NRCE： A New Random Cluster Election Algorithm

The Intrusion Detection System （IDS） is a secure solution for Mobile Ad Hoc Networks （MANET）. Cluster management can improve cooperating detection ability of mobile nodes. In this paper, a New Random Cluster Election （NRCE） algorithm, which is applied to the intrusion detection system for MANET, is proposed based on analyzing some popular cluster algorithms. NREC considering security and fairness adequately is working in a distrustful mode where mobile nodes distrust each other and the process of electing clusterhead is random. It is a new method for cluster management of IDS for MANET.     

Discretization of nonlinear non-affine time delay systems based on second-order hold

When calculating the sampled-date representation of nonlinear systems second-order hold(SOH) assumption can be applied to improving the precision of the discretization results. This paper proposes a discretization method based on Taylor series and the SOH assumption for the nonlinear systems with the time delayed non-affine input. The mathematical structure of the proposed discretization method is explored. This proposed discretization method can provide a precise and finite dimensional discretization model for the nonlinear time-delayed non-affine system by keeping the truncation order of the Taylor series. The performance of the proposed discretization method is evaluated by doing the simulation using a nonlinear system with the time-delayed non-affine input.Different input signals, time-delay values and sampling periods are considered in the simulation to investigate the proposed method.The simulation results demonstrate that the proposed method is practical and easy for time-delayed nonlinear non-affine systems.The comparison between SOH assumption with first-order hold(FOH) and zero-order hold(ZOH) assumptions is given to show the advantages of the proposed method.     

Research on the Architecture of Distributed Web Mining System

A new distributed web-mining system based on CORBA （DWMBC） is proposed. It is based on web and takes advantage of CORBA for distributed processing. Distributed data processing and web mining are effectively combined to meet the demand of multi-platform in the system, and the safety and sealability of the system are reinforced. In addition, the Java technology is applied to meet the real time requirement of the presentation of the mining results. At last, a system instance is developed to implement this distributed system model.     

Fractionally Delayed Kalman Filter

The conventional Kalman filter is based on the assumption of non-delayed measurements. Several modifications appear to address this problem, but they are constrained by two crucial assumptions: 1) the delay is an integer multiple of the sampling interval, and 2) a stochastic model representing the relationship between delayed measurements and a sequence of possible non-delayed measurements is known. Practical problems often fail to satisfy these assumptions, leading to poor estimation accuracy and frequent track-failure. This paper introduces a new variant of the Kalman filter, which is free from the stochastic model requirement and addresses the problem of fractional delay.The proposed algorithm fixes the maximum delay(problem specific), which can be tuned by the practitioners for varying delay possibilities. A sequence of hypothetically defined intermediate instants characterizes fractional delays while maximum likelihood based delay identification could preclude the stochastic model requirement. Fractional delay realization could help in improving estimation accuracy. Moreover, precluding the need of a stochastic model could enhance the practical applicability. A comparative analysis with ordinary Kalman filter shows the high estimation accuracy of the proposed method in the presence of delay.     

Energy Theft Detection in Smart Grids: Taxonomy,Comparative Analysis,Challenges, and Future Research Directions

Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)techniques.Here,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature.The challenges of different ETD techniques and their mitigation are suggested for future work.It is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft tracking.This can help in the prevention of energy theft,in the future,as well as for ETD.     

CC与SSE-CMM结合的信息安全评估方法

The information security evaluation is an important part of information field. It is a general method to execute evaluation to the information security products under the instruction of Common Criteria (CC).A new method of information security evaluation, based on the combination of CC and Systems Security Engineering Capability Maturity Model (SSE-CMM),has been proposed in the paper. The basic idea of this method is using the reference of the security system engineer. Based on the experiment of a Target of Evaluation (TOE)in CC, the evaluation result of security assurance by this new method is proved to be more accurate, more comprehensive and more acceptable.     

基于单应性矩阵的室内移动机器人视觉定位研究

Monocular visual positioning for indoor mobile robot is concerned in this paper. A new visual positioning method based on homography matrix in Euclidean space is proposed. It can calculate the position and pose of the mobile robot according to the intrinsic parameters of camera and two position-known points in a plane. It is very simple and low cost in computation. The experimental results show its effectiveness.     

Study on Visual Positioning Based on Homography for Indoor Mobile Robot

Monocular visual positioning for indoor mobile robot is concerned in this paper.A new visual positioning method based on homography matrix in Euclidean space is proposed.It can calculate the position and pose of the mobile robot according to the intrinsic parameters of camera and two position-known points in a plane.It is very simple and low cost in computation.The experimental results show its effectiveness.     

