2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)最新文献_第9页

Research on the Application of ERP Financial Software in Enterprises ERP财务软件在企业中的应用研究

2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) Pub Date : 2021-11-01 DOI: 10.1109/ICCSMT54525.2021.00046

Ping Mu

引用次数: 1

The Impact of User Perceived Overload on Continuance Intention to Use Social Commerce: —Based on Stimulus-Organism-Response Model 用户感知超载对社交商务持续使用意愿的影响:基于刺激-机体-反应模型

2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) Pub Date : 2021-11-01 DOI: 10.1109/ICCSMT54525.2021.00039

Wangchun Zhang

引用次数: 0

Seismic Facies Classification Algorithm Based on the EarthTransNet 基于EarthTransNet的地震相分类算法

2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) Pub Date : 2021-11-01 DOI: 10.1109/ICCSMT54525.2021.00058

Haoran Liang, Yanxin Yang, Liang Shi, Qingqiang Wu

{"title":"Seismic Facies Classification Algorithm Based on the EarthTransNet","authors":"Haoran Liang, Yanxin Yang, Liang Shi, Qingqiang Wu","doi":"10.1109/ICCSMT54525.2021.00058","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00058","url":null,"abstract":"Seismic exploration is an interdisciplinary subject. Combined with artificial intelligence, it can automatically identify seismic dips and distinguish faults. The application of Deep Neural Network can reduce the error of manual recognition and improve the efficiency of recognition. Most seismic exploration datasets lack labels, so the supervised learning algorithm cannot be used to extract image features in order to obtain better seismic facies classification effect. Due to the proposal of the F3 dataset which contains real labels in 2019, the supervised learning algorithm can be used on 3D seismic data to take less time and get better prediction results. It is an effective means of evaluation. However, the classification effect of some deep learning models is not satisfactory, especially the neglect of underlying features and the misclassification of small categories on the F3 dataset. Therefore, we apply firstly the TransUNET to the F3 dataset, and modify the input method to 3D volume data, the Transformer layers are added at the end of the CNN layers to collect deep and potential information. The output of the decoder needs to be integrated in the X, Y and Z directions to get the final result. Finally, we propose EarthTransNet, which is applied to the seismic dataset to obtain higher accuracy and better boundary characterization ability.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121680158","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

Research on public safety emergency management of “Smart city” “智慧城市”公共安全应急管理研究

2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) Pub Date : 2021-11-01 DOI: 10.1109/ICCSMT54525.2021.00041

Shuguang Wang, Mengshan Li

引用次数: 0

Pruning Deep Feature Networks Using Channel Importance Propagation 基于信道重要性传播的深度特征网络剪枝

2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) Pub Date : 2021-11-01 DOI: 10.1109/ICCSMT54525.2021.00080

Honglin Chen, Chunting Li

{"title":"Pruning Deep Feature Networks Using Channel Importance Propagation","authors":"Honglin Chen, Chunting Li","doi":"10.1109/ICCSMT54525.2021.00080","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00080","url":null,"abstract":"Deep convolutional neural networks use their powerful feature representation capability to extract deep information of the targets, which is conducive to the improvement of model accuracy. However, its model is more complex, with a heavier computational burden and greater demand on computational and memory resources, which affects the real-time performance and lightness of the model. To address the above limitations of deep convolutional neural networks, we define a new metric for measuring the importance of convolutional kernels in conjunction with feature maps, introduce a non-linear mapping function that maps feature maps to important convolutional kernels, propose a continuous and smooth pruning strategy for deep convolutional neural networks, and obtain the Pruning deep feature networks using channel importance propagation model to reduce the complexity of the network and reduce the computational burden, and improve the accuracy and training efficiency of the model, while ensuring the feature network representation capability and the system performance loss is small. Our proposed model was tested on three datasets, CIFAR-10, CIFAR-100 and SVHN, and the test results demonstrated the validity of the model.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129734081","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: 2

Research on Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision 基于模糊决策的轻量级深度相关滤波跟踪算法研究

2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) Pub Date : 2021-11-01 DOI: 10.1109/ICCSMT54525.2021.00076

Chunting Li, Honglin Chen

{"title":"Research on Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision","authors":"Chunting Li, Honglin Chen","doi":"10.1109/ICCSMT54525.2021.00076","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00076","url":null,"abstract":"Deep correlation filter tracking method based on the fusion of correlation filter and deep convolutional neural network is one of the research hot topics in the field of visual object tracking. But how to choose an effective decision-making mechanism for implementing the online updating of feature network to fully adapt to the changes of target and environment in the tracking process is one of the key problems in the research of deep correlation filter tracking. It is obvious that the decision-making mechanism that only considers single factor can hardly meet the complex situation of the changes of target and environment. To address such an issue, this paper proposes a “Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision”. In the process of tracking, the cosine similarity based on Siamese network and the SSIM similarity both for the predicting tracking targets in two consecutive frames are calculated in real time. And then these two kinds of the similarity are fused together into the final similarity of the predicting tracking targets by full use of the fuzzy decision, which is taken as the criterion to determine whether the feature network needs updating and whether the tracking fails. When the feature network needs to be updated, the model is updated online while the tracking continues. In the case of tracking failure, the target is searched again, and the tracking is resumed. We tested the model on the OTB data set, and the experiments show that the tracking model designed in this paper can improve the tracking accuracy under the conditions of real-time tracking.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127961971","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

Research and application of Substation Fire Protection System based on big data 基于大数据的变电站消防系统研究与应用

2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) Pub Date : 2021-11-01 DOI: 10.1109/ICCSMT54525.2021.00116

Nan Cheng, Hailiang Wu, Zhong Liu, Yamin Wang, Wuchen Zhang, Jizhi Su

引用次数: 0

A Fast and Efficient Lines Matching Method via Multi-depth-layer Strategy 一种基于多深度层策略的快速高效线条匹配方法

2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) Pub Date : 2021-11-01 DOI: 10.1109/ICCSMT54525.2021.00084

Qiang Chen, Lingkun Luo, Jiyuan Cai, Shiqiang Hu

引用次数: 0

Multi-Relational Graph Convolutional Network Based on Relational Correlation for Link Prediction 基于关系关联的多关系图卷积网络链接预测

2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) Pub Date : 2021-11-01 DOI: 10.1109/ICCSMT54525.2021.00018

Lianhong Ding, Shengchang Gao

引用次数: 0

Threshold regression analysis is used to analyze the impact of computer software and information technology service industry agglomeration on manufacturing competitiveness 采用阈值回归分析方法，分析了计算机软件和信息技术服务业集聚对制造业竞争力的影响

2021 2nd International Conference on Computer Science and Management Technology (ICCSMT) Pub Date : 2021-11-01 DOI: 10.1109/ICCSMT54525.2021.00010

Hai-Feng Zhang, Yeqiu Wang

引用次数: 0