A new health estimation model for CNC machine tool based on infinite irrelevance and belief rule base

To guarantee the normal workflow and determine scheme of optimal maintenance, it is important to accurately estimate the health condition of computerized numerical control (CNC) machine tool. In current studies, the health condition of CNC machine tool is modeled by using one feature. Due to the complexity of CNC machine tool, the estimating accuracy of the current models is poor and real-time performance cannot be satisfied when multiple features are chosen. Moreover, it is difficult to obtain more effective monitoring data when the CNC machine tool is from normal to failure. To solve the problems, based on infinite irrelevance and belief rule base (BRB), a health estimation model which is named as the infinite irrelevance BRB model is proposed in this paper. In particular, the infinite irrelevance method is used to select key features to optimize the model structure, and BRB is applied to estimate the health condition according to the monitoring data and expert knowledge. Thus, the quantitative monitoring data and expert knowledge can be used effectively to improve accuracy and real-time performance of health estimation. Furthermore, because the initial values of the parameters in the proposed infinite irrelevance BRB model given by experts may not be accurate, the constraint covariance matrix adaptation evolution strategy (CMA-ES) algorithm is employed to train the parameters. A case study for the servo system of the CNC milling machine is used to verify the effective and accuracy of the proposed model. The results show that the infinite irrelevance BRB model can accurately estimate the health condition of the servo system.     

A Novel Heuristic Approach Based Trust Worthy Architecture for Wireless Sensor Networks

A Wireless Sensor Networks (WSNs) consist of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. WSNs are more vulnerable to attacks and failures due to the involvement of many numbers of tiny sensor nodes. As the technology is tremendously increasing in the recent past, the implementation of this for various time critical applications is quite interesting and challenging. Moreover, WSNs have no specific hierarchical structures, leads to security and maintenance problems. Trust in WSN is defined as the degree of belief or confidence about the nodes based on the past interactions and observations has which become a mandatory requirement for reliable communication in WSN under security constraints. In this paper, we propose a Heuristic Approach based Trust Worthy Architecture for WSN that considers the challenges of the system and focus on the collaborative mechanism for trust evaluation and maintenance. Our proposed Architecture could also be capable of fulfilling critical security, reliability, mobility and performance requirements for reliable communication while being readily adaptable to different applications. The simulation results of the proposed architecture outperformed the recent trust worthy architecture using the analysis of the performance requirements such as communication overhead, memory requirements and energy consumption.     

一种无线传感器网络的多Agent系统模型

针对无线传感器网络缺乏普遍的理论模型,提出一种多Agent系统模型,包括形式化语言、模型、语义以及相应的公理和模型约束.该模型将无线传感器网络抽象为由具有特定信念、期望、意图和能力等认知成分的Agent组成的认知系统,所有这些认知成分构成多Agent系统认知模型.本文将为独立于内部结构和具体实现来理解、分析和预测无线传感器网络行为提供形式化的理论基础和分析工具.     

Network Intrusion Detection Based on Directed Acyclic Graph and Belief Rule Base

Intrusion detection is very important for network situation awareness. While a few methods have been proposed to detect network intrusion, they cannot directly and effectively utilize semi‐quantitative information consisting of expert knowledge and quantitative data. Hence, this paper proposes a new detection model based on a directed acyclic graph (DAG) and a belief rule base (BRB). In the proposed model, called DAG‐BRB, the DAG is employed to construct a multi‐layered BRB model that can avoid explosion of combinations of rule number because of a large number of types of intrusion. To obtain the optimal parameters of the DAG‐BRB model, an improved constraint covariance matrix adaption evolution strategy (CMA‐ES) is developed that can effectively solve the constraint problem in the BRB. A case study was used to test the efficiency of the proposed DAG‐BRB. The results showed that compared with other detection models, the DAG‐BRB model has a higher detection rate and can be used in real networks.     

Evaluation of Cloud Service Reliability Based on Classified Statistics and Hierarchy Variable Weight

With the rapid growth of Cloud Computing, more and more organizations choose cloud service to support their business. And the reliability of cloud service has been widely concerned. To better serve the use of cloud service as well as efficiently decide the reliability of cloud service, it is important to know how to deal with the evaluation. In this paper, we establish a cloud service reliability model. This model can be presented to solve the problems with cloud service reliability evaluation which is significantly affected by subjective factors and to further improve its scientific nature. Meanwhile, we proposed a method based on classified statistics and hierarchy variable weight to efficiently evaluate the cloud service reliability based on the model. The experimental results show that the model and method constructed in this paper can be used to efficiently evaluate the cloud service reliability through the classified processing and hierarchical division of subjective and objective characteristics/ subcharacteristics.

