• 한국항공운항학회지
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• 제14권4호
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• pp.17-23
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• 2006

This paper describes the preliminary design procedure for the hybrid propulsion system. For a given mission defined by velocity increment, the design of a polyethylene/LOX hybrid rocket was implemented. In addition, Seven-cluster multi-port fuel-grain was considered. After determining the system size including the combustion chamber, the performance parameters such as specific impulse, thrust, characteristic velocity, and thrust coefficient can be predicted by using empirical regression rate correlation, though most of preliminary design code assume constant regression rate. The results of the performance prediction indicated that besides the widely used HTPB/LOX, polyethylene/LOX hybrid motor can be a viable alternative to the more widely used SRMs.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2863-2865
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• 1999

In this paper we present the modeling of ozone prediction system using polynomial neural network. The Polynomial Neural Network is a useful tool for data learning, nonlinear function estimation and prediction of dynamic system. The mechanism of ozone concentration is highly complex, nonlinear, nonstationary. The purposed method shows that the prediction to the ozone concentration based upon a polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation.

• 제어로봇시스템학회논문지
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• 제11권4호
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• pp.385-391
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• 2005

As the Internet real-time multimedia applications increases, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example satisfying this necessity. The TCP-Friendly Rate Control (TFRC) is an UDP-based protocol that controls the transmission rate that is based on the available round trip time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used in the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.

• 한국해양공학회지
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• 제38권1호
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• pp.18-29
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• 2024

Waves are a complex phenomenon in marine and coastal areas, and accurate wave prediction is essential for the safety and resource management of ships at sea. In this study, three types of machine learning techniques specialized in nonlinear data processing were used to predict the waves of Korea waters. An optimized algorithm for each area is presented for performance evaluation and comparison. The optimal parameters were determined by varying the window size, and the performance was evaluated by comparing the mean absolute error (MAE). All the models showed good results when the window size was 4 or 7 d, with the gated recurrent unit (GRU) performing well in all waters. The MAE results were within 0.161 m to 0.051 m for significant wave heights and 0.491 s to 0.272 s for periods. In addition, the GRU showed higher prediction accuracy for certain data with waves greater than 3 m or 8 s, which is likely due to the number of training parameters. When conducting marine and offshore research at new locations, the results presented in this study can help ensure safety and improve work efficiency. If additional wave-related data are obtained, more accurate wave predictions will be possible.

• 한국항해항만학회지
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• 제40권3호
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• pp.97-103
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• 2016

최적의 선박 운항 항로를 찾기 위해서는 선박의 정확한 추진성능을 추정하는 것이 매우 중요하다. 본 논문은 선박 최적운항시스템의 추진성능 데이터베이스를 생성하기 위한 전산프로그램의 개발에 대해 기술하고 있다. 실해역에서의 추진성능은 표류와 표면 거칠기 등 선체 상태뿐 만 아니라 파랑과 바람 등 해상 상태의 영향을 받는다. 이 부가저항 추정 방법들은 ISO 15016:2002 표준의 실선 속력시운전 해석법을 근간으로 하고 있으며, 추가로 바람과 선체 표면 거칠기에 대한 몇 가지 추정 방법이 보완되었다. 이 추정 방법들은 종합적인 전산프로그램으로 만들어졌다. 그리고 향후 최적 운항경로 계산에 활용될 쇄빙연구선 아라온 호에 대해서 데이터베이스 계산이 수행되었다. 이 프로그램은 모든 선박의 항로 최적화 계산에 유용하게 사용될 수 있을 것으로 판단된다.

• 한국인터넷방송통신학회논문지
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• 제21권4호
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• pp.183-188
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• 2021

협력 필터링 기반의 추천 시스템은 현재 다양한 분야의 상업용 시스템의 필수불가결한 기능으로서, 사용자들이 선호할만한 상품을 맞춤형으로 제공해 주는 유용한 서비스이다. 그러나, 사용자들의 평가 데이타가 불충분할 경우 선호상품의 예측이 부정확할 우려가 크다. 본 연구에서는 이러한 단점을 해결하기 위하여 단계적으로 상품의 평가치를 예측하는 방안을 제시한다. 각 단계에 해당하는 예측 방법의 적용 조건을 만족하지 못할 경우 다음 단계의 방법을 적용한다. 제안 방법의 성능 평가를 위해, 공개 데이터셋을 활용한 실험을 진행하였으며, 제안 방법은 여러 전통적 유사도 척도를 도입한 협력 필터링 시스템의 예측 성능과 정밀도 성능을 크게 향상시켰고, 평가데이터 희소성 해결을 위한 기존 방식들의 성능을 능가하는 결과를 보였다.

• 한국항공운항학회지
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• 제23권3호
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• pp.28-34
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• 2015

An analysis method of trajectory prediction has been suggested and the developed trajectory prediction module, which is an important functional component of the Arrival Manager (AMAN) of Jeju airport, has been tested by applying the suggested method. The objective of this method is to improve prediction performance of the trajectory prediction module. The trajectory prediction module predicts the trajectories based on the real-time track data and flight plans. Therefore, the suggested analysis method includes the simulation framework which is based on real-time playback, recording, and graphic display systems for testing. Besides, the definition of time error, which is a important index for the time based scheduling system, such as AMAN, is included in the suggested analysis method. An example of arrival time prediction accuracy improvement through the suggested analysis method has also been presented.

• 한국항공운항학회지
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• 제25권4호
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• pp.124-129
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• 2017

Clouds are an important factor in aircraft flight. In particular, a significant impact on small aircraft flying at low altitude. Therefore, we have verified and characterized the low level cloud prediction data of the Unified Model(UM) - based Local Data Assimilation and Prediction System(LDAPS) operated by KMA in order to develop cloud forecasting service and contents important for safety of low-altitude aircraft flight. As a result of the low level cloud test for seven airports in Korea, a high correlation coefficient of 0.4 ~ 0.7 was obtained for 0-36 leading time. Also, we found that the prediction performance does not decrease as the lead time increases. Based on the results of this study, it is expected that model-based forecasting data for low-altitude aviation meteorology services can be produced.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.176-179
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• 2002

In recent years, an increasing amount of computer network research has focused on the problem of cluster system in order to achieve higher performance and lower cost. The load unbalance is the major defect that reduces performance of a cluster system that uses parallel program in a form of SPMD (Single Program Multiple Data). Also, the load unbalance is a problem of MPP (Massive Parallel Processors), and distributed system. The cluster system is a loosely-coupled distributed system, therefore, it has higher communication overhead than MPP. Dynamic load balancing can solve the load unbalance problem of cluster system and reduce its communication cost. The cluster systems considered in this paper consist of P heterogeneous nodes connected by a switch-based network. The master node can predict the average execution time of tasks for each slave node based on the information from the corresponding slave node. Then, the master node redistributes remaining tasks to each node considering the predicted execution time and the communication overhead for task migration. The proposed dynamic load balancing uses execution time prediction to optimize the task redistribution. The various performance factors such as node number, task number, and communication cost are considered to improve the performance of cluster system. From the simulation results, we verified the effectiveness of the proposed dynamic load balancing algorithm.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 춘계정기학술대회
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• pp.399-408
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• 2001

This paper presents a simultaneous optimization approach of case-based reasoning (CBR) using a genetic algorithm(GA) for the prediction of stock price index. Prior research suggested many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most studies, however, used the GA for improving only a part of architectural factors for the CBR system. However, the performance of CBR may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of CBR outperforms other conventional approaches for the prediction of stock price index.