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检索条件"机构=School of Control and Computer Engineering"

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An Attention-ReXNet Network for Long Tail Road Scene Classification 13

An Attention-ReXNet Network for Long Tail Road Scene Classif...

引用

13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Guo, Yuan H. Zhu, Jun R. Yang, Chang C. Yang, B. School of Computer Science and Artificial Intelligence Changzhou University Changzhou213000 China School of Microelectronics and Control Engineering Changzhou University Changzhou213000 China

ISBN: (纸本)9798350315196

With the development of assisted driving and autonomous driving, road scene classification has become increasingly important due to its ability to provide environmental information for drivers and autonomous driving, which is beneficial to reduce traffic accidents. However, road scene classification is limited by dataset imbalance and running speed, making it difficult to apply practically. To solve the problems mentioned above, we proposed an Attention-ReXNet network to conduct road scene classification. A balance Softmax cross-entropy is introduced to solve the class imbalance problem in road scene classification. The proposed method is evaluated in the Road Surface Classification Dataset (RSCD), which contains 27 categories and 1 million images with class imbalance. Experimental results show that Attention-ReXNet and balance Softmax cross entropy can achieve the best classification performance, with accuracy and top-1 being 87.67% and 88.52%, respectively. © 2023 IEEE.

关键词： Autonomous vehicles

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Green and Low Carbon Load Side Carbon Emission Measurement in Power Systems Based on Opportunity Constraints 11

Green and Low Carbon Load Side Carbon Emission Measurement i...

引用

11th China International Conference on Electricity Distribution, CICED 2024

作者： Xu, Zhiyuan Cheng, Liang School of Control and Computer Engineering North China Electric Power University Beijing China Institute of Software Chinese Academy of Sciences Tca Lab Beijing China

ISBN: (纸本)9798350368345

In the context of global warming, traditional carbon measurement methods have failed to completely capture the carbon emission dynamics of the power system under various operating states, resulting in biased carbon emission data. Moreover, there is still significant research space for measurement models on the power load side. To improve the accuracy and real-time performance of carbon emission measurement, this study designs a power system load side carbon emission measurement model based on opportunity constraints. The proposed model is built on opportunity constraints and fairness principles to allocate carbon emission responsibilities on the load side. On this basis, considering the dynamic characteristics of the power system, a two-stage carbon emission measurement model is raised, which uses dynamic carbon emission measurement method and node carbon potential method to calculate carbon emissions in the first and second stages, respectively. The outcomes validated that the carbon emissions of Unit 1 in the fixed carbon emission factor model were 620.07 tons, while they were reduced to 470.11 tons in the proposed model. After implementing demand response measures, the total carbon emissions of the system during peak electricity usage decreased by 3130 kg. The fixed carbon emission factor econometric model, traditional carbon market model, and the proposed model resulted in a total carbon emissions of 18306.61 tons, 16939.79 tons, and 19045.21 tons, respectively. Therefore, the proposed model performs better in regulating demand, can more accurately measure carbon emissions on the load side of the power system, and contribute to promoting green and low-carbon development of the energy system. © 2024 IEEE.

关键词： Low emission

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Based on Linkable Ring Signature Map Review Scheme for Zero-Trust Autonomous Vehicles 8

Based on Linkable Ring Signature Map Review Scheme for Zero-...

引用

8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Zhang, Min Ye, Qing Deng, Kaihuan Naval University of Engineering Department of Information Security Wuhan China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350331035

The real-time map plays a vital role in the Internet of Vehicles (IoV) navigation. Compared with the existing map review, the real-time map review method is based on crowdsensing enabling faster data updates and shortening the map period from map collection to service. However, the crowdsensing method requires frequent interaction between the vehicle and nearby devices or servers, increasing the risk of user privacy leakage. How to ensure the privacy of users is a challenge in the real-time map review. Starting from the typical architecture of IoV and combining the core idea of zero-trust 'never trust, continuous verification', IoV-ZT conceptual model architecture is designed. This paper proposes a based on linkable ring signature map review scheme for zero-trust autonomous vehicles (LRSMR). In the LRSMR, a linkable ring signature based on the SM2 digital signature algorithm is used to protect user identity privacy and ensures while ensuring signature linkability by hiding the identity of the actual signer in the ring. Through the requirement for user credit, the reliability of the review data is improved. At the same time, the distribution of rewards increases users' motivation. At last, the security analysis indicates that the LRSMR satisfies correctness, unforgeability, unconditional anonymity, linkability and nonslanderability. The simulation results illustrate that the LRSMR is efficient in terms of communication overhead and computation cost. © 2023 IEEE.

