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A Reinforcement Learning Framework for Efficient Task Allocation among AGVs in Smart Warehouse

IEEE Internet of Things Journal 2025年第11期12卷 16947-16961页

作者： Li, Shaofeng Zhao, Zejian Wang, Di Li, Ke Liu, Gang Wang, Quan Xidian University Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province the School of Computer Science and Technology Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China

In smart warehouses that use automated guided vehicles (AGVs) for goods transportation, task allocation has a great impact on operational efficiency. Currently, warehouse task allocation is typically modeled as a pickup and delivery problem (PDP), which requires vehicles to start and return from the same depot to construct several closed-loop routes. This approach increases the vehicle travel distance without load in high-throughput warehouses and results in resource wastage. Thus we re-model the task allocation problem as an open-loop routing problem with heterogeneous starting points and name it capacitied multiagent open pickup and delivery problem (CMOPDP), which has more complex solution space and constraints than PDP. The solving speed of existing heuristic methods cannot meet the real-time processing demands of large-scale warehouses. And deep reinforcement learning (DRL)-based methods typically satisfy constraints through the output mask of decoders, which leads to unsatisfactory quality of solutions under complex constraints. To address these limitations, we design an DRL-based model with encoder-decoder architecture to solve the CMOPDP. Specifically, first, an encoder with heterogeneous attention is designed to fully explore constraint relationships between nodes. Second, we utilize dual decoders and information sharing to maximize vehicle-customer nodes matching. Finally, entropy rewards are introduced to enhance exploration during reinforcement learning, preventing the model from getting stuck in local optima. Extensive experiments on random datasets and various warehouse maps demonstrate that our method improves solution quality by at least 1.76% over baselines, while maintaining competitive solving time and exhibiting good generalization performance. © 2025 IEEE.

关键词： Deep reinforcement learning

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Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification

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Tsinghua Science and technology 2025年第1期30卷 112-123页

作者： Ertong Shang Hui Liu Jingyang Zhang Runqi Zhao Junzhao Du School of Computer Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of EducationXi’an 710126China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an 710126China

Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated learning have focused on fully supervised learning settings,assuming that all the data are annotated with ground-truth ***,this work considers a more realistic and challenging setting,Federated Semi-Supervised Learning(FSSL),where clients have a large amount of unlabeled data and only the server hosts a small number of labeled *** to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this *** this paper,we propose a new FSSL algorithm for image classification based on consistency regularization and ensemble knowledge distillation,called *** algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled ***,we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled *** experiments on several image classification datasets show that our EKDFSSL outperforms current baseline methods.

关键词： federated learning semi-supervised learning federated semi-supervised learning knowledge distillation

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Multidimensional image morphing-fast image-based rendering of open 3D and VR environments

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 155-172页

作者： Simon SEIBT Bastian KUTH Bartosz von Rymon LIPINSKI Thomas CHANG Marc Erich LATOSCHIK Game Tech Laboratory Faculty of Computer ScienceNuremberg Institute of TechnologyNuremberg 90489Germany Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and ArtsCoburg 96450Germany Human-Computer Interaction Group Institute of Computer ScienceUniversity of WuerzburgWuerzburg 97074Germany

Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and ***,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains *** This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and ***,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image *** this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of *** rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image *** detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in *** addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light *** Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,*** achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering *** Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR ***,the handling of morphing artifacts in the parallax image regions remains a topic for future resea

关键词： computer graphics 3D real-time rendering computer vision Image morphing Virtual reality

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Spatial grids assisted multi-task fusing framework for incremental point cloud mapping of large scenes

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Luo, Nan Hu, Daoru Liang, Jie Wang, Yifeng Zhao, Hui Wang, Quan Xidian University Key Laboratory of Smart Human Computer Interaction and Wearable Technology of Shannxi Province School of Computer Science and Technology Xi'an710126 China

Recovering the point cloud maps of large-scale scenes from multi-sensor data is the fundamental building block of numerous 3D related applications. Available SLAM (Simultaneous Localization and Mapping) frameworks require rigorous constraints in data capturing and suffer from heavy workload when facing large-scale scenes. To push the research to further extent, this work proposes an incremental multi-task mapping framework for large-scale scenes under the guidance of spatial grids. (1) This framework first encodes the target scene into spatial grids according to the GNSS (The Global Navigation Satellite System) coordinates, then the mapping task is accomplished via multiple sub-scene SLAM tasks that can be executed independently, and finally the mapped point clouds are incrementally aligned with the assistance of encoded spatial grids to form the whole picture of the large-scale scene. (2) In each sub-scene SLAM task, we develop a new fast loop detection method based on spatial grids and perceptual hashing, which replaces the memory-expensive BoW (bag-of-words) model for image similarity determination, saves the unnecessary computational cost and lifts the localization accuracy. (3) In the aligning part, a new grid-wise merging method is designed for sub-scene point clouds, which improves the robustness and efficiency of point cloud registration and the applicability for large-scale scenes, supporting incremental multi-task mapping. We extensively evaluate the localization accuracy, loop detection efficiency on indoor EuRoC dataset, and multi-machine point cloud mapping on outdoor KITTI dataset. Experimental results reveal that the proposed framework provides a feasible multi-task fusing solution for incremental mapping of large scenes. © 1963-2012 IEEE.

