• Bulletin of the Korean Mathematical Society
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• v.6 no.1
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• pp.37-39
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• 1969

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.415-424
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• 2009

As one of the unsupervised learning neural network methods, self-organizing maps(SOM) are applied to various fields. It reduces the dimension of multidimensional data by representing observations on the low dimensional manifold. On the other hand, the minimal spanning tree(MST) of a graph that achieves the most economic subset of edges connecting all components by a single open loop. In this study, we apply the MST technique to SOM with subnodes. We propose SOM's with embedded MST and a distance measure for optimum choice of the size and shape of the map. We demonstrate the method with Fisher's Iris data and a real gene expression data. Simulated data sets are also analyzed to check the validity of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.184-194
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• 1997

In the recent years number of commercial off-line character recognition systems have been appeared in the Korean market. This paper describes a "self -organizing" data structure for representing a large dictionary which can be searched in real time and uses a practical amount of memory, and presents a study on the character correction for off-line printed sentence marks, Japanese, English, and Chinese character recognition. Self-organizing algorithm can be recommenced as particularly appropriate when we have reasons to suspect that the accessing probabilities for individual words will change with time and theme. The wrongly recognized characters generated by OCR systems are collected and analyzed Error types of English characters are reclassified and 0.5% errors are corrected using an English character confusion table with a self-organizing dictionary containing 25,145 English words. And also error types of Chinese characters are classified and 6.1% errors are corrected using a Chinese character confusion table with a self-organizing dictionary carrying 34,593 Chinese words.ese words.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.439-448
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• 2006

Previous recognition/clustering algorithms such as Kohonen SOM(Self-Organizing Map), MLP(Multi-Layer Percecptron) and SVM(Support Vector Machine) might not adapt to unexpected input pattern. And it's recognition rate depends highly on the complexity of own training patterns. We could make up for and improve the weak points with lowering complexity of original problem without losing original characteristics. There are so many ways to lower complexity of the problem, and we chose a kernel concepts as an approach to do it. In this paper, using a kernel concepts, original data are mapped to hyper-dimension space which is near infinite dimension. Therefore, transferred data into the hyper-dimension are distributed spasely rather than originally distributed so as to guarantee the rate to be risen. Estimating ratio of recognition is based on a new similarity-probing and learning method that are proposed in this paper. Using CEDAR DB which data is written in cursive letters, 0 to 9, we compare a recognition/clustering performance of kSOM that is proposed in this paper with previous SOM.

• Journal of the Korean Society for information Management
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• v.24 no.3
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• pp.321-341
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• 2007

It will be possible to prevent the infringement of the trademarks and the insueing disputes regarding the originality of the trademarks by using an efficient content-based trademark image retrieval system. In this paper, we describe a content-based image retrieval system using the Self Organizing Map(SOM) algorithm. The SOM algorithm utilizes the visual features, which were derived from the gray histogram representation of the images. In addition, we made the objective effectiveness evaluation possible by coming up with a quantitative measure to gauge the effectiveness of the content-based image retrieval system.

• Korean Institute of Interior Design Journal
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• no.13
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• pp.181-186
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• 1997

If you compare recent works in small commercial places along with the terms such as Post-Modernism, Late-Modernism, and Dismantling, to those of 60's and 70's, you will discover for sure that recent works try to model individually with 'surface'. Expressing the responses to the multilateral cultural desires is available through the organization of surface, so are the re-presentationsof classic notions by adding decorations to the vertical walls, which are different from modern constructions that have persisted on only space itself as a main point. These trends are various organizing techniques with forms, decorations, materials, color etc. and made the surface modeling develop. What a 'surface element' means as a concrete notion in a design process in a commercial place is enormous. So, it implies lots of notions and logic, and can be a strong message-conveying means as well as an expressional language. At this point of view, the research was limited to Facade, which is treated as a creational object to a designer all the time in order to study what kind of system the surface organization has in a small commercial place. This is to find out what kind of system the designer uses to make the spatial images, orders and forms through surface, but ultimately it is to discover the designer's basic tecniques that he or she uses when planning. To sum up, we have studied the meanings of the composition and the changes of the organization of Facade to analyze the organizing techniques in Pasad in a small commercial construction which designers planned as material. We have also investigated the organizing techniques of surface by examining and analyzing the elements of Facade organizing techniques, i.e. its form, decoration, material, pattern and color.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.408-415
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• 2020

In this study, the AE-SOM method, which combines auto-encoder and self-organizing map, is used to detect and diagnose faults in EVA production process. Then, the fault propagation pathways are identified using Granger causality test. One year and seven months of operation data were obtained to detect faults of the process, and the process variables of the autoclave reactor are mainly analyzed. In the data pretreatment process, the data are standardized and 200 samples of each grade are randomly chosen to obtain a fault detection model. After that, the best matching unit (BMU) of each grade is confirmed by applying AE-SOM. The faults are determined based on each BMU. When a fault is found, the most causative variable of the fault is identified by using a contribution plot, and the fault propagation pathway is identified by Granger causality test. The prognostic of the two shutdowns is detected, and the fault propagation pathway caused by the faulty variable was analyzed.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.515-522
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• 2001

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Map(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented about k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.