• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.713-719
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• 2000

In order to Prepare the$Ge_xSi_{1-x}/Si$(111) heterosructure by solid phase epitaxy (SPE), about 1000A of Au and about 1000A Ge were sequentially deposited on the Si(111) substrate. The resulting Ge/Au/Si(111) samples were isochronically annealed in the high vacuum condition. The behaviors of Au and Ge during thermal annealing and the structural Properties of $Ge_xSi_{1-x}$ films were characterized by Auger electron spectroscopy (AES), X-ray diffraction (XRD) and high resolution transmission electron microscopy (TEM). The a-Ge/Au/Si(111) structure was converted to the Au/GeSi/Si(111) structure. Defects such as stacking faults, point defects and dislocations were found at the GeXSil-X(111) interface, but the film was grown epitaxially with the matching face relationship of $Ge_xSi_{1-x}/$(111)/Si(111). Twin crystals were also found in the $Ge_xSi_{1-x}/$(111) matrix.

• 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.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.2
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• pp.92-102
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• 1998

Prediction of failure behavior in filament-wound composite motor cases is one of the important issues for their reliable application. Acoustic emission during hydroproof testing of the cases is used to solve this problem. Based on the acoustic emission behavior, failure sites can be located successfully. The identification of failure modes is also possible using the distribution of acoustic emission amplitude. Due to the limitation in the number of samples, it is not possible to predict the final burst pressure of motor cases and the effect of impact damage on the final burst pressure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2891-2898
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• 2014

It is feasible to develop a fault tolerant system through module level redundancy on the Integrated Modular Avionics (IMA). However, its great implementation complexity is one of important challenges when asynchronous hardware environment is naturally assumed. To solve this problem, Physically Asynchronous Logically Synchronous (PALS) on IMA has been proposed. But, it has adaptation problem by not addressing specific architecture for IMA system. In the paper, we propose how to synchronize the input data on the IMA system under primary/secondary redundancy architecture by referring to existing PALS. In the proposed scheme, we introduce window frame by considering rate monotonic scheduling and analyze the adequate the synchronization time. Finally, we verify the feasibility of the proposed design pattern through the systematic experiments.

• Journal of IKEEE
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• v.5 no.2 s.9
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• pp.190-200
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• 2001

This paper is addressed to welding defect feature vector selection and implementation method of welding defect classifier using fuzzy techniques. We compare IAV, zero-crossing number as time domain analysis, power spectrum coefficient as frequency domain, histogram as both domain for welding defect feature selection. We choose histogram as feature vector by graph analysis and find out that maximum frequent occurrence number and section of corresponding signal scale in relative histogram show obvious difference between normal welding and voiding with penetration depth defect. We implement a fuzzy welding defect classifier using these feature vector, test it to verify its effectiveness for 695 welding data frame which consist of 4000 sampled data. As result of test, correct classification rate is 92.96%. Lab experimental results show a effectiveness of fuzzy welding defect classifier using relative histogram for practical Laser welding monitoring system in industry.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.45-52
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• 2004

There are many cities installing ITS(Intelligent Transportation Systems) and running TMC(Trafnc Management Center) to improve mobility and safety of roadway transportation by providing roadway information to drivers. There are many devices in ITS which collect real-time traffic data. We can obtain many valuable traffic data from the devices. But it's impossible to avoid missing traffic data for many reasons such as roadway condition, adversary weather, communication shutdown and problems of the devices itself. We couldn't do any secondary process such as travel time forecasting and other transportation related research due to the missing data. If we use the traffic data to produce AADT and DHV, essential data in roadway planning and design, We might get skewed data that could make big loss. Therefore, He study have explored some imputation techniques such as heuristic methods, regression model, EM algorithm and time-series analysis for the missing traffic volume data using some evaluating indices such as MAPE, RMSE, and Inequality coefficient. We could get the best result from time-series model generating 5.0$\%$, 0.03 and 110 as MAPE, Inequality coefficient and RMSE, respectively. Other techniques produce a little different results, but the results were very encouraging.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.669-678
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• 2018

