• Journal of the Korean Society for Precision Engineering
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• v.14 no.1
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• pp.185-193
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• 1997

This paper presents the on-line monitoring of the surface roughness in a face milling operation. The cut- ting force was used to monitor the surface roughness, since the insert run-outs not only deteriorate surface roughness but also change cutting force. AR model and band energy method were taken to extract the fea- tures from the cutting force. The features extracted from AR modelling are more accurate about the moni- toring than those from band energy method, whereas, the computing speed of the former is slow. An artifi- cal neural network discriminated the level of the surface roughness by using the features extracted via signal processing.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.149-156
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• 1995

This paper presents a method of detecting chatter vibration in end-mill process. The detecting system consists of an adaptive signal processing scheme which uses an autore- gressive time-series model and a neural network is proposed and is verified its effectiveness by using acceleration and cutting force signals recorded during slotting in end-mill operations. Expeerimental results indicate that the proposed system provides excellent detection when chatter is occured within the ranges of cutting conditions considered in this study and an effectiveness of the integration of signals is confirmed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1153-1159
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• 2000

This paper applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the lido utilization that QoS of the network is greatly influenced by and proposes the prediction algorithm of threshold violation in line utilization using the seasonal ARIMA model. We can predict the time of threshold violation in line utilization and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold and a detection probability, it thus appears that we have maximized the performance of this algorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.171-171
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• 2011

상수도의 합리적인 운용과 관리를 위해서는 급수량 예측이 매우 중요하다. 기존 급수량 예측은 신경망과 칼만 필터법을 사용한 연구들이 대부분이었다. 이러한 연구결과들은 높은 상관결과를 갖고 있지만 이는 자기상관계수에 대한 높은 의존도에 따른 결과로 볼 수 있다. 즉, 예측의 결과가 전날 수요량을 거의 그대로 따라오는 경향을 띄어, 급수량 예측 그래프가 기존 그래프를 오른쪽으로 이동시킨 것과 같이 나타난다. 본 연구에서는 이러한 문제점들을 해결하기 위해서 물수요량을 예측하는데 있어서 효과적인 예측인자를 도출하는 것이 우선되어야 할 것으로 판단되었다. 이에, 물수요량 특성을 효과적으로 나타내어 줄 수 있는 예측인자로서 강수량, 최저온도, 최고온도, 평균온도 등을 1차적으로 선정하였다. 이들 예측인자들과 서울시 물수요량과의 상관성을 평가하여 최적의 예측인자 Set과 지체시간 등을 산정하였다. 이렇게 선정된 예측인자와 Bayesian 통계기법 기반의 회귀분석 모형을 구축하여 물수요량을 예측하였다. 본 연구에서 적용하고자 하는 계층적 Bayesian 모형은 유사한 특성을 가지는 자료계열들 사이에서 서로 보완이 될 수 있는 정보들을 추출함으로써 모형이 갖는 불확실성을 상당히 줄일 수 있는 방법이다. 이러한 모형적 특징은 생산량 예측에 대한 불확실성 저감 측면에서 장점이 있을 것으로 판단된다. 본 연구에서는 광암, 암사, 구의, 뚝도, 영등포, 강북 정수장을 대상으로 모형의 적합성을 평가하였다. 이러한 연구결과는 향후 정수장 운영계획 및 동일한 시스템을 갖는 상수도 급수량 예측 시 유용하게 사용할 수 있을 것이다.

• The KIPS Transactions:PartC
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• v.9C no.6
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• pp.841-846
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• 2002

We applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the line utilization that QoS of the network is greatly influenced by. And this paper proposes the learning algorithm of dynamic threshold in line utilization using the SARIMA model. We can find the proper dynamic threshold in timely line utilization on the various network environments and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold on real network. Network manager can overcome a shortcoming of original threshold method and maximize the performance of this algorithm.

• Journal of the Korean association of regional geographers
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• v.17 no.4
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• pp.432-442
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• 2011

Climatic variables such as temperature and precipitation tend to vary both in space and in time simultaneously. Thus, it is necessary to include space-time autocorrelation into conventional spatial interpolation methods for reliable time-series mapping. This paper introduces and applies space-time variogram modeling and space-time kriging to generate time-series temperature maps using hourly Automatic Weather System(AWS) temperature observation data for a one-month period. First, temperature observation data are decomposed into deterministic trend and stochastic residual components. For trend component modeling, elevation data which have reasonable correlation with temperature are used as secondary information to generate trend component with topographic effects. Then, space-time variograms of residual components are estimated and modelled by using a product-sum space-time variogram model to account for not only autocorrelation both in space and in time, but also their interactions. From a case study, space-time kriging outperforms both conventional space only ordinary kriging and regression-kriging, which indicates the importance of using space-time autocorrelation information as well as elevation data. It is expected that space-time kriging would be a useful tool when a space-poor but time-rich dataset is analyzed.

