The 2nd International Conference on Computing and Data Science最新文献_第2页

Tourism English Translation System Based on Fuzzy Clustering Algorithm 基于模糊聚类算法的旅游英语翻译系统

The 2nd International Conference on Computing and Data Science Pub Date : 2021-01-28 DOI: 10.1145/3448734.3450837

J. Cui

{"title":"Tourism English Translation System Based on Fuzzy Clustering Algorithm","authors":"J. Cui","doi":"10.1145/3448734.3450837","DOIUrl":"https://doi.org/10.1145/3448734.3450837","url":null,"abstract":"With the rapid development of economic globalization and the Internet, international exchanges and cooperation have become increasingly extensive and in-depth. Language differences have become the biggest obstacle to international communication and cooperation. The main research of this paper is the tourism English translation system based on fuzzy clustering algorithm. This system uses black box testing to test and verify system functions. Performance indicators mainly include response time, throughput, and so on. Complete the performance test of the system by checking the monitoring points in the performance test cases. To confirm whether the basic performance requirements of the system are met, performance testing is very important. In this teaching system, the response time and throughput of the system are mainly tested. In the system performance test cases, the use cases are divided according to the number of online users, and the response time of the client is tested in each use case. If the indicator requirements are met, the use case is deemed qualified, otherwise it is unqualified, and the system defects are recorded according to the actual test structure. The data shows that the correct clustering data of C-FCA is further improved to 95% based on the better PCM algorithm. The results show that the fuzzy clustering algorithm can effectively improve the accuracy of the tourism English translation system.","PeriodicalId":105999,"journal":{"name":"The 2nd International Conference on Computing and Data Science","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123913215","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

Identification and Classification of Chinese Traditional Musical Instruments Based on Deep Learning Algorithm 基于深度学习算法的中国传统乐器识别与分类

The 2nd International Conference on Computing and Data Science Pub Date : 2021-01-28 DOI: 10.1145/3448734.3450836

P. Cao

引用次数: 1

Research on Multi-granularity Ensemble Learning Based on Korean 基于朝鲜语的多粒度集成学习研究

The 2nd International Conference on Computing and Data Science Pub Date : 2021-01-28 DOI: 10.1145/3448734.3450777

Jingxuan Jin, Yahui Zhao, Rong-yi Cui

引用次数: 2

Based on a prediction method for improving WOA-Elman air quality prediction 基于改进WOA-Elman空气质量预测方法的研究

The 2nd International Conference on Computing and Data Science Pub Date : 2021-01-28 DOI: 10.1145/3448734.3450773

Zhuang Chen, Dingwen Cai

引用次数: 1

Research on cutting quality prediction technology of aviation structural parts based on JAYA-GABP algorithm 基于JAYA-GABP算法的航空结构件切削质量预测技术研究

The 2nd International Conference on Computing and Data Science Pub Date : 2021-01-28 DOI: 10.1145/3448734.3450848

Yan-ge Ma, Z. Hou, Tingting Du

引用次数: 0

Target Detection Algorithm Based on MMW Radar and Camera Fusion 基于毫米波雷达与相机融合的目标检测算法

The 2nd International Conference on Computing and Data Science Pub Date : 2021-01-28 DOI: 10.1145/3448734.3450908

Shouyi Lu, Z. Bao, Yongshuai Zhi, Sumin Zhang

{"title":"Target Detection Algorithm Based on MMW Radar and Camera Fusion","authors":"Shouyi Lu, Z. Bao, Yongshuai Zhi, Sumin Zhang","doi":"10.1145/3448734.3450908","DOIUrl":"https://doi.org/10.1145/3448734.3450908","url":null,"abstract":"With the increasing demand of consumers for car safety and comfort, intelligent driving technology has received extensive attention and research. A robust and reliable vehicle detection and tracking system is one of the key modules for intelligent vehicles to perceive the surrounding environment. At present, the mass-produced intelligent driving system mainly uses millimeter-wave radar and monocular camera to detect and track vehicles. Despite their advantages, the drawbacks of these two sensors make them insufficient when used separately. Therefore, in order to improve the target detection ability of the system, the research on multi-sensor information fusion is particularly important. This paper presents an information fusion approach based on a millimeter-wave radar and a monocular camera. Firstly, the millimeter-wave radar and monocular camera are used for target detection separately. Then the target data is extracted and recognized. Finally, the same target data detected by the two sensors are fused at the decision level. The proposed algorithm is tested on typical urban roads and urban expressways. Experiments show that the proposed algorithm can effectively improve the stability and reliability of target recognition and reduce the probability of missing target detection.","PeriodicalId":105999,"journal":{"name":"The 2nd International Conference on Computing and Data Science","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129987664","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

