A Classical Parameter Identification Method and?a?Modern Test Generation Algorithm

Many methods have been presented for the testing and diagnosis of analog circuits. Each of these methods has its advantages and disadvantages. In this paper we propose a novel sensitivity analysis algorithm for the classical parameter identification method and a continuous fault model for the modern test generation algorithm, and we compare the characteristics of these methods. At present, parameter identification based on the component connection model (CCM) cannot ensure that the diagnostic equation is optimal. The sensitivity analysis algorithm proposed in this paper can choose the optimal set of trees to construct an optimal CCM diagnostic equation, and enhance the diagnostic precision. But nowadays increasing attention is being paid to test generation algorithms. Most test generation algorithms use a single value in the fault model. But the single values cannot substitute for the actual faults that may occur, because the possible faulty values vary over a continuous range. To solve this problem, this paper presents a continuous fault model for the test generation algorithm which has a continuous range of parameters. The test generation algorithm with this model can improve the treatment of the tolerance problem, including the tolerances of both normal and faulty parameters, and enhance the fault coverage rate. The two methods can be applied in different situations.     

基于改进LSSVM的城市轨道交通供电故障诊断方法

小样本条件下供电系统故障快速诊断是保证城市轨道交通安全稳定运行的保证.文中提出了一种基于量子粒子群优化最小二乘支持向量机(LSSVM)的供电系统故障诊断方法.该方法首先基于主成分分析提取能够表征系统运行状态的特征参数,并降低数据维数.然后利用LSSVM构建小样本故障诊断模型,通过量子粒子群算法对LSSVM模型参数进行优...     

多样本状态加权合成的CGHMM训练算法

为解决隐马尔可夫模型(HMM)中参数很多,实际当中难以提供足够多训练数据的问题,根据观察值序列的状态分布情况,描述了一种基于状态加权合成的连续高斯混合密度隐马尔可夫模型(CGHMM)训练算法,对多个CGHMM模型进行加权合成,并将此方法应用于轴承故障诊断进行仿真实验。实验结果表明,平均训练时间为12.86 s,诊断时间为0.189 s,诊断准确度为96%。可见,多样本状态加权合成的CGHMM轴承故障诊断方法确实有效可行,具有良好的应用前景。     

Improved image decoding over noisy channels using minimummean-squared estimation and a Markov mesh

Joint source-channel (JSC) decoding based on residual source redundancy is a technique for providing channel robustness to quantized data. Previous work assumed a model equivalent to viewing the encoder/noisy channel tandem as a discrete hidden Markov model (HMM) with transmitted indices the hidden states. We generalize this HMM-based (1-D) approach for images, using the more powerful hidden Markov mesh random field (HMMRF) model. While previous state estimation methods for HMMRFs base estimates on only a causal subset of the observed data, our new method uses both causal and anticausal subsets. For JSC-based image decoding, the new method provides significant benefits over several competing techniques.     

Real Time Fault Diagnosis with Tests of Uncertain Quality for Multimode Systems and its Application in a Satellite Power System

Dynamic fault diagnosis must consider complex fault situations such as fault evolution, coupling, unreliable tests and so on. Previous dynamic fault diagnostic models and inference algorithms are mainly designed for the steady state systems, which are not suitable for the multimode systems. In this paper, a time varying dynamic model to solve the multimode fault diagnosis problem is proposed. Its structure and formulation are presented. Fault diagnosis based on this model is realized by means of inference calculation given the test result, which is formulated as an optimization problem. A new algorithm to solve this problem is proposed. Simulation experiments on different scenarios are carried out to validate the model and the algorithm. As an example, the case of a satellite electrical power system is studied in detail. Both the simulation result and the application result show that the method proposed in this paper can be used to solve the dynamic fault diagnosis problem for multimode systems considering the complex circumstances such as uncertain tests and system delay.     

