• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.302-305
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• 2000

This paper proposed an optimal register resource allocation algorithm using graph coloring for minimal register at high level synthesis. The proposed algorithm constructed interference graph consist of the intermediated representation CFG to description VHDL. and at interference graph fur the minimal select color selected a position node at stack, the next inserted spill code and the graph coloring process executes for optimal register allocation. The proposed algorithm proves to effect that result compare another allocation techniques through experiments of bench mark.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1313-1316
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• 1993

This paper presents a fuzzy system that estimates the optimal bit allocation matrices for the spatially active subimage classes of adaptive transform image coding in noisy channels. Transform image coding is good for image data compression but it requires a transmission error protection scheme to maintain the performance since the channel noise degrades its performance. The fuzzy system provides a simple way of estimating the bit allocation matrices from the optimal bit map computed by the method of minimizing the mean square error between the transform coefficients of the original and the reconstructed images.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.3
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• pp.217-234
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• 2015

In this tutorial the theoretical background of LQ optimal guidance is reviewed, starting from calculus of variations. LQ optimal control is then introduced and applied to missile guidance to obtain the basic form of LQ optimal guidance laws. Extension of LQ optimal guidance methodology for handling weighted cost function, dynamic lag associated with the missile dynamics and the autopilot, constrained impact angle, and constrained impact time is also described with a brief discussion on the asymptotic properties of the optimal guidance laws. Furthermore, an introduction to polynomial guidance and generalized impactangle-control guidance, which are closed related with LQ optimal guidance, is provided to demonstrate the current status of missile guidance techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2514-2520
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• 2010

The objective of this paper is to propose the methodology for automated generation of the optimal security defense strategies using evolutionary techniques. As damages by penetration exploiting vulnerability in computer systems and networks are increasing, security techniques have been researched actively. However it is difficult to generate optimal defense strategies because it needs to consider various situations on network environment according to countermeasures. Thus we have adopted a genetic algorithm in order to generate an optimal defense strategy as combination of countermeasures. We have represented gene information with countermeasures. And by using simulation technique, we have evaluated fitness through evaluating the vulnerability of system having applied various countermeasures. Finally, we have examined the feasibility by experiments on the system implemented by proposed method.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.187-194
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• 2011

Optimizing truck dispatching-intervals is imperative in ready mixed concrete (RMC) delivery process. Intervals shorter than optimal may induce queuing of idle trucks at a construction site, resulting in a long delivery cycle time. On the other hand, intervals longer than optimal can trigger work discontinuity due to a lack of available trucks where required. Therefore, the RMC delivery process should be systematically scheduled in order to minimize the occurrence of waiting trucks as well as guarantee work continuity. However, it is challenging to find optimal intervals, particularly in urban areas, due to variations in both traffic conditions and concrete placement rates at the site. Truck dispatching intervals are usually determined based on the concrete plant managers' intuitive judgments, without sufficient and reliable information regarding traffic and site conditions. Accordingly, the RMC delivery process often experiences inefficiency and/or work discontinuity. Automatic data collection (ADC) techniques (e.g., RFID or GPS) can be effective tools to assist plant managers in finding optimal dispatching intervals, thereby enhancing delivery performance. However, quantitative evidence of the extent of performance improvement has rarely been reported to data, and this is a central reason for a general reluctance within the industry to embrace these techniques, despite their potential benefits. To address this issue, this research reports on the development of a discrete event simulation model and its application to a large-scale building project in Abu Dhabi. The simulation results indicate that ADC techniques can reduce the truck idle time at site by 57% and also enhance the pouring continuity in the RMC delivery process.

• Journal of Biosystems Engineering
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• v.15 no.4
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• pp.355-364
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• 1990

• Water for future
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• v.16 no.4
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• pp.199-203
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• 1983

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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• pp.335-342
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• 2000

Preventive maintenance(PM) is an action taken on a repairable system while it is still operating, which needs to be carried out in order to keep the system at the desired level of successful operation. The PM improves the reliability of the system by predicting the possible failures and thereby preventing such failures from its occurrence. In this paper, we develop the optimal preventive maintenance policies based on the aperiodic PM model. We investigate an aperiodic preventive maintenance policy and propose several optimal PM policies which minimize the expected cost over an infinite time span. Park, Jung and Yum(2000) determine the optimal period and the optimal number of PMs based on Canfield's(1986) periodic model. Our techniques to derive the optimal preventive maintenance policies based on our aperiodic PM model is similar to those in Park, Jung and Yum(2000), which can be considered as the special case of our results.

• The Journal of Information Systems
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• v.31 no.3
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.263-278
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• 2004

This paper reviews dynamic programming (DP), surveys approximate solution methods for it, and considers their applicability to process control problems. Reinforcement Learning (RL) and Neuro-Dynamic Programming (NDP), which can be viewed as approximate DP techniques, are already established techniques for solving difficult multi-stage decision problems in the fields of operations research, computer science, and robotics. Owing to the significant disparity of problem formulations and objective, however, the algorithms and techniques available from these fields are not directly applicable to process control problems, and reformulations based on accurate understanding of these techniques are needed. We categorize the currently available approximate solution techniques fur dynamic programming and identify those most suitable for process control problems. Several open issues are also identified and discussed.