关键词： Global positioning system

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Multisource Adversarial Feature Disentanglement Method for Cross-Subject Gesture Recognition Using sEMG Signals

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Su, Kejia Liu, Kai Wan, Bo Qiao, Hanbing Huang, Jiayang Feng, Min Liu, Jinhui Xidian University Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Shaanxi Engineering Research Center of High-Confidence Embedded Computing and the School of Computer Science and Technology Xi’an710126 China

Cross-subject gesture recognition remains a challenge in domain adaptation (DA), as most existing methods focus on extracting domain-invariant (DI) class-relevant features, treating all source domains as a single entity. These methods often neglect the importance of domain-specific (DS) class-relevant features, which are crucial for capturing individual subject variations. To address this, we propose a novel multisource adversarial feature disentanglement (MSAFD) method that separates DI and DS features, improving gesture recognition accuracy and robustness. The MSAFD method introduces a two-stage transfer learning (TL) strategy. In the pretraining stage, a multisource adversarial network extracts generalized DI features by minimizing domain discrepancies across multiple source domains. In the calibration stage, a pretrained DI feature extractor captures shared features between source and target domains. Simultaneously, a DS feature extractor, enhanced with an orthogonal projection layer, extracts target DS features by projecting target domain features orthogonally to the DI features. This ensures that DS features capture only the unique variations of the target domain. The disentangled DI and DS features are then processed through an attention module to improve gesture classification. This approach enables precise feature separation, preserves critical DS information, and enhances generalization across subjects. Experimental results on the self-collected XDMyo and public MYO datasets show that MSAFD outperforms benchmark methods, achieving the highest classification accuracy and lowest standard deviation. Real-time experiments demonstrate that the method achieves an average gesture recognition latency of 111.86 ± 0.73 ms with a recognition accuracy of 98.33% ± 1.03 %, highlighting its suitability for real-time gesture recognition systems. © 1963-2012 IEEE.

关键词： Adversarial machine learning

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Synchronization in a neuronal network with delayed hybrid synapses

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The European Physical Journal Special Topics 2025年 1-11页

作者： Xi, Xiaojian Yang, Rui Zhang, Peijun Tian, Huaigu Liu, Xiaogang Xi’an Key Laboratory of Human–Machine Integration and Control Technology for Intelligent Rehabilitation School of Computer Science Xijing University Xi’an People’s Republic of China

Synchronization is a fundamental phenomenon in various fields, particularly in neuronal networks, where it underpins critical biological processes. In this paper, we present a novel investigation into synchronization dynamics in a network of memristive Rulkov neuron models with hybrid synapses, incorporating both short-range electrical and long-range chemical interactions. To enhance biological realism, we introduce delays in chemical synaptic transmission, considering both fixed and random delay scenarios. Our work systematically examines the impact of distinct coupling topologies by exploring four cases: (i) both electrical and chemical synapses are globally coupled, (ii) both follow a small-world structure, (iii) electrical synapses are globally coupled while chemical synapses exhibit small-world connectivity, and (iv) the reverse configuration. By quantifying synchronization error, a robust numerical index of synchrony, we reveal that the structure of chemical synapses plays a pivotal role in determining the level of coherence and the ability to achieve synchronization, while the electrical coupling topology significantly influences sensitivity to delays. Notably, our results demonstrate that globally coupled chemical synapses promote stronger synchronization, but the extent of coherence depends on the electrical network structure and delay values. These findings provide new insights into the intricate interplay between synaptic mechanisms, network topology, and delays in shaping neuronal synchronization, offering valuable guidance for designing and analyzing biologically realistic neuronal networks.

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DFE: Deep Flow Embedding for Robust Network Traffic Classification

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 1597-1612页

作者： Wang, Zhijiong Zhang, Anguo Li, Hung Chun Yin, Yadong Chen, Wei Lam, Chan Tong Mak, Peng Un Vai, Mang I. Gao, Yueming Pun, Sio Hang Zhuhai UM Science and Technology Research Institute Lingyange Semiconductor Incorporated Joint Laboratory Zhuhai519031 China Lingyange Semiconductor Inc. Zhuhai519030 China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI Institute of Microelectronics 999078 China Fuzhou University School of Physics and Information Engineering Fuzhou350108 China Fudan University Center for Intelligent Medical Electronics School of Information Science and Technology Human Phenome Institute Shanghai200437 China Fudan University Human Phenome Institute Shanghai200437 China Macau Polytechnic University Faculty of Applied Sciences 999078 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology 999078 China Fuzhou University Key Lab of Medical Instrumentation and Pharmaceutical Technology of Fujian Province Fuzhou350108 China

People's increasing demand for high-quality network services has prompted the continuous attention and development of network traffic classification (NTC). In recent years, deep flow inspection (DFI) is considered to be the most effective and promising method to solve the NTC. However, DFI still cannot effectively address the problem of changes in flow characteristics of complex packet flows and the discovery of new traffic categories. In this paper, we propose a metric learning based deep learning solution with feature compressor, named deep flow embedding (DFE). The feature compressor is used to compress the feature information transmitted layer by layer in DL backbone while maintaining the computational accuracy, so that the backbone can remove as much noise, redundancy, and other irrelevant information from the input data as possible, and achieve more robust feature extraction of network traffic flow. The deep learning (DL) backbone generates an embedding vector for each network packet flow. Then the embedding vector is compared with the vector template preset for each traffic type in the template library to determine the category of the packet flow. Experimental results verify that our method is more effective than the traditional DFI methods in overcoming the problems of flow characteristics variation and new category discovery. © 2013 IEEE.