     

An asymmetric post-processing for correspondence problem

This paper presents a novel approach that performs post-processing for stereo matching. We improve the performance of stereo matching by performing consistency check and adaptive filtering in an iterative filtering scheme. The consistency check is only done with asymmetric information so that very few additional computational loads are necessary. The information in the valid pixels is propagated into invalid pixels through the adaptive filtering. The proposed post-filtering method can be used in various methods for stereo matching. We demonstrate the validity of the proposed method by applying it to hierarchical belief propagation and semi-global matching. The performance of the post-processing method for hierarchical belief propagation is comparable to state-of-the-art methods in the Middlebury stereo datasets. In order to verify the performance of asymmetric consistency check, we compare it with other reliability estimation methods in the proposed post-processing scheme. Moreover, in order to verify the performance of post-filtering method in noisy environment, the proposed post-filtering method is applied to the stereo images denoised by NL-means algorithm. We find that the iterative filtering scheme reduce an error which may be caused in stereo matching for the denoised images and improve the performance of stereo matching.     

基于代数连通度的虚拟骨干网构造方法

随着无线传感器网络的大量运用,通过分级将网络规模缩小显得十分必要。与已有的一些无线传感器网络的分簇算法不同,本文旨在构造一个健壮的、稳定的虚拟骨干网。通过引入图论中的代数连通度,并考虑无线传感器网络的信道特性,使用基于连接概率的代数连通度来表征虚拟骨干网的健壮性。同时提出度量参数网络质量,结合考虑骨干网的健壮性与代价,通过调节参数因子进行健壮性与代价因素之间的权衡。对该网络优化问题,提出集中式算法进行求解,并通过仿真得到健壮的虚拟骨干网。     

Service reliability modeling and evaluation of active-active cloud data center based on the IT infrastructure

As cloud data center has caught the eye of the information-intensive society since its birth, it keeps on a flourishing development due to its advantages such as high availability and resource utilization, rapid elasticity and disaster recovery. However, as a complex system, it means the centralization of failures and risks, which exerts great influence on the service that cloud data center provides to customers. Thus, the service reliability of the cloud data center is always a key concern. In order to evaluate and further improve the service reliability of the cloud data center, modeling analysis is absolutely necessary but difficult to apply because of the complicated cloud control flows, massive-scale service sharing and complex real-world infrastructures. Focused on the active-active data center, which is a typical mode of the cloud data center, a methodology of the service reliability modeling and analysis based on IT infrastructure is proposed. In this methodology, the queuing theory and graph theory are used to formulate the service reliability model, but the Monte Carlo simulation is used for statistical evaluation. During the modeling, the operational process of the active-active data center is divided into two parts—the request stage and execution stage, which are modeled respectively. Then, the modeling and evaluation approaches are applied in a use case, which verifies the applicability and creditability of our approach. Meanwhile, by sensitivity analysis considering the variation of uncertain or key factors both internal and external, several parameters are identified as high-sensitive factors, which can enlighten service providers on service reliability improvement.     

军事通信网络系统可靠性的分层指标体系研究

根据军事通信网络的体系结构,结合ISO协议的分层方式,给出了一种通信网络可靠性指标的层次分析模型与方法。定义了通信网络可靠性的内涵,描述了可靠性的分层定义、指标、及其关键测度。该分析方法可应用于基于通信网络系统的分析、规划、评价和质量管理等领域,也可应用于系统的时效特性、协同性、及时性等性能参数的工程分析。     

An optimal evaluating method for uncertainty metrics in reliability based on uncertain data envelopment analysis

In reliability engineering, there always exist uncertainties caused by the scarcity of data and information. Various metrics are developed to measure reliability under uncertainty, including evidence-theory-based reliability, interval-analysis-based reliability, fuzzy-interval-analysis-based reliability, posbist reliability and belief reliability. As these five metrics are difficult to be fully understood, it is hard to select the most appropriate one for a specific condition. This paper will propose an uncertain evaluating model to conduct an objective evaluation on these uncertainty metrics. An evaluating index system is established from the aspects of capabilities and adaptabilities. Uncertainty theory is adopted to deal with subjective uncertainties in the quantification process of the index system. Then an evaluating method based on uncertain data envelopment analysis is proposed to provide decision-makers with a succinct result for a given operational context. Finally, the evaluating method is illustrated with a numerical example, which shows that final metric choices vary with different requirements.     

