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IET Blockchain 2024年第3期4卷 276-286页

作者： Gao, Muyun Lin, Shenwen Tian, Xin He, Xi He, Ketai Chen, Shifeng School of Mechanical Engineering University of Science and Technology Beijing Beijing China Internet Financial Security Technology Key Laboratory National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

There are service communities with different functions in the Bitcoin transactions system. Identifying community categories helps to further understand the Bitcoin transactions system and facilitates targeted regulation of anonymized Bitcoin transactions. To this end, a Bitcoin service community classification method based on Random Forest and improved K-Nearest Neighbor (KNN) algorithm is proposed. First, the transaction characteristics of different types of communities are analyzed and summarized, and the corresponding transaction features are extracted from the address and entity levels;then multiple classification algorithms are compared, the optimal model to filter the effective features is selected, and the feature vector of entity addresses is constructed. Finally, a classification model is constructed based on Random Forest and improved KNN algorithm to classify the entities. By constructing different classification models for experimental comparison, the accuracy and stability advantages of the proposed method for classification in service community classification research are verified. © 2024 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology.

关键词： Classification (of information)

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Sea clutter suppression via cuttable encoder-decoderaugmentation network

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Journal of Systems engineering and Electronics 2024年第6期35卷 1428-1440页

作者： ZANG Chuanfei WANG Yumiao WANG Xiang XU Congan CUI Guolong School of Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Advanced Technology Research Institute Beijing Institute of TechnologyJinan 250300China Naval Aviation University Yantai 264000China

This paper considers the problem of sea clutter *** propose the cuttable encoder-decoder-augmenta-tion network(CEDAN)to improve clutter suppression perfor-mance by enriching the contrast information between the target and ***,the plug-and-play residual U-block(ResUblock)is proposed to augment the feature representation ability of the clutter suppression *** CEDAN first extracts and fuses the multi-scale features using the encoder and the decoder composed of the ***,the fused features are processed by the contrast information augmenta-tion module(CIAM)to enhance the diversity of target and clutter,resulting in encouraging sea clutter suppression *** addi-tion,we propose the result-consistency loss to further improve the suppression *** result-consistency loss enables CEDAN to cut some blocks of decoder and CIAM to reduce the inference time without significantly degrading the suppression *** results on measured and simulated data show that the CEDAN outperforms state-of-the-art sea clutter suppression methods in sea clutter suppres-sion performance and computation efficiency.

关键词： radar sea clutter suppression deep learning

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Multitask Semantic Segmentation Network Using Adaptive Multiscale Feature Fusion

Multitask Semantic Segmentation Network Using Adaptive Multi...

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2022 International Conference on Computing, communication, Perception and Quantum technology, CCPQT 2022

作者： Chen, Huilin Yang, Shengsong Lyu, Ting School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9781665470209

ISBN: (纸本)9781665470209

A multi-task semantic segmentation network architecture based on adaptive multi-scale feature fusion is proposed, which improves segmentation target edge details and small-scale target segmentation accuracy by combining boundary detection tasks and semantic segmentation tasks. The critical component of the architecture is the adaptive multi-scale feature fusion module, which can adaptively fuse the semantic feature information and boundary feature information of different scales, extract semantic features that contain more spatial data, and reduce the loss of spatial information of small-scale targets, thereby enhancing the network's ability to learn small-scale target features and boundary features. Experiments show that our designed network architecture can improve the segmentation accuracy of small-scale objects and optimize the edge details of segmented objects. © 2022 IEEE.

关键词： Semantic Segmentation

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UNSUPERVISED FEATURE REPRESENTATION FOR OCEAN-GOING SHIP DETECTION

UNSUPERVISED FEATURE REPRESENTATION FOR OCEAN-GOING SHIP DET...

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IET International Radar Conference 2023, IRC 2023

作者： Li, Haoyan Wang, Zhuowei Zhang, Zekai Zhou, Shichao Beijing Information Science & Technology University School of Information and Communication Engineering Beijing China

ISBN: (纸本)9781839539954

Feature representation plays a crucial role in ocean-going ships detection with remote sensing imagery. The robustness of traditional hand-craft features is heavily influenced by prior knowledge, and modern deep neural network models require a large amount of annotated data. In this case, the large field of view characteristic of remote sensing imageries leads to high and challenging annotation costs. Here, we investigate an unsupervised feature representation method, utilizing the spatial context of similar affinity to enhance local descriptors. The refined local descriptors are embedded into a subspace constructed by learned landmarks, aiming to achieve a linear approximation and derive equivalent feature representation. Further, visual cosine similarity is utilized for labelling similar samples in a large-scale data environment. Compared with one-shot object detection methods with classical hand-craft features, the experimental results demonstrate that the proposed method achieves superior detection efficiency with little training instances. © The Institution of engineering & technology 2023.

关键词： Deep neural networks

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Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

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computers, Materials & Continua 2023年第9期76卷 3623-3651页

作者： Yang Yang Yuhan Long Yijing Lin Zhipeng Gao Lanlan Rui Peng Yu State Key Laboratory ofNetworking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China School of Computer Science and Engineering Nanyang Technological University639798Singapore Science and Technology on Communication Networks Laboratory The 54th Research Institute of China Electronics Technology Group CorporationShijiazhuang050051China

With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant *** existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and *** paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge ***,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss *** utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high ***,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side *** propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro *** comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault *** addition,experiments show that our approach has a smooth training process and good balance.

关键词： Fault diagnosis knowledge distillation edge-side lightweight model high similarity

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A Real-time Semantic Segmentation Method Based on Multi-level Feature Fusion

A Real-time Semantic Segmentation Method Based on Multi-leve...

