarwickCoventry CV47ALUnited Kingdom National Engineering Laboratory for Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China School of Control Science and Engineering Shandong UniversityJinan 250061China Department of Energy Technology Aalborg UniversityAalborg 9220Denmark School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia IEEE

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification,smart grid,but also strengthen the battery supply *** battery inevitably ages with time,losing its capacity to store charge and deliver it *** directly affects battery safety and efficiency,making related health management *** advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management *** paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime ***,AI-based battery manufacturing and smart battery to benefit battery health are *** the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and *** through designing suitable AI solutions to enhance battery longevity are also ***,the main challenges involved and potential strategies in this field are *** work will inform insights into the feasible,advanced AI for the health-conscious manufacturing,control and optimization of battery on different technology readiness levels.

关键词： Artificial intelligence battery health management battery life diagnostic battery manufacturing smart battery

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STFormer: A dual-stage transformer model utilizing spatio-temporal graph embedding for multivariate time series forecasting

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Journal of Intelligent and Fuzzy Systems 2024年第3期46卷 6951-6967页

作者： Xiao, Yuteng Liu, Zhaoyang Yin, Hongsheng Wang, Xingang Zhang, Yudong Jinan China Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Xuzhou University of Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing China

Multivariate Time Series (MTS) forecasting has gained significant importance in diverse domains. Although Recurrent Neural Network (RNN)-based approaches have made notable advancements in MTS forecasting, they do not effectively tackle the challenges posed by noise and unordered data. Drawing inspiration from advancing the Transformer model, we introduce a transformer-based method called STFormer to address this predicament. The STFormer utilizes a two-stage Transformer to capture spatio-temporal relationships and tackle the issue of noise. Furthermore, the MTS incorporates adaptive spatio-temporal graph structures to tackle the issue of unordered data specifically. The Transformer incorporates graph embedding to combine spatial position information with long-term temporal connections. Experimental results based on typical finance and environment datasets demonstrate that STFormer surpasses alternative baseline forecasting models and achieves state-of-the-art results for single-step horizon and multistep horizon forecasting. © 2024 - IOS Press. All rights reserved.

关键词： Recurrent neural networks

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Exploiting Machine Learning and LSTM for Human Activity Recognition: Using Physiological and Biological Sensor Data from Actigraph

Exploiting Machine Learning and LSTM for Human Activity Reco...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Matthew Oyeleye Tianhua Chen Pan Su Grigoris Antoniou Department of Computer Science School of Computing and Engineering University of Huddersfield UK School of Control and Computer Engineering North China Electric Power University China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Human activity recognition involves identifying the daily living activities of an individual through the utilization of sensor attributes and intelligent learning algorithms. The identification of intricate human activities proves to be a labo-rious task, given the inherent difficulty of capturing long-term dependencies and extracting efficient features from unprocessed sensor data. For this purpose, this study aims at recognizing and classifying human activities using physiological and biological sensor data generated by Actigraph, as they can accurately measure moderate-to-vigorous intensity physical which is mostly affected by body composition and also better suited for self-monitoring. We examined the effectiveness of these features by applying prevalent machine learning classifiers and long short-term memory (LSTM) networks on recently publicly available data, which includes accelerometer and heart rate recordings. The results from our experiments showed that LSTM models performed better than conventional ML classifiers with the best result achieving an accuracy of 86.5%. The findings also confirms the significance of the heart rate in accurately classifying and identification of human activity more.

关键词： Heart rate Machine learning Feature extraction Recording Human activity recognition Biosensors Biomedical monitoring

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Adaptive Nonsingular Fast Terminal Sliding Mode control for Multilateral Teleoperation Systems with Varying Time Delay

Adaptive Nonsingular Fast Terminal Sliding Mode Control for ...

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International Conference on Systems and control (ICSC)

作者： Asma Ounissi Neila Mezghani Ben Romdhane Mohamed Boukattaya Laboratory of sciences and technique of automatic control and computing (Lab-STA) National school of engineering of Sfax Sfax Tunisia Laboratory of sciences and technique of automatic control and computing (Lab-STA) Sfax Preparatory Engineering Institute Sfax Tunisia

This paper deals with the development of control law to achieve the consensus tracking on a multilateral teleoperation system (MTS) that is proposed is to achieve a complex goal which is hard to achieve by a single manipulator with communication time delay. In MTS, each robot shares its information to the other robots to complete the task. In order to ensure rapid convergence, prevent singularities, and bolster resilience against uncertainties and external disturbances (ED), a novel approach is employed in the multilateral teleoperation system. This technique, referred to as Adaptive Non-Singular Fast Terminal Sliding Mode control (ANFTSMC), is implemented on master-slave-followers manipulators confronting challenges such as unidentified upper limits on system uncertainty (SU), varying-time delay (VTD), and external disturbances. The controller exhibits a finite-time convergence and is robust in the presence of the external force and (VTD). The Lyapunov stability theory is employed for stability analysis, and simulation investigations are carried out to confirm the efficacy of the control strategy that have been developed.

