• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.2
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• pp.175-185
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• 2012

Predictive watershed transform is a popular object segmentation algorithm which achieves a speed-up by identifying image regions that are different from the previous frame and performing object segmentation only for those regions. However, incorrect segmentation is often generated by the predictive watershed transform which uses only local information in merge-split decision on boundary regions. This paper improves the predictive watershed transform to increase the accuracy of segmentation results by using the additional information about the root of boundary regions. Furthermore, the proposed algorithm is processed in a block-based manner such that an image frame is decomposed into blocks and each block is processed independently of the other blocks. The block-based approach makes it easy to implement the algorithm in hardware and also permits an extension for parallel execution. Experimental results show that the proposed watershed transform produces more accurate segmentation results than the predictive watershed transform.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.223-226
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• 2002

This Paper presents an interval type-2 fuzzy perceptron algorithm that is an extension of the type-1 fuzzy perceptron algorithm proposed in [1]. In our proposed method, the membership values for each Pattern vector are extended as interval type-2 fuzzy memberships by assigning uncertainty to the type-1 memberships. By doing so, the decision boundary obtained by interval type-2 fuzzy memberships can converge to a more desirable location than the boundary obtained by crisp and type-1 fuzzy perceptron methods. Experimental results are given to show the effectiveness of our method

• Journal of Forest and Environmental Science
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• v.32 no.4
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• pp.392-399
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• 2016

This review paper presents guidelines which stakeholders use in addressing on-farm tree planting configuration, establishment, tending, silvi- cultural management, management of pests and diseases, challenges and opportunities as practiced in the medium to high potential areas of Kenya. The tree planting configurations discussed includes blocks planting (woodlot), boundary, compound planting, home/fruit gardens, trees intercropped or mixed with pasture, trees on riverbanks and roadside. Participatory monitoring and evaluation techniques have been highlighted. The main challenges facing tree planting activities include culture and attitude of local people, land and tree tenure, inadequate technical support, lack of recognition and integration of technical information and indigenous knowledge, capital and labour shortages, lack of appropriate incentives measures, damage by domestic and wild animals, conflict over trees on the boundary and policy and legal issues. This guideline targets forest managers, extension agents, students and other practitioners in policy and day to day decision making processes in Kenya.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.112-119
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• 2000

A die-casting junction method is usually used to manufacture the tub of an IH(induction heating) jar. If there is a very small air bubble in the junction area, the thermal conductivity is deteriorated and local overheat occurs. Such problem brings serious inferiority of the IH jar. In this paper, we propose a new method to detect such defect with simply measured thermal data. Thermal distribution of preheated tubs is obtained by scanning with infrared thermal sensors and analyzed with the statistic pattern recognition method. By defining the characteristic feature as the temperature difference between sensors and using ellipsoid function as decision boundary, a supervised learning method of genetic algorithm is proposed to obtain the required parpameters. After applying the proposed method to experiment, we have proved that the rate of recognition is high even for a small number of data set.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.57-65
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• 2018

Artificial neural network is inspired by the biological neural network. For simplicity, in computer science, it is represented as a set of layers. Many research has been made in evaluating the number of neurons in the hidden layer but still, none was accurate. Several methods are used until now which do not provide the exact formula for calculating the number of thehidden layer as well as the number of neurons in each hidden layer. In this paper model selection approach was presented. Proposed model is based on geographical analysis of decision boundary. Proposed model selection method is useful when we know the distribution of the training data set. To evaluate the performance of the proposed method we compare it to the traditional architecture on IRIS classification problem. According to the experimental result on Iris data proposed method is turned out to be a powerful one.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1503-1517
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• 1994

