• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.923-928
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• 1993

Genetic algorithms are powerful optimization methods based on the mechanism of natural genetics and natural selection. Genetic algorithms reduce chance of searching local optima unlike most conventional search algorithms and especially show good performances in complex nonlinear optimization problems because they do not require any information except objective function value. This paper presents a new model based on sexual reproduction in nature. In the proposed Sexual Reproduction model(SR model), individuals consist of the diploid of chromosomes, which are artificially coded as binary string in computer program. The meiosis is modeled to produce the sexual cell(gamete). In the artificial meiosis, crossover between homologous chromosomes plays an essential role for exchanging genetic informations. We apply proposed SR model to optimization of the design parameters of Single-sided Linear Induction Motor(SLIM). Sequential Unconstrained Minimization Technique(SUMT) is used to transform the nonlinear optimization problem with many constraints of SLIM to a simple unconstrained problem, We perform optimal design of SLIM available to FA conveyer systems and discuss its results.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1564-1573
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• 2016

With the spread of social media and mobile devices, people spend more time on online than ever before. As more people participate in various online activities, much research has been conducted on how to make use of the time effectively and productively. In this paper, we propose two methods which can be used to extract highlights and make searchable media indexes using online social data. For highlight extraction, we collected the comments from the online baseball broadcasting website. We adopted peak-finding algorithm to analyze the frequency of comments uploaded on the comments section of the website. For each indexes, we collected postings from soap opera forums provided by a popular web service called DCInside. We extracted all the instances when a character's name is mentioned in postings users upload after watching TV, which can be used to create indexes when the character appears on screen for the given episode of the soap opera The evaluation results shows the possibility of the crowdsourcing-based media interaction for both highlight extraction and index building.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.815-816
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• 2009

Owing to the development of computer technologies, to process data derived from various sensors is recently demanding. It is also increasing to demand the moving object based servies like the services of location based and mobile application services. That's why it is needed the processing of visualizing the services for education aspects. In this paper, we show the implemented results about $R^*$ tree algorithm to insert, delete and search a object in two dimension environment.

• Steel and Composite Structures
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• v.39 no.1
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• pp.81-93
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• 2021

Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study offers novel methodologies for the early prediction of this parameter. Artificial neural network (ANN), which is a leading predictive method, is synthesized with two metaheuristic algorithms, namely atom search optimization (ASO) and equilibrium optimizer (EO) to achieve an optimal tuning of the weights and biases. The models are applied to data collected from the published literature. The sensitivity of the ASO and EO to the population size is first investigated, and then, proper configurations of the ASO-NN and EO-NN are compared to the conventional ANN. Evaluating the prediction results revealed the excellent efficiency of EO in optimizing the ANN. Accuracy improvements attained by this algorithm were 13.26 and 11.41% in terms of root mean square error and mean absolute error, respectively. Moreover, it raised the correlation from 0.89958 to 0.92722. This is while the results of the conventional ANN were slightly better than ASO-NN. The EO was also a faster optimizer than ASO. Based on these findings, the combination of the ANN and EO can be an efficient non-destructive tool for predicting the STS.

• Smart Structures and Systems
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• v.30 no.2
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• pp.195-207
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• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.668-671
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• 2010

최근 AODV 라우팅 프로토콜은 무선센서네트워크에서 노드 간에 데이터 전송방식을 추구하므로 테이블구동방식 중 가장 널리 사용되고 있다. AODV 라우팅 프로토콜은 목적지노드가 멀리 있거나 없는 경우엔 목적지노드를 찾기 위한 라우팅 패킷이 급격하게 증가하고 이로 인해 네트워크의 성능이 크게 저하된다. 이러한 문제점을 해결하기 위해 불필요한 RREQ 메시지를 제어하는 ERS(expanding ring search)방법을 사용한다. 그러나 ERS 방법은 유선 멀티캐스팅을 기본으로 하기 때문에 무선네트워크환경의 NTT(node traversal time)를 고려하지 않았다는 단점을 가지고 있다. 따라서 본 논문에서는 이동 무선 센서 노드들이 불규칙적으로 이동하는 무선센서네트워크에서 목적지 노드로부터 소스노드까지 전송되는 RREP의 소요시간, 노드간의 거리, 에너지량을 고려하여 효율적인 NTT 알고리즘을 제안한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.29-43
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• 2019

The calibration of microscopic traffic simulation models has received much attention in the simulation field. Although no standard has been established for it, a genetic algorithm (GA) has been widely employed in recent literature because of its high efficiency to find solutions in such optimization problems. However, the performance still falls short in simulation analyses to support fast decision making. This paper proposes a new calibration procedure using a dual GA and central composite design (CCD) in order to improve the efficiency. The calibration exercise goes through three major sequential steps: (1) experimental design using CCD for a quadratic response surface model (RSM) estimation, (2) 1st GA procedure using the RSM with CCD to find a near-optimal initial population for a next step, and (3) 2nd GA procedure to find a final solution. The proposed method was applied in calibrating the Gipps car-following model with respect to maximizing the likelihood of a spacing distribution between a lead and following vehicle. In order to evaluate the performance of the proposed method, a conventional calibration approach using a single GA was compared under both simulated and real vehicle trajectory data. It was found that the proposed approach enhances the optimization speed by starting to search from an initial population that is closer to the optimum than that of the other approach. This result implies the proposed approach has benefits for a large-scale traffic network simulation analysis. This method can be extended to other optimization tasks using GA in transportation studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.652-661
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• 2020

Recently, the Korean government developed the RE3020 (renewable energy) policy to overcome environmental problems, such as fine dust, climate change, and large-scale PV systems interconnected with a distribution system. When a large-scale PV system is interconnected in the distribution system, however, a malfunction can occur, and the protection devices may not be operated because of the dividing effect depending on the magnitude and direction of fault current as well as connection types and location of the PV system. Therefore, this paper proposes a search algorithm for the malfunction pattern of protection devices based on various scenarios, when large-scale PV systems are operated and interconnected in a distribution system. This paper presents a malfunction mechanism of protection devices according to the installation locations of recloser (R/C). Furthermore, the modeling of a distribution system with large-scale PV systems was performed using Off-DAS S/W, and the malfunction patterns of protection devices were analyzed based on a range of scenarios. From the simulation results with the proposed model and algorithm for searching for protection devices, it was confirmed that they are useful and effective in identifying a malfunction phenomenon depending on the installation location of the R/C and connection type of PV system.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.569-578
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• 2021

Thanks to web development today, we can easily access online news via various media. As much as it is easy to access online news, we often face fake news pretending to be true. As fake news items have become a global problem, fact-checking services are provided domestically, too. However, these are based on expert-based manual detection, and research to provide technologies that automate the detection of fake news is being actively conducted. As for the existing research, detection is made available based on contextual characteristics of an article and the comparison of a title and the main article. However, there is a limit to such an attempt making detection difficult when manipulation precision has become high. Therefore, this study suggests using a verifying article to decide whether a news item is genuine or not to be affected by article manipulation. Also, to improve the precision of fake news detection, the study added a process to summarize a subject article and a verifying article through the summarization model. In order to verify the suggested algorithm, this study conducted verification for summarization method of documents, verification for search method of verification articles, and verification for the precision of fake news detection in the finally suggested algorithm. The algorithm suggested in this study can be helpful to identify the truth of an article before it is applied to media sources and made available online via various media sources.