• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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• pp.934-938
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• 2008

To customize individual characteristics of HRTF, a spherical model has been used for structural modeling technique. A pseudo-code of prolate spheroidal HRTF caused by incident acoustic point source is already developed, and it can be used a head shadow filter for structural modeling of HRTF. In this research, to see the necessity and efficiency of spheroidal head modeling, ITD optimization is performed on CIPIC HRTF database. From given cost function, ITD-optimized spheroidal head model, whose ITD information is the most matched version of measured ITD information, is found by varying head parameters subject by subject. By comparing results of ITD-optimized spheroids and ITD-optimized spheres, we concluded that a spherical head model is more efficient way of generating head shadow effect than a spheroidal head model does.

• Wind and Structures
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• 제17권6호
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• pp.565-587
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• 2013

Obvious vortex induced vibration (VIV) was observed during section model wind tunnel tests for a single main cable suspension bridge. An optimized section configuration was found for mitigating excessive amplitude of vibration which is much larger than the one prescribed by Chinese code. In order to verify the maximum amplitude of VIV for optimized girder, a full bridge aeroelastic model wind tunnel test was carried out. The differences between section and full aeroelastic model testing results were discussed. The maximum amplitude derived from section model tests was first interpreted into prototype with a linear VIV approach by considering partial or imperfect correlation of vortex-induced aerodynamic force along span based on Scanlan's semi-empirical linear model. A good consistency between section model and full bridge model was found only by considering the correlation of vortex-induced force along span.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.263-272
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• 2024

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

• Bulletin of the Korean Chemical Society
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• 제23권1호
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• pp.107-111
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• 2002

The OSS2 (Oj？me-Shavitt-Singer 2)[L. Oj？me et al., J. Chem. Phys. 109, 5547 (1998)] model for the solvated proton in water is examined for $H_2O,\;H_3O^+,\;H_5O_2^+,\;H_7O_3^+,\;and\;H_9O_4^-$ by molecular dynamics (MD) simulations. The equilibrium molecular geometries and energies obtained from MD simulations at 5.0 and 298.15 K agree very well with the optimized calculations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.146-148
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• 2005

In this paper, we propose a genetically optimized identification of information granulation(IG)-based fuzzy model. To optimally design the IG-based fuzzy model we exploit a hybrid identification through genetic alrogithms(GAs) and Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the seleced input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the inital parameters are tuned effectively with the aid of the genetic algorithms and the least square method. And also, we exploite consecutive identification of fuzzy model in case of identification of structure and parameters. Numerical example is included to evaluate the performance of the proposed model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1722-1737
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• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.

• International Journal of Ocean System Engineering
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• 제2권4호
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• pp.233-238
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• 2012

In this study, we carried out a precedent investigation for an ultra-large container ship, which is expected to be a higher value-added vessel. We studied a preliminary optimized design technique for estimating the principal dimensions of an ultra-large container ship. Above all, we have developed optimized dimension estimation models to reduce the building costs and weight, using previous container ships in shipbuilding yards. We also applied a generalized estimation model to estimate the shipping service costs. A Genetic Algorithm, which utilized the RFR (required freight rate) of a container ship as a fitness value, was used in the optimization technique. We could handle uncertainties in the shipping service environment using a Monte-Carlo simulation. We used several processes to verify the estimated dimensions of an ultra-large container ship. We roughly determined the general arrangement of an ultra-large container ship up to 1500 TEU, the capacity check of loading containers, the weight estimation, and so on. Through these processes, we evaluated the possibility for the practical application of the preliminary design model.

• Spatial Information Research
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• 제10권2호
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• pp.275-287
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• 2002

HEC-HMS와 HEC-GeoHMS를 이용하여 황룡강유역의 유출분석을 수행하였다. 황룡강유역을 HEC-GeoHMS와 GIS를 이용하여 3개의 소유역으로 분할하고 입력자료를 구축하였다. HEC-HMS의 SCS 유출곡선지수 손실계산모형, Snyder 단위유량도 직접유출모형, 지수함수 감수 기저유출모형, Muskingum 추적모형을 사용하여 선택된 홍수사상에 대한 유출량을 모의하였고 매개변수의 최적화를 실시하였다. 최적화된 매개변수를 사용하여 계산한 첨두유량을 관측치와 비교한 결과 매우 양호한 결과를 얻을 수 있었다. 최적화된 매개변수값은 향후 황룡강유역의 유출량을 계산할 때 사용될 수 있을 것이다.

• Journal of Power Electronics
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• 제15권2호
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• pp.479-486
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• 2015

The application of soft-switching techniques is increasing in the DC/DC converter area. It is important to design soft-switching parameters to ensure the converter operates properly and efficiently. An optimized design method is presented in this paper. The objective function is the total power loss of a converter, while the variables are soft-switching parameters and the constraints are the electrical requirements for soft-switching. Firstly, a response surface methodology (RSM) model with a high precision is built, and the rough optimized parameters can be obtained with the help of a genetic algorithm (GA) in the solution space determined by the constraints. Secondly, a re-optimization is conducted with a SPICE model and a GA, and accurate optimized parameters can be obtained. Simulation and experiment results show that the proposed method performs well in terms of a wide adaptability, efficiency, and global optimization.

• 지능정보연구
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• 제12권1호
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• pp.139-161
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• 2006

본 논문에서는 새로운 조합경매(combinational auction)모형인 다자간 최적화 경매모형(N-Bilateral Optimized Combinatorial Auction; N-BOCA)을 제시하였다. N-BOCA는 다수의 공급자 및 다수의 구매자간 최적화된 거래를 지원하는 조합경매모형이다. 특히 아키텍처, 거래규약, 거래전략 세가지 관점에서 N-BOCA 시스템을 설계하였다. 경매시장 참여자인 경매자들과 입찰자들은 특정 아키텍처 및 거래규약하에서 최적 거래 대상자 선정을 위한 다양한 전략을 가지게 되며 이러한 거래전략에 따른 유연한 의사결정 모델링 지원을 필요로 한다. 이에 본 논문에서는 최적의 입찰 및 경매자 선정을 위한 Integer Programming 모형 기반의 에이전트 시스템을 제시하였다. 아울러 N-BOCA모형의 유용성을 입증하기 위해 프로토타입과 실험결과를 제시하였다. 실험결과, 기존의 일대다 조합경매 모형 대비 높은 거래 성과를 나타내었다.