bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal design of system reliability by an improved genetic algorithm
Authors: Yokota Takao , Gen Mitsuo , Ida Kenichi , Taguchi Takeaki 
Published: July 2007

AbstractWhen genetic algorithms are applied to large‐scale optimization problems taking only integer‐valued solutions, too often the chromosomes generated fail to satisfy the constraint conditions. In such cases, the selection operator is unable to produce high‐quality chromosomes, worsening the search efficiency. As a consequence, many times good solutions cannot be obtained. In this paper, in order to cope with such a problem, a new algorithm with improved chromosome selection mechanism is proposed, taking into account the evaluation function including some information about chromosomes not contained in the feasible region of the nonlinear integer programming problem under consideration. The proposed method considers how far each unfeasible chromosome is from the feasible region. The improved algorithm was applied to a large‐scale problem of optimal design of system reliability formulated as a nonlinear integer programmng problem and numerical experiments confirmed the effectiveness of the method.
An optimal design of differentiators by recursive narrowband filters
Authors: Tachibana Yasuo , Maniwa Hisakazu , Kameda Hiroshi , Kondo Michimasa 
Published: July 2007

AbstractThis paper proposes a narrowband filter, which is a finite impulse response (FIR) filter where the phase response exhibits a constant phase‐linear characteristic, independently of the center frequency, and the execution format is a sum of stable recursive filters. the optimal design of the differentiator (differentiation filter) using that narrowband filter is discussed.To realize a sharp narrowband characteristic, higher‐order FIR filters are needed. the narrowband filter proposed in this paper has the feature that it is the sum of the recursive filters, and the operation can be executed with the number of operations which is independent of the order. the filter considered is obtained as a linear combination of a number of narrowband filters with distributed center frequencies.By connecting the first‐order sum or difference filter in series to the above filter, a filter can be constructed which has an arbitrary amplitude response and approximately constant linear‐phase response. Especially, the optimal design procedure is given for the set of bandpass differentiator filters and application examples are shown for the estimation of the transfer function of the linear system.
Bifurcation sets in forced rayleigh oscillator containing a diode
Authors: Inaba Naohiko , Baba Norio , Ohnishi Masafumi 
Published: July 2007

AbstractThe forced Rayleigh equation is known as the differential equation for which the existence of the aperiodic solution is shown for the first time. From the viewpoint of the history of the research of chaos, it is very important to examine the bifurcvation in this equation. the authors considered the forced Rayleigh oscillator containing a diode. By considering the limit α → ∞ for a parameter α contained in the system, the ideal diode constrained equation is defined. Then, the rigorous analysis of the bifurcation phenomenon is made for the forced Rayleigh oscillator.This paper considers the forced Rayleigh oscillator containinig a large parameter α and shows that the bifurcation concerning the chaos generated in the oscillator can be accounted for by the ideal diode constrained equation by constructing and comparing the detailed bifurcation diagrams for the two systems.
An algorithm tracing out the tangent bifurcation curves and its application to duffing's equation
Authors: Kitajima Hiroyuki , Kawakami Hiroshi 
Published: July 2007

AbstractThe bifurcation diagram is constructed in the analysis of the bifurcation phenomenon. A problem in the conventional method of bifurcation diagram construction is that the computation is interrupted at the cusp point on the tangent bifurcation curve. Consequently, it has been necessary to determine the bifurcation set independently, excluding the cusp point, and then connect the curves afterward. This increases the time for constructing the bifurcation diagram when there exists a cusp point. Especially, when there exist multiple cusp points in the range where the parameter takes a small value, it has been almost impossible to trace the bifurcation curve.From such a viewpoint, this paper proposes an algorithm based on the concept of the vector field, where the tangent bifurcation curve can be determined as a continuous curve, even if cusp points are included. the proposed algorithm is applied to the Duffing equation, and the result of computation is shown when the tangent bifurcation set including multiple cusp points is traced.
“Individually normalized” least mean square algorithm suitable for fixed‐point processing and its application to active noise control
Authors: Fujii Kensaku , Ohga Juro 
Published: July 2007

