• 전기학회논문지
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• 제60권1호
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Clinical and Experimental Reproductive Medicine
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• 제48권2호
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• pp.163-173
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• 2021

Objective: This study aimed to characterize a validated model for predicting oocyte retrieval in controlled ovarian stimulation (COS) and to construct model-based nomograms for assistance in clinical decision-making regarding the gonadotropin protocol and dose. Methods: This observational, retrospective, cohort study included 636 women with primary unexplained infertility and a normal menstrual cycle who were attempting assisted reproductive therapy for the first time. The enrolled women were split into an index group (n=497) for model building and a validation group (n=139). The primary outcome was absolute oocyte count. The dose-response relationship was tested using modified Poisson, negative binomial, hybrid Poisson-E_max, and linear models. The validation group was similarly analyzed, and its results were compared to that of the index group. Results: The Poisson model with the log-link function demonstrated superior predictive performance and precision (Akaike information criterion, 2,704; λ=8.27; relative standard error (λ)=2.02%). The covariate analysis included women's age (p<0.001), antral follicle count (p<0.001), basal follicle-stimulating hormone level (p<0.001), gonadotropin dose (p=0.042), and protocol type (p=0.002 and p<0.001 for short and antagonist protocols, respectively). The estimates from 500 bootstrap samples were close to those of the original model. The validation group showed model assessment metrics comparable to the index model. Based on the fitted model, a static nomogram was built to improve visualization. In addition, a dynamic electronic tool was created for convenience of use. Conclusion: Based on our validated model, nomograms were constructed to help clinicians individualize the stimulation protocol and gonadotropin doses in COS cycles.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2627-2629
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• 2003

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized networks, and to be much more flexible and preferable neural network than the conventional SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented with using nonlinear system data.

• Steel and Composite Structures
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• 제48권5호
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• pp.547-564
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• 2023

This study investigates the effect of bolt preloading on the rotational stiffness of stainless steel end-plate connections. An experimental programme incorporating 11 full-scale joint specimens are carried out comparing the behaviours of fully pre-tensioned (PT) and snug-tightened (ST) flush/extended end-plate connections, made of austenitic or lean duplex stainless steels. It is observed from the tests that the presence of bolt preloading leads to a significant increase in the rotational stiffness. A parallel finite element analysis (FEA) validated against the test results demonstrates that the geometric imperfection of end-plate has a strong influence on the moment-rotation response of preloaded end-plate connections, which is crucial to explain the observed "two-stage" behaviour of these connections. Based on the data obtained from the tests and FE parametric study, the performance of the Eurocode 3 predictive model is evaluated, which exhibits a significant deviation in predicting the rotational stiffness of stainless steel end-plate connections. A modified bi-linear model, which incorporates three key properties, is therefore proposed to enable a better prediction. Finally, the effect of bolt preloading is demonstrated at the system (structure) level considering the serviceability of semi-continuous stainless steel beams with end-plate connections.

• 대한전자공학회논문지SP
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• 제42권2호
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• pp.131-142
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• 2005

현재 개발된 4 kbit/s이하의 저 전송율 음성부호화 시스템은 STC(Sinusoidal Transform Coding)나 MBE (Multi-band Excitation Coding)에 바탕을 두고 있다. 이러한 저 전송율 부호화기들은 대표적인 전이구간 신호인 유성음의 시작점과 끝점에서의 혼합신호(onset signal, offset signal), 비주기적인 신호(non-period signal) 등은 정확히 표현하지 못하기 때문에 자연스런 음질을 만들어 내지 못한다. 본 논문에서는 유성음에는 하모닉 모델, 무성음에서는 스토케스틱 모델, 전이구간에는 하모닉 기반의 비주기적인 펄스의 위치를 추적하는 방식을 사용하여 효과적으로 전이구간을 모델링 하는 방법과 2.4 kbit/s 다중모드 부호화방법을 제안한다. 제안한 방법은 원본신호에서 선형예측 부호화 방법으로 추출된 잔여신호를 신호의 성격에 따라 모델을 달리하는 방법이며, 자각의 신호의 성격에 따라 좋은 성능을 나타내는 모델을 사용하였다. 또한 효율적인 전이구간 모델링 방법의 도입으로 저 전송율에서 CELP(Code Excitation Linear Predictive) 부호화 방식에 의해 시간축에서 합성되는 여기신호와 선형위상을 이용한 하모닉 부호화 방식에 의해 주파수축에서 합성되는 여기신호를 효율적으로 결합이 가능하다는 것이 제안된 2.4 kbit/s 다중모드 부호화기의 장점이다. 제안된 방법의 2.4kbit/s 다중모드 부호화기는 미국 연방 표준부호화기인 2.4 kbit/s MELP(Mixed Excitation Linear Prediction) 부호화기보다 더 좋은 성능을 나타낸다.

