• 한국해양공학회지
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• 제33권2호
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• pp.161-167
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• 2019

This paper focuses on a newly designed ice load formula based on the ARAON's 2016 Arctic field data in order to improve a structural design against ice loads. The strain gage signals from ARAON's hull plating were converted to the local ice pressure upon the hull plating using the influence coefficient matrix and finite element analysis. First, a traditional pressure-area relationship is derived by applying probabilistic approaches to handle the strains measured onboard the ARAON. Then, the local ice load prediction formula is re-analyzed after reviewing the ARAON's additional field data to consider information about the ship speed and thickness of the sea ice. It is shown that the newly developed pressure-area relationship well reflects the influence of other design parameters such as the ship speed and ice thickness in the prediction of local ice loads on Arctic vessels.

• 대한안전경영과학회지
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• 제23권4호
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• pp.93-104
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• 2021

In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.

• 한국항공우주학회지
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• 제50권10호
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• pp.701-708
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• 2022

압축력을 받는 발사체의 추진제 탱크 구조는 좌굴에 의한 파손이 발생할 위험이 크다. 탱크 구조와 같이 두께가 얇고 반지름이 큰 대형 경량 구조물은 제작 과정이 어렵고 복잡하므로 시험 후 사용을 위해 비파괴적 시험법을 이용한 좌굴 하중 예측이 요구된다. 압축 하중-고유 진동수와의 관계를 이용하여 좌굴 하중을 예측하는 Vibration Correlation Technique(VCT)에 관한 많은 연구가 수행되었으나 좌굴 하중을 정확히 예측하기 위하여 큰 압축 하중을 필요로 하는 시험이 요구되었고 구조물의 내부 압력이 증가됨에 따라 예측 정확도가 현저히 떨어지는 경향을 보였다. 본 논문에서는 내압 증가에 따라 예측 정확도가 저하되는 경향과 원인을 분석하고 유한요소해석 결과와 압축 시험 결과를 혼합한 VCT를 제안하여 시험 후 추진제 탱크의 사용이 가능할 정도의 낮은 압축 하중 시험 값에서도 좌굴 하중 예측 정확도를 증대시킬 수 있는 방법을 제안하였다. 제안된 방법에 의한 좌굴 예측값은 실제 좌굴 시험 값과 매우 잘 일치하였다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제4권2호
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• pp.47-56
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• 2015

에너지 절감형 서버 클러스터 환경에서는 서버 전원 모드가 부하상황에 따라 제어된다. 다시 말하면 현재 부하를 처리하는 데 필요한 대수의 서버들만 ON하고 나머지 서버들은 OFF한다. 이 알고리즘은 정상적인 상황에서는 잘 동작하지만 부하가 급증 또는 급감하는 비정상적인 상황에서는 QoS를 보장할 수 없다. 왜냐하면 서버가 OFF에서 ON으로 바뀌는 데 필요한 지연시간 때문에 ON 서버 대수를 당장 증가시킬 수 없기 때문이다. 본 논문에서는 정상적인 상황뿐만 아니라 비정상적인 상황에서도 QoS를 향상시키는 새로운 소비 전력 예측 알고리즘을 제안한다. 제안된 예측 알고리즘은 기존 시계열 분석에 기반한 예측과 추세를 반영한 예측 조정의 두 부분으로 구성된다. 15대의 서버 클러스터를 이용하여 실험이 수행되었고, 4가지 유형의 기존의 시계열 예측 모델과 본 논문에서 제안하는 4가지 유형의 수정된 모델에 대해 성능을 비교하였다. 실험 결과 4가지 유형 중 추세조정 지수평활법(ESTA)과 본 논문에서 제안된 ESTA(MESTA)가 표준화된 QoS 및 단위전력당 좋은 응답수 측면에서 가장 우수한 성능을 보였으며, 또한 본 논문에서 제안한 MESTA 알고리즘이 기존의 ESTA 알고리즘에 비해 가상 부하패턴과 실제 부하패턴에 대해 QoS가 7.5%, 3.3% 각각 향상됨을 보여주었다.

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.622-624
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• 2005

This paper proposes a time series prediction method for the nonllinear system using the fuzzy system and its genetic algorithm, At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction series system may be obtained. We obtain a good result for the time prediction of the electric load.

• 설비공학논문집
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• 제22권11호
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• pp.798-804
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• 2010

New methodology is proposed to predict the hourly cooling load of the next day using maximum/minimum temperature and building area. The maximum and minimum temperature are obtained from forecasted weather data. The cooling load parameters related to building area are set through a database provided from reference buildings. To validate the performance of the proposed method, the predicted cooling loads in hourly bases are calculated and compared with the measured data. The predicted results show fairly good agreement with the measured data for benchmarking building.

• 대한전기학회논문지:전력기술부문A
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• 제55권7호
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• pp.306-312
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• 2006

Support Vector Machine(SVM), of which the foundations have been developed by Vapnik (1995), is gaining popularity thanks to many attractive features and promising empirical performance. In this paper, we propose a new short-term load forecasting technique based on SVM. We discuss the input vector selection of SVM for load forecasting and analyze the prediction performance for various SVM parameters such as kernel function, cost coefficient C, and $\varepsilon$ (the width of 8 $\varepsilon-tube$). The computer simulation shows that the prediction performance of the proposed method is superior to that of the conventional neural networks.

• 대한전기학회논문지
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• 제32권6호
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• pp.191-198
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• 1983

This paper describes the aplication of different model which can be used for short-term load prediction. The model is based on Bohlin's approach to first develop a load profile model representing the nominal load component and the Box-Jenkins approach is used to predict residuals. An on-line algorithm using Kalman Filter and Time Series is implemented for and hour-ahead prediction. In the Kalman Filter system equation and measurement equation were fixed and parameters of Time Series were varied week after week. A set of data for Korea Electric Power Corporation from April to June 1981 was used for the evaluation of the model. As the result of this simulation 1.2% rms error was acquired.

• 전기학회논문지
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• 제66권12호
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• pp.1751-1758
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• 2017

This paper presents a fuzzy logic system with data preprocessing to make the accurate electric power load prediction system. The fuzzy logic system acceptably treats the hidden characteristic of the nonlinear data. The data preprocessing processes the original data to provide more information of its characteristics. Thus the combination of two methods can predict the given data more accurately. The former uses TSK fuzzy logic system to apply the linguistic rule base and the linear regression model while the latter uses the linear interpolation method. Finally, four regional electric power load data in taiwan are used to evaluate the performance of the proposed prediction system.