• Journal of the Korean Society of Physical Medicine
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• v.5 no.2
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• pp.193-201
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• 2010

Purpose : This study achieved to search the effect of the circuit exercise and conventional exercise on walking ability(walking speed, endurance, dynamic balance, speed, endurance and pedestrian crossing) in chronic stroke. Methods : Since is diagnosed by stroke, to 30 chronic stroke patients who more than 1 year past the 15 circuit exercise group, the 15 conventional exercise group random the circuit exercise group applied circuit exercise 3th 8 weeks each week after neurological treatment because assigning and the conventional exercise group executed round trip walk exercise in parallel bar 3th 8 weeks each week after neurological treatment. The data of 25 patients who complete experimental course were statistically analysed. Results : The results of this dissertation were as following : 1) There were significantly increased after experimental of 10 meter walk test, 6 minutes walk test and Timed "Up and Go" test in circuit exercise group (p<.001). 2) There were significantly increased after experimental of 2, 4 and 6 lane road crossing mobility in Walking circuit exercise group(p<.01). 3) There were significantly differences after experimental of 10 meter walk test, 6 minutes walk test and Timed "Up and Go" test change quantity between circuit exercise group and conventional exercise group(p<.05). 4) There were correlations were found between the TUG test and 2, 4 and 6 lane road (2 lane road; r=.463, p<.01., 4 lane road; r=515, p<.01., 6lane road; r=.710, p<.01), and there were correlations were found between the 10 meter walk test and 6 minutes walk test(r=.595, p<.01), TUG test(r=.662, p<.01) and 6 lane road(r=.527, p<.01). Conclusion : Even if improvement of walk function through training consists in room, transfer of actuality pedestrian crossing is no change outside the room. Because it is much variable of the weather, seasonal factor, temperature, pedestrian number, state of underneath etc. outside the room. Then, in room after direction promotion of walk function to be promotion of walk function in actuality life and need development of connectable training method consider.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.14-21
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• 2021

In this paper, it is shown how the performance of the monopulse algorithm in the presence of an additive noise can be obtained analytically. In the previous study, analytic performance analysis based on the first-order Taylor series and the second-order Taylor series has been conducted. By adopting the third-order Taylor series, it is shown that the analytic performance based on the third-order Taylor series can be made closer to the performance of the original monopulse algorithm than the analytic performance based on the first-order Taylor series and the second-order Taylor series. The analytic MSE based on the third-order Taylor approximation reduces the analytic MSE error based on the second-order Taylor approximation by 89.5%. It also shows faster results in all cases than the Monte Carlo-based MSE. Through this study, it is possible to explicitly analyze the angle estimation ability of monopulse radar in an environment where noise jamming is applied.

• Journal of Technology Innovation
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• v.27 no.2
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• pp.99-122
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• 2019

ODA (Official Development Assistance) aims for practicing international humanitarianism in developing countries. However, ODA donors also seek to find convincing evidence meeting the national economic & political interests in the international community. In this regards, precise & unbiased estimation of the policy effects of ODA aid on the donors' exports to the recipient countries has recently become one of the primary concerns of the ODA donors, especially developing countries including Korea of which economy structure heavily relies on exports for economic growth. Based on the basic gravity model, this study empirically analyzes the effects of technical cooperation ODA delivering skills, knowledge and technical know-how on Korea's exports to the ODA recipient countries using 10-year panel data from 2007 to 2016. Specifically, by incorporating major variables affecting trade such as GDP, distance, FDI etc, the effect of technical cooperation ODA on Korea's exports to the ODA recipient countries is estimated with various kinds of panel models. As a result, technical cooperation ODA has a statistically significant impact on Korea's exports to ODA recipient countries, especially in the exports of intermediate goods. And the detail process of this black-boxed mechanism is scrutinized through case studies on Uzbekistan, The Philippines, and Morocco.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.781-791
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• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Safety and Health at Work
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• v.13 no.3
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• pp.263-268
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• 2022