AbstractBy the fixed‐point procesing, a cheap and fast processor can be obtained which is an effective approach in reducing the production cost. In the adaptive filter, where the coefficient correction which is calculated in each sampling period to be added to the filter coefficient, the adjustment of the filter coefficient is made impossible if the coefficient correction is less than the minimum value that can be represented by the fixed‐point representation.In the normalized least mean sqaure (NLMS) algorithm, the normalization using the norm is applied, which increases the probability that the coefficient correcton is less than the minimum value. This problem is solved by extracting the coefficient correction as the “difference between the impulse response of the unknown signal trnsmission system as the object of identification and its estimation.” In the “individually normalized” least mean square (LMS) algorithm, this difference is derived by calculating the ratio between the product‐sum of the residual response with adaptive filter output and the square‐sum of the tap output. Then, the result is normalized individually for each coefficient.This paper then preents the stability condition which serves as the design principle of the proposed method. the parallel‐shifting integrating configuration is presented, which has the computional complexity less than that of the NLMS algorithm. the method is applied to the filtered‐x algorithm, and the usefulness is verified.
A new recursive algorithm for linear equation and its application to adaptive signal processing
Authors: Furukawa Toshihiro , Maeda Tsunenori , Kubota Hajime 
Published: July 2007

AbstractThe solution of a system of linear equations is very important in various fields of engineering, and various methods have been presented for the numerical solution. Especially, in the adaptive signal processing and adaptive control, the system of equations is often considered where the coefficient matrix is (pseudo‐) positive‐definite and symmetrical. the conjugate graduate method is known as a means to solve such a system of equations. Letting the rank of the coefficient matrix be R, it is a method where the solution for the system of equatins is determined by iterating a certain procedure R times. In this metho, even if the iterative process is terminated on the way due to the constraint for the computation time, for example, an approximate solution which is somewhat accurate is obtained. This method, however, still has room for improvement with respect to the accuracy of the approximate solution.From such a viewpoint, this paper examines geometrically the iterative computation process in the conjugate gradient method and, based on that result, proposes a new method of iterative solution for the system of linear equations with symmetrical coefficient matrix. Then the original method is slightly modified and an adaptive algorithm is derived by applying the result to the parameter estimation of the adaptive filter. Those methods are based on the orthogonal projection operation from the solution to be determined to the linear space. Consequently, it is expected that, even if the iterative computation is terminated on the way, a more accurate approximate solution is obtained, compared to the case of the conjugate gradient method or the adaptive algorithm based on the conjugate gradient method. By the property of the method proposed in this paper the method is useful when the coefficient matrix is ill‐conditioned. It is expected that the method will effectively be utilized in the signal processing and control.
Approximation of group delay response of allpass filters using weighted least square method
Authors: Okuda Masahiro , Matsuyama Kojiro , Ikehara Masaaki 
Published: July 2007

AbstractAllpass filters with prescribed group‐delay response are useful in compensating such response. In general, the approximation of group‐delay response is formulated as a nonlinear problem; thus, its approximation becomes difficult. the design methods with nonlinear optimization have the problem that initial values of filter coefficients are needed and heavy computation is required. the design method is simplified in the conventional method where magnitude response is approximated instead of group‐delay response, but the approximation error is increased in the indirect methods.This paper proposes a method in which group‐delay response is approximated by a linear function and optimal solution is obtained in the least square sense by iteratively solving the linear equations. This method has the merit that the error of group‐delay response is directly evaluated. Moreover, applying Lawson's algorithm to this method, an equiripple solution can be obtained and maximum value of error can be decreased.
Improvement in stability and convergence speed on learning identification method
Authors: Fukumoto Masahiro , Kubota Hajime , Tsujii Shigeo 
Published: July 2007

AbstractLearning identification is one of the most widely utilized adaptive algorithms due to its small computational complexity. A problem with this algorithm is that a division by the square norm of the state vector is included in the coefficient update process which easily produces an instability when the nonstationary signal such as speech is given as the input. A method known to cope with such a problem and to realize the stable convergence involves not updating the coefficient when small input signals continue. This paper discusses this approach. As a first step, the situation is assumed where the square norm of the state vector is less than the specified threshold and the effect of interrupting the coefficient update is analyzed. the convergence and the time‐constant are formulated and the guarantee of the estimation accuracy for the convergence value is discussed. Then, the step gain is derived for the specified guarantee value so that the convergence speed is the fastest in the stochastic sense while ensuring stability. Lastly, the effectiveness of the proposed method is verified by computer simulation. It is shown that, even if the input signal is nonstationary, the fastest convergence speed is realized, together with the satisfactory estimation accuracy, which is always obtained according to the specified guarantee value after the convergence.
Optimization of the nonlinear function of a neural net‐type filter
Authors: Arakawa Kaoru , Yamakawa Koji , Koyama Mitsuo 
Published: July 2007

