• Smart Media Journal
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• v.4 no.2
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• pp.26-33
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• 2015

In this paper, we propose an efficient automatic pedestrian removal system from a frame in a video sequence. It firstly finds pedestrians from the frame using a Histogram of Oriented Gradient(HOG) / Linear-Support Vector Machine(L-SVM) classifier, searches for proper background patches, and then the patches are used to replace the deleted pedestrians. Background patches are retrieved from the reference video sequence and a modified feather blender algorithm is applied to make boundaries of replaced blocks look naturally. The proposed system, is designed to automatically detect object and generate natural-looking patches, while most existing systems provide search operation in manual. In the experiment, the average PSNR of the replaced blocks is 19.246

• Journal of the Korean Magnetics Society
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• v.20 no.1
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• pp.24-27
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• 2010

This paper presents the design optimization of a linear actuator for fast response of electromagnetic engine valve. The optimization is performed using generic algorithm which is one of global search techniques and not highly dependent on either initial conditions or constraints in the solution domain to maximize the mechanical frequency of the armature mass and valve spring stiffness for fast response of the engine valve. In the results, the mechanical frequency is improved by 30 %.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.621-632
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• 2013

In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.90-94
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• 1996

Code Excited Linear Prediction(CELP) vocoder exhibits good performance at data rates below 8 kbps. The major drawback of CELP type coders is a large amount of computation. In this paper, we propose a new pitch searching method that preserves the quality of the CELP vocoder reducing computational complexity. The basic idea is that grasps preliminary pitches using the first formant of speech signal and performs pitch search only about the preliminary pitches. As applying the proposed method to the CELP vocoder, we can reduce complexity by 64% in the pitch search.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.564-582
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• 2015

This paper proposes a novel swarm intelligence optimization method which integrates bacterial foraging optimization (BFO) with quantum computing, called quantum bacterial foraging optimization (QBFO) algorithm. In QBFO, a multi-qubit which can represent a linear superposition of states in search space probabilistically is used to represent a bacterium, so that the quantum bacteria representation has a better characteristic of population diversity. A quantum rotation gate is designed to simulate the chemotactic step for the sake of driving the bacteria toward better solutions. Several tests are conducted based on benchmark functions including multi-peak function to evaluate optimization performance of the proposed algorithm. Numerical results show that the proposed QBFO has more powerful properties in terms of convergence rate, stability and the ability of searching for the global optimal solution than the original BFO and quantum genetic algorithm. Furthermore, we examine the employment of our proposed QBFO for cognitive radio spectrum allocation. The results indicate that the proposed QBFO based spectrum allocation scheme achieves high efficiency of spectrum usage and improves the transmission performance of secondary users, as compared to color sensitive graph coloring algorithm and quantum genetic algorithm.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.1
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• pp.93-101
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• 1997

Generally the traditional optimization methods have possibilities not only to give a different optimum value according to their starting point, but also to get to local optima. On the other hand, Genetic Algorithm (GA) has an ability of robust global search. In this paper, a new optimization method - the combination of traditional optimization method and genetic algorithm - is presented so as to overcome the above disadvantage of traditional methods. In order to increase the efficiency of the hybrid optimization method, a knowledge-based reasoning is adopted in the part of mathematical modeling, algorithm selection, and process control. The validation of the developed knowledge-based hybrid optimization method was examined and verified applying the method to nonlinear mathematical models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.217-229
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• 2007

In this paper, Genetic Algorithm (GA) is used to find the Maximum Weight Independent Set (MWIS) of a graph. First, MWIS problem is formulated as a 0-1 integer programming optimization problem with linear objective function and a single quadratic constraint. Then GA is implemented with the help of this formulation. Since GA is a heuristic search method, exact solution is not reached in every run. Though the suboptimal solution obtained is very near to the exact one. Computational result comprising an average performance is also presented here.

• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.280-287
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• 1993

Code Excited Linear Prediction(CELP) speech coders exhibit good performance at data rates as low as 4800bps. The major drawback to CELP type paper, a comparative performance study of three pitch searching algorithms for the CELP vocoder was conducted. For each of the algorithms, a standard pitch searching algorithm was used by the sequential pitch searching algorithm that was implimented in the QCELP vocoder. The algorithms used in this study were 1) using the skip table(TABLE), 2) using the symmetrical property of the autocorrelation(SYMMT), and 3) using the preprocessing autocorrelation(PREPC). Performance scores are presented for each of the three pitch searching algorithms based on computation speed and on pitch prediction error.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.188-194
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• 1994

Code excited linear prediction speech coders exhibit good performance at data rates as low as 4800bps. But the major drawback to CELP type coders is their large computational requirements. Therefore, in this paper a comparative performance study of three pitch searching algorithms for the CELP vocoder was conducted. For each of the algorithms, a standard pitch searching algorithm was used by the full pitch searching algorithm that was implimented in the QCELP vocoder. The algorithms used in this study is to reduce the pitch searching time 1) using the skip table, 2) using the symmetrical property of the autocorrelation , and 3) using the preprocessing autocorrelation, 4) using the positive autocorrelation, 5) using the preliminary pitch. Performance scores are presented for each of the five pitch searching algorithms based on computation speed and on pitch prediction error.