• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.692-700
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• 2017

In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models' performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data.

• Journal of the Korean Society of Food Science and Nutrition
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• v.26 no.5
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• pp.866-871
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• 1997

This study was carried out to determine the effect of osmotic dehydration as pretreatment on the qualities of dried cherry-tomatoes. The weight reduction and solid gain in osmosed cherry-tomato were increased by increasing sugar concentration, immersion temperature and time; among three parameters, the immersion temperature affected more than sugar concentration and immersion time did. The moisture content was decreased as increasing sugar concentration, immersion temperature and time, and it was the lowest at the osmotic conditions of 7$0^{\circ}C$, 60$^{\circ}$Brix and 11hr. To determine the optimum processing condition by RSm, the polynomial optimum models were established. The regression models was significant (p<0.05). It was used contour plots to optimize osmotic dehydration. The optimum condition for osmotic dehydration as pretreatments for drying of cherry-tomatoes were immersion temperature of 47~53$^{\circ}C$, sugar concentration of 39~43$^{\circ}$Brix, and immersion time of 7hr, in which process conditions were 78~86% moisture content, 8.5~10$^{\circ}$Brix sugar content and 80~86% weight reduction.

• Water for future
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• v.18 no.1
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• pp.85-94
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• 1985

A method is developed for the separation of the major base flow in a river hydrograph combining the numerical techniques and the empirical methods. The linearized Boussinesq equation and the storage function are used to obtain the base flow recession. The shape of base flow curve made by the recharge of the groundwater table aquifer resulting from rainfall in determined by the Singh and Stall's graphical method, and the continuous from for the curve is approximated by the multiple and polynomial regression. this procedure was successfully tested for the separation of base flow and the establishment of hydrograph in a natural watershed. It was found that the direct numerical method applied to the homogeneous linear second order ordinary differential equation system is not suited to obtain the recession curve, and the case that the loss is generated in the partially penetrating stream can not be solved by the method of this study.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Journal of Biosystems Engineering
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• v.40 no.1
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• pp.89-93
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• 2015

Purpose: Machine vision-based image processing methods can be useful for estimating the fresh weight of plants. This study analyzes the ability of two different image processing methods, i.e., morphological and pixel-value analysis methods, to measure the fresh weight of lettuce grown in a closed hydroponic system. Methods: Polynomial calibration models are developed to relate the number of pixels in images of leaf areas determined by the image processing methods to actual fresh weights of lettuce measured with a digital scale. The study analyzes the ability of the machine vision- based calibration models to predict the fresh weights of lettuce. Results: The coefficients of determination (> 0.93) and standard error of prediction (SEP) values (< 5 g) generated by the two developed models imply that the image processing methods could accurately estimate the fresh weight of each lettuce plant during its growing stage. Conclusions: The results demonstrate that the growing status of a lettuce plant can be estimated using leaf images and regression equations. This shows that a machine vision system installed on a plant growing bed can potentially be used to determine optimal harvest timings for efficient plant growth management.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.240-252
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• 2016

In this paper, an on-board orbit propagator and compressing trajectory method based on B-spline for a lunar explorer are proposed. An explorer should recognize its own orbit for a successful mission operation. Generally, orbit determination is periodically performed at the ground station, and the computed orbit information is subsequently uploaded to the explorer, which would generate a heavy workload for the ground station and the explorer. A high-performance computer at the ground station is employed to determine the orbit required for the explorer in the parking orbit of Earth. The method not only reduces the workload of the ground station and the explorer, but also increases the orbital prediction accuracy. Then, the data was compressed into coefficients within a given tolerance using B-spline. The compressed data is then transmitted to the explorer efficiently. The data compression is maximized using the proposed methods. The methods are compared with a fifth order polynomial regression method. The results show that the proposed method has the potential for expansion to various deep space probes.

• Food Science and Biotechnology
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• v.18 no.1
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• pp.31-35
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• 2009

Total folate content was determined by microbiological assay using Lactobacillus casei spp. rhamnosis (ATCC 7469) with a 96-well microplate technique. Using roasted peanut and red kidney beans as representative legume samples, response surface methodology (RSM) was supplied to optimize the trienzyme procedures for the determination of folate in legumes. After response surface regression (RSREG), the second-order polynomial equation was fitted to the experimental data. Ridge analysis showed that the optimal digestion times were <2 hr for $Pronase^{(R)}$ and $\alpha$-amylase, and <5 hr for conjugase to obtain maximal folate values for legume samples. This study confirms that established digestion times for cereal products (AOAC Method 2004.05) of 3 for protease and 2 hr for $\alpha$-amylase are applicable to legumes. Conjugase treatment can be reduced to 5 from 16 hr and the conjugase level to 5 from 20 mg per sample, providing significant cost saving.

• Food Science of Animal Resources
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• v.37 no.1
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• pp.139-146
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• 2017

Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. In the analysis of response surface data, a second-order polynomial regression model is usually used. However, sometimes we encounter situations where the fit of the second-order model is poor. If the model fitted to the data has a poor fit including a lack of fit, the modeling and optimization results might not be accurate. In such a case, using a fullest balanced model, which has no lack of fit, can fix such problem, enhancing the accuracy of the response surface modeling and optimization. This article presents how to develop and use such a model for the better modeling and optimizing of the response through an illustrative re-analysis of a dataset in Park et al. (2014) published in the Korean Journal for Food Science of Animal Resources.

• Journal of Aerospace System Engineering
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• v.3 no.2
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• pp.12-19
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• 2009

The research for the fatigue analysis is regarded greatly as important in aerospace field. Moreover, a study on the fatigue characteristic is very actively progressing. In this study, the fatigue life estimation was performed for Flaperon Joint which has FCL(fatigue critical location) of tilt-rotor UAV. The Flaperon Joint should be taken the various loads by several missions profiles of UAV. The fatigue load spectrum of Flaperon Joint is generated by the standard mission segment for the tilt-rotor UAV, and this spectrum is used for the fatigue test and analysis. The in-house fatigue analysis program is applied to calculate the fatigue life based on Stress-Life(S-N) method. The S-N curve is generated from the S-N data of Mil-Handbook by second order polynomial regression method. Moreover, the coefficient of determination is used to ensure how accuracy it has. In addition, the Goodman equation is used to consider the mean stress effect for evaluating more accurate fatigue life. Finally, the result of fatigue analysis is verified by comparing with the fatigue test result for the Flaperon Joint.

• ETRI Journal
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• v.11 no.1
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• pp.109-122
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• 1989

We developed new quadtree and octree representation schemes which reduce the storage requirements from exponential to polynomial. The new schemes not only lessen the large storage requirements of the existing quadtree and octree representation schemes but guarantee an exact representation of the original object. These are made possible by adopting a new set of termination conditions that ensure finiteness of the quadtree and octree during the decomposition. These new data structures are analyzed theoretically and tested empirically. For space complexity, we analyzed its best case, worst case, and average case. Given an $n_e$-gon, we show that the expected number of nodes in our quadtree isO($$$n_e^1.292$) For a polyhedron with $n_f$ faces, the expected number of nodes in the new octree is O($$$n_f^1.667$). For time complexity, we again analyzed the best, worst, and average cases for constructing such quadtree and octree and find the average to be the same as those of the space complexity. Finally, random $n_e$- gons are generated as test data. Regression equations are fitted and are shown to support the claims on the average case performance.