• Title, Summary, Keyword: Transfer Behavior of Training

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An analysis of structural relationships among employee training, servant leadership, self-efficacy, transfer behavior of training, and knowledge sharing (교육훈련, 서번트 리더십, 자기효능감, 교육훈련 전이, 지식공유 간의 구조적 관계 분석)

  • Song, In-Sook;Kwon, Sang-Jib
    • Knowledge Management Research
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    • v.18 no.4
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    • pp.261-286
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    • 2017
  • Key factors enhancing transfer behavior of training and knowledge sharing are of great interest to researchers and executives because training transfer and knowledge sharing activities are remarkable predictors of organizational growth. This study investigates the core motivations for boosting transfer behavior of training and knowledge sharing. To empirically test the impacts of employee training, servant leadership and self-efficacy, a survey was conducted in small-medium sized companies. The data (N=292) were analyzed using structural equation modeling analysis. The results indicate that higher employee training positively leads to self-efficacy and transfer behavior of training. Servant leadership is positively leads to self-efficacy, transfer behavior of training, and knowledge sharing. Self-efficacy of employees induces greater transfer behavior of training and knowledge sharing. Finally, transfer behavior of training encourages workers to increase knowledge sharing. This study represents an initial step to examine the psychological mechanism of improving employees' transfer behavior of training and knowledge sharing activities based on the employee training qualities and servant leaderships.

Validity and Reliability of the Korean Version of a Tool to Measure Uncivil Behavior in Clinical Nursing Education (간호학생이 임상실습에서 경험하는 무례함 한국어판 측정도구의 타당도와 신뢰도)

  • Jo, Su Ok;Oh, Jina
    • The Journal of Korean Academic Society of Nursing Education
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    • v.22 no.4
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    • pp.537-548
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    • 2016
  • Purpose: This study aims to develop a Korean version of a tool to measure uncivil behavior in clinical training to examine the experiences of nursing students. Methods: The "Uncivil Behavior in Clinical Nursing Education Scale" was developed by Anthony and Yastik in 2011. This study procedure was based on DeVellis' instrument development guidelines. Data were collected from 220 senior-year nursing students from four different universities in four different locations. Two hundreds surveys were analyzed using SPSS software and AMOS. Results: Out of 20 questions, 13 were selected after reviewing the content validity, face validity, construct validity, and reliability. The factors of the Korean version scale were specified as "exclusion", "contempt", and "refusal." The general characteristics of the subjects that showed significant differences in the occurrence of incivility were gender, age, transfer student status, level of satisfaction with clinical training, and level of satisfaction with the clinical training environment. Conclusion: The "Korean-Uncivil Behavior in Clinical Nursing Education Scale" was partially modified to account for differences in language and culture, but its validity and reliability were verified. We suggest that nurse educators and supervisors will be able to better understand the relationship between nurses and nursing students in clinical training.

Human Activity Recognition Based on 3D Residual Dense Network

  • Park, Jin-Ho;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.23 no.12
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    • pp.1540-1551
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    • 2020
  • Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

Optimized Neurocontroller for Human Control Skill Transfer

  • Seo, Kap-Ho;Changmok Oh;Lee, Ju-Jang
    • 제어로봇시스템학회:학술대회논문집
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    • pp.42.3-42
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    • 2001
  • A human is an expert in manipulation. We have acquired skills to perform dexterous operations based upon knowledge and experience attained over a long period of time. It is important in robotics to understand these human skills, and utilize them to bring about better robot control and operation It is hoped that the neurocontroller can be trained and organized by simply presenting human teaching data, which implicate human intention, strategy and expertise. In designing a neurocontroller, we must determine the size of neurocontroller. Improper size may not only incur difficulties in training neural nets, e.g. no convergence, but also cause instability and erratic behavior in machines. Therefore, it is necessary to determine the proper size of neurocontroller for human control transfer. In this paper, a new pruning method is developed, based on the penalty-term methods. This method makes ...

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The influences of dental health behaviors on the subjective dental health status and knowledge in some middle school students (일부 중학생의 주관적 구강건강상태와 구강건강지식이 구강건강행동에 미치는 영향)

  • Yeo, An-Na;Lee, Su-Young
    • Journal of Korean society of Dental Hygiene
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    • v.18 no.4
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    • pp.585-595
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    • 2018
  • Objectives: The purposes of this study were to comprehend the subjective dental health status and the level of dental health knowledge in some middle school students and to analyze the correlation with dental health behaviors. Methods: A survey was conducted in some middle school students and the final 637 survey data were analysed. As the statistical analysis methods, the subjective dental health status, dental health knowledge and dental health behaviors according to the general characteristics were analyzed by independent t-test, one way ANOVA and Scheffe. The correlations among the subjective dental health status, dental health knowledge and dental health behavior were found by Pearson's correlation and multiple regression analysis. Results: Through correlation analysis of the subjective dental health status, dental health knowledge and dental health behavior, all showed a significant correlation. As a result of the factor analysis affecting dental behaviors, subjective dental health status was the highest (${\beta}=0.304$, p<0.001). Conclusions: The results of this study suggest that the improvement of subjective dental health status and dental health knowledge related to dental behaviors health in the middle school students should be considered. In addition, dental health education should focus on improving subjective dental health status through motivation rather than knowledge transfer training. Moreover, development programs appropriate for the middle school students whose behavioral changes are hard to obtain are needed.

