• Journal of the Korea Fashion and Costume Design Association
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• v.10 no.3
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• pp.127-136
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• 2008

The main focus of the study resides in antecedents of price acceptability. Levels of acceptable price may be related to the consumers' perception on reasonable or expected price. Price acceptability is known to have several psychological antecedents. One of the antecedents to price acceptability reported by prior researches is price-quality inference, a tendency to correlate high price to excellence in quality. In addition, price-conscious consumers are likely to show lower level of price acceptability level. Another well-known antecedent is sale proneness. Sales-prone consumers may relate price of apparel products to product quality information. Moreover, it was reported that involved consumers should be more concerned with the products to its price and thus should have higher levels of price acceptability. A conceptual model with price consciousness, sale proneness and product involvement as the exogenous variable, price-quality inference and price acceptability as the endogenous variable was developed for the empirical study. Measures of research variables were developed based on previous studies. Questionuaires from 298 respondents were analyzed for the study. The average age of respondents was 27. About 60% of the respondents were married and about 65% of them had college degrees. Empirical results supported all of the hypothesized relationships. Price consciousness had significant negative influence on price-quality inference and price acceptability. Sale proneness significantly influenced price-quality inference, while apparel involvement had significant impact on price-quality inference and price acceptability. Price-quality affected price acceptability significantly. This study generated a framework to help scholars understand antecedents of price acceptability of apparel products. Price has been shown to playa dual role in consumer's perceptions, either positively or negatively. Price consciousness played a negative role, and product involvement had a positive role in evoking higher level of price acceptability. This study also suggests additional source of positive, yet indirect role of price, sale proneness. This study also affirmed the importance of price-quality inference in arousing higher level of price acceptability.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.63-71
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• 2011

We present a method to modify the recognition of neural networks by the fuzzy inference in a handwritten digit recognition with large deformations, and we verified the method by the experiment. The neural networks take long time in learning and recognize 100% on the learning pattern. But the neural networks don't show a good recognition on the testing pattern. So, we apply the modified method as the fuzzy inference. As a result, the recognition and false recognition of neural networks was improved 90.2% and 9.8% respectively at 89.6% and 10.4% initially. This approach decreased especially the false recognition on digit 3, 5. We used the density of digit to extract the fuzzy membership function in this experiment. But, because the handwritten digit have varified input patterns, we will get a better recognition by extracting varifed characteristics and applying the composite fuzzy inference. We also propose the application of fuzzy inference on matching the input pattern, than applying strictly the fuzzy inference.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.161-168
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• 2004

A single-index model is useful in fields which employ multidimensional regression models. Many methods have been developed in parametric and nonparametric approaches. In this paper, posterior inference is considered and a wavelet series is thought of as a function approximated to a true function in the single-index model. The posterior inference needs a prior distribution for each parameter estimated. A prior distribution of each coefficient of the wavelet series is proposed as a hierarchical distribution. A direction $\beta$ is assumed with a unit vector and affects estimate of the true function. Because of the constraint of the direction, a transformation, a spherical polar coordinate $\theta$, of the direction is required. Since the posterior distribution of the direction is unknown, we apply a Metropolis-Hastings algorithm to generate random samples of the direction. Through a Monte Carlo simulation we investigate estimates of the true function and the direction.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.254-259
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• 1998

A new design methology is proposed to identify the structure and parameters of fuzzy model using PNN and a fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Handling), and uses several types of polynomials such as linear, quadratic and cubic besides the biquadratic polynomial used in the GMDH. The FPNN(Fuzzy Polynomial Neural Networks) algorithm uses PNN(Polynomial Neural networks) structure and a fuzzy inference method. In the fuzzy inference method, the simplified and regression polynomial inference methods are used. Here a regression polynomial inference is based on consequence of fuzzy rules with a polynomial equations such as linear, quadratic and cubic equation. Each node of the FPNN is defined as fuzzy rules and its structure is a kind of neuro-fuzzy architecture. In this paper, we will consider a model that combines the advantage of both FPNN and PNN. Also we use the training and testing data set to obtain a balance between the approximation and generalization of process model. Several numerical examples are used to evaluate the performance of the our proposed model.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.764-770
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• 2001

In this paper, we propose a novel PCI bus based parallel fuzzy inference system for transferring and inferencing the large volumes of fuzzy data in high speed. For this, the PCI 9050 interface chip is used to connect a local bus design as a PCI target core using FPGA to the PCI bus. We design and implement the PCI target core by using VHDL to be processed in parallel by considering the points of parallelyzing each element of the membership functions and each block of the condition and/or consequent parts. The proposed system can be used in a system requiring a rapid inference time in a real-time system or pattern recognition on the large volume of satellite images that have many inference variables in the condition and consequent parts.

• Child Studies in Asia-Pacific Contexts
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• v.5 no.2
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• pp.83-94
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• 2015

The purpose of this study was to understand the influence of personality trait information on young children's perception of initial attraction in peer relationships. The sample consisted of 90 children of three to five years of age in South Korea. Children were presented with an inductive inference task where they had to make inference of a target character's preference on novel-play and prosocial act based on trait labels (smart-not smart, outgoing-shy, nice-mean) and perceptual (toy) similarity information of two test characters. Children showed difference in their use of trait information depending on the perceptual similarity information, trait valence, and inference question with age. This result provides initial support that not only do young children understand the significance of trait in peer attraction but also know when trait label is more informative to use to infer others depending on the situation.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.341-344
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• 1991

Rules in expert system have meaning of assigning never-happen-minterms. Overall logical relations of variables can be achived by making all prime implicants of never-happen-minterms. From prime implicants, two tables, which are necessary in the process of inference, are constructed. There are two inferencing modes. One excutes inference only one variable which the user is interested in, and the other excutes inference all variables simultaneously. Outputs of inference have not only 'true' or 'false' but also 'unknown' which is different from conventional expert system. In this paper, an efficient approach is presented, which can check logical inconsistency in knowledge base and contradiction between input facts and rules. The methods in the paper may be available in the field of diagnosis and alarm processing.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.101-107
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• 2006

In this research, we propose a hybrid inference architecture for medical diagnosis based on dynamic knowledge map (DKM) and relational database (RDB). Conventional expert systems (ES) and developing tools of ES has some limitations such as, 1) time consumption to extend the knowledge base (KB), 2) difficulty to change the inference path, 3) inflexible use of inference functions and operators. To overcome these Limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. The DKM also can help the knowledge engineers to change the inference path rapidly and easily. Then, RDB and its management systems help us to transform the relationships from diagram to relational table.

• KDI Journal of Economic Policy
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• v.38 no.2
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• pp.1-20
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• 2016

In this paper, we conduct uniform inference of two widely used versions of the Phillips curve, specifically the random-walk Phillips curve and the New-Keynesian Phillips curve (NKPC). For both specifications, we propose a potentially time-varying natural unemployment (NAIRU) to address the uncertainty surrounding the inflation-unemployment trade-off. The inference is conducted through the construction of what is known as the uniform confidence band (UCB). The proposed methodology is then applied to point-ahead inflation forecasting for the Korean economy. This paper finds that the forecasts can benefit from conducting UCB-based inference and that the inference results have important policy implications.