• Journal of Labour Economics
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• v.23 no.2
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• pp.107-126
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• 2000

In this study I am concerned with the impact of training for the unemployed on reemployment in Korea. The data is based on the survey that was conducted on those who participated in training programs in 1998 and those who did not. The matching criteria was the length of the spell of nonemployment that preceded entry to training programs. This data design allows to apply the quasi-experimental evaluation method. My estimation results indicate that the participation in training raises the hazard rate into reemployment, but training characteristics such as training contents, agencies do not affect the hazard rate significantly. This results imply that training participation increases reemployment possibility by preventing withdrawal of participants from the labor market, but training programs make little contribution to improving skills.

• Journal of the Korea Convergence Society
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• v.8 no.2
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• pp.27-33
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• 2017

To efficiently provide cloud computing services to users over the Internet, IT resources must be configured in the data center based on virtualization and distributed computing technology. This paper focuses specifically on the problem that new training data can be added at any time in a wide range of fields, and new attributes can be added to training data at any time. In such a case, rule generated by the training data with the former attribute set can not be used. Moreover, the rule can not be combined with the new data set(with the newly added attributes). This paper proposes further development of the new inference engine that can handle the above case naturally. Rule generated from former data set can be combined with the new data set to form the refined rule.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.535-545
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• 2004

The clustering method suggested in this paper produces clusters based on the 'rules of variables' by merging the 'training' and the identically structured reference data and then by filtering it to obtain the clusters of the 'training data' through the use of the 'tree classification model'. The reference dataset is generated by spatially contrasting it to the 'training data' through the 'reverse arcing' algorithm to effectively identify the clusters. The strength of this method is that it can be applied even to the mixture of continuous and discrete types of 'training data' and the performance of this algorithm is illustrated by applying it to the simulated data as well as to the actual data.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.4
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• pp.362-370
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• 2004

In this work, the collapse moment due to wall-thinning defects is estimated by using fuzzy neural networks. The developed fuzzy neural networks have been applied to the numerical data obtained from the finite element analysis. Principal component analysis is used to preprocess the input signals into the fuzzy neural network to reduce the sensitivity to the input change and the fuzzy neural networks are trained by using the data set prepared for training (training data) and verified by using another data set different (independent) from the training data. Also, two fuzzy neural networks are trained for two data sets divided into the two classes of extrados and intrados defects, which is because they have different characteristics. The relative 2-sigma errors of the estimated collapse moment are 3.07% for the training data and 4.12% for the test data. It is known from this result that the fuzzy neural networks are sufficiently accurate to be used in the wall-thinning monitoring of elbows.

• Hwankyungkyoyuk
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• v.14 no.2
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• pp.68-75
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• 2001

The purpose of study is to provide fundamental data for the improvement of the teacher inservice training for environmental education through analysis of current inservice training programmes. The subject of analysis are documents on training programmes which was conducted after 2000 by 10 training organizations. Based on the results of this study, inservice training programmes is classified with 6 organizations which consist of education training institute, education & scientific research institute, national · public organizations, colleges of an attached organizations, civil organizations, teacher research society. The strategies for improvement of proposed in this study can be summarized as follows: First,'60 hours training programmes for general competencies improvement of environmental teacher' have to reconsider about scarcity areas to analysis of programmes. Second, this training programmes need to establish in training programmes of nothing region for increase in training opportunity of teachers. Third,'the core training programmes'is continued to be complementing about this programmes and need to establish about training programmes of teaching method of environmental education, environmentally value and attitude, etc

• Journal of the Korea Safety Management & Science
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• v.24 no.3
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• pp.85-92
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• 2022

In the case of incumbent training, unlike training for the unemployed, it is difficult to show the results of training. In this study, factors affecting the performance of vocational competency development training were selected and the main factors were derived. Data were collected through Focus Group Interview(FGI), and regression analysis was performed through factor analysis and reliability analysis. As a result, empathy, training participation type, reliability, working experience, and training operation type were derived as factors affecting job satisfaction. did. Confidence and training participation patterns were derived as factors affecting training satisfaction, and the results showed that the larger the variable, the more positive it was. Therefore, as factors affecting the vocational competency development training performance, there are empathy, training participation type, reliability, work experience, training operation type, certainty, and training participation type. It was confirmed that the results had an effect.

• Economic and Environmental Geology
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• v.52 no.2
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• pp.199-212
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• 2019

In the machine learning techniques, the sampling strategy of the training data affects a performance of the prediction model such as generalizing ability as well as prediction accuracy. Especially, in landslide susceptibility analysis, the data sampling procedure is the essential step for setting the training data because the number of non-landslide points is much bigger than the number of landslide points. However, the previous researches did not consider the various sampling methods for the training data. That is, the previous studies selected the training data randomly. Therefore, in this study the authors proposed several different sampling methods and assessed the effect of the sampling strategies of the training data in landslide susceptibility analysis. For that, total six different scenarios were set up based on the sampling strategies of landslide points and non-landslide points. Then Random Forest technique was trained on the basis of six different scenarios and the attribute importance for each input variable was evaluated. Subsequently, the landslide susceptibility maps were produced using the input variables and their attribute importances. In the analysis results, the AUC values of the landslide susceptibility maps, obtained from six different sampling strategies, showed high prediction rates, ranges from 70 % to 80 %. It means that the Random Forest technique shows appropriate predictive performance and the attribute importance for the input variables obtained from Random Forest can be used as the weight of landslide conditioning factors in the susceptibility analysis. In addition, the analysis results obtained using specific sampling strategies for training data show higher prediction accuracy than the analysis results using the previous random sampling method.

• Journal of Korean Society for Quality Management
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• v.28 no.3
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• pp.68-81
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• 2000

In this paper, we studied the four stages of quality related education and training and identified alignment factors that have influence on successful TQM education and training. Based on extensive literature reviews the four stages are extracted such as quality concepts training, quality tools training, special topics training, and leadership training. Also we determine the alignment factors. A framewok of research model including above factors is developed and tested statistically. The perceived data are collected from managers of quality departments of 140 Korean firms through survey. The results show that alignment factors which achieve success in Quality related education training are using relevant examples and implementing training at the top in quality concepts training, providing time and opportunity to master skills in quality tools training, organizing courses into a logical curriculum in special topics training, and providing ongoing feedback in leadership training. We also offered numerous suggestions that can help organizations develop effective training programs to meet their objectives.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.27-33
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• 1997

A neural network approach has been developed to determine the depth of a surface breaking crack in a steel plate from ultrasonic backscattering data. The network is trained by the use of feedforward three-layered network together with a back-scattering algorithm for error correction. The signal used for crack insonification is a mode converted 70$^{\circ}$transverse wave. A numerical analysis of back scattered field is carried out based on elastic wave theory, by the use of the boundary element method. The numerical data are calibrated by comparison with experimental data. The numerical analysis provides synthetic data for the training of the network. The training data have been calculated for cracks with specified increments of the crack depth. The performance of the network has been tested on other synthetic data and experimental data which are different from the training data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.