• ETRI Journal
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• v.32 no.4
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• pp.530-539
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• 2010

As the conventional silicon metal-oxide-semiconductor field-effect transistor (MOSFET) approaches its scaling limits, quantum mechanical effects are expected to become more and more important. Accurate quantum transport simulators are required to explore the essential device physics as a design aid. However, because of the complexity of the analysis, it has been necessary to simulate the quantum mechanical model with high speed and accuracy. In this paper, the modeling of double gate MOSFET based on an adaptive neuro-fuzzy inference system (ANFIS) is presented. The ANFIS model reduces the computational time while keeping the accuracy of physics-based models, like non-equilibrium Green's function formalism. Finally, we import the ANFIS model into the circuit simulator software as a subcircuit. The results show that the compact model based on ANFIS is an efficient tool for the simulation of nanoscale circuits.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.6
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• pp.877-888
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• 2002

Price is a product attribute, which is determined by the function of the producing cost and profit. It is also identified as one of the most important components of the marketing mix. For consumers, price is an always-existing cue, definite evaluation criteria, and easily accessible information in the purchasing process. Considering the concept of the clothing-price in a comprehensive perspective encompassing economic, psychological and marketing perspectives, a theoretical model was developed. The model includes souses and dimensions of price perception and related behaviors. Souses of price perception were: the actual retail price at selling point, the internal reference price and external reference price. The dimensions of price perception included sacrifice perception, economic value perception, inference, savings perception and price as information perception. Clothing price related behaviors that flowed these dimensions were: low price consciousness, value for money consciousness, price-quality inference, price-prestige inference, sale proneness and price mavenism. An empirical study was conducted to validate the theoretical model. A questionnaire was developed and data were collected from 680 adult women living in Seoul, Korea. Confirmatory factor analysis as well as exploratory factor analysis results showed that theorized price related behaviors were successful classifications.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1139-1158
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• 2015

This paper proposes an efficient system identification method for modeling nonlinear behavior of civil structures. This method is developed by integrating three different methodologies: principal component analysis (PCA), artificial neural networks, and fuzzy logic theory, hence named PANFIS (PCA-based adaptive neuro-fuzzy inference system). To evaluate this model, a 3-story building equipped with a magnetorheological (MR) damper subjected to a variety of earthquakes is investigated. To train the input-output function of the PANFIS model, an artificial earthquake is generated that contains a variety of characteristics of recorded earthquakes. The trained model is also validated using the1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes. The adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. It is demonstrated from the training and validation processes that the proposed PANFIS model is effective in modeling complex behavior of the smart building. It is also shown that the proposed PANFIS produces similar performance with the benchmark ANFIS model with significant reduction of computational loads.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.2
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• pp.575-584
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• 1996

The objective of this paper is to develop an expert system for nuclear piping integrity. This paper describes the selection methodology of integrity evalution method and the inference of material properties. To select the integrity evaluation method, the weight factor for respective material properties was obtained by the sensitivity analysis of the effect of material properties on integrity evaluation method. Subsequently the possession ratio for respective integrity evaluation method was computed, and the most appropriate integrity evaluation method for given input information is selected. In the material properties inference, stress-strain curves and J-R curves were predicted from tensile properties such as yield strength and tensile strength.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.411-430
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• 2018

The Bayesian approach is a suitable alternative in constructing appropriate models for observed record values because the number of these values is small. This paper provides an objective Bayesian analysis method for upper record values arising from the Rayleigh distribution. For the objective Bayesian analysis, the Fisher information matrix for unknown parameters is derived in terms of the second derivative of the log-likelihood function by using Leibniz's rule; subsequently, objective priors are provided, resulting in proper posterior distributions. We examine if these priors are the PMPs. In a simulation study, inference results under the provided priors are compared through Monte Carlo simulations. Through real data analysis, we reveal a limitation of the appropriate confidence interval based on the maximum likelihood estimator for the scale parameter and evaluate the models under the provided priors.

• Journal of the Korean Society of Clothing and Textiles
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• v.19 no.2
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• pp.264-277
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• 1995

is designed to study the college women's desirable clothing behavior in campus, and to find out the difference in person perception according to appropriate or inappropriate clothing. Detailed object is to find out the following differences according to appropriate and inappropriate clothing in campus: 1) formation of impression 2) inference of value. Addi\ulcorner tionally the difference in person perception according to major, grade and preference group are studied. For data collection, 460 college women who are attending Ewha Woman's University are included, and convenience sampling method is used. Frequency, percentage, mean, factor analysis, t-test, ANDV A. duncan test, correspondent analysis are used for data analysis. The result are as follows: 1) Wearer's impression is devided into four factors: appearence evaluation, personality evaluation, ability and activity. 2) There are significant differences in impression formation and value inference according to situational appropriateness. 3) There are significant differences in person perception according to major, grade and preference group.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.779-802
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• 2013

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

• Journal of the Korean School Mathematics Society
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• v.26 no.1
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• pp.21-47
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• 2023

This study examines the activity system of teaching and learning about informal statistical inference using sampling simulation, based on cultural-historical activity theory. The research explores what contradictions arise in the activity system and how the system changes as a result of these contradictions. The participants were 20 elementary school students in the 5th to 6th grades who received classes on informal statistical inference using sampling simulations. Thematic analysis was used to analyze the data. The findings show that a contradiction emerged between the rule and the object, as well as between the mediating artifact and the object. It was confirmed that visualization of empirical sampling distribution was introduced as a new artifact while resolving these contradictions. In addition, contradictions arose between the subject and the rule and between the rule and the mediating artifact. It was confirmed that an algorithm to calculate the mean of the sample means was introduced as a new rule while resolving these contradictions.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.853-866
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• 2011

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

• Journal of KIISE:Software and Applications
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• v.37 no.10
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• pp.736-744
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• 2010

Log data collected from mobile devices contain diverse and meaningful personal information. However, it is not easy to implement a context-aware mobile agent using this personal information due to the inherent limitation in mobile platform such as memory capacity, computation power and its difficulty of analysis of the data. We propose a method of selective inference for modular Bayesian Network for context-aware mobile agent with effectiveness and reliability. Each BN module performs inference only when it can change the result by comparing to the history module which contains evidences and posterior probability, and gets results effectively using a method of influence score of the modules. We adopt memory decay theory and virtual linking method for the evaluation of the reliability and conservation of casual relationship between BN modules, respectively. Finally, we confirm the usefulness of the proposed method by several experiments on mobile phones.