• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2003.11a
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• pp.230-248
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• 2003

In this work an integrated model for paper plants combining wet-end and dry section is developed and a model predictive control scheme based on the plant model is proposed. Closed-loop process identification method is employed to produce a state-space model. Thick stock, filler flow, machine speed and steam pressure are selected as Input variables and basis weight, ash content and moisture content are considered as output variables. The desired output trajectory is constructed in the form of 1st-order dynamics. Results of simulations for control of grade change operations are compared with plant operation data collected during the grade change operations under the same conditions as in simulations. From the comparison, we can see that the proposed model predictive control scheme reduces the grade change time and achieves stable steady-state.

• Korean Journal of Social Welfare
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• v.66 no.3
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• pp.5-28
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• 2014

School absenteeism, one of the early warning signs of behavioral problems, has been known to be a complex and heterogeneous problem. However, much of the research assumes that school absentee adolescents comprise a homogeneous group. This study explored the heterogeneity of school absentee adolescents, based on school adaptation, to provide a more nuanced understanding of school absenteeism and examined predictive and risk factors associated with each typology of school absentee adolescents. Latent profile analysis was conducted using sample 477 middle school students who were reported absent in the previous year from the 3rd wave of Korean Children and Youth Panel Study. Multinomial logistic regression and ANOVA was also employed to examine the effects of predictive variables. As a result, three profiles were identified: low, middle, and high adaptive group. Group membership was found to be associated deferentially with gender, mental health, parenting neglect, delinquent friends, and delinquent behaviors. These findings propose more specific and targeted interventions designed to meet the needs and risk factors associated with the different typologies of school absentee adolescents.

• Journal of Korean Academy of Nursing
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• v.35 no.7
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• pp.1285-1294
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• 2005

Purpose: This study was done to evaluate the predictive value of aspects of the Transtheoretical model (TTM) of behavior change as applied to smoking cessation in a rural population. Method: A convenience sample was recruited from a public health center in a community. A total of 484 participants were recruited, including 319 smokers, 116 ex-smokers and 49 non-smokers. A cross-sectional and descriptive design was used in this study. Data was analyzed using descriptive statistics, frequency statistics, ANOVA and Logistic regression. Result: The major findings were 1) The participants were assessed at baseline for their current Stage of Change resulting in a distribution with $42.1\%$ in Precontemplation, $24.1\%$ in Contemplation, $9.7\%$ in Preparation, $6.2\%$ in Active, and $17.9\%$ in the Maintenance stage. 2) There were statistically significant differences of processes of change, decisional balance and situational temptation across the stages of change. 3) The main factors that affect smoking cessation were age, number of years smoking, age when began smoking, self-liberation and negative/affective situations, which combined explained $33.2\%$ of the smoking cessation. Conclusion: TTM variables measured prior to a smoking cessation program added little predictive value for cessation outcome beyond that explained by demographic and smoking history variables.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.165-175
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• 2015

Energy storage system has been widely applied in power distribution sectors as well as in renewable energy sources to ensure uninterruptible power supply. This paper presents a model predictive algorithm to control a bidirectional AC-DC converter, which is used in an energy storage system for power transferring between the three-phase AC voltage supply and energy storage devices. This model predictive control (MPC) algorithm utilizes the discrete behavior of the converter and predicts the future variables of the system by defining cost functions for all possible switching states. Subsequently, the switching state that corresponds to the minimum cost function is selected for the next sampling period for firing the switches of the AC-DC converter. The proposed model predictive control scheme of the AC-DC converter allows bidirectional power flow with instantaneous mode change capability and fast dynamic response. The performance of the MPC controlled bidirectional AC-DC converter is simulated with MATLAB/Simulink(R) and further verified with 3.0kW experimental prototypes. Both the simulation and experimental results show that, the AC-DC converter is operated with unity power factor, acceptable THD (3.3% during rectifier mode and 3.5% during inverter mode) level of AC current and very low DC voltage ripple. Moreover, an efficiency comparison is performed between the proposed MPC and conventional VOC-based PWM controller of the bidirectional AC-DC converter which ensures the effectiveness of MPC controller.

