2018 5th International Conference on Dependable Systems and Their Applications (DSA)最新文献

Indoor Positioning System Based on Zigbee and Inertial System 基于Zigbee和惯性系统的室内定位系统

2018 5th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2018-09-01 DOI: 10.1109/dsa.2018.00023

Cheng-de Han, Zhongtao Wang, Longxu Wang

引用次数: 4

Process Capability Evaluation of Product Development Based on Information Entropy 基于信息熵的产品开发过程能力评价

2018 5th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2018-09-01 DOI: 10.1109/dsa.2018.00025

Lixiang Wang, W. Dai, Yubing Huang

引用次数: 0

DSA 2018 Program Committee DSA 2018项目委员会

2018 5th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2018-09-01 DOI: 10.1109/dsa.2018.00009

引用次数: 0

A Multi-Chain Model for CBDC CBDC的多链模型

2018 5th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2018-09-01 DOI: 10.1109/DSA.2018.00016

W. Tsai, Zihao Zhao, Chi Zhang, Lian Yu, Enyan Deng

引用次数: 14

A Monte Carlo Simulation-Based Algorithm for a Repairable System in GO Methodology 基于蒙特卡罗模拟的可修系统GO方法

2018 5th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2018-09-01 DOI: 10.1109/DSA.2018.00029

Ang Li, Yi Ren, Dezhen Yang, Zhifeng Li

{"title":"A Monte Carlo Simulation-Based Algorithm for a Repairable System in GO Methodology","authors":"Ang Li, Yi Ren, Dezhen Yang, Zhifeng Li","doi":"10.1109/DSA.2018.00029","DOIUrl":"https://doi.org/10.1109/DSA.2018.00029","url":null,"abstract":"GO methodology is a significant system probability analysis technique. Because the GO model is similar to system schematic diagram, it is easy to establish and often used to evaluate the reliability and safety of complex systems. The GO model can not only describe multi-state and time-sequential characteristics, but also express maintenance behaviors of systems. However, the solutions for repairable GO model through analytical approaches such as the numerical method and methods combined with Markov models or dynamic Bayesian networks have limitations in terms of shared signals, complex dynamic maintenance behaviors, the number of operators, non-exponential failure or repair probability distributions and the size of networks. Based on this, a Monte Carlo simulation-based algorithm for repairable GO model is proposed in this paper. The process of generating state time diagrams and the simulation principle of several typical operators are given and the result of an example validates the algorithm. By this new algorithm, the curves of reliability with time and the instantaneous reliability indices on determined time can be figured out without considering the limitations mentioned above.","PeriodicalId":117496,"journal":{"name":"2018 5th International Conference on Dependable Systems and Their Applications (DSA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126496657","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

Message from the DSA 2018 Program Chairs 来自2018年DSA项目主席的信息

2018 5th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2018-09-01 DOI: 10.1109/dsa.2018.00007

引用次数: 0

Publisher's Information 出版商的信息

2018 5th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2018-09-01 DOI: 10.1109/dsa.2018.00034

引用次数: 0

An Algebraic Binary Decision Diagram for Analysis of Dynamic Fault Tree 动态故障树分析的代数二元决策图

2018 5th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2018-09-01 DOI: 10.1109/DSA.2018.00018

Wei Jiang, Siwei Zhou, Luyao Ye, Dongdong Zhao, Jing Tian, W. E. Wong, Jianwen Xiang

{"title":"An Algebraic Binary Decision Diagram for Analysis of Dynamic Fault Tree","authors":"Wei Jiang, Siwei Zhou, Luyao Ye, Dongdong Zhao, Jing Tian, W. E. Wong, Jianwen Xiang","doi":"10.1109/DSA.2018.00018","DOIUrl":"https://doi.org/10.1109/DSA.2018.00018","url":null,"abstract":"Dynamic fault tree (DFT) is an extension of traditional static fault tree, in which several dynamic gates are introduced to model sequential dependency between fault events. As for the quantitative analysis of DFT, sequential binary decision diagram (SBDD) has been proposed by incorporating sequential nodes into traditional binary decision diagram (BDD). With SBDD, a DFT can be reduced into a sum of disjoint products (SDP) of basic events and sequential nodes, which can avoid the space explosion problem and exponential complexity caused by traditional Markov chain and inclusion/exclusion based solutions, respectively. However, the SBDD is not developed base on a well-defined temporal or sequential algebra. Rather, it is developed based on a precedence operator with specific and very limited number of reduction rules. The applications of SBDD is thus restricted to the DFTs whose dynamic gates consist of inputs of only basic events or some specific events. In this paper, we present an algebraic binary decision diagram (ABDD) based on the algebraic framework for DFT proposed by G. Merle et al. In addition to the existing laws for the reduction of arbitrary DFT with any structure, we introduce a set of new laws for the reduction of SDP in the ABDD. Thanks to the sound algebraic framework and complete set of laws, ABDD is applicable for the analysis of general DFT without any structure restriction. We illustrate our approach and compare the difference with SBDD by several examples.","PeriodicalId":117496,"journal":{"name":"2018 5th International Conference on Dependable Systems and Their Applications (DSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125042671","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: 6

Message from the DSA 2018 Steering Committee Chair 2018年DSA指导委员会主席致辞

2018 5th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2018-09-01 DOI: 10.1109/dsa.2018.00004

引用次数: 0

Failure Diagnosis for UAV-Born Inertial Navigation Device Based on BP-Adaboost Model 基于BP-Adaboost模型的无人机惯性导航装置故障诊断

2018 5th International Conference on Dependable Systems and Their Applications (DSA) Pub Date : 2018-09-01 DOI: 10.1109/DSA.2018.00020

Yingjie Ren, Yang Hu

{"title":"Failure Diagnosis for UAV-Born Inertial Navigation Device Based on BP-Adaboost Model","authors":"Yingjie Ren, Yang Hu","doi":"10.1109/DSA.2018.00020","DOIUrl":"https://doi.org/10.1109/DSA.2018.00020","url":null,"abstract":"When a certain UAV conduct a predetermined task, its flying track is planned in advance. The accumulated historical data of position information and respective state of the error can be used to diagnose the real time motion and position information deviation of the UAV-born inertial navigation devices through BP-Adaboost methods. The relationship between the position information deviation and characteristic parameters of UAV-born Inertial Navigation Device are in a high level of nonlinearity. As an efficacious non-linear classifier, BP neural network is capable of delineating the above nonlinear relationship. However, the performance and preciseness of the BP network are sensitively subject to the infuence of its initial weights, which is likely to incur the relatively unstable training outcomes. In order to address the contradiction and attain the training results we want, this paper provides a new approach to the UAV-born inertial navigation devices failure diagnosis based on BPAdaboost model. The first step is utilizing BP neural network as a weak classifier to discern the relationship between the failure states and the parameters. Each of the several BP networks chosen beforehand are applied to the repeated training process to become weak classifier one by one. Secondly, Adaboost algorithm is conducted by amalgamating the volidated weak classifier aiming at a strong classifier to diagnose the failure of UAV-born Inertial Navigation Device. Then, application of the BP-Adaboost method to a practical cases evinces that the failure diagnosis model of the strong classifier gains more satisfied accuracy than the weak classifiers, which can meet the requirements of the practices and offer another reference for the position deviation judgment during some repeating tasks for UAV-born inertial navigation devices, especially under the condition that GPS and inertial navigation devices are not stable and reliable.","PeriodicalId":117496,"journal":{"name":"2018 5th International Conference on Dependable Systems and Their Applications (DSA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126134144","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