MADDPG-based Task Offloading and Resource Management for Edge System 基于madpg的边缘系统任务卸载与资源管理

The 2nd International Conference on Computing and Data Science Pub Date : 2021-01-28 DOI: 10.1145/3448734.3450782

Haojie Lin, Wenjing Hou, Hong Wen, Wenxin Lei, Sihui Wu, Zhiwei Chen

引用次数: 1

Anomalous State Detection of Power Transformer Based on K-Means Clustering Algorithm 基于k均值聚类算法的电力变压器异常状态检测

The 2nd International Conference on Computing and Data Science Pub Date : 2021-01-28 DOI: 10.1145/3448734.3450907

Hai-Jun Luo, Bo Gao, Qingping Zhang, Hongwei Han, Junyu Guo, Xuefeng Li

{"title":"Anomalous State Detection of Power Transformer Based on K-Means Clustering Algorithm","authors":"Hai-Jun Luo, Bo Gao, Qingping Zhang, Hongwei Han, Junyu Guo, Xuefeng Li","doi":"10.1145/3448734.3450907","DOIUrl":"https://doi.org/10.1145/3448734.3450907","url":null,"abstract":"As an important hub equipment of power system, the safe and stable operation of transformer is the top priority to ensure the continuous supply of high-quality electric energy and the normal operation of social life. The state estimation of the transformer is the key to the operation state maintenance method. The existing transformer state estimation methods mainly use gas content and other data, but can not use the massive transformer electrical quantity monitoring data accumulated in the monitoring system. Therefore, a k-means clustering method for transformer state anomaly detection based on voltage, current and power data of transformer is proposed. Firstly, based on the monitoring data of transformer with normal maintenance history, a state detection model based on K-means clustering is constructed. Then, according to the clustering results of historical normal data, the appropriate threshold is selected, and the distance between the new data and each cluster center is analyzed to judge the operation status of the transformer. Finally, the correctness of the model is verified by an example. The results show that the proposed method can make full use of the electrical data of the transformer and realize the real-time detection of the transformer state, which is convenient for engineering application.","PeriodicalId":105999,"journal":{"name":"The 2nd International Conference on Computing and Data Science","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130077617","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

New Short Binary Sequence Test Algorithm: First Interval Test 一种新的短二值序列检验算法:第一区间检验

The 2nd International Conference on Computing and Data Science Pub Date : 2021-01-28 DOI: 10.1145/3448734.3450878

Jinchi Li, Sheng Lin

{"title":"New Short Binary Sequence Test Algorithm: First Interval Test","authors":"Jinchi Li, Sheng Lin","doi":"10.1145/3448734.3450878","DOIUrl":"https://doi.org/10.1145/3448734.3450878","url":null,"abstract":"The randomness test is particularly important in the evaluation of encryption algorithms. The ciphertext generated by the encryption algorithm should have a certain degree of randomness, otherwise the security of the algorithm cannot be guaranteed. Besides, block cipher algorithm and hash function algorithm cannot generate a long binary sequence, up to 512 bits, and the detection results of traditional detection algorithms are not ideal when detecting short fixed length sequences. Therefore, a new detection algorithm is proposed for the randomness of short binary sequences, which is named as the First Interval Test. It focuses on checking whether the length of the interval between the first element and its nest position is random, using the first interval, FI, as a new statistic, and transforming the detection sequence, so that this algorithm can be applied to the randomness detection of integer sequences and binary sequences. The specific probability distribution at each length is given, the parameter selection of the algorithm is defined, and the real p-value distribution is derived to further apply the chi-square goodness of fit test. Taking the correlation between plaintext and ciphertext as the standard, a comparative experiment was carried out on block cipher algorithm and hash function algorithm. The results show that the First Interval Test improves the accuracy of the randomness test.","PeriodicalId":105999,"journal":{"name":"The 2nd International Conference on Computing and Data Science","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133893601","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

Weakly supervised mitosis detection using ellipse label on attention Mask R-CNN 基于椭圆标记的弱监督有丝分裂检测

The 2nd International Conference on Computing and Data Science Pub Date : 2021-01-28 DOI: 10.1145/3448734.3450486

Xiaoxue Liu, Xinwei Li, Wei Zhang, Peng Ran, Bing-qing Zhang, Zhangyong Li

引用次数: 0