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A Group-Based Many-Task Collaborative Optimization Framework for Evolutionary Robots Design

IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3492-3505页

作者： Hou, Yaqing Yu, Zhaoping Guo, Zheng Pei, Wenbin Wu, Yaoxin Ge, Hongwei Xue, Bing Zhang, Mengjie Dalian University of Technology College of Computer Science and Technology Dalian116024 China Avic Xi'an Aeronautics Computing Technique Research Institute 18th Research Department Xi'an710068 China Eindhoven University of Technology Department of Industrial Engineering and Innovation Sciences Eindhoven5612 AZ Netherlands Victoria University of Wellington School of Engineering and Computer Science Wellington6140 New Zealand

In evolutionary robotics (ER), the evolution of a robot's morphology (i.e., physical structure) or controller (i.e., control algorithm or instruction sequence) often entails tackling an extensive number of tasks. The use of evolutionary multitasking (EMT) in ER, which optimizes multiple tasks simultaneously by reusing potentially useful knowledge across diverse tasks, could improve the performance of problem-solving to each task. However, existing EMT methods do not fully use intertask correlations, limiting knowledge sharing. In view of this, this study introduces a novel framework, termed adaptive group-based collaborative optimization, tailored for handling optimization problems involving a large number of tasks within the ER domain simultaneously. The proposed framework divides tasks into groups according to their similarity and then proceeds through two principal stages, namely, intergroup knowledge separation and intragroup knowledge reunion. During intergroup knowledge separation stage, an adaptive method for selecting crossover operators enables source tasks to share useful knowledge to the target task across groups. During intragroup knowledge reunion stage, an adaptive knowledge combination strategy facilitates the target task in assimilating knowledge from multiple sources intragroup. We validated the efficacy of the proposed framework in both planar manipulators and hexapod robot experiments. The results indicate that our method outperforms existing state-of-the-art algorithms (i.e., MME, MMKT) on several metrics (e.g., mean fitness and quality diversity metrics). The proposed method can effectively improve the effectiveness and diversity of solutions in solving ER problems with a large number of tasks (e.g., 5 000 or 10 000), and has broad potential in practical ER applications. © 2025 IEEE.

关键词： Collaborative robots

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Flexible Thin-Film Transistor and Integration Strategies for Future Intelligent Displays

IEEE Open Journal on Immersive Displays

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IEEE Open Journal on Immersive Displays 2024年 1卷 187-203页

作者： Zhang, Yuqing Wang, Xu Zhang, Sixin Wang, Chunxiu Zhang, Min Peking University School of Electronic and Computer Engineering Shenzhen518055 China Hong Kong Applied Science and Technology Research Institute Hong Kong

Future flexible intelligent displays are driving demands for greater flexibility, higher resolution, faster refresh rates, and enhanced functionality. This review mainly focuses on recent advances and ongoing challenges in flexible thin-film transistors (TFTs) based on low-temperature polycrystalline silicon (LTPS), amorphous oxide semiconductor (AOS), and low-temperature polysilicon and oxide (LTPO) for flexible backplane technologies. Furthermore, the developments of process and function integration techniques, including device scaling, device stacking, as well as functional circuits and applications, are discussed from the perspective of innovation and significance. Besides, the integration of intelligent systems is briefly introduced, emphasizing the role of synaptic transistors, including metal oxide and carbon nanotube-based ones, in real-time data processing and sensing functions. By providing insights into the status and future directions, this review provides a comprehensive perspective and discussion on the development of next-generation flexible intelligent display technologies. © 2024 IEEE.

