• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.343-346
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• 1996

A new method for on-line induction motor fault detection is presented in this paper. This system utilizes unsupervised-learning clustering algorithm, the Dignet, proposed by Thomopoulos etc., to learn the spectral characteristics of a good motor operating on-line. After a sufficient training period, the Dignet signals one-phase ground fault, or a potential failure condition when a new cluster is formed and persists for some time. Since a fault condition is found by comparison to a prior condition of the machine, on-line failure prediction is possible with this system without requiring information on the motor of load characteristics.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.87-90
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• 1997

Mountain Method의 다른 형태인 Subtractive 클러스터링 알고리듬은 계산이 간단하고 기존의 클러스터링 방법들과는 달리 초기 클러스터 중심의 개수 선정이 필요 없기 때문에 클러스터를 추정하는데 효과적인 알고리듬이다. 또한 클러스터의 간격을 결정하는 파라미터의 값에 따라 클러스터의 개수를 다르게 할 수 있다. 그러나 이 파라미터에 의해 동일한 그룹(Class)내에서 여러 개의 클러스터 중심이 발생될 수도 있다. 본 논문에서는 Subtractive HyperBox 알고리듬을 사용하여 이 파라미터의 영향을 줄이고 발생한 클러스터 중심이 속한 그룹의 경계를 판정함으로서 같은 그룹내에서 하나의 클러스터만 발생하도록 하고, 순차적으로 클러스터링 한 후 결과를 Subtractive 클러스터링 알고리듬과 비교하여 보았다.

• 전기전자학회논문지
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• 제2권1호
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• pp.24-33
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• 1998

Independent Component analysis (ICA) is a new statistical method for extracting statistically independent components from their linear instantaneous mixtures which are generated by an unknown linear generative model. The recognition model is learned in unsupervised manner so that the recovered signals by the recognition model become the possibly scaled estimates of original source signals. This paper addresses the neural learning approach to ICA. As recognition models a linear feedforward network and a linear feedback network are considered. Associated learning algorithms for both networks are derived from maximum likelihood and information-theoretic approaches, using natural Riemannian gradient [1]. Theoretical results are confirmed by extensive computer simulations.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.798-801
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• 2002

This paper proposes unsupervised classification of multiple attributes via five-layer autoassociative neural network with bottleneck layer. In the conventional methods, high dimensional data are compressed into low dimensional data at bottleneck layer and then feature extraction is performed (Fig.1). In contrast, in the proposed method, analog data is compressed into digital data. Furthermore bottleneck layer is divided into two segments so that each attribute, which is a discrete value, is extracted in corresponding segment (Fig.2).

• ETRI Journal
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• 제38권3호
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• pp.487-493
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• 2016

Two new methods are proposed for an unsupervised adaptation of a language model (LM) with a single sentence for automatic transcription tasks. At the training phase, training documents are clustered by a method known as Latent Dirichlet allocation (LDA), and then a domain-specific LM is trained for each cluster. At the test phase, an adapted LM is presented as a linear mixture of the now trained domain-specific LMs. Unlike previous adaptation methods, the proposed methods fully utilize a trained LDA model for the estimation of weight values, which are then to be assigned to the now trained domain-specific LMs; therefore, the clustering and weight-estimation algorithms of the trained LDA model are reliable. For the continuous speech recognition benchmark tests, the proposed methods outperform other unsupervised LM adaptation methods based on latent semantic analysis, non-negative matrix factorization, and LDA with n-gram counting.

• Spatial Information Research
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• 제2권2호
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• pp.175-188
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• 1994

본 연구에서는 최근 많은 분야데서 패턴인식을 위한 효과적인 기법으로 이용되고 있는 신경망 기법을 원격탐사자료의 군집화 기법으로서 적용하고자 하였다. 이를 위해 선택된 신경망 모델은 경쟁학습 신경망이며 이를 구성하는 각종 변수들을 재구성하여 원격탐사자료의 군집화를 위한 신경망모델을 설정하였다. 본 신경망을 이용한 군집화 기법은 항공기를 이용하여 획득된 원격탐사자료를 이용하여 순차적(sequential)군집화 기법 K 평균 군집화 기법과 비교되었다. 계산시간은 순차적 기법이나 K 평균기법에 비하여 더 많이 소요되나 정확도면에 있어서는 비교적 우수한 결과를 나타냈다.

• International journal of advanced smart convergence
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• 제7권2호
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• pp.33-37
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• 2018

In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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• pp.254-268
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• 2013

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1415-1417
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• 2003

In this study, as the passing of a year, the changes of tidal flat area in Seohan Bay, North Korea was monitored through using Landsat ETM Data and the ancient topological map. The map to present tidal flat distribution characteristic based on the ancient topographical map (1918) was constructed as GIS DB. In addition, a tidal flat distribution map was estimated by using the satellite images with unsupervised classification method. Even though it is difficult to approach to study area, it was possible to gain the data and to monitor the change of the coast tidal flat by comparing to area change yielded.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.384-387
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• 2004

The direction and the type of a fault on a transmission line need to be identified rapidly and correctly, The work described in this paper addresses the problem encountered by a conventional algorithm in a fault type classification in double circuit line, this arises due to a mutual coupling and CT saturation under the fault condition. We present an approach to identify fault type with novel neural network on double circuit transmission line. The neural network based on combined unsupervised training method provides the ability classify the fault type by different patterns of the associated voltages and currents.