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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Sachin Gupta Shivali Gupta Shaik Mohammed Raith Anand Kumar Salil Bharany Vishnu Kant Ch.Sandeep Department of Robotics and Control Engineering School of Electronics and Control Engineering Lovely Professional University Phagwara India Department of Chemistry School of Chemical Engineering and Physical Sciences Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Warangal Telangana

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Dynamic path planning and collision prevention is one of the key tasks for autonomous vehicles in relation to dynamic movement of obstacles. In this paper, a novel hybrid approach based on integration of Slime Mold Algorithm (SMA), Triangle Inequality Principle (TIP), and Partition Method Strategy (PMS) for solution of this problem statement. The SMA algorithm is very flexible and adaptive by nature due to its biological working mechanism which is associated with TIP and PMS strategies in relation to their optimization and efficient path planning capabilities and robust solutions. This integrated module is tested in virtual environment and the results are compared with other updated algorithms such as A*, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the same static and dynamic environment. It has been observed from the results that this hybrid strategy significantly improves the path planning task several times than other algorithms in terms of multiple aspects. Under testing conditions, the path length was reduced to 25% than other algorithms in a dynamic environment. During this calculation, the computational efficiency of the system has been improved to 60% than conventional methods. The chances of collision have been improved to 34.23% especially in environment where the obstacles are moving. The capability of SMA has been improved due to the synergy between TIP and PMS. The dynamic and adaptive nature of this algorithm is suitable for various applications such as search-and-rescue operations, manufacturing units’ automation and other hazardous environment exploration.

关键词： Navigation Heuristic algorithms Virtual environments Path planning Partitioning algorithms Safety Mobile robots Vehicle dynamics Testing Genetic algorithms

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Parameters Extraction of Photovoltaic Cell Using Particle Swarm Optimization 4th

Parameters Extraction of Photovoltaic Cell Using Particle Sw...

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4th International Conference on Electronic engineering and Renewable Energy Systems, ICEERE 2024

作者： Chermite, Charaf Douiri, Moulay Rachid Elamri, Oumaymah Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco LAMIGEP EMSI of Marrakech Systems and Applications Engineering Laboratory National School of Applied Sciences Cadi Ayyad University Marrakech Marrakesh Morocco

ISBN: (纸本)9789819799749

Extracting parameters accurately and effectively from solar photovoltaic (PV) models is crucial for detailed simulation, evaluation, and management of PV systems. Although there has been an increase in the development of analytical, numerical, and metaheuristic methods targeting this task, accurately determining these parameters continues to be a significant challenge. This study focuses on applying Particle Swarm Optimization (PSO) for the extraction of key parameters of RTC France solar cells, a critical step towards enhancing PV system efficiency and reliability. Additionally, the PSO's performance is evaluated against other metaheuristic algorithms including Enhanced Chaotic JAYA (CJAYA), Performance-Guided JAYA (PGJAYA), and Enhanced Gradient-Based Optimizer (EGBO), underscoring the effectiveness of the proposed PSO-based approach. Utilizing MATLAB-SIMULINK, comprehensive analysis and results are presented, shedding light on the efficacy of PSO in addressing the intricacies of parameter extraction in PV systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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FMCW Radar-Based Human Activity Recognition: A Machine Learning Approach for Elderly Care

FMCW Radar-Based Human Activity Recognition: A Machine Learn...

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IEEE Conference on Wireless Communications and Networking

作者： Mohammadreza Mashhadigholamali Ali Samimi Fard Samaneh Zolfaghari Hajar Abedi Mainak Chakraborty Luigi Borzí Masoud Daneshtalab George Shaker Department of Control and Computer Engineering Politecnico di Torino Turin Italy School of Innovation Design and Engineering Mädlardalen University Västerås Sweden Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we propose a novel system prototype for human activity recognition using a low-cost, low-power millimeter-wave (mmWave) frequency-modulated continuous wave (FMCW) radar. Our approach applies the Fast Fourier Transform on the slow time axis and employs a Capon filter to generate range-Doppler, range-azimuth, and range-elevation maps, respectively. It can also effectively mitigate noise and multipath effects. We then use principal component analysis for feature reduction, reducing the dimensionality of the feature vectors extracted from these maps, which can be used to train conventional machine learning classifiers. This approach aims to achieve a balance between computational complexity, accuracy, and overall system performance. Our proposed system demon-strates promising recognition rates and robustness across varying levels of activity granularity, achieving recognition rates from 90.28% for four activities up to 70.97% for seven fine-grained ac-tivities. These findings highlight the potential of millimeter wave radar and suggested range maps combined with conventional machine learning classifiers for noninvasive, privacy-preserving activity recognition, with significant implications for healthcare, elderly care, and ambient assisted living.

