• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.2
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• pp.59-68
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• 2001

The job shop scheduling problem is not only NP-hard, but is one of the well known hardest combinatorial optimization problems. The goal of this research is to develop an efficient scheduling method based on hybrid genetic algorithm to address job shop scheduling problem. In this scheduling method, generating method of initial population, new genetic operator, selection method are developed. The scheduling method based on genetic algorithm are tested on standard benchmark job shop scheduling problem. The results were compared with another genetic algorithm0-based scheduling method. Compared to traditional genetic, algorithm, the proposed approach yields significant improvement at a solution.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.610-619
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• 2003

This paper presents a concept of cellular automata and a modular exponentiation algorithm and implementation of a basic EIGamal encryption by using cellular automata. Nowadays most of modular exponentiation algorithms are implemented by a linear feedback shift register(LFSR), but its structure has disadvantage which is difficult to implement an operation scheme when the basis is changed frequently The proposed algorithm based on a cellular automata in this paper can overcome this shortcomings, and can be effectively applied to the modular exponentiation algorithm by using the characteristic of the parallelism and flexibility of cellular automata. We also propose a new fast multiplier algorithm using the normal basis representation. A new multiplier algorithm based on normal basis is quite fast than the conventional algorithms using standard basis. This application is also applicable to construct operational structures such as multiplication, exponentiation and inversion algorithm for EIGamal cryptosystem.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.369-371
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• 2006

The AIS(Automatic Identification System) has to be developed SOTDMA(Self-Organized Time Division Multiple Access) Algorithm which is important on wireless communication method because It is based on ITU(International Telecommunication Union) M.1371-1 of the international standard therefore, we need to develop a performance evaluation simulator efficiently to develop and analyze SOTDMA Algorithm. This paper shows the method of designing it. The SOTDMA Algorithm driving state verifies the performance evaluation simulator by IEC(International Electrotechnical Commission) 61993-2. After verifying results the performance evaluation simulator is correctly satisfied with IEC 61993-2. So we expect that it helps not only the AIS technology developed but also verify new SOTDMA Algorithm.

• Journal of the military operations research society of Korea
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• v.9 no.1
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• pp.29-49
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• 1983

The algorithm developed in this paper utilized kettelle's [1] idea of the undominated allocation sequence and his way of tableau computation to solve the more general spares allocation problem in the system availability optimization. The algorithm is to optimally allocate resources to the independent modules which are connected to be series/parallel/mixed system configurations. It has advantages over the standard dynamic programming algorithm by eliminating the need for backtracking and by solving the allocation problem for any budget size. By careful heuristic inspection the algorithm can be made very efficient for manual calculations because large blocks of cells can be eliminated from computation. A numerical example is provided to illustrate the allocation algorithm.

• The Journal of the Acoustical Society of Korea
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• v.25 no.3E
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• pp.123-130
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• 2006

The adaptive echo canceller (AEC) has become an important component in speech communication systems, including mobile station. In these applications, the acoustic echo path has a long impulse response. We propose a running-average least mean square (RALMS) algorithm with a detection method for acoustic echo cancellation. Using colored input models, the result clearly shows that the RALMS detection algorithm has a convergence performance superior to the least mean square (LMS) detection algorithm alone. The computational complexity of the new RALMS algorithm is only slightly greater than that of the standard LMS detection algorithm but confers a major improvement in stability.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.3
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• pp.395-402
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• 2009

This paper presents the newly developed Pareto-based multi-objective Bees Algorithm with weighted sum technique for solving a power system multi-objective nonlinear optimization problem. Specifically, the Pareto-based Bees Algorithm with memorized zone has been developed to alleviate both difficulties from classical techniques and intelligent techniques for multi-objective problems (MOP) and successfully applied to an Environmental/Economic (electric power) dispatch (EED) problem. This multi-objective Bees Algorithm has been examined and applied to the standard IEEE 30-bus six-generator test system. Simulation results have been compared to those obtained using other approaches. The comparison shows the potential and effectiveness of the proposed Bees Algorithm for solving the multi-objective EED problem.

• 한국가시화정보학회:학술대회논문집
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• 2001.12a
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• pp.75-85
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• 2001

The hierarchic recursive local-correlation PIV algorithm with CBC(correlation based correction) method was developed to increase the spatial resolution of PIV results and to reduce error vectors. This new algorithm was applied to the single-frame and double-frame cross-correlation PIV techniques. In order to evaluate its performance, the recursive algorithm was tested using synthetic images, PIV standard images from Visualization Society of Japan, real flows including ventilation flow inside a vehicle passenger compartment and wake behind a circular cylinder with rib let surface. As a result, most spurious vectors were suppressed by employing CBC method. In addition, the hierarchical recursive correlation algorithm improved largely the sub-pixel accuracy of PIV results by decreasing the interrogation window size, increasing spatial resolution significantly.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.10
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• pp.1441-1447
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• 2014

A robust new algorithm for R wave detection named for Multiscale-based Peak Detection(MSPD) is assessed in this paper using MIT/BIH Arrhythmia Database. MSPD algorithm is based on a matrix composed of local maximum and find R peaks using result of standard deviation in the matrix. Furthermore, By reducing needless procedure of proposed algorithm, improve algorithm ability to detect R peak efficiently. And algorithm performance is assessed according to detection rates about various arrhythmia database.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.778-790
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• 2023

Although density peak clustering can often easily yield excellent results, there is still room for improvement when dealing with complex, high-dimensional datasets. One of the main limitations of this algorithm is its reliance on geometric distance as the sole similarity measurement. To address this limitation, we draw inspiration from the information bottleneck theory, and propose a novel density peak clustering algorithm that incorporates this theory as a similarity measure. Specifically, our algorithm utilizes the joint probability distribution between data objects and feature information, and employs the loss of mutual information as the measurement standard. This approach not only eliminates the potential for subjective error in selecting similarity method, but also enhances performance on datasets with multiple centers and high dimensionality. To evaluate the effectiveness of our algorithm, we conducted experiments using ten carefully selected datasets and compared the results with three other algorithms. The experimental results demonstrate that our information bottleneck-based density peaks clustering (IBDPC) algorithm consistently achieves high levels of accuracy, highlighting its potential as a valuable tool for data clustering tasks.

• The Journal of Korea Robotics Society
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• v.19 no.3
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• pp.254-265
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• 2024

Pattern recognition for surface electromyogram (sEMG) suffers from its nonstationary and stochastic property. Although it can be relieved by acquiring new training data, it is not only time-consuming and burdensome process but also hard to set the standard when the data acquisition should be held. Therefore, we propose an adaptive sEMG pattern recognition algorithm using principal component analysis. The proposed algorithm finds the relationship between sEMG channels and extracts the optimal principal component. Based on the relative distance, the proposed algorithm determines whether to update the existing patterns or to register the new pattern. From the experimental result, it is shown that multiple patterns are generated from the sEMG data stream and they are highly related to the motion. Furthermore, the proposed algorithm has shown higher classification accuracy than k-nearest neighbor (k-NN) and support vector machine (SVM). We expect that the proposed algorithm is utilized for adaptive and long-lasting pattern recognition.