• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.135-142
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• 2019

Given the ability and skills required by modern people, marketing can be divided into knowledge-related skill such as marketing plans, market segmentation, and marketing mix management and supportive skill such as communication, inter-organizational management, creativity, and decision making. Knowledge related skills can be nurtured in existing marketing classes, but it is recognized that special educational programs such as self marketing are needed to develop and train supportive skills regardless of education levels or major education. This paper is aimed to design for marketing educational program for the self marketing. In this study, a DACUM method job analysis to extract contents by specialists such as model setting of task and job, job statement, job analysis, education course development, and so on. In the first place, this report presents job analysis model by procedures for developing selection criteria of examination questions of the self marketing qualification. The first step is preparation for job analysis, the second step: the establishment of job models, the third step : the job specification and task analysis, the fourth step: the review of job model, the fifth step: the establishment of subjects for examination matrix table for making questions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3654-3670
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• 2019

This paper proposes an Image Texture Median Filter (ITMF) to analyze and detect Android malware on Drebin datasets. We design a model of "ITMF" combined with Image Processing of Median Filter (MF) to reflect the similarity of the malware binary file block. At the same time, using the MAEVS (Malware Activity Embedding in Vector Space) to reflect the potential dynamic activity of malware. In order to ensure the improvement of the classification accuracy, the above-mentioned features(ITMF feature and MAEVS feature)are studied to train Restricted Boltzmann Machine (RBM) and Back Propagation (BP). The experimental results show that the model has an average accuracy rate of 95.43% with few false alarms. to Android malicious code, which is significantly higher than 95.2% of without ITMF, 93.8% of shallow machine learning model SVM, 94.8% of KNN, 94.6% of ANN.

• Structural Engineering and Mechanics
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• v.78 no.4
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• pp.425-437
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• 2021

Under foundation shock mats have been used in the current practice in order to reduce/damp vibrations received by buildings through the surrounding environment. Although some investigations have been made on under foundation shock mats performance, their effectiveness in the reduction of railway induced-vibrations has not been fully studied, particularly with the consideration of underneath soil media. In this regard, this research is aimed at investigating performance of shock mat used beneath building foundation for reduction of railway induced-vibrations, taking into account soil-structure interaction. For this purpose, a 2D finite/infinite element model of a building and its surrounding soil media was developed. It includes an elastic soil media, a railway embankment, a shock mat, and the building. The model results were validated using an analytical solution reported in the literature. The performance of shock mats was examined by an extensive parametric analysis on the soil type, bedding modulus of shock mat and dominant excitation frequency. The results obtained indicated that although the shock mat can substantially reduce the building vibrations, its performance is significantly influenced by its underneath soil media. The softer the soil, the lower the shock mat efficiency. Also, as the train excitation frequency increases, a better performance of shock-mats is observed. A simplified model/method was developed for prediction of shock mat effectiveness in reduction of railway-induced vibrations, making use of the results obtained.

• ETRI Journal
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• v.42 no.6
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• pp.886-898
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• 2020

Unusual data patterns or outliers can be generated because of human errors, incorrect measurements, or malicious activities. Detecting outliers is a difficult task that requires complex ensembles. An ideal outlier detection ensemble should consider the strengths of individual base detectors while carefully combining their outputs to create a strong overall ensemble and achieve unbiased accuracy with minimal variance. Selecting and combining the outputs of dissimilar base learners is a challenging task. This paper proposes a model that utilizes heterogeneous base learners. It adaptively boosts the outcomes of preceding learners in the first phase by assigning weights and identifying high-performing learners based on their local domains, and then carefully fuses their outcomes in the second phase to improve overall accuracy. Experimental results from 10 benchmark datasets are used to train and test the proposed model. To investigate its accuracy in terms of separating outliers from inliers, the proposed model is tested and evaluated using accuracy metrics. The analyzed data are presented as crosstabs and percentages, followed by a descriptive method for synthesis and interpretation.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.12-21
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• 2021

For successful automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, SAR target images of the database should have the identical or highly similar resolution with those collected from SAR sensors. However, it is time-consuming or infeasible to construct the multiple databases with different resolutions depending on the operating SAR system. In this paper, an approach for resolution conversion of SAR target images is proposed based on conditional generative adversarial network(cGAN). First, a number of pairs consisting of SAR target images with two different resolutions are obtained via SAR simulation and then used to train the cGAN model. Finally, the model generates the SAR target image whose resolution is converted from the original one. The similarity analysis is performed to validate reliability of the generated images. The cGAN model is further applied to measured MSTAR SAR target images in order to estimate its potential for real application.

