2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)最新文献_第7页

Synchronous modeling technology for satellite manufacturing systems driven by real-time perception information 实时感知信息驱动的卫星制造系统同步建模技术

2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142340

Bin Wang, Fenglong Yang, Jie Zhao, Lixia Liu, Wangzhu Yu, Aijing Ni, Qing Guo, Yong Zhou, Junjie Zhong, Youxie Hong, Qiong Wu

{"title":"Synchronous modeling technology for satellite manufacturing systems driven by real-time perception information","authors":"Bin Wang, Fenglong Yang, Jie Zhao, Lixia Liu, Wangzhu Yu, Aijing Ni, Qing Guo, Yong Zhou, Junjie Zhong, Youxie Hong, Qiong Wu","doi":"10.1109/CISCE58541.2023.10142340","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142340","url":null,"abstract":"In light of the need for spacecraft manufacturing to enhance real-time control capabilities and production efficiency in the manufacturing process, this paper designed a synchronous modeling scheme for the satellite manufacturing system driven by real-time perception information, based on the optimal layout of the workshop's Internet of Things (IoT) system. This paper focuses on the research of manufacturing process-oriented multimodal recognition technology of perception information and manufacturing system synchronous modeling technology based on real-time perception. The real-time, data-driven, synchronous modeling of the manufacturing system realized the complete mapping and control of the product manufacturing process in a virtual environment. It constructed a manufacturing system synchronous modeling system based on real-time perception. The system had been applied in the development of several models of honeycomb sandwich panels, including the Mars rover, and has achieved the expected results.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133997802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Approach For Generating High Quality Structured Data 一种生成高质量结构化数据的方法

2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142328

Yunfei Jia, Xinhuan Zhang

引用次数: 0

Computer Aided Design of Microwave Circuits Based on BP Neural Networks 基于BP神经网络的微波电路计算机辅助设计

2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142939

Junze Liu, Miao Yu

{"title":"Computer Aided Design of Microwave Circuits Based on BP Neural Networks","authors":"Junze Liu, Miao Yu","doi":"10.1109/CISCE58541.2023.10142939","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142939","url":null,"abstract":"In addition to meeting basic electrical characteristics, the design of a microwave circuit must also consider the ease and accuracy of the circuit model, the time required for circuit design, the availability of tolerance analysis, and the ability to optimize output. Artificial Neural Network (ANN) technology has recently been introduced into microwave circuit CAD and optimization due to its nonlinear mapping function and high real-time computing capability. This technology has been shown to significantly improve the accuracy of modeling and compensate for errors in traditional microwave circuit design methods. In this study, we propose a BP neural network-based modeling method for microwave integrated circuits. This model can be used for computer-aided integrated microsystem simulation and analysis. The fitting error(RMSE) between the original simulation data and the test data is reduced from 0.868 to 0.274 before and after compensation, demonstrating the effectiveness of the proposed method. Experimental results show that the accuracy of the circuit model is improved after the optimization of the BP neural network.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126047027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Aux-ViT : Classification of Alzheimer's Disease from MRI based on Vision Transformer with Auxiliary Branch 基于视觉变压器辅助分支的MRI对阿尔茨海默病的分类

2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142358

Yaofei Duan, Rongsheng Wang, Yukun Li

{"title":"Aux-ViT : Classification of Alzheimer's Disease from MRI based on Vision Transformer with Auxiliary Branch","authors":"Yaofei Duan, Rongsheng Wang, Yukun Li","doi":"10.1109/CISCE58541.2023.10142358","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142358","url":null,"abstract":"Alzheimer's disease (AD) has become a major public health concern, and reliable screening and diagnosis remain difficult. Magnetic resonance image (MRI) can be helpful in distinguishing AD patients from individuals with normal cognition (NC). Deep neural networks have demonstrated strong capacities for extracting intricate nonlinear correlations from brain imaging data recently. However, this requires large amounts of data for training to avoid overfitting problems, but data is scarce and precious in medical field. In this work, we propose a Vision Transformer network architecture called Aux-ViT, which solves the problem of losing shallow features by adding a class auxiliary branch. Specifically, we choose ViT as the backbone network and add an Auxiliary Multi-layer Perceptron Head to output auxiliary prediction results for calculating prediction errors. Based on the characteristics of MRI, we also developed a brain MRI data preprocessing method called Multi-Information Fusion Improvement, while further achieving data enhancement using a random synthetic mask based on pixel weighting fusion. We conducted extensive experiments using the ADNI-3 dataset to validate our algorithm. When compared to the baseline ViT model, the Aux-ViT model obtains an accuracy of 89.58%, which is an increase in accuracy of 3.93% and a decrease in training time of 47.7%. Our study provides a practical approach for early Alzheimer's diagnostics utilizing MRI data.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121815042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Prediction of nuclear power valve faults using sample expansion method, multi-domain feature-optimal screening method and GA-SVM: (Prediction of nuclear power valve faults) 基于样本展开法、多域特征优化筛选法和GA-SVM的核电阀门故障预测(核电阀门故障预测)

2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142417

Yanjun Xia, Yanghong Pan, Zhangchun Tang

{"title":"Prediction of nuclear power valve faults using sample expansion method, multi-domain feature-optimal screening method and GA-SVM: (Prediction of nuclear power valve faults)","authors":"Yanjun Xia, Yanghong Pan, Zhangchun Tang","doi":"10.1109/CISCE58541.2023.10142417","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142417","url":null,"abstract":"Valves are extensively applied in the nuclear power field. They commonly serve under high temperature, radiation, corrosion and other harsh environments for a long time, and once a failure occurs, it will lead to serious accidents, and thus it is of great significance for the prediction of nuclear power valve faults. The typical fault data relevant to nuclear power valves is usually limited, i.e., small sample. In addition, multi-domain features including time domain and frequency domain are commonly employed to predict nuclear power valve faults, in which redundant features may reduce the prediction accuracy. A sample expansion method is first proposed to overcome the difficulty of the small sample, and then a feature-optimal screening method is employed to address the issue relevant to redundant features in multi-domain features. Further, Genetic Algorithm Support Vector Machines (GA-SVM) is employed to predict nuclear power valve faults. The results demonstrate that the proposed method can obtain good prediction accuracy.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127824388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of electrical certificate examination system based on virtual simulation technology 基于虚拟仿真技术的电工证书考试系统设计

2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142778

Qinghua You

引用次数: 0

Multi-channel EEG signal acquisition system based on nRF52832 基于nRF52832的多通道脑电信号采集系统

2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142276

Wencan Chen

引用次数: 0

TS-GGNN: Combining Graph and Sequence Features for Vulnerability Detection in Source Code TS-GGNN:结合图与序列特征的源代码漏洞检测

2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142859

Xin Zhou, Jianmin Pang, Chunyan Zhang, F. Yue, Junchao Wang, Guangming Liu

引用次数: 0

Underwater Image Processing with New Dark Channel Prior Dehazing 新暗通道先验去雾的水下图像处理

2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142806

Ruikang Hu, Yuhan Li

引用次数: 0

Application of Gpu parallel in BIM model lightweight Gpu并行在BIM模型轻量化中的应用

2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142347

Bo Cai, Qinghua Zhou

引用次数: 0