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Proceedings of the 5th International Conference on Computer Science and Application Engineering最新文献

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An Intelligent Lane Changing Decision Method for Connected Vehicles 网联车辆智能变道决策方法
Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487175
Zheyu Cui, Jianming Hu
{"title":"An Intelligent Lane Changing Decision Method for Connected Vehicles","authors":"Zheyu Cui, Jianming Hu","doi":"10.1145/3487075.3487175","DOIUrl":"https://doi.org/10.1145/3487075.3487175","url":null,"abstract":"Aiming at the problem of driving scenarios redundancy in the lane change decision-making, this paper proposes a deep reinforcement learning method (DRL) for lane change decision based on embedded attention mechanism (CADQN). The algorithm introduces the Convolutional Attention Mechanism Module (CBAM) into the DQN network to optimize the scenarios in time and space dimensions, and assist connected vehicles in making lane changing decisions. The algorithm is verified by the traffic simulation platform under the highway environment, and the results show that CADQN is helpful to improve the global traffic efficiency, and with the increase of traffic flow density, the benefit is more significant. Moreover, the visualization results of the attention layer in the CADQN can guide the optimization of the driving scenario.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116002624","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
Sales Demand Prediction Model of Gated Recurrent Unit Neural Network Based on Improved Shape Distance Loss Function 基于改进形状距离损失函数的门控循环单元神经网络销售需求预测模型
Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487139
H. Lou, Zhiwei Zhang, Baihui Zha
{"title":"Sales Demand Prediction Model of Gated Recurrent Unit Neural Network Based on Improved Shape Distance Loss Function","authors":"H. Lou, Zhiwei Zhang, Baihui Zha","doi":"10.1145/3487075.3487139","DOIUrl":"https://doi.org/10.1145/3487075.3487139","url":null,"abstract":"Under the background of diversification and refinement of chemical products, product demand prediction is playing a guiding role in production planning. In this paper, a new sales demand prediction model based on improved shape distance Loss function of Gated Recurrent Unit Neural Network (ISD_GRUNN) is proposed for the long-term prediction of the sales quantity of chemical products. The improved shape distance is determined by the change trend, amplitude and distance between the two points. Compared with MSE which only considers the difference between the corresponding time point sequence values as the loss function, the change trend and range of the time series will be taken into account in the improved shape distance as the loss function. The experimental results show that the improved shape distance as the loss function can be better used for sales long-term prediction.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126397381","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 and Implementation of a Remote Diagnosis System for Congenital Heart Disease Based on DjangoRestFramework 基于DjangoRestFramework的先天性心脏病远程诊断系统的设计与实现
Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487123
Hefei Tan, Rong Zong, Hongbo Yang, T. Guo, Pengfei Yu
{"title":"Design and Implementation of a Remote Diagnosis System for Congenital Heart Disease Based on DjangoRestFramework","authors":"Hefei Tan, Rong Zong, Hongbo Yang, T. Guo, Pengfei Yu","doi":"10.1145/3487075.3487123","DOIUrl":"https://doi.org/10.1145/3487075.3487123","url":null,"abstract":"Congenital heart disease, referred to as CHD, is a disease that seriously harms the health of children and adolescents. The earlier the treatment, the better the effect. The distribution of domestic medical resources is uneven, and the level of medical staff's auscultation is uneven, and it is impossible to provide accurate initial diagnosis results for patients with congenital heart disease. Many remote hospitals are not equipped with cardiac color ultrasound equipment and cannot make a final diagnosis of congenital heart disease. Therefore, the research integrates DjangoRestFramework and other framework technologies to design a set of congenital heart disease database management and remote auscultation system, adopting a front-end and back-end separation architecture to achieve low coupling and high cohesion procedures, which is convenient for later maintenance and management of the system. The system includes modules such as personnel management, collection information management, data statistics visualization, and uses wavelet to denoise the signals collected by Bluetooth heart sound devices. The algorithm effectively filters out environmental noises and physiological noises such as patient breathing, making the doctor's remote auscultation effect consistent with the effect of using a stethoscope. After the stress test, the system works well and can provide remote diagnosis services for congenital heart disease over the Internet for people in economically underdeveloped areas.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128083364","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
Analysis of Frequency Domain Characteristics of Crosstalk for Train Network Control Communication Cable 列车网络控制通信电缆串扰频域特性分析
Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487180
Yang Liu, Changxiang Li, Bozhen Ma
{"title":"Analysis of Frequency Domain Characteristics of Crosstalk for Train Network Control Communication Cable","authors":"Yang Liu, Changxiang Li, Bozhen Ma","doi":"10.1145/3487075.3487180","DOIUrl":"https://doi.org/10.1145/3487075.3487180","url":null,"abstract":"Aiming at crosstalk problem of communication cable in train network control, mechanism of crosstalk generation is analyzed based on multi-conductor transmission lines (MTL) theory, and calculation method of distributed parameter of shielding cable in homogeneous dielectric is given. Frequency domain method of generalized two-port network is used. Frequency domain solution of the coupling response in the cable terminal is derived by solving shielded twisted pair (STP) chain-parameter matrix. Finally, influence factors of crosstalk is simulated. The results show that crosstalk is inversely proportional to the distance between lines and is directly proportional to the height from the ground and the coupling length, and the amplitude of variation increases significantly when the frequency is greater than 80MHz. For shielding layer, double-end grounding has the strongest anti interference ability. Single-end grounding has a significant inhibitory effect on low frequency interference, but has limited suppression of high-frequency interference. Double-end floating has a worse anti interference effect.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"261 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114014718","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
Implicit Discourse Relation Classification Based on Semantic Graph Attention Networks 基于语义图注意网络的隐式语篇关系分类
Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487156
Yuhao Ma, Yuan Yan, Jie Liu
{"title":"Implicit Discourse Relation Classification Based on Semantic Graph Attention Networks","authors":"Yuhao Ma, Yuan Yan, Jie Liu","doi":"10.1145/3487075.3487156","DOIUrl":"https://doi.org/10.1145/3487075.3487156","url":null,"abstract":"Theimplicit discourse relation classification is of great importance to discourse analysis. It aims to identify the logical relation between sentence pair. Compared with the linear network model, the graph neural network has a more complex structure to capture cross-sentence interactions. Therefore, this article proposes a semantic graph neural network for implicit discourse relation classification. Specifically, we design a semantic graph to describe the syntactic structure of sentences and semantic interactions between sentence pair. Then, convolutional neural network (CNN) with different convolutional kernels to extract the multi-granularity semantic features. The experimental results on Penn Discourse TreeBank 2.0 (PDTB 2.0) prove that our work performed well.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133242885","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
Object Tracking Algorithm Based on Channel-interconnection-spatial Attention Mechanism and Siamese Region Proposal Network 基于通道-互联-空间注意机制和暹罗区域建议网络的目标跟踪算法
Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487120
Junchang Zhang, Siqi Lei
{"title":"Object Tracking Algorithm Based on Channel-interconnection-spatial Attention Mechanism and Siamese Region Proposal Network","authors":"Junchang Zhang, Siqi Lei","doi":"10.1145/3487075.3487120","DOIUrl":"https://doi.org/10.1145/3487075.3487120","url":null,"abstract":"The target tracking algorithm based on the Siamese network has become one of the most mainstream and best tracking algorithms because of the balance of accuracy and speed. However, target tracking algorithms based on the Siamese network are affected by factors such as occlusion, illumination changes, motion changes, size changes and other factors in natural scenes, making designing a robust tracking algorithm a challenging task. In order to improve the feature extraction and discrimination capabilities of the algorithm in complex scenes, a tracking algorithm combining channel-interconnection-spatial attention mechanism was proposed. First a Siamese tracking framework with a deep convolutional network ResNet-50 as the backbone network was built to enhance feature extraction capabilities, then the channel-interconnection-spatial attention module was integrated to enhance the adaptability and discrimination capabilities of the model, then the multi-layer response maps were weighted and fused to make results more accurate, and finally the largescale datasets were used to train the network, and tracking tests on the benchmark OTB-2015 and VOT2016 and VOT2018 were completed. The experimental results show that the proposed algorithm is more robust and better adapt to complex scenes such as target appearance changes, similar distractors, and occlusion than the current mainstream.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131689503","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
Multi Scale Attention Network for Crowd Counting 人群计数的多尺度注意网络
Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487097
Xiangpeng Yang, Xiaobo Lu
