2022 9th International Conference on Dependable Systems and Their Applications (DSA)最新文献_第9页

A Survey of Autonomous Driving Scenarios and Scenario Databases 自动驾驶场景与场景数据库研究综述

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00107

Hongping Ren, Hui Gao, He Chen, Guangzhen Liu

{"title":"A Survey of Autonomous Driving Scenarios and Scenario Databases","authors":"Hongping Ren, Hui Gao, He Chen, Guangzhen Liu","doi":"10.1109/DSA56465.2022.00107","DOIUrl":"https://doi.org/10.1109/DSA56465.2022.00107","url":null,"abstract":"With the development of autonomous driving technology, traditional road testing methods can no longer meet the needs of autonomous driving testing. These methods lack sufficiency, comprehensiveness and efficiency. Using the autonomous driving scenario databases for testing can greatly shorten the test time and cost, and can improve the safety and reliability of the test. By systematically sorting out a large number of related publications, this paper summarizes the research progress of autonomous driving scenarios and scenario databases. The article firstly compares and analyzes the different definitions of autonomous driving scenarios, clarifies the connotation of the scenarios, summarizes the types of elements of the scenarios, and introduces the scenario layered model; secondly, we outline the description standards of scenario. We mainly summarize the two scenario data formats, OpenDRIVE and OpenSCENARIO, which are commonly used in the world. Thirdly, the scenario data collection and research work carried out at home and abroad is reviewed from the perspective of scenario data sources, and different datasets are compared; In addition, the definition of the scenario database, the construction process of the scenario database and the typical scenario databases are summarized; Finally, the problems and future development trends of autonomous driving scenarios and scenario databases are discussed and prospected.","PeriodicalId":208148,"journal":{"name":"2022 9th International Conference on Dependable Systems and Their Applications (DSA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115312849","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

An Ontology-based Knowledge Base System for Military Software Testing 基于本体的军用软件测试知识库系统

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00043

Chiyang Gao, Wenbing Luo, Fei Xie

引用次数: 0

Intelligent Mineral Identification and Classification based on Vision Transformer 基于视觉变压器的智能矿物识别与分类

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00095

Xiaobo Cui, Cheng Peng, Hao Yang

{"title":"Intelligent Mineral Identification and Classification based on Vision Transformer","authors":"Xiaobo Cui, Cheng Peng, Hao Yang","doi":"10.1109/DSA56465.2022.00095","DOIUrl":"https://doi.org/10.1109/DSA56465.2022.00095","url":null,"abstract":"Image recognition technology in deep learning plays a vital role in many research fields. The intelligent recognition of mineral images based on deep learning technology brings new ideas for the development of traditional fields. It improves the efficiency of identification and brings some economic benefits. This paper proposes an intelligent mineral recognition classification model based on Vision Transformer. Firstly, More than 2000 images of twelve minerals, such as biotite, bornite, phoenix, and quartz, were collected. The data set was expanded by the data enhancement method, which was used to train and test the model, and the model's generalization ability was enhanced. Secondly, a self-attentive mechanism is introduced for feature extraction, and a new activation function is used to optimize the convergence speed of the model further. In the end, the accuracy of this model on the test set Top-1 reached 96.08 %, and the F1 score was 95.40 %. Compared with the network models such as ResNet50, VGG16, and DenseNet, the proposed model's recognition accuracy is higher, and the recognition stability is also better. According to the analysis of the experimental results, the pre-processing of the data also has a particular influence on the accuracy of the model, which provides an essential reference for the subsequent intelligent recognition and classification of minerals.","PeriodicalId":208148,"journal":{"name":"2022 9th International Conference on Dependable Systems and Their Applications (DSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129026165","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

FastTransLog: A Log-based Anomaly Detection Method based on Fastformer FastTransLog:基于Fastformer的基于日志的异常检测方法

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00065

Yidan Wang, Xuge Li

引用次数: 0

Weld Image Recognition based on Deep Learning 基于深度学习的焊缝图像识别

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00041

Yaling Zhao, H. Du, Hai Wang, Chunlai Yang, Yongmin Liu, L. Wang, Manman Xu, Jingsong Gui, Tielong Tan, Xiangdong Wang