AbstractWhen a random noise is superposed on a non‐Gaussian signal with an abruptly changing component, as in the case of the image signal, it is impossible for the conventional linear filter to eliminate the noise effectively. This is one of the cases where the nonlinear filter should be applied.This paper proposes a nonlinear digital filter based on the layered neural network, including the optimization of the nonlinear function. the proposed filter can effectively eliminate the noise by utilizing the nonlinearity of the layered neural network and its learning function. Another point is that the performance can be improved by the piecewise‐linear approximation of the nonlinear function and the automatic design of the optimal nonlinear function. the effectiveness of the proposed filter is demonstrated by eliminating the noise from the two‐dimensional image signal.
Dynamic network for identification of character locations using self‐organized clustering
Authors: Kondo Masanori , Miyanaga Yoshikazu , Tochinai Koji 
Published: July 2007

AbstractThis paper proposes a new network that evaluates the locations of targets, i.e., target characters, and recognizes them in an image mixed with other objects, i.e., noncharacters. This network consists of a self‐organized clustering layer, a simple perceptron layer, and a controller. The purpose of the self‐organized clustering is to evaluate the stochastic characteristics of an input data set; the self‐organization is executed by calculating the distance between stochastic characteristics in this layer and the stochastic characteristics of an input vector. The simple perceptron layer determines the location of a target character and simultaneously recognizes its character by linearly dividing all outputs given from the self‐organized clustering layer. In the proposed system, the location identification and its recognition of a target character can be adaptively performed by using two small different compensative networks. The location identification and its recognition of real target characters, which are obtained in printed boards, are also shown in this paper.
High‐speed and high‐accuracy learning using a self‐organization network and a supervised network
Authors: Miyanaga Yoshikazu , Tochinai Koji 
Published: July 2007

AbstractThis paper introduces a feedforward network with a new learning algorithm into which both a self‐organized mechanism and a supervised training mechanism are implemented. In particular, from some experiments, it is seen that this network can realize sophisticated signal processing and analysis data with high‐speed training ability. the network is divided into two layers, that is, a self‐organized layer and a supervised layer. However, in the self‐organized layer, the training data of the network also are used for realizing high precision. In addition, the supervised layer also is trained in order to realize quite high accuracy for data recognition. In other words, the training data are used for both layers. Accordingly, the total precision of the network is improved greatly. In addition, the high‐speed organization and the parallel processing which are embedded in the original self‐organization also are realized in the new network.In this paper, some comparisons in which conventional neural networks, i.e., multilayer perceptron and self‐organization models, are used also are shown. In particular, as a new application of speech ARMA modelling, a new spectrum envelope estimation is explained by using this network.
Natural observation method for discrete‐time waveforms
Authors: Iwaki Mamoru , Iijima Taizo 
Published: July 2007

AbstractThe natural observation method provides a procedure to analyze the time‐varying characteristics of the continuous‐time waveform with mathematical rigor and can resolve the fundamental problem encountered in Fourier analysis method. In this paper, for discrete‐time waveforms a discrete natural observation method is proposed which has characteristics similar to those for the continuous natural observation method. Similar to the continuous natural observation method. Similar to the continuous natural observation theory of normal type, the discrete natural observation theory is introduced by requiring that the waveform values and the power component can be expressed in terms of sum forms. The discrete natural observation method is complete by itself as a discrete type, and not only is it similr in nature to those of the continuous type but also can be shifted naturally to the continuous type as the sampling interval vanishes.
A study of information‐presentation rate in a visual and auditory display
Authors: Kuma Hideki , Yokoi Hirokazu 
Published: July 2007

AbstractThis paper explores the information presentation rate in a visual and auditory display from the viewpoint of multimodal interface and carries out a comparison with that in a visual display or an auditory display. That is, using the time‐continuous model of short‐term memory, the optimum‐apportionment mode of letter presentation time, for which letters are most easily retained in shortterm memory, was determined for four subjects, when six randomly selected alphabet letters are sequentially presented in the same position on the screen and through a headset acoustically. This mode has shown the same tendency as that in a visual display or in an auditory display, but minimum presentation time has been slightly shorter than in the auditory display. the number of recalled letters in the case of presentation under the optimum‐apportionment mode is greater for all subjects than the maximum value of the number of recalled letters on condition that the presentation time of each letter is made equal. Moreover, there is a significant difference between the former and the latter. Accordingly, the effectiveness of the method employed here has been confirmed in the visual and auditory display as well. Furthermore, in the visual and auditory display, the number of recalled letters under the optimum‐apportionment mode was greater than that in the visual display or the auditory display for all subjects. In this case, significant difference was recognized for three subjects in a comparison within the visual display and for two subjects in the comparison within the auditory display.
Improved accessibility system—communication environment for the visually handicapped
Authors: Ohtake Nobuyuki , Ogawa Yasuhiko , Yonezawa Yoshimichi 
Published: July 2007

