• Journal of Korean Society of Steel Construction
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• v.19 no.3
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• pp.259-270
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• 2007

This paper presents an analytical method of evaluating the flexural behavior of RC (reinforced concrete) beams strengthened with high-strengh bars. The former experimental results were used to compare with the analytical results. The experimental results also outline the advantages of externally strengtheng method with high-strenght bars. To evaluate the flexural behavior of RC beams strenghtend with unbonded high-strength bars, this paper proposes a method involving a simple strength-summation method. This method basically assumes that the total strength of RC beams strengthened with high-strength bars is equal to the sum of the strengths of the RC beams and the high-strength bars. This analytical method also includes the effects of compressive force due to the tension from high-strength bars. A comparison of the analytical and experimental results leads to the conclusion that the simple strength-summation mothod can simulate the flexural behavior of RC beams strengthened with high-strength bars with a good level of accuracy.

• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.67-77
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• 2007

This study deals with the traffic accidents at the unsignalized intersections in Cheongju. The purpose is to analyze the characters and the relations between road environmental factors and traffic accidents. The correlation analyses among the above factors show that the accidents are strongly related to traffic volumes and sight distances in 3-legged, and the cross angles, maximum vertical grades and sight distances in 4-legged unsignalized intersections. Also the multiple linear and nonlinear regression analyses represent that the accidents in the 3-legged increase as the traffic volume and the number of double stop-lines increase, and that the accidents in the 4-legged increase as the cross angle approaches to the 90 degree and decrease as the maximum vertical grade increases. It could be expected that this results give the good implications to the future intersection improvement projects in Cheongju.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• Applied Chemistry for Engineering
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• v.31 no.5
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• pp.520-525
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• 2020

In this study, we developed a machine learning-based model for predicting the production stage temperature of distillation process. It is necessary to predict an accurate temperature for control because the control of the distillation process is done through the production stage temperature. The temperature in distillation process has a nonlinear complex relationship with other variables and time series data, so we used the recurrent neural network algorithms to predict temperature. In the model development process, by adjusting three recurrent neural network based algorithms, and batch size, we selected the most appropriate model for predicting the production stage temperature. LSTM₁₂₈ was selected as the most appropriate model for predicting the production stage temperature. The prediction performance of selected model for the actual temperature is RMSE of 0.0791 and R² of 0.924.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.2
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• pp.30-36
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• 2002

This paper describes a nonlinear adaptive noise canceler(ANC) using neural networks(NN) based on filter to make up for the drawback of the conventional ANC with the linear adaptive filter. The proposed ANC was tested its noise rejection performance using broadband time-varying noise signal and compared with the ANC of TDL linear filter. Experimental results show that in cases of nonlinear correlations between the noise of primary input and reference input, the neural network based ANC outperforms the linear ANC with respect to mean square error It is also verified that the recurrent NN adaptive filter is superior to the feedforward NN filter. Thus, we identify that the NN adaptive filter is more effective than the linear adaptive filter for rejection of broadband time-varying noise in the ANC.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.137-146
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• 2011

In order to support increased consumer awareness regarding energy consumption, we present new ways of monitoring and predicting with energy in electric appliances. The proposed system is a design of a common electrical power outlet called smart plug that measures the amount of current passing through current sensor at 0.5 second. To acquire data for training and testing the proposed neural network, weather parameters used include average temperature of day, min and max temperature, humidity, and sunshine hour as input data, and power consumption as target data from smart plug. Using the experimental data for training, the neural network model based on Back-Propagation algorithm was developed. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the proposed neural network model can predict the power consumption quite well with correlation coefficient was 0.9965, and prediction mean square error was 0.02033.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.457-466
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• 2016

To achieve eco-friendly hospitals it is necessary to empirically verify the effect of meteorological factors on the power consumption of the hospital. Using daily meteorological big data from 2009 to 2013, we studied the weather conditions impact to power consumption and analyzed the patterns of power consumption of two hospitals. R analysis revealed that temperature among the meteorological factors had the greatest impact on the hospital power consumption, and was a significant factor regardless of hospital size. The pattern of hospital power consumption differed considerably depending on the hospital size. The larger hospital had a linear pattern of power consumption and the smaller hospital had a quadratic nonlinear pattern. A typical pattern of increasing power consumption during a hot summer and a cold winter was evident for both hospitals. The results of this study suggest that a hospital's functional specificity and meteorological factors should be considered to improve energy savings and eco-friendly building.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1
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• pp.23-33
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• 1996

In this paper we point out that if the classical principal domain for bispectra is utilized to determine a second-order Volterra model's output, such and output will be incomplete. This deficiency is associated with the periodic nature of the DFT. For this reason, the objective of this paper is to present an "extended" principal domain for Volterra kernels which leads to an improved estimate of the nonlinear system's response. In order to define the extended principal domain, we derive a new discrete frequency-domain Volterra model from a discrete time-domain Volterra model utilizing 2-dimensional DFT and the relationship between the quadratic component of the Volterra model and a square filter. The effect of the extended domain on the model output is interpreted in terms of the periodicity of DFT. Through computer simulations, we demonstrate the effects of the extended principal domain on the Volterra modeling. The simulation results indicate that the extended principal domain plays and important role in computing Volterra model outputs and estimating Volterra model coefficients.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.4
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• pp.567-578
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• 2002

The main purpose of this study is to investigate the dynamic behaviour of containment building in nuclear power plant excited by aircraft impact loading using a lower order 8-node solid element. The yield and failure surfaces for concrete material model is formulated on the basis of Drucker-Prager yield criteria and are assumed to be varied by taking account of the visco-plastic energy dissipation. The standard 8-node solid element has prone to exhibit the element deficiencies and the so-called B bar method proposed by Hughes is therefore adopted in this study. The implicit Newmark method is adopted to ensure the numerical stability during the analysis. Finally, the effect of different levels of cracking strain and several types of aircraft loading are examined on the dynamic behaviour of containment building and the results are quantitatively summarized as a future benchmark.

• Nuclear Engineering and Technology
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• v.16 no.4
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• pp.181-194
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• 1984

The in-core fuel management problem was studied by use of the calculus of variations. Two functions of interest to a public power utility, the profit function and the cost function, were subjected to the constraints of criticality, the reactor turnup equations and an inequality constraint on the maximum allowable power density. The variational solution of the initial profit rate demonstrated that there are two distinct regions of the reactor, a constant power region and a minimum inventory or flat thermal flux region. The transition point between these regions is dependent on the relative importance of the profit for generating power and the interest charges for the fuel. The fuel cycle cost function was then used to optimize a three equal volume region reactor with a constant fuel enrichment. The inequality constraint on the maximum allowable power density requires that the inequality become an equality constraint at some points in the reactor. and at all times throughout the core cycle. The finite difference equations for reactor criticality and fuel burnup in conjunction with the equality constraint on power density were solved, and the method of gradients was used to locate an optimum enrichment. The results of this calculation showed that standard non-linear optimization techniques can be used to optimize a reactor when the inequality constraints are properly applied.