Intelligent Motion Planning System(IMPS) is presented for a robot to achieve an efficient path toward the given target point in two dimensional unknown environment is constructed with unrestricted obstacle shapes. IMPS consists of three components for making intelligent motion. These components are real-time motion planning algorithm based on a discontinous boundary method, fuzzy neural network decision system for heuristic knowledge representation, and world modeling with forgetting and reinforcing memory cells. First of all, in real-time motion planning algorithm, the behavior-based architectural method is used to generate subgoal. A behavior generates a subgoal independently by using the method of discontinuous boundary in sensed area. The discontinuous boundary method is a new proposed fast obstacle avoidance algorithm. The second component is fuzzy neural network decision system for accomplishing the subgoal. The heuristic rules are imbedded on the fuzzy neural network to make an intelligent decision. The last one is a forgetting, reinforcing memory technique for the construction of external world map. The activation values of all activated memory cells in grid space are decreased monotonically and after all they are burned out. Therefore, after sufficient journey, robot can have a stationary world map even if the dynaic obstacles exist. Using the IMPS, several simulations show the efficient achievement of target point in unknown enviroment with obstcles of various shapes.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.504-516
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• 2011

In this paper, we will present a method to detect human hand and recognize hand gesture. For detecting the hand region, we use the feature of human skin color and hand feature (with boundary complexity) to detect the hand region from the input image; and use algorithm of optical flow to track the hand movement. Hand gesture recognition is composed of two parts: 1. Posture recognition and 2. Motion recognition, for describing the hand posture feature, we employ the Fourier descriptor method because it's rotation invariant. And we employ PCA method to extract the feature among gesture frames sequences. The HMM method will finally be used to recognize these feature to make a final decision of a hand gesture. Through the experiment, we can see that our proposed method can achieve 99% recognition rate at environment with simple background and no face region together, and reduce to 89.5% at the environment with complex background and with face region. These results can illustrate that the proposed algorithm can be applied as a production.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.421-425
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• 2006

It can be important framework data to monitor the change of land-use pattern of river boundary in design and management of river. This study analyzed the change of land-use pattern of Gab and Yudeung River using time-series aerial images. To do this, we carried out radiation and geometric correction of image, and estimated land-use changes in inland and floodplain. As the analysis of inland, the ratio of residential, commercial, industrial, educational and public area, that is urbanized element, increases, but that of agricultural area shows a decline on the basis of 1990. Also, Minimum Distance Method, which is a kind of supervised classification method, is applied to extract water-body and sand bar layer in floodplain. As the analysis of land-use, the ratio of level-upped riverside land and water-body increases, but that of sand bar decreases. These time-series land use information can be important decision making data to evaluate the urbanization of river boundary, and especially it gives us goodness in river development project such as the composition of ecological habitat.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.8-20
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• 1999

In this paper, a design and estimation procedure for the stochastically dependent nonstationary multiple acceptance sampling plans is developed. At first, the rough-cut acceptance and rejection numbers are given as an initial solution from the corresponding sequential sampling plan. A Monte-Carlo algorithm is used to find the acceptance and rejection probabilities of a lot. The conditional probability formula for a sample path is found. The acceptance and rejection probabilities are found when a decision boundary is given. Several decision criteria and the design procedure to select optimal plans are suggested. The formula for measuring performance of these sampling plans is developed. Type I and II error probabilities are also estimated. As a special case, by setting the stage size as 1 in a dependent sampling plan, a sequential sampling plan satisfying type I and II error probabilities is more accurate and a smaller average sample number can be found. In a numerical example, a Polya dependent process is examined. The sampling performances are shown to compare the selection scheme and the effect of the change of the dependency factor.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.1
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• pp.31-36
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• 2000

To enhance the fault diagnosis ability for the dissolved gas analysis(DGA) of the power transformer, this paper proposes a learning vector quantization(LVQ) for the incipient fault recognition. LVQ is suitable expecially for pattern recognition such as fault diagnosis of power transformer using DGA because it improves the performance of Kohonen neural network by placing emphasis on the classification around the decision boundary. The capabilities of the proposed diagnosis system for the transformer DGA decision support have been extensively verified through the practical test data collected from Korea Electrical Power Corporation.