AbstractThe word communication in the computer science field is associated with data communication. In the original context, communication is “person‐to‐persons communication” using some language or the human voice. Nowadays, handicapped people are assisted in communication by informational equipment. the authors have developed several tools as welfare communication tools for handicapped people and related volunteers. the main theme is a support system for the visually impaired and blind. Not only is research level equipment developed, but also commercial products. In this paper, several are described, along with some problems encountered in the development of welfare communication and accessibility to handicapped people.
Memorizing and regenerating spatiotemporal patterns with a structured recurrent neural network
Authors: Li Yisheng , Miyanaga Yoshikazu , Tochinai Koji 
Published: July 2007

AbstractA local connected recurrent neural network with the ability to memorize and regenerate some nonlinear complex dynamics is proposed in this paper and a new learning algorithm for this network is also developed. the network, constructed of adaptive oscillating modules, is easily applied to the realization of nonlinear dynamics. the module consists of two simple neuron nodes with recurrent connections. the new learning algorithm can independently train each module with suitable speed for given input data. the network size is also adaptively determined during the learning process. This network also has a suitable structure of a parallel VLSI system. Finally, some simulation results are given to verify the effectiveness on the proposed network structure and the learning algorithm.
Constraints‐programmed macro‐model of information selection—limits of information‐processing and the basic principle of information behavior
Authors: Nagaya Tatsuhito 
Published: July 2007

AbstractInformation‐related activites are strongly influenced by many constraints at the individual level: physiology, time, economics, degree of interest and memory, and feelings of overload or saturation. However, excluding research on human recognizing and perceiving abilities, no attempts have been made so far to study theoretically, comprehensively, and systematically individual‐level information activities and choices in relation to the interconnected workings of such constraints programmed in the body as “interest,” “physiology,” “time,” and “money”.This paper first recognizes human beings as “living information‐processing systems,” and it studies mutual relations between information selection activities and the limits of human information‐processing capacity and intellectual‐physiological functions. This study is based on a “macro‐model of information selection” at the individual level. It also examines the framework for information selection and human functions for picking up information.
Human interface using surface electromyography signals
Authors: Koike Yasuharu , Kawato Mitsuo 
Published: July 2007

AbstractWe have been working toward the construction of a forward model arm employing a neural network model with electromyographic signals as the control input. We have succeeded in estimating 1) the joint torque during isometric contraction in a plane from the electromyographic signals, 2) the path of motion from the degree of joint acceleration as well as 3) the path of motion from the joint torque and 4) the position in three‐dimensional space. In this paper, we present a new human interface employing a model of an arm, robot control of an artifical hand, and the learning of motion capability. In addition, based on the fact that we can estimate the position of the arm in three‐dimensional space, we discuss as an example of the human interface the results of generating motion from electromyographic signals in a hypothetical arm using a neural network model.
Theoretical analysis on the allowable number of vehicles in automated guided vehicle systems
Authors: Sasaki Atsushi , Masuyama Shigeru , Yamakawa Eiki 
Published: July 2007

AbstractRecently, automated guided vehicle (AGV) systems have been introduced widely into plants. In most cases, the performance the AGV system is evaluated only by simulation and other similar methods. The authors are conducting research to analyze theoretically the performance of the AGV system. As the first step toward their goal, this paper reports on the result concerning the number of vehicles that can be contained in the system.
Multivalued regularization network‐a theory of multilayer networks for learning many‐to‐h mappings
Authors: Shizawa Masahiko 
Published: July 2007

AbstractThe regularization network (RN) is a network for learning input‐output mapping proposed by Poggio and Girosi from the viewpoint of learning = function approximation. the RN, which is derived from the standard regularization theory, is extended to an approximation of multivalued mappings from examples to make the learning of inverse models of nonlinear systems possible. This extension is called multivalued regularization network (MVRN). the MVRN is derived based on the multivalued standard regularization theory (MVSRT), which in turn is a based on direct representation of the mulitivalued functions by using Kronecker's tensor product. In the MVRN, the learning of the weight parameters from the traning data results in simultaneous linear equations that are similar to those of conventional regularization networks. In the present theory, the clustering operations for separating training data into those correponding to single‐valued element functions are unnecessary. As with the conventional methods, the radial basis function (RBF), generalized radial basis function (GRBF), spline approximations, and Hyper‐BF networks can be extended to those of multivalued functions by specializaing MVRN or by marking the number of basis functions smaller than the number of training samples. Direct representation of the multivalued function used in the present theory has broad application as a basic equation for more general classes of networks to learn multivalued mappings.