基于WA-ABC-LSSVM的电气故障诊断

为了改善当前电气故障诊断的效果,提出一种基于小波消噪和人工蜂群优化最小二乘支持向量机的电气故障诊断方法(WA-ABC-LSSVM)。首先收集电气状态信息,并采用小波变换对其进行去噪处理,消除噪声的干扰,然后提取电气状态中的特征,并且进行归一化处理,最后采用训练样本对最小二乘支持向量机进行训练,采用人工蜂群算法优化最小二乘支持向量机参数,建立电气故障诊断分类器。仿真实验结果表明,本文方法可以较好描述电气系统的工作状态,诊断性能要明显优于其它的电气故障诊断方法。     

Throughput Maximization in Cognitive Radio System with Transmission Probability Scheduling and Traffic Pattern Prediction

In this paper, we propose a novel transmission probability scheduling (TPS) scheme for the opportunistic spectrum access based cognitive radio system (OSA-based CRS), in which the secondary user (SU) optimally schedules its transmission probabilities in the idle period of the primary user (PU), to maximize the throughput of the SU over a single channel when the collision probability perceived by the PU is constrained under a required threshold. Particularly, we first study the maximum achievable throughput of the SU when the proposed TPS scheme is employed under the assumption that the distribution of the PU idle period is known and the spectrum sensing is perfect. When the spectrum sensing at the SU is imperfect, we thoroughly quantify the impact of sensing errors on the SU performance with the proposed TPS scheme. Furthermore, in the situation that the traffic pattern of the PU and its parameters are unknown and the spectrum sensing is imperfect, we propose a predictor based on hidden Markov model (HMM) for the proposed TPS scheme to predict the future PU state. Extensive simulations are conducted and show that the proposed TPS scheme with the HMM-based predictor can achieve a reasonably high SU throughput under the PU collision probability constraint even when the sensing errors are severe.     

神经网络专家系统在电机故障诊断中的应用

针对传统专家系统不能进行自学习、自适应的问题,本文提出了基于神经网络专家系统的异步电机故障诊断方法。本文将小波神经网络和专家系统相结合,充分发挥了二者故障诊断的优点,很大程度上降低了对电机的诊断误差。仿真结果验证了该算法的有效性。     

基于HMM和PNN的混合语音识别模型

基于隐马尔可夫模型(HMM)和改进后的概率神经网络(PNN)模型提出了一种用于语音识别的混合模型,该模型首先利用HMM生成最佳语音状态序列,然后对最佳状态序列进行时间规整,最后通过PNN神经网络进行分类识别。给出了HMM参数训练及时间规整的算法。实验结果表明这种模型比HMM具有更好的识别效果。     

Application of the Gibbs distribution to hidden Markov modeling inspeaker independent isolated word recognition

A method of integrating the Gibbs distributions (GDs) into hidden Markov models (HMMs) is presented. The probabilities of the hidden state sequences of HMMs are modeled by GDs in place of the transition probabilities. The GDs offer a general way in modeling neighbor interactions of Markov random fields where the Markov chains in HMMs are special cases. An algorithm for estimating the model parameters is developed based on Baum reestimation, and an algorithm for computing the probability terms is developed using a lattice structure. The GD models were used for experiments in speech recognition on the TI speaker-independent, isolated digit database. The observation sequences of the speech signals were modeled by mixture Gaussian autoregressive densities. The energy functions of the GDs were developed using very few parameters and proved adequate in hidden layer modeling. The results of the experiments showed that the GD models performed at least as well as the HMM models     

中文HMM参数化语音合成系统构建

在语音合成领域,大语料库拼接合成方式有一些固有弱点,例如语料库建设成本过高,合成稳定性差等。而基于隐马尔可夫模型(HMM)的语音合成技术在多样化语音合成、多语言支持、系统资源占用方面优势明显。分析了基于HMM的参数化语音合成技术的基本结构和核心算法,研究语料库建设,声学参数提取,建模单元和HMM拓扑结构选择等问题,给出适合于中文语音的参数设置,实现基于HMM的参数化中文语音合成。     

