• Title/Summary/Keyword: The Logistic Curve

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Modified sigmoid based model and experimental analysis of shape memory alloy spring as variable stiffness actuator

  • Sul, Bhagoji B.;Dhanalakshmi, K.
    • Smart Structures and Systems
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    • v.24 no.3
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    • pp.361-377
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    • 2019
  • The stiffness of shape memory alloy (SMA) spring while in actuation is represented by an empirical model that is derived from the logistic differential equation. This model correlates the stiffness to the alloy temperature and the functionality of SMA spring as active variable stiffness actuator (VSA) is analyzed based on factors that are the input conditions (activation current, duty cycle and excitation frequency) and operating conditions (pre-stress and mechanical connection). The model parameters are estimated by adopting the nonlinear least square method, henceforth, the model is validated experimentally. The average correlation factor of 0.95 between the model response and experimental results validates the proposed model. In furtherance, the justification is augmented from the comparison with existing stiffness models (logistic curve model and polynomial model). The important distinction from several observations regarding the comparison of the model prediction with the experimental states that it is more superior, flexible and adaptable than the existing. The nature of stiffness variation in the SMA spring is assessed also from the Dynamic Mechanical Thermal Analysis (DMTA), which as well proves the proposal. This model advances the ability to use SMA integrated mechanism for enhanced variable stiffness actuation. The investigation proves that the stiffness of SMA spring may be altered under controlled conditions.

Railway Noise Exposure-response Model based on Predicted Noise Level and Survey Results (예측소음도와 설문결과를 이용한 철도소음 노출-반응 모델)

  • Son, Jin-Hee;Lee, Kun;Chang, Seo-Il
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.21 no.5
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    • pp.400-407
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    • 2011
  • The suggested method of previous Son's study dichotomized subjective response data to modeling noise exposure-response. The method used maximum liklihood estimation instead of least square estimation and the noise exposure-response curve of the study was logistic regression analysis result. The method was originated to modeling community response rate such as %HA or %A. It can be useful when the subjective response was investigated based on predicted noise level. It is difficult to measure the single source emitting noise such as railway because various traffic noise sources combined in our life. The suggested method was adopted to model in this study and railway noise-exposure response curves were modeled because the noise level of this area was predicted data. The data of this study was used by previous Ko's paper but he dealt the area as combined noise area and divided the data by dominant noise source. But this study used all data of this area because the annoyance response to railway noise was higher than other noise according to the result of correlation analysis. The trend of the %HA and %A prediction model to train noise of this study is almost same as the model based on measured noise of previous Lim's study although the investigated areas and methods were different.

A Study on the Comparison of Predictive Models of Cardiovascular Disease Incidence Based on Machine Learning

  • Ji Woo SEOK;Won ro LEE;Min Soo KANG
    • Korean Journal of Artificial Intelligence
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    • v.11 no.1
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    • pp.1-7
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    • 2023
  • In this paper, a study was conducted to compare the prediction model of cardiovascular disease occurrence. It is the No.1 disease that accounts for 1/3 of the world's causes of death, and it is also the No. 2 cause of death in Korea. Primary prevention is the most important factor in preventing cardiovascular diseases before they occur. Early diagnosis and treatment are also more important, as they play a role in reducing mortality and morbidity. The Results of an experiment using Azure ML, Logistic Regression showed 88.6% accuracy, Decision Tree showed 86.4% accuracy, and Support Vector Machine (SVM) showed 83.7% accuracy. In addition to the accuracy of the ROC curve, AUC is 94.5%, 93%, and 92.4%, indicating that the performance of the machine learning algorithm model is suitable, and among them, the results of applying the logistic regression algorithm model are the most accurate. Through this paper, visualization by comparing the algorithms can serve as an objective assistant for diagnosis and guide the direction of diagnosis made by doctors in the actual medical field.

The Relation between Verbal Aggression by Parents and Children's Maladjusted Emotional Behavior (부모의 언어적 학대와 아동의 정서적 부적응행동과의 관계)

  • Kim, Hye Ryun;Lee, Jae Yeon
    • Korean Journal of Child Studies
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    • v.15 no.1
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    • pp.91-108
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    • 1994
  • This study investigated the relation between verbal abuse by parents and children's maladjusted emotional behavior. The sampling consisted of 628 children in 16 classes out of every three elementary schools and one middle school. Instruments used for this study were the Verbal Abuse Measure, Parent-to-child violence items of the Conflict Tactics Scales, Emotional Maladjustment Behavior Scale, and Socioeconmic Status. Methods applied to data analysis were multiple regression, logistic regression and logistic curve graphic display. The major findings were ; (1) Of all subjects, almost 20% experienced at least one instance in which they were victims of verbal abuse during the year covered by this study. (2) As the amount of physical abuse by parents increased the verbal abuse by parents increased. The older children experienced more verbal abuse than the younger ones. (3) Verbal abuse by parents was more highly related to maladjusted emotional behavior of the children than physical abuse by parents. (4) Regardless of the physical violence by parents, verbal abuse by parents was associated with maladjusted emotional behavior of children. Children who were subjected to both verbal and physical abuse were more strongly related to withdrawal, hyperactivity, and obsessive-compulsions than children experienced either one or the other.

