• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2003년도 추계학술발표논문집
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• pp.230-248
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• 2003

본 연구에서는 제지공정에서의 wet-end 와 dry section 부분을 통합한 모델을 구하고 이를 바탕으로 하여 지종교체 공정의 모델예측제어 방법을 제안하였다. 폐회로 공정 인식기법을 이용하여 state-space 모델을 구한 후 지종교체 제어를 모사한 결과와 실제 제지공장의 지종교체 운전데이터를 비교 분석하였다. 입력 변수로서 이전까지는 간과되어 왔던 4가지 변수(thick stock, filler flow, speed, steam pressure), 그리고 출력변수로서 3가지 변수(basis weight, ash content, moisture content)를 고려하였으며, output trajectory는 1차 전달함수 형식으로 하여 적용하였다. 모델예측제어 모사결과를 지종교체 운전데이터와 비교하여 본 결과 지종교체 시간이 짧아지고 보다 안정적으로 정상상태에 이르는 것을 확인할 수 있었다. 아울러 모델예측제어로 인하여 지종교체 이후 입력 변수들이 큰 진동이 없이 보다 신속하게 정상상태에 도달함을 확인하였다.

• 한국사회복지학
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• 제66권3호
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• pp.5-28
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• 2014

본 연구는 학교결석 청소년을 대상으로 학교적응 유형을 분류하고, 각 유형에 영향을 미치는 예측요인을 개인, 가족, 또래 및 지역사회의 다양한 차원에서 살펴보고, 유형별 비행행동의 차이를 분석하는데 그 목적이 있다. 이를 위해 한국아동 청소년패널조사 자료를 활용하여 학교결석 중학생 477명을 대상으로 잠재프로파일분석을 실시하였고, 분류된 유형별 예측요인과 비행행동과의 관계를 알아보기 위해 다항로지스틱 분석과 분산분석을 실시하였다. 분석결과, 저적응, 중적응, 고적응 집단으로 분류되었으며, 유형별 예측요인과 비행행동에 통계적으로 유의미한 차이가 나타났다. 이는 학교결석 청소년이 다양하고 이질적인 집단으로 구성되었으며, 이것이 발달결과에 중요한 차이를 불러일으킬 수 있다는 사실을 경험적으로 증명한 것이다. 마지막으로 본 연구에서는 청소년 문제의 효과적인 예방을 위해 학교결석 청소년에 대한 차별화된 개입의 중요성을 제언하였다.

• 대한간호학회지
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• 제35권7호
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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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• 제10권1호
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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.

• 한국항만학회지
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• 제14권1호
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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.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1999년도 추계학술대회논문집
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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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• 제64권4호
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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.

• 산업경영시스템학회지
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• 제44권4호
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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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• 제67권1호
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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.

• 한국보육지원학회지
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• 제12권2호
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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.