This study developed information technology infrastructures for building a driving environment analysis platform using various big data, such as vehicle sensing data, public data, etc. First, a small platform server with a parallel structure for big data distribution processing was developed with H/W technology. Next, programs for big data collection/storage, processing/analysis, and information visualization were developed with S/W technology. The collection S/W was developed as a collection interface using Kafka, Flume, and Sqoop. The storage S/W was developed to be divided into a Hadoop distributed file system and Cassandra DB according to the utilization of data. Processing S/W was developed for spatial unit matching and time interval interpolation/aggregation of the collected data by applying the grid index method. An analysis S/W was developed as an analytical tool based on the Zeppelin notebook for the application and evaluation of a development algorithm. Finally, Information Visualization S/W was developed as a Web GIS engine program for providing various driving environment information and visualization. As a result of the performance evaluation, the number of executors, the optimal memory capacity, and number of cores for the development server were derived, and the computation performance was superior to that of the other cloud computing.

• The KIPS Transactions:PartD
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• v.12D no.7 s.103
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• pp.993-1000
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• 2005

Recently, software quality evaluation based on ISO/IEC 9126 and ISO/IEC 14598 has been widely accepted in various areas. However, these standards for software quality do not provide practical guidelines to apply the quality model and the evaluation process of software product=5. And the qualify management in most software projects has been conducted by managing defects without applying the standards for software qualify. Thus, we present a quantitative quality evaluation approach of artifacts in the Component Based Development (CBD). Particularly, our evaluation approach allows most of the standard evaluation process and adopts a quantitative quality model which uses the weights of quality characteristics obtained through carefully selected questionnaires for stakeholder and Analytic Hierarchical Process(AHP). Moreover, we have also examined the proposed evaluation approach with applying the checklists for the artifacts of the CBD to a small-scale software project. As a result, we believe that the proposed approach will be helpful for acquiring the high quality software.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.531-539
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• 2009

Logistic regression is widely used as a datamining technique for the customer relationship management. The maximum likelihood estimator has highly inflated variance when multicollinearity exists among the regressors, and it is not robust against outliers. Thus we propose the robust principal components logistic regression to deal with both multicollinearity and outlier problem. A procedure is suggested for the selection of principal components, which is based on the condition index. When a condition index is larger than the cutoff value obtained from the model constructed on the basis of the conjoint analysis, the corresponding principal component is removed from the logistic model. In addition, we employ an algorithm for the robust estimation, which strives to dampen the effect of outliers by applying the appropriate weights and factors to the leverage points and vertical outliers identified by the V-mask type criterion. The Monte Carlo simulation results indicate that the proposed procedure yields higher rate of correct classification than the existing method.

• Journal of Korean Tunnelling and Underground Space Association
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• v.8 no.1
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• pp.3-12
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• 2006

The purpose of this study is to describe the Non-Destructive Testing(NDT) of the rockbolt and investigate the applicability of the NDT methods to estimate the integrity of the rockbolt. To examine the rockbolt integrity including rockbolt itself and grouting material, two methods are adopted: numerical and experimental methods. In the numerical method, the numerical code DISPERSE is used to analyze the dispersion of the rockbolt. The dispersion curve shows the effects of the thickness and stiffness of grouted materials on the embedded rockbolt. Therefore, the optimal frequency for the integrity test of the rockbolt is obtained: 20~120kHz in L(1,0) mode. In the experimental methods, destructive and non-destructive tests are carried out in a laboratory. In the non-destructive test, the low frequency mode generated by an impact and t he high frequency mode generated by an ultrasonic transducer seem to characterize the rockbolt condition readily. The experimental results show that the guided waves attenuate more significantly when the stiffness of the grouted material increases and/or the zone of the defect increases. Meanwhile, the ultimate capacity of rockbolt was evaluated through the pull-out tests and is compared to the NDT results. This study demonstrates that the NDT is a valuable tool for the rockbolt integrity evaluation.