• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.6
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• pp.345-352
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• 2015

Recently, by developing many training systems in battle field, the demand for interconnecting and internetworking between Live, Virtual, Constructive training systems has been increased to support efficient data distribution and system control. But, there are lots of problems for them to interwork, because the existing researches only support L-L, V-V, C-C Interoperability. Therefore, we propose L-V-C gateway to provide interoperable simulation environment based on HLA and DDS between them. First, we illustrate FOM Management that parses RPR-FOM XML file to acquire Data information to be shared between them, and generates common data structure and source code used for L-V-C Gateway. L-V-C Gateway created from FOM Management supports Data Conversion and Quality of Service between HLA and DDS. HLA Federate and DDS Domainparticipant in L-V-C Gateway play a role of logical communication channel and relay data from HLA Federation to DDS Domain and vice versa.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.113-124
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• 2001

CHAID, logistic regression, bagging trees, and bagging trees are compared on SAS artificial data set as HMEQ in terms of classification accuracy and training time. In error rates, bagging trees is at the top, although its run time is slower than those of others. The run time of logistic regression is best among given models, but there is no uniformly efficient model satisfied in both criteria.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.6
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• pp.775-783
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• 2019

The big data industry market continues to grow and is expected to grow further. In this paper, based on the five-point Likert scale of college students in the process of developing big data young people, the satisfaction of instructors in big data training and improvement of job (education) ability based on AI convergence The survey was conducted on the expectations of the participants and their intention to participate in the training process for the young talents. Male students were more satisfied than students. In terms of students, students who are less than 6th semester have the highest satisfaction, but students who are less than 7th and 8th semesters are less satisfied. By department, the satisfaction level of science and statistics students was the highest, while the satisfaction level of other students was low. According to the average of college credits, the satisfaction of students under 3.5~4.0 was the highest, and the satisfaction of students below 3.0 was the lowest.

• Journal of Agricultural Extension & Community Development
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• v.4 no.1
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• pp.305-315
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• 1997

The propose of this study was to analyze the relationships among the levels of training & development evaluation (reaction, learning, transfer). The study has been conducted on 730 trainees who attended in the basic accounting program in L training and development institution through three incidents of tracked research such as reaction survey right after the conclusion of training, learning evaluation through test, and an evaluation of the transferability after 3 months of training. Questionnaires and test papers for analyses were used after their reliability, validity, difficulty, and discrimination have been verified on a pre-test. The research has been conducted for six months from 4 March 1996 to the end of August 1996, and data have been collected through direct research and survey through mail. The collected data have been worked on at SAS program for Windows with a statistical significance level of 5%. Statistical method that had been used was Pearson's correlation coefficient. The result and conclusion acquired from this study were as follows: Between reaction and learning, learning and transfer of training, only a weak positive correlation exists and explanation or prediction variance showing hierarchical relationship was quite weak with 1%. Thus, this research not only does not strongly support Kirkpatrick(1976)'s hierarchical model of $reaction{\rightarrow}learning{\rightarrow}transfer$, but also indicates that the separate measurement on each levels of training evaluation needs to be done. On the other hand, there was a relatively strong positive correlation between reaction and transfer of training. Based on the result, the conclusion, and the restriction perceived through this study, the following suggestions were made. 1. There is a need to empirically analyze and verify the hierarchy of all levels of training evaluation including the evaluation of the fourth level (result) such as organizational productivity, organizational satisfaction, and separation rate. 2. A great deal of efforts will be needed to systematically analyze what the relationships are among the methods measuring the level of evaluation of the training and development, and to apply this result to the training field.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.

• Journal of Korea Multimedia Society
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• v.20 no.10
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• pp.1671-1677
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• 2017

In this paper, we propose a machine learning method for random noise removal in image deblurring. The proposed method uses a logistic regression to select reliable data to use them, and, at the same time, to exclude data, which seem to be corrupted by random noise, in the deblurring process. The proposed method uses commonly available images as training data. Simulation results show an improved performance of the proposed method, as compared with the median filtering based reliable data selection method.

• Physical Therapy Korea
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• v.10 no.1
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• pp.29-35
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• 2003

Body weight support treadmill training is a new and promising therapy in gait rehabilitation of patients with hemiplegia. The purpose of this study was to identify the effects of body weight support treadmill training on gait and standing balance in patients with hemiplegia. Eighteen patients with hemiplegia participated in the study. A 10 m-timed walk test, measurements of step length and standing balance score were administered. Intervention consisted of body weight support treadmill training five times a week for 2 weeks. The data were analyzed by paired t-test. Body weight support treadmill training scoring of standing balance, step length and 10 m-timed walk test showed a definite improvement. Body weight support treadmill training offers the advantages of task-oriented training with numerous repetitions of a supervised gait pattern. The outcomes suggest that patients with hemiplegia can improve their gait ability and standing balance through body weight support treadmill training.

• Journal of Distribution Science
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• v.18 no.4
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• pp.27-37
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• 2020

Purpose: This exploratory research is to give managerial implication to sales personal management. This study focused on antecedents of job satisfaction and organizational commitment specially in HRD programs and system by participation and effect toward job. Research design, data and methodology: This research focuses on relationship analysis among job satisfaction, organizational commitment and HRD programs of logistics and sales personnel in Korea. HRD program consider two parts one is participation and other is effect toward job. And three HRD program is included education & training, system and self-directed Learning. This study used 7th HCCP data from KRIVET and 748 employee data is analyzed. SPSS18 is used and frequency, reliability, correlation and regression analysis are conducted. Results: Result shows that job satisfaction is positively affected by education & training participation, HRD system participation and HRD system effect toward job. Organizational commitment is positively affected by education & training participation, HRD system participation, education & training effect toward job and HRD system effect toward job. However self-directed Learning participation negatively affect organizational commitment. Lastly job satisfaction partially mediates between HRD and organizational commitment. Conclusions: Based on the results, this paper provide implication to academic, practical HRD and suggest feature research.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.147-153
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• 2018

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

• The Korean Journal of Physiology and Pharmacology
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• v.25 no.6
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• pp.545-553
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• 2021

Fixed-dose combinations development requires pharmacokinetic drugdrug interaction (DDI) studies between active ingredients. For some drugs, pharmacokinetic properties such as long half-life or delayed distribution, make it difficult to conduct such clinical trials and to estimate the exact magnitude of DDI. In this study, the conventional (non-compartmental analysis and bioequivalence [BE]) and model-based analyses were compared for their performance to evaluate DDI using amlodipine as an example. Raw data without DDI or simulated data using pharmacokinetic models were compared to the data obtained after concomitant administration. Regardless of the methodology, all the results fell within the classical BE limit. It was shown that the model-based approach may be valid as the conventional approach and reduce the possibility of DDI overestimation. Several advantages (i.e., quantitative changes in parameters and precision of confidence interval) of the model-based approach were demonstrated, and possible application methods were proposed. Therefore, it is expected that the model-based analysis is appropriately utilized according to the situation and purpose.