• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.11
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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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• v.22 no.3
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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.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.421-428
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• 2002

The local prediction method utilizing embedding vector loses the prediction power when the parameter r estimation is not exact for predicting the chaos time series induced from the high order differential equation. In spite of the fact that there have been a lot of suggestions regarding how to estimate the delay time ($\tau$), no specific method is proposed to apply to any time series. The inclinded linear model, which utilizes inclinded netter, yields satisfying degree of prediction power without estimating exact delay time ($\tau$). The usefulness of this approach has been indicated not only theoretically but also in practical situation when the method w8s applied to economical time series analysis.

• Nuclear Engineering and Technology
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• v.46 no.3
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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.

• Geotechnical Engineering
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• v.13 no.2
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• pp.65-76
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• 1997

It has been a growing concern how to use Nanji-Island landfill and other refuse landfills located around metropolitan areas. In this paper, settlement characteristics of Nanji -Island landfill were studied by analyzing the data collected over the period of two years. The settlement characteristics were similar to the analyzed settlement characteristics of 24 refuse landfills in the United States. The model proposed by Bjarngard and Edger(1990) model is considered to be suitable for the long-term prediction of Wnil -Island landfill. The ten-year -period prediction value of Bjarngard and Edger (1990) model is considerably different from that of Power Creep Model. If existing settlement models used for long-term prediction of the settlement characteristics of landfill are not analyzed thoroughly there remains the possibility of including considerable prediction errors.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.2
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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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• v.10 no.3
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.10
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• pp.616-618
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• 2014

In this paper, we designed the accelerated life testing(ALT) and presented the lifetime prediction method of the RF SAW duplexer. We determined RF input power as an accelerated stress when designing an accelerating life testing and defined the lifetime of the duplexer as the period during which the insertion loss increased by 0.5[dB]. Lifetime prediction results of duplexer was estimated for 82,900hours at an ambient temperature of $85^{\circ}C$ and RF input power of 30[dBm].

• Journal of Power System Engineering
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• v.16 no.5
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• pp.55-62
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• 2012

Centrifugal compressors are widely used and each operating condition is different. However, it cannot be manufactured according to the every operating condition. In the this study, performance of compressor was evaluated with various rotational speeds of impeller and various stagger angles of diffuser in order to apply a typical model widely. A centrifugal compressor was designed and manufactured based on the design point. On this machines, an experiment was conducted and the performance was predicted at off-design point. The performance prediction was validated with the experimental result and the numerical result. Although the isentropic efficiency on the prediction was slightly lower than that on the experimental result due to the heat loss in the experiment, the pressure ratio was predicted well and also the predicted results were matched well with the numerical results. When the rotational speed of the impeller and the stagger angle of the diffuser were changed together, the compressor can be worked in the high efficiency region and avoided operating in the stall region.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.355-362
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• 2012

The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict Pressure Tube (PT) diametral creep employing the previously measured PT diameters and operating conditions. There are twelve bundles in a fuel channel, and for each bundle a linear model was developed by using the dependent variables, such as the fast neutron fluences and the bundle coolant temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3, and 4 of the Wolsung nuclear plant in Korea were used to develop the BPLM. The data from the remaining 10 channels were used to test the developed BPLM. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from Units 2, 3, and 4. Two error components for the BPLM, which are the epistemic error and the aleatory error, were generated. The diametral creep prediction and two error components will be used for the generation of the regional overpower trip setpoint at the corresponding effective full power days. The root mean square (RMS) errors were also generated and compared to those from the current prediction method. The RMS errors were found to be less than the previous errors.