• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1212-1222
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• 2018

This paper presents a model predictive control (MPC) method to reduce the common-mode voltage (CMV) for inverters connected in parallel, which increase the capacity of energy storage systems (ESSs). The proposed method is based on subdivided voltage vectors, and the resulting algorithm can be applied to control the inverters. Furthermore, we use more voltage vectors than the conventional MPC algorithm; consequently, the quality of grid currents is improved. Several methods were proposed in order to reduce the CMV appearing during operation and its adverse effects. However, those methods have shown to increase the total harmonic distortion of the grid currents. Our method, however, aims to both avoid this drawback and effectively reduce the CMV. By employing phase difference in the carrier signals to control each inverter, we successfully reduced the CMV for inverters connected in parallel, thus outperforming similar methods. In fact, the validity of the proposed method was verified by simulations and experimental results.

• 한국안전학회지
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• 제18권3호
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• pp.154-160
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• 2003

원격지에 위치한 분산제어 시스템과 센서 데이터 또는 제어 명령을 주고받을 때에는 불확실한 시간지연이 발생한다. TCP/IP 프로토콜을 이용한 데이터 네트워크와 마찬가지로 데이터 네트워크와 디바이스 네트워크를 결합한 가상 디자이스 네트워크도 불확실한 시간지연이 내재되어 있다. 이러한 시간지연은 분산제어시스템의 성능을 저하시키고 불안전성을 야기하는 원인이 된다. 본 논문에서는 이러한 네트워크상에 내재하는 시간지연을 평가하고 부정적인 효과를 최소화하기 위하여 Smith Predictor를 적용하였다. 제안된 제어 알고리즘은 실시간 서보제어를 통하여 효과를 입증하였으며 가상 디바이스 네트워크 개념에 근거한 분산제어 시스템을 이용하여 실시간 예지보전을 수행할 때 효과가 있음을 제시하였다.

• 한국인공지능학회지
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• 제11권3호
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• 복식문화연구
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• 제24권6호
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• pp.709-724
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• 2016

The purpose of this study is to develop a multi-dimensional scale measuring consumers' perceived challenge in shopping fashion products online, and to verify its validity and reliability. Relevant literature is first reviewed to identify possible dimensions of perceived challenge. Next, Study 1 is conducted in order to explore the dimensions empirically and to see whether the dimensions that emerged were consistent with prior findings. A total of 190 responses to an open-ended question was qualitatively analyzed by using content analysis. The findings of Study 1 generate 26 items reflecting four dimensions (i.e., product knowledge, previous experience, website functionality, and product availability), which correspond to the dimensions suggested in literature review. Study 2 is subsequently conducted to refine the items so that the perceived challenge scale establishes cross-validation, convergent validity, discriminant validity, reliability, and predictive validity. A total of 238 responses is quantitatively analyzed by using exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. In the results of Study 2, the perceived challenge scale is found to consist of a total of 16 items reflecting three dimensions: E-commerce Challenge (corresponding to Previous Experience reported in Study 1), Retailer Challenge (corresponding to Website Functionality), and Product Challenge (corresponding to Product Knowledge); all Product Availability items have been eliminated through the item refinement process. Specifically, E-commerce Challenge and Retailer Challenge are found to predict flow, supporting flow theory, while Product Challenge fails to lead to flow significantly. Implications, limitations, and suggestions for future studies are also discussed.

• 한국항행학회논문지
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• 제18권6호
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• pp.582-587
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• 2014

본 연구는 장기간의 해상활동으로 의료기관 방문이 어려운 해상 활동자의 심혈관계질환 예측인자로 검사가 용이한 생체전기저항분석법 (BIA; bioelectrical impedance analysis)의 활용이 가능한지를 확인해보았다. 현재 심혈관성질환의 예측인자로 사용되고 있는 총콜레스테롤 측정치를 기준으로 BIA의 측정치와 관련성을 통계적 방법으로 분석한 결과 인체부위별 지방두께와 상관관계를 보였으며, 특히 왼쪽(왼) 허벅지의 지방두께가 총 콜레스테롤 측정치와 높은 상관관계를 나타냈다. 이 결과로 장기적인 해상 활동을 요하는 사람들은 BIA 검사를 통해 왼허벅지의 지방두께 변화를 심혈관질환의 예측인자로 활용할 수 있을 것이다. 하지만 선행연구의 부재로 후속연구가 필요하고 해상이라는 특수 상황이 고려된 측정도구의 정확성과 타당성 진단이 이루어져야 될 것으로 사료된다.

