• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.157-167
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• 2020

With the recent advancement of computer hardware and the contribution of open source libraries to facilitate access to artificial intelligence technology, the use of machine learning (ML) and deep learning (DL) technologies in various fields of exploration geophysics has increased. In addition, ML researchers have developed complex algorithms to improve the inference accuracy of various tasks such as image, video, voice, and natural language processing, and now they are expanding their interests into the field of automatic machine learning (AutoML). AutoML can be divided into three areas: feature engineering, architecture search, and hyperparameter search. Among them, this paper focuses on hyperparamter search with Bayesian optimization, and applies it to the problem of facies classification using seismic data and well logs. The effectiveness of the Bayesian optimization technique has been demonstrated using Vincent field data by comparing with the results of the random search technique.

• Journal of Korea Multimedia Society
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• v.14 no.2
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• pp.288-294
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• 2011

In this paper we define the probability models for determining the disparity map given stereo images and derive the methods for solving the problem, which is proven to be equivalent to an energy-based stereo matching. Under the assumptions the difference between the pixel on the left image and the corresponding pixel on the right image and the difference between the disparities of the neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameter is proposed. The proposed method alternates between estimating the parameters with the intermediate disparity map and estimating the disparity map with the estimated parameters, after computing it with random initial parameters. Our algorithm is applied to the stereo matching algorithms based on the dynamic programming and belief propagation to verify its operation and measure its performance.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.1
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• pp.7-15
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• 2016

Ocean wave parameters are important for safety and efficiency of operation and routing of marine traffic. In this paper, by using X-band marine radar, we try to develop an effective algorithm for collecting ocean surface information such as current velocity, wave parameters. Specifically, by exploiting iterative refinement flow instead of using fixed control schemes, an effective algorithm is designed in such a way that it can not only compute efficiently the optimized current velocity but also introduce new cost function in an optimized way. Experimental results show that the proposed algorithm is very effective in retrieving the wave information compared to the conventional algorithms.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.365-366
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• 2023

본 논문은 F1TENTH 와 같은 자율주행 경주 대회를 위한 고전적인 자율주행 알고리즘의 파라미터 최적화에 관한 연구를 다룬다. 고전적인 자율주행 알고리즘은 하이퍼파라미터의 영향을 크게 받고 더 나아가서 하이퍼파라미터의 설정에 따라서 성능의 차이가 크다. 이 하이퍼파라미터를 빠르게 찾기 위하여 Piece and Conquer Fireworks 방법을 제안한다. 결과적으로Random search에 비해서 일반 Fireworks알고리즘은 약8.3배, Piece and Conquer Fireworks알고리즘은 약 28.5배 빠른 성능을 보여준다.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.252-256
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• 2016

본 논문에서는 한국어에 최적화된 단어 임베딩을 학습하기 위한 방법을 소개한다. 단어 임베딩이란 각 단어가 분산된 의미를 지니도록 고정된 차원의 벡터공간에 대응 시키는 방법으로, 기계번역, 개체명 인식 등 많은 자연어처리 분야에서 활용되고 있다. 본 논문에서는 한국어에 대해 최적의 성능을 낼 수 있는 학습용 말뭉치와 임베딩 모델 및 적합한 하이퍼 파라미터를 실험적으로 찾고 그 결과를 분석한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.168-171
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• 2006

본 논문에서는 Particle Swarm Optimization 기법을 이용한 뉴로-퍼지 시스템의 파라미터 동정을 실시한다. PSO의 학습 및 군집 특성을 이용하여 시스템을 학습한다. 유전 알고리즘과 같은 무작위 탐색법을 이용하며 하나의 해 군집에 대해 다수 객체들이 탐색하는 기법을 통하여 최적해 부분의 탐색성능을 높여 전체 모델의 학습성능을 개선하고자 한다. 제안된 기법의 유용성을 시뮬레이션을 통하여 보이고자 한다.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.157-158
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• 2015

본 논문 에서는 펄스폭 변조 영전류 천이 (PWM-ZCT) 승압형 컨버터에서 보조 회로로 흐르는 전류로 인한 도통 손실을 최소화 할 수 있는 공진 파라미터 최적화 방안을 제시한다. 제안된 해석을 통한 소프트 스위칭 기술 적용 시 추가적으로 발생한 보조 회로의 도통 손실을 최소화할 수 있다. 시뮬레이션 결과를 통하여 제안된 해석의 타당성을 입증 하였다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.6
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• pp.876-885
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• 2000

A new and simple method is presented for determining the parasitic resistances of MESFET and HEMT from the measured S-parameters under normal active bias without depending on additional DC measurements or iteration or optimization process. The presented method is based on the fact that the difference between source resistance(Rs) and drain resistance(Rd) can be obtained from the measured Z-parameters under zero bias condition. It is possible to define the new internal device including intrinsic device and 3 parasitic resistances by elimination the parasitic inductances and capacitances from the measured S-parameters. Three parasitic resistances are calculated easily from the fact that the real parts of Yint,11 and Yint,12 of intrinsic Y-parameters are zero theoretically and the relations between S-,Z-, Y-matrices. The calculated parasitic resistances using the presented method and successively calculated equivalent circuit parameters give modeled S-parameters which are in good agreement with the measured S-parameters up to 400Hz.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.395-402
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• 2016

This paper presents a design methodology for improving the energy efficiency by considering the inertial properties of serial manipulators. This method employed is to put the inertia matrix, which has a critical effect on the equation of motion, into the constraints of the optimization problem. Through the optimization process, we propose a design algorithm that can double-check whether the optimized parameters satisfy the required performance or not by using an auxiliary index associated with the inertia and energy. Using this design algorithm, we were able to improve the energy efficiency by minimizing the torque. We applied this method to a 3 degrees of freedom serial manipulator and simulated it.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1357-1366
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• 2022

It is important to quickly and accurately build a disaster network or tactical mobile communication network adapting to the field. In configuring the traditional wireless communication systems, the parameters of the base station are set through cell planning. However, for cell planning, information on the environment must be established in advance. If parameters which are not appropriate for the field are used, because they are not reflected in cell planning, additional optimization must be carried out to solve problems and improve performance after network construction. In this paper, we present a rapid mobile communication network construction and optimization method using artificial intelligence and SON technologies in mobile communication base stations. After automatically setting the base station parameters using the CNN model that classifies the terrain with path loss prediction through the DNN model from the location of the base station and the measurement information, the path loss model enables continuous overage/capacity optimization.