安全关键软件需求中的相关知识大多需要手工提取,既费时又费力。近年来,人工智能技术逐渐被应用于安全关键软件设计与开发过程中,以减少工程师的手工劳动,缩短软件开发的生命周期。文中提出了一种安全关键软件术语推荐和需求分类方法,...安全关键软件需求中的相关知识大多需要手工提取,既费时又费力。近年来,人工智能技术逐渐被应用于安全关键软件设计与开发过程中,以减少工程师的手工劳动,缩短软件开发的生命周期。文中提出了一种安全关键软件术语推荐和需求分类方法,为安全关键软件需求规约提供了基础。首先,基于词性规则和依存句法规则对候选术语进行提取,通过术语相似度计算和聚类方法对候选术语进行聚类,将聚类结果推荐给工程师;其次,基于特征提取方法和分类方法将安全关键软件需求自动分为功能、安全性、可靠性等需求;最后,在AADL(Architecture Analysis and Design Language)开源建模环境OSATE中实现了原型工具TRRC4SCSTool,并基于工业界案例需求、安全分析与认证标准等构建实验数据集进行了实验验证,证明了所提方法的有效性。展开更多
In hierarchical networks, nodes are separated to play different roles such as CHs and cluster members. Each CH collects data from the cluster members within its cluster, aggregates the data and then transmits the data...In hierarchical networks, nodes are separated to play different roles such as CHs and cluster members. Each CH collects data from the cluster members within its cluster, aggregates the data and then transmits the data to the sink. Each algorithm that is used for packet routing in quality of service (QoS) based applications should be able to establish a tradeoffs between end to end delay parameter and energy consumption. Therefore, enabling QoS applications in sensor networks requires energy and QoS awareness in different layers of the protocol stack. We propose a QoS based and Energy aware Multi-path Hierarchical Routing Algorithm in wireless sensor networks namely QEMH. In this protocol, we try to satisfy the QoS requirements with the minimum energy via hierarchical methods. Our routing protocol includes two phase. In first phase, performs cluster heads election based on two parameters: node residual energy and node distance to sink. In second phase, accomplishes routes discovery using multiple criteria such as residual energy, remaining buffer size, signal-to-noise ratio and distance to sink. When each node detect an event can send data to the CH as single hop and CH to the sink along the paths. We use a weighted traffic allocation strategy to distribute the traffic amongst the available paths to improve the end to end delay and throughput. In this strategy, the CH distributes the traffic between the paths according to the end to end delay of each path. The end to end delay of each path is obtained during the paths discovery phase. QEMH maximizes the network lifetime as load balancing that causes energy consume uniformly throughout the network. Furthermore employs a queuing model to handle both real-time and non-real-time traffic. By means of simulations, we evaluate and compare the performance of our routing protocol with the MCMP and EAP protocols. Simulation results show that our proposed protocol is more efficient than those protocols in providing QoS requirements and minimizing energy consumption.展开更多
采用数据挖掘技术从企业已有的产品或服务规划知识中提取顾客需求和设计需求之间的映射规则,是进行产品或服务规划分析的重要方法。针对常用Apriori关联规则挖掘算法运算量大的问题,提出了基于PIETM(Principle of Inclusion-Exclusion a...采用数据挖掘技术从企业已有的产品或服务规划知识中提取顾客需求和设计需求之间的映射规则,是进行产品或服务规划分析的重要方法。针对常用Apriori关联规则挖掘算法运算量大的问题,提出了基于PIETM(Principle of Inclusion-Exclusion and Transaction Mapping)算法的顾客需求映射规则挖掘方法,提取强关联规则。针对规划设计数据量较大时,规则挖掘会产生大量冗余规则的问题,通过采用基于粗糙集的聚类方法对顾客需求以及设计需求可选值进行聚类,实现顾客需求映射规则的聚类分析。最后以某企业叉车方案规划中,顾客需求映射规则的挖掘和聚类分析为例,验证了所提方法的有效性。展开更多
文摘安全关键软件需求中的相关知识大多需要手工提取,既费时又费力。近年来,人工智能技术逐渐被应用于安全关键软件设计与开发过程中,以减少工程师的手工劳动,缩短软件开发的生命周期。文中提出了一种安全关键软件术语推荐和需求分类方法,为安全关键软件需求规约提供了基础。首先,基于词性规则和依存句法规则对候选术语进行提取,通过术语相似度计算和聚类方法对候选术语进行聚类,将聚类结果推荐给工程师;其次,基于特征提取方法和分类方法将安全关键软件需求自动分为功能、安全性、可靠性等需求;最后,在AADL(Architecture Analysis and Design Language)开源建模环境OSATE中实现了原型工具TRRC4SCSTool,并基于工业界案例需求、安全分析与认证标准等构建实验数据集进行了实验验证,证明了所提方法的有效性。
文摘In hierarchical networks, nodes are separated to play different roles such as CHs and cluster members. Each CH collects data from the cluster members within its cluster, aggregates the data and then transmits the data to the sink. Each algorithm that is used for packet routing in quality of service (QoS) based applications should be able to establish a tradeoffs between end to end delay parameter and energy consumption. Therefore, enabling QoS applications in sensor networks requires energy and QoS awareness in different layers of the protocol stack. We propose a QoS based and Energy aware Multi-path Hierarchical Routing Algorithm in wireless sensor networks namely QEMH. In this protocol, we try to satisfy the QoS requirements with the minimum energy via hierarchical methods. Our routing protocol includes two phase. In first phase, performs cluster heads election based on two parameters: node residual energy and node distance to sink. In second phase, accomplishes routes discovery using multiple criteria such as residual energy, remaining buffer size, signal-to-noise ratio and distance to sink. When each node detect an event can send data to the CH as single hop and CH to the sink along the paths. We use a weighted traffic allocation strategy to distribute the traffic amongst the available paths to improve the end to end delay and throughput. In this strategy, the CH distributes the traffic between the paths according to the end to end delay of each path. The end to end delay of each path is obtained during the paths discovery phase. QEMH maximizes the network lifetime as load balancing that causes energy consume uniformly throughout the network. Furthermore employs a queuing model to handle both real-time and non-real-time traffic. By means of simulations, we evaluate and compare the performance of our routing protocol with the MCMP and EAP protocols. Simulation results show that our proposed protocol is more efficient than those protocols in providing QoS requirements and minimizing energy consumption.
文摘采用数据挖掘技术从企业已有的产品或服务规划知识中提取顾客需求和设计需求之间的映射规则,是进行产品或服务规划分析的重要方法。针对常用Apriori关联规则挖掘算法运算量大的问题,提出了基于PIETM(Principle of Inclusion-Exclusion and Transaction Mapping)算法的顾客需求映射规则挖掘方法,提取强关联规则。针对规划设计数据量较大时,规则挖掘会产生大量冗余规则的问题,通过采用基于粗糙集的聚类方法对顾客需求以及设计需求可选值进行聚类,实现顾客需求映射规则的聚类分析。最后以某企业叉车方案规划中,顾客需求映射规则的挖掘和聚类分析为例,验证了所提方法的有效性。
