A new fast Newton‐type adaptive filtering algorithm for stereophonic acoustic echo cancellation (SAEC)

This paper addresses the problem of acoustic echo cancellation. We propose a new version of the fast Newton transversal filter algorithm for stereophonic acoustic echo cancellation applications. This algorithm can be viewed as an efficient implementation of the extended two‐channel fast transversal filter algorithm. Moreover, it fits naturally within the frame of the fast version of the recursive least‐squares (RLS) algorithm, applied to the two‐channel case. To stabilize the proposed two‐channel algorithm, we have adapted and then applied a new numerical stabilization technique that has been proposed recently. The computational complexity of the proposed two‐channel algorithm is less than half the complexity of the fastest two‐channel RLS versions and very close to that of the two‐channel normalized least mean squares algorithm when its predicting part length is chosen to be small. Simulation results and comparisons in term of complexities, convergence speed and tracking with the two‐channel algorithms are presented. Copyright © 2009 John Wiley & Sons, Ltd.     

A new two‐microphone Gauss‐Seidel pseudo affine projection algorithm for speech quality enhancement

This study addresses the problem of speech quality enhancement by adaptive and nonadaptive filtering algorithms. The well‐known two‐microphone forward blind source separation (TM‐FBSS) structure has been largely studied in the literature. Several two‐microphone algorithms combined with TM‐FBSS have been recently proposed. In this study, we propose 2 contributions: In the first, a new two‐microphone Gauss‐Seidel pseudo affine projection (TM‐GSPAP) algorithm is combined with TM‐FBSS. In the second, we propose to use the new TM‐GSPAP algorithm in speech enhancement. Furthermore, we show the efficiency of the proposed TM‐GSPAP algorithm in speech enhancement when highly noisy observations are available. To validate the good performances of our algorithm, we have evaluated the adaptive filtering properties in computational complexity and convergence speed performance by system mismatch criteria. A fair comparison with adaptive and nonadaptive noise reduction algorithms are also presented. The adaptive algorithms are the well‐known two‐microphone normalized least mean square algorithm, and the recently published two‐microphone pseudo affine projection algorithm. The nonadaptive algorithms are the one‐microphone spectral subtraction and the two‐microphone Wiener filter algorithm. We evalute the quality of the output speech signal in each algorithm by several objective and subjective criteria as the segmental signal‐to‐noise ratio, cepstral distance, perceptual evaluation of speech quality, and the mean opinion score. Finally, we validate the superior performances of the proposed algorithm with physically measured signals.     

Contribution estimation of participants for human interaction recognition

In this paper, we propose an efficient algorithm to recognize actions of human interaction. Unlike previous algorithms using two participants' actions, the proposed algorithm estimates the action contribution of participants to determine which participant's action is the major action for correct interaction recognition. To estimate this contribution, we construct a contribution interaction model for each interaction category in which both participants carry out major actions. Using the contribution models, we design a method that automatically estimates the contribution of participants and classifies interaction samples into “co‐contribution” and “single‐contribution” interactions. At the same time, the major actions in the “single‐contribution” interactions are determined. We evaluate our method on the UT‐interaction dataset and our original interaction dataset (LIMU). Recognition results indicate the robustness of the proposed method and the high estimation accuracy obtained: estimation accuracies of 96 and 98% in set 1 and set 2 of the UT dataset, respectively, and 97.8% in the LIMU dataset. Based on the estimation results, we extract the major action information for interaction recognition. Average recognition accuracies of 93.3% in set 1 and 91.7% in set 2 of the UT dataset were obtained. Our result is at least 5% better than those obtained with previous algorithms. For the LIMU dataset, recognition accuracy reached 91.1%. It was 8.9% higher than the recognition result without contribution estimation. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

State estimation of discrete‐time systems with arbitrarily correlated noises

In this paper, the state estimation problem for discrete‐time systems is considered where the noises affecting such systems do not require any constraint condition for the correlation and distribution, that is, the noises can be arbitrarily correlated and arbitrarily distributed random vector. For this, two filtering algorithms based on the criterion of linear minimum mean‐square error are proposed. The first algorithm is an optimal algorithm that can exactly compute the linear minimum mean‐square error estimate of system states. The second algorithm is a suboptimal algorithm that is proposed to reduce the computation and storage load of the proposed optimal algorithm. Computer simulations are carried out to evaluate the performance of the proposed algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