关键词： Internet of vehicles Linkable ring signature Map review Zero-trust environment

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Research on Autonomous Decision-Making of Multi-UAV Air Combat Based on Deep Reinforcement Learning 7th

Research on Autonomous Decision-Making of Multi-UAV Air Comb...

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhou, Zhiming Qi, Hongmin Liu, Zhen Qian, Dianwei Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733354

To solve the autonomous decision-making problem of multi-UAV air combat, a multi-UAV reinforcement learning self-game autonomous decision-making method for air combat with parameter sharing is proposed. Firstly, a self-game policy pool training framework is designed. The game ability of the multi-UAV autonomous decision-making model is improved by self-game confrontation with a policy pool containing different training generations. Secondly, a parameter-sharing mechanism is proposed to train homogeneous UAVs to improve the efficiency of reinforcement learning training and the utilization of interactive data. Finally, a multi-UAV autonomous decision-making model using the multi-agent deep deterministic policy gradient algorithm is designed so that it can explore effectively in the continuous action space. In addition, experiments are designed to verify the effectiveness and migration of the designed algorithm and training framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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Multi-task Collaborative Network for Image-Text Retrieval 1

引用

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Qin, Xueyang Li, Lishuang Hao, Jing Ge, Meiling Huang, Jiayi Pang, Guangyao School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Computer Engineering Weifang University Weifang261061 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China

ISBN: (数字)9783031533112

ISBN: (纸本)9783031533105

Image-text retrieval aims to capture semantic relevance between images and texts. Most existing approaches rely solely on the image-text pairs to learn visual-semantic representation through fine-grained alignments while neglecting the potential beneficial impact of unimodal tasks on cross-modal retrieval. To this end, we present a Multi-Task Collaborative Network (MTCN) that leverages the synergy between multiple tasks to enhance the performance of image-text retrieval. Specifically, we introduce three unimodal tasks, including text-text matching, image multi-label classification, and text multi-label classification, and train together with target task image-text retrieval from the perspective of semantic constraints. Additionally, we employ a modality interaction module for image-text retrieval to discover interrelationships between these two modalities. Subsequently, a cascaded graph convolutional network combined with a multi-layer perceptron is used to infer the correlation scores between images and texts. We conduct comprehensive experiments on two benchmark datasets, Flickr30K and MSCOCO, and the quantitative and qualitative experimental results demonstrate the effectiveness of the proposed method. The source code is available at https://***/FlyCuteBird/MTCN. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Clustering of Temperature Measurement Points for Key Components of Offshore Wind Turbines Considering Spatiotemporal Multi-Modality Characteristics 16

Clustering of Temperature Measurement Points for Key Compone...

引用

16th IEEE PES Asia-Pacific Power and Energy engineering Conference, APPEEC 2024

作者： Zhang, Yangyu Cheng, Yi Hu, Yang Fu, Daoyi Hu, Haopeng School of Control and Computer Engineering North China electric Power University Beijing China Haizhuang Windpower Equipment Co. Ltd. Chongqing China

ISBN: (纸本)9798350386127

The offshore environment is constantly changing and complex, causing changes in environmental temperature and operating conditions with seasonal variations, resulting in a spatiotemporal multimodal correlation between temperature measurement points of key components within wind turbines. A clustering method for temperature measurement points of key components of wind turbines based on Spearman correlation analysis and VGAE algorithm is proposed in this paper, which provides a reasonable modeling strategy for temperature characteristic perception modeling of offshore wind turbines. Analyze the raw data of wind turbines based on seasonal changes, generate spatiotemporal multimodal characteristic data, and use Spearman correlation coefficient to preliminarily analyze the nonlinear correlation of characteristic variables, defining the undirected graph relationship nodes for feature variable;Then, the potential feature dependencies and temperature variation characteristics of the undirected graph relationship nodes are excavated, and the Variational graph auto encoders algorithm is used. The encoder composed of GCN and variational inference method are used to analyze the potential representations and reconstruct the undirected graph relationship nodes;Finally, the K-means clustering method is used to cluster the temperature measurement points of key components of offshore wind turbines. The results indicate the effectiveness of the method. © 2024 IEEE.

关键词： Offshore wind turbines

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Named Entity Recognition in Chinese Medical Texts Based on RoBERTa-WWM-IDCNN-CRF 25

Named Entity Recognition in Chinese Medical Texts Based on R...

