• Journal of the Korea Computer Industry Society
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• v.9 no.1
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• pp.29-38
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• 2008

This treatise analyzed theoretical principle of Cellular Automata systematically. Specially, Cellular Automata is hinting that can handle function of various form using transition rule. Cellular Automata can embody various and complicated principle with simple identifying marks that is "State", "Neighborhood", "Program Rules". Specially, have eminent cognitive faculty in image processing field. Examined closely that the ability excels flying important Edge Detection in image processing using this Cellular Automata in treatise that see therefore.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.731-739
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• 1998

The probabilistic relaxation(PR) scheme based on the conditional probability and probability space partition has the important property that when its compatibility coefficient matrix (CCM) has uniform components it can classify m-dimensional probabilistic distribution vectors into different classes. When consistency or inconsistency measures have been defined, the properties of PRs are completely determined by the compatibility coefficients among labels of labeled objects and influence weight among labeled objects. In this paper we study the properties of PR in which both compatibility coefficients and influence weights are uniform, and then a learning rule for such PR system is derived. Experiments have been performed to verify the effectiveness of the learning rule.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.767-772
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• 2005

The Internet is a packet switched network which offers best-effort service, but current IP network provide enhanced services such Quality of Services, Virtual Private Network (VPN) services, Distribute Firewall and IP Security Gateways. All such services need packet classification for determining the flow. The problem is performing scalable packet classification at wire speeds even as rule databases increase in size. Therefore, this research offer packet classification algorithm that increase classifier performance when working with enlarge rules database by rearrange rule structure into Bitmap Intersection Lookup (BIL) tables. It will use packet's header field for looking up BIL tables and take the result with intersection operation by logical AND. This approach will use simple algorithm and rule structure, it make classifier have high search speed and fast updates.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.357-360
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• 2008

There are many control strategies for HEV in today. Expanding motor-driving and operating at good-efficient point in engine is the key of the HEV control to increase fuel economy. There are two types of HEV supervisory control. One is rule-based control and the other is optimization control. MAX-SOC control, thermostat control, baseline status control and state-machine control are in deterministic RBC. It is simple, but powerful and easy to apply in real-time circumstance. In this study, we analysis these four control strategies in RBC (Rule-based control) and identify the each advantage and disadvantage.

• Journal of the Korea Society for Simulation
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• v.9 no.3
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• pp.77-89
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• 2000

This paper suggests a multi-attribute dispatching rule for an automated guided vehicle(AGV). The attributes to be considered are the number of queues in outgoing buffers of workstations, distance between an idle AGV and a workstation with a job waiting for the service of vehicle, and the number of queues in input buffers of the destination workstation of a job. The suggested rule is based on the simple additive weighting method using a normalized score for each attribute. A neural network approach is applied to obtain an appropriate weight vector of attributes based on the current status of the manufacturing system. Backpropagation algorithm is used to train the neural network model. The proposed dispatching rules and some single attribute rules are compared and analyzed by simulation technique. A number of simulation runs are executed under different experimental conditions to compare the several performance measures of the suggested rules and some existing single attribute dispatching rules each other.

• Nonlinear Functional Analysis and Applications
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• v.27 no.1
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• pp.121-140
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• 2022

In this paper, we study the strong convergence of new methods for solving classical variational inequalities problems involving semistrictly quasimonotone and Lipschitz-continuous operators in a real Hilbert space. Three proposed methods are based on Tseng's extragradient method and use a simple self-adaptive step size rule that is independent of the Lipschitz constant. The step size rule is built around two techniques: the monotone and the non-monotone step size rule. We establish strong convergence theorems for the proposed methods that do not require any additional projections or knowledge of an involved operator's Lipschitz constant. Finally, we present some numerical experiments that demonstrate the efficiency and advantages of the proposed methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.550-552
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• 2022

A video wall controller has a special type of multi-monitor that displays multiple monitors on a single large screen by arranging them consecutively. Operator maps and stores the video and monitor in advance. In a small system the mapping task of videos and monitors is simple. But as the number of monitors increases, the number of mapping cases increases, and thus work efficiency decreases. In this paper, we propose a association rule-based recommender system which help improve the efficiency of mapping task.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.574-578
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• 2001

The stability analysis for the fuzzy logic controller (FLC) has widely been reported. Furthermore many research in the FLC has been introduced to decrease the number of parameters representing the antecedent part of the fuzzy control rule. In this paper we briefly explain a single-input fuzzy logic controller (SFLC) or simple-structured FLC which uses only a single input variable. And then we analyze that it is absolutely stale based on the sector bounded condition. We also show the feasibility of the proposed stability analysis through a numerical example of a mass-damper-spring system.

• Industrial Engineering and Management Systems
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• v.14 no.2
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• pp.122-128
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• 2015

The purpose of this paper is to propose an appropriate evaluation scheme for improvement activities, based on a simple model comprising cash inflow by sales as well as variable and fixed cost expenditures. The paper distinguishes capacity surplus and capacity shortage situations, and examines economic benefits gained by yield increase improvement and capacity increase. The paper then proposes a basic rule of thumb for economic evaluation of improvement activities. The logic is simple but useful in practice, being conducive towards improvement activities under current economic conditions with uncertainties.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.170-178
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• 1994

The nearest neighbor classification rule is widely used because it is not only simple but the error rate is asymptotically less than twice Bayes theoretical minimum error. But the method basically use the whole training patterns as the reference vectors. so that both storage and classification time increase as the number of training patterns increases. LVQ(Learning Vector Quantization) resolved this problem by training the reference vectors instead of just storing the whole training patterns. But it is a heuristic algorithm which has no theoretic background there is no terminating condition and it requires a lot of iterations to get to meaningful result. This paper is to propose a new training method of the reference vectors. which minimize the given error function. The nearest neighbor network,the network version of the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule and the reference vectors are represented by the weights between the nodes. The network is trained to minimize the error function with respect to the weights by the steepest descent method. The learning algorithm is derived and it is shown that the proposed method can adjust more reference vectors than LVQ in each iteration. Experiment showed that the proposed method requires less iterations and the error rate is smaller than that of LVQ2.