• Journal of Korean Medicine Rehabilitation
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• v.18 no.4
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• pp.85-101
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• 2008

Objectives : The purposes of this study are to understand Craniosacral Techniques(CST) from the oriental medical point of view and to apply it clinically. Methods : The similarities of theories and techniques between CST and the oriental medicine were researched. Results : CST is similar to Doin chuna(導引推拿) which is usually used for improving the muscle and joint movement. Conclusions : CST can be applied for not only autism, ADHD, learning disability but also various diseases of the whole body in company with acupuncture, herbal medicines, chuna(推拿), etc.

• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.45-54
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• 2003

Enterprise Resource Planning(ERP) systems offer pervasive business functionality the applications encompass virtually all aspects of the business. Understanding and managing this pervasiveness will result in a successful and productive business application platform. Because of this pervasiveness, implementations have ranged from great successes to complete failures. This article has two distinctive parts. The first proposes and discusses a systematic process based on consulting experiences of LG CNS (leading information system company in Korea) for ERP selection. Also, the second provides the key factors that are critical to the successful implementation of ERP. The second part reports the results of a study carried out to assess a number of different ERP implementations in different organizations. A case study method of investigation was used, and the experiences of five Korean manufacturing companies were documented. The critical factors in the adoption of ERP are identified as: learning from the experiences of others, appointment of a process innovator, establishment of committees and project teams, training and technical support for the users, and appropriate changes to the organizational structure and managerial responsibilities.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.211-220
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• 2021

The process of inspecting SMDs on the PCB boards improves the product quality, performance and reduces frequent issues in this field. However, undesirable scenarios such as assembly failure and device breakdown can occur sometime during the assembly process and result in costly losses and time-consuming. The detection of these components with a model based on deep learning may be effective to reduce some errors during the inspection in the manufacturing process. In this paper, YOLO models were used due to their high speed and good accuracy in classification and target detection. A SMD detection and classification method using YOLO networks based on robust data preprocessing and augmentation techniques to deal with various types of variation such as illumination and geometric changes is proposed. For 9 different components of data provided from a PCB manufacturer company, the experiment results show that YOLOv4 is better with fast detection and classification than YOLOv3.

• Journal of Platform Technology
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• v.6 no.4
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• pp.18-25
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• 2018

This paper describes a real-time human and robot tracking method in Intelligent Space with multi-camera networks. The proposed method detects candidates for humans and robots by using the histogram of oriented gradients (HOG) feature in an image. To classify humans and robots from the candidates in real time, we apply cascaded structure to constructing a strong classifier which consists of many weak classifiers as follows: a linear support vector machine (SVM) and a radial-basis function (RBF) SVM. By using the multiple view geometry, the method estimates the 3D position of humans and robots from their 2D coordinates on image coordinate system, and tracks their positions by using stochastic approach. To test the performance of the method, humans and robots are asked to move according to given rectangular and circular paths. Experimental results show that the proposed method is able to reduce the localization error and be good for a practical application of human-centered services in the Intelligent Space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.721-739
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• 2023

Rating prediction is an important issue in recommender systems, and its accuracy affects the experience of the user and the revenue of the company. Traditional recommender systems use Factorization Machinesfor rating predictions and each feature is selected with the same weight. Thus, there are problems with inaccurate ratings and limited data representation. This study proposes a deep recommendation model based on self-attention Factorization (SAFMR) to solve these problems. This model uses Convolutional Neural Networks to extract features from user and item reviews. The obtained features are fed into self-attention mechanism Factorization Machines, where the self-attention network automatically learns the dependencies of the features and distinguishes the weights of the different features, thereby reducing the prediction error. The model was experimentally evaluated using six classes of dataset. We compared MSE, NDCG and time for several real datasets. The experiment demonstrated that the SAFMR model achieved excellent rating prediction results and recommendation correlations, thereby verifying the effectiveness of the model.

• Annual Conference of KIPS
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• 2021.05a
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• pp.369-372
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• 2021

Scheduling the movements of trains in the modern railway system is becoming essential and important. Swiss Federal Railway Company (SBB) and machine learning researchers began collaborating to make a simulation environment and held a Flatland challenge. In this paper, the methodologies of the winners of this competition are analyzed to achieve insight and research trends. This problem is similar to the Multi-Agent Path Finding (MAPF) and Vehicle Rescheduling Problem (VRSP). The potential of the attempted methods from the Flatland challenge to be applied to various transportation systems as well as railways is discussed.

• Smart Media Journal
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• v.12 no.5
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• pp.9-16
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• 2023

In the recommendation system of the credit card company, it is necessary to understand the customer patterns to predict a customer's next merchant based on their histories. The data we want to model is much more complex and there are various patterns that customers choose. In such a situation, it is necessary to use an effective model that not only shows the relevance of the merchants, but also the relevance of the customers relative to these merchants. The proposed model aims to predict the next merchant for the customer. To improve prediction performance, we propose a novel model, called Customer-based Recommendation Model (CRM), to produce a more efficient representation of customers. For the next merchant recommendation system, we use a synthetic credit card usage dataset, BC'17. To demonstrate the applicability of the proposed model, we also apply it to the next item recommendation with another real-world transaction dataset, IJCAI'16.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.

• Journal of vocational education research
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• v.35 no.6
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• pp.1-19
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• 2016

This study is to analyze the effect of job relatedness of content in learning, job stress and organization communication on turnover intention and to examine whether the job satisfaction has a mediating effect by using HCCP 2013 panel data. The data was analyzed through Windows SPSS 23.0 program. Major findings of the study were as follows. First, It has significant result that job relatedness of content in learning, job stress and organization communication affects turnover intention. Second, job satisfaction mediates between job relatedness of content in learning and turnover intention. The job relatedness of content in learning level is higher, the turnover intention will be lower through mediating job satisfaction. Third, job satisfaction is also significant effect between job stress and turnover intention. Although the job stress gets higher, the turnover intention can be lower because of job satisfaction. Finally, the relationship between organization communication and turnover intention is mediated by job satisfaction. Therefore, the company should prepare employee's turnover intention to control through this study.