• Title/Summary/Keyword: predictive validity

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A Progressive Failure Analysis Procedure for Composite Laminates I - Anisotropic Plastic Constitutive Model (복합재료 거동특성의 파괴해석 I - 이방성 소성 적합모델)

  • Yi, Gyu-Sei
    • Journal of the Korean Society for Advanced Composite Structures
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    • v.5 no.4
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    • pp.1-10
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    • 2014
  • A progressive failure analysis procedure for composite laminates is developed in here and in the companion paper. An anisotropic plastic constitutive model for fiber-reinforced composite material, is developed, which is simple and efficient to be implemented into computer program for a predictive analysis procedure of composites. In current development of the constitutive model, an incremental elastic-plastic constitutive model is adopted to represent progressively the nonlinear material behavior of composite materials until a material failure is predicted. An anisotropic initial yield criterion is established that includes the effects of different yield strengths in each material direction, and between tension and compression. Anisotropic work-hardening model and subsequent yield surface are developed to describe material behavior beyond the initial yield under the general loading condition. The current model is implemented into a computer code, which is Predictive Analysis for Composite Structures (PACS), and is presented in the companion paper. The accuracy and efficiency of the anisotropic plastic constitutive model are verified by solving a number of various fiber-reinforced composite laminates with and without geometric discontinuity. The comparisons of the numerical results to the experimental and other numerical results available in the literature indicate the validity and efficiency of the developed model.

A Numerical Approach for Lightning Impulse Flashover Voltage Prediction of Typical Air Gaps

  • Qiu, Zhibin;Ruan, Jiangjun;Huang, Congpeng;Xu, Wenjie;Huang, Daochun
    • Journal of Electrical Engineering and Technology
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    • v.13 no.3
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    • pp.1326-1336
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    • 2018
  • This paper proposes a numerical approach to predict the critical flashover voltages of air gaps under lightning impulses. For an air gap, the impulse voltage waveform features and electric field features are defined to characterize its energy storage status before the initiation of breakdown. These features are taken as the input parameters of the predictive model established by support vector machine (SVM). Given an applied voltage range, the golden section search method is used to compute the prediction results efficiently. This method was applied to predict the critical flashover voltages of rod-rod, rod-plane and sphere-plane gaps over a wide range of gap lengths and impulse voltage waveshapes. The predicted results coincide well with the experimental data, with the same trends and acceptable errors. The mean absolute percentage errors of 6 groups of test samples are within 4.6%, which demonstrates the validity and accuracy of the predictive model. This method provides an effectual way to obtain the critical flashover voltage and might be helpful to estimate the safe clearances of air gaps for insulation design.

Common-mode Voltage Reduction for Inverters Connected in Parallel Using an MPC Method with Subdivided Voltage Vectors

  • Park, Joon Young;Sin, Jiook;Bak, Yeongsu;Park, Sung-Min;Lee, Kyo-Beum
    • Journal of Electrical Engineering and Technology
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    • v.13 no.3
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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.

Real-time Distributed Control in Virtual Device Network with Uncertain Time Delay for Predictive Maintenance (PM) (가상 디바이스 네트워크상에서 불확실한 시간지연을 갖는 실시간 분산제어를 이용한 예지보전에 관한 연구)

  • Kiwon Song;Gi-Heung Choi
    • Journal of the Korean Society of Safety
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    • v.18 no.3
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    • pp.154-160
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    • 2003
  • Uncertain time delay happens when the process reads the sensor data and sends the control input to the plant located at a remote site in distributed control system. As in the case of data network using TCP/IP, VDN that integrates both device network and data network has uncertain time delay. Uncertain time delay can cause degradation in performance and stability of distributed control system based on VDN. This paper first investigates the transmission characteristic of VDN and suggests a control scheme based on the Smith's predictor to minimize the effect of uncertain varying time delay. The validity of the proposed control scheme is demonstrated with real-time velocity control of DC servo motor located in remote site.

Development of Big Data-based Cardiovascular Disease Prediction Analysis Algorithm

  • Kyung-A KIM;Dong-Hun HAN;Myung-Ae CHUNG
    • Korean Journal of Artificial Intelligence
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    • v.11 no.3
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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).

Perceived challenges in fashion shopping online: Scale development and validation (온라인 패션 쇼핑 시 도전감의 척도 개발 및 타당성 연구)

  • Shim, Soo In
    • The Research Journal of the Costume Culture
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    • v.24 no.6
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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.

BIA Feasibility Analysis as Predictors of Cardiovascular Disease in the Sea (Total Cholesterol Compared with Fat Thickness by Region) (해상에서 심혈관질환 예측인자로 BIA 활용가능성 분석 (혈중 총콜레스테롤과 부위별 지방두께 비교))

  • Na, Seung-Kwon;Park, Eun-Ju
    • Journal of Advanced Navigation Technology
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    • v.18 no.6
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    • pp.582-587
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    • 2014
  • This study have researched on feasibility of bioelectrical impedance analysis (BIA, which is simple useful evaluation tool for predictive factor of cardiovascular disease) to patients who have to travel along the sea for a long-period time and have difficulty in visiting medical institutions. We studied on the basis of total cholesterol value, which is nowadays widely used tool for predictive factor of cardiovascular disease, and also studied its association with BIA value via statistical analysis. Our result showed correlation with fat thickness of individual sites, and especially, fat thickness of left thigh showed high relation with total cholesterol value. This result shows that people who are in travel of long-period of time at sea are feasible of using BIA to evaluate changes of left thigh fat thickness as predictive factor for cardiovascular disease. Due to lack of advanced researches further studies should be done. And based on special circumstances in sea, more studies should be done to validity concerning this circumstances and accuracy of this evaluation tool.

Trajectory tracking control system of unmanned ground vehicle (무인자동차 궤적 추적 제어 시스템에 관한 연구)

  • Han, Ya-Jun;Kang, Chin-Chul;Kim, Gwan-Hyung;Tac, Han-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.10
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    • pp.1879-1885
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    • 2017
  • This paper discusses the trajectory tracking system of unmanned ground vehicles based on predictive control. Because the unmanned ground vehicles can not satisfactorily complete the path tracking task, highly efficient and stable trajectory control system is necessary for unmanned ground vehicle to be realized intelligent and practical. According to the characteristics of unmanned vehicle, this paper built the kinematics tracking models firstly. Then studied algorithm solution with the tools of the optimal stability analysis method and proposed a tracking control method based on the model predictive control. The controller used a kinematics-based prediction model to calculate the predictive error. This controller helps the unmanned vehicle drive along the target trajectory quickly and accurately. The designed control strategy has the true robustness, simplicity as well as generality for kinematics model of the unmanned vehicle. Furthermore, the computer Simulink/Carsim results verified the validity of the proposed control method.

Detection and characterization of Clostridium difficile infections tracking the trends of Clostridium difficile culture

  • Ock, Min-Su;Oh, Jin-Sun;Kim, Hwa-Jung;Lyu, Yong-Man;Lee, Moo-Song
    • Quality Improvement in Health Care
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    • v.22 no.2
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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.

LSTM-based Deep Learning for Time Series Forecasting: The Case of Corporate Credit Score Prediction (시계열 예측을 위한 LSTM 기반 딥러닝: 기업 신용평점 예측 사례)

  • Lee, Hyun-Sang;Oh, Sehwan
    • The Journal of Information Systems
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    • v.29 no.1
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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.