The intelligent CAI system for chemistry based on automated reasoning

A new type of intelligent CAI system for chemistry is developed in this paper based on automated reasoning with chemistry knowledge.The system has shown its ability to solve chemistry problems,to assist students and teachers in studies and instruction with the automated reasoning functions.Its open mode of the knowledge base and its unique style of the interface between the system and human provide more opportunities for the users to acquire living knowledge through active participation.The automated reasoning based on basic chemistry knowledge also opened a new approach to the information storage and management of the ICAI system for sciences.     

Agent-Oriented Probabilistic Logic Programming

Currently, agent-based computing is an active research area, and great efforts have been made towards the agent-oriented programming both from a theoretical and practical view. However, most of them assume that there is no uncertainty in agents＇ mental state and their environment. In other words, under this assumption agent developers are just allowed to specify how his agent acts when the agent is 100% sure about what is true/false. In this paper, this unrealistic assumption is removed and a new agent-oriented probabilistic logic programming language is proposed, which can deal with uncertain information about the world. The programming language is based on a combination of features of probabilistic logic programming and imperative programming.     

Equipment PHM using non-stationary segmental hidden semi-Markov model

Health monitoring and prognostics of equipment is a basic requirement for condition-based maintenance (CBM) in many application domains where safety, reliability, and availability of the systems are considered mission critical. As a key complement to CBM, prognostics and health management (PHM) is an approach to system life-cycle support that seeks to reduce/eliminate inspections and time-based maintenance through accurate monitoring, incipient faults. Conducting successful prognosis, however, is more difficult than conducting fault diagnosis. A much broader range of asset health related data, especially those related to the failures, shall be collected. The asset health progression can then be possibly extracted from the congregated data, which has proved to be very challenging. This paper presents a non-stationary segmental hidden semi-Markov model (NSHSMM) based prognosis method to predict equipment health. Unlike previous HSMMs, the proposed NSHSMM no longer assumes that the state-dependent transition probabilities keep the same value all the time. That is, the probability of transiting to a less healthy state does not increase with the age. “Non-stationary” means the transition probabilities will change with time. In the proposed method, in order to characterize a deteriorating equipment, three kinds of aging factor that discount the probabilities of staying at current state while increasing the probabilities of transitions to less healthy states are introduced. The performances of these aging factors are compared by using historical data colleted from three hydraulic pumps. The hazard function (h.f.) has been introduced to analyze the distribution of lifetime with a combination of historical failure data and on-line condition monitoring data. Using h.f., PHM is based on a failure rate that is a function of both the equipment age and the equipment conditions. The state values of the equipment condition considered in PHM, however, are limited to those stochastically increasing over time and those having non-decreasing effect on the hazard rate. The estimated state duration probability distributions can be used to predict the remaining useful life of the systems. With the equipment PHM, the behavior of the equipment condition can be predicted.     

CHMM在设备故障诊断和预测中的应用研究

有效的故障诊断和预测是在工业中大范围推广基于状态维修的先决条件。提出了利用连续隐马尔可夫模型对设备实施故障诊断和预测（剩余寿命预测）的方法和步骤。研究了模型的设计和训练方法。最后,滚动轴承振动实验台数据分析验证了方法的有效性。     

基于HSMM的两阶段设备缺陷状态识别方法*

针对传统马尔可夫模型(HMM)状态停留时间必须服从指数分布假设的不足,提出了一种基于隐半马尔可夫模型(HSMM)的两阶段设备缺陷状态识别方法.首先,通过分析HSMM模型的参数构成及基本特点,并结合两阶段设备的劣化过程特点提出合理的假设条件,建立起用于描述两阶段设备运行状态的HSMM模型;其次,针对HSMM模型的参数估计问题,引入最大似然估计法,并提出了小样本条件下求解状态持续时间的方法;再次,基于建立的HSMM模型,给出了两阶段设备缺陷状态早期识别的计算公式及步骤,通过对状态停留时间的概率估计实现了对缺陷状态的早期识别;最后,通过计算机仿真方法模拟了HSMM模型的建模、参数估计及缺陷状态识别过程,从而验证了该方法的有效性和准确性.     

基于HMM的设备剩余寿命预测框架及其实现

在设备退化状态识别中剩余寿命预测是设备实施状态维修的一个重要内容,也是实现真正意义上精确维修的前提和基础。隐马尔可夫模型作为一种统计分析算法,具有较强的模式分类能力,能够对设备退化过程进行有效识别。但是传统隐马尔可夫模型状态持续时间分布的不合理性,不能直接用于剩余寿命的预测。考虑状态识别和剩余寿命预测的连续性,应用隐马尔可夫模型的改进算法——隐半马尔可夫模型,构建设备剩余寿命的预测框架,实现了在设备退化状态识别基础上的剩余寿命预测。通过对滚动轴承实测数据的仿真试验,对框架进行了仿真验证。仿真结果表明框架能够有效识别设备退化过程和实现剩余寿命的预测。     

