• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.2
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• pp.193-200
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• 2008

Cohesive and frictional materials such as concrete and soil are pressure dependent. In general, failure criterion for such materials inclined with respect to positive hydrostatic axis in Haigh-Westergaard stress space. Consequently, inelastic volumetric strain always positive with associated flow rule. In this study, to overcome this shortcoming, non-associated flow rule which controls volumetric component of plastic flow is adopted. Numerical analysis based on a constitutive model using nonuniform hardening plasticity with five parameter failure criterion and non-associated flow rule has conducted to predict concrete behavior under multi-axial stress state and verified with experimental result.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.3
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• pp.322-333
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• 2012

AESA radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability. For this reason, Radar Resource Management(RRM) becomes new challenging issue. RRM is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. Especially radar beam scheduling is the most critical component for the success of RRM. In this paper, we proposed a rule-based scheduling algorithm and Simulated Annealing(SA) based scheduling algorithm, which are alternatively selected and applied to beam scheduler according radar load status in real-time. The performance of the proposed algorithm was evaluated on the multi-function radar scenario. As a result, we showed that our proposed algorithm can process a lot of beams at the right time with real time capability, compared with applying only rule-based scheduling algorithm. Additionally, we showed that the proposed algorithm can save scheduling time remarkably, compared with applying only SA-based scheduling algorithm.

• Journal of the Korea Society for Simulation
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• v.14 no.1
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• pp.9-18
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• 2005

Attempts to attack hosts in the network have become diverse, due to crackers developments of new creative attacking methods. Under these circumstances the role of intrusion detection system as a security system component gets considerably importance. Therefore, in this paper, we have suggested multiple intrusion detection system based on the contract net protocol which provides the communication among multiple agents. In this architecture, fuzzy rule based system has been applied for agent selection among agents competing for being activated. The simulation models are designed and implemented based on DEVS formalism which is theoretically well grounded means of expressing discrete event simulation models.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.619-622
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• 2005

최근 금융권의 수익 기반이 되고 있는 수수료는 다양한 형태의 규칙을 내포하고 있으며, 복잡성이 증가하고 있어 유연하고 동적인 수수료 구조가 요구된다. 이러한 요구 사항을 충족시키기 위해서 업무 규칙(business rule)이 활용될 수 있다. 본 논문에서는 은행권의 수수료를 분석하여, 수수료 부과 기준을 업무 규칙으로 정의하고, 이를 파라미터 드리븐(parameter driven) 방식의 룰 데이터베이스(rule database)로 설계하였다. 이를 통하여 복합 수수료를 즉시 적용할 수 있는 유연한 설계로 어플리케이션 구조를 단순화 할 수 있는 업무 규칙 기반 수수료 처리 컴포넌트를 설계하였다.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.807-813
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• 2006

The objective of this study is to develop a rule-based fault detection algorithm and an experimental verification using an artificial air handling unit. To develop an analytical algorithm which precisely detects a tendency of faulty component, energy equations at each control volume of AHU were applied. An experimental verification was conducted on the HVAC simulator. The rule based FDD algorithm isolated a faulted sensor from HVAC components in summer and winter conditions.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1004-1019
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• 2021

To solve the problems of the low image contrast, fuzzy edge details and edge details missing in noisy image fusion, this study proposes a noisy infrared and visible light image fusion algorithm based on non-subsample contourlet transform (NSCT) and an improved bilateral filter, which uses NSCT to decompose an image into a low-frequency component and high-frequency component. High-frequency noise and edge information are mainly distributed in the high-frequency component, and the improved bilateral filtering method is used to process the high-frequency component of two images, filtering the noise of the images and calculating the image detail of the infrared image's high-frequency component. It can extract the edge details of the infrared image and visible image as much as possible by superimposing the high-frequency component of infrared image and visible image. At the same time, edge information is enhanced and the visual effect is clearer. For the fusion rule of low-frequency coefficient, the local area standard variance coefficient method is adopted. At last, we decompose the high- and low-frequency coefficient to obtain the fusion image according to the inverse transformation of NSCT. The fusion results show that the edge, contour, texture and other details are maintained and enhanced while the noise is filtered, and the fusion image with a clear edge is obtained. The algorithm could better filter noise and obtain clear fused images in noisy infrared and visible light image fusion.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.200-208
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• 2003

This paper proposes a separation and feature extraction of image signals using the independent component analysis(ICA) based on neural networks of efficient learning rule. The proposed learning rule is a hybrid fixed-point(FP) algorithm based on secant method and momentum. Secant method is applied to improve the performance by simplifying the 1st-order derivative computation for optimizing the objective function, which is to minimize the mutual informations of the independent components. The momentum is applied for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution. The proposed algorithm has been applied to the composite images generated by random mixing matrix from the 10 images of $512\times512$-pixel. The simulation results show that the proposed algorithm has better performances of the separation speed and rate than those using the FP algorithm based on Newton and secant method. The proposed algorithm has been also applied to extract the features using a 3 set of 10,000 image patches from the 10 fingerprints of $256\times256$-pixel and the front and the rear paper money of $480\times225$-pixel, respectively, The simulation results show that the proposed algorithm has also better extraction speed than those using the another methods. Especially, the 160 basis vectors(features) of $16\times16$-pixel show the local features which have the characteristics of spatial frequency and oriented edges in the images.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.165-179
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• 2005

Close collaboration among businesses is required in B2B EC environment. Furthermore, emergency requests or critical information among businesses should be processed in an immediate mode. Most current systems, however, due to firewalls for the systems' security and autonomy, can not handle these requirements appropriately, but handle them in an ad hoc manner In this paper a method of timely collaboration among businesses and an active functionality component to support it in B2B EC environment are proposed. Since the active functionality component supports high level ECA rule patterns and event-based immediate processing, system administrators and programmers can easily program and maintain the timely collaboration independently to the application logic. The proposed active functionality component uses HTTP protocol to be applied through firewalls and is designed using a commercial DBMS for practical purpose.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.9 no.1
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• pp.1-7
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• 2013

In around 2000, the U.S. NPPs have developed the various advanced engineering processes based on the INPO AP-913(Equipment Reliability Process Description) and showed the high performance in availability. With these benchmarking cases, the Korean NPPs have introduced the advanced engineering technology since 2005. The first step of the advanced engineering is to analyze and determine component importance for all components of a plant. This process is called Functional Importance Determination(FID). These results are basically utilized to determine the priority with limited resources in various areas. However, because the consistency of FID results is insufficient despite applying the same criteria in the existing operating NPPs, the degree of application is low. Therefore, this paper presents the improved methodology for FID interfacing system functions of Maintenance Rule Program and results of Single Point Vulnerability(SPV). This improved methodology is expected to contribute to enhance the reliability of FID data.