• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.246-246
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• 2021

본 연구는 강우 발생시 유량을 추정하는 것에 목적이 있다. 이를 위해 본 연구는 선행연구의 모형 개발방법론에서 벗어나 딥러닝 알고리즘 중 하나인 합성곱 신경망 (convolution neural network)과 수문학적 이미지 (hydrological image)를 이용하여 강우 발생시 유량을 추정하였다. 합성곱 신경망은 일반적으로 분류 문제 (classification)을 해결하기 위한 목적으로 개발되었기 때문에 불특정 연속변수인 유량을 모의하기에는 적합하지 않다. 이를 위해 본 연구에서는 합성곱 신경망의 완전 연결층 (Fully connected layer)를 개선하여 연속변수를 모의할 수 있도록 개선하였다. 대부분 합성곱 신경망은 RGB (red, green, blue) 사진 (photograph)을 이용하여 해당 사진이 나타내는 것을 예측하는 목적으로 사용하지만, 본 연구의 경우 일반 RGB 사진을 이용하여 유출량을 예측하는 것은 경험적 모형의 전제(독립변수와 종속변수의 관계)를 무너뜨리는 결과를 초래할 수 있다. 이를 위해 본 연구에서는 임의의 유역에 대해 2차원 공간에서 무차원의 수문학적 속성을 갖는 grid의 집합으로 정의되는 수문학적 이미지는 입력자료로 활용했다. 합성곱 신경망의 구조는 Convolution Layer와 Pulling Layer가 5회 반복하는 구조로 설정하고, 이후 Flatten Layer, 2개의 Dense Layer, 1개의 Batch Normalization Layer를 배열하고, 다시 1개의 Dense Layer가 이어지는 구조로 설계하였다. 마지막 Dense Layer의 활성화 함수는 분류모형에 이용되는 softmax 또는 sigmoid 함수를 대신하여 회귀모형에서 자주 사용되는 Linear 함수로 설정하였다. 이와 함께 각 층의 활성화 함수는 정규화 선형함수 (ReLu)를 이용하였으며, 모형의 학습 평가 및 검정을 판단하기 위해 MSE 및 MAE를 사용했다. 또한, 모형평가는 NSE와 RMSE를 이용하였다. 그 결과, 모형의 학습 평가에 대한 MSE는 11.629.8 m3/s에서 118.6 m3/s로, MAE는 25.4 m3/s에서 4.7 m3/s로 감소하였으며, 모형의 검정에 대한 MSE는 1,997.9 m3/s에서 527.9 m3/s로, MAE는 21.5 m3/s에서 9.4 m3/s로 감소한 것으로 나타났다. 또한, 모형평가를 위한 NSE는 0.7, RMSE는 27.0 m3/s로 나타나, 본 연구의 모형은 양호(moderate)한 것으로 판단하였다. 이에, 본 연구를 통해 제시된 방법론에 기반을 두어 CNN 모형 구조의 확장과 수문학적 이미지의 개선 또는 새로운 이미지 개발 등을 추진할 경우 모형의 예측 성능이 향상될 수 있는 여지가 있으며, 원격탐사 분야나, 위성 영상을 이용한 전 지구적 또는 광역 단위의 실시간 유량 모의 분야 등으로의 응용이 가능할 것으로 기대된다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.146-157
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• 2018

Over the last 10 years, the number of disasters has been increasing in Korea. As a result, the need for temporary residences or shelters for disaster conditions is increasing. In this study, post-disaster refugees housing was developed using lightweight composite panels that are lighter than the materials that make up the existing shelter. To accomplish this, the structural performance of the lightweight composite panel was validated. Among the performance tests on the panels, the transverse load test was conducted according to the ASTM E 72 criteria. As a result of the experiment, when each specimen was subjected to a uniformly distributed load, the allowable load was determined according to the span. All the experiments were ended due to a loss of adhesive at the junction of the skin and core. Further analysis was conducted to calculate the shear stress when the junction was dropped. The mean shear stress at the adhesive surface of a specimen, 150 mm and 200 mm in thickness, was 0.0170MPa and 0.0156MPa, respectively. This suggests that similar values were obtained from panels of equal thickness. In addition, this stress provides a criterion of judgment that could be used to inspect the structural performance of the panels. The performance of the panel was evaluated based on the allowable load, but it may be possible to increase the strength of the lightweight composite panel by improving the joining method to avoid separation from the junction.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3141-3147
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• 2010

