• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2077-2078
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• 2006

This paper describes the development or a closed-loop Strapdown Attitude Reference System (SARS) algorithm integrated filtering estimator for determining attitude reference for railway and aviation system using fuzzy inference. The SARS consists of 3 single-axis rate gyms in conjunction with 2 single-axis accelerometers. For optimal values of fuzzy systems, we utilize on-line scheduling method for initial values and then use genetic algorithms for fine tuning. Implementation using experimental test data of unmanned aerial vehicle has been performed in order to verify the estimation. The proposed fuzzy inference based SARS demonstrate that more accurate performance can be achieved in comparison with conventional one. The estimation results were compared with the on-board vertical gyro as the reference standard.

• Genomics & Informatics
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• v.10 no.4
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• pp.266-270
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• 2012

Grammatical inference methods are expected to find grammatical structures hidden in biological sequences. One hopes that studies of grammar serve as an appropriate tool for theory formation. Thus, we have developed JSequitur for automatically generating the grammatical structure of biological sequences in an inference framework of string compression algorithms. Our original motivation was to find any grammatical traits of several cancer genes that can be detected by string compression algorithms. Through this research, we could not find any meaningful unique traits of the cancer genes yet, but we could observe some interesting traits in regards to the relationship among gene length, similarity of sequences, the patterns of the generated grammar, and compression rate.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.663-675
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• 2004

A battlefield simulation is the evaluation and analysis of the battlefield area, based on the data for terrain, climate, unit's maneuver and tactics basically required in battlefield simulation. Because it is difficult for the military authorities to collect all of the information perfectly for the reason of communication technology, jamming, and tactics, the military authorities need the future moving status for the target units by using acquired moving information. Therefore, we propose a moving object management system that concurrently provides domain reasoning function for the battlefield simulation. In order to implement the proposed system, we show the data modeling of the moving object for the battlefield simulation, and propose an inference engine using domain rule base and spatiotemporal operation. Also, we analyze the query response rate by inference function to verify domain reasoning of the implemented system.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.141-151
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• 1999

The resistance Spot Welding is widely used in the field of assembling the plates. However we don't still have any satisfactory solution, which is non-destructive quality evaluation in real-time or on-line, against it. Moreover, even though the rate of welding under the condition of expulsion has been high until now, quality control of welding against expulsion hasn't still been established. In this paper, it was proposed on the quality assurance technique of resistance spot welding pieces using Neuro-Fuzzy algorithm. Four parameters from electrode separation signal in the case of non-expulsion, and dynamic resistance patterns in the case of expulsion are selected as fuzzy input parameters. The parameters consist of Fuzzy Inference System are determined through Neuro-Learning algorithm. And then, fuzzy Inference System is constructed. It was confirmed that the fuzzy inference values of strength have within ${\pm}$4% error specimen in comparison with real strength for the total strength range, and the specimen percent having within ${\pm}$1% error was 88.8%. According to KS(Korean Industrial Standard), tensile-shear strength limit for electric coated of zinc is 400kgf/mm2. Judging to the quality of welding is good or bad, according to this criterion and the results of inference, the probability of misjudgement that good quality is valuated into poor one was 0.43%, on contrary it was 2.59%. Finally, the proposed Neuro-Fuzzy Inference System can infer the tensile-shear strength of resistance spot welding pieces with high performance for all cases-non-expulsion and expulsion. And On-Line Welding Quality Inspection System will be realized sooner or later.

• 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 the Korean Institute of Telematics and Electronics
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• v.27 no.11
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• pp.103-110
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• 1990

This paper studies on an on-line recognition of handwritten Hangeul characters using the fuzzy inference. To solve the ambiguity due to the variations of writing style by writes, these handwri-tten characters are recognized by means of the fuzzy inference on the production rule which is generated with every relative position information between strokes. In order to reduce the processing time, a subgroup which is previously classified with the number of strokes of reference characters is selected according to the number of strokes of input character, and the tolerance limit of distances between input character and reference characters of a subgroup is introduced to reduce the reference characters which is applied to the fuzzy inference. Experimental results for handwritten Hanguel charters 3990 by 10 writers show the recognition rate of $99.5{\%}$and the average processing time of 0.4sec/character.

