• Journal of Advanced Navigation Technology
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• v.12 no.4
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• pp.378-383
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• 2008

This paper presents a method of image analysis based on discrete cosine transform (DCT) and fuzzy inference(Fl). It concentrated not only on the design of fuzzy inference algorithm but also on incorporating human visual parameter(HVP) into transform coefficients. In the first, HVP such as entropy, texture degree are calculated from the coefficients matrix of DCT. Secondly, using these parameters, fuzzy input variables are generated. Mamdani's operator as well as ${\alpha}$-cut function are involved to simulate the proposed approach, and consequently, experimental results are presented to testify the performance and applicability of the proposed scheme.

• Science of Emotion and Sensibility
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• v.17 no.1
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• pp.71-78
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• 2014

We propose the sentiment pattern as a novel sentiment feature for more accurate text sentiment analysis, and introduce the rating inference of movie reviews using it. The text sentiment analysis is a task that recognizes and classifies sentiment of text whether it is positive or negative. For that purpose, the sentiment feature is used, which includes sentiment words and phrase pattern that have specific sentiment like positive or negative. The previous researches for the sentiment analysis, however, have a limit to understand accurately total sentiment of either a sentence or text because they consider the sentiment of sentiment words and phrase patterns independently. Therefore, we propose the sentiment pattern that is defined by arranging semantically all sentiment in a sentence, and use them as a new sentiment feature for the rating inference that is one of the detail subjects of the sentiment analysis. In order to verify the effect of proposed sentiment pattern, we conducted experiments of rating inference. Ratings of test reviews is inferred by using a probabilistic method with sentiment features including sentiment patterns extracted from training reviews. As a result, it is shown that the result of rating inference with sentiment patterns are more accurate than that without sentiment patterns.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.7-13
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• 2002

Almost applications using fuzzy theory are based on the fuzzy inference. However fuzzy inference needs much time in calculation process for the fuzzy system with many input variables or many fuzzy labels defined on each variable. Inference time is dependent on the number of arithmetic Product in computation Process. Especially, the inference time is a primary constraint to fuzzy control applications using microprocessor or PC-based controller. In this paper, a simple fast fuzzy inference algorithm(FFIA), without loss of information, was proposed to reduce the inference time based on the fuzzy system with triangular membership functions in antecedent part of fuzzy rule. The proposed algorithm was induced by using partition of input state space and simple geometrical analysis. By using this scheme, we can take the same effect of the fuzzy rule reduction.

• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• The Journal of Korean Association of Computer Education
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• v.11 no.5
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• pp.77-88
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• 2008

Having observed the restriction of the current Web technology, the semantic Web has been developed, and it now has grown up with the core help of the W3C to a level where it recommends the OWL Web ontology language. Besides, in order to deduce the information out of OWL data, several inference systems have been developed such as Jena, Jess, and JTP. Unfortunately, however, quite few systems can effectively handle recently developed OWL data, and further, due to the limitation of file-based operation, the current inference systems cannot meet the requirements for handing huge OWL data. An efficient method for storing and searching ontology data is essential for ensuring stable information inference processes. In this study, firstly, we proposed a model based on the database management system to transform and store OWL data and to enable deduction process from the database. Secondly, we designed and implemented an effective OWL storing system based on our model. Finally, we compare our system with the previous inference systems through experimental performance analysis.

• Ocean and Polar Research
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• v.33 no.3
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• pp.309-319
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• 2011

