This article presents the past, present and future of research on fu=y theory, soft com-puting and those neighborhoods in AIJ, related Japan Society for Fuzzy Theory and Systems (SOFT) and Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT). Further, some examples from Shingu Lab., Nihon University will be introduced.

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The elements and the composition logic in nursing science are examined in order to clarify whether the nursing science can be reconstructed from the standpoint of the soft science. Most of medical applications of the soft science has been diagnosis of diseases from the numerical data or the diagnostic imaging, and there were few applications to nursing itself Therefore, it is important to determine the direction of the research as the beginning stage. In this paper, it is discussed what is the essence of the nursing, and after the discussion, the scientific part in nursing is selected as the elements of nursing science. The importance is pointed out that various methods of the soft science such as fuzzy, neuro-network, SOM, Genetic Algorithm should be introduced, and knowledge system of if-then rules should be built up on practice wisdom of the expert nurses including sentences and images.

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It is well known that fuzzy mapping plays an important and fundamental role in fuzzy analysis. In this paper, we attempt to determine extensive classes of these fuzzy mappings that are represented by their Taylor expansion. We first obtain that a continuous function f (x) can be transformed into a fuzzy mapping f (u) by using the extension principle. Furthermore we discuss some properties of the series of fuzzy numbers. From these properties we get that f (u) has the analytic properties similar to those of the analytic function f (x). Finally as applications, the author gives some examples.

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The gene exchange method in the inversion of GA is categorized into three types. Three modes show three different patterns about the exchange probability of genes, and the exchange probabilities of genes depend on their locations, except mode 3. In mode 1, gene exchange probability in the center area is higher than in the ends. In mode 2, gene exchange probability in the center area is smaller than in the ends. In mode 3, the exchange probability of genes does not depend on the location of those loci. The calculated results of gene exchange probability show clear agreement with our simulations.

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Conducting hierarchical cluster analysis, the optimal number of clusters should be de-cided in many cases. That is, it is a problem of which cutting level is optimal in a partition tree. Concerning this problem, whi le the steepest decent method in multivariate analysis and AIC method in statistical analysis has been designed, the author proposes the fuzzy decision method in fuzzy theory. The fuzzy decision method effectively enables us to clarify J. Moreno 's sociometry analysis concerning social psychology and so on. In this paper, we will not only discuss the decision method analysis of the optimal number of clusters, but also illustrate its practical effectiveness through an application in sociome-try analysis.

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This paper is a research of the fuzy theory to educational evaluation. In the first section, the authors introduced the difference between the evaluation in mathematics and drawing. Weighted mean would be also introduced in the same section since it is the most common evaluation method in school. For this reason, the authors describe an example of evaluation in mathematics to explain how it is used in school. In the second section, the authors discuss the way how to apply fuzzy theory to educational evaluation and also compare it with weighted mean. In the last section, the approximate reasoning would be extended to the approximate reasoning on fuzzy number. It would also show that the approximate reasoning on fuzzy number is a new approach to educational evaluation.

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Inexact information such as human behavior and cognition could be properly analyzed and clarified by applying fuzzy theory. In this paper, the authors present an analysis method of the opinion survey by applying fuzzy graph and also illustrate its practical effectiveness with the case study concerning the sport preference of college students.

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The authors could generally analyze the inexact information efficiently and investigate the fuzzy relation by applying the fuzzy graph theory. We would extend the fuzzy graph theory, and propose a fuzzy node fuzy graph. And we transform it to a fuzzy graph by using T-norm family. In this paper, we would discuss about four subjects, (1) fuzzy node fuzzy graph, (2) new T-norm "Uesu product", (3) decision analysis of the optimal fuzzy graph in the fuzzy graph sequence. By using the fuzzy node fuzzy graph theory and this new T-norm "Uesu product ", we could clarify the relational structure of fuzzy information, and by using the decision of an optimal level on a partition tree, we could analyze the clustering relation among nodes. Moreover, we would illustrate its practical effectiveness with the case study concerning sociometry analysis.

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It is suggested that the local maxima of spectrally dissociated vibration are detected in the cochlea of the inner ear for coding all the acoustic information of pitch, loudness and timbre in the auditory nerves. The margin of errors in time and amplitude on the extrema in the narrow-band waveforms of the equivalent vibration in the cochlea were emphasized in the previous study, ascer-taining that all sounds have been successfully reconstructed by using the sinusoidal interpolation between two successive extrema. In order to elucidate further the neuronal extraction process of acoustic attributes, we collated the statistics of phase and quantization errors in extrema and the measurement of subjective scores and event-related potentials, in response to synthesized sounds using manipulated extrema with either phase error or quantization error and estimation of probable extrema. The observations could be interpreted as supporting the assumption that the band-limited structure of sounds is restorable in symmetry about the time-axis, in which reduction of phase error is prior to quantization error, suggesting the most suitable index that is sensitive to the sound quality.

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Brain atrophy in CT images is often found in patients with Senile dementia of Alzheimer type(AD), but occasionally it isn 't prominent in some patients with symptoms of dementia. On the contrary, some patients with few symptoms of dementia sometimes show a remarkable atrophy in their brain CT This is one of the reasons why the diagnosis ofAD in the early stage is difficult. Therefore, it is very important that we develop an explicit method to measure the atrophy objectively. Fractal analysis is a method to estimate the complexity of morphology In this report, we applied this analysis to patients with AD and investigated the relationship between the fractal dimension of cortex surface and the score of HDS-R (Revised version of Hasegawa's dementia scale). As a result, we could not find any significant difference between them, unfortunately, in this preliminary study. Further study is needed, but we think that it will be possible to distinguish fine brain atrophy.

