• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.191-196
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• 1997

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-Means(FCM) algorithm is most frequently used for fuzzy clustering. But some fixed point of FCM algorithm, know as Tucker's counter example, is not a reasonable solution. Moreover, FCM algorithm is impossible to perform the on-line learning since it is basically a batch learning scheme. This paper presents unsupervised learning networks as an attempt to improve shortcomings of the conventional clustering algorithm. This model integrates optimization function of FCM algorithm into unsupervised learning networks. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of a fuzzy objective function. Using the result of formal derivation, two algorithms of fuzzy cluster analysis, the batch learning version and on-line learning version, are devised. They are tested on several data sets and compared with FCM. The experimental results show that the proposed algorithms find out the reasonable solution on Tucker's counter example.

• Annual Conference on Human and Language Technology
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• 2000.10d
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• pp.133-137
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• 2000

구문태그가 부착되지 않은 코퍼스를 사용하여 문법규칙의 확률을 훈련하는 비통제 학습(unsupervised learning) 방법의 대표적인 것이 CNF(Chomsky Normal Form)의 CFG(Context Free Grammar)를 입력으로 하는 inside-outside 알고리즘이다. 본 연구에서는 의존문법을 CNF로 변환하는 기법에 대해 논하고 의존문법을 위해 변형된 inside-outside 알고리즘을 논한다. 또한 이 알고리즘을 사용하여 실제 훈련한 결과를 보이고, 의존규칙과 구문구조 확률을 같이 사용하는 hybrid방식 구문분석기에 적용한 결과를 보인다.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.23-28
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• 2002

Among effective feature extraction methods from the small-patched image set, independent component analysis (ICA) is recently well known stochastic manner to find informative basis images. The ICA simultaneously learns both basis images and independent components using high order statistic manners, because that information underlying between pixels are sensitive to high-order statistic models. The topographic ICA model is adapted in our experiment. This paper deals with an unsupervised classification strategies using learned ICA basis images. The experimental result by proposed classification technique shows superior performance than classic texture analysis techniques for the panchromatic KOMPSAT imagery.

• Proceedings of the KSRS Conference
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• 2003.11a
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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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.402-404
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• 2003

We present two methods for the automatic selection of the threshold values in unsupervised change detection. Both methods consist of the same two procedures: 1) to determine the parameters of Gaussian mixtures from a difference image or ratio image, 2) to determine threshold values using the Bayesian rule for minimum error. In the first method, the Expectation-Maximization algorithm is applied for estimating the parameters of the Gaussian mixtures. The second method is based on the iterative thresholding that successively employs thresholding and estimation of the model parameters. The effectiveness and applicability of the methods proposed here are illustrated by an experiment on the multi-temporal KOMPAT-1 EOC images.

• Proceedings of the KIEE Conference
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• summer
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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.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.517-524
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• 2008

In order to classify an satellite imagery into geospatial features of interest, the supervised classification needs to be trained to distinguish these features through training sampling. However, even though an imagery is classified, different results of classification could be generated according to operator's experience and expertise in training process. Users who practically exploit an classification result to their applications need the research accomplishment for the consistent result as well as the accuracy improvement. The experiment includes the classification results for training process used VITD polygons as a prior probability and training parameter, instead of manual sampling. As results, classification accuracy using VITD polygons as prior probabilities shows the highest results in several methods. The training using unsupervised classification with VITD have produced similar classification results as manual training and/or with prior probability.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.215-225
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• 2004

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using the conventional agglomerative approach. Using simulation data, the proposed method was compared with another hierarchical clustering technique based on 'mutual closest neighbor.' The experimental results show that the new approach proposed in this study considerably increases in computational efficiency for larger images with a low number of bands. The technique was then applied to classify the land-cover types using the remotely-sensed data acquired from the Korean peninsula.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.4
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• pp.297-305
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• 2021

Monocular depth estimation helps the robot to understand the surrounding environments in 3D. Especially, deep-learning-based monocular depth estimation has been widely researched, because it may overcome the scale ambiguity problem, which is a main issue in classical methods. Those learning based methods can be mainly divided into three parts: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning trains the network from dense ground-truth depth information, unsupervised one trains it from images sequences and semi-supervised one trains it from stereo images and sparse ground-truth depth. We describe the basics of each method, and then explain the recent research efforts to enhance the depth estimation performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1825-1844
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• 2019

Political campaigns circulate manipulative opinions in online communities to implant false beliefs and eventually win elections. Not only is this type of manipulation unfair, it also has long-lasting negative impacts on people's lives. Existing tools detect political manipulation based on a supervised classifier, which is accurate when trained with large labeled data. However, preparing this data becomes an excessive burden and must be repeated often to reflect changing manipulation tactics. We propose a practical detection system that requires moderate groundwork to achieve a sufficient level of accuracy. The proposed system groups opinions with similar properties into clusters, and then labels a few opinions from each cluster to build a classifier. It also models each opinion with features deduced from raw data with no additional processing. To validate the system, we collected over a million opinions during three nation-wide campaigns in South Korea. The system reduced groundwork from 200K to nearly 200 labeling tasks, and correctly identified over 90% of manipulative opinions. The system also effectively identified transitions in manipulative tactics over time. We suggest that online communities perform periodic audits using the proposed system to highlight manipulative opinions and emerging tactics.