• Journal of the Korea Convergence Society
• /
• v.8 no.11
• /
• pp.77-83
• /
• 2017

For efficient predictive analysis, self-healing research is needed that enables the system to recover autonomously by self-cognition and diagnosing system problems. However, software development does not provide formal contextual information analysis and appropriate presentation structure according to external situation. In this paper, we propose a prediction analysis method based on the change contents by applying the extraction rule to the functions that can act, data, and transaction based on the new Goal-scenario. We also evaluated how well the predictive analysis met through the performance indicators for achieving the requirements goal. Compared with the existing methods, the proposed method has a maximum 32.8% higher matching result through performance measurement, resulting in a 28.9% error rate and a 45.8% reduction in the change code. This shows that it can be processed into a serviceable form through rules, and it shows that performance can be expanded through predictive analysis of changes.

• Journal of Korean Academy of Nursing
• /
• v.44 no.6
• /
• pp.595-607
• /
• 2014

Purpose: The Braden Scale is one of the most intensively studied risk assessment scales used in identifying the risk of developing pressure sore. However, not all studies show that the predictive validity of this scale is sufficient. The purpose of this study was to evaluate the Braden Scale for predicting pressure ulcer development. Methods: Articles published 1946 and 2013 from periodicals indexed in Ovid Medline, Embase, CINAHL, KoreaMed, NDSL and other databases were selected, using the following keywords: 'pressure ulcer'. The QUADAS-II was applied to assess the internal validity of the diagnostic studies. Selected studies were analyzed using meta-analysis with MetaDisc 1.4. Results: Thirty-eight diagnostic studies with high methodological quality, involving 17,934 patients, were included. Results of the meta-analysis showed that the pooled sensitivity and specificity of the Braden Scale were 0.74 (95% CI: 0.72-0.76), 0.75 (95% CI: 0.74-0.76) respectively. However the predictive validity of the Braden Scale has limitation because there was high heterogeneity between studies. Conclusion: The Braden Scale's predictive validity of risk for pressure ulcer is interpreted as at a moderate level. However there is a limitation to the interpretation of the results, because of high heterogeneity among the studies.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.27 no.4
• /
• pp.179-186
• /
• 2015

In this study, the actual energy consumption of the secondary side District Heating System (DHS) with different hot water supply temperature control methods is compared. The two methods are Outdoor Temperature Reset Control and Outdoor Temperature Predictive Control. While Outdoor Temperature Reset Control has been widely used for energy savings of the secondary side system, the results show that the Outdoor Temperature Predictive Control method saves more energy. In general, the Outdoor Temperature Predictive Control method lowers the supply temperature of hot water, and it reduces standby losses and increases the overall heat transfer value of heated spaces due to more flow into the space. During actual energy consumption monitoring, the Outdoor Temperature predictive Control method saves about 6.6% of energy when compared to the Outdoor Temperature Reset Control method. Also, it is found that at partial load condition, such as during daytime, the fluctuation of hot water supply temperature with Outdoor Temperature Reset Control is more severe than that with Outdoor Temperature Predictive Control. Thus, it proves that Outdoor Temperature Predictive Control is more stable even at partial load conditions.

• Journal of Digital Contents Society
• /
• v.19 no.3
• /
• pp.529-535
• /
• 2018

Data is explosively growing, but many companies are still using data analysis only for descriptive analysis or diagnostic analysis, and not appropriately for predictive analysis or enterprise technology strategy analysis. In this study, we analyze the structured & unstructured patent data such as IPC code, inventor, filing date and so on by using big data analysis techniques such as network analysis and TF-IDF. Through this analysis, we propose analysis process to understand the core technology and technology distribution of competitors and prove it through data analysis.

• Asia pacific journal of information systems
• /
• v.26 no.4
• /
• pp.526-541
• /
• 2016

This research aims to examine the predictive power of multiple online stock message boards, namely, NAVER Finance and PAXNET, which are the most popular stock message boards in South Korea, in stock market activities. If predictive power exists, we then compare the predictive power of multiple online stock message boards. To accomplish the research purpose, we constructed a panel data set with close price, volatility, Spell out acronyms at first mention.PER, and number of posts in 40 companies in three months, and conducted a panel vector auto-regression analysis. The analysis results showed that the number of posts could predict stock market activities. In NAVER Finance, previous number of posts positively influenced volatility on the day. In PAXNET, previous number of posts positively influenced close price, volatility, and PER on the day. Second, we confirmed a difference in the prediction power for stock market activities between multiple online stock message boards. This research is limited by the fact that it only considered 40 companies and three stock market activities. Nevertheless, we found correlation between online stock message board and stock market activities and provided practical implications. We suggest that investors need to focus on specific online message boards to find interesting stock market activities.

• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.236-238
• /
• 2005

In this paper, we propose a new discrete-time predictive current controller for a PMLSM(permanent magnet linear synchronous motor). The main objectives of the current controllers are that the measured stator current is tracked the command current value accurately and the transient interval is shorten as much as possible, in order to obtain high-performance of ac drive system. The conventional predictive current controller is hard to implement in full digital current controller since a finite calculation time causes a delay between the current sensing time and the time that take to apply the voltage to motor. A new control strategy is the schema that gets the fast adaptation of transient current change, the fast transient response tracking. Moreover, the simulation results will be verified the improvements of Predictive controller and accuracy of the current controller.

• Journal of Power Electronics
• /
• v.13 no.6
• /
• pp.1016-1023
• /
• 2013

Predictive current control offers the potential for achieving more precise current control with a minimum of distortion and harmonic noise. However, the predictive method is difficult to implement and has a greater computational burden. This paper introduces a theoretical analysis and experimental verification for an improved predictive current control technique applied to single phase grid connected voltage source inverters (VSI). The proposed technique has simple calculations. An ATmega1280 microcontroller board is used to implement the proposed technique for a simpler and cheaper control system. To enhance the current performance and to obtain a minimum of current THD, an improved tri-level PWM switching strategy is proposed. The proposed switching strategy uses six operation modes instead of four as in the traditional strategy. Simulation results are presented to demonstrate the system performance with the improved switching strategy and its effect on current performance. The presented experimental results verify that the proposed technique can be implemented using fixed point 8-bit microcontroller to obtain excellent results.

• Physical Therapy Rehabilitation Science
• /
• v.12 no.2
• /
• pp.80-91
• /
• 2023

Objective: The purpose of this study is to identify factors that affect the incidence of hypertension using logistic regression and decision tree analysis, and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 9,859 subjects from the Korean health panel annual 2019 data provided by the Korea Institute for Health and Social Affairs and National Health Insurance Service. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In logistic regression analysis, those who were 60 years of age or older (Odds ratio, OR=68.801, p<0.001), those who were divorced/widowhood/separated (OR=1.377, p<0.001), those who graduated from middle school or younger (OR=1, reference), those who did not walk at all (OR=1, reference), those who were obese (OR=5.109, p<0.001), and those who had poor subjective health status (OR=2.163, p<0.001) were more likely to develop hypertension. In the decision tree, those over 60 years of age, overweight or obese, and those who graduated from middle school or younger had the highest probability of developing hypertension at 83.3%. Logistic regression analysis showed a specificity of 85.3% and sensitivity of 47.9%; while decision tree analysis showed a specificity of 81.9% and sensitivity of 52.9%. In classification accuracy, logistic regression and decision tree analysis showed 73.6% and 72.6% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. It is thought that both analysis methods can be used as useful data for constructing a predictive model for hypertension.

• Physical Therapy Rehabilitation Science
• /
• v.11 no.3
• /
• pp.285-295
• /
• 2022

Objective: The purpose of this study is to use logistic regression and decision tree analysis to identify the factors that affect the success or failurein the national physical therapy examination; and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 76,727 subjects from the physical therapy national examination data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was pass or fail, and the input variables were gender, age, graduation status, and examination area. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In the logistic regression analysis, subjects in their 20s (Odds ratio, OR=1, reference), expected to graduate (OR=13.616, p<0.001) and from the examination area of Jeju-do (OR=3.135, p<0.001), had a high probability of passing. In the decision tree, the predictive factors for passing result had the greatest influence in the order of graduation status (x²=12366.843, p<0.001) and examination area (x²=312.446, p<0.001). Logistic regression analysis showed a specificity of 39.6% and sensitivity of 95.5%; while decision tree analysis showed a specificity of 45.8% and sensitivity of 94.7%. In classification accuracy, logistic regression and decision tree analysis showed 87.6% and 88.0% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. Additionally, whether actual test takers passed the national physical therapy examination could be determined, by applying the constructed prediction model and prediction rate.

• Korean Journal of Adult Nursing
• /
• v.27 no.5
• /
• pp.559-571
• /
• 2015

Purpose: This study is to determine the predictive validity of the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) for inpatients' fall risk. Methods: A literature search was performed to identify all studies published between 1946 and 2014 from periodicals indexed in Ovid Medline, Embase, CINAHL, KoreaMed, NDSL and other databases, using the following key words; 'fall', 'fall risk assessment', 'fall screening', 'mobility scale', and 'risk assessment tool'. The QUADAS-II was applied to assess the internal validity of the diagnostic studies. Fourteen studies were analyzed using meta-analysis with MetaDisc 1.4. Results: The predictive validity of STRATIFY was as follows; pooled sensitivity .75 (95% CI: 0.72~0.78), pooled specificity .69 (95% CI: 0.69~0.70) respectively. In addition, the pooled sensitivity in the study that targets only the over 65 years of age was .89 (95% CI: 0.85~0.93). Conclusion: The STRATIFY's predictive validity for fall risk is at a moderate level. Although there is a limit to interpret the results for heterogeneity between the literature, STRATIFY is an appropriate tool to apply to hospitalized patients of the elderly at a potential risk of accidental fall in a hospital.