Hidden Markov Model Based Localization Using Array Antenna

We present a hidden Markov model (HMM) based localization using array antenna. In this method, we use the eigenvector spanning signal subspace as a location dependent feature. The eigenvector does not depend on received signal strength but on direction of arrival of incident signals. As a result, the eigenvector is robust to fading and noise. In addition, the eigenvector is unique to the environment of propagation due to indoor reflection and diffraction of the radio wave. The conventional localization method based on fingerprinting does not take previous information into account. In our proposal algorithm with HMM, we take previous state of estimation into account by comparing the eigenvector obtained during observation with the one stored in the database. The database has the eigenvector obtained at each reference point according to setting in advance. In an indoor environment represented in a quantized grid, we design the transition probability due to previous estimated position. Because of this, target’s movable range is obtained. In addition, we use maximum likelihood estimation method based on statics of correlation values. The correlation value is an indicator of pattern matching in a fingerprinting method. The most likely trajectory is calculated by Viterbi algorithm with above mentioned probabilities. The experimental results show that the localization accuracy is improved owing to the use of HMM.     

Training a Hidden Markov Model with a Bayesian Spiking Neural Network

It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. This paper explores a novel hybrid approach for classifying segments of sequential data, such as individual spoken works. The approach combines a hidden Markov model (HMM) with a spiking neural network (SNN). The HMM, consisting of states and transitions, forms a fixed backbone with nonadaptive transition probabilities. The SNN, however, implements a biologically based Bayesian computation that derives from the spike timing-dependent plasticity (STDP) learning rule. The emission (observation) probabilities of the HMM are represented in the SNN and trained with the STDP rule. A separate SNN, each with the same architecture, is associated with each of the states of the HMM. Because of the STDP training, each SNN implements an expectation maximization algorithm to learn the emission probabilities for one HMM state. The model was studied on synthesized spike-train data and also on spoken word data. Preliminary results suggest its performance compares favorably with other biologically motivated approaches. Because of the model’s uniqueness and initial promise, it warrants further study. It provides some new ideas on how the brain might implement the equivalent of an HMM in a neural circuit.     

基于隐马尔可夫模型的切换飞行控制系统性能分析

该文研究了一种由电磁环境对电子设备产生的数字干扰来驱动的切换飞行控制系统性能分析模型。其中,从电磁干扰的产生原理角度,采用隐马尔可夫模型(Hidden Markov Model, HMM)描述数字电磁干扰特性并对其进行建模分析,同时针对HMM参数训练算法存在对初值选择敏感的问题,提出一种快速的初值选择策略,可以在经典Baum-Welch算法迭代下达到指定的收敛精度。最后将HMM电磁干扰注入分布式飞行控制系统性能观测平台,从理论与仿真的角度对比了不同电磁环境下分布式飞行控制系统的性能下降情况。仿真实验表明：与已有的数字电磁干扰建模分析方法相比,HMM具有更高的准确度,并且仿真所得性能下降程度与理论分析一致     

Blind equalization of IIR channels using hidden Markov models andextended least squares

In this paper, we present a blind equalization algorithm for noisy IIR channels when the channel input is a finite state Markov chain. The algorithm yields estimates of the IIR channel coefficients, channel noise variance, transition probabilities, and state of the Markov chain. Unlike the optimal maximum likelihood estimator which is computationally infeasible since the computing cost increases exponentially with data length, our algorithm is computationally inexpensive. Our algorithm is based on combining a recursive hidden Markov model (HMM) estimator with a relaxed SPR (strictly positive real) extended least squares (ELS) scheme. In simulation studies we show that the algorithm yields satisfactory estimates even in low SNR. We also compare the performance of our scheme with a truncated FIR scheme and the constant modulus algorithm (CMA) which is currently a popular algorithm in blind equalization     