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Analysis of Factors Influencing Driver Lane-Changing Intentions Based on a Naturalistic Trajectory Data Set on Highways 24

Analysis of Factors Influencing Driver Lane-Changing Intenti...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Guangchen Lu, Guangquan Wang, Jinghua Liu, Miaomiao School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Accurately analyzing and predicting driver lane-changing intentions is of paramount importance, as it significantly enhances the safety of self-driving vehicles in their decision-making processes, holding great promise for the future of intelligent transportation systems. This study extracts fundamental lane-changing characteristic parameters from the Next Generation Simulation (NGSIM) trajectory data set and employs Structural Equation Modeling (SEM) to construct a model that elucidates the factors influencing drivers’ lane-changing intentions. Furthermore, it provides insight into how lane-changing intentions are influenced by lane-changing behavior and explores the intricate connection between intention and associated risks. The results emphasize the substantial negative impact of the longitudinal speed difference between the self-vehicle and the preceding vehicle on lane-changing intentions. Additionally, the consideration of lane-changing risk plays a pivotal role in drivers’ decisions to engage in lane-changing behavior. These findings have significant implications for a deeper comprehension of lane-changing intentions, guiding lane-changing decisions, and enhancing overall traffic safety. © ASCE.

关键词： Autonomous vehicles

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Design of a Digital Phase Lead-and-Lag Voltage control based on Frequency Response Approach for Single-Phase Half-Bridge DC-AC Converters

Design of a Digital Phase Lead-and-Lag Voltage Control based...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Henrique Eissmann Buzzi Tiago Davi Curi Busarello Adriano Péres Dept. of Control Automation and Computing Engineering Federal University of Santa Catarina Blumenau Brazil

Grid-forming inverters is getting special attention of industries and academia due to their important role in the realization of microgrids. A crucial feature of them is to have an accurate voltage control. This paper proposes a digital phase lead-and-lag voltage control based on frequency response approach for single-phase half-bridge DC-AC converters. Initially, the system is modeled through the small-signal technique. Then, the controller is designed based on the frequency response approach. The discretization through Tustin and Backward methods are presented and compared. Moreover, designing the controller in discrete-time domain by conventional methods using the fictitious w-domain is also presented. The system is tested in a controller Hardware-in-Loop setup, where the power topology runs in the HIL402 device and the digital voltage controller runs in the TMS320FF28335 digital signal controllers. Scenarios like initialization, nonlinear load and oscillation at the DC voltage are performed. Results show the efficacy of the methodology proposed in this paper.

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Robust Target Detection and Localization for UAV Autonomous Landing with Partially Occluded Targets 15th

Robust Target Detection and Localization for UAV Autonomous ...

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15th International Conference on Genetic and Evolutionary computing, ICGEC 2023

作者： Shao, Mingmei Liu, Yanyan Tang, Jiaze Liu, Huanyu Li, Junbao Faculty of Computing Harbin Institute of Technology Harbin150080 China National Key Laboratory of Electromagnetic Space Security Tianjin300308 China Electrical Measurement Technology and Intelligent Control Institute School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819700677

This paper presents an approach for robust target detection and localization in the context of UAV autonomous landing with partially occluded targets. Since the performance of the autonomous landing system largely depends on the accurate detection and positioning of the landing target, it is very important to deal with the local occlusion caused by obstacles or environmental factors. In order to improve the overall performance of the system, we propose a fault-tolerant target detection method and target completion strategy. The proposed target detection method combines image processing technology with feature extraction algorithm, which can recognize and locate the landing target even in the case of occlusion. The target completion strategy aims to recover the missing part of the occluded target and make the detection and location algorithm execute more reliably. We perform experiments on image data sets with different horizontal object occlusion to evaluate the performance of the proposed method. The results show that our method successfully detects and locates the landing target under different occlusion levels, and significantly improves the performance of UAV autonomous landing system in the real scene. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN 2

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Krishna, Tiruveedula Gopi Sreenivas, Nune Ajay Kumar, M. Reddy, Chandra Sekhar Rathee, Davinder Sing Urgessa, Teklu College of Electrical Engineering and Computing Adama Science and Technology University Department of Computer Science and Engineering Adama Ethiopia Andhra Loyala institute of Engineering and Technology Department of Electrical and Electronics Engineering Vijayawada India College of Electrical Engineering and Computing Adama Science and Technology University Department of Electrical Power and Control Engineering Adama Ethiopia College of Electrical Engineering and Computing Adama Science and Technology University Department of ECE Adama Ethiopia

ISBN: (纸本)9798331505745

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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