• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.399-408
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• 2018

This study was examined to confirm the influence of personality traits, social support, and career decision self-efficacy on career preparation behavior in nursing college students based on the social recognition career self-management model. The subjects were nursing students attending 3rd and 4th grade in two nursing colleges in Gwangju and Chonnam area. The 208 data collected were analyzed by descriptive statistics, Pearson's correlation analysis, and multiple regression analysis with SPSS 23.0 for windows. Personality traits, social support, and career decision self-efficacy were positively correlated with career preparedness behaviors. Conscientiousness and career decision self-efficacy were reported to have a statistically significant effect on career preparation behavior. Extraversion and social support were reported to have no statistically significant effect on career preparation behavior. These findings suggest that in order to promote career preparation behavior of nursing college students, the strategies to improve conscientiousness and career decision self-efficacy should be provided in nursing education field.

• 한국벤처창업학회:학술대회논문집
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• 2023.04a
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• pp.93-99
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• 2023

경기 침체와 더불어 고용 불안, 그에 따른 사회의 혼란 속에서 국가의 성장 동력의 대안 중 하나로 창업 활성화의 요구가 높아지고 있다. 우리나라를 비롯한 많은 국가에서 창업 활성화를 중장기 목표로 설정하고 다방면으로 노력하고 있다. 이에 따라 창업의도를 높일 수 있는 요인에 대한 연구가 진행되어 왔고, 특히 자기효능감과 창업기회인식 등의 개인적 역량 요소가 창업의도를 높인다는 연구 결과들이 지속적으로 제시되고 있다. 이러한 창업의도를 높일 수 있는 자기효능감, 창업기회인식을 고취시키기 위해 학계의 연구활동 뿐 아니라 정부의 정책적 접근 또한 활발하게 이루어지고 있다. 창업교육 활성화부터 사회적 환경 조성을 위한 창업 롤모델 활용, 미디어를 통한 창업 활동 홍보 등 긍정적인 창업 경험을 공유하도록 하기 위한 연구 역시 계속되고 있다. 그러나 개인적 역량 요소와 사회적 환경 조성 외에 문화적 특성이 창업의도에 영향을 미치는지에 대한 연구는 부족하였다. 본 연구는 문화적 특성이 창업의도를 높일 수 있는 요인으로 개인적 역량 요소와 사회적 환경 조성과 함께 의미가 있을 것이라는 물음에서 시작하였다. 가설 검증을 위하여 SPSS 26버전을 활용하여 로지스틱 회귀분석 하였고 GEM KOREA의 2017년 데이터를 분석하였으며, 자기효능감과 창업기회인식은 창업의도에 긍정적인 영향을 미친다는 기존 연구 동일한 결과가 나왔다. 본 연구의 특징은 문화적 특성을 집단주의와 관계주의로 구분하여 자기효능감, 창업기회인식과 창업의도에 영향을 미치는 과정에서의 조절효과를 검증하였다는 것이다. 문화적 특성 중 집단주의 특성은 유의하지 않았으나 관계주의 특성이 유의하여 조절효과를 가진다는 결과를 얻어냈다. 이는 국가에 새로운 성장 동력이 필요한 상황에서 창업의도가 없거나 낮은 개인들도 관계주의 특성을 활용하여 창업의도를 높일 수 있다는 연구 결론으로 이어진다. 지금까지 알려진 바와 달리 한국은 집단주의 보다 관계주의가 강하기 때문에 관계주의 문화 특성을 고려하여 선배 창업가 또는 로컬 창업가들과의 관계를 만들고 유지할 수 있도록 하는 등의 정책을 수립할 필요가 있다는 것을 시사한다. 하지만 이미 설계된 GEM 데이터를 활용하였다는 점, 문화적 특성이 각기 다른 국가들과의 비교연구가 필요하다는 점 등은 본 연구의 한계라고 할 수 있다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.27-36
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• 2023

The purpose of this study was to examine the relationships between health knowledge, health attitude, social support, self-efficacy, and health promotion behaviors among nursing students in medically underserved areas using the Information-Motivation-Behavioral Skills Model, as well as to identify the factors influencing health promotion behaviors. The study was conducted from October 1 to October 20, 2022, with 157 nursing students residing in medically underserved areas G. Data analysis was performed using SPSS 25.0 program, including descriptive statistics, t-test, one way ANOVA, correlation analysis, and multiple regression analysis. The results showed that the factors affecting health promotion behaviors were school life satisfaction, stress management, social support, and self-efficacy, with social support being the most significant factor. The total explanatory power was 84.9%. Based on these results, we hope to develop a health promotion program that can increase the interest of nursing students in health promotion and encourage active participation in health behaviors, which can contribute to becoming a healthy nurse.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.96-105
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• 2019

