• Journal of Korean Academy of Nursing Administration
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• v.9 no.3
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• pp.495-505
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• 2003

Purpose: The purpose of this study was to develop 360 feedback for nursing unit manager based on the current personnel evaluation system and to evaluate the new tool according to evaluation subjects. Total of 277 subjects of nurse unit managers and staff nurses were participated in this study. Method: The study was conducted in three phases each for development, application, and analysis of 360 feedback. SAS program was utilized for data analysis with descriptive statistics, t-test, and analysis of variance. Result: The evaluation criteria of the developed 360 feedback tool consisted of 13 subscales such as professional knowledge, apprehension & judgement, job performance, applicability, creativity, leadership, responsibility, promptness & accuracy, administrative ability & sense of mission, activeness, cooperation, communicability, and general attitude. The internal consistency of the tool was Cronbach's alpha .939. The evaluation score by! peers(M=4.30) was the highest one, followed by self-evaluation(M=4.23), evaluation by supervisor(M=4.17), and evaluation by subordinate(M=4.10). The differences in the total evaluation scores among the subjects supervisor, self, peer, and subordinate were not statistically significant, but significant differences were found in some subscales scores. Conclusion: Further research is required to test the reliability and validity of the $360^{\circ}$ feedback tool, and to test the outcome and the process of $360^{\circ}$ feedback system.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.13 no.1
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• pp.16-23
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• 2006

Purpose: The purpose of this study was to examine morale and related factors of hospital based home care nurses. Method : The subjects were 159 home care nurses from 94 hospitals and clinics which were operating home care programs around the country. Data were collected for 40days from March 14, to April 24, 2005. The questionnaire consisted of 51 items including 12 general variables and 39 items of nurses' morale. The reliability of the questionnaire by Cronbach's ${\alpha}$, was .88. Result : The level of the morale was found as mean score 2.69 in 4 point scale The high ranks of morale were self actualization($3.05{\pm}0.43$) and job satisfaction($3.03{\pm}0.43$), the factors which showed lower points were evaluation of work ($2.47{\pm}0.53$), welfare($2.42{\pm}0.42$), promotion system ($2.35{\pm}0.45$) and wages($2.23{\pm}0.54$). The level of morale according to the general variables were significantly different in such variables ; home care nursing antecedent(p = .000), motivation for job selection(p= .030), intention to quit the job(P= .000). Variables of intention to quit the job(15.7%) and home care nursing antecedent; 6.7%(p=.001) showed 22.4% of explanatory persuasion effect on level of morale. Conclusion : To improve a morale of home care nurses, the arrangement of nursing department should be consider nurse's aptitude and interest and allow them to have longer period of work in that part. Also wages, promotion system and welfare should be reformed as relevant as their career.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.51-63
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• 2021

Recently, virtual communication has become a standard tool due to the outbreak of COVID-19. Likewise online communication is emerging as an essential competency. In this study, we aimed to develop a comprehensive and systematic digital literacy measurement tool reflecting the changes and needs of society. Construct variables were drawn by characterizing existing digital literacy measurement tools. Thirty-four items corresponding to the concept of each variable were developed. The developed measurement tool was then evaluated in the form of surveys from university students belonging to the digital native generation, and the reliability and validity were performed through exploratory and confirmatory factor analysis. The digital literacy measurement tool contained five sub-factors and twenty-five questions. In addition, hierarchical regression analysis was performed to verify the predictive validity of digital literacy sub-factors. Based on these findings, the implication of future research is discussed.

• Journal of Integrative Natural Science
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• v.13 no.4
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• pp.132-140
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• 2020

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.

• The Korean Journal of Franchise Management
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• v.11 no.4
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• pp.31-44
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• 2020

Purpose: The purpose of this study is to examine the effects of CEO's self-determination on entrepreneurship, business performance (operational and financial performance). Also, this research provide some strategic insights for improving business performance. In the proposed model, self-determination consists of autonomy, competence, and relatedness, and entrepreneurship consists of innovation, initiative and risk sensitivity, and proactiveness. More specifically, this study proposes a framework that entrepreneurship and operational performance will play mediating roles between self-determination and financial performance. Research design, data, methodology: In this study, an online survey was conducted on SME CEOs for analysis, and a total of 122 samples were used. In the analysis process for hypothesis verification and evaluation, frequency analysis was first performed to identify the demographic characteristics of the respondents, and confirmatory factor analysis was conducted to assess the reliability and validity of the measurement model. In addition, a structural model analysis was conducted to examine the structural relationships between CEO's self-determination, entrepreneurship, and business performance (operational and financial performance) using SmartPLS 3.0. Results: The findings and summary are as follows. First, the autonomy of self-determination has a positive effect on entrepreneurship. Second, the competence of self-determination affects entrepreneurship and operational performance. Third, it affects the innovation, initiative and risk sensitivity of the CEO's entrepreneurship, and ultimately, its operational performance. The results show that the business performance of Start-up also increases when self-determination can be a factor in increasing entrepreneurship in three sub-dimensionalities. Conclusions: The conclusion of this study is that in order for SMEs to develop into a sustainable company by securing competitiveness after start-up, external motivation such as external help and support from the state (local government) is important, but competence and relationship, which are components of self-determination. The intrinsic motivation of the CEO may be more important. To this end, CEO's should prioritize learning for competency development, and the government should pay attention to providing various educational programs through establishment of education policies and education systems to enhance the competency of start-up CEO's.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1202-1208
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• 2020

