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2021 2nd International Conference on Computing and Data Science (CDS)最新文献

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Research on Motion Modeling and Control of Tracking Car Based on Neural Network 基于神经网络的履带车运动建模与控制研究
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00056
Jing Qiao
{"title":"Research on Motion Modeling and Control of Tracking Car Based on Neural Network","authors":"Jing Qiao","doi":"10.1109/CDS52072.2021.00056","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00056","url":null,"abstract":"In order to solve the problem of path recognition, speed management and tracking control, BP neural network was used to build the motion model of the tracking car. BP neural network has strong nonlinear fitting ability and learning ability. It obtained different connection weight parameters according to different training sets and made the model more simple, accurate and universal. Based on the BP neural network model of the tracking car, the tracking strategy with the fuzzy control algorithm was proposed. Experiment results showed that this method improved the stability and robustness of the intelligent tracking car. Moreover, the BP neural network we used has strong generalization ability and can be applied to different modeling environments.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129369995","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
Satellite based analysis on covariation of air pollution and human activities in Guangdong during COVID-19 lockdown 基于卫星的广东封城期间空气污染与人类活动共变分析
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00110
Zu-Ren Li
{"title":"Satellite based analysis on covariation of air pollution and human activities in Guangdong during COVID-19 lockdown","authors":"Zu-Ren Li","doi":"10.1109/CDS52072.2021.00110","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00110","url":null,"abstract":"The relationship between air pollution emissions and economic development has been a research hot spot in the last few decades. Lockdowns caused by novel coronavirus provide an unprecedented opportunity to deepen our understanding to the possible mechanism of the covariation between human economical activities and air pollutant events. In this paper, satellite derived aerosol optical depth (AOD) and Nighttime light (NTL) data were used as agencies for air pollution and economic activities respectively. The results show that NTL in Feb. 2020 is lower than the same month of previous year in most of the eight cities of Guangdong Province, Northern China. That indicates lower NTL may due to less activities of social activities, tourism and industrial production because of lockdown during COVID-19 pandemic. Meanwhile, AOD also sharply decreases in Feb. 2020. And, both trends of NTL and AOD turned over as soon as March 2020. The correlations between AOD and NTL of these eight cities are quite different from each other, somehow them are more correspondingly close to 1 during COVID-19 from Dec. 2019 to Feb. 2020. That may suggest solid covariation between AOD and NTL, and may indicate human economic activities may be one of the reasons leading to air pollution.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129259251","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
Improvement in Multi-Person 2D Pose Estimation: Applying Polar Representation in OpenPose 多人2D姿态估计的改进:在OpenPose中应用极坐标表示
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00061
Weixi Cai
{"title":"Improvement in Multi-Person 2D Pose Estimation: Applying Polar Representation in OpenPose","authors":"Weixi Cai","doi":"10.1109/CDS52072.2021.00061","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00061","url":null,"abstract":"Recent pose machines provide a relative accurate estimation in 2D real-time multi-person situations. In this work, we demonstrate an advanced open-pose design with a sequential stages of prediction and use of polar coordinate system. The main contribution of this paper is to denote a pose machine frame work based on the available open-pose model, which performs improvement in both efficiency and accuracy in image-dependent spatial models learning. We achieve this by considering additional information of image features with both a sequential structure of convolutional networks and the support of part affinity fields, as well as the advantages of using polar coordinate system, which efficiently predicting accurate estimates in multi-person cases. Our approach characterizes how the concept of part affinity fields can be used in key points connection. We perform competing methods on standard data sets including COCO data set, compare our result with several bottom-up approach and illustrate the result in straightforward ways.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133929727","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
Proceedings 2021 2nd International Conference on Computing and Data Science CDS 2021 - Title page 第二届计算与数据科学国际会议文集2021 -标题页
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/cds52072.2021.00001
{"title":"Proceedings 2021 2nd International Conference on Computing and Data Science CDS 2021 - Title page","authors":"","doi":"10.1109/cds52072.2021.00001","DOIUrl":"https://doi.org/10.1109/cds52072.2021.00001","url":null,"abstract":"The proceedings contain 103 papers. The topics discussed include: distantly supervision for relation extraction via LayerNorm gated recurrent neural networks;model optimization techniques for embedded artificial intelligence;a survey on single image deblurring;prediction of house price index based on machine learning methods;review of deep learning-based approaches for COVID-19 detection;comparative unsupervised clustering approaches for customer segmentation;technical change and development trend of automatic driving;target localization method based on virtual anchor nodes of one single unmanned aerial vehicle;one improved OpponentSIFT and HOPDFG descriptor based image classification algorithm;and business intelligence based novel marketing strategy approach using automatic speech recognition and text summarization.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129655828","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
Review on Application of Intelligent Mining Technology in Safety 智能采矿技术在安全中的应用综述
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00046
Xiang Li
{"title":"Review on Application of Intelligent Mining Technology in Safety","authors":"Xiang Li","doi":"10.1109/CDS52072.2021.00046","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00046","url":null,"abstract":"There are still many accidents that cause casualties in the coal industry every year. Intelligent mining technology can not only reduce labor intensity, reduce environmental pollution and improve mining efficiency in the current application scenarios, but also play a significant role in the mining safety. This paper summarized the role of intelligent mining technology on the safety risk reduction, specifically how to reduce the probability of occurrence and consequences of risks from the perspective of unsafe human behaviors and unsafe state of affairs. Finally, the paper discussed the limitations of this technology in the current practical application, including the lack of technical personnel and insufficient automation degree of equipment, and proposed some suggestions on the future development of this technology.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130869903","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