关键词： Thin film transistors

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Clinical deep model to analyse medical multivariate time-series data for health diagnosis

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Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

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IPH2O: Island Parallel-Harris Hawks Optimizer-Based CLSTM for Stock Price Movement Prediction

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Annals of Data science 2024年第6期11卷 1959-1974页

作者： Joel, Linda Parthasarathy, S. Venkatesan, P. Nandhini, S. Department of Mathematics SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai89 India Research and Statistics Sri Ramachandra Medical College and RI Sri Ramachandra University Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Stock price movement forecasting is the process of predicting the future price of a financial and company stock from chaotic data. In recent years, many financial institutions and academics have shown interest in stock market forecasting. The accurate and successful predictions of the future price of stock yield a substantial profit. However, the current approaches are a major challenge due to the dynamic, chaotic, high-noise, non-linear, highly complex, and nonparametric characteristics of stock data. Furthermore, it is not sufficient to consider only the target firms' information because the stock prices of the target firms may be influenced by their related firms. Significant profits can be made by correct forecasting of stock prices, while poor forecasts can cause huge problems. Thus, we propose a novel Island Parallel-Harris Hawks Optimizer (IP-HHO)-optimized Convolutional Long Short Term Memory (ConvLSTM) with an autocorrelation model to predict stock price movement. Then, using the IP-HHO algorithm, the hyperparameters of ConvLSTM are optimized to minimize the Mean Absolute Percentage Error (MAPE). Four different types of financial time series datasets are utilized to validate the performance of the evaluation measures such as root mean square error, MAPE, Index of Agreement, accuracy, and F1 score. The results show that the IP-HHO-optimized ConvLSTM model outperforms others by improving the prediction rate accuracy and effectively minimizing the MAPE rate by 19.62%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Profitability

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Deep Learning Algorithms in Mobile Edge with Real-Time Abnormal Event Detection for 5G-IoT Devices

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第17期17卷 59-71页

作者： Praveenchandar, J. Kumar, S. Vinoth Paul, A. Christopher Mukunthan, M.A. Maharajan, K. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Avadi India Department of Information Technology Karpagam Institute of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India

IoT is becoming increasingly popular due to its quick expansion and variety of applications. In addition, 5G technology helps with communication and network connectivity. This work integrates C-RAN with IoT networks to provide an experimental 5G testbed. In a 5G IoT environment, this experience is utilized to enhance both perpendicular and flat localization (3D localization). DRCaG, an acronym for the proposed model, stands for a deep, complicated network with a gated layer on top. The performance of the proposed model has been demonstrated through extensive simulations in terms of learning reduction, accuracy, and matrix disorientation, with a variable signal-to-noise ratio (SNR) spanning from 20 dB to + 20 dB, which illustrates the superiority of DRCaG compared to others. An online, end-to-end solution based on deep learning techniques is presented in this study for the fast, precise, reliable, and automatic detection of diverse petty crime types. By detecting tiny crimes like hostility, bag snatching, and vandalism, the suggested system may not only identify unusual passenger behavior like vandalism and accidents but also improve passenger security. The solution performs admirably in a variety of use cases and environmental settings. © 2023 by the authors of this article. Published under CC-BY.

关键词： Crime

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Smart Cap Design For Visually Impaired

Smart Cap Design For Visually Impaired

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Jain, Rashmi Gawande, Kavita Fadnavis, Atharva Bansod, Harsh Patne, Priyanshu Sawarkar, Srushti Polade, Suyog S. B. Jain Institute of Technology Management & Research Computer Science & Engineering Nagpur India

ISBN: (纸本)9798331529833

The technologies constantly try to make human lifestyles easier. The folks who are visually Impaired they faces many problems throughout navigation This paper presents the layout and implementation of a clever Cap utilising text-to-speech (TTS) to help visually impaired individuals in ordinary activities. The smart Cap is equipped with a camera that captures printed text from books, labels, or symptoms and converts it into audible speech. thru actual-time audio feedback, customers can receive information approximately their environment, read documents, or understand product details. The tool additionally carries voice command functionality, taking into consideration smooth control and interaction. © 2025 IEEE.

