A comparative study of recent relay autotuning methods for multivariable systems

A typical procedure for designing multivariable controllers is the following: build a model for the multivariable process, choose the control structure, calculate the control parameters, test the controller (possibly with simulation) and then retune controller parameters as necessary. This procedure is complex and time consuming even for scalar control loops. For multivariable controllers, the procedure is even more daunting. Automation of the design method is and has been a concern of many researchers. There has been a large number of papers on relay autotuning of control systems. The choice of relay feedback to solve the design problem is justified by the possible integration of system identification and control into the same design strategy, giving birth to relay autotuning. In this paper, nine different relay autotuning methods for multivariable systems are compared. Most of these methods have common basics but they may differ in the tuning procedure, convergence, identification method, control structure and performance achievement. The paper summarizes these methods and investigates the advantages and drawback of each algorithm.     

Parameter estimation of stochastic linear systems with noisy input

This paper addresses the problem of parameter estimation of stochastic liner systems with noisy input–output measurements. A new and simple estimation scheme for the variances of the white input and output measurement noises is presented, which is only based on expanding the denominator polynomial of the system transfer function and makes no use of the average least-squares errors. The attractive feature of the iterative least-square based parametric algorithm thus developed is its improved convergence property. The effectiveness of the developed identification algorithm is demonstrated through numerical illustrations.     

A method for parameter estimation of a class of non-linear distributed systems under noisy observations

A method is presented for estimating an unknown parameter of a distributed parameter system which depends on the system state. The system considered is modelled by a class of non-linear partial differential equations of a parabolic type. Noisy observations are assumed to be taken through an arbitrary number of sensors allocated on the spatial region. First, the explicit form of the stationary solution of the state equation is discussed. Second, use is made of the maximum likelihood approach to obtain the optimal estimate of the unknown parameter. Consistency properties w.p.1 of the optimal estimate obtained are also shown. Finally, results of digital simulation experiments are included to support the theoretical aspects.     

Novel methodologies and a comparative study for manufacturing systems performance evaluations

The purpose of this work is to establish complex fuzzy methodologies in the evaluation of a manufacturing system’s performance. Many empirical studies have been presented about the evaluation of manufacturing system’s performance. However, the performance evaluation is quite subjective, since it relies on the individual judgment of the managers who have different, various and multi-factor assessment methods of a system’s performance. In this study, two fuzzy modeling designs were developed and in the construction of the models, a hierarchy process was used. In the first method, the performance factors and the Analytic Hierarchy Process (AHP) were fuzzified and the use of fuzzy numbers and a fuzzy AHP for this problem was recommended. Also, the relative importance of these factors with respect to each other and their contribution to the overall performance was quantified with fuzzy linguistic terms. In the other method, we proposed Approximate Reasoning (AR) based on experts’ knowledge which is represented with the collection of the rules. These fuzzy rule bases are “if-then” linguistic rules that are formed with linguistic variables such as poor, below average, average, above average and superior. Additionally, the problem was structured with the normal AHP and System-With-Feedback (SWF), Finally, these methods were compared. The results showed that fuzzy AHP leads to the best result. It is expected that the recommended models would have an advantage in the competitive manufacturing including cost, flexibility, quality, speed and dependability.     

结合语音增强的基音检测改进方法

针对低信噪比情况下,语音信号传统的基音检测方法鲁棒性较差的问题,提出一种结合语音增强的基音检测改进方法。通过基于听觉掩蔽的多频带谱减法减小带噪语音信号背景噪声,得到较干净的语音;将增强后的语音作为基音检测的待处理语音,利用能零积和能零比的多门限法对其进行清浊音判决;在平均幅度差函数（AMDF）加权自相关函数（ACF）的基础方法上进行改进,实现精确的基音检测。理论与仿真结果表明,在信噪比为－10dB时,该方法依然能够精确检测基音周期,鲁棒性明显提高。     

Suboptimal state estimation for continuous-time nonlinear systems from discrete noisy measurements

This paper presents the derivation of the dynamical equations of a second-order filter which estimates the states of a non-linear plant on the basis of discrete noisy measurements. The filter equations contain terms involving the second-order partial derivatives of the plant and output equations. Simulation results are presented which yield a comparison of the performance of the first-versus the second-order filter when applied to a nonlinear third-order system. The results indicate that the inclusion of second-order terms can markedly improve the filter performance.     

基音周期标准化与嵌入式语音识别系统

根据不同人发相同音节时,一个基音周期内的波形具有一定相似性的特点,提出一种新的基音周期标准化的语音信号预处理方法。该方法在一个嵌入式的、非特定人、孤立数字的语音识别系统中进行了验证,实验结果表明基音周期标准化能有效提高语音识别的准确率。     

On the estimation of state variables and parameters for noisy dynamic systems

The problem of estimating state variables and parameters is considered for discrete-time systems in the presence of random disturbances and measurement noise. The solution of the linear problem is given and an approximation technique is developed for nonlinear systems. A dynamic programming formulation of the estimation problem is also developed.     

