• Korean Journal of Computational Design and Engineering
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• v.22 no.1
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• pp.80-88
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• 2017

Product data analytics (PDA) is a data-driven analysis method that uses product data management (PDM) databases as its operational data. It aims to understand and evaluate product development processes indirectly through the analysis of product data from the PDM databases. To educate and train PDA efficiently, this study proposed an approach that employs courses for both product development and PDA in a class. The participant group for product development provides a PDM database as a result of their product development activities, and the other group for PDA analyses the PDM database and provides analysis result to the product development group who can explain causes of the result. The collaboration between the two groups can enhance the efficiency of the education and training course on PDA. This study also includes an application example of the approach to a graduate class on PDA and discussion of its result.

• Journal of Drive and Control
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• v.19 no.3
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• pp.9-15
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• 2022

This paper proposes a method for constructing and verifying datasets used in deep learning technology, to prevent safety accidents in automated construction machinery or autonomous vehicles. Although open datasets for developing image recognition technologies are challenging to meet requirements desired by users, this study proposes the interface of virtual simulators to facilitate the creation of training datasets desired by users. The pixel-level training image dataset was verified by creating scenarios, including various road types and objects in a virtual environment. Detecting an object from an image may interfere with the accurate path determination due to occlusion areas covered by another object. Thus, we construct a database, for developing an occlusion area detection algorithm in a virtual environment. Additionally, we present the possibility of its use as a deep learning dataset to calculate a grid map, that enables path search considering occlusion areas. Custom datasets are built using the RDBMS system.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.127-134
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• 2009

Universities or job training institutes need to develop training courses that match up with requirements of enterprises. Therefore, job analysis system that analyzes skills which are needed for job performing and makes it possible to be reflected in the curriculums is requested for deriving generalized curriculum system. In this paper, to develop a curriculum that fulfills the requirements of enterprises, we implemented a series of DACUM(Developing A CurriculUM) based process that performs verification of tasks for workers and roadmap for curriculum, etc. And, we analyzed a instance of this application system. The proposed system is implemented by Java and MS Access database. The system is possible to work with central database realized by MS SQL Server through the Internet.

• Phonetics and Speech Sciences
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• v.6 no.2
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• pp.29-34
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• 2014

In the Vector Taylor Series (VTS)-based noisy speech recognition methods, Hidden Markov Models (HMM) are usually trained with clean speech. However, better performance is expected by training the HMM with noisy speech. In a previous study, we could find that Minimum Mean Square Error (MMSE) estimation of the training noisy speech in the log-spectrum domain produce improved recognition results, but since the proposed algorithm was done in the log-spectrum domain, it could not be used for the HMM adaptation. In this paper, we modify the previous algorithm to derive a novel mathematical relation between test and training noisy speech in the cepstrum domain and the mean and covariance of the Multi-condition TRaining (MTR) trained noisy speech HMM are adapted. In the noisy speech recognition experiments on the Aurora 2 database, the proposed method produced 10.6% of relative improvement in Word Error Rates (WERs) over the MTR method while the previous MMSE estimation of the training noisy speech produced 4.3% of relative improvement, which shows the superiority of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.11
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• pp.1631-1638
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• 2015

This paper describes the design methodology of the operator training simulator for power monitor and control system in the railway system. In power system, the purpose of energy management system was to monitor, control, and analyze the performance of generation and transmission system based on H/W and S/W. Network analysis applications provide a clear picture of power system characteristics using state estimation, power flow and short circuit analysis. In this respect, the operator training system in the railway system should be equipped with the methodology of these systems. First, the proposed database structure in the railway system was introduced. Then the overall structure of operator training system based on railway analysis applications was proposed. Finally, a methodology to verify the performance of the developed applications was described.

• ETRI Journal
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• v.39 no.5
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• pp.643-651
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• 2017

Human age estimation is one of the key factors in the field of Human-Robot Interaction/Human-Computer Interaction (HRI/HCI). Owing to the development of deep-learning technologies, age recognition has recently been attempted. In general, however, deep learning techniques require a large-scale database, and for age learning with variations, a conventional database is insufficient. For this reason, we propose an age estimation method using artificially generated data. Image data are artificially generated through 3D information, thus solving the problem of shortage of training data, and helping with the training of the deep-learning technique. Augmentation using 3D has advantages over 2D because it creates new images with more information. We use a deep architecture as a pre-trained model, and improve the estimation capacity using artificially augmented training images. The deep architecture can outperform traditional estimation methods, and the improved method showed increased reliability. We have achieved state-of-the-art performance using the proposed method in the Morph-II dataset and have proven that the proposed method can be used effectively using the Adience dataset.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• Journal of Oriental Neuropsychiatry
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• v.27 no.3
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• pp.157-167
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• 2016

Objectives: This study was undertaken to evaluate the effectiveness of the Neurofeedback training programme (NFT), and systematically search for factors related to the NFT.Methods: This study applied meta-analysis to thesis and journal articles published in Korea. A total of 42 papers published between 2001 to August in 2015 were evaluated, which were selected through a database search.Results: Summarizing the evaluation, the quality of results was low. The meta-analysis revealed that the effect size of the neurofeedback programme training was 0.691, over the median. Also, the variables were statistically significant to the neurofeedback programme training effect, and were consistent with the subject characteristics, their disabilities/non disabilities, and level of education. The results were also statistically significant to the neuro-feedback programme training effect according to the research method (training method), the sessions per a week, total sessions, and training time.Conclusions: The RoBANS result of 42 studies is at a risk of being highly biased. However, statistically, the meta-analysis result of the factors evaluated is significantly high.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.589-595
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• 2010

In this paper, we have designed and implemented a personalized health training system which provides health training methods using 3D animation based on the data from a professional trainer, after a trainee inputs individual physical characteristics. Many trainers at fitness centers provide only sketchy training method and usage of fitness machines not appropriate training method for trainee's physical characteristics. Individual characteristics. Individual characteristics prepared tabular input which consists of exercise goals, exercise areas, whether or not the normal movement, and RM. The system provides the training methods, the effects of exercise, and the health training motions through searching the database more accurately.

• Journal of Labour Economics
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• v.28 no.2
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• pp.93-117
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• 2005

This study aims to examine the transition of the youth to the labor market by types of education and training institutes focusing employment rate. To construct the dataset for the analysis, the list of the graduates from 4-year universities, junior colleges, polytechnic colleges and the graduates from vocational training institutes as of February 2001 has been merged with the database for the insured in the Unemployment Insurance Database. This data enables tracking down of these graduates in terms of their mobility in the labor market. For graduates from universities and junior colleges, their scores on the Scholastic Aptitude Test have been matched. One of major findings is that the longer the schooling period is, the better the employment results are. Among those who finished 4-year universities, those who went to schools in the metropolitan area achieve a relatively better record in job finding than those who attended schools in the local areas. Meanwhile it is confirmed that the SAT score is highly co-related with the performance in the labor market among those who finished 4-year universities. The co-relation of one's major with his/her employment is not negligible also.