• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.110-115
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• 2005

This paper proposes the method to detect contours of a face, eyes and a mouth in a color image for making an avatar automatically. First, we use the HSI color model to exclude the effect of various light condition, and we find skin regions in an input image by using the skin color is defined on HS-plane. And then, we use deformable templates and Genetic Algorithm(GA) to detect contours of a face, eyes and a mouth. Deformable templates consist of B-spline curves and control point vectors. Those can represent various shape of a face, eyes and a mouth. And GA is very useful search procedure based on the mechanics of natural selection and natural genetics. Second, an avatar is created automatically by using contours and Fuzzy C-means clustering(FCM). FCM is used to reduce the number of face color As a result, we could create avatars like handmade caricatures which can represent the user's identity, differing from ones generated by the existing methods.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.103-116
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• 2008

This paper proposes quay crane (QC) scheduling algorithms that determine the working sequence of QCs over ship bays in a container vessel in automated container terminals. We propose two scheduling algorithms that examine the distribution of export containers in the stacking yard and determine the sequence of ship bays to balance the workload distribution among the yard blocks. One of the algorithms is a simple heuristic algorithm which dynamically selects the next ship bay based on the entropy of workloads among yard blocks whenever a QC finishes loading containers at a ship bay and the other uses genetic algorithm to search the optimal sequence of ship bays. To evaluate the fitness of each chromosome in the genetic algorithm, we have devised a method that is able to calculate an approximation of loading time of container vessels considering the workloads among yard blocks. Simulation experiments have been carried out to compare the efficiency of the proposed algorithms. The results show that our QC scheduling algorithms are efficient in reducing the turn-around time of container vessels.

• Journal of Korean Society of Transportation
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• v.19 no.4
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• pp.85-96
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• 2001

This paper discusses the problem of finding the Origin-Destination(O-D) shortest path in urban road networks that have variable special qualifies such as time windows for passing as well as geometrical special qualities such as U-turn and left-turn prohibition. The focus of this paper is motivated by the problem of finding minimum travel time paths for an advanced traveler information system (ATIS) in the context of intelligent transportation system(ITS) application. The transportation network with variable and geometrical special qualities is a more realistic representation of the urban road network in the real word. But, the traditional and existing shortest path algorithms can not search practical shortest path that variable special quality is reflected. This paper presents a shortest path algorithm which can search reasonable shortest path information for the urban ATIS application within a real time. The algorithm is based on genetic algorithm(GA). The high performance of the proposed algorithm is demonstrated by computer simulations.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.862-866
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• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.

• Journal of Power System Engineering
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• v.21 no.6
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• pp.56-61
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• 2017

The check valves are mechanical valves that permit fluids to flow in only one direction, preventing flow from reversing. It is classified as one way directional valves. There are various types of check valves that used in a marine application. A lift type check valve uses the disc to open and close the passage of fluid. The disc lift up from seat as pressure below the disc increases, while drop in pressure on the inlet side or a build up of pressure on the outlet side causes the valve to close. An important concept in check valves is the cracking pressure which is the minimum upstream pressure at which the valve will operate. On the other hand, optimization is a process of finding the best set of parameters to reach a goal while not violating certain constraints. The AMESim software provides NLPQL(Nonlinear Programming by Quadratic Lagrangian) and genetic algorithm(GA) for optimization. NLPQL is the implementation of a SQP(sequential quadratic programming) algorithm. SQP is a standard method, based on the use of a gradient of objective functions and constraints to solve a non-linear optimization problem. A characteristic of the NLPQL is that it stops as soon as it finds a local minimum. Thus, the simulation results may be highly dependent on the starting point which user give to the algorithm. In this paper, we carried out optimization design of the check valve with NLPQL algorithm.