关键词： Optical character recognition

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Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

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computers, Materials & Continua 2024年第3期78卷 3909-3927页

作者： Yilei Wang Xin Sun Guiping Zheng Ahmar Rashid Sami Ullah Hisham Alasmary Muhammad Waqas Zhejiang Electric-Power Corporation Research Institute Zhejiang310014China Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Trusty Cloud Technology Co. Ltd.Beijing100022China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and EngineeringTopi23640Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir18050Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent *** sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and ***,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion ***,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart *** response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these *** scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT *** then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy *** comprehensive approach reduces the impact of subjective factors on trust ***,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious ***,in turn,enhances the security and reliability of the smart grid *** effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation ***,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

关键词： IIoT trusted measure energy efficient

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Pre-trained SAM as data augmentation for image segmentation

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CAAI Transactions on Intelligence technology 2025年第1期10卷 268-282页

作者： Junjun Wu Yunbo Rao Shaoning Zeng Bob Zhang Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhouChina School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengduChina Pattern Analysis and Machine Intelligence Research Group Department of Computer and Information ScienceUniversity of MacaoMacaoChina

Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the ***,data augmentation mainly involved some simple transformations of ***,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative ***,these methods required a mass of computation of training or *** this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for *** the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved *** this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation *** comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and *** this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,***,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.

关键词： data augmentation image segmentation large model segment anything model

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Boost diagnostic performance in retinal disease classification utilizing deep ensemble classifiers based on OCT

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Multimedia Tools and Applications 2024年 1-21页

作者： Khan, Umer Sadiq Khan, Saif Ur Rehman School of Computer and Information Science Hubei Engineering University Xiaogan432000 China School of Computer Science and Information Engineering Hubei University Wuhan430062 China Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China School of Computer Science and Engineering Central South University Yuelu District 932 Lushan S Rd Changsha Hunan China

Damage to the retinal blood vessels is critical in diabetic retinopathy, a progressively emerging health concern that often advances quietly without explicit symptoms. Optical coherence tomography-OCT has emerged as a favored noninvasive imaging technique for diagnosing diabetic retinopathy promptly and accurately. However, timely and precise diagnoses from OCT images are essential in prevention of blindness. Moreover, accurate interpretation of OCT images is challenging. Single model learning debilitates in managing diverse data types and structures, constraining its adaptability to varied environments. Its limitations become apparent in tasks requiring expertise from multiple domains, delaying overall performance. Moreover, learning may exhibit susceptibility to overfitting with large and heterogeneous datasets, resulting in compromised generalization capabilities. In this study, we propose a hybrid learning model for the classification of four distinct classes of retinal diseases in OCT images with improved generalization capabilities. Our hybrid model is constructed upon the well-established architectural foundations of ResNet50 and EfficientNetB0. By pre-training the hybrid model on extensive datasets like ImageNet and then fine-tuning it on publicly available OCT image datasets, we capitalize on the strengths of both architectures. This empowers the hybrid model to excel in discerning intricate image patterns while efficiently extracting hierarchical prediction from various regions within the images. To enhance classification accuracy and mitigate overfitting, we eliminate the fully connected layer from the base model and introduce a concatenate layer to combine two objective learning prediction. A dataset comprising 84,452 OCT images, each expertly graded for illnesses. we conducted training and evaluation of our proposed model, which demonstrated superior performance compared to existing methods, achieving an impressive overall classification accuracy of 97.

关键词： Optical tomography

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Poster: FlexibleBP: Blood Pressure Monitoring Using Wrist-worn Flexible Sensor 24

Poster: FlexibleBP: Blood Pressure Monitoring Using Wrist-wo...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Zhang, Yujing Li, Bing Peng, Yanxi Li, Jiao Sun, Tao Zhang, Jin Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798400706974

We propose FlexibleBP, a novel cuffless blood pressure monitoring system using a wrist-worn flexible sensor to enhance comfort and accuracy. By capturing pulse wave signals from the radial artery, we develop a personalized estimation framework incorporating a Transformer model with fine-tuning. Experiments with 36 participants confirm FlexibleBP's accuracy, meeting AAMI standards. This work marks a step toward more user-friendly, advanced wearable BP monitoring solutions. © 2024 Copyright held by the owner/author(s).

关键词： Pressure regulators

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