This is a preliminary study of the feasibility of obtaining reliable tidal current harmonic constants, using one month of current observations, to verify the accuracy of a tidal model. An inference method is commonly used to separate out the tidal harmonic constituents when the available data spans less than a synodic period. In contrast to tidal constituents, studies of the separation of tidal-current harmonics are rare, basically due to a dearth of the long-term observation data needed for such experiments. We conducted concurrent and monthly harmonic analyses for tidal current velocities and heights, using 2 years (2006 and 2007) of current and sea-level records obtained from the Tidal Current Signal Station located in the narrow waterway in front of Incheon Lock, Korea. Firstly, the l-year harmonic analyses showed that, with the exception of $M_2$ and $S_2$ semidiurnal constituents, the major constituents were different for the tidal currents and heights. $K_1$, for instance, was found to be the 4th major tidal constituent but not an important tidal current constituent. Secondly, we examined monthly variation in the amplitudes and phase-lags of the $S_2$ and $K_1$ current-velocity and tide constituents over a 23-month period. The resultant patterns of variation in the amplitudes and phase-lags of the $S_2$ tidal currents and tides were similar, exhibiting a sine curve form with a 6-month period. Similarly, variation in the $K_1$ tidal constant and tidal current-velocity phase lags showed a sine curve pattern with a 6-month period. However, that of the $K_1$ tidal current-velocity amplitude showed a somewhat irregular sine curve pattern. Lastly, we investigated and tested the inference methods available for separating the $K_2$ and $S_2$ current-velocity constituents via monthly harmonic analysis. We compared the effects of reduction in monthly variability in tidal harmonic constants of the $S_2$ current-velocity constituent using three different inference methods and that of Schureman (1976). Specifically, to separate out the two constituents ($S_2$ and $K_2$), we used three different inference parameter (i.e. amplitude ratio and phase-lag diggerence) values derived from the 1-year harmonic analyses of current-velocities and tidal heights at (near) the short-term observation station and from tidal potential (TP), together with Schureman's (1976) inference (SI). Results from these four different methods reveal that TP and SI are satisfactorily applicable where results of long-term harmonic analysis are not available. We also discussed how to further reduce the monthly variability in $S_2$ tidal current-velocity constants.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.287-308
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• 2023

After launching its service in November 2022, ChatGPT has rapidly increased the number of users and is having a significant impact on all aspects of society, bringing a major turning point in the history of artificial intelligence. In particular, the inference ability of large language models such as ChatGPT is improving at a rapid pace through prompt engineering techniques. This reasoning ability can be considered as an important factor for companies that want to adopt artificial intelligence into their workflows or for individuals looking to utilize it. In this paper, we begin with an understanding of in-context learning that enables inference in large language models, explain the concept of prompt engineering, inference with in-context learning, and benchmark data. Moreover, we investigate the prompt engineering techniques that have rapidly improved the inference performance of large language models, and the relationship between the techniques.

• Journal of Global Scholars of Marketing Science
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• v.9
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• pp.47-75
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• 2002

The purpose of this study is to identify the effects of reference price, price rationale and price-quality inference of consumer on the evaluation of apparel quality. The experimental materials developed for this study were a set of stimulus and response sheet. The stimuli were six print ads, which was manipulated by reference price and price rationale for a jacket of national brand. This study used a 2(reference price: offer and non offer)$\times$3(price rationale: non offer, stock disposal, sales promotion) $\times$2(price-quality inference of consumer: high and low level) between-subjects experiment. Subjects were 371 female university students. The data were analyzed by factor analysis, ANOVA and t-test. The results were as follows. First, three apparel attributes were identified: sewing/fabrics and label by factor analysis. Second, the significant interaction effects of reference price, price rationale and price-quality inference of consumer were found on evaluating quality of sewing/fabrics and label of apparel. So, reference price effect differed to depending on type of price rationale and levels of price-quality inference. Third, the significant main effect of price-quality inference of consumer existed on evaluating construction quality of apparel.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.25-31
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• 2009

Widely used color information recognition methods based on the RGB color model with static fuzzy inference rules have limitations due to the model itself-the detachment of human vision and applicability of limited environment. In this paper, we propose a method that is based on HSI model with new inference process that resembles human vision recognition process. Also, a user can add, delete, update the inference rules in this system. In our method, we design membership intervals with sine, cosine function in H channel and with functions in trigonometric style in S and I channel. The membership degree is computed via interval merging process. Then, the inference rules are applied to the result in order to infer the color information. Our method is proven to be more intuitive and efficient compared with RGB model in experiment.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.2
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• pp.252-261
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• 2014

A Bayesian network (BN) based fault diagnosis framework for semiconductor etching equipment is presented. Suggested framework contains data preprocessing, data synchronization, time series modeling, and BN inference, and the established BNs show the cause and effect relationship in the equipment module level. Statistically significant state variable identification (SVID) data of etch equipment are preselected using principal component analysis (PCA) and derivative dynamic time warping (DDTW) is employed for data synchronization. Elman's recurrent neural networks (ERNNs) for individual SVID parameters are constructed, and the predicted errors of ERNNs are then used for assigning prior conditional probability in BN inference of the fault diagnosis. For the demonstration of the proposed methodology, 300 mm etch equipment model is reconstructed in subsystem levels, and several fault diagnosis scenarios are considered. BNs for the equipment fault diagnosis consists of three layers of nodes, such as root cause (RC), module (M), and data parameter (DP), and the constructed BN illustrates how the observed fault is related with possible root causes. Four out of five different types of fault scenarios are successfully diagnosed with the proposed inference methodology.