• 전기학회논문지
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• 제62권7호
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• pp.913-917
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• 2013

During the restoration process of primary restorative transmission system, some over voltages may happen due to nonlinear interaction between unloaded transformers and transmission systems. These over voltages caused by harmonic resonance can be suppressed by inserting damping loads before energizing transformers. But it is very difficult to predict the occurrence possibility of harmonic resonance and complex simulation must be repeated to estimate the sufficient damping loads. This paper presents a damping loads prediction system to prevent harmonic resonance. Detailed analysis of the relationship between harmonic resonance and the amount of damping loads is discussed. The prediction system is developed using a curve fitting and a neural network based on this relationship. A curve fitting used a Gaussian function based on non-linear least square method and multi-layer back-propagation neural network is applied. The system is applied to primary restorative transmission lines in korean power system and the result showed satisfactory performance.

• 동력기계공학회지
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• 제19권5호
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• pp.45-51
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• 2015

Grade 91 steel is used for the major structural components of Generation-IV reactor systems such as a very high temperature reactor (VHTR) and sodium-cooled fast reactor (SFR). Since these structures are designed for up to 60 years at elevated temperatures, the prediction of long-term creep life is very important to determine an allowable design stress of elevated temperature structural component. In this study, a large body of creep rupture data was collected through world-wide literature surveys, and using these data, the long-term creep life was predicted in terms of three methods: Larson-Miller (L-M), Manson-Haferd (M-H) and Wilshire methods. The results for each method was compared using the standard deviation of error. The L-M method was overestimated in the longer time of a low stress. The Wilshire method was superior agreement in the long-term life prediction to the L-M and M-H methods.

• 대한기계학회논문집A
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• 제21권11호
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• pp.1796-1808
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• 1997

In order to perform leak-before-break design of nuclear piping systems and integrity evaluation of reactor vessels, full stress-strain curves and fracture resistance(J-R) curves are required. However it is time-consuming and expensive to obtain J-R curves experimentally. To resolve these problems, three different methods for predicting J-R curves from tensile data were proposed by the authors previously. The objective of this paper is to develop a computer program based on those J-R curve prediction methods. The program consists of two major parts ; the main program part for the J-R curve prediction and the database part. Several case studies were performed to verify the program, and it was shown that the predicted results were, in general, in good agreement with the experimental ones.

• 대한기계학회논문집A
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• 제21권11호
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• pp.1786-1795
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• 1997

In order to perform leak-before-break design of nuclear piping systems and integrity evaluation of reactor vessels, full stress-strain curves and fracture resistance (J-R) curves are required. However it is time-consuming and expensive to obtain J-R curves experimentally. The objective of this paper is to modify two J-R curve prediction methods previously proposed by the authors and to propose an additional J-R curve prediction method for nuclear piping materials. In the first method which is based on the elastic-plastic finite element analysis, a blunting region handling procedure is added to the existing method. In the second method which is based on the empirical equation, a revised general equation is proposed to apply to both carbon steel and stainless steel. Finally, in the third method, both full stress-strain curve and finite element analysis results are used for J-R curve prediction. A good agreement between the predicted results based on the proposed methods and the experimental ones is obtained.

• Environmental Engineering Research
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• 제22권3호
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• 정보처리학회논문지B
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• 제9B권4호
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• pp.421-428
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• 2002

지금까지 삽입(Embedding)백터를 이용한 국소적예측방법은 고차미분방정식으로부터 생성된 카오스 시계열을 예측할 때, 파라메타 $\tau$의 추정이 정확하지 않으면 예측성능은 떨어졌다. 지금까지 지연시간 ($\tau$)의 값을 추정하는 방법은 많이 제안되어있지만 실제로 고차원미분방정식부터 생성되어진 수많은 시계열에 모두 적용 가능한 방법은 아직 없다. 이것을 기울기 백터를 이용한 기울기 선형모델을 도입하는 것에 의해 정확한 지연시간 ($\tau$)의 값을 추정하지 않아도 예측성능에 만족할 수 있는 결과를 표시했다. 이것을 이론뿐이 아니고 경제시계열에도 적용해서 종래의 예측방법과 비교해서 그 유효성을 표시했다.

• Nuclear Engineering and Technology
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• 제46권3호
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• pp.373-380
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• 2014

Safety-related parameters are very important for confirming the status of a nuclear power plant. In particular, the reactor vessel water level has a direct impact on the safety fortress by confirming reactor core cooling. In this study, the reactor vessel water level under the condition of a severe accident, where the water level could not be measured, was predicted using a fuzzy neural network (FNN). The prediction model was developed using training data, and validated using independent test data. The data was generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The informative data for training the FNN model was selected using the subtractive clustering method. The prediction performance of the reactor vessel water level was quite satisfactory, but a few large errors were occasionally observed. To check the effect of instrument errors, the prediction model was verified using data containing artificially added errors. The developed FNN model was sufficiently accurate to be used to predict the reactor vessel water level in severe accident situations where the integrity of the reactor vessel water level sensor is compromised. Furthermore, if the developed FNN model can be optimized using a variety of data, it should be possible to predict the reactor vessel water level precisely.

• 한국지반공학회지:지반
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• 제13권2호
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• pp.65-76
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• 1997

난지도 쓰레기 매립지 뿐만 아니라 대도시 주변 매립지의 사후활용 방안에 대한 관심이 점차로 고조되고 있다. 본 논문에서는 난지도 쓰레기 매립지에서 2년여간 계측된 침하자료를 분석하여 침하양상을 규명하고자 하였다. 침하분석결과 난지도 쓰레기 매립지의 침하양상은 미국내 24개 매립지에 대해 분석된 침하경향과 유사함을 알 수 있었다. 계측된 침하자료에 대한 해석을 근거로 할 경우, Bjarngard와 Edgers의 침하모델이 난지도 매립지의 장기침하량 예측에 적합한 것으로 사료된다. 10년 후 장기침하량을 예측할 때 Bjarngard와 Edgers의 침하모델은 Power Creep Model과 상당한 예측의 차이를 보였다. 난지도 쓰레기 매립지 침하양상에 대한 분석이 이루어지지 않은 상황에서 단지 기존의 침하모델만을 사용하는 것은 장기침하량 예측시 상당한 오류를 범할 수 있음을 알 수 있었다.

• 대한임베디드공학회논문지
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• 제4권2호
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• pp.55-62
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• 2009

The power consumption of a high-end microprocessor increases very rapidly. High power consumption will lead to a rapid increase in the chip temperature as well. If the temperature reaches beyond a certain level, chip operation becomes either slow or unreliable. Therefore various approaches for Dynamic Thermal Management (DTM) have been proposed. In this paper, we propose a learning based temperature prediction scheme for a multi-core system. In this approach, from repeatedly executing an application, we learn the thermal patterns of the chip, and we control the temperature in advance through DTM. When the predicted temperature may go beyond a threshold value, we reduce the temperature by decreasing the operation frequencies of the corresponding core. We implement our temperature prediction on an Intel's Quad-Core system which has integrated digital thermal sensors. A Dynamic Frequency System (DFS) technique is implemented to have four frequency steps on a Linux kernel. We carried out experiments using Phoronix Test Suite benchmarks for Linux. The peak temperature has been reduced by on average $5^{\circ}C{\sim}7^{\circ}C$. The overall average temperature reduced from $72^{\circ}C$ to $65^{\circ}C$.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.877-887
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• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.