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Rubin (1975) identified seven major characteristics of "good language learners." I would like to focus specifically on Rubin's assertion that being a good guesser (deriving the meaning of language. from context) is essential for language learning. Research has shown that many Asian countries including Japan focus on rote memorization and translation when learning English. However, in my opinion such strategies ignore an essential part of the Japanese experience: Paying close attention to situational norms and context to achieve the desired result. In other words, Japa-nese people often make very accurate guesses during their personal and professional interactions. Using both outside research as well as my own in classroom exercises as case studies, I will show how Japanese teachers and students can harness this natural ability to learn how to better communicate in English.

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Brain tumor image classification and segmentation are an important but inherently difficult problem in magnetic resonance (MR) medical images. Artificial neural networks employed for image classification problems do not guarantee high accuracy besides being computationally heavy. The necessity for a large training set to achieve high accuracy is another drawback of ANN. On the other hand, fuzzy logic technique which promises better accuracy depends heavily on expert knowledge, which may not always available. Even though it requires less convergence time, it rely on trial and error method in selecting either the fuzzy membership Junctions or the fuzy rules. These problems are overcome by the hybrid model namely, neuro-fuzzy model. This system removes the stringent requirements since it enjoys the benefits of both ANN and the fuzzy logic systems. In this paper, the application of Adaptive neuro-fuzzy inference systems (ANFIS) for MR brain tumor classification has been demonstrated. Abnormal brain tumor images from four classes namely metastase, meningioma, glioma and astrocytoma are used in this work. A comprehensive feature set and fuzzy rules are selected to classify an abnormal image to the corresponding tumor type. Experimental results illustrate promising results in terms of classification accuracy and convergence rate. A comparative analysis is performed with the representatives of ANN and fuzzy systems to show the superior nature of ANFIS systems.

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We here propose a new evolutionary approach with learning to create a variety of behavioral patterns in autonomous robots. The conventional evolution or learning is to optimize a cost function such as.frtness function and error function. In practice, the robot encounters situations where exist multiple so-lutions having quite similar fitness or error values. The optimum solution is generally selected, while oth-ers are eliminated, even if the difference in the fitness or error is very little between the solutions. This causes an essential problem for behavior-based robots. Ideally, the robot should be able to select one of the behaviors by perceiving a slight difference in the sensory information, but the ability is lost. To over-come this problem, we introduced a structural learning during the evolution of neural network ensemble (NNE). Motor outputs were generated by summing outputs of component neural networks of an NNE, and they were trained to segregate each other by negative correlation learning between generations. In ex-periment, each component network exhibited different functionality, producing a variety of behaviors as a whole. The proposed evolution of NNE with negative-correlation learning thus can be a practical solution for the plasticity-stability problem in robotics.

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The EEG frequency bands are brain rhythms that indicate the activity level of the brain. This paper investigates the effects of the sub-band frequency on the classification of motor imagery of hand movements. Ten sub-bands of MHz width between 0 to 100 H_ are chosen. Band power features of the sub-bands are classified using a neural classifier. Motor imagery signals recorded from the C3 and C4 channels for four tasks are used in the analysis. Classification rates of 89.23% - 94.47% were achieved for sub-band frequencies ranging from 21HZ to 40 H_ for motor imagery signals. Results show that apart from mu and beta, low gamma frequencies are also better suited.for motor imagery classification

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Human or face tracking in a diverse environment is very important for various applications in computer vision, especially for video surveillance. Usually, a color cue offers many advantages over motion or geometric information which cannot robustly handle partial occlusion, rotation, scale and resolution changes. In this paper, we present a robust face tracking system by employing a color-based particle filter. The face detection technique is realized based on a Haar-like features algorithm. Here we exploit skin color cues for face-tracking, and it also proposes a body-part particle distribution system. This system is robust against occlusion by human or others and it can perform the tracking in real-time. We conducted experiments in both indoor and outdoor environments, with either a single or multiple persons in a view. Based on the color-based and body-part particle filter, we tracked a person's face satisfactorily by a developed simple robot system.

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Analysis of bio-signals in the clinical point of view is gaining wide range of application nowadays. These signals are degraded in their quality due the Motion artifacts which are as alike Gaussian noise in nature. This paper discusses a methodology to analysis the significance of.Photo-Plethysmographic (PPG) waveform with motion artifacts using four different wavelets such as Haar, db2, db4 and near symmetric wavelet (symlet-8). The hard and soft Thersholding techniques are compared. In this study four healthy subjects are analyzed. The Peak Signal to Noise Ratio (PSNR) measurements show the efficacy of the wavelet Thersholding at two levels (1 dB and 2dB) of added Gaussian noise to the signal. The Hard Thersholding outperforms the Soft one with higher PSNR values.

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Wisdom is considered as a kind of judgment based on the retrieved and extracted knowledge from miscellaneous information, and wisdom promotes the corresponding action. If we can get the wisdom from other person widely, we can do the effective action to achieve our purposes. When knowledge and information based on one's experience are understood, it becomes wisdom. So to get the wisdom it is the first step to collect a lot of knowledge which is composed of a set of low-level information, for instance, the component attributes of knowledge are situation, purpose, time, action and result of expectation. So we call them for a Knowledge capsule, and we try to evaluate the Knowledge capsule on the effect and cost. This paper describes the study to collect a lot of low-level knowledge for Project management by the Pro-totype system using WEB technology. This system has the input form and has the function to search infor-mation. By the way, Team work is necessary to accomplish the project. So, it is the problem how to collect the Knowledge capsule and how to use them to accomplish the project by team work in future.

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