A fast algorithm for parallel model combination for noisy speech recognition

Based on the log-normal assumption, parallel model combination (PMC) provides an effective method to adapt the cepstral means and variances of speech models for noisy speech recognition. In addition, the log-add method has been derived to adapt the mean by ignoring the cepstral variance during the process of PMC. This method is efficient for speech recognition in a high signal-to-noise ratio (SNR) environment. In this paper, a new interpretation of the log-add method is proposed. This leads to a modified scheme for performing the adaptation procedure in PMC. This modified method is shown to be efficient in improving recognition accuracy in low SNR. Based on this modified PMC method, we derive a direct adaptation procedure for the variance of speech models in the cepstral domain. The proposed method is a fast algorithm because the computation for the transformation of the covariance matrix is no longer required. Three recognition tasks are conducted to evaluate the proposed method. Experimental results show that the proposed technique not only requires lower computational cost but it also outperforms the original PMC technique in noisy environments.     

Auditory driven subband speech enhancement for automatic recognition of noisy speech

Speech recognizers achieve high recognition accuracy under quiet acoustic environments, but their performance degrades drastically when they are deployed in real environments, where the speech is degraded by additive ambient noise. This paper advocates a two phase approach for robust speech recognition in such environment. Firstly, a front end subband speech enhancement with adaptive noise estimation (ANE) approach is used to filter the noisy speech. The whole noisy speech spectrum is portioned into eighteen dissimilar subbands based on Bark scale and noise power from each subband is estimated by the ANE approach, which does not require the speech pause detection. Secondly, the filtered speech spectrum is processed by the non parametric frequency domain algorithm based on human perception along with the back end building a robust classifier to recognize the utterance. A suite of experiments is conducted to evaluate the performance of the speech recognizer in a variety of real environments, with and without the use of a front end speech enhancement stage. Recognition accuracy is evaluated at the word level, and at a wide range of signal to noise ratios for real world noises. Experimental evaluations show that the proposed algorithm attains good recognition performance when signal to noise ratio is lower than 5 dB.     

Adaptive nonlinear observer for state and unknown parameter estimation in noisy systems

This paper proposes a novel adaptive observer for Lipschitz nonlinear systems and dissipative nonlinear systems in the presence of disturbances and sensor noise. The observer is based on an H_∞ observer that can estimate both the system states and unknown parameters by minimising a cost function consisting of the sum of the square integrals of the estimation errors in the states and unknown parameters. The paper presents necessary and sufficient conditions for the existence of the observer, and the equations for determining observer gains are formulated as linear matrix inequalities (LMIs) that can be solved offline using commercially available LMI solvers. The observer design has also been extended to the case of time-varying unknown parameters. The use of the observer is demonstrated through illustrative examples and the performance is compared with extended Kalman filtering. Compared to previous results on nonlinear observers, the proposed observer is more computationally efficient, and guarantees state and parameter estimation for two very broad classes of nonlinear systems (Lipschitz and dissipative nonlinear systems) in the presence of input disturbances and sensor noise. In addition, the proposed observer does not require online computation of the observer gain.     

Source code size estimation approaches for object-oriented systems from UML class diagrams: A comparative study

BackgroundSource code size in terms of SLOC (source lines of code) is the input of many parametric software effort estimation models. However, it is unavailable at the early phase of software development.ObjectiveWe investigate the accuracy of early SLOC estimation approaches for an object-oriented system using the information collected from its UML class diagram available at the early software development phase.MethodWe use different modeling techniques to build the prediction models for investigating the accuracy of six types of metrics to estimate SLOC. The used techniques include linear models, non-linear models, rule/tree-based models, and instance-based models. The investigated metrics are class diagram metrics, predictive object points, object-oriented project size metric, fast&&serious class points, objective class points, and object-oriented function points.ResultsBased on 100 open-source Java systems, we find that the prediction model built using object-oriented project size metric and ordinary least square regression with a logarithmic transformation achieves the highest accuracy (mean MMRE = 0.19 and mean Pred(25) = 0.74).ConclusionWe should use object-oriented project size metric and ordinary least square regression with a logarithmic transformation to build a simple, accurate, and comprehensible SLOC estimation model.     