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Perceptual Boundary on a Synthesized Korean Vowel /o/-/u/ Continuum by Chinese Learners of Korean Language (/오/-/우/ 합성모음 연속체에 대한 중국인 한국어 학습자의 청지각적 경계)

  • Yun, Jihyeon;Kim, EunKyung;Seong, Cheoljae
    • Phonetics and Speech Sciences
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    • v.7 no.4
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    • pp.111-121
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    • 2015
  • The present study examines the auditory boundary between Korean /o/ and /u/ on a synthesized vowel continuum by Chinese learners of Korean language. Preceding researches reported that the Chinese learners have difficulty pronouncing Korean monophthongs /o/ and /u/. In this experiment, a nine-step continuum was resynthesized using Praat from a vowel token from a recording of a male announcer who produced it in isolated form. F1 and F2 were synchronously shifted in equal steps in qtone (quarter tone), while F3 and F4 values were held constant for the entire stimuli. A forced choice identification task was performed by the advanced learners who speak Mandarin Chinese as their native language. Their experiment data were compared to a Korean native group. ROC (Receiver Operating Characteristic) analysis and logistic regression were performed to estimate the perceptual boundary. The result indicated the learner group has a different auditory criterion on the continuum from the Korean native group. This suggests that more importance should be placed on hearing and listening training in order to acquire the phoneme categories of the two vowels.

A Study on the Development of Readmission Predictive Model (재입원 예측 모형 개발에 관한 연구)

  • Cho, Yun-Jung;Kim, Yoo-Mi;Han, Seung-Woo;Choe, Jun-Yeong;Baek, Seol-Gyeong;Kang, Sung-Hong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.4
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    • pp.435-447
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    • 2019
  • In order to prevent unnecessary re-admission, it is necessary to intensively manage the groups with high probability of re-admission. For this, it is necessary to develop a re-admission prediction model. Two - year discharge summary data of one university hospital were collected from 2016 to 2017 to develop a predictive model of re-admission. In this case, the re-admitted patients were defined as those who were discharged more than once during the study period. We conducted descriptive statistics and crosstab analysis to identify the characteristics of rehospitalized patients. The re-admission prediction model was developed using logistic regression, neural network, and decision tree. AUC (Area Under Curve) was used for model evaluation. The logistic regression model was selected as the final re-admission predictive model because the AUC was the best at 0.81. The main variables affecting the selected rehospitalization in the logistic regression model were Residental regions, Age, CCS, Charlson Index Score, Discharge Dept., Via ER, LOS, Operation, Sex, Total payment, and Insurance. The model developed in this study was limited to generalization because it was two years data of one hospital. It is necessary to develop a model that can collect and generalize long-term data from various hospitals in the future. Furthermore, it is necessary to develop a model that can predict the re-admission that was not planned.

Growth curve estimates for wither height, hip height, and body length of Hanwoo steers (Bos taurus coreanae)

