• Korean Journal of Computational Design and Engineering
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• v.11 no.5
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• pp.365-374
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• 2006

The ubiquitous environment is to support people in their everyday life in an inconspicuous and unobtrusive way. This environment requires information such as the person, his/her preferences, and habits which is available in the ubiquitous system. In this paper, we propose the context aware system that can provide the tailored information service for user in ubiquitous computing environment. Our system architecture is divided into 4 domain models such as context awareness, presentation, interface and inference domain. Each domain model can perform some predefined tasks independently. And we suggest the hybrid algorithm combined with fuzzy and Bayesian method in order to reason what is the suitable information for user. We show the possibility for the real application through applying the system to the TGA (Tour Guide Assistant) for Kyoungju historical site.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.447-452
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• 1998

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model non linear systems. In this paper, we proposes an FDI system for non linear systems using ANFIS. The proposed diagnositc system consists of two ANFISs which operate in two different modes (parallel-and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis function) network to identify the faults. The proposed FDI scheme has been tested by simultation on a two-tank system

• KIEE International Transactions on Electrophysics and Applications
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• v.5C no.3
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• pp.138-142
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• 2005

In this paper, we compared recognition rates between NN(neural networks) and clustering method as a scheme of off-line PD(partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for classification were acquired from PD detector. And then statistical distributions are calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP(Back propagation algorithm) and ANFIS(adaptive network based fuzzy inference system) pre-processed FCM(fuzzy c-means) clustering method. So, classification rate of BP were somewhat higher than ANFIS. But other items of ANFIS were better than BP; learning time, parameter number, simplicity of algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.2
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• pp.53-61
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• 1988

Games such as baseball, soccer are scheduled by a given game type such as tournament, league or their mixed form. The objective of this paper is to find an efficient game-scheduling method with respect to traveling distance, break-time and other conditions. In this paper we first present two models which minimize traveling distance. The first model that a match is played once each other is solved by a heuristic method. In the second model that a match is played more than once, teams are paired by a modified 0 - 1 programming, and the pairs are rearranged in order to generate a number of workable schedules. Then Expert Systems is applied to solve breake-time and other conditions. In order to represent expertise's knowledge effectively, we present a new design of knowledge-base and data-base, inference engine including many rules and meta-rules which controls the global system. In knowledge-base, binary relation among various attributes is used to ease not only knowledge acquisition but also system execution.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.4
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• pp.510-516
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• 1999

This paper proposes methods of estimating lifetime distribution from incomplete field data under parametric regression models. Failure-record data-failure times and covariates-reported to the manufacturer can be seriously incomplete for satisfactory inference since only reported failures are recorded. This paper assumes that within-warranty data are reported with probability $P_1$ ($\leq1$) and after-warranty data are reported with Methods of obtaining pseudo and after-warranty data are reported with $P_2$ (< $P_1$). Methods of obtaining pseudo maximum likelihood estimators(PMLEs) are outlined, their asymptotic properties are studied, and specific formulas for Weibull distribution are obtained. Simulation studies are perfumed to investigate the effects of follow-up percentage on the PMLEs.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1952-1954
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• 2001

In this paper, we propose the neuro-fuzzy modeling method using VmGA (Virus messy Genetic Algorithm) for the complex nonlinear system. VmGA has more effective and adaptive structure than sGA. in this paper, we suggest a new coding method for applying the model's input and output data to the optimal number of rules in fuzzy models and the structure and parameter identification of membership functions simultaneously. The proposed method realizes the optimal fuzzy inference system using the learning ability of neural network. For fine-tune of parameters identified by VmGA, back- propagation algorithm is used for optimizing the parameter of fuzzy set. The proposed fuzzy modeling method is applied to a nonlinear system to prove the superiority of the proposed approach through comparing with ANFIS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.8 no.11
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• pp.27-34
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• 1985

Recently the distributed lag models for time series data have been used in several quantitative analyses. But the analyses of time series which have the serial correlations in error terms and the lagged values of dependent variables violate the hypothesis of OLS method. This paper suggests that the approach technique of distributed lay model with serial correlation should be applied by the Bayesian inference to estimate the parameters. For the application of distributed lag model by Bayesian analysis, the data for monthly consumption expenditure per household by items of commodities from 1972 to 1981 are used in order to estimate the lagged coefficient of processed food and the regression coefficient of the food and beverage.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.371-383
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• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1071-1082
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• 2021

Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.

• Asian Journal of Innovation and Policy
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• v.11 no.3
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• pp.320-341
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• 2022

Empirical applications to explain criminogenic events are abundant. While much of the research in criminal studies concentrates on understanding the motivations of offenders and preventing victimization from a micro perspective, there have been recent theoretical advancements that give priority to the role of spatial factors in directly impacting crime rates. The primary purpose of this study is to investigate the empirical inference between violent crime incidence and spatial characteristics of local areas focusing particularly on spatial accessibility conditions in the areas. Applying discrete spatial econometrics models, this study reveals a significant relationship between spatial accessibility and the formation of violent crime hot spots in South Korea. Along with other variables, it is revealed that road accessibility has a clear association with violent crime hot spots. Based on the findings, this study suggests some policy implications such as effective surveillance systems, land use restrictions, and advanced street lighting.