• Korean Chemical Engineering Research
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• 제59권4호
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• pp.532-541
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• 2021

최근 인공지능에 대한 관심이 높아짐에 따라 화학공정분야에서도 인공지능을 활용한 연구가 많아지고 있다. 그러나 인공지능 기반 모델이 충분히 일반화되지 않아 학습에 이용되지 않은 새로운 데이터에 대한 예측률이 떨어지는 과적합 현상이 빈번하게 일어나고 있으며, 교차검증은 과적합을 해결하는 방법 중 하나이다. 본 연구에서는 2,3-BDO 분리 공정 온도 예측 모델의 초매개변수 중에서 배치 개수와 반복횟수를 조정하기 위해 시계열 교차검증을 적용하고 일반적으로 사용되는 K 겹 교차검증과 비교하였다. 결과적으로 K 겹 교차검증을 사용했을 때 보다 시계열 교차검증 방식을 사용했을 때 MAPE는 0.61% 증가한 반면 RMSE는 9.06% 감소하였고 학습 시간은 198.29초 적게 소요되었다.

• 한국보건간호학회지
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• 제28권3호
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• pp.495-508
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• 2014

Purpose: This study was conducted in order to evaluate the reliability, validity, sensitivity, and specificity of the Short Form of Bobath Memorial Hospital Fall Risk Assessment Scale (BMFRAS-SF). Methods: A validation study was conducted on 207 elderly patients aged over 65 who were admitted to Bobath Memorial Hospital. Fall risk scores of BMFRAS, composed of eight subscales (age, fall history, physical activity, consciousness level, communication, fall risk factors, underlying disease, and medications) were assessed from the electronic medical record. BMFRAS-SF was derived from eight subscales of the BMFRAS representing the significance between fallers and non-fallers (fall history, physical activity, fall risk factors, underlying disease, and medications). Internal consistency reliability and interrater reliability were assessed by Cronbach's alpha and kappa coefficient. Validity was assessed by Spearman correlation analysis, factor analysis. Sensitivity, specificity, positive predictive and negative predictive values, and a receiver-operating characteristic curve (ROC) were generated. Results: Fallers had significantly higher risk scores than non-fallers in fall history, physical activity, fall risk factors, underlying disease, and medication scales. The BMFRAS-SF demonstrated acceptable Cronbach's alpha (.706) and kappa coefficients of .95. The BMFRAS-SF subscales showed good convergent validity and construct validity. The BMFRAS-SF presented good sensitivity(86.7%), specificity(67.9%), positive predictive value(42.9%) and good negative predictive value(94.8%) at a cut-off score of 5. Areas under the ROC curves were .860 for the BMFRAS and .861 for the BMFRAS-SF. Conclusion: The BMFRAS-SF was proved to be reliable and valid. It could be used for time-saving assessment and evaluation of the high risks for falls in clinical practice settings.

• 한국식품위생안전성학회지
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• 제34권4호
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• pp.374-379
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• 2019

본 연구에서는 대표적인 신선 잎채소류인 상추의 세척 단계에서 초음파 (37 kHz) 와 염소 (100~300 ppm) 의 병용처리 후 냉장 ~ 실온저장 ($10{\sim}25^{\circ}C$)에 따른 이 식품 중의 Salmonella Typhimurium의 성장예측모델을 개발하였다. 1 차 모델 개발을 위해 Gompertz 방정식을 활용하여 각기 다른 실험 조건에서의 S. Typhimurium의 생육도 (SGR 과 LT)를 조사했다. 본 방정식에 의한 1 차 모델 개발시 $R^2$가 0.92 이상으로 우수하게 나타났으며 저장온도가 낮을수록 초음파에 사용된 염소의 농도가 높을수록 SGR 값은 감소하였고 LT 값은 증가하였다. 이를 바탕으로 2 차 polynomial 모델을 개발하여 다양한 통계적 지표 ($R^2$, MSE, $A_f$ 및 $B_f$)를 통해 분석한 결과 개발된 모델의 적합성을 확인할 수 있었다. 따라서 개발된 모델이 초음파와 염소의 병용 세척에 따른 저장 중 상추에 대한 S. Typhimurium의 성장예측모델로 사용 가능하다고 판단되어지며, 신선 잎채소류에서의 식중독을 예방하고 미생물학적 위생관리기준을 설정하는데 기초자료로 활용될 수 있을 것으로 사료된다.

• Structural Engineering and Mechanics
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• 제47권5호
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• pp.679-700
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• 2013

Two new predictive design methods are presented in this study. The first is a hybrid method, called neuro-fuzzy, based on neural networks with fuzzy learning. A total of 280 experimental datasets obtained from the literature concerning concentric punching shear tests of reinforced concrete slab-column connections without shear reinforcement were used to test the model (194 for experimentation and 86 for validation) and were endorsed by statistical validation criteria. The punching shear strength predicted by the neuro-fuzzy model was compared with those predicted by current models of punching shear, widely used in the design practice, such as ACI 318-08, SIA262 and CBA93. The neuro-fuzzy model showed high predictive accuracy of resistance to punching according to all of the relevant codes. A second, more user-friendly design method is presented based on a predictive linear regression model that supports all the geometric and material parameters involved in predicting punching shear. Despite its simplicity, this formulation showed accuracy equivalent to that of the neuro-fuzzy model.

