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A Framework for Solving Quadratic Knapsack Problem based on Deep Reinforcement Learning 13

A Framework for Solving Quadratic Knapsack Problem Based on ...

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13th international conference on intelligent Control and information Processing, ICICIP 2025

作者： Huang, Jiajia Zhao, Shijie Fu, Xiaoyu Gu, Shenshen School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798331516147

the quadratic knapsack problem (QKP) with constraints has been a focus of attention in combinatorial optimization, where traditional exact algorithms face difficulties in solving large-scale problems due to the problem’s NP-hard nature, and heuristic algorithms have difficulty in generating high-quality solutions in effective time. To solve QKP quickly and accurately, this paper proposes a graph pointer network (GPN) framework with hierarchical reinforcement learning (HRL). the first stage of the method trains low-level neural network to find feasible solutions that satisfy the constraint as much as possible. the second stage trains the high-level neural network based on the first stage using the objective function as a penalty term. After the HRL, our trained hierarchical GPN (HGPN) model can effectively solve the QKP. In addition, we add a greedy strategy in the decoding process to further improve the solution accuracy of the neural network solver. Experiments show that our proposed method can approximate the optimal solution produced by the Docplex solver and outperforms most end-to-end neural network solvers. © 2025 IEEE.

关键词： Combinatorial optimization

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Clustering Algorithm for Vessel Trajectory Anomaly Detection based on Binary Hashing Method 13

Clustering Algorithm for Vessel Trajectory Anomaly Detection...

引用

13th international conference on intelligent Control and information Processing, ICICIP 2025

作者： Liang, Yuhan Ng, Wing W.Y. Li, Qihua Tian, Xing Su, Xiong Qiu, Qin Hou, Zhengzhi School of Computer Science and Engineering South China University of Technology Guangzhou China School of Artificial Intelligence South China Normal University Guangzhou China Anti-Drug Tethnology Center of Guangdong Province Guangzhou China

ISBN: (纸本)9798331516147

Timely identification of abnormal ships through trajectories make daily commercial trade transportation safer. However, current methods for identifying abnormal ships through vessel trajectories have issues such as high time cost and high sensitivity to parameters. To address such issues efficiently, a clustering method based on hashing algorithm applying Long Short-Term Memory neural networks is proposed to capture abnormal local and temporal information in trajectories. the trajectories are converted into hash codes that present thier characteristics succinctly. then, the dominant set clustering method that does not require setting hyperparameters is used to cluster and find abnomal trajectories according to similarity relationship computed by corresponding hash codes. the proposed method is validated on a dataset of real vessel trajectories, which basically achieves the identification of abnormal vessel trajectories with lower time cost compared to other methods. © 2025 IEEE.

关键词： Clustering algorithms

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Enhanced Infrared-Visible Image Fusion Method with an ISP based Degradation Model 13

Enhanced Infrared-Visible Image Fusion Method with an ISP Ba...

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13th international conference on intelligent Control and information Processing, ICICIP 2025

作者： Guo, Xueyi Liu, Yisha Xue, Weimin Zhang, Zhiwei Information Science and Technology College Dalian Maritime University Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

the performance of infrared (IR) and visible (VIS) image fusion tasks depends on the quality of source images, which are usually impacted by various degradation factors in real-world scenarios, leading to poor quality and visual effects of the fused images. By learning the mapping from low-quality (LQ) source images to high-quality (HQ) fused images, neural networks can remove the impacts of degradation factors, generating HQ fused images. However, the lack of paired LQ source images and corresponding HQ fused images poses a significant challenge in training a fusion network. Considering the effectiveness of the image processing pipeline (ISP) in processing IR and VIS images, reversing specific steps of ISP can accurately simulate the image degradation process. Building upon this research, we propose a novel ISP-based degradation model that reverses several ISP steps while incorporating various degradation factors, thereby generating LQ IR and VIS images from source images to train the fusion network. Experimental results on multiple image fusion datasets demonstrate that our method significantly enhances the quality and visual effects of fused images. © 2025 IEEE.

关键词： Image fusion

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A Non-Intrusive Load Monitoring Method based on Relative Position Matrix and Residual Network 13

A Non-Intrusive Load Monitoring Method Based on Relative Pos...

