• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2504-2526
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• 2016

Wireless sensors are always deployed in brutal environments, but as we know, the nodes are powered only by non-replaceable batteries with limited energy. Sending, receiving and transporting information require the supply of energy. The essential problem of wireless sensor network (WSN) is to save energy consumption and prolong network lifetime. This paper presents a new communication protocol for WSN called Dynamical Threshold Control Algorithm with three-parameter Particle Swarm Optimization and Ant Colony Optimization based on residual energy (DPA). We first use the state of WSN to partition the region adaptively. Moreover, a three-parameter of particle swarm optimization (PSO) algorithm is proposed and a new fitness function is obtained. The optimal path among the CHs and Base Station (BS) is obtained by the ant colony optimization (ACO) algorithm based on residual energy. Dynamical threshold control algorithm (DTCA) is introduced when we re-select the CHs. Compared to the results obtained by using APSO, ANT and I-LEACH protocols, our DPA protocol tremendously prolongs the lifecycle of network. We observe 48.3%, 43.0%, and 24.9% more percentages of rounds respectively performed by DPA over APSO, ANT and I-LEACH.

• Proceedings of the KSME Conference
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• 2001.06e
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• pp.548-554
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• 2001

A GA(Genetic Algorithm) based 3D-PTV technique has been developed. The measurement system consists of three CCD cameras, Ar-ion laser, an image grabber and a host computer. The fundamental of the developed technique was based on that one-to-one correspondence is found between two tracer particles selected at two different image frames taking advantage of combinatorial optimization of the genetic algorithm. The fitness function controlling reproductive success in the genetic algorithm was expressed by a kind of continuum theory on the sparsely distributed particles in space. In order to verify the capability of the constructed measurement system, a performance test was made using the LES data set of an impinging jet. The developed 3D-PTV system was applied to the measurement of flow characteristics of the wake of a circular cylinder.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.1
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• pp.122-130
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• 2001

In order to improve the efficiency of the design process and the quality of the resulting design, this study proposes a design method for determining design variables of an automotive wheel-bearing unit of double-row angular-contact ball bearing type by using a genetic algorithm. The desired performance of the wheel-bearing unit is to maximize system life while satisfying geometrical and operational constraints without enlarging mounting spae. The use of gradient-based optimization methods for the design of the unit is restricted because this design problem is characterized by the presence of discrete design variables such as the number of balls and standard ball diameter. Therefore, the design problem of rolling element bearings is a constrained discrete optimization problem. A genetic algorithm using real coding and dynamic mutation rate is used to efficiently find the optimum discrete design values. To effectively deal with the design constraints, a ranking method is suggested for constructing a fitness function in the genetic algorithm. A computer program is developed and applied to the design of a real wheel-bearing unit model to evaluate the proposed design method. Optimum design results demonstrate the effectiveness of the design method suggested in this study by showing that the system life of an optimally designed wheel-bearing unit is enhanced in comparison with that of the current design without any constraint violations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.601-606
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• 1998

In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. Futhermore proper fuzzy partitioning is not deterministic ad there is no unique solution. So we propose a co-evolutionary method finding optimal fuzzy rules and proper fuzzy membership functions at the same time. Predator-Prey co-evolution and symbiotic co-evolution algorithms, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. Our algorithm is that after constructing two population groups made up of rule base and membership function, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the propose method to a path planning problem of autonomous mobile robots when moving objects applying the proposed method to a pa h planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.9
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• pp.1039-1048
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• 2015

In this paper, we propose an efficient stair locomotion method for a quadruped robot with mechanism of insectile legs using genetic algorithm(GA). In the proposed method, we first define the factors and the reachable region for the stair locomotion. In addition, we set the gene and the fitness function for GA and generate the gait trajectory by searching the landing position of a quadruped robot, which has the minimun distance of movement and the optimal energy stability margin(ESM). Finally, we verify the effectiveness and superiority of the proposed stair locomotion method through the computer simulations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.2
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• pp.406-414
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• 2002

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is Proposed for identifying a global solution for continuous optimization problems. In order to improve the local fine-tuning capability of GA, we introduced a new method whereby the search space is zoomed around the design point with the best fitness per 100 generation. Furthermore, the reliability of the optimized solution is determined based on the theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro genetic algorithm, and the proposed algorithm were tested as regards for the minimization of a multiminima function as well as simple functions. The results confirmed that the proposed SZGA significantly improved the ability of the algorithm to identify a precise global minimum. As an example of structural optimization, the SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the standard genetic algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.987-992
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• 2002

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. In this paper, pareto-based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. This algorithm is based on Continuous Evolutionary Algorithms to solve single objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche-formation method fur fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto-optimal tradeoff surface. Finally, the validity of this method has been demonstrated through a numerical example.

• Journal of Fashion Business
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• v.8 no.1
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• pp.91-106
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• 2004

This study focuses on the sportism expressed in the modern fashion. Many factors attribute to the advent of sportism such as rapid development and cultural changes toward sports, increase in leisure time, new fashion materials resulting from new technologies, youth culture and postmodernism. Designers gazing into the future are inspired by the details and functionality of clothing for snow boarding, skiing, rock-climbing and fitness. While the sportswear is the term whith stemmed from the need for functionally in sports, the Sportism is the style inspired by the formative elements, that is, the details, the silhouette, and the colors of the sportswear. New technologies for sports, the powerful influence of youthful culture, and the celebritizations of the sports stars made the sports look more popular. It can be categorized into three aesthetic values, i.e., the functional sportism, the street sportism, and the futuristic sportism. The functional sportism is expressed with the details of function, simplicity, and no useless ornament, the street sportism with fun, androgynous and unisex mode and the image of hip-hop look and traditional look, the futuristic sportism with new high tech fabrics and cyber style. The characters of these are a sence of unisex, sensualness, ostentation, renovation.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.3 s.168
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• pp.146-153
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• 2005

In this paper, a multi-step optimization using genetic algorithm with variable penalty function is introduced to the structural design optimization of a grinding machine. The design problem, in this study, is to find out the optimum configuration and dimensions of structural members which minimize the static compliance, the dynamic compliance, and the weight of the machine structure simultaneously under several design constraints such as dimensional constraints, maximum deflection limit, safety criterion, and maximum vibration amplitude limit. The first step is shape optimization, in which the best structural configuration is found by getting rid of structural members that have no contributions to the design objectives from the given initial design configuration. The second and third steps are sizing optimization. The second design step gives a set of good design solutions having higher fitness for lightweight and minimum static compliance. Finally the best solution, which has minimum dynamic compliance and weight, is extracted from the good solution set. The proposed design optimization method was successfully applied to the structural design optimization of a grinding machine. After optimization, both static and dynamic compliances are reduced more than 58.4% compared with the initial design, which was designed empirically by experienced engineers. Moreover the weight of the optimized structure are also slightly reduced than before.