基于相位信息的旋转、缩放、位移不变的图像水印技术

数字水印技术将在电力系统中得到广泛应用。这些应用包括数字信号版权保护、内容认定、质量估计、数据库索引和检索等。该文介绍一种基本上不受旋转、缩放、位移影响的新的数字图像水印技术。笔者首先对原始图像进行傅立叶变换，然后再将水印埋嵌在该傅氏变换的幅度频谱的对数极坐标域。图像在空域中的旋转对应于该对数极坐标域中沿着角坐标的位移；图像在空域中的缩放对应于该对数极坐标域中沿着对数半径坐标的位移；而图像在空域中的水平位移对该对数极坐标域没有影响。为了计算和校正旋转和缩放在该对数极坐标域中造成的水印位置平移，笔者提出一种新的滤波方法。该滤波方法需要使用原始图像对数极坐标域中的一小方块信息来计算水印位置的平移。该水印技术能以最小的代价避免穷举搜索，以节省计算时间和减少误检率。实验数据证明该技术非常实用。     

The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle

For a special class of nonlinear systems (ie, bilinear systems) with autoregressive moving average noise, this paper gives the input‐output representation of the bilinear systems through eliminating the state variables in the model. Based on the obtained model and the maximum likelihood principle, a filtering‐based maximum likelihood hierarchical gradient iterative algorithm and a filtering‐based maximum likelihood hierarchical least squares iterative algorithm are developed for identifying the parameters of bilinear systems with colored noises. The original bilinear systems are divided into three subsystems by using the data filtering technique and the hierarchical identification principle, and they are identified respectively. Compared with the gradient‐based iterative algorithm and the multi‐innovation stochastic gradient algorithm, the proposed algorithms have higher computational efficiency and parameter estimation accuracy. The simulation results indicate that the proposed algorithms are effective for identifying bilinear systems.     

Global optimization with the PSO coupling‐type discrete gradient chaos model

In this paper, we propose a new multiagent discrete gradient chaos model using a coupling structure which PSO has. Concretely, first, we introduce a multiagent‐type optimization model whose agents search autonomously with the discrete gradient chaos model which is the simplest dynamical global search model, and they are coupled by convective coupling. Convective coupling in this model is used to aim at overcoming of emergence of boundary crisis which is a problem of the original discrete gradient chaos model. Second, we introduce PSO coupling structure, where population drifts to the “gbest” and the “pbest”, into discrete gradient chaos model. Then, we propose “PSO coupling‐type discrete gradient chaos model” with the search strategy based on objective function's value. In this paper, our proposed models are applied to several benchmark problems. The results show that our proposed models have better global optimization ability than the original discrete gradient chaos model and PSO model. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 165(4): 67–75, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20563     

Solving the m‐way graph partitioning problem using a genetic algorithm

The m‐way graph partitioning problem is of central importance in combinatorial optimization. It has many important applications in fields such as VLSI circuit design, task allocation in distributed computing systems, and network partitioning. In this paper, we propose an efficient genetic algorithm to solve this problem. The proposed method searches a large solution space and finds the best possible solution by adjusting the intensification and diversification automatically during the optimization process. The proposed method is tested on a large number of instances and compared with some existing algorithms. The experimental results show that the proposed algorithm is superior to its competitors in terms of computation time and solution quality. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Digital travelling-wave protection of transmission lines

Almost all the algorithms proposed so far for the digital protection of transmission lines are of the distance type and involve the extraction of the fundamental components from the complex post-fault waveforms through the use of filters, whereas those based on the travelling-wave theory need only a short data window and no filtering. This paper presents two new algorithms of the latter type along with the results of digital simulation tests which confirm their viability.     

Moment-based approaches in imaging part 3: computational considerations [A Look at...]

This paper discusses the computation of moments in imaging. Faster algorithms are needed to decrease the computational complexity for real-time environment applications.     

