• 노동경제논집
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• 제32권2호
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• pp.95-124
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• 2009

본 연구는 외환위기 이후 상장제조기업의 생산성 추이 및 교육훈련효과를 분석하였다. 분석 결과, 외환위기 이후 상장제조기업은 시간이 흐를수록 저 생산성 국면에 빠지게 되었으며, 그 과정에서 상장제조기업의 고용 없는 성장, 물적 투자 없는 성장, 인적자본투자 없는 성장 현상이 관찰되었다. 그리고 생산성이 높은 기업의 고용비중이 늘고, 낮은 기업의 고용비중이 주는 효율적인 노동시장이 거의 작동하지 않았다. 또한 교육훈련이 생산성에 미치는 영향을 분석해 보기 위해 동적 패널 분석을 실시할 경우 교육훈련의 과소투자가 발생하고 있음에도 불구하고 교육훈련의 생산성에 대한 지나치게 높은 긍정적 효과가 나타나는 모순적 현상이 상당 부분 해결되었다.

• 유통과학연구
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• 제13권9호
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• pp.29-36
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• 2015

Purpose - The purpose of this study is to examine the influence of effective role-play and training on employee education satisfaction and customer orientation. Evidence of the suggested objective is obtained by monitoring the effectiveness of hotel service training. Research design, data, and methodology - Data were collected from 280 role-play sessions performed in a Korean Hotel and examined using a frequency analysis, reliability/validity assessments, correlation analysis, and regression analysis using SPSS 19.0. Results - 1) Entrepreneurs and training instructors should enthusiastically apply service education in order to enhance each employee d evaluation in terms of customer satisfaction and customer orientation. 2) The most effective factor on customer satisfaction and orientation is the instructor's qualifications. 3) Since a higher level of education-training satisfaction leads to better customer orientation, effective education-training is essential to achieve this result. Conclusions - The study was able to obtain practical evidence that can confirm that service education-training through role-play positively affects employee customer service orientation. In future advanced research on training effects on customer orientation, various internal factors of a business should also be considered.

• 한국측량학회지
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• 제38권6호
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• pp.635-644
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• 2020

딥러닝(DL)을 이용한 객체인식, 탐지 및 분할하는 연구는 여러 분야에서 활용되고 있으며, 주로 영상을 DL 모델의 학습 데이터로 사용하고 있지만, 본 논문은 영상뿐 아니라 공간정보 특성을 포함하는 다양한 학습 데이터(multimodal training data)를 향상된 영역기반 합성곱 신경망(R-CNN)인 Detectron2 모델 학습에 사용하여 객체를 분할하고 건물을 탐지하는 것이 목적이다. 이를 위하여 적외선 항공영상과 라이다 데이터의 내재된 객체의 윤곽 및 통계적 질감정보인 Haralick feature와 같은 여러 특성을 추출하였다. DL 모델의 학습 성능은 데이터의 수량과 특성뿐 아니라 융합방법에 의해 좌우된다. 초기융합(early fusion)과 후기융합(late fusion)의 혼용방식인 하이브리드 융합(hybrid fusion)을 적용한 결과 33%의 건물을 추가적으로 탐지 할 수 있다. 이와 같은 실험 결과는 서로 다른 특성 데이터의 복합적 학습과 융합에 의한 상호보완적 효과를 입증하였다고 판단된다.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.551-560
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• 1998

This paper presents a neural network model representing complex hydro-thermo-dynamic characteristics of a steam generator in nuclear power plants. The key modeling processes include training data gathering process, analysis of system dynamics and determining of the neural network structure, training process, and the final process for validation of the trained model. In this paper, we suggest a training data gathering method from an unstable steam generator so that the data sufficiently represent the dynamic characteristics of the plant over a wide operating range. In addition, we define the inputs and outputs of neural network model by analyzing the system dimension, relative degree, and inputs/outputs of the plant. Several types of neural networks are applied to the modeling and training process. The trained networks are verified by using a class of test data, and their performances are discussed.

