• Journal of Korean Tunnelling and Underground Space Association
• /
• v.21 no.3
• /
• pp.419-432
• /
• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• Journal of Korean Academy of Nursing
• /
• v.29 no.6
• /
• pp.1244-1253
• /
• 1999

The purpose of this study was to explore and describe the experience of Dan-Jeon breathing training and of Qi as a essential substance in forming human body. The sample consists of 7 participants who are Dan-Jeon Breathing training in a Training center, Pusan, Korea. They were asked open-ended questions in order for them to talk about their experiences. With permission of the subjects, the interviews were recorded and transcribed. The summarized results of this research are following. 1. The purpose of Dan-Jeon Breathing The interview data was organized by themes into 4 categories : hope for health recovery, a concern about Dan-Jeon Breathing, seeking meaning of life, change of lifestyle 2. The experience of Qi during Dan-Jeon Breathing training The interview data was organized by themes into 3 categories : an autonomic movement of body, spiritual experience, conviction of existence of Qi. 3. The change after Dan-Jeon Breathing training. The interview data was organized by themes into 7 categories : physical health promotion, emotional relaxation, promoting brain function, positive attitude about life, love to others, investigation for self, improvement on Qi feeling..

• Proceedings of the IEEK Conference
• /
• 2001.06c
• /
• pp.173-176
• /
• 2001

This paper proposes training algorithms of neuro-fuzzy systems. First, we introduce a structure training algorithm, which produces the necessary number of hidden nodes from training data. From this algorithm, initial fuzzy rules are also obtained. Second, the parameter training algorithm using evolution strategy is introduced. In order to show their usefulness, we apply our neuro-fuzzy system to a nonlinear system identification problem. It was found from experiments that proposed training algorithms works well.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.12 no.10
• /
• pp.799-807
• /
• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• Journal of Engineering Education Research
• /
• v.19 no.2
• /
• pp.97-101
• /
• 2016

This paper examines the industry field training education model, analyze the operational status proposed improvement measures. Data were analyzed using a field training participating students participating industry last three years. On the other hand analysis field training participating students increased, industry participation has decreased. And most of the students took part in the seasonal short-term job training. In addition, it was difficult to analyze the employment status field training operations follow-up member. In this paper, a field training operations support system management models and practical training courses organized field trips how to improve. Field training operations support will be strengthened through the work associated with the company expanding participation model introduced and is expected to increase in the long-term practical training, students participate in field training system improvement. Run the job training Improvement in future research presented in this paper attempts to analyze the students' employment status and results of operations involved.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.2
• /
• pp.200-209
• /
• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• English Language & Literature Teaching
• /
• v.16 no.4
• /
• pp.107-131
• /
• 2010

The goals of the present study are two-fold: 1) to examine NNS teachers' needs for training period in improving their general communicative competence and classroom communicative competence, and 2) to explore the relationships of teachers' needs for the training period, and their current levels of general/classroom communicative competence and other background variables. Data was collected from seventy primary and secondary school English teachers (N=70) who participated in the six-month intensive teacher training program in South Korea. The teacher trainees responded to four questionnaires of 1) the self-diagnosis of their current levels of four language skills (L/S/R/W) in both general/classroom communicative competence, 2) the training period required to improve their general/classroom communicative competence for teaching both English and other subjects through English, 3) the period of their English teaching, and 4) the proportion of their English use in class. The data analysis has shown that there were the strong relationships between trainee needs for the training period and their teaching period, and the proportion of their English use in class. In terms of trainees' communicative competence, the significant relations of both their general/classroom communicative competence and their needs for the training period were found. Implications of the findings are discussed.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.10
• /
• pp.37-45
• /
• 2021

The purpose of this study was to explore and investigate: the direct impact of training on entrepreneurial performance and self-efficacy, the direct impact of leadership attitudes on entrepreneurial performance, and self-efficacy, the direct impact of self-efficacy on entrepreneurial performance, self-efficacy as a mediator of the effect of training on entrepreneurial performance, and self-efficacy as a mediator of the effect of leadership attitudes on entrepreneurial performance. This study purposively involved 131 entrepreneurs in Village-Owned Enterprises, Kuningan, Indonesia. The data was collected using a questionnaire. The data obtained was analyzed using Path Analysis with SPSS statistical software. This study has several findings. First, training has a significant effect on entrepreneurial performance and self-efficacy. Second, leadership attitudes have a significant effect on entrepreneurial performance and self-efficacy. Third, self-efficacy has a significant effect on entrepreneurial performance. Fourth, self-efficacy mediates the effect of training on entrepreneurial performance. Fifth, self-efficacy mediates the effect of leadership attitudes on entrepreneurial performance. The findings demonstrated that using self-efficacy-based training and leadership attitudes can enhance entrepreneurial self-confidence and assist them to improve their performance.

• The Journal of Asian Finance, Economics and Business
• /
• v.10 no.1
• /
• pp.169-179
• /
• 2023

This study aims to determine the factors that influence training programs in increasing entrepreneurial success as a new model for developing entrepreneurship training in a new era. It intended to provide a suggestion for building an entrepreneurship training model for Beginner Young Entrepreneurs (BYE) organized by the Ministry of Youth and Sports of Indonesia. The study used a quantitative method by collecting data through a Google form questionnaire distributed via the WhatsApp group. This study employs samples from 358 BYE training participants for 2017-2020, and data was processed using Amos SEM software to analyze factors that influence the success of entrepreneurship. The results showed that entrepreneurial motivation is a partial mediator in increasing the effect of training on its success by BYE participants. Furthermore, the key factor for increasing entrepreneurial motivation is challenging young people to start businesses. This study recommends that BYE program policymakers build a training model by considering many practical case studies to increase motivation as an important mediator in influencing entrepreneurial success. Meanwhile, to boost the morale of training participants, it is necessary to add significant real challenges for participants to start entrepreneurship. Moreover, future studies should add other independent variables, such as personality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.33 no.4
• /
• pp.267-275
• /
• 2015

Those land cover maps have widely been used in various fields, such as environmental studies, military strategies as well as in decision-makings. This study proposes a method to extract training data, automatically and classify the cover using ingle satellite images and national land cover maps, provided by the Ministry of Environment. For this purpose, as the initial training data, those three were used; the unsupervised classification, the ISODATA, and the existing land cover maps. The class was classified and named automatically using the class information in the existing land cover maps to overcome the difficulty in selecting classification by each class and in naming class by the unsupervised classification; so as achieve difficulty in selecting the training data in supervised classification. The extracted initial training data were utilized as the training data of MLC for the land cover classification of target satellite images, which increase the accuracy of unsupervised classification. Finally, the land cover maps could be extracted from updated training data that has been applied by an iterative method. Also, in order to reduce salt and pepper occurring in the pixel classification method, the MRF was applied in each repeated phase to enhance the accuracy of classification. It was verified quantitatively and visually that the proposed method could effectively generate the land cover maps.