Health care workers (HCWs) are more than ten times more likely to be infected with coronavirus infectious disease 2019 (COVID-19) than the general population, thus demonstrating the burden of COVID-19 among HCWs. Factors that expose HCWs to a differentially high-risk of COVID-19 acquisition are important to elucidate, enable appropriate public health interventions to mitigate against high risk and reduce adverse outcomes from the infection. We conducted a systematic review and meta-analysis to summarize and critically analyze the existing evidence on SARS-CoV-2 risk factors among HCWs. With no geographical limitation, we included studies, in any country, that reported (i) the PCR laboratory diagnosis of COVID-19 as an independent variable (ii) one or more COVID-19 risk factors among HCWs with risk estimates (relative risk, odds ratio, or hazard ratio) (iii) original, quantitative study design, and published in English or Mandarian. Our initial search resulted in 470 articles overall, however, only 10 studies met the inclusion criteria for this review. Out of the 10 studies included in the review, inadequate/lack of protective personal equipment, performing tracheal intubation, and gender were the most common risk factors of COVID-19. Based on the random effects adjusted pooled relative risk, HCWs who reported the use of protective personal equipment were 29% (95% CI: 16% to 41%) less likely to test positive for COVID-19. The study also revealed that HCWs who performed tracheal intubations were 34% (95% CI: 14% to 57%) more likely to test positive for COVID-19. Interestingly, this study showed that female HCWs are at 11% higher risk (RR 1.11 95% CI 1.01-1.21) of COVID-19 than their male counterparts. This article presents initial findings from a living systematic review and meta-analysis, therefore, did not yield many studies; however, it revealed a significant insight into better understanding COVID-19 risk factors among HCWs; insights important for devising preventive strategies that protect them from this infection.

• The Journal of Information Systems
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• v.31 no.3
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

• Journal of the Korean Regional Science Association
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• v.37 no.4
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• pp.49-59
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• 2021

This study aims to identify the characteristics of the cancelled districts of housing redevelopment and housing reconstruction project. The subject of this study is 189 project districts(121 promoted districts, 68 cancelled districts). Both 121 promoted districts and 68 cancelled districts were analyzed by Decision Tree Analysis. The first separation of the release zone influencing factors was made by the Development Actors. In other words, the most important independent variable for determining the release zone influence factor was shown to be the presence or absence of propulsion actors. Of the 89 districts without propellers, 41 were lifted and 48 were promoted, and 9 out of 100 districts with propellers were lifted and 91 were promoted. The second separation of the impact factors on the zone was then made by Land Owners, and the probability of cancellation increased if the number of landowners was less than 468 and 37 out of 62 were removed. On the other hand, four out of 27 districts with more than 468 landowners were lifted and 23 districts were promoted. The third separation was made by the Average Land Assessment, and 35 zones were lifted below the standard of KRW 269.64 million/m² approximately KRW 8.91 million per pyeong, and two zones were lifted at higher official prices. In the second division, the number of landowners was 468 or more, and in node4, four areas were removed from areas with a public land area ratio of 29.43% or more, and no areas less were released. This study used SPSS Statistics 26 S/W for analysis.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.353-366
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• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

• Economic Analysis
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• v.27 no.1
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• pp.67-90
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• 2021

This study found an interesting fact that the nonlinear relationship structure between volatility and trading volume changed before and after the COVID-19 pandemic according to empirical analysis using Bitcoin (BTC) market data that sensitively reflects investors' trading behavior. That is, their relationship appeared positive (+) in a stable market state before COVID-19 pandemic, as in theory based on the information flow paradigm. In a state under severe market stress due to COVID-19 pandemic, however, their dependence structure changed and even negative (-). This can be seen as a consequence of increased market stress caused by COVID-19 pandemics from a behavioral economics perspective, resulting in structural changes in the asset market and a significant impact on the nonlinear dependence of volatility and trading volume (in particular, their dependence at extreme quantiles). Hence, it should be recognized that in addition to information flows, psychological phenomena such as behavioral biases or herd behavior, which are closely related to market stress, can be a key in changing their dependence structure. For empirical analysis, this study performs a test of Ross (2015) for detecting a structural change, and proposes a Copula Regression Quantiles (CRQ) approach that can identify their nonlinear relationship structure and the asymmetric dependence in their distribution tails without the assumption of i.i.d. random variable. In addition, it was confirmed that when the relationship between their extreme values was analyzed by linear models, incorrect results could be derived due to model specification errors.