分级分布式软件系统可靠性评估算法

针对分级分布式软件系统的可靠性,提出了一种评估算法和流程。在确定软件失效判据的基础上给出了可靠性建模方案,采用自下而上的方法,利用模块分析法求解底层软件模块的可靠度,利用层次分析法求解模型中的权重系数,最后求解出整个软件系统的可靠度。通过实例说明了分级分布式软件系统的可靠性评估算法的流程。     

基于分等级对象语义图模型的复杂目标自动识别方法研究

目标自动识别是图像处理领域的研究热点。针对现有方法的不足,该文提出一种新的基于分等级对象语义图模型的复杂目标自动识别方法。该方法通过构建分等级对象语义图模型增强对目标与背景间、目标部件间语义约束的利用,引入置信对象网络统计局部特性,利用消息机制传递对象间相互影响,实现概率语义分析。训练中还将产生式和判别式方法结合,提高了目标识别的准确度。在自然和遥感部分目标类别数据集上的测试结果表明,该方法能完成对多种类型和复杂结构目标的识别和提取,具有一定的实用价值。     

Energy optimized micro genetic algorithm based LEACH protocol for WSN

This article presents the design, analyses and implementation of the novel routing protocol for energy optimization based on LEACH for WSN. Network Lifetime is the major problem in various routing protocols used in WSN. In order to overcome that problem, our proposed routing protocol is developed, which is a combination of Micro Genetic algorithm with LEACH protocol. Our proposed µGA-LEACH protocol, strengthen the cluster head (CH) selection and also reduce the energy consumption of the network when compared to existing protocols. This paper shows the improvement of network lifetime and energy consumption with the optimal CH selection based on a micro genetic algorithm and also compared the results with an existing hierarchical routing protocol like LEACH, LEACH-C, LEACH GA and GADA LEACH routing protocol with various packet sizes, and initial energy.

     

基于钟型函数的基本信度指派生成方法

针对认知目标属性为单点值情况下,利用模糊相似度生成证据理论的基本信度指派（Basic Belief As-signment,BBA）存在悖论的问题,本文提出一种基于钟型函数（Bell Function,Bell-F）的相似度确定方法和相应的BBA生成算法。该方法避免了上述悖论算例与实际相左的情况,且能够细粒度刻画相似度评估模型,进一步提高了认知描述的准确性。通过对鸢尾花数据集进行识别实验,结果证明了该方法的有效性,并且对已知样本数量不足的非确定环境具备较好的适应能力。     

基于云计算和动态贝叶斯博弈的WSN恶意程序传播优化抑制方法

为有效地应用入侵检测系统检测WSN（wireless sensor network,无线传感网络）恶意程序从而抑制WSN恶意程序传播,在考虑WSN节点资源有限和云计算平台资源几乎无限的现状基础上,借助云计算平台提出WSN入侵检测网络结构。依据传感节点和WSN入侵检测代理之间博弈过程的分析,使用动态贝叶斯博弈建立了考虑WSN入侵检测代理监控数据发送能耗和传感节点隐私保护需求的WSN恶意程序传播抑制博弈模型。依据建立的博弈类型,并基于精炼贝叶斯均衡提出抑制WSN恶意程序传播的优化策略,并给出具体的算法。实验分析了影响WSN入侵检测代理选择优化策略的因素,为具体应用提供了实验依据。     

