• 한국항해항만학회지
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• 제33권8호
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• pp.555-562
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• 2009

컨테이너 터미널의 주요 생산성 지표는 안벽에서의 작업 효율성이라 할 수 있다. 안벽에서는 Q/C(Quay Crane)이라는 장비가 접안 선박의 컨테이너를 하역한다. Q/C의 작업 생산성을 높이기 위해서는 좀 더 효율적인 Y/T(Yard Tractor)운영 방식이 필요하다. 기존 작업 방식(싱글 사이클)에서는 양하작업 이후 적하 작업이 이루어진다. 듀얼 사이클이란 양하작업과 적하 작업을 동시에 함으로써 안벽 생산성과 야드 트랙터의 이용률을 높이는 방법이다. 터미널에서 듀얼 사이클의 도입은 추가적인 장비의 도입 없이 운영에서의 변화만을 요구한다. 즉, 기존의 dedicate 시스템에서 pooling 시스템으로의 변화가 필요하다. 본 논문에서는 듀얼 사이클을 이용하는 항만에서의 작업 효율성을 증대시키기 위한 선적 계획 방법을 제시하고자 한다. 이 문제를 풀기위해 유전 알고리즘과 타부서치를 제시하였다.

• 한국철도학회논문집
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• 제19권2호
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• pp.234-242
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• 2016

기존 도시철도의 급행화 방안으로 가감속도가 향상된 고성능 열차를 완행에 투입하고 기존 성능의 열차를 급행에 투입하는 방안은 대피선 수를 줄이는 효과를 가진다. 본 연구에서는 고성능 열차를 완행으로 투입하는 급행화 방안을 토대로, 대피선 수를 최소화하면서 완행 열차의 운행시간을 최소화 할 수 있는 동시 최적화 방안을 제안하였다. 최적화 방안은 유전알고리즘을 기반으로 하여 차두시간, 대피선 위치 및 대피 시간을 설계 변수로 정의하였다. 결과적으로 제안한 최적화 방법론을 서울 7호선에 적용하여 타당한 최적화 결과를 얻을 수 있음을 확인하였다.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• 한국해양공학회지
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• 제30권1호
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• pp.10-17
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• 2016

This paper reports a simulation-based design method for the optimized arrangement design of buoyancy modules in a marine riser system. A buoyancy module is used for the safe operation and structural stability of the riser. Engineers design buoyancy modules based on experience and experimental data. However, they are difficult to design because of the difficulty of conducting real sea experiments and quantifying the data. Therefore, a simulation-based design method is needed to tackle this problem. In this study, we developed a simulation-based design algorithm using a multi-body dynamic simulation and genetic algorithm to perform optimization arrangement design of a buoyancy module. The design results are discussed in this paper.

• 한국정밀공학회지
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• 제29권12호
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• pp.1304-1312
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• 2012

According to the successful development of hybrid vehicle, hybridization of construction equipments like excavator, wheel loader, and backhoe etc., is gaining increasing attention. However, hybridization of excavator and commercial vehicle is very different. Therefore a specialized energy management control algorithm for excavator should be developed. In this paper, hybridization of excavators is investigated and a new energy management control algorithm is proposed. Four control parameters, i.e., lower baseline, upper baseline, idling generation speed, and idling generation torque, are newly introduced and a new operating principle using those four control parameters is proposed. The use of Genetic Algorithm for the optimization of the four control parameters from the view point of minimization of fuel consumption for standard excavating operation is suggested. In order to verify the proposed algorithm, dedicated simulation program of hybrid excavator was developed. The proposed algorithm is applied to a specific hydraulic excavator and 20.7% improvement of fuel consumption is achieved.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.317-320
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• 1997