• 한국건설관리학회논문집
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• 제5권6호
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• pp.138-145
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• 2004

공사에 소요되는 기간은 시설물의 품질과 비용에 직접적인 영향을 미치지만, 고등학교 시설공사의 경우 경험과 직관에 의거하여 공기를 산정하고 있어 공사수행과정에서 계약당사자 간에 분쟁이 많이 발생하고 있다. 본 논문은 고등학교 시설공사에 소요되는 기간의 산정에 영향을 미치는 다양한 요인을 분석하여 공기 산정기준을 제안하는 것으로, 인천지 역에서 최근에 개교한 고등학교의 실적자료를 수집하여 다중선형 회귀분석 하였다. 회귀분석 결과로 얻은 순 공사기간에 인천지 역의 기후특성을 고려하여 산정한 작업불가능기간을 더하여 총 공사기간을 산출 하였다. 본 논문에서 제안한 공기 산정식은 공사발주 및 계약 시 계약공기를 정확하게 산정 하는데 도움을 줄 수 있을 것이다

• Animal Bioscience
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• 제36권7호
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• pp.1003-1009
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• 2023

Objective: The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Methods: Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. Results: The random contemporary group (CG) effect explained about 50%, 45%, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. Conclusion: The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.

• Computers and Concrete
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• 제21권4호
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

• 산업경영시스템학회지
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• 제40권4호
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• Asian Pacific Journal of Cancer Prevention
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• 제17권4호
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• pp.1861-1864
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• 2016

Although early diagnosis of thyroid nodule type is very important, the diagnostic accuracy of standard tests is a challenging issue. We here aimed to find an optimal combination of factors to improve diagnostic accuracy for distinguishing malignant from benign thyroid nodules before surgery. In a prospective study from 2008 to 2012, 345 patients referred for thyroidectomy were enrolled. The sample size was split into a training set and testing set as a ratio of 7:3. The former was used for estimation and variable selection and obtaining a linear combination of factors. We utilized smoothly clipped absolute deviation (SCAD) logistic regression to achieve the sparse optimal combination of factors. To evaluate the performance of the estimated model in the testing set, a receiver operating characteristic (ROC) curve was utilized. The mean age of the examined patients (66 male and 279 female) was $40.9{\pm}13.4years$ (range 15- 90 years). Some 54.8% of the patients (24.3% male and 75.7% female) had benign and 45.2% (14% male and 86% female) malignant thyroid nodules. In addition to maximum diameters of nodules and lobes, their volumes were considered as related factors for malignancy prediction (a total of 16 factors). However, the SCAD method estimated the coefficients of 8 factors to be zero and eliminated them from the model. Hence a sparse model which combined the effects of 8 factors to distinguish malignant from benign thyroid nodules was generated. An optimal cut off point of the ROC curve for our estimated model was obtained (p=0.44) and the area under the curve (AUC) was equal to 77% (95% CI: 68%-85%). Sensitivity, specificity, positive predictive value and negative predictive values for this model were 70%, 72%, 71% and 76%, respectively. An increase of 10 percent and a greater accuracy rate in early diagnosis of thyroid nodule type by statistical methods (SCAD and ANN methods) compared with the results of FNA testing revealed that the statistical modeling methods are helpful in disease diagnosis. In addition, the factor ranking offered by these methods is valuable in the clinical context.