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arXiv 2021年

作者： Zhang, LiHua Liu, Quan School of Computer Science and Technology Soochow University Soochow China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Jilin China

Imitation learning (IL) is a framework that learns to imitate expert behavior from demonstrations. Recently, IL shows promising results on high dimensional and control tasks. However, IL typically suffers from sample inefficiency in terms of environment interaction, which severely limits their application to simulated domains. In industrial applications, learner usually have a high interaction cost, the more interactions with environment, the more damage it causes to the environment and the learner itself. In this article, we make an effort to improve sample efficiency by introducing a novel scheme of inverse reinforcement learning. Our method, which we call Model Reward Function Based Imitation Learning (MRFIL), uses an ensemble dynamic model as a reward function, what is trained with expert demonstrations. The key idea is to provide the agent with an incentive to match the demonstrations over a long horizon, by providing a positive reward upon encountering states in line with the expert demonstration distribution. In addition, we demonstrate the convergence guarantee for new objective function. Experimental results show that our algorithm reaches the competitive performance and significantly reducing the environment interactions compared to IL methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Demonstrations

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Lightweight Vision Model-based Multi-user Semantic Communication Systems

arXiv

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arXiv 2025年

作者： Jiang, Feibo Tu, Siwei Dong, Li Wang, Kezhi Yang, Kun Liu, Ruiqi Pan, Cunhua Wang, Jiangzhou Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China Wireless and Computing Research Institute ZTE Corporation Beijing100029 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

Semantic Communication (SemCom) is a promising new paradigm for next-generation communication systems, emphasizing the transmission of core information, particularly in environments characterized by uncertainty, noise, and bandwidth constraints. However, existing image SemCom systems face several challenges, such as inefficient knowledge base construction, insufficient semantic encoding, and lack of multi-user semantic sharing. To address these issues, we propose a Lightweight Vision Model-based Multi-user Semantic Communication System (LVM-MSC). First, we construct a Lightweight Knowledge Base (LKB) based on the fast Segment Anything Model (SAM). LKB incorporates the extensive image knowledge of the SAM model while significantly reducing the number of parameters through its convolutional architecture. Next, we design an Efficient Semantic Codec (ESC) based on the Masked AutoEncoder (MAE) architecture. ESC enhances semantic compression at both the pixel and semantic levels and implements lightweight semantic decoding tailored for user devices. Furthermore, we propose a Multi-user Semantic Sharing (MSS) transmission for the multi-user SemCom. By calculating the similarity of semantic information among different users in the sharing semantic space, we unify the transmissions of similar semantic information through broadcasting, further improving the transmission efficiency. Finally, simulation results demonstrate the feasibility and effectiveness of the proposed LVM-MSC system. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

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Large-Scale Target Detection and Classification Based on Improved Candidate Regions 11th

Large-Scale Target Detection and Classification Based on Imp...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Xi, Runping Han, Qianqian Jia, Gaoyun Kou, Xuefeng School of Computer Science and Techology Northwest Polytechnical University Xi’an710072 China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Xi’an710129 China Shaanxi Baocheng Aviation instrument Co. Ltd. Baoji China

ISBN: (纸本)9783030873578

Target detection of aerial images has become a frontier subject of concern in the image processing field. Using existing method to detect and classify large-scale building objects in aerial images, the accuracy is still a little low. This is mainly because the current method does not make full use of the prior information of the target to be detected, so there are too much redundant information in the candidate box. In this paper, our own dataset were built and then utilize the Hough transform to filter out the images that may exist in the sequence image. For images with dense lines or circles, it is possible that there is an artificial building target which will be detected, otherwise it is excluded directly. Besides, this paper exploits significance analysis from the filtered image and then extract the area of interest where the potential target is located. The results of the above-mentioned processing lay a good foundation for the subsequent detection and classification which can help improve the accuracy. © 2021, Springer Nature Switzerland AG.