固定边界矩阵的并行潮流牛顿算法

为了克服基于BBDF分块的牛顿潮流的协调量计算过大的瓶颈,通过固定处理边界矩阵,提出了一种固定边界矩阵牛顿潮流并行算法,该法可显著减少每次迭代的协调计算量,提高并行效率。在IEEE118和IEEE300标准网络上,使用P42.4G的PC机对各个网络在分块数为2、4、8、12和16块时计算的结果表明,该算法在减少计算量的同时还具有较好收敛性,在大型互连网络上能提高计算速度。     

一种适用于微机保护的新的递推DFT算法

为减少离散傅里叶变换(DFT)算法的计算量,人们利用一种很自然的增减数据项的方法提出了递推傅里叶变换算法,但在某些情况下,该传统递推算法并不太适用。文中基于三角函数的和差公式以及线性方程组的求解,提出了一种新的递推离散傅里叶变换算法,它的计算量与传统递推算法接近,但可以解决传统递推算法不易解决的许多问题,如适用于人们提出的克服电力系统中衰减直流分量对DFT影响的一些方法中,因而具有广阔的应用前景     

Real‐time calculation of power system bus voltage using a hybrid approach combining the Newton–Raphson method and dynamic programming

The calculation of the magnitudes and phase angles of the bus voltage is a challenging task in real‐time applications for power systems. Voltage profile, which denotes the present conditions of a power system, is determined by executing the traditional AC power flow program or by searching the supervisory control and data acquisition system. The AC power flow program is not suitable for several real‐time applications, such as contingency analysis and security control calculations, because of its complexity and convergence problems. Fast computation is the major concern in such applications. In this paper, a new method based on sensitivity factors, referred to as Jacobian‐based distribution factors (JBDFs), is proposed for calculating the magnitudes and phase angles of bus voltages. This method requires setting up JBDFs and deriving optimal solution paths of bus voltage for non‐swing buses through dynamic programming under base‐case loading conditions. Under real‐time conditions, the proposed method initially calculates real and reactive power line flows via JBDFs, and then computes the voltage magnitudes and phase angles of non‐swing buses through the derived optimal solution paths. The excellence of the proposed hybrid calculation method is verified by IEEE test systems. Simulation results demonstrate that the proposed method exhibits fast computation and high accuracy. Thus, the method is suitable for real‐time applications. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

An information filtering method for emergency management

Information filtering is a function to select useful or interesting information for the user among a large amount of information. This function has become very important as network technology develops rapidly. This paper focuses on information filtering for emergency management. When a large‐scale disaster happens, the problem of information flood can be very serious because a great deal of information occurs in a short time and is sent to a person or an organization that is responsible for managing the situation. In this paper, we first consider features of information in emergency situations, and propose an information filtering system for emergency management. The proposed system is composed of three parts: message structuring part, causal analysis part, and information classification part. A prototype system is developed and its effectiveness is evaluated by experiments. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 147(1): 60–69, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10233     

Reactive power dispatch considering voltage stability with seeker optimization algorithm

Optimal reactive power dispatch (ORPD) has a growing impact on secure and economical operation of power systems. This issue is well known as a non-linear, multi-modal and multi-objective optimization problem where global optimization techniques are required in order to avoid local minima. In the last decades, computation intelligence-based techniques such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA) based method is proposed for ORPD considering static voltage stability and voltage deviation. The SOA is based on the concept of simulating the act of human searching where search direction is based on the empirical gradient by evaluating the response to the position changes and step length is based on uncertainty reasoning by using a simple Fuzzy rule. The algorithm's performance is studied with comparisons of two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms on the IEEE 57 and 118-bus power systems. The simulation results show that the proposed approach performed better than the other listed algorithms and can be efficiently used for the ORPD problem.     

Seeker Optimization Algorithm for Optimal Reactive Power Dispatch

Optimal reactive power dispatch problem in power systems has thrown a growing influence on secure and economical operation of power systems. However, this issue is well known as a nonlinear, multimodal and mixed-variable problem. In the last decades, computation intelligence-based techniques, such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA)-based reactive power dispatch method is proposed. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, the algorithm's performance is evaluated on benchmark function optimization. Then, the SOA is applied to optimal reactive power dispatch on standard IEEE 57- and 118-bus power systems, and compared with conventional nonlinear programming method, two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms. The simulation results show that the proposed approach is superior to the other listed algorithms and can be efficiently used for optimal reactive power dispatch.      