• Geomechanics and Engineering
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• v.24 no.2
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• pp.143-156
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• 2021

Rail transit lines usually pass through many complicated topographies in mountain areas. The influence of inclined bedrock on the train-induced soil vibration response was investigated. Model tests were conducted to comparatively analyze the vibration attenuation under inclined bedrock and horizontal bedrock conditions. A three-dimension numerical model was built to make parameter analysis. The results show that under the horizontal bedrock condition, the peak velocity in different directions was almost the same, while it obviously changed under the inclined bedrock condition. Further, the peak velocity under inclined bedrock condition had a larger value. The peak velocity first increased and then decreased with depth, and the trend of the curve of vibration attenuation with depth presented as a quadratic parabola. The terrain conditions had a significant influence on the vibration responses, and the inclined soil surface mainly affected the shallow soil. The influence of the dip angle of bedrock on the peak velocity and vibration attenuation was related to the directions of the ground surface. As the soil thickness increased, the peak velocity decreased, and as it reached 173% of the embedded pile length, the influence of the inclined bedrock could be neglected.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.382-395
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• 2021

In this study, character recognition using deep learning is performed among the various defects in the PCB, the purpose of which is to check whether the printed characters are printed correctly on top of components, or the incorrect parts are attached. Generally, character recognition may be perceived as not a difficult problem when considering MNIST, but the printed letters on the PCB component data are difficult to collect, and have very high redundancy. So if a deep learning model is trained with original data without any preprocessing, it can lead to over fitting problems. Therefore, this study aims to reduce the redundancy to the smallest dataset that can represent large amounts of data collected in limited production sites, and to create datasets through data enhancement to train a flexible deep learning model can be used in various production sites. Moreover, ResNet model verifies to determine which combination of datasets is the most effective. This study discusses how to reduce and augment data that is constantly occurring in real PCB production lines, and discusses how to select coresets to learn and apply deep learning models in real sites.

• Journal of Distribution Science
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• v.19 no.9
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• pp.5-17
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• 2021

Purpose: As mobile devices are commonly used and contact-free services are widespread due to the COVID-19 pandemic in the recent distribution environment, this study suggests retail strategies for consumers using high-speed railways. To this end, we analyzed how consumer perception on technologies necessary for use of mobile apps is related to the attitude that drives consumers to continue using the app services. Research design, data and methodology: Based on the extended unified theory of technology acceptance and use of technology model by Venkatesh, Morris, Davis and Davis (2003), we added variables proposed by existing theories that studied the technology acceptance model from multiple perspectives and empirically analyzed the relationship between user satisfaction and use intention with structural equation modeling. Results: As expected, factors necessary for the use of app services such as performance expectancy, social influence, price value, facilitating conditions, security, and aesthetics had positive effects on user satisfaction, whereas the effect of effort expectancy on user satisfaction was rejected. And user satisfaction was found to have a significant effect on intention to use. Conclusions: The results provide implications that strategic retail management of the above factors can motivate passengers to continuously use high-speed railways.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1057-1070
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• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2927-2941
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• 2022

The primary task of machine fault diagnosis is to judge whether the current state is normal or damaged, so it is a typical binary classification problem with mutual exclusion. Mutually exclusive events and out-of-domain detection have one thing in common: there are two types of data and no intersection. We proposed a fusion model method to improve the accuracy of machine fault diagnosis, which is based on the mutual exclusivity of events and the commonality of out-of-distribution detection, and finally generalized to all binary classification problems. It is reported that the performance of a convolutional neural network (CNN) will decrease as the recognition type increases, so the variational auto-encoder (VAE) is used as the primary model. Two VAE models are used to train the machine's normal and fault sound data. Two reconstruction probabilities will be obtained during the test. The smaller value is transformed into a correction value of another value according to the mutually exclusive characteristics. Finally, the classification result is obtained according to the fusion algorithm. Filtering normal data features from fault data features is proposed, which shields the interference and makes the fault features more prominent. We confirm that good performance improvements have been achieved in the machine fault detection data set, and the results are better than most mainstream models.