关键词： Antennas

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Online Attentive Kernel-Based Temporal Difference Learning

arXiv

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arXiv 2022年

作者： Yang, Guang Chen, Xingguo Yang, Shangdong Wang, Huihui Dong, Shaokang Gao, Yang The the Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications National Engineering Laboratory for Agri-Product Quality Traceability Beijing Technology and Business University China The State Key Laboratory for Novel Software Technology Nanjing University China The PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference, creating difficulty for the deep neural network to be applied to an online RL algorithm in a fully online setting. Therefore, a simpler and more adaptive approach is introduced to evaluate value function with the kernel-based model. Sparse representations are superior at handling interference, indicating that competitive sparse representations should be learnable, non-prior, non-truncated and explicit when compared with current sparse representation methods. Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA. In this paper, we propose an Online Attentive Kernel-Based Temporal Difference (OAKTD) algorithm using two-timescale optimization and provide convergence analysis of our proposed algorithm. Experimental evaluations showed that OAKTD outperformed several Online Kernel-based Temporal Difference (OKTD) learning algorithms in addition to the Temporal Difference (TD) learning algorithm with Tile Coding on public Mountain Car, Acrobot, CartPole and Puddle World tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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Adaptive Variational Mode Time-frequency Analysis of Ship Radiated Noise 7

Adaptive Variational Mode Time-frequency Analysis of Ship Ra...

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7th International Conference on information Science and Control Engineering, ICISCE 2020

作者： Zhang, Haoyi Junejo, Naveed Ur Rehman Sun, Weitao Chen, Hailan Yan, Jiaquan Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou China Ilma University Department of Computer Science Karachi Pakistan Northwestern Polytechnical University School of Marine Science and Technology Xi'an China Jimei University School of Science Xiamen China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

ISBN: (纸本)9781728164069

In this paper, we have proposed a novel time-frequency analysis method implementing adaptive variational mode decomposition (AVMD) and Wigner-Ville Distribution (WVD) to visualize the local features of ship radiated noise. Unlike the traditional methods are invalid under low signal to noise ratio (SNR), the proposed method is suitable for constructing time-frequency (TF) image with the capability of mining the local feature information accurately. Firstly, we employ complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) on the raw signal to determine the numbers of variational modes which is equal to intrinsic mode functions (IMFs) numbers. Secondly, we perform VMD algorithm to extract the local features absolutely by decomposing the raw signal into IMFs, where we set the IMFs gained by CEEMDAN as the input parameter of VMD. Finally, we adopt Wigner-Ville distribution (WVD) to draw TF signatures of all the IMFs, then superpose them to reconstruct the objected signal that can solve the cross-item problem. We have conducted the comparative experiments by the real oceanic data. Under the different SNRs, we have verified the effectiveness and robustness of the proposed method, also obtained better performance in terms of resolution than compared algorithm. © 2020 IEEE.

关键词： Intrinsic mode functions

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A multimodal progressive fusion bearing fault diagnosis algorithm based on residual network

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IEEE Sensors Journal 2025年

作者： Zhu, Wenbing Ni, Haibin Li, Zhuo Cao, Ji Ni, Bo Chang, Jianhua Nanjing University of Information Science and Technology School of Electronics and Information Engineering Nanjing210044 China Jiangsu JITRI Integrated Circuit Application Technology Innovation Center Wuxi214101 China Nanjing University of Information Science and Technology Jiangsu Provincial Key Laboratory of Meteorological Observation and Information Processing Nanjing210044 China

