• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1524-1529
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• 2005

Particle filter is a kind of conditional density propagation model. Its similar characteristics to both selection and mutation operator of evolutionary strategy (ES) due to its Bayesian inference rule structure, shows better performance than any other tracking algorithms. When a new object is entering the region of interest, particle filter sets which have been swarming around the existing objects have to move and track the new one instantaneously. Moreover, there is another problem that it could not track multiple objects well if they were moving away from each other after having been overlapped. To resolve reinitialization problem, we use competitive-AVQ algorithm of neural network. And we regard interfarme difference (IFD) of background images as potential field and give priority to the particles according to this IFD to track multiple objects independently. In this paper, we showed that the possibility of real-time object tracking as intelligent interfaces by simulating the deformable contour particle filters.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.138.4-138
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• 2001

The purpose of this research is to build a quality-improved occupancy grid map for path-planning of an autonomous mobile robot(AMR) based on the measurements from a single ultrasonic sensor, which are acquired when the autonomous mobile robot explores unknown indoor environment. The AMR navigates in the unknown space by following the wall and gathers the range data using the ultrasonic sensor, from which the occupancy grid map is constructed by associating the range data with occupancy certainties. In order to increase the quality of the map we modify the Bayesian probability updating rule, reject non-systematic measurement errors and correct the predictable error of the AMR itself. These procedures are implemented and tested using an AMR, and primary results are presented in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.720-727
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• 2009

This paper proposes a new integrated navigation system for a mobile robot in indoor environments. This system consists of five frameworks which are classified by function. This architecture can make the navigation system scalable and flexible. The robot can recover from exceptional situations, such as environmental changes, failure of entering the narrow path, and path occupation by moving objects, using the exception recovery framework. The environmental change can be dealt with using the probabilistic approach, and the problems with the narrow path and path occupation are solved using the ray casting algorithm and the Bayesian update rule. The proposed navigation system was successfully applied to several robots and operated in various environments. Experimental results showed good performance in that the exception recovery framework significantly increased the success rate of navigation. The system architecture proposed in this paper can reduce the time for developing robot applications through its reusability and changeability.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.165-168
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• 1999

The support or automation of various kinds of intelligent work is urged at large, integrated control centers. Given this demand, a decision making system for wash timing of polluted insulators, applying the Bayesian rule theory, has been developed in order to support maintenance work in the power system. The results of this system application revealed that exact wash timing of the insulators could be determined automatically, equivalent in precision to judgement by skilled operators, thus contributing to further work efficiency.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.4
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• pp.251-258
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• 2005

Automatic document classification may differ significantly according to the characteristics of documents that are subject to classification, as well as classifier's performance. This research identifies e-mail document's characteristics to apply a three-step preprocessing algorithm that can minimize e-mail document's atypical characteristics. In the first 5go, uncertain based sampling algorithm that used Mean Absolute Deviation(MAD), is used to address the question of selection learning document for the rule generation at the time of classification. In the subsequent stage, Weighted vlaue assigning method by attribute is applied to increase the discriminating capability of the terms that appear on the title on the e-mail document characteristic level. in the third and last stage, accuracy level during classification by each category is increased by using Naive Bayesian Presumptive Algorithm's Dynamic Threshold. And, we implemented an E-Mail Recommendtion System using a three-step preprocessing algorithm the enable users for direct and optimal classification with the recommendation of the applicable category when a mail arrives.

• 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 Korea Information Processing Society Conference
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• 2008.05a
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• pp.141-144
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• 2008

본 논문에서는 세포영상을 분할하고 분류하는 알고리즘을 제안한다. 우선, 배경으로부터 세포를 분할한 후, 학습데이터로부터 얻은 Compactness, Smoothness, Moments와 같은 형태학적 특징을 추출한다. 전경세포들이 분할된 후에, 보다 정밀한 세포분석을 위해서 군집세포(Overlapped Cell)와 독립세포(Isolated Cell)를 분류 할 수 있는 알고리즘의 개발이 필수적이다. 이를 위해서 본 논문에서는 베이지안 네트워크와 각 노드에 대한 3개의 확률밀도함수를 사용하여 각 세포 영역을 분류한다. 분류된 군집세포영역은 향후 정확한 세포 분석을 위해서 군집세포가 포함하는 독립세포의 수만큼 마커를 찾고, Watershed 알고리즘과 병합과정을 거쳐 하나의 독립세포를 분리하게 된다. 현미경으로부터 얻은 세포영상에 대한 실험 결과는 이전 논문들에서 제안한 방법들과 비교했을 때, 각 군집세포의 독립세포로의 분리 이전에 세포영역에 대한 분류과정을 먼저 수행하였기 때문에 분할 성능이 크게 향상되었음을 확인할 수 있다.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.9-18
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• 2011

In this paper, we propose a method which recognizes human activity using model-based gaze direction estimation in an indoor environment. The method consists of two steps. First, we detect a head region and estimate its gaze direction as prior information in the human activity recognition. We use color and shape information for the detection of head region and use Bayesian Network model representing relationships between a head and a face for the estimation of gaze direction. Second, we recognize event and scenario describing the human activity. We use change of human state for the event recognition and use a rule-based method with combination of events and some constraints. We define 4 types of scenarios related to the gaze direction. We show performance of the gaze direction estimation and human activity recognition with results of experiments.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.445-455
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• 2011

A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.