In the water purification plant, chemicals are injected for quick purification of raw water. It is clear that the amount of chemicals intrinsically depends on the water quality such as turbidity, temperature, pH and alkalinity etc. However, the process of chemical reaction to improve water quality by the chemicals is not yet fully clarified nor quantified. The feedback signal in the process of coagulant dosage, which should be measured (through the sensor of the plant) to compute the appropriate amount of chemicals, is also not available. Most traditional methods focus on judging the conditions of purifying reaction and determine the amounts of chemicals through manual operation of field experts or jar-test results. This paper presents the method of deriving the optimum dosing rate of coagulant, PAC(Polymerized Aluminium Chloride) for coagulant dosing process in water purification system. A neural network model is developed for coagulant dosing and purifying process. The optimum coagulant dosing rate can be derived the neural network model. Conventionally, four input variables (turbidity, temperature, pH, alkalinity of raw water) are known to be related to the process, while considering the relationships to the reaction of coagulation and flocculation. Also, the turbidity in flocculator is regarded as a new input variable. And the genetic algorithm is utilized to identify the neural network structure. The ability of the proposed scheme validated through the field test is proved to be of considerable practical value.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1028-1032
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• 2004

In order to prevent machine tool feed slide system from transient vibrations during operations, machine tool designers usually adopt some typical design solutions; box-in-box typed feed slides, optimizing moving body for minimum weight and dynamic compliance, and so on. Despite all efforts for optimizing design, a feed drive system may experience severe transient vibrations during high-speed operation if its feed rate control is unsuitable. A rough feed rate curve having discontinuity in its acceleration profile causes a serious vibration problem in the feed slides system. This paper presents a feed rate optimization of a ball screw driven machine tool feed slide system for its minimum vibration. Firstly, a ball screw feed drive system was mathematically modeled as a 6-degree-of-freedom lumped parameter system. Next, a feed rate optimization of the system was carried out for minimum vibrations. The main idea of the feed rate optimization is to find out the most appropriate smooth acceleration profile with jerk continuity. A genetic algorithm was used in this feed rate optimization

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.57-62
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• 2005

Microarray gene expression profiling technology is one of the most important research topics in clinical diagnosis of disease. Given thousands of genes, only a small number of them show strong correlation with a certain phenotype. To identify such an optimal subset from thousands of genes is intractable, which plays a crucial role when classify multiple-class genes express models from tumor samples. This paper proposes an efficient classifier design method to simultaneously select the most relevant genes using an intelligent genetic algorithm (IGA) and design an accurate classifier using Support Vector Machine (SVM). IGA with an intelligent crossover operation based on orthogonal experimental design can efficiently solve large-scale parameter optimization problems. Therefore, the parameters of SVM as well as the binary parameters for gene selection are all encoded in a chromosome to achieve simultaneous optimization of gene selection and the associated SVM for accurate tumor classification. The effectiveness of the proposed method IGA/SVM is evaluated using four benchmark datasets. It is shown by computer simulation that IGA/SVM performs better than the existing method in terms of classification accuracy.

• Industrial Engineering and Management Systems
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• 제14권1호
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• pp.1-10
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• 2015

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

• Journal of Microbiology and Biotechnology
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• 제5권2호
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• pp.100-104
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• 1995

Production of $poly-{\beta}-hydroxybutyrate$ (PHB) in a strain of Azotobacter chroococcum, a nitrogen-fixing bacteria, was investigated at various levels of nitrogen and oxygen. Feeding nitrogen source increased both cell growth and PHB accumulation. Oxygen supply appeared to be one of the most important operating parameters for PHB production. Both cell growth and PHB accumulation increased with the sufficient supply of air in the fed-batch fermentation of the strain. However, it was also noted that keeping the oxygen level under limited condition was critical to achieve high PHB productivity. A high titer of PHB (52 g/l) with a high cellular content (60%) was obtained after 48 hr of fed-batch operation by controlling the oxygen supply. Dual limitation of nitrogen and oxygen did not further increase the PHB accumulation probably due to the greater demand for reducing power and ATP for nitrogen fixation.