In line with the current era of the 4th Industrial Revolution, it is necessary to prepare for the future by integrating AI elements in the ship sector. In addition, it is necessary to respond to this in the field of power management for the appearance of autonomous ships. In this study, we propose a battery-linked electric propulsion system (BLEPS) algorithm using machine learning's DNN. For the experiment, we learned the pattern of ship power consumption for each operation mode based on the ship data through LabView and derived the battery status through Python to check the flexibility of the generator and battery interlocking. As a result of the experiment, the low load operation of the generator was reduced through charging and discharging of the battery, and economic efficiency and reliability were confirmed by reducing the fuel consumption of 1% of LNG.

• Journal of the Korean Society for information Management
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• v.40 no.3
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• pp.77-98
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• 2023

Large Language Models (LLMs) are becoming the major trend in the natural language processing field. These models were built based on research data, but information such as types, limitations, and risks of using research data are unknown. This research would present how to analyze and evaluate the LLMs that were built with research data: LLaMA or LLaMA base models such as Alpaca of Stanford, Vicuna of the large model systems organization, and ChatGPT from OpenAI from the perspective of research data. This quality evaluation focuses on the validity, functionality, and reliability of Data Quality Management (DQM). Furthermore, we adopted the Holistic Evaluation of Language Models (HELM) to understand its evaluation criteria and then discussed its limitations. This study presents quality evaluation criteria for LLMs using research data and future development directions.

• Convergence Security Journal
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• v.23 no.1
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• pp.27-33
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• 2023

Digitalization due to COVID-19 has rapidly developed artificial intelligence-based voice recognition technology. However, this technology causes unfair social problems, such as race and gender discrimination if datasets are biased against some groups, and degrades the reliability and security of artificial intelligence services. In this work, we compare and analyze accuracy-based unfairness in biased data environments using VGGNet (Visual Geometry Group Network), ResNet (Residual Neural Network), and MobileNet, which are representative CNN (Convolutional Neural Network) models of artificial intelligence. Experimental results show that ResNet34 showed the highest accuracy for women and men at 91% and 89.9%in Top1-accuracy, while ResNet18 showed the slightest accuracy difference between genders at 1.8%. The difference in accuracy between genders by model causes differences in service quality and unfair results between men and women when using the service.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.99-107
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• 2023

Recently, the rate of death and safety accidents at construction sites is the highest among all kinds of industries. In order to apply artificial intelligence technology to construction sites, it is essential to secure a dataset which can be used as a basic training data. In this paper, a number of image data were collected through actual construction site, for which major construction equipment objects mainly operated in civil engineering sites were defined. The optimal training dataset construction was completed by annotation process of about 90,000 image dataset. Reliability of the dataset was verified with the mAP of over 90 % in use of YOLO, a representative model in the field of object detection. The construction equipment training dataset built in this study has been released which is currently available on the public data portal of the Ministry of Public Administration and Security. This dataset is expected to be freely used for any application of object detection technology on construction sites especially in the field of construction safety in the future.

• Smart Media Journal
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• v.12 no.5
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• pp.81-87
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• 2023

In this paper, accidents in high-voltage overhead distribution lines, which provide stable power supply in the power system, cause inconvenience in life and disruption of production of companies. 22.9 [kV] high-voltage overhead power distribution lines aim to improve reliability and stability, such as damage caused by rain, snow, wind, etc., or electric shock prevention. Therefore, in order to prevent wire disconnection accidents due to deterioration of electrical conductivity or tensile strength due to corrosion of overhead distribution lines, it is necessary to prevent unexpected accidents in the future through regular inspection and repair. In order to diagnose deterioration due to corrosion of distribution lines, a diagnostic system (measuring instrument) is installed on the wires to monitor the condition of the wires. The manager on the ground receives the measured data through ZigBee wireless communication, controls the diagnosis system through the diagnosis system program, and grasps the condition of the overhead distribution line through the measured data and photographed photos, and predicts the life of the wire along with the visual inspection method. developed a program.