  • Park, Hu-Rak;Eum, Seung-Hoon;Roh, Seung-Hee;Sun, Du-Won;Seo, Jakyeom;Cho, Seong-Keun;Lee, Jung-Gyu;Kim, Byeong-Woo
    • Korean Journal of Agricultural Science
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    • v.44 no.3
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    • pp.384-391
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    • 2017
  • Growth curves in Hanwoo steers were estimated by Gompertz, Von Bertalanffy, Logistic, and Brody nonlinear models using growth data collected by the Hanwoo Improvement Center from a total of 6,973 Hanwoo (Bos taurus coreanae) steers 6 to 24 months old that were born between 1996 and 2015. The data included three parameters: A, mature size of body measurement; b, growth ratio; and, k, intrinsic growth rate. Nonlinear regression equations for wither height according to Gompertz, Von Bertalanffy, Logistic, and Brody models were $Y_t=144.7e^{-0.5869e^{-0.00301t}}$, $Y_t=145.3(1-0.1816e^{-0.00284t})^3$, $Y_t=143.1(1+0.7356e^{-0.00352t})^{-1}$, and $Y_t=146.8(1+0.4700e^{-0.00249t})^1$, respectively, while those for hip height were $Y_t=144.5e^{-0.5549e^{-0.00312t}}$, $Y_t=145.0(1-0.1724e^{-0.00295t})^3$, $Y_t=143.1(1+0.6863e^{-0.00360t})^{-1}$, and $Y_t=146.2(1+0.4501e^{-0.00263t})^1$, respectively. Equations for body length $Y_t=174.1e^{-0.8342e^{-0.00289t}}$, $Y_t=175.8(1-0.2500e^{-0.00265t})^3$, $Y_t=170.0(1+1.1548e^{-0.00363t})^{-1}$, and $Y_t=180.3(1+0.6077e^{-0.00215t})^1$, respectively, for the same models. Among the four models, the Brody model resulted in the lowest mean square error, with mean square errors of 31.79, 30.57, and 42.13, respectively, for wither height, hip height, and body length. Also, an estimated birth wither height, birth hip height, and birth body length (77.98, 80.57, and 70.97 cm, respectively) were lower in the Brody model than in other models. An inflection point was not observed during the growth phase of Hanwoo steer according to the growth curves calculated using Gompertz, Von Bertalanffy, and Logistic models. Based on the results, we concluded that the regression equation using the Brody model was the most appropriate among the four growth models. To obtain more accurate parameters, however, using data from a wider production period (from birth to shipping) would be required, and the development of a suitable model for body conformation traits would be needed.

Evaluation and Analysis of Gwangwon-do Landslide Susceptibility Using Logistic Regression (로지스틱 회귀분석 기법을 이용한 강원도 산사태 취약성 평가 및 분석)

  • Yeon, Young-Kwang
    • Journal of the Korean Association of Geographic Information Studies
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    • v.14 no.4
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    • pp.116-127
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    • 2011
  • This study conducted landslide susceptibility analysis using logistic regression. The performance of prediction model needs to be evaluated considering two aspects such as a goodness of fit and a prediction accuracy. Thus to gain more objective prediction results in this study, the prediction performance of the applied model was evaluated considering two such evaluation aspects. The selected study area is located between Inje-eup and Buk-myeon in the middle of Kwangwon. Landslides in the study area were caused by heavy rain in 2006. Landslide causal factors were extracted from topographic map, forest map and soil map. The evaluation of prediction model was assessed based on the area under the curve of the cumulative gain chart. From the results of experiments, 87.9% in the goodness of fit and 84.8% in the cross validation were evaluated, showing good prediction accuracies and not big difference between the results of the two evaluation methods. The results can be interpreted in terms of the use of environmental factors which are highly related to landslide occurrences and the accuracy of the prediction model.

A Comparative Study on Prediction Performance of the Bankruptcy Prediction Models for General Contractors in Korea Construction Industry

  • Seung-Kyu Yoo;Jae-Kyu Choi;Ju-Hyung Kim;Jae-Jun Kim
    • International conference on construction engineering and project management
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    • 2011.02a
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    • pp.432-438
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    • 2011
  • The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.

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Initial D-dimer level as early prognostic tool in blunt trauma patients without significant brain injury (중증 뇌손상이 없는 둔상 환자에서 초기 중증도 예측인자로서 D-dimer의 역할)

  • Sohn, Seok Woo;Lee, Jae Baek;Jin, Young Ho;Jeong, Tae Oh;Jo, Si On;Lee, Jeong Moon;Yoon, Jae Chol;Kim, So Eun
    • Journal of The Korean Society of Emergency Medicine
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    • v.29 no.5
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    • pp.430-436
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    • 2018
  • Objective: The purpose of this study was to evaluate whether or not the d-dimer level indicating hyperfibrinolysis could be a predictor of early poor outcome (massive transfusion, death within 24 hours) associated with trauma-induced coagulopathy in blunt trauma without significant brain injury. Methods: This study was a retrospective observational study using 516 blunt trauma patients without significant brain injury. The poor outcome group, including patients receiving massive transfusion and those who died within 24 hours, consisted of 33 patients (6.4%). The variables were compared between the poor outcome group and good outcome group, and logistic regression analysis was performed using statistically significant variables. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the poor outcome prediction ability of the initial d-dimer level. Results: The poor outcome group showed more serious anatomical, physiological, and laboratory data than the good outcome group. In the ROC curve analysis for evaluation of the poor outcome prediction of the d-dimer level, the area under the curve value was 0.87 (95% confidence interval [CI], 0.84-0.90) while the cut-off value was 27.35 mg/L. In the logistic regression analysis, the high d-dimer level was shown to be an independent predictor of poor outcome (adjusted odds ratio, 14.87; 95% CI, 2.96-74.67). Conclusion: The high d-dimer level (>27.35 mg/L) can be used as a predictor for the poor outcome of patients with blunt trauma without significant brain injury.