• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.175-180
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• 2012

Currently datamining sector is interested and applied in many areas. In other words, datamining is predicting the future to discover hidden correlations and make decisions. To interpret data on various aspects can be converted to real expectation. Analyzing the results even a simple can be found big difference. The properties associated with breast cancer by about applying bayesian theory is used to predict the probability. In the past patient data, doctors may be obtaining by applying evidence-based care for patients with the results of examination and By using the the past patient data.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1037-1040
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• 1999

This paper investigates a RBF(Radial Basis Function) equalizer for channel equalization. RBF network has an identical structure to the optimal Bayesian symbol-decision equalizer solution. Therefore RBF can be employed to implement the Bayesian equalizer. Proposed algorithm of this paper makes channel states estimation to be unncessary, also makes center number which is needed indivisual channel to be minimum. Bayesian Equalizer has the theorical optimum performance. Proposed Equalizer performance is compared with this Baysian equalizer performance.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2840-2842
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• 2000

This paper describes sensor-based occupancy grid map construction method through complete coverage navigation algorithm in unknown environment. In this paper, we use the updated Baysian model for probabilistic grid map. For map construction, complete coverage navigation method in which mobile robot can navigate complete field through as short path as possible in unknown environment, is used. The computer simulations result show that map construction method using complete coverage algorithm is efficient.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.7-16
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• 2009

Some clustering algorithms have a problem that an image is over-segmented since both the spatial information between the segmented regions is not considered and the number of the clusters is defined in advance. Therefore, they are difficult to be applied to the applicable fields. This paper proposes the new post-processing methods, a reclassification of the inhomogeneous clusters and a region merging using Baysian algorithm, that improve the segmentation results of the clustering algorithms. The inhomogeneous cluster is firstly selected based on variance and between-class distance and it is then reclassified into the other clusters in the reclassification step. This reclassification is repeated until the optimal number determined by the minimum average within-class distance. And the similar regions are merged using Baysian algorithm based on Kullbeck-Leibler distance between the adjacent regions. So we can effectively solve the over-segmentation problem and the result can be applied to the applicable fields. Finally, we design a classification system for the segmented regions to validate the proposed method. The segmented regions are classified by SVM(Support Vector Machine) using the principal colors and the texture information of the segmented regions. In experiment, the proposed method showed the validity for various real-images and was effectively applied to the designed classification system.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.6
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• pp.973-980
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• 2011

In this paper, we gaze upon the movement faces the problem points are difficult to identify a user based on points and that corrective action is needed to solve the identification system is proposed a new eye. Kalman filter, the current head of the location information was used to estimate the future position in order to determine the authenticity of the face facial features and structural elements, the information and the processing time is relatively fast horizontal and vertical elements of the face using the histogram analysis to detect. And an infrared illuminator obtained by constructing a bright pupil effect in real-time detection of the pupil, the pupil was tracked - geulrinteu vectors are extracted.

• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.157-168
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• 2006

Spatial detection system such as AVI, GPS, and Beacon etc. can provide spatial travel time only after a vehicle Passes through a road section. In this context, majority of the existing studies on the link travel time estimation area has focused on the arrival time-based link travel time estimation. rather than departure time-based link travel time estimation. Even if some of the researches on this area have developed departure time-based link travel time estimation algorithms, they are limited in that they are not applicable in a real-time mode. The objective of this study is to develop an departure time-based link travel time estimation algorithm which is applicable in a real-tine mode. Firstly, this study discussed the tradeoff between accuracy and timeliness of the departure time-based on-line link travel time estimates. Secondly, this study developed an departure time-based on-line link travel time estimation algorithm which utilizes the Baysian inference logic. It was found that the proposed approach could estimate departure time-based link travel times in a real-time context with an acceptable accuracy and timeliness.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.20-27
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• 2006

Multi-level thresholding is a method that is widely used in image segmentation. However most of the existing methods are not suited to be directly used in applicable fields and moreover expanded until a step of image segmentation. This paper proposes region-based multi-level thresholding as an image segmentation method. At first we classify pixels of each color channel to two clusters by using EWFCM(Entropy-based Weighted Fuzzy C-Means) algorithm that is an improved FCM algorithm with spatial information between pixels. To obtain better segmentation results, a reduction of clusters is then performed by a region-based reclassification step based on a similarity between regions existing in a cluster and the other clusters. The clusters are created using the classification information of pixels according to color channel. We finally perform a region merging by Bayesian algorithm based on Kullback-Leibler distance between a region and the neighboring regions as a post-processing method as many regions still exist in image. Experiments show that region-based multi-level thresholding is superior to cluster-, pixel-based multi-level thresholding, and the existing mettled. And much better segmentation results are obtained by the post-processing method.