关键词： Support vector machines Deep learning Radar imaging Millimeter wave radar Feature extraction Vectors Human activity recognition Older adults Millimeter wave communication Long short term memory

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One-dimensional Continuous Dynamic Modeling and Optimal Sensor Placement Strategy of Heat Storage Tank

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Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical engineering 2025年第8期45卷 3120-3131页

作者： Zhang, Yi Liu, Jianbang Yang, Tingting Liu, Jinfeng Fang, Fang School of Control and Computer Engineering North China Electric Power University Changping District Beijing102206 China School of Electrical and Information Engineering Jiangsu University Jiangsu Province Zhenjiang212013 China Department of Chemical & Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada

Thermocline heat storage tanks, known for their high net energy and exergy efficiency, are becoming a mainstream thermal energy storage solution. However, flow direction switching caused by real-time variations in charging/discharging mass flow creates discontinuities in conventional temperature stratification models. To address this, this study employs continuous smooth functions to approximate the 0-1 variables characterizing working fluid flow direction, resulting in a one-dimensional continuous dynamic stratified temperature model along the tank height. Building on this model, we propose an optimal sensor placement strategy based on sensitivity matrices. The system observability index is redefined using information derived from sensitivity matrices, with the minimum sensor count and optimal positions determined by each location's contribution to system observability. Simulation results demonstrate that the proposed model accurately captures vertical temperature stratification across various operating scenarios when an appropriate smoothing parameter μ is selected. Among all observable sensor combinations with equal sensor numbers, this strategy achieves the smallest mean and variance of RMSE in moving horizon estimation (MHE) observation errors, confirming its superior system observability performance. © 2025 Chinese Society for Electrical engineering. All rights reserved.

关键词： Tanks (containers)

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Correction to: Generalized Robot Vision-Language Model via Linguistic Foreground-Aware Contrast (International Journal of computer Vision, (2025), 10.1007/s11263-024-02340-z)

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International Journal of computer Vision 2025年 1-1页

作者： Liu, Kangcheng Wang, Chaoqun Han, Xiaodong Liu, Yong-Jin Chen, Baoquan Changsha China Pasadena United States The School of Control Science and Engineering Shandong University Jinan China The School of Control Engineering Minjiang University Fuzhou China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Artificial Intelligence Peking University Beijing China

The original version of this article contained inadvertent typos requiring the following revisions. The article title was given as "Generalized Robotic Vision-Language Learning Model via Linguistic Foreground-Aware Contrast" but should have been "Generalized Robot Vision-Language Model via Linguistic Foreground-Aware Contrast". The affiliation details for Author Chaoqun Wang were given as "The Department of computer Science and Technology, Tsinghua University, Beijing, China" but should have been "The school of control Science and engineering, Shandong University, Jinan, China". The affiliation details for Author Xiaodong Han were given as "The school of control Science and engineering, Shandong University, Jinan, China" but should have been "The school of control engineering, Minjiang University, Fuzhou, China". The affiliation details for Author Yong-Jin Liu were given as "The school of control engineering, Minjiang University, Fuzhou, China" but should have been "The Department of computer Science and Technology, Tsinghua University, Beijing, China". In this article, the part of the caption to Fig. 3 "Please refer to the colored figures online for detailed information" was inadvertently omitted. The complete caption of Fig. 3 is given here. Figure 3 Visualizations of projected point correlation maps over the indoor ScanNet (1st-4th rows) and the outdoor KITTI (5th-8th rows) with respect to the query points highlighted by yellow crosses. The View 1 and View 2 in each sample show the intra-view and cross-view correlations, respectively. We compare FAC with the state-of-the-art CSC (Hou et al., 2021) on segmentation (rows 1–4) and ProCo (Yin et al., 2022) on detection (rows 5–8). FAC clearly captures better feature correlations within and across views (columns 3–4). Please refer to the colored figures online for detailed information In this article, the footnote "The bold highlights the results of our proposed approaches" to Table 1 was inadvertently omitted. In Tab

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3dmnet: A 3d Multi-Attention Network for Pulmonary Nodule Detection in 3d Ct Scans

SSRN

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SSRN 2025年

作者： Jin, Haodong Zhang, Linsong Jian, Muwei Yu, Hui School of Control Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Computer Science and Technology Shandong University of Finance and Economics Jinan China School of Computer Science & Engineering Linyi University Linyi China cSCAN University of Glasgow Glasgow United Kingdom

Artificial intelligence (AI)-assisted diagnosis has become an urgent and active research topic in modern healthcare. The automatic detection of pulmonary nodules through three-dimensional computed tomography (CT) not only significantly reduces the workload of primary care physicians but also aids in the early screening of lung cancer, providing patients with critical additional treatment time. However, existing methods primarily focus on single-slice nodule detection and fail to fully leverage the 3D information embedded within the CT volume, ultimately leading to suboptimal detection accuracy. To overcome this limitation, we propose 3DMNet, a 3D multi-attention convolutional neural network that effectively utilizes the 3D information embedded in multi-dimensional CT data. Specifically, to simulate the clinical diagnostic process of radiologists identifying nodule locations across consecutive CT slices, we develop an innovative 3D spatial attention mechanism. This mechanism enhances feature representations by applying projection transformations across multiple dimensions of the feature map, followed by rigorous attention calculations, thereby capturing intra- and inter-slice spatial information. Furthermore, by integrating data-driven global attention and channel attention modules, we achieve a more refined capture of global spatial dependencies and inter-channel relationships. Extensive comparative analysis on widely used datasets demonstrates that the proposed framework outperforms mainstream methods. © 2025, The Authors. All rights reserved.

