• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• Structural Engineering and Mechanics
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• v.85 no.5
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• pp.635-644
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• 2023

The design of honeycomb sandwich structures is often challenging because these structures can be tailored from a variety of possible cores and face sheets configurations, therefore, the design of sandwich structures is characterized as a time-consuming and complex task. A data-driven computational approach that integrates the analytical method and Artificial Neural Network (ANN) is developed by the authors to rapidly predict the design of sandwich structures for a targeted maximum structural deflection. The elaborated ANN reverse design approach is applied to obtain the thickness of the sandwich core, the thickness of the laminated face sheets, and safety factors for composite sandwich structure. The required data for building ANN model were obtained using the governing equations of sandwich components in conjunction with the Monte Carlo Method. Then, the functional relationship between the input and output features was created using the neural network Backpropagation (BP) algorithm. The input variables were the dimensions of the sandwich structure, the applied load, the core density, and the maximum deflection, which was the reverse input given by the designer. The outstanding performance of reverse ANN model revealed through a low value of mean square error (MSE) together with the coefficient of determination (R2) close to the unity. Furthermore, the output of the model was in good agreement with the analytical solution with a maximum error 4.7%. The combination of reverse concept and ANN may provide a potentially novel approach in designing of sandwich structures. The main added value of this study is the elaboration of a reverse ANN model, which provides a low computational technique as well as savestime in the design or redesign of sandwich structures compared to analytical and finite element approaches.

• Korean Journal of Organic Agriculture
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• v.20 no.1
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• pp.91-99
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• 2012

This study was conducted to develop a functional feed additive for pig with spent $Lycium$ $chinense$ Mill fruit. We investigated the optimum conditions for the extraction of polyphenol from spent $Lycium$ $chinense$ Mill using methanol. Methanol concentration as a solvent for extraction, extraction time and the volume of solvent per a gram of solid (ground spent Lyceum chinense Mill) were selected as parameters. Three levels of parameters were configured according to Box Behnken experiment design, a fractional factorial design, and total 15 trials were employed. Total polyphenol concentration from each trial was used as response from experiment system and effects of parameters on total polyphenol extraction efficiency were determined using response surface model. As a result, all terms in analysis of variance, regression ($p$ = 0.001), linear ($p$ = 0.002), square ($p$ = 0.017) and interaction ($p$ = 0.047) was significant and adjusted determination coefficient ($R^2$) was 94.7%. Total polyphenol extraction efficiency was elevated along increased methanol content and decreased solvent to solid ratio. However extraction time did not affect the efficiency. This study provides a primary information for the optimum extraction conditions to maximize total polyphenol recovery from spent Lycium chinens Mill fruit and this result could be applied to re-use of argo-industrial by-products and to develop of functional feed additives in organic farming.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• Journal of Korean Academy of Nursing
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• v.47 no.1
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• pp.121-132
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• 2017

Purpose: In this study the reliability and validity of the Korean version of the Cancer Stigma Scale (KCSS) was evaluated. Methods: The KCSS was formed through translation and modification of Cataldo Lung Cancer Stigma Scale. The KCSS, Psychological Symptom Inventory (PSI), and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Core 30 (EORTC QLQ-C30) were administered to 247 men and women diagnosed with one of the five major cancers. Construct validity, item convergent and discriminant validity, concurrent validity, known-group validity, and internal consistency reliability of the KCSS were evaluated. Results: Exploratory factor analysis supported the construct validity with a six-factor solution; that explained 65.7% of the total variance. The six-factor model was validated by confirmatory factor analysis (Q (${\chi}^2/df$)= 2.28, GFI=.84, AGFI=.81, NFI=.80, TLI=.86, RMR=.03, and RMSEA=.07). Concurrent validity was demonstrated with the QLQ-C30 (global: r=-.44; functional: r=-.19; symptom: r=.42). The KCSS had known-group validity. Cronbach's alpha coefficient for the 24 items was .89. Conclusion: The results of this study suggest that the 24-item KCSS has relatively acceptable reliability and validity and can be used in clinical research to assess cancer stigma and its impacts on health-related quality of life in Korean cancer patients.

• Fire Science and Engineering
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• v.29 no.6
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• pp.45-50
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• 2015

The flash point is one of the most important properties for characterizing the fire and explosion hazard of liquid solutions. In this study, a Tag open-cup apparatus was used to measure the flash points of two flammable binary mixtures, n-decane + n-octanol and acetic acid + n-butanol. The flash point temperature was estimated using the UNIFAC (Universal Functional Activity Coefficient) group contribution model and optimization method. The experimentally derived flash point was also compared with the predicted flash point. The two methods can estimate the flash point fairly well for the n-decane + n-octanol and acetic acid + n-butanol systems.

• Journal of Home Health Care Nursing
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• v.18 no.2
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• pp.108-117
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• 2011

Purpose: The purpose of this study is to develop a homecare nursing assessment tool for terminal cancer patients, testing the validity and reliability of the tool. Methods: This was a methodological study. The tool was developed in four stages: first, preliminary items were developed based on Gordon' functional health pattern model; second, a panel of specialists reduced the number of preliminary items using validity tests for content; third, final items were selected from the results of a pre-test. Finally, from August 4th, 2011 to August 26th, 2011, reliability and validity were tested using a sample of 125 terminal cancer patients in Seoul and Gyeonggi-do. Results: The final tool consisted of 39 items, with Cronbach's ${\alpha}$ 0.70. Using factor analysis, 10 factors were extracted; the correlation coefficient of these was over 0.3. Conclusion: The tool developed in this study was identified as having a high degree of reliability and validity. Given this, the tool can be effectively utilized for implementing and improving home care for patients with terminal cancer.

• Journal of Information Science Theory and Practice
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• v.5 no.2
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• pp.17-32
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• 2017

We compared nanotechnology web portal requirements using a Kano method, to identify similarities and dissimilarities in Kano-categorizations of features and functions required of nanotechnology among users in universities, government research institutes, and industry. Based upon data obtained from 130 user members of the National Nanotechnology Policy Centre, this study analyzed assessed asymmetries in web users' feelings based on hypothesized provision and non-provision of web portal requirements. In doing this, this study utilized measures and procedures suggested in the literature such as the most frequent-response categorization, customer satisfaction (dissatisfaction) coefficient, category strength and total strength, and Fong test. This study found that overall, sectors were an important factor in explaining the relationships between web portal requirements and user satisfaction/expectations. When these requirements were classified, users' perceptions of information contents requirements were consistent across the sectors, but the other functional requirements including communication and collaborations considerably varied.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.1
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• pp.93-99
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• 2011

In this study, a series of laboratory experiments are carried out to analyze fluid behaviors around multi-arranged (2 pieces) floating breakwaters with various parameters such as distance between structures, wave periods and steepness. The rate of wave transmission is shown to be affected directly by wave periods of incident waves and the breakwaters with multi-arranged structures show the highest rate of wave protection compared with other cases. The velocity fields around the breakwaters are measured by using the Laser Doppler Velocimetry system. The transmission coefficients are also measured in laboratory experiments. Finally, laboratory observed data are compared with numerical experimental results and analyzed in detail.