• 한국정보통신학회논문지
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• 제21권10호
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• pp.1879-1885
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• 2017

본 논문에서는 시간에 따라 방향 속도와 위치가 변하는 무인자동차의 궤적 추적 제어시스템에 대해 논한다. 무인자동차는 운전자의 도움이 없어도 스스로 주위환경을 인식하여 지정된 도로를 주행할 수 있는 자동차로 올바른 주행을 위해 고려해야 할 변수가 다양하다. 무인자동차의 궤적 추적 시스템에서 인식한 정보는 이산적인 값을 가지므로 센스 간의 간격으로 인하여 비연속성 및 비선형성을 가지고 있다. 이로 인하여 목표 궤적을 정확하게 추적하는 것 어렵다. 본 논문은 차량의 운동학 모델링을 통하여 선형오차, 제약 조건, 제어 목표함수의 세 가지 조건을 갖는 무인자동차 궤적 추적시스템을 제안한다. 제안된 궤적 추적시스템을 기반으로 동적 시뮬레이션 소프트웨어-카심(Dynamic Simulation Software-CarSim)의 결합시뮬레이션을 통해 시스템의 성능을 평가하였고, 그 결과로 더욱 정밀하게 목표 궤적을 추적할 수 있음을 확인하였다.

• 한국의료질향상학회지
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• 제22권2호
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• pp.15-25
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• 2016

Objectives: In this study, we examined the validity of Clostridium difficile culture results as a proxy measure of Clostridium difficile infection, and inferred the epidemiologic characteristics of Clostridium difficile infection by tracking the trends of Clostridium difficile culture results. Methods: We reviewed the medical records to figure out the actual possibilities of Clostridium difficile infection of those with positive or negative results of Clostridium difficile culture during the time span from January 2012 to March 2012. We calculated the positive and negative predictive value of Clostridium difficile culture results for Clostridium difficile infection. Furthermore, epidemiologic characteristics of Clostridium difficile infection in a tertiary general hospital in 2012 were analyzed. Result: The estimated positive predictive value of Clostridium difficile culture tests for Clostridium difficile infection was 100%, and the estimated negative predictive value was around 94.4~99.3% depending on the cutoff value of possibility of Clostridium difficile infection. A total of 622 cases were identified as Clostridium difficile infection in a tertiary general hospital in 2012 and there were 4.9 patients with Clostridium difficile infection per 1,000 inpatients. Conclusion: In conclusion, we identified that Clostridium difficile culture results can be used as a proxy measure of Clostridium difficile infection.

• 한국정보시스템학회지:정보시스템연구
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• 제29권1호
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• 임상간호연구
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• 제19권2호
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• pp.245-254
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• 2013

Purpose: This study was done to examine predictive validity of Critical Care Non-verbal Pain Scale (CNPS) and develop criteria for pain assessment using CNPS with critically ill patients who have communication problems. Methods: Data were collected from intensive care units at three major general hospitals in Seoul and Kyunggi province. During each observation, a nurse assessed pain severity using CNPS ratings (range 0-9) at four treatment stages: at rest, during central catheter dressing change (nonpainful procedure), position change and suctioning (routine painful procedures). Patients also assessed their pain using a self-report 4-point VRS-4. Results: There were significant differences between the four treatment stages except between "at rest" and "nonpainful procedure". Strong correlations were found between CNPS and VRS-4 for "at rest" (r=.552, p<.001), central catheter dressing change (r=.505, p<.001), position change (r=.709, p<.001), and suctioning (r=.662, p<.001). ROC curve analysis of CNPS based on 3 point on VRS-4 showed the cutoff point was 3 for CNPS, the starting point for pain management with 73% sensitivity, 92.2% specificity, 73% positive predictive value, and 92.8% negative predictive value. Conclusion: Results indicate that CNPS is a valid tool for measuring pain in critically ill patients with communication problems and 3 point should be the standardized pain treatment point.

• Nutrition Research and Practice
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• 제13권3호
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• pp.256-262
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• 2019

BACKGROUND/OBJECTIVES: The objectives of this study were to evaluate the accuracy of the Dietary Reference Intakes (DRI) for estimating the energy requirements of older adults, and to develop and validate new equations for predicting the energy requirements of this population group. MATERIALS/METHODS: The study subjects were 25 men and 23 women with a mean age of $72.2{\pm}3.9\;years$ and $70.0{\pm}3.3\;years$, and mean BMI of $24.0{\pm}2.1$ and $23.9{\pm}2.7$, respectively. The total energy expenditure (TEE) was measured by using the doubly labeled water (DLW) method, and used to validate the DRI predictive equations for estimated energy requirements (EER) and to develop new EER predictive equations. These developed equations were cross-validated by using the leave-one-out technique. RESULTS: In men, the DRI equation had a -7.2% bias and accurately predicted the EER (meaning EER values within ${\pm}10%$ of the measured TEE) for 64% of the subjects, whereas our developed equation had a bias of -0.1% and an accuracy rate of 84%. In women, the bias was -6.6% for the DRI equation and 0.2% for our developed equation, and the accuracy rate was 74% and 83%, respectively. The predicted EER was strongly correlated with the measured TEE, for both the DRI equations and our developed equations (Pearson's r = 0.915 and 0.908, respectively). CONCLUSIONS: The DRI equations provided an acceptable prediction of EER in older adults and these study results therefore support the use of these equations in this population group. Our developed equations had a better predictive accuracy than the DRI equations, but more studies need to be performed to assess the performance of these new equations when applied to an independent sample of older adults.