• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.505-511
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• 2003

Production rule based knowledge representation method has many advantages, but has the difficulties in maintaining the consistency of knowledge. Since the consistency maintenance of knowledge exercises a marked effect on the reliability of inference results, the system for consistency maintenance of knowledge is indispensable to increase the reliability. In the most popular pairwise checking method among consistency verification methods, the valuable rules can be omitted and it takes much time in checking the consistency when the rules are numerous. So, this paper is to propose and implement the verification system which can remove the structural errors and semantic ones, making up for the defects of pairwise checking method by using the certain property list and eventual property list and improving the steps of verification.

• Journal of KIISE:Software and Applications
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• v.27 no.4
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• pp.412-421
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• 2000

In spite that the main contents of mathematical and scientific learning are understanding principles and their applications, most of existing educational softwares are based on rote learning, thus resulting in limited educational effects. In the artificial intelligence research, some progress has been made in developing automatic tutors based on proving and simulation, by adapting the techniques of knowledge representation, search and inference to the design of tutors. However, these tutors still fall short of being practical and the turor, even a prototype model, for learning problem solving is yet to come out. The geometry problem-solving tutor proposed by this research involves dynamic inference performed in parallel with learning. As an ontology for composing the problem space within a real-time setting, we have employed the notions of propositions, hypotheses and operators. Then we investigated the mechanism of interactive learning of problem solving in which the main target of inference involves the generation and the test of these components. Major accomplishment from this research is a practical model of a problem tutor embedded with a series of inference techniques for algebraic manipulation, which is indispensable in geometry problem solving but overlooked by previous research. The proposed model is expected to be applicable to the design of problem tutors in other scientific areas such as physics and electric circuitry.

• Journal of the Korean Society for information Management
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• v.25 no.1
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• pp.149-171
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• 2008

Actually a diffusion of a Semantic Web application and utilization are situations insufficient extremely. Technology most important in Semantic Web application is construction of the Ontology which contents itself with characteristics of Semantic Web. Proposed a suitable a Method of Building Web Ontology for characteristics of Semantic Web and Web Ontology as we compared the existing Ontology construction and Ontology construction techniques proposed for Web Ontology construction, and we analyzed. And modeling did Ontology to bases to Description Logic and the any axiom rule that used an expression way of SWRL, and established Inference-based Web Ontology according to proposed ways. Verified performance of Ontology established through Ontology inference experiment. Also, established an Web Ontology-based Intelligence Image Retrieval System, to experiment systems for performance evaluation of established Web Ontology, and present an example of implementation of a Semantic Web application and utilization. Demonstrated excellence of a Semantic Web application to be based on Ontology through inference experiment of an experiment system.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.2
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• pp.100-109
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• 2007

Object recognition of service robots is very important for most of services such as delivery, and errand. Conventional methods are based on the geometric models in static industrial environments, but they have limitations in indoor environments where the condition is changable and the movement of service robots occur because the interesting object can be occluded or small in the image according to their location. For solving these uncertain situations, in this paper, we propose the method that exploits observed objects as context information for predicting interesting one. For this, we propose the method for modeling domain knowledge in probabilistic frame by adopting Bayesian networks and ontology together, and creating knowledge model dynamically to extend reasoning models. We verify the performance of our method through the experiments and show the merit of inductive reasoning in the probabilistic model

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.32-41
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• 1994

To solve the frequency variation of speech patterns which consist of LPC sequences, new membership function view from LPC, spectrum and the relations between the order of LPC and spectrum is proposed. To solve the time variation, multi-secation equi-segmentation method which equally divide the speech section into several section are applied. False recognition mainly occur at time when the same syllable is placed at the same utterance. To reduce the error, fuzzy inference is executed using the proposed membership function and weights are assigned into sectional certainty and then the decision method for recognized the section up to the third candidate. To testify the validation of this method, we experimented the recognition test of 28 DDD area names. The recognition rate of the fuzzy inference by the triangle membership function is $92\%$. That of the combined method of the fuzzy inference and the dicision method is $92.9\%$ and that of fuzzy inference by the proposed membership funtion is $93.8\%$.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.77-84
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• 2022

In this paper, we propose a Label Reassignment Strategy to improve the performance of an object detection algorithm. Our approach involves two stages: an inference stage and an assignment stage. In the inference stage, we perform multi-scale inference with predefined scale sizes on a trained model and re-infer masked images to obtain robust classification results. In the assignment stage, we calculate the IoU between bounding boxes to remove duplicates. We also check box and class occurrence between the detection result and annotation label to re-assign the dominant class type. We trained the YOLOX-L model with the re-annotated dataset to validate our strategy. The model achieved a 3.9% improvement in mAP and 3x better performance on AP_S compared to the model trained with the original dataset. Our results demonstrate that the proposed Label Reassignment Strategy can effectively improve the performance of an object detection model.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.409-416
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• 2018

Significant research has been conducted in the financial sector on the behavior of investors in the stock market. In this paper, we directly measure the degree of interest using the ranking of the frequency mentioned in the stock message board operated by Daum Communications Corp. and test the fact that the higher ranking of the frequency results in the higher stock returns in order to investigate the attention effect on the stock returns in the Korean stock market. We also propose and apply the likelihood ratio test procedure for order restricted hypotheses in order to test the attention effect. The test results shows that the higher rank in the frequency mentioned in the message board is related to stock returns (p-value < $10^{-6}$). Therefore, we conclude that an investors' attention effects exist in the Korean stock market.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1149-1158
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• 2004

In this thesis, we created a Fuzzy rule in a Fuzzy logic that are Fuzzy logic which is composed of linguistic rules and Fuzzy inference engine for effective traffic control in ATM networks. The parameters of the Fuzzy rules are adapted to minimize the given performance index in both cases. In other words, the difuzzification value controls the threshold in the buffer to arrival ratio to traffic priority (low or high) using fuzzy set theory for traffic connected after reasoning. Also, show experiment result about rule by MATLAB6.5 and on-line bulid-up to verify validity of created Fuzzy rule. As a result, we can verify that threshold value in buffer is efficiently controlled by the traffic arrival ratio.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1034-1041
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• 2014

This paper propose a fuzzy inference model for obstacle avoidance for a mobile robot with an active camera, which is intelligently searching the goal location in unknown environments using command fusion, based on situational command using an vision sensor. Instead of using "physical sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data. In this paper, "command fusion" method is used to govern the robot motions. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. We describe experimental results obtained with the proposed method that demonstrate successful navigation using real vision data.