MT-ResNet: A Multi-Task Deep Network for Facial Attractiveness Prediction MT-ResNet:面部吸引力预测的多任务深度网络
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00015
Jiankai Xu
{"title":"MT-ResNet: A Multi-Task Deep Network for Facial Attractiveness Prediction","authors":"Jiankai Xu","doi":"10.1109/CDS52072.2021.00015","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00015","url":null,"abstract":"Facial attractiveness prediction (FAP) is an intriguing and challenging problem that draws attention of researchers in recent years. Unlike other objective computer vision topics such as face detection, FAP also involves deep facial feature extraction and attractiveness pattern recognition which is relatively subjective. The work of FAP requires both mass collection of people's appreciations of beauty and the learning, replication of people's aesthetic standards by the model. Work regarding FAP in the early stage focuses on representing facial features using machine learning algorithms. In recent years, neutral networks, especially convolutional neural networks show its great performance in related areas. In this paper, a multi-task FAP model, MT-ResNet is proposed which could automatically predict the facial attractiveness score and the gender given a portrait. The results are compared with other existing models, which shows MT-ResNet's efficiency and high-accuracy among similar works.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"38 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114290746","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: 4
The Application of Using Convolutional Neural Network to Classify MRI Brain Tumor 卷积神经网络在MRI脑肿瘤分类中的应用
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00075
Qiqi Liu
{"title":"The Application of Using Convolutional Neural Network to Classify MRI Brain Tumor","authors":"Qiqi Liu","doi":"10.1109/CDS52072.2021.00075","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00075","url":null,"abstract":"Brain tumor is a severe disease that requires accurate classification before giving treatment. As traditional diagnosing is time consuming and has a great reliance on experience, the deep learning method is recommended for classifying brain tumor. Deep learning is a newly developed technology that is commonly applied in image recognition field. Many successful applications of deep learning surely increased the efficiency and accuracy of the classification procedure. Deep learning requires sufficient datasets to train the computer, but for brain tumor usually we don't have enough data to input. This paper mainly discussed two methods-data augmentation, and transfer learning to deal with this issue. This paper focused on introducing the types and the effect of data augmentation, then presenting different kinds of transfer learning skills which are applied in different circumstances. Results shows that both transfer learning and data augmentation are advantageous for training the deep convolutional neural network model","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117151150","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 Backward Collision Warning and Avoidance System when On-street Parking Using LiDAR 基于激光雷达的道路停车倒车预警与避免系统设计
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00048
Qizhang Shen
{"title":"Design of Backward Collision Warning and Avoidance System when On-street Parking Using LiDAR","authors":"Qizhang Shen","doi":"10.1109/CDS52072.2021.00048","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00048","url":null,"abstract":"In the past decades, Collision Avoidance System has grown to a level more developed than it has ever been, actively and significantly improving the safety of passengers of cars. However, it only focused on the period of time when the vehicles are driving, and ignored the moment when passengers are most vulnerable - when they are getting off the car as their attention to the surrounding drops and are no longer protected by the structure of the vehicle. Therefore, in this paper, an additional system is proposed to warn the passengers to open the doors and get off with caution, and even interfere to stop them getting off temporarily.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122528407","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
Deontic Rule of Rule-Based Service Choreographies 基于规则的服务编排的道义规则
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00094
Nor Najihah Zainanl Abidin, Nurulhuda A. Manaf, S. Moschoyiannis, Nur Amalina Jamaludin
{"title":"Deontic Rule of Rule-Based Service Choreographies","authors":"Nor Najihah Zainanl Abidin, Nurulhuda A. Manaf, S. Moschoyiannis, Nur Amalina Jamaludin","doi":"10.1109/CDS52072.2021.00094","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00094","url":null,"abstract":"Service choreography describes the interaction across different participating services capturing the ordering constraints of global message exchanges. Semantics of Business Vocabulary and Rules (SBVR) model, an Object-Oriented Management (OMG) standard is proposed to specify services interaction (service choreographies), The specification focuses on a deontic rule expressing both prohibition and obligation which can be progressively helpful in working with coordinating service interactions. Then Alloy Analyzer, a relational constraint solver, is used to transform a developed service choreographies, an SBVR model into an Alloy model. This transformation allows to generate and to verify, the choreography for the developed SBVR model and the realisability of the generated choreography, automnatically.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131989814","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
Deep Spatio-Temporal Convolutional Neural Network for City Traffic Flow Prediction 基于深度时空卷积神经网络的城市交通流预测
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00037
Zhiyuan Zhou, Yanjun Qin, Haiyong Luo
{"title":"Deep Spatio-Temporal Convolutional Neural Network for City Traffic Flow Prediction","authors":"Zhiyuan Zhou, Yanjun Qin, Haiyong Luo","doi":"10.1109/CDS52072.2021.00037","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00037","url":null,"abstract":"Forecasting transportation flow is of vital significance for relieving traffic congestion and improving public safety. However, it is very challenging to achieve it precisely because many factors such as weather condition, traffic control and big celebration events can lay great influence on it. To better fulfill this challenging task, we propose a deep-learning-based approach called Spatio-Temporal Convolutional Neural Network. We first model three temporal properties of transportation flow (closeness, period, trend). Each property is assigned with a convolutional neural network, each of which models the corresponding property of public traffic. This model also fuses the aggregation of the output of the three properties with external elements, for example weather condition and some big events, to gain a better performance in citywide traffic flow prediction. Experiments on Beijing taxi flow and the New York city bike flow show that our ST-CNN model outperforms many well-known passenger flow prediction methods.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128677986","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
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