A comparative study of probability estimation methods for reliability analysis

In this paper we investigate the performance of probability estimation methods for reliability analysis. The probability estimation methods typically construct the probability density function (PDF) of a system response using estimated statistical moments, and then perform reliability analysis based on the approximate PDF. In recent years, a number of probability estimation methods have been proposed, such as the Pearson system, saddlepoint approximation, Maximum Entropy Principle (MEP), and Johnson system. However, no general guideline to suggest a most appropriate probability estimation method has yet been proposed. In this study, we carry out a comparative study of the four probability estimation methods so as to derive the general guidelines. Several comparison metrics are proposed to quantify the accuracy in the PDF approximation, cumulative density function (CDF) approximation and tail probability estimations (or reliability analysis). This comparative study gives an insightful guidance for selecting the most appropriate probability estimation method for reliability analysis. The four probability estimation methods are extensively tested with one mathematical and two engineering examples, each of which considers eight different combinations of the system response characteristics in terms of response boundness, skewness, and kurtosis.     

Hybrid continuous speech recognition systems by HMM,MLP and SVM: a comparative study

This paper presents a new hybrid method for continuous Arabic speech recognition based on triphones modelling. To do this, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities within the Hidden Markov Models (HMM) standards. In this work, we describe a new approach of categorising Arabic vowels to long and short vowels to be applied on the labeling phase of speech signals. Using this new labeling method, we deduce that SVM/HMM hybrid model is more efficient then HMMs standards and the hybrid system Multi-Layer Perceptron (MLP) with HMM. The obtained results for the Arabic speech recognition system based on triphones are 64.68 % with HMMs, 72.39 % with MLP/HMM and 74.01 % for SVM/HMM hybrid model. The WER obtained for the recognition of continuous speech by the three systems proves the performance of SVM/HMM by obtaining the lowest average for 4 tested speakers 11.42 %.     

Multivariate density estimation: A comparative study

This paper is a continuation of the authors' earlier work [1], where a version of the Tråvén's [2] Gaussian clustering neural network (being a recursive counterpart of the EM algorithm) has been investigated. A comparative simulation study of the Gaussian clustering algorithm [1], two versions of plug-in kernel estimators and a version of Friedman's projection pursuit algorithm are presented for two- and three-dimensional data. Simulations show that the projection pursuit algorithm is a good or a very good estimator, provided, however, that the number of projections is suitably chosen. Although practically confined to estimating normal mixtures, the simulations confirm general reliability of plug-in estimators, and show the same property of the Gaussian clustering algorithm. Indeed, the simulations confirm the earlier conjecture that this last estimator proivdes a way of effectively estimating arbitrary and highly structured continuous densities on R^d, at least for small d, either by using this estimator itself or, rather, by using it as a pilot estimator for a newly proposed plug-in estimator.     

Single-channel speech separation using empirical mode decomposition and multi pitch information with estimation of number of speakers

Speech separation is an essential part of any voice recognition system like speaker recognition, speech recognition and hearing aids etc. When speech separation is applied at the front-end of any voice recognition system increases the performance efficiency of that particular system. In this paper we propose a system for single channel speech separation by combining empirical mode decomposition (EMD) and multi pitch information. The proposed method is completely unsupervised and requires no knowledge of the underlying speakers. In this method we apply EMD to short frames of the mixed speech for better estimation of the speech specific information. Speech specific information is derived through multi pitch tracking. To track multi pitch information from the mixed signal we apply simple-inverse filtering tracking and histogram based pitch estimation to excitation source information along with estimating the number of speakers present in the mixed signal.     

Some performance benchmarks for isolated work speech recognition systems

The performance of isolated word speech recognition system has steadily improved over time as we learn more about how to represent the significant events in speech, and how to capture these events via appropriate analysis procedures and training algorithms. In particular, algorithms based on both template matching (via dynamic time warping (DTW) procedures) and hidden Markov models (HMMs) have been developed which yield high accuracy on several standard vocabularies, including the 10 digits (zero to nine) and the set of 26 letters of the English alphabet (A-Z). Results are given showing currently attainable performance of a laboratory system for both template-based (DTW) and HMM-based recognizers, operating in both speaker trained and speaker independent modes, on the digits and the alphabet vocabularies using telephone recordings. We show that the average error rates of these systems, on standard vocabularies, are significantly lower than those reported several years back on the exact same databases, thereby reflecting the progress which has been made in all aspects of the speech recognition process.     

A lower bound on the estimation performance for stochastic control systems

A lower bound is derived on the mean-square-error in estimating the state of a nonlinear, stochastic, feedback-control system from nonlinear, non-Gaussian measurements.     

Sparse estimation from noisy observations of an overdetermined linear system

This note studies a method for the estimation of a finite number of unknown parameters from linear equations, which are perturbed by Gaussian noise. In the case the unknown parameters have only few nonzero entries, the proposed estimator performs more efficiently than a traditional approach. The method consists of three steps: (1) a classical Least Squares Estimate (LSE); (2) the support is recovered through a Linear Programming (LP) optimization problem which can be computed using a soft-thresholding step; (3) a de-biasing step using a LSE on the estimated support set. The main contribution of this note is a formal derivation of an associated ORACLE property of the final estimate. That is, with probability 1, the estimate equals the LSE based on the support of the true parameters when the number of observations goes to infinity.