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2022 International Conference on Computing, communication, Perception and Quantum technology, CCPQT 2022

作者： Xu, Jinyan Lyu, Ting School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9781665470209

ISBN: (纸本)9781665470209

The performance improvement for real-time segmentation networks is generally to accelerate the segmentation speed of the model at the cost of computational cost, ignoring the problem of semantic inconsistency of neighborhood features, which causes the accuracy of segmented images to decrease. Therefore, it is crucial to take into account the segmentation efficiency while ensuring the accuracy of model segmentation. In this paper, a lightweight model based on Multi-level Feature Fusion Semantic Segmentation Network (MLFFNet) is proposed, and the network as a whole adopts a two-branch structure to differentiate different types of features. The model obtained 81.4 FPS forward inference speed and 71.3% segmentation accuracy on the Cityscapes dataset, which is capable of real-time semantic segmentation tasks and proposes a new idea for the semantic segmentation problem in a complex context. © 2022 IEEE.

关键词： Semantic Segmentation

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A Hybrid Reasoning Method of Knowledge Graph for On-line Arts Education based on Reinforcement Learning 7

A Hybrid Reasoning Method of Knowledge Graph for On-line Art...

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7th International Conference on computer and communication Systems, ICCCS 2022

作者： Li, Gang Han, Ruixin University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

With the popularization of online education mode, online arts education has entered the public field of vision. However, due to the non-systematic and non-standard art teaching at home and abroad, there is still a huge room for improvement in its scale and knowledge system. In this paper, a hybrid reasoning method of knowledge graph based on Reinforcement Learning - Multi relational GCN reasoning combined with reinforcement learning (RL-URGCN) is introduced, which uses the knowledge reasoning technology to mine the knowledge standardization. The scattered knowledge can be formed into a relational and structured knowledge system, so as to improve the learning efficiency and promote the teaching process in the process of art education. © 2022 IEEE.

关键词： Reinforcement learning

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Prediction Model of the End-point Carbon Content in BOF by Constructing the Graph of Heats

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ISIJ International 2025年第5期65卷 613-618页

作者： Ning, Wen Hongbing, Wang Li, Li Anjun, Xu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Metallurgical and Ecological Engineering University of Science and Technology Beijing Beijing100083 China

For the differences in composition and proportions of input materials, end-point quality requirements and slagging specifications, it is difficult to construct a generalized model to guide steelmaking production. There is a complex connection among different heats in BOF, and this combination of correlations can be thought of as a hidden working pattern. A method named "attribute level division" is developed to exploit the correlations among heats to construct the graph of heats. The model based on the combination of label propagation algorithm (LPA) and back propagation neural network (BPNN), LPA-BP, is proposed for end-point carbon content prediction in BOF. LPA is used to discover the different community in the graph of heats and BPNN is trained to construct different models for end-point carbon content prediction for the heats from different community. The results of comparative experiment show that the LPA-BP model is higher than the baseline 2.5% when prediction error is within ±0.012%. The LPA-BP model also outperforms in some metrics, such as RMSE, MAE. This model provides a novel idea to improve the endpoint hit rate by distinguishing different communities to uncover the hidden working patterns among heats and constructing different models. © 2025 The Iron and Steel Institute of Japan.

关键词： Steelmaking

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A multimodal feature learning framework with graph structure learning and CLIP for apparent personality analysis 10

A multimodal feature learning framework with graph structure...

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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Rongquan Xu, Xianyu Yang, Hao Wei, Lin Wang, Tao University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Tianjin University of Science and Technology College of Artificial Intelligence Tianjin China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China Flight Technology College Civil Aviation Flight University of China Guanghan China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798331520861

Automatic personality prediction has become a challenging topic in computer vision. This paper proposes a novel multimodal feature learning framework with graph structure learning and CLIP for analyzing personality from short videos (GSL-CLIP). We construct a facial graph for the visual modality and propose a Geo method based on GCN and CNN to extract static facial appearance and geometric features. Furthermore, the pre-trained CLIP and VGGish CNN are used as feature extractors to extract their spatial features. Then, we design a graph relation attention network to mine their temporal feature in visual and audio modalities. Finally, we aggregate these two features through MLP to form a scene-audio association feature. Additionally, to enhance our framework's robustness, we employ the pre-trained VGGish CNN for audio spatial features, and the BiRNN with an attention mechanism is used to extract significant audio temporal features. Finally, we introduce a multimodal channel residual attention network for fusing multimodal features and leverage an MLP regression model to predict scores for personality traits. The experimental results indicate that the effectiveness of our proposed GSL-CLIP is superior to state-of-the-art methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Prediction of segregation characterization based on granular velocity and concentration in rotating drum

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Particuology 2023年第2期73卷 17-25页

作者： Wenzheng Xiu Ra Li Quan Chen Qicheng Sun Vladimir Zivkovic Hui Yang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai200093China State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing100084China School of Engineering Newcastle UniversityNE17RUUnited Kingdom

In a binary granular system composed of two types of particles with different granule sizes and the same density,particle sorting occurs easily during the flow *** segregation pattern structure is mainly affected by the granular velocity and granular concentration in the flow *** paper reports on the experimental velocity and concentration measurement results for spherical particles in a quasi-two-dimensional rotating *** relationship between the granular velocity along the depth direction of the flow layer and granular concentration was established to characterize structures with different degrees of *** corresponding relationships between the granular velocity and concentration and the segregation pattern were further analyzed to improve the theoretical models of segregation(convection-diffusion model and continuous flow model)and provide a reference for granular segregation control in the production process.

关键词： Granular segregation Rotating drum Granular velocity Granular concentration

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