A Study on Participation of Korean a university graduate at Youth TLO Applying the Expectancy Theory (국내 대학 졸업생의 기대이론을 적용한 청년TLO 참여연구)

  • Yang, Jong-Gon;Kim, Jin-Gyu
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.5
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    • pp.200-212
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    • 2019
  • The purpose of this study is to examine the motivational factors of university graduates participating in 'Youth Technology Transfer Specialist Training Project(Youth TLO)' by applying Vroom's expectancy theory. Moreover, it is verified that the effect of actual participation behavior and individual performance improvement for the university graduates in Gyeonggi-do, Busan regions. The motivation factors were consisted of valence, instrumentality, and expectancy. An empirical analysis was conducted of the effects on the verification of the demographic characteristics of the target, the behaviour of personal business participation in the Valence and Force model, and the improvement of performance. Three results were inferred from 322 collected data as follows; First, comparative analysis about expectancy, which related to work experience, according to demographic characteristics such as gender, residence, age, and employment period revealed no significant differences in mean value, except career duration. Especially, the university graduates in 'Youth TLO' who had internship experience had the highest level of recognition for the expectancy. Second, both of valence and force model had influence on participation behavior and performance improvement. Notably, determination of coefficient for the valence model were higher than those for the force model. Third, level of mediation effects for the valence model were higher than those for the force model in respect of direct, indirect, and the total. Moreover, it was verified that the three motivation factors could improve individual performance and participation behavior had partial mediation effect.

A Deep Learning Based Approach to Recognizing Accompanying Status of Smartphone Users Using Multimodal Data (스마트폰 다종 데이터를 활용한 딥러닝 기반의 사용자 동행 상태 인식)

  • Kim, Kilho;Choi, Sangwoo;Chae, Moon-jung;Park, Heewoong;Lee, Jaehong;Park, Jonghun
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.163-177
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    • 2019
  • As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

PID Control of a Shell and Tube Heat Exchanger System Incorporating Feedforward Control and Anti-windup Techniques (피드포워드 제어와 안티와인드업 기법을 결합한 셀-튜브 열교환기 시스템의 PID 제어)

  • Ahn, Jong-Kap;So, Gun-Baek;Lee, Ju-Yeon;Lee, Yun-Hyung;So, Myong-Ok;Jin, Gang-Gyoo
    • Journal of Institute of Control, Robotics and Systems
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    • v.20 no.5
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    • pp.543-550
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    • 2014
  • In many industrial processes and operations, such as power plants, petrochemical industries and ships, shell and tube heat exchangers are widely used and probably applicable for a wide range of operating temperatures. The main purpose of a heat exchanger is to transfer heat between two or more medium with temperature differences. Heat exchangers are highly nonlinear, time-varying and show time lag behavior during operation. The temperature control of such processes has been challenging for control engineers and a variety of forms of PID controllers have been proposed to guarantee better performance. In this paper, a scheme to control the outlet temperature of a shell and tube heat exchanger system that combines the PID controller with feedforward control and anti-windup techniques is presented. A genetic algorithm is used to tune the parameters of the PID controller with anti-windup and the feedforward controller by minimizing the IAE (Integral of Absolute Error). Simulation works are performed to study the performance of the proposed method when applied to the process.

Interrelationship between Records and Information (기록과 정보의 상관관계)

  • Song, Byoung-Ho
    • The Korean Journal of Archival Studies
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    • no.20
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    • pp.3-32
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    • 2009
  • When the record management faces to the information environment, the practices based on self-judgment needs more open and considerate policies. New viewpoint that treat records as information and treat information as records will produce new mutual-conscious behavior that create records based on the value of information usage and maintain information data based on the reliability as an record. As the internal aspect how to create records well, how to transfer them well, and how to archives them well used to be the focus of record management, existing legislation, guidelines, and training seem to be mainly related to this front steps. We should also address issues according to the succeeding information services, including opening to the relevant, sharing, duplicating, information security, privacy protection, and constructing collections with continual supplement. This paper observe the confusion of the viewpoints in the recent reports, explain the need of fusion viewpoint, and suggest interconnecting feedback cycle between record management system and general information system.