The most important cause for shortening LED lighting efficiency and life is the junction temperature rises and, to solve this problem, various studies such as thermally efficient packaging, highly conductive material development, contact resistance improvement or heat sink optimization have been studied. However, most studies so far assumed that the LED lights are in the atmosphere, and thermal performance has not been therefore reported when the LED lights are mounted on the ceiling with ceiling unit. Thus, this study investigates the variation of junction temperature of the MR16 LED light under actual installation conditions and more accurate thermal design for the efficiency and life of LED lights is therefore achieved.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.636-640
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• 2011

Film cooling technique has been applied to effectively reduce thermal load on liquid rocket combustion chambers by direct injection of a portion of propellant, which flows through the regeneratively cooling channels, into the chamber wall. This study developed a comprehensive model to quantitatively predict the effects of kerosene film cooling on propulsive performance and wall cooling at supercritical pressure conditions, and assessed the predictive capability against hot-firing tests of an actual combustor. The present model is expected to be utilized as a design and analysis tool to meet the conflicting requirements in terms of performance, cooling, pressure loss and weight.

• The Journal of the Acoustical Society of Korea
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• v.11 no.2
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• pp.38-49
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• 1992

The structure borne sound is one of the most important factor in building acoustics. Nevertheless, there is not yet sufficient knowledge to predict its behavior in preparing the acoustical design of a building. We are concerned with the concrete floating floor construction, which is one of the most promising ways to control floor impact sound. This study is to develop floating screeds isolated from the conventional concrete floor structures, to improve the concrete floor systems for the purpose of the good sound insulation performance which protects the propagation of the structure borne sound. Floor impact sound in many apartment house buildings and developed floating floors was measured, and we can save many floor impact sound data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7A
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• pp.828-835
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• 2004

An adaptive hexagon based search(AHBS) algorithm is proposed in this paper to perform block motion estimation in video coding. The AHBS evaluates the value of a given objective function starting from a diamond-shaped checking block and then continues its process using two hexagon-shaped checking blocks until the minimum value is found at the center of checking blocks. Also, the determination of which checking block is used depends on the position of minimum value occurred in previous searching step. The AHBS is compared with other fast searching algorithms including full search(FS). Experimental results show that the proposed algorithm provides competitive performance with slightly reduced computational complexity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.3069-3078
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• 2014

This research handles the performance improvement effect by the Modeling & Simulation and shows the design, implementation, test results of the new 3-axis magnetometer which is the core component of strategic offensive deploying mine. The submarine is modelled by using the commercial electromagnetic field analysis tool on numerical value, and its magnetic field characteristic is predicted in order to apply the new magnetometer to the future underwater weapon system. The method to take the performance test results of new 3-axis magnetometer in the land is shown instead of the real test result in sea by making the miniature submarine.

• Journal of the Korean Solar Energy Society
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• v.25 no.4
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• pp.141-153
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• 2005

PV 태양광 발전은 PV 재료가 고가이므로 일반 전력비용에 비해 상대적으로 비용이 높아진다. 저가형 광학 집광기술과 PV를 통합하게 되면, 비용뿐만 아니라 설치면적 등에서 유리하게 되나, 집광기의 단점이 함께 추가되게 된다. 집광기는 작은 수광각과 송신광선을 갖고 있어 PV 모듈에 필요한 태양광, 광학손실의 손실정도를 최소화하기 위한 신중한 시스템 디자인과 2축형 트레킹 장치가 필요하다. 고정식 비집광 시스템보다 더 많은 에너지를 얻기 위해서는 광학시스템의 손실율을 줄이고, 고효율의 PV 모듈을 이용한 PV셀의 상호연결이 필요하다. 본 논문에서는 우선, 비이미지 프레넬 렌즈 집광기를 사용한 PV 시스템에 대하여 간단하게 설명한 후, 출력전력값을 이론적으로 예측하고 PV 효율과 시스템 성능을 제시하였다. 프레넬 렌즈 선형 집광기 통합 PV 시스템과 비집광 PV 모듈의 출력전력값과 시스템 비용을 비교하면, PV 전력비용을 줄일 수 있는 집광기의 이용이 유용한 것을 알 수 있다. 따라서, 집광형 PV 시스템은 미래의 에너지 이용에 매우 유리한 시스템이라 할 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.11-17
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• 2010

This paper proposes an improved SMO solving a quadratic optmization problem for class imbalanced learning. The SMO algorithm is aproporiate for solving the optimization problem of a support vector machine that assigns the different regularization values to the two classes, and the prosoposed SMO learning algorithm iterates the learning steps to find the current optimal solutions of only two Lagrange variables selected per class. The proposed algorithm is tested with the UCI benchmarking problems and compared to the experimental results of the SMO algorithm with the g-mean measure that considers class imbalanced distribution for gerneralization performance. In comparison to the SMO algorithm, the proposed algorithm is effective to improve the prediction rate of the minority class data and could shorthen the training time.