• Smart Structures and Systems
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• v.13 no.3
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• pp.389-406
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• 2014

Optimal sensor placement techniques play a significant role in enhancing the quality of modal data during the vibration based health monitoring of civil structures, where many degrees of freedom are available despite a limited number of sensors. The literature has shown a shift in the trends for solving such problems, from expansion or elimination approach to the employment of heuristic algorithms. Although these heuristic algorithms are capable of providing a global optimal solution, their greatest drawback is the requirement of high computational effort. Because a highly efficient optimisation method is crucial for better accuracy and wider use, this paper presents an improved simulated annealing (SA) algorithm to solve the sensor placement problem. The algorithm is developed based on the sensor locations' coordinate system to allow for the searching in additional dimensions and to increase SA's random search performance while minimising the computation efforts. The proposed method is tested on a numerical slab model that consists of two hundred sensor location candidates using three types of objective functions; the determinant of the Fisher information matrix (FIM), modal assurance criterion (MAC), and mean square error (MSE) of mode shapes. Detailed study on the effects of the sensor numbers and cooling factors on the performance of the algorithm are also investigated. The results indicate that the proposed method outperforms conventional SA and Genetic Algorithm (GA) in the search for optimal sensor placement.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.39-50
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• 2015

Genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1049-1060
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• 2009

The expansion of volatility in Korean Stock Market made it more difficult for the individual to invest directly and increased the weight of indirect investment through a fund. The purpose of this study is to construct the EIF(enhanced index fund) model achieves an excessive return among several types of fund. For this purpose, this paper propose portfolio optimization model to manage an index fund by using GA(genetic algorithm), and apply the trading amount and the closing price of standard index to earn an excessive return add to index fund return. The result of the empirical analysis of this study suggested that the proposed model is well represented the trend of KOSPI 200 and the new investment strategies using this can make higher returns than Buy-and-Hold strategy by an index fund, if an appropriate number of stocks included.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.4
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• pp.323-330
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• 2003

GIS-based applications for route guidance are increasingly developed recently, but most of them are for self$.$driven cars. Although some of them are intended for public transportation, they show limitations in dealing with time constraints problems taken place in transfer areas. Developing a public transportation guidance system requires the fallowing aspects: (i) people may change transportation means not only within the same type but also among different modes such as between buses and subways, and (ii) the system should take into account the time taken in transfer from one mode to the other. This study suggests the framework for developing a public transportation guidance system that generates optimized paths in the transportation network of mixed means including buses, subways and other modes. For this study, the Genetic Algorithms are used to find the best routes that take into account transfer time and other service-time constraints. The method for constructing the data structure in the GIS was also suggested.

• Journal of Communications and Networks
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• v.18 no.1
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• pp.50-64
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• 2016

Nowadays, smart meter (SM) technology is widely effectively used. In addition, power allocation (PA) and channel selection (CS) are considered problems with many proposed approaches. In this paper, we will suggest a specific scenario for an SM configuration system and show how to solve the optimization problem for transmission between SMs and the data concentrator unit (DCU), the center that collects the data from several SMs, via simulation. An efficient CS with PA scheme is proposed in the TV white space system, which uses the TV band spectrum. On the basic of the optimal configuration requirements, SMs can have a transmission schedule and channel selection to obtain the optimal efficiency of using spectrum resources when transmitting data to the DCU. The optimal goals discussed in this paper are the maximum capacity or maximum channel efficiency and the maximum allowable power of the SMs used to satisfy the quality of service without harm to another wireless system. In addition, minimization of the interference to the digital television system and other SMs is also important and needs to be considered when the solving coexistence scenario. Further, we propose a process that performs an interference analysis scheme by using the spectrum engineering advanced Monte Carlo analysis tool (SEAMCAT), which is an integrated software tool based on a Monte-Carlo simulation method. Briefly, the process is as follows: The optimization process implemented by genetic evolution optimization engines, i.e., a genetic algorithm, will calculate the best configuration for the SM system on the basis of the interference limitation for each SM by SEAMCAT in a specific configuration, which reaches the solution with the best defined optimal goal satisfaction.