Ocean reverberation is the most limiting factor in designing realistic and real-time system for sonar simulator. The simulation for an ocean reverberation requires a lot of computational loads, so it is hard to embed program and generate real-time signal in the sonar simulator. In this study, we simulate a time-domain bottom reverberation signal based on Harrison's energy-flux bottom reverberation model by applying Doppler effects as ship maneuvering and autoregressive model. Finally, the bottom reverberation signal with realistic characteristics could be generated for the simulation of ONR reverberation modeling workshop-I problem XI and East Sea ocean environments.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.491-494
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• 2020

최근 언어 모델(Language model)의 기술이 발전함에 따라, 자연어처리 분야의 많은 연구들이 좋은 성능을 내고 있다. 정해진 주제 없이 인간과 잡담을 나눌 수 있는 오픈 도메인 대화 시스템(Open-domain dialogue system) 분야에서 역시 이전보다 더 자연스러운 발화를 생성할 수 있게 되었다. 언어 모델의 발전은 응답 선택(Response selection) 분야에서도 모델이 맥락에 알맞은 답변을 선택하도록 하는 데 기여를 했다. 하지만, 대화 모델이 답변을 생성할 때 일관성 없는 답변을 만들거나, 구체적이지 않고 일반적인 답변만을 하는 문제가 대두되었다. 이를 해결하기 위하여 화자의 개인화된 정보에 기반한 대화인 페르소나(Persona) 대화 데이터 및 태스크가 연구되고 있다. 페르소나 대화 태스크에서는 화자마다 주어진 페르소나가 있고, 대화를 할 때 주어진 페르소나와 일관성이 있는 답변을 선택하거나 생성해야 한다. 이에 우리는 대용량의 코퍼스(Corpus)에 사전 학습(Pre-trained) 된 언어 모델을 활용하여 더 적절한 답변을 선택하는 페르소나 대화 시스템에 대하여 논의한다. 언어 모델 중 자기 회귀(Auto-regressive) 방식으로 모델링을 하는 GPT-2, DialoGPT와 오토인코더(Auto-encoder)를 이용한 BERT, 두 모델이 결합되어 있는 구조인 BART가 실험에 활용되었다. 이와 같이 본 논문에서는 여러 종류의 언어 모델을 페르소나 대화 태스크에 대해 비교 실험을 진행했고, 그 결과 Hits@1 점수에서 BERT가 가장 우수한 성능을 보이는 것을 확인할 수 있었다.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.205-212
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• 2017

This study examined the factors affecting the intention to use the wearable device consumers and the factors that have positive effects on the intention to use the wearable device or the consumers who have not yet used the wearable device. The purpose of this study was to examine the perceived usefulness, perceived usefulness, consumer individuality (individual innovation, self-efficacy, subjective norm) and wearable device characteristics (aesthetics, compatibility) of technology acceptance model (TAM). The results of the analysis are as follows. This study focused on the effect of consumers on the intention to use. (TPB) and Davis (1989) proposed technology acceptance model which is applied to various fields to predict the intention to use as it is proved validity and usefulness as a theory explaining various social behaviors. TAM) in order to examine the intention to use. In addition, we added consumer characteristics and variables related to product characteristics of wearable devices, which have not been studied much in previous studies.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.343-351
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• 2021

The purpose of this study was to identify the nursing students' COVID-19 vaccination intention based on health belief model. A total of 169 nursing students who were freshman to senior grade from one college participated in this study. An online self-administered questionnaire was used for data collection from April 5 to 16, 2021 and data were analyzed using the SPSS/Window 21.0 program. Multiple regression analysis conducted to verify the factors on COVID-19 vaccination intention. The significant influential factors for COVID-19 vaccination intention were self-efficacy(β=.345, p<.001), cues to action(β=.307, p<.001), perceived benefits(β =.143, p= .034), and knowledge(β=.116, p=.042). The model explained 50.8% of the variance in the COVID-19 vaccination intention. This result points to the importance of fostering nursing student's self-efficacy, cues to action, perceived benefits, and knowledge to promote COVID-19 vaccination uptake.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.481-491
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• 2020

The development of agricultural land at Saemangeum has required a significant increase in agricultural water use. It has been well acknowledged that salinity plays a critical role in the farming system. Therefore, a systematic study in salinity is necessary to better manage agricultural water. This study aims to develop a stochastic salinity simulation model that simultaneously simulates salinities obtained from different layers. More specifically, this study proposed a two-stage Autoregressive Exgeneous (ARX) model within a hierarchical Bayesian modeling framework. We derived posterior distributions of model parameters and further used them to obtain the predictive posterior distribution for salinities at three different layers. Here, the BIC values are used and compared to determine the optimal model from a set of candidate models. A detailed discussion of the model is provided.

• Journal of KIISE
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• v.44 no.9
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• pp.954-965
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• 2017

A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1341-1347
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• 2020

Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%.