Set‐theoretic adaptive filtering based on data‐driven sparsification

In this article, we propose a fast and efficient algorithm named the adaptive parallel Krylov‐metric projection algorithm. The proposed algorithm is derived from the variable‐metric adaptive projected subgradient method, which has recently been presented as a unified analytic tool for various adaptive filtering algorithms. The proposed algorithm features parallel projection—in a variable‐metric sense—onto multiple closed convex sets containing the optimal filter with high probability. The metric is designed based on (i) sparsification by means of a certain data‐dependent Krylov subspace and (ii) maximal use of the obtained sparse structure for fast convergence. The numerical examples show the advantages of the proposed algorithm over the existing ones in stationary/nonstationary environments. Copyright © 2011 John Wiley & Sons, Ltd.     

Dynamic optimal route search algorithm for car navigation systems with preferences by dynamic programming

Optimal route search to the destination is one of the most important functions of car navigation devices. The development of road traffic infrastructure has made it possible to receive real‐time information of the traffic situation. Route search algorithms for car navigation devices make use of this information to avoid the traffic congestions. Such algorithms should find the new optimal route efficiently when the traffic situation changes. Usually, the minimum traveling time or distance is considered to define the optimal route. However, the minimum traveling time or distance is not always what the user is looking for. The user may prefer to travel on a certain route even at the cost of traveling time or distance. Car navigation devices should consider such preferences when finding the optimal route. In this paper, we propose a dynamic programming algorithm to find the optimal route considering that it should deal with the changes of the traffic situation and multiple criteria. The proposed method uses the information from the previous computation to find the new optimal route considering user preferences when the traveling time of the road section changes. The proposed method was applied to a real road network to find the optimal route. Results show that the proposed method can find the user‐preferred optimal route. Simulation results also show better calculation time of the proposed method compared to the Dijkstra algorithm. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Estimation algorithm for system with non‐Gaussian multiplicative/additive noises based on variational Bayesian inference

This paper considers estimation algorithms for linear and nonlinear systems contaminated by non‐Gaussian multiplicative and additive noises. Based on the variational idea, in order to derive optimal estimation algorithms, we combine the multiplicative noise with states as the joint parameters to estimate. The application of variational Bayesian inference to joint estimation of the state and the multiplicative noise is established. By treating the states as unknown quantities as well as the multiplicative noise, there are now correlations between the states and multiplicative noise in the posterior distribution. There are two main goals in Bayesian learning. The first is approximating the marginal likelihood (PDF of multiplicative noise) to perform model comparison. The second is approximating the posterior distribution over the states (also called a system model), which can then be used for prediction. The two goals constitute the iterative algorithm. The rules for determining the loop is the Kullback‐Leibler divergence between the true distribution of state and a chosen fixed tractable distribution, which is used to approximate the true one. The iterative algorithm is deduced, which is initialized based on the idea of sampling. Meanwhile, the convergence analysis of the proposed iterative algorithm is presented. The numerical simulation results in a comparison between the proposed method and these existing classic algorithms in the context of nonlinear hidden Markov models, state‐space models, and target‐tracking models with non‐Gaussian multiplicative noise demonstrate the superiorities, not only in speed, precision, and computation load but also in the ability to process non‐Gaussian complex noise.     

基于瞬时无功功率理论的新型谐波检测算法

为降低谐波提取环节的运算量,快速、准确地检测出三相系统的谐波电流,本文在传统的基于瞬时无功功率理论的id-iq法基础之上,提出了将谐波检测环节中锁相环的鉴相部分与Park变换相结合的新型谐波检测算法。所提方法不使用电压信息直接提取谐波,消除了传统方法中由于电压畸变、不对称和电压采集所带来的检测误差,且鉴相环节和Park变换相结合,节省了运算时间。与基于广义积分器和传统id、iq法的谐波检测算法相比,该方法可以更为快速准确地提取谐波。通过仿真和实验,验证了该谐波检测方法的正确性和优越性。