2019 6th International Conference on Dependable Systems and Their Applications (DSA)最新文献_第6页

Research on Industrial Software Testing Knowledge Database Based on Ontology 基于本体的工业软件测试知识库研究

2019 6th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2020-01-01 DOI: 10.1109/DSA.2019.00066

P. Chen, An Xi

引用次数: 3

FPGA Implementation of Stepping Correlation Acquisition Algorithm for Beidou II B1 Signal 北斗II号B1信号步进相关采集算法的FPGA实现

2019 6th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2020-01-01 DOI: 10.1109/DSA.2019.00049

Zhiming Hu, Bo Zhang, Jinjian Lu

引用次数: 1

Blind Identification of LDPC Code Based on Deep Learning 基于深度学习的LDPC码盲识别

2019 6th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2020-01-01 DOI: 10.1109/DSA.2019.00073

Yanqin Ni, Shengliang Peng, Lin Zhou, Xi Yang

引用次数: 10

Research on CPI Prediction Based on Space-Time Model 基于时空模型的CPI预测研究

2019 6th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2020-01-01 DOI: 10.1109/DSA.2019.00058

Songyan Ji, Jian Dong, Ye Wang, Yanxin Liu

引用次数: 2

FPGA Software Security Testing Excitation Random Generation Based on SFMEA and SFTA 基于SFMEA和SFTA的FPGA软件安全测试激励随机生成

2019 6th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2020-01-01 DOI: 10.1109/DSA.2019.00074

Ying-chao Wang, Wei Liu, Peng Chen, Cong Zhang, Chunjingming Li

引用次数: 0

Chinese Long Text Sentiment Analysis Based on the Combination of Title and Topic Sentences 基于标题句和主题句结合的中文长文情感分析

2019 6th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2020-01-01 DOI: 10.1109/DSA.2019.00053

Nan-chang Cheng, Y. He, Peixi Zhong, Yujia Wang, Yonglin Teng, Min Hou

{"title":"Chinese Long Text Sentiment Analysis Based on the Combination of Title and Topic Sentences","authors":"Nan-chang Cheng, Y. He, Peixi Zhong, Yujia Wang, Yonglin Teng, Min Hou","doi":"10.1109/DSA.2019.00053","DOIUrl":"https://doi.org/10.1109/DSA.2019.00053","url":null,"abstract":"The long text in our study involves a complete discourse structure with a title and approximately 1500 Chinese characters within. Long text sentiment analysis has faced a lot of difficulties on the grounds that a long text often comprises multiple sentiments and diverse focuses. This paper proposes to analyze the sentiment of long texts based on the combination of title and topic sentence. Based on the fine-labeled texts, the analysis extracted the important features of topic sentences, such as location, feature words, degree of topic relevance and emotional words. Then two topic sentences were extracted from each text that can best represent the topic through weighted calculation of these multi-dimensional features. Next this paper combined the two topic sentences with the title to complete the sentiment analysis of the whole document. The topic sentence extraction method has achieved good results in the task of extracting and judging key emotional sentences of news in the 6th COAE (Chinese Opinion Analysis Evaluation) (2014), which shows that the method is effective. This method has been put into practice in the National Language Public Opinion Monitoring system, with an accuracy of 0.82.","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125661011","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

Study on Comprehensive Detection Technology of Insulation Defects in Cable Joints of Medium Voltage Distribution Network 中压配电网电缆接头绝缘缺陷综合检测技术研究

2019 6th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2020-01-01 DOI: 10.1109/DSA.2019.00036

Lu Chen, Xueyou Huang, Caigan Nie, Hui Liu

引用次数: 0

Research on Development Strategy of Test and Evaluation Assessment for Equipment Software 装备测试评估软件开发策略研究

2019 6th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2020-01-01 DOI: 10.1109/DSA.2019.00059

Yang Liu, Youchun Zhang, Yu Zhu, Yunfeng Lu, Tengfei Shi

{"title":"Research on Development Strategy of Test and Evaluation Assessment for Equipment Software","authors":"Yang Liu, Youchun Zhang, Yu Zhu, Yunfeng Lu, Tengfei Shi","doi":"10.1109/DSA.2019.00059","DOIUrl":"https://doi.org/10.1109/DSA.2019.00059","url":null,"abstract":"How to check and assess the whole process of Test and Evaluation on equipment software is one of the major challenges faced by the development of equipment software at present. In order to solve above problems, this paper proposed the development strategies of Test and Evaluation assessment for equipment software in terms of the overall design, data-driven, assessment system and method construction. Then the process of Test and Evaluation assessment on equipment software was checked to verify the development ability, design and organization ability. Therefore, the assessment loop of quality for equipment software as well as the assessment loop of design, development, and stereotyping for equipment software could be gotten through. In this way, the assessment of Test and Evaluation provides supports and traction for equipment software. Moreover, the development strategy of Test and Evaluation assessment on equipment software were made for the operational and in-service usage, which could finally and greatly improve the quality and operational effectiveness of equipment software.","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128988489","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

The Design of Automatic Correction System for Spoken English Pronunciation Errors Based on Phonemes 基于音位的英语口语语音错误自动纠错系统设计

2019 6th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2020-01-01 DOI: 10.1109/DSA.2019.00038

Yang Xin

引用次数: 1

A Comparative Evaluation of AutoEncoder-Based Unsupervised Anomaly Detection Methods Applied on Space Payload 基于自编码器的空间载荷无监督异常检测方法的比较评价

2019 6th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2020-01-01 DOI: 10.1109/DSA.2019.00041

Junrong Du, Lei Song, Taisheng Zheng, Lili Guo, Chao Ma

{"title":"A Comparative Evaluation of AutoEncoder-Based Unsupervised Anomaly Detection Methods Applied on Space Payload","authors":"Junrong Du, Lei Song, Taisheng Zheng, Lili Guo, Chao Ma","doi":"10.1109/DSA.2019.00041","DOIUrl":"https://doi.org/10.1109/DSA.2019.00041","url":null,"abstract":"The anomaly detection technology is the basis for ensuring the safe and stable operation of the on-rail payload. The traditional threshold-based anomaly detection method has low accuracy and poor flexibility, and cannot detect abnormalities in real time. In addition, due to the lack of abnormal samples, the distribution of positive and negative samples is extremely imbalanced, which increases the difficulty of abnormal detection. Therefore, this paper proposes an unsupervised learning method based on AutoEncoder and its variants, the Basic AutoEncoder, Deep AutoEncoder and Sparse AutoEncoder are used to verify the algorithm on three public datasets. And using the above three algorithms to carry out the case application on the real load dataset. The experiments show whether in the public dataset or the real data of the payload, the three methods of AutoEncoder have achieved good results, proving the AutoEncoder and its variants have a good application in anomaly detection. At the same time, it is verified that the three algorithms have different effects on different datasets, which proves that the AutoEncoder with different characteristics need to be selected in different scenarios.","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131760861","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