Yuko Osana

Bio: Yuko Osana is an academic researcher from Tokyo University of Technology. The author has contributed to research in topics: Content-addressable memory & Self-organizing map. The author has an hindex of 10, co-authored 91 publications receiving 348 citations.

Chaotic Complex-Valued Bidirectional Associative Memory

Abstract: In this paper, we propose a Chaotic Complex-valued Bidirectional Associative Memory (CCBAM) which can realize one-to-many associations of multi-valued patterns. The proposed model is based on the Bidirectional Associative Memory, and is composed of complex-valued neurons and chaotic complex-valued neurons. In the proposed model, associations of multi-valued patterns are realized by using complex-valued neurons, and one-to-many associations are realized by using chaotic complex-valued neurons. We carried out a series of computer experiments and confirmed that the proposed model can realize one-to-many associations of multi-valued patterns.

Chaotic Complex-Valued Associative Memory

Abstract: —In this paper, we propose a chaotic complexvalued associative memory which can realize a dynamic association of multi-valued patterns. The proposed model is based on a complex-valued associative memory and a chaotic associative memory. The complex-valued associative memory can treat multi-valued patterns, and the chaotic associative memory can recall stored patterns dynamically. The proposed model utilizes the properties of these conventional models to realize the dynamic association of multi-valued patterns. We carried out a series of computer experiments and confirmed that the proposed model realizes the dynamic association. And, we show the influence of some parameters and state number for the dynamic association.

Self-Organizing Map with Refractoriness and Its Application to Image Retrieval

Abstract: In this research, we propose a self-organizing map with refractoriness (SOMR) and apply it to a similarity-based image retrieval. The proposed SOMR is based on the self-organizing map (SOM) and the refractoriness which is observed in the real neuron is introduced. In the proposed SOMR, the plural neurons in the Map Layer corresponding to the input can fire sequentially because of the refractoriness. The image retrieval system using the SOMR makes use of this property in order to retrieve plural similar images. In this image retrieval system, as the image feature, not only color information but also impression words and key words are employed. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed SOMR and the image retrieval system using the SOMR.

Improved chaotic associative memory using distributed patterns for image retrieval

Abstract: We propose an improved chaotic associative memory using distributed patterns for image retrieval. This model is based on our previous work, chaotic associative memory using distributed patterns that is based on the chaotic associative memory which can separate superimposed patterns and the multi winners self-organizing neural network which has the ability to generate distributed representation patterns corresponding to input in a self-organizing manner.

Similarity-based Image Retrieval by Self-Organizing Map with Refractoriness

Abstract: In this research, we proposed a similarity-based image retrieval by self-organizing map with refractoriness. In the self-organizing map with refractoriness, the plural neurons in the map layer corresponding to the input can fire sequentially because of the refractoriness. The image retrieval system using the self-organizing map with refractoriness makes use of this property in order to retrieve plural similar images. In this image retrieval system, as the image feature, not only color information but also spectrum, impression words and key words are employed. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed system.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

Pattern Recognition by Self-Organizing Neural Networks

Abstract: Pattern Recognition by Self-Organizing Neural Networks edited by Gail A. Carpenter and Stephan Grossberg, MIT Press, 1991, ISBN 0-262-03176-0.

Intelligent Evacuation Management Systems: A Review

Abstract: Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios.

Computational Intelligence in Music Composition: A Survey

Abstract: Composing music is an inspired yet challenging task, in that the process involves many considerations such as assigning pitches, determining rhythm, and arranging accompaniment. Algorithmic composition aims to develop algorithms for music composition. Recently, algorithmic composition using artificial intelligence technologies received considerable attention. In particular, computational intelligence is widely used and achieves promising results in the creation of music. This paper attempts to provide a survey on the computational intelligence techniques used in music composition. First, the existing approaches are reviewed in light of the major musical elements considered in composition, to wit, musical form, melody, and accompaniment. Second, the review highlights the components of evolutionary algorithms and neural networks designed for music composition.