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13th international conference on intelligent Control and information Processing, ICICIP 2025

作者： Huang, Jin-Yang Chen, Chun-Hua Jiang, Yi Yan, Junwei Zhang, Jun Zhan, Zhi-Hui School of Software Engineering South China University of Technology Guangzhou China Department of Electrical and Electronic Engineering Hanyang University Ansan Korea Republic of School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China College of Artificial Intelligence Nankai University Tianjin China

ISBN: (纸本)9798331516147

Non-Intrusive Load Monitoring (NILM) technology identifies various appliances’ types and operational status by analyzing electrical data collected from sensors in residential or commercial buildings, offering significant practical value. However, due to the limitations of existing methods for constructing appliance features, the identification performance of NILM models needs improvement. Addressing this, we propose a method for constructing appliance features based on a relative position matrix (RPM), which more accurately captures the temporal characteristics of electrical data compared to other methods. Additionally, a deep learning model based on the improved ResNet18 with multi-scale fusion is proposed for the load identification task. In this paper, comparative experiments were conducted on the PLAID dataset. We compared the RPM with three appliance feature construction methods: voltage-current trajectory (VI), markov transition field (MTF), and gramian angular field (GAF). We also compared our results with advanced methods reported in other literature. the results show that this method outperforms existing methods in terms of precision, recall, and F1 score across various evaluation metrics. © 2025 IEEE.

关键词： Markov processes

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FedRFD: Heterogeneous Federated Learning via Refining Feature-level Distillation 24

FedRFD: Heterogeneous Federated Learning via Refining Featur...

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13th international conference on Computing and Pattern Recognition, ICCPR 2024

作者： Yu, Peng Yuan, Yuwen Wang, Youquan Shi, Wei Shi, Zhihui Hua, Yue College of Information Engineering Nanjing University of Finance and Economics Jiangsu Nanjing China School of Computer Science and Engineering Taizhou Institute of Sci.&Tech. NJUST Jiangsu Taizhou China College of Liberal Arts and Sciences University of Illinois at Chicago ChicagoIL United States Department of Financial Technology Jiangsu Sushang Bank Co. Ltd. Jiangsu Nanjing China School of Marxism Jiangsu University Jinagsu Zhenjiang China

ISBN: (纸本)9798400717482

Federated learning faces a significant challenge in effectively aggregating knowledge from diverse local models with varying learning capabilities while preserving privacy through collaborative, local updates on private data. Previous methods for addressing such challenges involve combining distilled logits-based knowledge from various local models using weights derived from their respective learning capabilities as weighting coefficients. However, logits-based knowledge distillation suffers from information loss, expression diversity limitations, and model dependency, leading to a less affluent knowledge transfer. therefore, we propose heterogeneous federated learning via refining feature-level distillation (FedRFD), which leverages richer knowledge of intermediate layer features for distillation. Specifically, (1) To facilitate communication between heterogeneous models, we align their feature-level with public data, eliminating the need for an additional global model. (2) To address feature alignment challenges in models with varying feature-level numbers, we propose the knowledge refining mechanism (KRM). (3) To increase the efficiency of collaborative learning, we propose a novel method to dynamically assign weights to each client. Extensive experiments confirm that our approach significantly outperforms state-of-the-art algorithms in heterogeneous model scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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TAPNet: Tunnelled Asymmetric Pyramid Network for Achieving a Pragmatic Semantic Segmentation Solution for Autonomous Driving 13

TAPNet: Tunnelled Asymmetric Pyramid Network for Achieving a...

引用

13th international conference on intelligent Control and information Processing, ICICIP 2025

作者： Atif, Nadeem Mazhar, Saquib Bhuyan, M.K. Ahamed, Shaik Rafi Dept. of Electronics and Electrical Engineering Indian Institute of Technology Assam Guwahati India

ISBN: (纸本)9798331516147

—Semantic segmentation, vital in autonomous driving, is a pixel-level prediction task that is used to partition images into distinct regions. Effective models must capture multi-scale context to enhance segmentation quality but also need to be computationally efficient for practical deployment on resource-constrained devices. Hence, the challenge is to maximize accuracy and speed while minimizing model complexity to transform semantic segmentation into a successful practical reality. In response, we propose a Tunneled Asymmetric Pyramid Network (TAPNet) that combines short and long-range context with high efficiency. It is based on the TAP module, which extracts multi-scale short-range context in a highly efficient manner. Unlike conventional methods, our feature tunneling technique reinforces feature maps with finer details, enhancing segmentation accuracy. To efficiently capture long-range context, we propose the Pairwise Aggregated Reinforced Scale-Enlarged Spatial Pyramid Pooling (PARSE-SPP) module, designed for ultra-low computational overhead. based on the TAP and PARSE-SPP modules, we are able to achieve a high-performing segmentation model that strikes an optimal balance between accuracy and efficiency. this makes TAPNet a strong candidate for autonomous driving in real-time scenarios. More specifically, with only 0.29 million parameters, it achieves 68.75% and 68.6% mIoU on Cityscapes validation and test set, respectively. More interestingly, it achieves 161.55 FPS on a single RTX 3090 card. © 2025 IEEE.

