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FSCIL-EACA: Few-Shot Class-Incremental Learning Network Based on Embedding Augmentation and Classifier Adaptation for Image Classification

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Chinese Journal of Electronics 2024年第1期33卷 139-152页

作者： Ruru ZHANG Haihong E Meina SONG School of Computer Science Beijing University of Posts and Telecommunications Education department information Network Engineering Research Center Beijing University of Posts and Telecommunications

The ability to learn incrementally is critical to the long-term operation of AI systems. Benefiting from the power of few-shot class-incremental learning(FSCIL), deep learning models can continuously recognize new classes with only a few samples. The difficulty is that limited instances of new classes will lead to overfitting and exacerbate the catastrophic forgetting of the old classes. Most previous works alleviate the above problems by imposing strong constraints on the model structure or parameters, but ignoring embedding network transferability and classifier adaptation(CA), failing to guarantee the efficient utilization of visual features and establishing relationships between old and new classes. In this paper, we propose a simple and novel approach from two perspectives: embedding bias and classifier bias. The method learns an embedding augmented(EA) network with cross-class transfer and class-specific discriminative abilities based on self-supervised learning and modulated attention to alleviate embedding bias. Based on the adaptive incremental classifier learning scheme to realize incremental learning capability,guiding the adaptive update of prototypes and feature embeddings to alleviate classifier bias. We conduct extensive experiments on two popular natural image datasets and two medical datasets. The experiments show that our method is significantly better than the baseline and achieves state-of-the-art results.

关键词： Training Adaptation models Visualization Image recognition Prototypes Self-supervised learning Power capacitors

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Low-Brightness Object Recognition Based on Deep Learning

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Computers, Materials & Continua 2024年第5期79卷 1757-1773页

作者： Shu-Yin Chiang Ting-Yu Lin department of information and telecommunications Engineering Ming Chuan UniversityTaoyuan City333Taiwan

This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition *** primary goal is to tackle issues related to recognizing objects with low brightness *** this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information *** detection scenarios are categorized into normal brightness and low brightness *** the system determines a normal brightness environment,normal brightness images are recognized using deep learning *** low-brightness situations,three methods are proposed for *** first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented *** second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for *** third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for *** experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition *** research outcome can be applied to nighttime surveillance systems or nighttime road safety systems

关键词： Low-brightness depth image image segmentation image recognition HDV HSD

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Multifunctional MXene/rGO aerogels loaded with Co/MnO nanocomposites for enhanced electromagnetic wave absorption,thermal insulation and pressure sensing

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Nano Research 2024年第9期17卷 7803-7813页

作者： Chan Guo Shiping Shao Xin Zhang Yunxiang Tang Luxue Wang Jiurong Liu Lili Wu Ke Bi Fenglong Wang Key Laboratory for Liquid-Solid Structural Evolution and processing of Materials Ministry of Education Shandong UniversityJinan 250061China State Key Laboratory of information Photonics and Optical Communications School of ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China

Aerogel-based composites hold promising application prospects as potential electromagnetic wave(EMW)absorption materials,yet the construction of such materials with ingenious microstructures,appropriate magnetic/dielectric multi-components,and integrated multifunctionality remains considerably ***,a multicomponent Co/MnO/Ti_(3)C_(2)T_(x) MXene/rGO(CMMG)hybrid aerogel featured with three-dimensional(3D)vertical directional channel architecture is *** from the synergistic effect arising from the 3D conductive networking structure,diverse heterogeneous interfaces,magnetic/dielectric multicomponent,and multiple loss pathways,the optimized CMMG-2 aerogel delivers fascinating EMW absorption capabilities,characterized by a minimal reflection loss(RL_(min))of-77.41 dB and an effective absorption bandwidth(EAB)of 6.56 ***,the remarkable hydrophobicity,exceptional thermal insulation capabilities,and outstanding photothermal properties of CMMG-2 aerogel make it highly promising for multiple application in diverse and demanding ***,the distinctive pore structure of hybrid aerogel also allows it for sensitive and reliable detection of electrical signals caused by pressure changes and human ***,this research provides a viable design strategy for the development of lightweight,efficient,and multifunctional aerogel-based EMW absorption materials for various application scenarios.

关键词： electromagnetic wave absorption Ti_(3)C_(2)qT_(x)MXene reduced graphene oxide magnetic-dielectric nanoparticles multifunctional aerogel

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A New Generation of Rules-based Approach:Mivar-based Intelligent Planning of Robot Actions(MIPRA)and Brains for Autonomous Robots

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Machine Intelligence Research 2024年第5期21卷 919-940页

作者： Oleg Varlamov Dmitry Aladin department of information processing and Management Systems RI MIVARBauman Moscow State Tchnical University(BMSTU)Moscow105005Russian Federation

To create autonomous robots,both hardware and software are *** enormous progress has already been made in the field of equipment,then robot software depends on the development of artificial *** article proposes a solution for creating“logical”brains for autonomous robots,namely,an approach for creating an intelligent robot action planner based on Mivar expert *** application of this approach provides opportunities to reduce the computational complexity of solving planning problems and the requirements for the computational characteristics of hardware platforms on which intelligent planning systems are *** theoretically and practically justify the expediency of using logically solving systems,in particular Mivar expert systems,to create intelligent planners,the MIPRA(Mivar-based Intelligent Planning of Robot Actions)planner was created to solve problems such as STRIPS for permutation cubes in the Blocks World *** planner is based on the platform for creating expert systems of the *** a result,the Mivar planner can process information about the state of the subject area based on the analysis of cause-effect relationships and an algorithm for automatically constructing logical inference(finding a solution from“Given”to“Find”).Moreover,an important feature of the MIPRA is that the system is built on the principles of a“white box”,due to which the system can explain any of its decisions and provide justification for the actions performed in the form of a retrospective of the stages of the decision-making *** preparing a set of robot actions aimed at changing control objects,expert knowledge is used,which is the basis for the functioning algorithms of the *** approach makes it possible to include an expert in the process of organizing the work of the intelligent planner and use existing knowledge about the subject *** experiments of this study have shown that instead of many hours and powerfu