• Journal of Korean Port Research
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• v.14 no.1
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• pp.66-75
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• 2000

From the traffic analysis, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results were obtained : ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy. ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period showed 10mph decrease when compared with the 24hours'average speed, but the speed did not show a big difference in the afternoon peak period. ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge section. ⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.111-121
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• 1999

From the traffic analyses, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results obtained: ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy.ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period shown 10mph decrease when compared with the 24hours' average speed, but the speed did not show a big difference in the afternoon peak period.ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge sectionⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• Clinical and Experimental Pediatrics
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• v.64 no.4
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• pp.180-187
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• 2021

Background: Severe perinatal asphyxia results in multiple organ involvement, neonate hospitalization, and eventual death. Purpose: This study aimed to investigate the predictive factors of death in newborns with hypoxic-ischemic encephalopathy (HIE) receiving selective head cooling. Methods: This cross-sectional descriptive-retrospective study was conducted from 2013 to 2018 in Fatemieh Hospital of Hamadan and included 51 newborns who were admitted to the neonatal intensive care unit with a diagnosis of HIE. Selective head cooling for patients with moderate to severe HIE began within 6 hours of birth and continued for 72 hours. The required data for the predictive factors of death were extracted from the patients' medical files, recorded on a premade form, and analyzed using SPSS ver. 16. Results: Of the 51 neonates with moderate to severe HIE who were treated with selective head cooling, 16 (31%) died. There were significant relationships between death and the need for advanced neonatal resuscitation (P=0.002), need for mechanical ventilation (P=0.016), 1-minute Apgar score (P=0.040), and severely abnormal amplitude-integrated electroencephalography (a-EEG) (P=0.047). Multiple regression of variables or data showed that the need for advanced neonatal resuscitation was an independent predictive factor of death (P=0.0075) and severely abnormal a-EEG was an independent predictive factor of asphyxia severity (P=0.0001). Conclusion: All cases of neonatal death in our study were severe HIE (stage 3). Advanced neonatal resuscitation was an independent predictor of death, while a severely abnormal a-EEG was an independent predictor of asphyxia severity in infants with HIE.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.14-21
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• 2024

Objective : The primary treatment goal of current endovascular thrombectomy (EVT) for emergent large-vessel occlusion (ELVO) is complete recanalization after a single maneuver, referred to as the 'first-pass effect' (FPE). Hence, we aimed to identify the predictive factors of FPE and assess its effect on clinical outcomes in patients with ELVO of the anterior circulation. Methods : Among the 129 patients who participated, 110 eligible patients with proximal ELVO (intracranial internal carotid artery and proximal middle cerebral artery) who achieved successful recanalization after EVT were retrospectively reviewed. A comparative analysis between patients who achieved FPE and all others (defined as a non-FPE group) was performed regarding baseline characteristics, clinical variables, and clinical outcomes. Multivariate logistic regression analyses were subsequently conducted for potential predictive factors with p<0.10 in the univariate analysis to determine the independent predictive factors of FPE. Results : FPE was achieved in 31 of the 110 patients (28.2%). The FPE group had a significantly higher level of functional independence at 90 days than did the non-FPE group (80.6% vs. 50.6%, p=0.002). Pretreatment intravenous thrombolysis (IVT) (odds ratio [OR], 3.179; 95% confidence interval [CI], 1.025-9.861; p=0.045), door-to-puncture (DTP) interval (OR, 0.959; 95% CI, 0.932-0.987; p=0.004), and the use of balloon guiding catheter (BGC) (OR, 3.591; 95% CI, 1.231-10.469; p=0.019) were independent predictive factors of FPE. Conclusion : In conclusion, pretreatment IVT, use of BGC, and a shorter DTP interval were positively associated with FPE, increasing the chance of acquiring better clinical outcomes.

• Korean Journal of Childcare and Education
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• v.12 no.2
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• pp.1-17
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• 2016

The purpose of this study was to investigate the difference in elementary school children's human rights sensitivity and the perception of human rights depending on child variables, family variables, school variables, and predictive influences among these variables. The participants were 1,364 elementary school children in the 'Current Status of Korean Children's and Youth's Rights(2013)'. The results of this study were as follows: First, the variables influencing children's human rights sensitivity were school life experience, grade, the degree to which adolescents think they are respected in deciding family issues, gender, experiences of teacher's swear words, experiences of being neglected, and experiences of being bullied at school. Second, the variables influencing children's perception of human rights were gender, experiences of parents' swear words, school life experience, the degree to which adolescents think they are respected in deciding family issues, and father's educational achievements. The results of this study offered fundamental data about the important issues in researching children's rights and the policy implications for enhancing them.