An advanced diagnostic method for delay faults in combinational faulty circuits

Due to physical defects or process variations, a logic circuit may fail to operate at the desired clock speed. So, verifying the timing behavior of digital circuits is always necessary, and needs to test for delay faults. When a delay fault has been detected, a specific diagnostic method is required to locate the site of the fault in the circuit. So, a reliable method for delay fault diagnosis is proposed in this paper. Firstly, we present the basic diagnostic method for delay faults, which is based on multivalued simulation and critical path tracing. Next, heuristics are given that decrease the number of critical paths and improve diagnosis results. In the second part of this paper, we provide an approximate method to refine the results obtained with the basic diagnostic process. We compute the detection threshold of the potential delay faults, and use statistical studies to classify the faults from the most likely to be the cause of failure to the less likely. Finally, results obtained with ISCAS'85 circuits are presented to show the effectiveness of the method.     

基于人工鱼群算法和LS_SVM的变压器故障诊断

针对变压器故障数据的特征信息不确定性以及传统诊断方法准确率较低的问题,文中采用人工鱼群算法和最小二乘支持向量机相结合的方法来进行变压器故障诊断。将IECTC10数据库中的DGA特征气体比值作为输入,建立基于最小二乘支持向量机的变压器故障诊断模型,并运用人工鱼群算法对最小二乘支持向量机的参数进行优化选取。然后根据诊断结果,选出分类效果最佳的多比值特征参量组合。实验验证结果显示,文中所提出的诊断方法准确率可达96.67%,拥有更高的故障诊断正确率。     

隐马尔科夫模型检测LDoS攻击方法的研究

针对低速率拒绝服务LDoS （Low-Rate Denial of Service）攻击具有平均速率低、隐蔽性强的特点,提出了一种基于隐马尔科夫模型的LDoS攻击检测方法。首先对网络状态建立隐马尔科夫模型,将归一化累计功率谱密度NCPSD（Normalized Cumulative Power Spectrum Density）方法的检测结果作为隐马尔科夫模型的观测值。利用前向算法得到不同观测值序列在该模型下的相似度作为检测依据。在NS 2中对本检测方法进行测试,实验结果表明本方法能够有效的检测LDoS攻击,与其他方法相比也具有更好的检测性能。通过假设检验得出检测率为99.96%。      

基于FVQ/HMM的无教师说话人自适应

本文提出了一种新的语音识别方法,它综合了VQ、HMM和无教师说话人自适应算法的优点,在每个状态通过用矢量量化误差值取代传统HMM的输出概率值来建立FVQ/HMM,同时采用基于模糊矢量量化的无教师自适应算法,来改变FVQ/HMM的各状态的码字,从而实现对未知说话人的码本适应.本文通过非特定人汉语数码(孤立和连续数码)语音识别实验,把该新的组合方法同基于CHMM的自适应和识别方法进行了比较,实验结果表明该方法的自适应和识别效果优于基于CHMM的方法.     

On-line estimation of hidden Markov model parameters based on theKullback-Leibler information measure

Sequential or online hidden Markov model (HMM) signal processing schemes are derived, and their performance is illustrated by simulation. The online algorithms are sequential expectation maximization (EM) schemes and are derived by using stochastic approximations to maximize the Kullback-Leibler information measure. The schemes can be implemented either as filters or fixed-lag or sawtooth-lag smoothers. They yield estimates of the HMM parameters including transition probabilities, Markov state levels, and noise variance. In contrast to the offline EM algorithm (Baum-Welch scheme), which uses the fixed-interval forward-backward scheme, the online schemes have significantly reduced memory requirements and improved convergence, and they can estimate HMM parameters that vary slowly with time or undergo infrequent jump changes. Similar techniques are used to derive online schemes for extracting finite-state Markov chains imbedded in a mixture of white Gaussian noise (WGN) and deterministic signals of known functional form with unknown parameters