关键词： Autonomous robot blocks world decision-making system expert systems intelligent planning

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Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete information

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 690-697页

作者： Yun Zhang Lulu Zhang Yunze Cai the department of Automation Shanghai Jiao Tong UniversityShanghai 200240 the Key Laboratory of System Control and information processing Ministry of Education of ChinaShanghai 200240China

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence *** players are divided into two groups in the learning process and adapt their policies *** method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based ***,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control *** efficacy of our method is illustrated by three examples.

关键词： Adaptive dynamic programming incomplete information multi-player differential game value iteration

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A New Framework for Software Vulnerability Detection Based on an Advanced Computing

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Computers, Materials & Continua 2024年第6期79卷 3699-3723页

作者： Bui Van Cong Cho Do Xuan department of information Technology University of Economics and Technical IndustriesHanoi100000Vietnam Faculty of information Security Posts and Telecommunications Institute of TechnologyHanoi100000Vietnam

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest *** paper proposes a new framework called DrCSE to improve software vulnerability *** uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software *** do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive *** which,the method(i)extracts the source code’s features based on the vertices and edges of the *** method of rebalancing data has the function of supporting the training process by balancing the experimental ***,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers *** experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum *** a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE *** performs better than other approaches,with a 5%boost in Precision and a 5%boost in ***,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

关键词： Source code vulnerability source code vulnerability detection code property graph feature profile contrastive learning data rebalancing

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Generative Multi-Modal Mutual Enhancement Video Semantic Communications

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2985-3009页

作者： Yuanle Chen Haobo Wang Chunyu Liu Linyi Wang Jiaxin Liu Wei Wu The College of Communication and information Engineering Nanjing University of Posts and TelecommunicationsNanjing210023China The College of Science Nanjing University of Posts and TelecommunicationsNanjing210023China

Recently,there have been significant advancements in the study of semantic communication in single-modal ***,the ability to process information in multi-modal environments remains *** by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality ***,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called *** upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of *** it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the ***,a multi-frame semantic detection module is designed to facilitate semantic transitions during video *** results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

关键词： Generative adversarial networks multi-modal mutual enhancement video semantic transmission deep learning

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A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

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China Communications 2024年第2期21卷 166-182页

作者： Guijin Tang Lian Duan Haitao Zhao Feng Liu Jiangsu Key Laboratory of Image processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual ***, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

关键词： image enhancement multi-stream fusion underwater image

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Monocular Distance Estimated Based on PTZ Camera

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Computers, Materials & Continua 2024年第5期79卷 3417-3433页

作者： Qirui Zhong Xiaogang Cheng Yuxin Song Han Wang Bell Honors School Nanjing University of Posts and telecommunications Nanjing210003China College of telecommunications and information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China College of Automation and College of Artificial Intelligence Nanjing University of Posts and TelecommunicationsNanjing210003China

This paper introduces an intelligent computational approach for extracting salient objects fromimages and estimatingtheir distance information with PTZ (Pan-Tilt-Zoom) cameras. PTZ cameras have found wide applications innumerous public places, serving various purposes such as public securitymanagement, natural disastermonitoring,and crisis alarms, particularly with the rapid development of Artificial Intelligence and global infrastructuralprojects. In this paper, we combine Gauss optical principles with the PTZ camera’s capabilities of horizontal andpitch rotation, as well as optical zoom, to estimate the distance of the *** present a novel monocular objectdistance estimation model based on the Focal Length-Target Pixel Size (FLTPS) relationship, achieving an accuracyrate of over 95% for objects within a 5 km range. The salient object extraction is achieved through a simplifiedconvolution kernel and the utilization of the object’s RGB features, which offer significantly faster computingspeeds compared to Convolutional Neural Networks (CNNs). Additionally, we introduce the dark channel beforethe fog removal algorithm, resulting in a 20 dB increase in image definition, which significantly benefits distanceestimation. Our system offers the advantages of stability and low device load, making it an asset for public securityaffairs and providing a reference point for future developments in surveillance hardware.

关键词： Public security location information PTZ camera image processing

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Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

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Computers, Materials & Continua 2024年第8期80卷 2815-2837页

作者： Mengmeng Zhao Haipeng Peng Lixiang Li Yeqing Ren information Security Center State Key Laboratory ofNetworking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China National Engineering Laboratory for Disaster Backup and Recovery Beijing University of Posts and TelecommunicationsBeijing100876China department of information Science and Engineering Zaozhuang UniversityZaozhuang277160China

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working *** the systems are attacked,timely identification of outliers in time series is critical to ensure *** many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these *** to the superior capability of Transformer in learning time series *** paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved ***,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are ***,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each *** scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each *** interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory *** introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph *** on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

关键词： Multivariate time series anomaly detection spatial-temporal network Transformer

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