关键词： Semantic Segmentation

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Complex scene fall detection method based on improved YOLOv5 24

Complex scene fall detection method based on improved YOLOv5

引用

13th international conference on Computing and Pattern Recognition, ICCPR 2024

作者： Li, Yifan Liu, Pengyu Kong, Lili Deng, Yiyang Liu, Weilong Chen, Shanji Chen, Jiali Yuan, Bowen School of Information Science and Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Hebei Hongyi Environmental Protection Technology Co. Ltd Dingzhou Hebei China Beijing-Dublin International College at BJUT Beijing University of Technology Beijing China Information Technology Co. Ltd Dingzhou Hebei China College of Physics and Electronic Information Engineering Qinghai Minzu University Qinghai China

ISBN: (纸本)9798400717482

Aiming at the existing deep learning-based fall detection model in multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, there are problems such as leakage detection and false detection. In this paper, we take the falling person as the research object, construct a dataset containing multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, and propose a fall detection model based on improved YOLOv5. By improving the backbone network and neck network structure of the detection model, the problem of low accuracy of fall detection due to incomplete target deformation features and target detail features in complex scenes is solved. the proposed algorithm in this paper achieves an average accuracy of 92.78% for fall detection in complex scenes, and the detection speed reaches 36 frames/second. © 2024 Copyright held by the owner/author(s).

关键词： Fall detection

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A Recurrent Neural Network based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

引用

13th international conference on intelligent Control and information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. the linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 IEEE.

关键词： Recurrent neural networks

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Automated Motion System of 5-DOF Hybrid Serial-Parallel Manipulator for Invasive Medical Procedures 13

Automated Motion System of 5-DOF Hybrid Serial-Parallel Mani...

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13th international conference on intelligent Control and information Processing, ICICIP 2025

作者： Abdeldaim, Abdelrahman M. Abdelrahman, Ahmed E. Ismail, Mohanad M.H. El Haj Youssef, Khaled Alkhedher, Mohammad Dept. of Mechanical and Industrial Engineering Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331516147

In the medical profession where accuracy is vitally critical, operations are increasingly adopting robotic technology to eliminate mistakes and improve precision. Current robotic-assisted surgical systems are sometimes limited in their usefulness in complex procedures because they lack real-time haptic feedback and adaptive motion control, despite improvements in the field. A 5-DOF hybrid serial-parallel manipulator is presented in this study, which has been developed to enhance dexterity, motion stability, and real-time force feedback in a range of minimally surgery applications. During surgical procedures, the device enhances precision and control by combining AI-assisted motion correction with a haptic glove-controlled master-slave mechanism. Increased trajectory stability, reduced force application errors, and greater surgical precision are all demonstrated by simulations and experimental validation in MATLAB Simulink. the findings indicate that adaptive AI and enhanced haptic feedback can significantly improve robotic-assisted procedures, making them more effective and reliable for a range of minimally invasive surgeries. In the future, the focus will be on improving response time, making the system more adaptive, and integrating it into the actual environment. © 2025 IEEE.

关键词： Robotic surgery

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Adaptive Local Neighborhood Window Laplacian Eigenmaps 24

Adaptive Local Neighborhood Window Laplacian Eigenmaps

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13th international conference on Computing and Pattern Recognition, ICCPR 2024

作者： Xu, Jiayi Huang, Qirui Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400717482

Manifold learning method is a dimensionality reduction method that treats the data in non-Euclidean space as a Euclidean space in a local scope. However, most existing manifold learning methods cannot obtain the true neighborbood relationship of each sample point well. therefore, we propose a manifold learning method based on adaptive local neighborhood. this method can adaptively select a neighborhood window. It can be approximated as a local Euclidean space based on the sample characteristics. To implement this method, a specific manifold learning method, named Laplacian Eigenmap of Adaptive Local Neighborhood Window (ALNWLE) is proposed. ALNWLE mainly contains two modules, namely the manifold structure learning module (MSLM) and the manifold structure pattern recognition module (MSPR). MSLM constructs an adaptive local neighborhood adjacency matrix to constrain the learning of the manifold structure. MSPR uses the interpolation method to recognize new sample patterns. the adaptive local neighborhood method proposed in this paper is a general method which is applicable to any manifold learning method. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

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