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.453-463
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• 2018

An analysis including ASE model accessing based on the intention of behavior performance of online university students is a new approach to improve academic persistence considering the characteristics of students with extensive personal variables, a uniqueness of learning environment. This study aimed to identify the relationship between ASE model including academic stress and academic persistence, and the effect of these factors on academic persistence of online university students. Data were collected from 181 sophomores in K open university from March to June, 2018. Frequency analysis, ${\chi}^2-test$, t-test, F-test, Pearson's correlation analysis, and multiple regression analysis used for data analysis. For factors affecting academic persistence, academic stress (${\beta}=-.16$, p=.016), online learning attitude (${\beta}=.44$, p<.001), and social support among social influential factors (${\beta}=.16$, p=.045) were statistically significant and the prediction model of academic persistence showed 29% explanation power (F=15.76, p<.001). To enhance academic persistence of online university students, it is needed to develop programs to reduce academic stress, improve attitude toward online learning, and improve social support.

• Journal of the Korean Applied Science and Technology
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• v.41 no.2
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• pp.472-483
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• 2024

This study evaluated the attitudes of the Korean population towards sunscreen use through the Health Belief Model (HBM) construct and investigated the psychological factors that influence sunscreen use. For this purpose, an online survey was conducted from 1 November 2023 to 1 January 2024, and a total of 303 participants were collected. The collected data were analysed using SPSS v. 25.0 programme using Cronbach's 𝛼, frequency analysis, descriptive statistics, correlation analysis, independent samples t-test, one way ANOVA, Scheffe's test, and multiple regression analysis. The results of the study showed that the mean score of sunscreen use was 3.26±1.384 out of 5, and there was a significant correlation between the variables of the health belief model and sunscreen use (p<.01). Gender, age, and skin colour were also associated with each variable, with women, the elderly, and those with lighter skin tending to be more proactive in sun protection. Multiple regression analyses revealed that self-efficacy (𝛽=.629, p<.001) and perceived vulnerability (𝛽=.139, p<.001), sub-factors of the Health Belief Model, had a statistically significant positive effect on sunscreen use, while perceived barriers (𝛽=-.261, p<.001) had a statistically significant negative effect on sunscreen use. These results may have important theoretical implications for the development and implementation of educational programmes to promote sunscreen use by providing insight into the psychosocial factors that influence sun protection.

• Korean journal of communication and information
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• v.48
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• pp.280-305
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• 2009

By conceptualizing UCC users' active behavior, this study newly presents two key concepts, 'self-expression' and 'artistic activity' of UCC users. Then, this study suggests a research framework to analyze text, image, and video UCC adoption processes by integrating those concepts with already-built UCC research tradition. Using the framework, we found that individuals who has a hard time expressing themselves in reality are likely to have a favorable attitude toward UCC, and the stronger the faith about the justice of self-expression and its freedom, the more the favorable attitude toward UCC. Furthermore, we confirmed that individuals who are very interested in artistic activity, and individuals who has high self-efficacy to their artistic talent are likely to participates more in UCC production. The role of other factors such as perceived enjoyment, the perception of critical mass of UCC adoption, and subjective norm also were discussed with limitations of this study.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.484-495
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• 2019

This study attempted to investigate the effects of self-leadership on organizational citizenship behavior and turnover intention among beauty salon employees and analyze the moderating effects of job satisfaction. For this purpose, data were collected for 330 people working in beauty salons and analyzed using SPSS v. 21.0. According to multiple regression analysis, self-leadership revealed a statistically significant positive effect on the OCB and urnover intention Therefore, it was shown that self-leadership is a critical factor. To determine if self-leadership having an influence on the OCB and turnover intention is moderated by job satisfaction, variables were applied to the following models, and hierarchical regression was performed: self-leadershipto model I, job satisfaction to model II, self-leadership and job satisfaction to model III. The results found a statistically significant positive influence in all three models. Therefore, this shows that job satisfaction has a moderating effect on self-leadership, OCB and turnover intention. Self-development education as well as technical training should be strengthened to enhance self-leadership, which leads beauty salon employees' thoughts and behavior in the right direction.