Event-to-sink reliable transport in wireless sensor networks

Wireless sensor networks (WSNs) are event-based systems that rely on the collective effort of several microsensor nodes. Reliable event detection at the sink is based on collective information provided by source nodes and not on any individual report. However, conventional end-to-end reliability definitions and solutions are inapplicable in the WSN regime and would only lead to a waste of scarce sensor resources. Hence, the WSN paradigm necessitates a collective event-to-sink reliability notion rather than the traditional end-to-end notion. To the best of our knowledge, reliable transport in WSN has not been studied from this perspective before. In order to address this need, a new reliable transport scheme for WSN, the event-to-sink reliable transport (ESRT) protocol, is presented in this paper. ESRT is a novel transport solution developed to achieve reliable event detection in WSN with minimum energy expenditure. It includes a congestion control component that serves the dual purpose of achieving reliability and conserving energy. Importantly, the algorithms of ESRT mainly run on the sink, with minimal functionality required at resource constrained sensor nodes. ESRT protocol operation is determined by the current network state based on the reliability achieved and congestion condition in the network. This self-configuring nature of ESRT makes it robust to random, dynamic topology in WSN. Furthermore, ESRT can also accommodate multiple concurrent event occurrences in a wireless sensor field. Analytical performance evaluation and simulation results show that ESRT converges to the desired reliability with minimum energy expenditure, starting from any initial network state.     

A Novel QoS Based Secure Unequal Clustering Protocol with Intrusion Detection System in Wireless Sensor Networks

Wireless sensor network (WSN) becomes a hot research topic owing to its application in different fields. Minimizing the energy dissipation, maximizing the network lifetime, and security are considered as the major quality of service (QoS) factors in the design of WSN. Clustering is a commonly employed energy-efficient technique; however, it results in a hot spot issue. This paper develops a novel secure unequal clustering protocol with intrusion detection technique to achieve QoS parameters like energy, lifetime, and security. Initially, the proposed model uses adaptive neuro fuzzy based clustering technique to select the tentative cluster heads (TCHs) using three input parameters such as residual energy, distance to base station (BS), and distance to neighbors. Then, the TCHs compete for final CHs and the optimal CHs are selected using the deer hunting optimization (DHO) algorithm. The DHO based clustering technique derives a fitness function using residual energy, distance to BS, node degree, node centrality, and link quality. To further improve the performance of the proposed method, the cluster maintenance phase is utilized for load balancing. Finally, to achieve security in cluster based WSN, an effective intrusion detection system using a deep belief network is executed on the CHs to identify the presence of intruders in the network. An extensive set of experiments were performed to ensure the superior performance of the proposed method interms of energy efficiency, network lifetime, packet delivery ratio, average delay, and intrusion detection rate.

     

适用于无线传感器网络的层次化分布式压缩感知

分布式压缩感知(Distributed Compressed Sensing, DCS)是在无线传感器网络(Wireless Sensor Network, WSN)中减少数据传输量、降低能量消耗的有效手段。该文面向分簇WSN,提出层次化分布式压缩感知(Hierarchical Distributed Compressed Sensing, HDCS)。在利用簇内DCS消除簇内时间、空间冗余的基础上,利用簇间DCS消除簇间空间冗余,减少簇头的数据发送量。针对分簇WSN采集信号的结构化稀疏特性,建立块稀疏簇内联合稀疏模型与块稀疏簇间联合稀疏模型,提出HDCS观测方案与层次化联合重构算法。仿真结果表明,与普通DCS相比,HDCS在保证重建信号质量的同时,能够有效减轻簇头的通信负担,并显著降低Sink上的信号重构时间。     

基于超声波的过金属壁无线通信系统研究

该文针对密闭金属结构内、外高可靠性通信方法等关键技术进行了理论与实验研究,提出了过金属壁双向信号可靠性数据传输方法,分别为由外向内的数字幅度调制数据传输和由内向外基于阻抗调制式的数据传输方式。同时研究了基于随机共振原理的微弱信号增强传输方法,提高了噪声背景下过金属壁数字信号传输的可靠性。构建了密闭金属结构内部状态监测的实验平台,对数据传输进行实验测试,验证了密闭金属容器内部温度传感器的数据传输特性,最后实现了4.8kbit/s的数据传输速率,表明了该系统方案具有实际工程应用价值。     

基于证据理论的系统非概率可靠性分析

为实现对复杂电子学系统进行可靠性量化分析,提出了一种基于证据理论的电子学系统非概率可靠性分析方法。利用证据理论来表征单元可靠性的不确定性,并通过响应函数获得系统的非概率可靠性输出,其区间的上下界分别为似真函数和信任函数,从而实现了不确定性的传播。与传统的概率可靠性分析方法相比,该方法不需要先验概率和条件概率,因而适合于对小子样的电子学系统进行可靠性量化分析,仿真结果表明了该方法的合理性及有效性。