Titlebook: Computer Safety, Reliability, and Security; 22nd International C Stuart Anderson,Massimo Felici,Bev Littlewood Conference proceedings 2003

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書目名稱Computer Safety, Reliability, and Security影響因子(影響力)




書目名稱Computer Safety, Reliability, and Security影響因子(影響力)學(xué)科排名




書目名稱Computer Safety, Reliability, and Security網(wǎng)絡(luò)公開度




書目名稱Computer Safety, Reliability, and Security網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Safety, Reliability, and Security被引頻次




書目名稱Computer Safety, Reliability, and Security被引頻次學(xué)科排名




書目名稱Computer Safety, Reliability, and Security年度引用




書目名稱Computer Safety, Reliability, and Security年度引用學(xué)科排名




書目名稱Computer Safety, Reliability, and Security讀者反饋




書目名稱Computer Safety, Reliability, and Security讀者反饋學(xué)科排名

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Infelicity

Visual Modeling and Verification of Distributed Reactive Systemsive systems. A distributed reactive system is composed of a collection of autonomous reactive nodes which communicate over buffered and/or unbuffered channels . Statecharts are industry accepted formal notation to model reactive systems but lack features to model communication. We have extended . [1

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江湖郎中

Improving System Reliability via Model Checking: The FSAP/NuSMV-SA Safety Analysis Platformeir environment. Such growing complexity requires an adequate increase in the capability of safety engineers to assess system safety, including analyzing the bahaviour of a system in degraded situations. Formal verification techniques, like symbolic model checking, have the potential of dealing with

Largess

Integrity Static Analysis of COTS/SOUPe static analysis was part of an overall software qualification programme, which also included the work reported in our paper presented at Safecomp 2002. Integrity static analysis focuses on unsafe language constructs and “covert” flows, where one thread can affect the data or control flow of anothe

Largess

Safety Lifecycle for Developing Safety Critical Artificial Neural Networksormance of neural networks for safety-critical systems. However, there is a complete absence of analytical certification methods for neural network paradigms. Consequently, their role in safety-critical applications, if any, is typically restricted to advisory systems. It is therefore desirable to e

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