• Structural Engineering and Mechanics
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• v.69 no.3
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• pp.317-326
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• 2019

This research aims to assess the tight seismic risk curve of the intake tower at Geumgwang reservoir by considering the recorded historical earthquake data in the Korean Peninsula. The seismic fragility, a significant part of risk assessment, is updated by using Bayesian inference to consider the uncertainties and computational efficiency. The reservoir is one of the largest reservoirs in Korea for the supply of agricultural water. The intake tower controls the release of water from the reservoir. The seismic risk assessment of the intake tower plays an important role in the risk management of the reservoir. Site-specific seismic hazard is computed based on the four different seismic source maps of Korea. Probabilistic Seismic Hazard Analysis (PSHA) method is used to estimate the annual exceedance rate of hazard for corresponding Peak Ground Acceleration (PGA). Hazard deaggregation is shown at two customary hazard levels. Multiple dynamic analyses and a nonlinear static pushover analysis are performed for deriving fragility parameters. Thereafter, Bayesian inference with Markov Chain Monte Carlo (MCMC) is used to update the fragility parameters by integrating the results of the analyses. This study proves to reduce the uncertainties associated with fragility and risk curve, and to increase significant statistical and computational efficiency. The range of seismic risk curve of the intake tower is extracted for the reservoir site by considering four different source models and updated fragility function, which can be effectively used for the risk management and mitigation of reservoir.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.844-855
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• 2005

This paper proposes a human action recognition scheme for Intelligent Web Browser. Based on the principle that a human action can be defined as a combination of multiple articulation movements, the inference of stochastic grammars is applied to recognize each action. Human actions in 3 dimensional (3D) world coordinate are measured, quantized and made into two sets of 4-chain-code for xy and zy projection planes, consequently they are appropriate for applying the stochastic grammar inference method. We confirm this method by experiments, that various physical actions can be classified correctly against a set of real world 3D temporal data. The result revealed a comparatively successful achievement of $93.8\%$ recognition rate through the experiments of 8 movements of human head and $84.9\%$ recognition rate of 60 movements of human upper body. We expect that this scheme can be used for human-machine interaction commands in a web browser.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.465-470
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• 2013

Dempster-Shafer Evidence Theory is available for multi-sensor data fusion. Basic Probability Assignment is essential for multi-sensor data fusion using Dempster-Shafer Theory. In this paper, we proposed a novel method of BPA calculation with signal assessment. We took notice of the signal that reported from the sensor mote at the time slot. We assessed the variation rate of the reported signal from the terminal. The trend of variation implies significant component of the context. We calculated the variation rate of signal for reveal the relation of the variation and the context. We could reach context inference with BPA that calculated with the variation rate of signal.

• Korean Journal of Cognitive Science
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• v.20 no.3
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• pp.335-355
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• 2009

We examined risk judgment and the accuracy of inference based on two kinds of probabilities in a Bayesian inference task: the death probability from a disease (base rates) and the probability of having a disease with positive results in the screening test (posterior probabilities). Risk information were presented in either a probability or a frequency format. In Study 1, we found a numerical format effect for both base rate and posterior probability. Participants rated information as riskier and inferred more accurately in the frequency condition than in the probability condition for both base rate and posterior probability. However, there was no frequency range effect, which suggested that the ranges of frequency format did not influence risk ratings. In order to find out how the analytic thought system influences risk ratings, we compared the ratings of a computation condition and those of a no-computation condition and still found the numerical format effect in computation condition. In Study 2, we examined the numerical format effect and frequency range effect in a high and a low probability condition and found the numerical format effect at each probability level. This result suggests that people feel riskier in the frequency format than in the probability format regardless of the base rates and the posterior probability. We also found a frequency range effect only for the low base rate condition. Our results were discussed in terms of the dual process theories.