关键词： computerized tomography

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On Finite Word Length Computing Error of Fixed-Point SAR Imaging Processing*

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Chinese Journal of Electronics 2025年第3期23卷 645-648页

作者： Yizhuang Xie Bocheng Zhu Wen Yan Weijie Zhang School of Information and Electronics Beijing Institute of Technology Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Science and Technology on Electronic Information Control Laboratory Chengdu China

Synthetic aperture radar (SAR) imaging system is generally realized by fixed-point with the purpose of reducing system implementation scale and enhancing real-time performance. Finite word length computing error of fixed-point SAR imaging system is studied. The characteristic of computing error in SAR imaging system is analyzed. A finite word length computing error model of SAR imaging system is built, by means of which the empirical formula of system's output noise-to-signal ratio is derived. Based on the empirical formula, SAR imaging system processing word length is presented according to different processing granularities. The validity of this proposed finite word length computing error model of SAR imaging system is verified by system level fixed-point simulation.

关键词： Computational modeling Imaging Transforms Radar polarimetry Real-time systems Synthetic aperture radar System implementation

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation

Adaptive Particle Swarm Optimization of U-net Parameters for...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Jin Zhao Jun Wang Min Han Jianchao Fan School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science and Department of Data Science City University of Hong Kong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models.

关键词： Deep learning Image segmentation Adaptation models Accuracy Heuristic algorithms Clustering algorithms Radar polarimetry Particle swarm optimization Tuning Aquaculture

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Multi-branch Space Sharing Feature Aggregation for contrastive multi-view clustering

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Pattern Recognition 2025年 167卷

作者： Zhang, Yuanyang Yan, Weiqing Tang, Chang Zhou, Wujie Jin, Jian School of Computer and Control Engineering Yantai University Yantai264005 China School of Computer Science China University of Geosciences Wuhan430074 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

Multi-view clustering has gained widespread attention for its ability to assign data samples to their respective categories in an unsupervised manner by learning a consensus representation. However, existing deep clustering methods typically employ either view-wise fusion or local cross-sample fusion within batches to obtain a consensus representation, but they often overlook the correlations among all samples. To address these issues, we propose a novel method called the Multi-branch Space Sharing Feature Aggregation for Contrastive Multi-view Clustering (MS2FA-CMVC). Specifically, we design a multi-branch space sharing module to filter out view-specific information and avoid local cross-sample fusion by learning the shared sample representation space across different batch samples. Moreover, we design a dual cross-view contrastive learning module to align feature representations from different views and capture the consistency of semantic information by mapping the learned consensus representation into the instance feature space and the semantic space, respectively. Extensive experimental results demonstrate that our proposed method outperforms state-of-the-art baseline methods. The code is available at https://***/zhangyuanyang21/MS2FA-CMVC. © 2025

关键词： Contrastive Learning

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Personalized Semantic Trajectory Privacy Protection in Location-Based Services: A TD3-Based Approach

Personalized Semantic Trajectory Privacy Protection in Locat...

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IEEE Conference on Wireless Communications and Networking

作者： Jun Ye Minghui Dai Minghui Min Jinling Song Hongliang Zhang Zhu Han School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The swift advancement of Location-Based Services (LBSs) raises the danger of trajectory privacy being breached, since the location semantic tags can easily disclose users' sensitive information. Additionally, attackers can exploit temporal correlations to infer sensitive personal information. This paper formulates a personalized semantic trajectory privacy protection framework designed to protect locations with varying sensitivities on the trajectory from the attacker with temporal correlation information. We model the trajectory privacy protection problem as a Markov Decision Process (MDP) and introduce the Reinforcement Learning (RL) technique to dynamically adjust the privacy parameters. Specifically, we leverage the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to enhance the stability and accuracy of policy evaluation, enabling efficient learning of optimal policies in continuous action spaces. Simulation results indicate that the TD3-based personalized semantic trajectory privacy protection mechanism effectively balances the Quality of Service and semantic trajectory privacy while realizing personalized trajectory privacy protection.

关键词： Privacy Correlation Sensitivity Simulation Semantics Quality of service Reinforcement learning Stability analysis Trajectory Protection

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