• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.656-662
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• 2018

Pattern matching algorithm is widely used in many application fields such as bio-informatics, intrusion detection, etc. Among many string matching algorithms, KMP (Knuth-Morris-Pratt) algorithm is commonly used because of its fast execution time when using large texts. However, the processing speed of KMP algorithm is also limited when the text size increases significantly. In this paper, we propose a high throughput parallel KMP algorithm considering CPU-GPU memory hierarchy based on OpenCL in GPGPU (General Purpose computing on Graphic Processing Unit). We focus on the optimization for the allocation of work-times and work-groups, the local memory copy of the pattern data and the failure table, and the overlapping of the data transfer with the string matching operations. The experimental results show that the execution time of the optimized parallel KMP algorithm is about 3.6 times faster than that of the non-optimized parallel KMP algorithm.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.109-114
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• 2012

In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2925-2948
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• 2019

In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.323-335
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• 1999

In this paper, we propose a new hybrid model of artificial neural networks(ANNs) and genetic algorithm (GA) to optimal feature transformation and feature weighting. Previous research proposed several variants of hybrid ANNs and GA models including feature weighting, feature subset selection and network structure optimization. Among the vast majority of these studies, however, ANNs did not learn the patterns of data well, because they employed GA for simple use. In this study, we incorporate GA in a simultaneous manner to improve the learning and generalization ability of ANNs. In this study, GA plays role to optimize feature weighting and feature transformation simultaneously. Globally optimized feature weighting overcome the well-known limitations of gradient descent algorithm and globally optimized feature transformation also reduce the dimensionality of the feature space and eliminate irrelevant factors in modeling ANNs. By this procedure, we can improve the performance and enhance the generalisability of ANNs.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1460-1463
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• 1997

The aim of this paper is to suggest a design method of the model following optimal boiler-turbine H.inf. control system using genetic algorithm. This boiler-turbine H.inf. control system is designed by applying genetic algortihm with reference model to the optimal determination of weighting functions and design parameter .gamma. that are given by Glover-Doyle algornithm whch can design H.inf. contrlaaer in the sate. space. The first method to do this is ghat the gains of weightinf functions and .gamma. are optimized simultaneously by genetic algroithm. And the second method is that not only the gains and .gamma. but also the dynamics of weighting functions are optimized at the same time by genetic algonithm. The effectiveness of this boiler-turbine H.inf. control system is verified and compared with LQG/LTR control system by computer simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.252-256
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• 1997

The success of a fuzzy neural network(FNN) control system solving any given problem critically depends on the architecture of the network. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. A messy genetic algorithm is used to obtain structurally optimized FNN models. Structural optimization is regarded important before neural networks based learning is switched into. We have applied the method to the problem of a numerical approximation

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.11
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• pp.938-943
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• 2013

In this paper, we propose a transmission parameter optimization scheme based on genetic algorithm for dynamic spectrum access systems. Specifically, we represent a multiple objective fitness function as a weighted sum of single objective fitness functions to optimize transmission parameters, and then, obtain optimized transmission parameters based on genetic algorithm for given transmission scenarios. From numerical results, we confirm that the transmission parameters are well optimized by using the proposed optimization scheme.

• Proceedings of the Safety Management and Science Conference
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• 2003.11a
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• pp.129-142
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• 2003

The SCM is that Supply Chain Network is Promptly and Voluntarily Optimized in Unstable Market Change Environment. The Cash flow Efficiency of Hole Supply Chain Network is Improved by Changing the Information and Changing the Foundation of Business Processes. The Role of WMS has been Changing Importantly with the Introduction of SCM. WMS Needed to Change to the Information Center in Order to Change Information in Real Time and the WMS of Information Storing in Order to Support an Idea Decision. This Development was Defined about the Importance of WMS in SCM Environment. The Criterion of Valuation is Normally Measured Time between Taking a Order Receive and Bringing the Items to Customer. The Decreasing Move Time of Order Picker in Warehouse is Directly Influence to the Job Execution. So, this Research is Defined about the Optimized Route of Order Picker and Suggests Algorithm. To do this, Past Algorithm is Studied. It's Easy to Introduce and this Study is Looking for Method about the Noticing of Order Picker. The Algorithm will Improve to be Adapt to Standard Process System.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.6
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• pp.590-597
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• 2009

The final quality of the workpiece is affected by the grinding process that has been conducted in final manufacturing stage. However the quality-satisfaction of ground workpiece depends on the skill of an expert in this process. Therefore, the process models of grinding have been developed to predict the states according to grinding process. In this paper, in order to find the optimized grinding condition to reduce the manufacturing expense and to meet requirements of ground workpiece optimization algorithm using E.S.(Evolutionary Strategy) is proposed. The proposed algorithm has been employed to find the optimal grinding and dressing condition using the grinding process models and nonlinear grinding constraints. The optimized results also presents the guide line of grinding process. The effectiveness of the proposed algorithm is verified through the experimental results.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.48-55
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• 2004

This study presents a genetic algorithm-based method fur optimizing control parameters in the pressure control of electro-hydraulic pump with variable displacement. Genetic algorithms are general-purpose optimization methods based on natural evolution and genetics and search the optimal control parameters maximizing a measure that evaluates the performance of a system. Four control gains of the PI-PD cascade controller for an electro-hydraulic pressure control system are optimized using a genetic algorithm in the experiment. Optimized gains are confirmed by inspecting the fitness distribution which represents system performance in gain spaces. It is shown that genetic algorithm is an efficient scheme in optimizing control parameters of the pressure control of electro-hydraulic pump with variable displacement.