• 보건의료산업학회지
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• 제12권4호
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• pp.203-217
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• 2018

Objectives : This study aimed to investigate the clinical practice ability and satisfaction of clinical training of health-medical information management major students. Methods : The data were collected from 68 persons from students finished clinical training at medical record (information) team using self administered questionnaires. The data were analyzed using t-test, ANOVA and correlation with SPSS 22.0 version. Results: Performance of data collection, data management, and data analysis were analyzed in three areas of the job area. In terms of academic characteristics and correlation, they were not related to the level of satisfaction with the practical experience. Conclusions : Research on a virtuous cycle clinical practice program that analyzes the factors by assessing the satisfaction level of clinical practice in each area of health care information management will be conducted continuously.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.995-1004
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• 2020

This study aims to analyze the effect of transformational leadership (TL), procedural justice (PJ), and training (T) on employee performance (EP) mediated by self-efficacy (SE). The object of this research is Rumah Sakit Umum Daerah (RSUD) M.Th. Djaman, a hospital in Sanggau Regency, while the subjects are the institution's staff. Data collection search uses purposive sampling with a total of 120 samples. Data are obtained through questionnaires distributed directly to respondents using the Google Form application. Data analysis techniques used in this study include standard error of mean (SEM) with AMOS software version 24.00. Methods use to test validity and reliability of data include Confirmatory Factor Analysis (CFA), Construct Reliability (CR) and VE. The results of the analysis show that only training has a significant effect on self-efficacy, and self-efficacy has a significant effect on employee performance. Also, self-efficacy is proven to mediate the role of training on employee performance; the other hypotheses are not significant. Training is the most prominent positive factor affecting self-efficacy and self-efficacy has a significant effect on employee performance at RSUD M.Th. Djaman. The results of this study can be used as a reference by management in determining what policy priorities should take precedence.

• 한국군사과학기술학회지
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• 제27권1호
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• pp.94-106
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• 2024

In this study, we conducted a survival analysis on battalion-level commanders(leaders), focusing on infantry battalion defensive operations using the big data of brigade-level KCTC(Korea Combat Training Center) training results. Unlike previous studies, we utilized the brigade-level KCTC training results data for the first time to conduct a survival analysis, and the research subjects were battalion-level commanders(leaders), which can affect the battle. At this time, the battle results were defined, and through cluster analysis, infantry battalions were divided into excellent, average, and insufficient units, and the difference in the survival rate of the commanders was analyzed through the Kaplan-Meier survival analysis. This provided an opportunity to objectively compare the differences between excellent and insufficient units. Subsequently, factors affecting the survival of commanders were derived using the Cox proportional hazard model, and it was possible to confirm the influencing factors from various angles by also using the survival tree model. Significance and limitations confirmed in the research process were presented as policy suggestions and future research directions.

• 한국콘텐츠학회논문지
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• 제8권7호
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• pp.66-74
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• 2008

소프트웨어 개발 프로세스의 초기 단계에서 결함경향성이 많은 모듈들을 예측하는 위험도 예측 모델은 프로젝트 자원할당에 도움을 주어 전체 시스템의 품질을 개선시키는 역할을 한다. 설계 복잡도 메트릭에 기반을 둔 여러 예측 모델들이 제안 되었지만 대부분 훈련 데이터 집합을 필요로 하는 모델들이었고 훈련 데이터 집합을 보유하고 있지 않은 대부분의 개발 집단들은 이들을 사용할 수 없다는 문제점이 있었다. 본 논문에서는 잘 알려진 감독형 학습 모델인 오류 역전파 신경망 모델에 SDL 시스템 명세를 정량화하여 적용한 예측 모델을 개발하였으며, 기존 학습 모델들의 문제점을 해결하기 위해 이 모델을 여러 제약조건을 가지고 만든 가상 훈련데이터집합으로 학습시켰다. 제안 모델의 사용가능성을 알아보기 위해 몇가지 모의실험을 수행 하였으며, 그 결과 제안 모델이 훈련 데이터 집합이 없는 개발 집단에서는 실제 데이터로 훈련된 예측 모델의 대안으로 사용될 수 있음을 보였다.

• 한국생활과학회지
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• 제14권6호
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• pp.1017-1025
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• 2005

The purpose of this paper was to investigate the relationship between customer orientation and sales training. Data were obtained from 297 apparel salespeople working at six department stores in Daegu. Statistics used for data analysis were frequency, factor analysis, correlation, and t-test. The respondents were classified into 3 groups; high, medium, and low customer-oriented groups based on the mean score of customer orientation, and the high and the low were compared in training contents and educational methods. Based on factor analysis, four factors were extracted from 27 items of training content. Two of four factors were significantly correlated with customer orientation. The regression analysis showed that customer service and duration of work had significant effects on customer orientation. Also, the results were found that there were significant differences between the high and the low customer-oriented group in training contents which salespeople want to have in the future. However, there were not significant differences between the two groups in educational methods.