引用

25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Tang, Lan Kong, Jie Xu, Liang Xi'an Shiyou University School of Computer Xi'an China Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems School of Electrical Engineering and Automation Tianjin China

ISBN: (纸本)9798350366600

Electronic medical records and doctor-patient conversations contain a wealth of useful information, such as disease symptoms, drug names, and cure cycles. Traditional deep learning approaches utilize bidirectional recurrent neural networks to encode text, which cannot effectively capture long-distance dependencies in text. In order to identify medical entity types in various texts more accurately, a named entity recognition approach based on RoBERTa-WWM-IDCNN-CRF is proposed. This approach introduces the RoBERTa-WWM pre-trained model, in order to improve the overall understanding of long Chinese texts for the first time. The model generates semantic representations containing prior knowledge. Then, the generated semantic representations are feed to the IDCNN to extract sentence level characteristics. Finally, the global optimal solution is obtained and the results of the named entity are produced through the CRF. The experiment indicates that the improvement of F1 score, accuracy, and recall of RoBERTa-WWM-IDCNN-CRF model are 7.94%, 4.52% and 10.02% comparing with the classical BiLSTM-CRF model. © 2024 IEEE.

关键词： Convolutional neural networks

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An Automata Machine Based Warehouse Management System 25

An Automata Machine Based Warehouse Management System

引用

25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Hua, Xiaomei Sun, Hongbo Yantai University School of Computer and Control Engineering Shandong Yantai264005 China

ISBN: (纸本)9781665405270

With the continuous development of industrialization, warehouse storage is becoming more and more intelligent. The storage state of goods in the warehouse is one of the very important components of the warehouse management system. In order to improve the utilization rate of the warehouse and the efficiency of warehouse storage, this paper proposes a warehouse management system based on state machine to deal with the state of goods in different situations, and the feasibility of this method is verified by an example. The warehouse management system using this method is more efficient and convenient than the traditional one. © 2022 IEEE.

关键词： Warehouses

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Target Localization of Short Vertical Line Array Based on Least Squares Method 6

Target Localization of Short Vertical Line Array Based on Le...

引用

6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Qu, Shaochun Cai, Zhipeng Ma, Jing Xi'an Technological University School of Computer Science and Engineering Xi'an China North Automatic Control Technology Institute Taiyuan China

ISBN: (纸本)9798350305388

In the shallow sea environment, array of small array elements may not be able to accurately locate targets due to undersampling. To address this issue, the paper proposed a method called sound field extension based on the reception of array of small array elements, which generates a virtual measurement field of a vertical array with lager number. The method utilizes the extended acoustic field for VTRM. This approach significantly improves the focusing and positioning performance of the TRM. The key implementation lies in estimating the normal wave mode coefficients from the acoustic field acquired by the short vertical line array. The paper uses the least squares method to estimate the mode coefficients. Experimental results demonstrate that compared to conventional time processing methods for an array of small array elements, the VTRM method using the a vertical array with a large number of elements can effectively enhance targeting and focusing performance of the short array. For array of small array elements with 21 elements, the SNR (Signal Interference to Noise Ratio) effectively improves by 4.32 dB. © 2023 IEEE.

关键词： Acoustic fields

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Research on Image Colorization Based on Deep Learning 7

Research on Image Colorization Based on Deep Learning

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7th International Conference on computer Network, Electronic and Automation, ICCNEA 2024

作者： Li, Ao Wang, Zhongsheng School of Computer Science and Engineering Xi’an Technological University Xi’an China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an Technological University Xi'an China

Gray scale picture colorization, a research hotspot in the domain of computer vision, seeks to assign reasonable colours to every single pixel of a grayscale picture, enriching the visual information of the coloured picture. Neural networks take grayscale images as input and output colorized images. From a psychological perspective, colours can provide observers with a more pleasant perceptual experience. Old photos can also be coloured, which are important documentary resources for recording real history and restoring the social landscape of the time. Additionally, due to the limitations of imaging mechanisms, most medical images are grayscale. Therefore, research on grayscale image colorization is a task of significant importance. However, existing automatic image colorization systems have issues such as "blurry boundaries", "colour overflow", "inappropriate colour choices" and "incorrect colour regions". This paper proposes a model combining adversarial neural networks and U-Net networks, optimizing them by incorporating global feature fusion modules, Pathway emphasis mechanisms and spatial focus mechanisms. The idea of Markov discriminator was added to the discriminator, and the loss function was optimized. The ultimate investigative conclusions demonstrate that the PSNR and SSIM of the suggested system are 24.45 and 0.941, accordingly, in relation to the preceding protocol, which are 3% and 5% higher than the previous algorithm, and the example has been ameliorated to some degree. And the loss function is modified, and the final algorithm can improve the effect of picture colouring, which encompasses a broad spectrum of application prospects. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Discriminators

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