• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.08a
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• pp.283-292
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• 1999

Temperature prediction model was developed for on-line application to plate rolling mills of POSCO. The adequate boundary conditions of heat transfer coefficients were obtained by comparing the predicted temperature with the measured temperatures taken by measuring system in plate rolling mill of POSCO. In obtaining the boundary condition which minimize the mean and standard deviation of the difference between prediction and measurement, orthogonal array for experimental design was used to reduce the calculation time of large data set. To predict the temperature drop at four edge of plate in one dimensional model, the energy change by heat transfer though directions perpendicular to thickness direction was treated like that by deformation. And the heat transfer through four edge directions was inferred from that through thickness direction with two coefficients of depth and severity of temperature drop at the edge. The boundary condition for the depth and severity of temperature drop were also determined using the measured temperature.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.11 no.5
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• pp.561-569
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• 1999

The effects of longitudinal heat conduction on the performance of heat transfer surfaces are investigated by using a single-blow method. In the transient testing method for determining the heat transfer characteristics, exponential inlet temperature variations are made by using screen-mesh heater with small time constant and low frontal velocities of the test section, and the experimentally determined inlet temperature profile is used as the inlet fluid temperature condition. The effects of longitudinal heat conduction are negligible only if $\gamma^\act<0.05\;and \;N_{tu}\le3$ and should be considered if $N_{tu}\le3$ The test results ate compared with the existing theoretical and experimental data and the validity of this technique is confirmed by the good agreement.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.14 no.4
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• pp.293-299
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• 1985

This paper describes an economical prediction procedure for heat transfer phenomenon through a channel containing an abrupt asymmetric expansion in flow cross-seetional area. Numerical solutions for the flow field are obtained by the finite difference numerical method applied to the modified boundary layer equations. Modified boundary energy equation is used to analyze heat transfer as modified boundary momentum equation. Predictions of the method compare very favorable with exprimental data. Results of this study by modified boundary layer equation are as follows : 1. The computation time required for the scheme is at least an order of magnitude less than for the numerical solution of the full Navier-stokes and Energy eguations. 2. In laminar flow, the maximum heat transfer occurs downstream of the reattachment point.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.96-102
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• 2023

In this paper, we proposed Channel Transfer Function estimation based on Delay and Doppler Profile for underwater acoustic OFDM communication system. It improved the estimation accuracy of the channel transfer function by linear time interpolation the change of Scattered Pilot (SP) insertion frequency in the time direction and the time by Delay and Doppler profile that analyzes the multipath situation of the channel investigated the performance of interpolation by simulation and report it. Previous works is inserted SP every 4 OFDM. It was effective under the environment without multipath, but it has observed that the effect of CTF compensation has been lowered in multipath channel condition. In addition to be better when inserted SP every 2 OFDM. But the amount of sending data will be decrease. Therefore, we conducted research to improve 4 OFDM with new interpolator. A computer simulation was performed as a comparison of SP inserted every 4 OFDM, SP inserted every 2 OFDM, and 4 OFDM with new interpolator. the performance of the proposed system is overwhelmingly improved, and the performance is slightly improved even 64 QAM.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.488-493
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• 2011

The object of this study is to develope the program for analyzing the fluid flow and heat transfer of PMSM electric motor. The program will be mainly used for inexperienced users of CFD analysis. So it has to be performed using the geometry data and the heat source of each part only. Interface program for converting the given data to the instruction of pre-processor is developed. The conjugate heat transfer between a flow passage of the motor and inner parts consisting of rotor and stator is regarded. In order to reduce the computational time and memory storage, cyclic boundary condition is applied. For the numerical simulation, MRF(Multi-Reference Frame) method is used to consider rotating operation of the rotor and heat source is applied to the copper, wire, and magnetic parts in the motor. On the screen of computer, the users can show the velocity distributions and the contours such as pressure, turbulent kinetic energy, turbulent dissipation rate and temperature.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.1
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• pp.7-14
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• 2009

Lately, NAND flash cards have been used to store massive amounts of multimedia data. However, these nand flash cells itself has a slow operation time and by that, the nand flash cards are not appropriate for high performance massive data transfer. Indeed, most flash card products have a disadvantage in that they require plenty of time to transfer massive amounts of data. Therefore, we propose a new architectural design for the hardware and software of the NAND flash cards by improving their data transfer rate. Our design is based on a multiprocessing which is different from the conventional serial processing method. We simulated our design under the VIP (Virtual IP) environment, and verified our work using FPGA test platforms. As a result, the downloading performances was approximately 160MB/s on VIP and 85.3MB/s on FPGA.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.92.1-92.1
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• 2011

NASA launched Solar Dynamics Observatory (SDO) on February 2011 in order to understand the cause of solar activities and their influences on the Earth and the near-Earth space. KASI is constructing Korean Data Center for SDO based on the letter of agreement between KASI and NASA for space weather research. SDO produces about 1.5 TB a day and its raw data amounts to about 550 TB in a year. Stanford University has been already operating the data center for scientific raw data, but there is a limit to use its data for space weather research and space weather service in real time because of network environment. Korean Data Center for SDO will provide scientific data not only to Korean institutes but also to international space weather societies. KASI has designed the data transfer system by using GLORIAD in order to get higher performance and stability. After the first construction of data transfer system and storage system in this year, we will increase the storage capacity of the data center in phases considering new developments in a storage technology and drop of their prices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.651-658
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• 2021

Because bolts with anti-loosening coatings are used mainly for joining safety-related components in automobiles, accurate automatic screening of these coatings is essential to detect defects efficiently. The performance of the convolutional neural network (CNN) used in a previous study [Identification of bolt coating defects using CNN and Grad-CAM] increased with increasing number of data for the analysis of image patterns and characteristics. On the other hand, obtaining the necessary amount of data for coated bolts is difficult, making training time-consuming. In this paper, resorting to the same VGG16 model as in a previous study, transfer learning was applied to decrease the training time and achieve the same or better accuracy with fewer data. The classifier was trained, considering the number of training data for this study and its similarity with ImageNet data. In conjunction with the fully connected layer, the highest accuracy was achieved (95%). To enhance the performance further, the last convolution layer and the classifier were fine-tuned, which resulted in a 2% increase in accuracy (97%). This shows that the learning time can be reduced by transfer learning and fine-tuning while maintaining a high screening accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.21-32
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• 2023

The amount of money processed in medicine from the Korea Consumer Agency was studied by the various time series models. The medical data set from the Korea Consumer Agency were consisted of counseling, damage relief and conciliation. For the analysis of time series, autoregressive moving average model, vector autoregressive model and the transfer function model were used. We considered the stationarity and cross correlation function for the identification and fitting. As a result, the transfer function model showed a better prediction. Whereas, the vector autoregressive model also provided good information for the degree and duration of the influence of variables.