• 한국지능시스템학회논문지
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• 제11권4호
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• pp.293-301
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• 2001

본 논문은 신경회로망과 같은 경험적 모델에서 출력별로 신뢰 구간을 추정하는 세 가지 대표적인 방법을 검토하고, 검토한 방법을 뉴로-퍼지 모델에 적용하여 장단점을 비교 분석한다. 본 논문에서 고려한 출력별 신뢰 구간 계산 방법은 cross-validation을 이용한 stacked generalization, 회귀 모델에서 유도된 predictive error bar, 지역 표현하는 신경회로망의 특성에 기반한 local reliability measure이다. 간단한 함수 근사화 문제와 혼돈 시계열 예측 문제를 이용하여 모의 실험을 수행하고, 세 가지 신뢰도 추정 방법의 성능을 정량적, 정성적으로 비교 분석한다. 분석 결과를 기초로 각 방법의 장단점 및 특성을 고찰하고, 모델링 문제에서 모델의 출력별 신뢰 구간 계산 방법의 실제 적용 가능성을 탐색한다.

• Journal of Microbiology
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• 제43권5호
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• pp.411-416
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• 2005

The high-throughput sequencing of microbial genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable research attention in recent years. This paper addresses the development of a predictive model, known as the dependence decomposition weight matrix model (DDWMM), which was designed to detect the core promoter region, including the -10 region and the transcription start sites (TSSs), in prokaryotic genomic DNA sequences. This is an issue of some importance with regard to genome annotation efforts. Our predictive model captures the most significant dependencies between positions (allowing for non­adjacent as well as adjacent dependencies) via the maximal dependence decomposition (MDD) procedure, which iteratively decomposes data sets into subsets, based on the significant dependence between positions in the promoter region to be modeled. Such dependencies may be intimately related to biological and structural concerns, since promoter elements are present in a variety of combinations, which are separated by various distances. In this respect, the DDWMM may prove to be appropriate with regard to the detection of core promoter regions and TSSs in long microbial genomic contigs. In order to demonstrate the effectiveness of our predictive model, we applied 10-fold cross-validation experiments on the 607 experimentally-verified promoter sequences, which evidenced good performance in terms of sensitivity.

• 한국도로학회논문집
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• 제14권5호
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• pp.189-199
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• 2012

PURPOSES: The purpose of this study is to propose a new methodology for developing statistical collision models and to show the validation results of the methodology. METHODS: A new modeling method of introducing variables into the model one by one in a multiplicative form is suggested. A method for choosing explanatory variables to be introduced into the model is explained. A method for determining functional forms for each explanatory variable is introduced as well as a parameter estimating procedure. A model selection method is also dealt with. Finally, the validation results is provided to demonstrate the efficacy of the final models developed using the method suggested in this study. RESULTS: According to the results of the validation for the total and injury collisions, the predictive powers of the models developed using the method suggested in this study were better than those of generalized linear models for the same data. CONCLUSIONS: Using the methodology suggested in this study, we could develop better statistical collision models having better predictive powers. This was because the methodology enabled us to find the relationships between dependant variable and each explanatory variable individually and to find the functional forms for the relationships which can be more likely non-linear.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.227-242
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• 2015

This paper proposes a weighting factor optimization method in predictive control algorithm for torque ripple reduction in an induction motor fed by an indirect matrix converter (IMC). In this paper, the torque ripple behavior is analyzed to validate the proposed weighting factor optimization method in the predictive control platform and shows the effectiveness of the system. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponds to minimum torque ripple and is compared with the results of conventional weighting factor based predictive control algorithm. The predictive control algorithm selects the optimum switching state that minimizes a cost function based on optimized weighting factor to actuate the indirect matrix converter. The conventional and introduced weighting factor optimization method in predictive control algorithm are validated through simulations and experimental validation in DS1104 R&D controller platform and show the potential control, tracking of variables with their respective references and consequently reduces the torque ripple.

• 제어로봇시스템학회논문지
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• 제14권12호
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• pp.1212-1217
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• 2008

This paper studies the feasibility of using the nonlinear model predictive control as a formation flight control algorithm for unmanned aerial vehicles. The optimal control inputs for formation flight are calculated through the cost function which incorporates the relative positions of the individual vehicles to maintain a desired formation and also the inequality constraints on inputs and states using the Karush-Kuhn-Tucker conditions. In the nonlinear model predictive control setting, the optimal control inputs are implemented in a receding horizon manner, which is suitable for dealing with dynamic constraints. Numerical simulations are executed for the validation of the proposed scheme.

• 한국화재소방학회논문지
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• 제24권5호
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• pp.60-67
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• 2010

화재분석모델이 제한사항 내에서 화재 피해를 신뢰성 있게 예측하기 위해서는 화재분석모델에 대한 검증분석이 반드시 이루어져야 하며, 이러한 검증분석 과정은 일반적으로 실증실험 결과와 비교함으로서 이루어진다. 본 연구의 목적은 화재분석모델인 FDS의 이중격실 Pool 화재에 대한 예측 능력을 평가하고, FDS의 중요 입력값(열방출률 및 환기량)의 미소변화에 따른 출력값(온도, 농도, 열유속)의 민감도를 분석하기 위함이다. FDS의 예측능력 평가와 FDS 입력변수의 민감도 분석을 위해 국제공동연구 PRISME 프로잭트로부터 화재실증 결과와 FDS 결과물을 비교분석하였다. 이중격실 Pool 화재에 대한 FDS의 예측능력은 화재실증 실험결과와 비교하여 약 ${\pm}$20% 오차범위를 나타내었다. 또한, FDS의 입력변수에 대한 민감도는 열방출율의 미소변화에 따라 비교적 높은 출력값의 변화가 나타났으며, 환기량의 미소변화에 따라 출력값 변화는 연소생성물의 농도에만 영향을 미쳤다.