引用次数: 0

Software Trustworthiness Evaluation Method based on Relationships Between Criteria 基于准则间关系的软件可信度评价方法

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00060

H. Tao

{"title":"Software Trustworthiness Evaluation Method based on Relationships Between Criteria","authors":"H. Tao","doi":"10.1109/DSA56465.2022.00060","DOIUrl":"https://doi.org/10.1109/DSA56465.2022.00060","url":null,"abstract":"Software trustworthiness evaluation (STE) is regarded as a Multi-Criteria Decision Making (MCDM) problem that consists of criteria. However, most of the current STE do not consider the relationships between criteria. This paper presents a software trustworthiness evaluation method based on the relationships between criteria. Firstly, the relationships between criteria is described by cooperative and conflicting degrees between criteria. Then, a measure formula for the substitutivity between criteria is proposed and the cooperative degree between criteria is taken as the approximation of the substitutivity between criteria. With the help of the substitutivity between criteria, the software trustworthiness measurement model obtained by axiomatic approaches is applied to aggregate the degree to which each optional software product meets each objective. Finally, the candidate software products are sorted according to the trustworthiness aggregation results, and the optimal product is obtained from the alternative software products on the basis of the sorting results. The effectiveness of the proposed method is demonstrated through case study.","PeriodicalId":208148,"journal":{"name":"2022 9th International Conference on Dependable Systems and Their Applications (DSA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116460569","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

Testing Photometric Stereo Applications 测试光度立体应用

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00029

Ledio Jahaj, F. Wotawa

引用次数: 0

Automatic Bug Inference via Deep Image Understanding 基于深度图像理解的自动Bug推断

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00051

Shengcheng Yu, Wanmin Huang, Jingui Zhang, Haitao Zheng

引用次数: 0

Kernel Subspace Clustering based on Block Diagonal Representation and Sparse Constraints 基于块对角表示和稀疏约束的核子空间聚类

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00055

Lili Fan, Gui-Fu Lu, Ganyi Tang, Yong Wang

{"title":"Kernel Subspace Clustering based on Block Diagonal Representation and Sparse Constraints","authors":"Lili Fan, Gui-Fu Lu, Ganyi Tang, Yong Wang","doi":"10.1109/DSA56465.2022.00055","DOIUrl":"https://doi.org/10.1109/DSA56465.2022.00055","url":null,"abstract":"Subspace clustering is an effective method for high-dimensional data clustering. On the premise of global linearity of data, it uses data self-representation to reconstruct each sample linearly, and achieves good results. However, the actual original data structure is usually nonlinear, which makes the subspace clustering algorithm designed on the premise of linear subspace not achieve satisfactory results in dealing with nonlinear data. In order to deal with nonlinear data better, we use kernel function to introduce block diagonal structure and sparse prior into kernel feature space, and propose a kernel subspace clustering method based on block diagonal representation and sparse constraints (KSCBS). Firstly, we perform subspace learning by combining block diagonal representation and sparse constraints. In this way, the obtained coefficient matrix can maintain the block diagonal structure and better reveal the real attributes of the data. Secondly, we use the kernel technique to map the nonlinear original data space into the appropriate high-dimensional feature space, and then transform it into linear data for processing to solve the nonlinear problem of subspace data. Finally, we use the alternating minimization algorithm to solve the objective function. Compared with other advanced linear subspace and nonlinear subspace algorithms, our algorithm has better clustering performance on several common data sets.","PeriodicalId":208148,"journal":{"name":"2022 9th International Conference on Dependable Systems and Their Applications (DSA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133497664","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

A Dual-channel Text Classification Model based on an Interactive Attention Mechanism 基于交互注意机制的双通道文本分类模型

2022 9th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2022-08-01 DOI: 10.1109/DSA56465.2022.00096

Wei Han, Cheng Peng

引用次数: 0