关键词： Network embeddings

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RL-Based USV Path Planning under the Marine Multimodal Features Considerations

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IEEE Internet of Things Journal 2025年第11期12卷 15274-15287页

作者： Lin, Quanbao Gou, Huaxing Tian, Peidong Zuo, Tian-Yu Zhang, Hanzhong Wang, Xin Sun, Poly Z. H. Shanghai Jiao Tong University School of Aeronautics and Astronautics Shanghai200240 China Southeast University School of Computer Science and Engineering Nanjing211189 China Shanghai University School of Microelectronics Shanghai200444 China Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Guangdong Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Shenzhen518055 China East China Normal University School of Psychology and Cognitive Science Shanghai200062 China Shanghai Jiao Tong University Department of Industrial Engineering Shanghai200240 China

Path planning is an important step in ensuring the safety of Unmanned Surface Vehicle (USV) navigation and executing missions quickly and efficiently. However, current USV path planning methods lack comprehensive consideration of electronic nautical charts and meteorological data, resulting in planned paths being unable to fully utilize marine environmental conditions, which may easily lead to collisions and long navigation times. Based on the above considerations, our study designs a USV path planning system that comprehensively considers the multimodal information from electronic nautical charts and meteorological data. The system consists of three parts: (1) image processing module, (2) meteorological analysis module, and (3) path planning module. In detail, the image processing module obtains the geographical feature information from the electronic chart and constructs a static obstacle environment. The meteorological analysis module obtains the meteorological feature information from meteorological data and constructs a dynamic meteorological vector field environment. The path planning module introduces a designed double deep Q-Network (DQN) structure, a multivariate weighted Dueling network, and a priority sampling mechanism to enhance the DQN algorithm for promising performance in USV path planning. Extensive experiments illustrate the superior performance of the proposed fusion DQN algorithm. Furthermore, the feasibility of the entire path planning system is confirmed. © 2014 IEEE.

关键词： Unmanned surface vehicles

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A Lightweight Method to Survey with Protecting Privacy yet Maintaining Accuracy 18th

A Lightweight Method to Survey with Protecting Privacy yet...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yuxuan Di, Xinyu Liu, Ningbo Zhang, Xianchao Ren, Wei School of Computer Science China University of Geosciences Wuhan China Department of Computer Science University of Toronto TorontoONM5S 1A1 Canada International College Chongqing University of Technology Chongqing China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing China Ministry of Education Chengdu China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China

ISBN: (纸本)9783031714665

In a survey, a lot of questions and answers are listed and participators will select one of the answers for each question. Sometimes, participators may not be willing to select the preferred one item in the answers because the item may damage the privacy of themselves. If this question is not answered, then it reveals that the question or the answers are sensitive, which also damages the privacy. Besides, each participator may have distinct privacy concerns even for the same questions and answers, thus the privacy-aware questions and answers cannot be determined and be removed in advance. Thus, the dilemma is how to select the answers for privacy-aware questions yet maintain the utility of the answering results in the survey. In this paper, we propose a general method to protect the various privacy in the investigation without the knowledge of privacy to be protected in advance. The main idea is that participators can randomly select answers if the privacy is the concern for themselves in the answers for this question. Our method is lightweight so as to be convenient in practices. We extensively analyze the accuracy of survey results to justify that our method can protect the privacy yet maintain the utility of the investigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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A Blockchain-Based Traceable and Verifiable Digital Circuit Trade Method with Dual-Signature Strategy 1st

A Blockchain-Based Traceable and Verifiable Digital Circuit...

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1st Conference on Blockchain and Web3.0 technology Innovation and Application, BWTAC 2024

作者： Wang, Wei Xie, Zhenping Liu, Yuan School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Key University Laboratory of Software and Media Technology under Human-Computer Cooperation Jiangnan University Wuxi214122 China

ISBN: (纸本)9789819794119

In the integrated circuit (IC) industry, due to the inadequate protection mechanisms for IP cores, the design achievements of engineers are difficult to effectively safeguard, leading to a large number of excellent IP cores unable to legally circulate among designers. To alleviate these troubles, we propose a blockchain-based trade method. First, we design unique chain structures for storing and verifying IP core ownership and transaction contracts to reduce the misuse of intellectual property circuits. Then, we also design smart contract algorithms to automates the generation and verification process of digital identities and contract subscriptions. Additionally, we propose a dual-signature strategy to ensure the non-repudiation of contract signatures. Finally, we implement a prototype system and conduct security analysis. Experimental results demonstrate that the model can effectively resist several attacks and has good performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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