Bearings are crucial components of rotating machinery in critical industrial equipment such as wind turbines, high-speed trains, and aerospace engines. Existing methods for bearing fault diagnosis are generally confined to superficial integration of multi-sensor or multi-domain data, constrained by either poor heterogeneous information integration in early fusion approaches or information loss and imbalanced modality representations caused by late fusion strategies, resulting in limited diagnostic effectiveness under complex and dynamic industrial operating conditions. To solve this issue, we propose a multimodal progressive fusion bearing fault diagnosis algorithm based on residual networks. The algorithm integrates multi-sensor and multi-domain data and automatically extracts fault features using residual networks. Then, an improved progressive feature fusion technique is applied to optimise the utilisation of the multimodal features, which aims to allow earlier layers to access later fused features, avoiding the loss of important information and improving the fusion representation over multiple iterations. The diagnostic efficacy of the proposed method is validated using two different bearing datasets, achieving a diagnostic accuracy of 99.97% for composite faults. This advancement shows the great potential for implementation on industrial IoT platforms, especially in scenarios such as power generation and transport where predictive maintenance is required, reducing unplanned downtime and maintenance costs. © 2001-2012 IEEE.

关键词： Bearings (machine parts)

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Adaptive Histogram Thresholding-Based Leukocyte Image Segmentation 15th

Adaptive Histogram Thresholding-Based Leukocyte Image Segmen...

引用

15th International Conference on Intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of information technology, Applications and Tools, FITAT 2019

作者： Zhou, Xiaogen Wang, Chuansheng Li, Zuoyong Zhang, Fuquan College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China

ISBN: (纸本)9789811397097

To improve the accuracy of leukocyte segmentation, this paper presents a novel method based on adaptive histogram thresholding (AHT). The proposed method first employs color component combination and AHT to extract the nucleus of leukocyte and utilizes image color features to remove the complex backgrounds such as red blood cells (RBCs) and substantial dyeing impurities. Then, Canny edge detection is performed to extract the entire leukocyte. Finally, the cytoplasm of the leukocyte is obtained by subtracting the nucleus with the entire leukocyte. Experimental results on an image dataset containing 60 leukocyte images show that the proposed method generates more accurate segmentation results than the counterparts. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Image segmentation

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Peripheral Blood Leukocyte Image Segmentation Based on Deep Convolutional Neural Network 3rd

Peripheral Blood Leukocyte Image Segmentation Based on Deep ...

引用

3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Chen, Yuanyuan Teng, Shenghua Li, Zuoyong College of Electronics and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (纸本)9783030624620

Counting and analysis of leukocyte on peripheral blood smears have great significance to assist doctors’ pathological diagnosis. Thus, accurate and automatic segmentation of leukocyte from blood smear images is a fundamental task. To achieve this task, we proposed an end-to-end peripheral blood leukocyte segmentation method based on deep convolutional neural networks, where the encoder-decoder network is its basic structure. Specifically, the proposed method first utilizes the feature encoder to extract multi-scale leukocyte features. Then, the context-aware feature decoder is introduced to reconstruct the segmentation mask of leukocyte. Finally, each layer generates a prediction map in the decoder module, and all these prediction maps are averaged to be the final prediction map. To demonstrate the effectiveness of the proposed method, we performed qualitative and quantitative comparisons with several existing methods on two publicly available datasets and a private dataset. Experimental results validate that the proposed method is superior for peripheral blood leukocyte image segmentation in terms of accuracy and robustness. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Cerebral Microbleeds Detection Based on 3D Convolutional Neural Network 1

引用

3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Zhao, Meng Jin, Chen Jin, Lingmin Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Cerebral microbleeds (CMBs) are important imaging and diagnostic biomarkers for cerebrovascular diseases and cognitive dysfunctions. Reliable detection of the location and amount of CMBs in brain tissue is crucial for the diagnosis, prevention and treatment of related diseases, where traditional Convolutional Neural Network (CNN) has been applied but may fail to achieve high enough detection accuracy. To alleviate this issue, we utilize 3D Fully Convolutional Networks (FCN) and 3D AlexNet to establish a cascade coarse-to-fine detection manner. Specifically, CMBs candidates are first screened out using 3D FCN, followed by 3D AlexNet which extracts the spatial features of CMBs and distinguishes the false positive samples from candidate regions. Experimental results show that the proposed method can realize precise detection of CMBs in magnetic resonance images (MRI) by improving detection sensitivity and reducing false positive samples. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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