{"title":"Multi Scale Attention Network for Crowd Counting","authors":"Xiangpeng Yang, Xiaobo Lu","doi":"10.1145/3487075.3487097","DOIUrl":"https://doi.org/10.1145/3487075.3487097","url":null,"abstract":"Reasonable management and control of extra crowded scenes have become a hot topic in recent years. Counting people from density map generated from the object location annotations is an effective way to analyze crowd information and control crowds in severely congested scenes. In this paper, we propose a novel end-to-end crowd counting method called MSANet for crowd counting. MSANet consists of the VGG16 backbone as the fronted part, two branches as the back-end part, including the attention map extractor to predict crowd states (means with people or not), and density map branch to regress the density map. What is more, to obtain high-resolution density map, we combine different scale maps from the front part to the back-end part. On the design of the loss function, to enhance the resolution of the predicted map and its structural similarity to ground truth, we proposed a new loss function for crowd counting. The test result based on the public dataset ShanghaiTech and Subway Crowd Counting Dataset supported by the Nanjing Metro demonstrates the effectiveness of our method.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128995744","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
A Dual-branch Graph Convolutional Network on Imbalanced Node Classification 非平衡节点分类的双分支图卷积网络
Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487162
Xiaoguo Wang, Jiali Chen
{"title":"A Dual-branch Graph Convolutional Network on Imbalanced Node Classification","authors":"Xiaoguo Wang, Jiali Chen","doi":"10.1145/3487075.3487162","DOIUrl":"https://doi.org/10.1145/3487075.3487162","url":null,"abstract":"Graph convolutional neural networks (GCNs) have attracted much attention in dealing with various node classification tasks on graphs. Some real-world node classification tasks face the situation that the number of minority class nodes is significantly less than that of majority class nodes. This makes us more concerned about how to effectively solve the problem of imbalanced node classification based on GCNs. To solve this problem, we propose a Dual-branch Graph Convolutional Network framework (D-GCN), which can reduce the dominant effect of majority class on topology aggregation and the negative impact of information differences caused by graph structure reconstruction. This framework achieves the goal of decreasing the possibility of misrecognizing the minority class nodes as majority class and improving the classification performance of minority class nodes. Experiments on several graph datasets demonstrate that D-GCN outperforms representative baselines in solving imbalanced node classification tasks.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133764793","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: 3
A Principal Component Analysis and Deep Back-Propagation Neural Network-based Approach to Gasoline Quality Prediction 基于主成分分析和深度反向传播神经网络的汽油质量预测方法
Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487129
Zihao Wang, Huawen Yang, Liang Chen, Wen-Xin Chen
{"title":"A Principal Component Analysis and Deep Back-Propagation Neural Network-based Approach to Gasoline Quality Prediction","authors":"Zihao Wang, Huawen Yang, Liang Chen, Wen-Xin Chen","doi":"10.1145/3487075.3487129","DOIUrl":"https://doi.org/10.1145/3487075.3487129","url":null,"abstract":"This paper proposes an approach that combines principal component analysis with a Deep Back-Propagation Neural Network model to solve high-latitude prediction problems. The approach is applied to establish a product quality prediction model for gasoline refinement. The simulation results have demonstrated effectiveness of the approach.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133396447","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
Data Matrix Completion Based on Pattern Classification 基于模式分类的数据矩阵补全
Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487091
Siyuan Lu, Xiaolan Tang, Yu Liu, Wenlong Chen
{"title":"Data Matrix Completion Based on Pattern Classification","authors":"Siyuan Lu, Xiaolan Tang, Yu Liu, Wenlong Chen","doi":"10.1145/3487075.3487091","DOIUrl":"https://doi.org/10.1145/3487075.3487091","url":null,"abstract":"In recent years, with the rapid development of big data technology, the matrix completion is often used for data recovery, and how to improve the accuracy of matrix completion is a key issue. This paper proposes a matrix completion method based on pattern classification, called PCRE, to improve data recovery performance. Since the hidden similarity within the data is a significant factor affecting the overall performance, the method PCRE uses non-negative matrix decomposition to extract the patterns of the data and accordingly rearranges the data matrix to fit for the matrix completion. Experiments are conducted by using PM 10 monitoring data collected by 34 sensors in Beijing in 2019 (totally 351 days). The results show that, compared with existing methods, PCRE improves the accuracy of data recovery with a shorter computation time.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125172396","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
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