基于隐马尔可夫模型的能量参数预测量化算法

为了充分利用能量与线性预测编码(Linear prediction coding,LPC)系数之间的相关性,提高能量参数量化效率,提出了一种基于隐马尔可夫模型(Hidden Markov model,HMM)的能量参数预测量化算法.通过适当假设,使用HMM模拟能量参数和LPC系数之间的相关性,其中离散化后的能量参数组成隐状态序列,量化后的LPC系数组成可现测序列.然后利用HMM预测每一超帧中的能量参数的变化轨迹,并根据预测出的能量轨迹对预测残差进行分模式矢量量化(Mode-based vector quantization,MBQ).仿真实验中能量参数量化后的平均失真为2.668 dB,与线性预测量化算法相比下降了14.O%,表明本文算法通过利用能量参数与LPC系数的相关性,能够有效地提高能量参数量化效率.     

基于HMM的设备故障预测方法研究

故障预测是设备实施基于状态维修的1个重要内容,是实现真正意义上精确维修的前提和基础。隐马尔可夫模型（HMM）作为1种统计分析算法,在设备的故障诊断中获得了成功应用。但对于故障的预测,传统隐马尔可夫模型存在很多缺陷,因此研究相关的改进算法,构建了基于隐马尔可夫的故障诊断和预测框架,使设备的故障诊断和预测能够同时进行。最后通过对滚动轴承实测数据的仿真验证,表明该算法具有较高的故障识别率并且对设备的剩余寿命能进行有效的预测。     

Fault features extraction for bearing prognostics

Over the past years, investigation on condition-based maintenance (CBM) technique on bearing has been conducted. Bearing diagnostics and prognostics are the important aspects in CBM. A key to the success of using vibration data for bearing fault diagnostics and bearing lifecycle prognostics is a quantified relationship between bearing damage and bearing fault features. To establish such a quantitative relationship, effective signal processing techniques to extract bearing fault features from vibration signals are needed. This paper describes a newly developed fault feature extraction method for bearing prognostics. The effectiveness of the method is demonstrated with two real bearing run-to-failure test datasets: one collected under normal operating conditions and another one under abnormal operating conditions. Experimental results show that the bearing fault features extracted using both traditional vibration analysis methods and the proposed method give clear bearing heath degradation trend for the dataset collected under normal operating conditions. However, for the data collected under abnormal operating conditions, bearing fault features obtained using traditional vibration analysis methods fail to show the bearing health degradation trend while the fault features extracted using the proposed method give consistent bearing degradation trends.     

SCALED SELF-ORGANIZING MAP – HIDDEN MARKOV MODEL ARCHITECTURE FOR BIOLOGICAL SEQUENCE CLUSTERING

The self-organizing hidden Markov model map (SOHMMM) introduces a hybrid integration of the self-organizing map (SOM) and the hidden Markov model (HMM). Its scaled, online gradient descent unsupervised learning algorithm is an amalgam of the SOM unsupervised training and the HMM reparameterized forward-backward techniques. In essence, with each neuron of the SOHMMM lattice, an HMM is associated. The image of an input sequence on the SOHMMM mesh is defined as the location of the best matching reference HMM. Model tuning and adaptation can take place directly from raw data, within an automated context. The SOHMMM can accommodate and analyze deoxyribonucleic acid, ribonucleic acid, protein chain molecules, and generic sequences of high dimensionality and variable lengths encoded directly in nonnumerical/symbolic alphabets. Furthermore, the SOHMMM is capable of integrating and exploiting latent information hidden in the spatiotemporal dependencies/correlations of sequences’ elements.     

燃气涡轮发动机PHM系统设计与实现技术研究

基于燃气涡轮发动机由计划维修模式向视情维修(Condition Based Maintenance, CBM)模式转变、由被动保障向主动保障发展的实际需求,针对目前燃气涡轮发动机在故障预测与健康管理(Prognostics and Health Management, PHM)系统设计上存在的整体性与系统性不强的问题,通过开展燃气涡轮发动机的PHM技术研究,围绕故障诊断、寿命预测、保障决策等PHM系统的核心功能要素,提出了一种新型燃气涡轮发动机PHM系统架构。该架构包括实时监测模块与保障决策模块,实时监测模块主要基于测试性建模实现故障诊断逻辑设计以满足涡轮发动机故障检测与健康管理的实时性要求,保障决策模块为燃气涡轮发动机的CBM保障提供了一种数据驱动的决策生成方法,通过长短时记忆网络(Long Short-Term Memory, LSTM)算法预测关键部件寿命,以匹配最佳的维修保障方案,提升燃气涡轮发动机的保障效能,最后给出了该系统架构的部分核心功能的设计。