• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.381-384
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• 2006

In order to evaluate the seismic performance of a reinforced concrete building structure, four different analyses are carried out. Firstly, conventional pushover analysis with code-specified inverted triangular load pattern is conducted. Secondly, the pushover analysis with uniform load pattern is performed. Thirdly, adaptive pushover analyses with spectral amplification for both EC 8 artificial and Northridge earthquake are carried out. Lastly, incremental dynamic analyses under a number of scaled PGA for both EC 8 artificial and Northridge earthquake record are performed. Comparative studies demonstrate that the adaptive pushover analysis may be able to explain the response characteristics that conventional pushover analysis with fixed load distribution fails to capture.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.7
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• pp.802-808
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• 1992

To investigate degradation procedure and life time of the oxidized PE and the unoxidized PE, alternative voltage is applied to the CIGRE Method-II (CM-II) electrode system, which is loaded artificial void, and measures the distribution of partial discharging generation. From the results, the samples etched by oxidation had wide degradation area of dielectric strength. Furthermore, discharge starting voltage was shifted to low voltage, the discharge generation frequency was high and consequently, the quantity of mean charge becomes small. Also, life time of the oxdized sample is shortened according as the oxidation time is longer.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.11a
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• pp.595-597
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• 2000

Poymeric insulators using in an heavy contamination area are easily attached to contaminants such as salt and by-products of industrial processes. To understand its effect on contaminants adhered to these insulators, we manufactured slurry mixed by some kaolin and salt as artificial contaminated solution. And then put samples in its slurry, for about one minute. And these samples are dried in natural condition for 6 days. We measured the degree of contaminant, AC leakage current for these contaminated samples.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.1
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• pp.184-189
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• 2004

In this paper, the warm forging process sequence has been determined to manufacture the warm forged product for the precision bevel gear used as the differential gear unit of a commercial automobile. The preform shape of bevel gear influences the dimensional accuracy and stiffness of final product. The aspect ratio and chamfer length are considered as design parameters to achieve adequate metal distribution in the finish forging operation. Then the optimal conditions of design parameters have been selected by artificial neural network (ANN). Finally, to verify the optimal preform shape, the experiments of the warm forging of the bevel gear have been executed. The proposed method can give more systematic and economically feasible means for designing the preform shape in metal forming process.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.11
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• pp.99-103
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• 1993

A monoleaflet polymer artificial heart valve which showed the remarkable improvement in pressure drop compared with other types of artificial valve was designed to decrease the deflection in vertical direction and the displacement or the valve tip in horizontal direction. Stress distribution change was studied as the location of the supporting members or the valve frame changed. And it was found that using the valve tip horizontal displacement the minimum valve thickness could be obtained in order to prevent the gap between the valve tip and the frame wall.

• Coupled systems mechanics
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• v.6 no.1
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• pp.55-74
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• 2017

The evaluation of torsional effects on multistory buildings remains an open issue, despite considerable research efforts and numerous publications. In this study, a large number of multiple test structures are considered with normally distributed topological attributes, in order to quantify the statistically derived relationships between the torsional criteria and response parameters. The linear regression analysis results, depict that the center of twist and the ratio of torsion (ROT) index proved numerically to be the most reliable criteria for the prediction of the modal rotation and displacements, however the residuals distribution and R-squared derived for the ductility demands prediction, was not constant and low respectively. Thus, the assessment of the torsional parameters' contribution to the nonlinear structural response was investigated using artificial neural networks. Utilizing the connection weights approach, the Center of Strength, Torsional Stiffness and the Base Shear Torque curves were found to exhibit the highest impact numerically, while all the other torsional indices' contribution was investigated and quantified.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.250-259
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• 2019

Big data processing technology and artificial intelligence (AI) are increasingly attracting attention. Natural language processing is an important research area of artificial intelligence. In this paper, we use Korean news articles to extract topic distributions in documents and word distribution vectors in topics through LDA-based Topic Modeling. Then, we use Word2vec to vector words, and generate a weight matrix to derive the relevance SCORE considering the semantic relationship between the words. We propose a way to recommend documents in order of high score.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.57-65
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• 2018

Artificial neural network is inspired by the biological neural network. For simplicity, in computer science, it is represented as a set of layers. Many research has been made in evaluating the number of neurons in the hidden layer but still, none was accurate. Several methods are used until now which do not provide the exact formula for calculating the number of thehidden layer as well as the number of neurons in each hidden layer. In this paper model selection approach was presented. Proposed model is based on geographical analysis of decision boundary. Proposed model selection method is useful when we know the distribution of the training data set. To evaluate the performance of the proposed method we compare it to the traditional architecture on IRIS classification problem. According to the experimental result on Iris data proposed method is turned out to be a powerful one.

• ETRI Journal
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• v.45 no.1
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• pp.119-130
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• 2023

We propose a quantized gradient search algorithm that can achieve global optimization by monotonically reducing the quantization step with respect to time when quantization is composed of integer or fixed-point fractional values applied to an optimization algorithm. According to the white noise hypothesis states, a quantization step is sufficiently small and the quantization is well defined, the round-off error caused by quantization can be regarded as a random variable with identically independent distribution. Thus, we rewrite the searching equation based on a gradient descent as a stochastic differential equation and obtain the monotonically decreasing rate of the quantization step, enabling the global optimization by stochastic analysis for deriving an objective function. Consequently, when the search equation is quantized by a monotonically decreasing quantization step, which suitably reduces the round-off error, we can derive the searching algorithm evolving from an optimization algorithm. Numerical simulations indicate that due to the property of quantization-based global optimization, the proposed algorithm shows better optimization performance on a search space to each iteration than the conventional algorithm with a higher success rate and fewer iterations.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.84-89
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• 2023

In water resource management, data prediction is performed using artificial intelligence, and companies, governments, and institutions continue to attempt to efficiently manage resources through this. LSTM is a model specialized for processing time series data, which can identify data patterns that change over time and has been attempted to predict groundwater level data. However, groundwater level data can cause sen-sor errors, missing values, or outliers, and these problems can degrade the performance of the LSTM model, and there is a need to improve data quality by processing them in the pretreatment stage. Therefore, in pre-dicting groundwater data, we will compare the LSTM model with the MSE and the model after normaliza-tion through distribution, and discuss the important process of analysis and data preprocessing according to the comparison results and changes in the results.