• Journal of KSNVE
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• v.10 no.4
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• pp.575-583
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• 2000

In this paper, we design a vibration control system that includes a 3-D.O.F. mass-spring-damper structure for the analytical model of a building that is excited at the base of this structure by an external dynamic force, and one Active Mass Damper(AMD) on the top of this structure to generate control forces fro attenuation of the structural response. Two robust controllers based on $\mu$-synthesis and H$\infty$ optimal control are designed for the structural system to show that the performance of a control system can be degraded by some parameter uncertainties such as mass, stiffness coefficients, and/or damping coefficients. The performance of the two controllers are compared in terms of nominal performance, robust stability and robust performance by simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.49-53
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• 2008

A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.233-236
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• 2002

We consider a single-input-single-output nonlinear system which can be represented in a normal form. The nonlinear system has a modeling uncertainties including the input coefficient uncertainties. A high-gain observer is used to estimate the states variables to reject a modeling uncertainty. A globally bounded output feedback integral sliding mode control is proposed to stabilize the closed loop system. The proposed integral sliding mode control can asymptotically stabilize the closed loop system in the it presence of input coefficient uncertainty.

• Agribusiness and Information Management
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• v.8 no.1
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• pp.11-16
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• 2016

This paper presents how Digital Knowledge Ecosystem such as "Govi Nena" (translates as agriculture intelligence) can be used to provide a more effective and practical solution to eliminate the inefficiencies in agricultural markets and achieve higher productivity and price stability. In order to establish the framework to analyze the system, this paper uses a set of hypothetical scenarios faced by value chain actors based on a review of the literature, established knowledge and recent developing country experiences. The scenario analysis reveals that "Govi Nena" enables farmers to make effective production decisions, deepens the level of value chain integration, and enhances the level of welfare for the society as a whole.

• Korean Journal of Optics and Photonics
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• v.21 no.2
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• pp.46-52
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• 2010

A theory of bidirectional reflectance-distribution function (BRDF), a newly developed BRDF measurement system, and a method for evaluating the uncertainty of BRDF measurements are presented. The BRDF measurement system which measures BRDF in a wavelength range of (380~1500) nm with an angle range of $(-75{\sim}75)^{\circ}$ was installed. The measurement uncertainties, consisting of correlated terms and uncorrelated terms, were evaluated for the BRDF measurement system, resulting in the relative expanded uncertainty less than 3% (k=2).

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.143-145
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• 1996

This paper presents the hybrid type based expert system for fault diagnosis in transformers. The proposed system uses the novel fault diagnostic technique based on dissolved gas analysis(DGA) in oil-immersed transformers. The uncertainty of key gas analysis, norm threshold, and gas ratio boundaries are managed by using a fuzzy set. Also, the uncertainty of the fault diagnostic rules are handled by using fuzzy measures. Finally, kohnen's feature map performs fault classification in transformers. To verify the effectiveness of the proposed diagnosis technique, the hybrid type based expert system for fault diagnosis has been tested by using KEPCO's transformer gas records.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.345-347
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• 1993

It is known that Feedback linearization has important limitations-the full state has to be measured; no robustness is guaranteed with respect to parameter uncertainty and unmodeled dynamics. In this paper, we construct a nonlinear feedback linearization controller for the system containing uncertain parameters and unknown states, in the case of EMS system with rail vibration. Performance of this controller is demonstrated by computer simulation.

• Korean Institute of Interior Design Journal
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• no.32
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• pp.105-112
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• 2002

The Purpose of this paper is to describe a decision model which can be used to establish an implementation system of renovation for university building. Any implementation model relies on the work scopes of renovation which varies design, project cost, construction duration. The renovation system is subject to evaluation of work steps which is different from each project. Accordingly, the decision model of renovation is necessary to use the application of the analytical hierarchy process. Many of the performance steps used in general renovation condition may be known with uncertainty. This research has shown how probabilities can be explicitly incorporated in the decision model of renovation to assess this uncertainty.

• Tunnel and Underground Space
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• v.15 no.4 s.57
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• pp.275-287
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• 2005

At the planning and design stages of a development of underground space or tunneling project, the information regarding ground conditions is very important to enhance economical efficiency and overall safety In general, the information can be expressed using RMR or Q-system and with the geophysical exploration image. RMR or Q-system can provide direct information of rock mass in a local scale for the design scheme. Oppositely, the image of geophysical exploration can provide an exthaustive but indirect information. These two types of the information have inherent uncertainties from various sources and are given in different scales and with their own physical meanings. Recently, RMR has been estimated in unsampled areas based on given data using geostatistical methods like Kriging and conditional simulation. In this study, simulated annealing(SA) is applied to overcome the shortcomings of Kriging methods or conditional simulations just using a primary variable. Using this technique, RMR and the image of geophysical exploration can be integrated to construct the spatial distribution of RM and to evaluate its uncertainty. The SA method was applied to solve an optimization problem with constraints. We have suggested the practical procedure of the SA technique for the uncertainty evaluation of RMR and also demonstrated this technique through an application, where it was used to identify the spatial distribution of RMR and quantify the uncertainty. For a geotechnical application, the objective functions of SA are defined using statistical models of RMR and the correlations between RMR and the reference image. The applicability and validity of this application are examined and then the result of uncertainty evaluation can be used to optimize the tunnel layout.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.111-117
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• 2010

To improve the performance of the prediction system, the system should reflect well the uncertainty of nonlinear data. Thus, this paper presents multiple prediction systems based on Type-2 fuzzy sets. To construct each prediction system, an Interval Type-2 TSK Fuzzy Logic System and difference data were used, because, in general, it has been known that the Type-2 Fuzzy Logic System can deal with the uncertainty of nonlinear data better than the Type-1 Fuzzy Logic System, and the difference data can provide more steady information than that of original data. Also, to improve each rule base of the fuzzy prediction systems, the HCBKA (Hierarchical Correlation Based K-means clustering Algorithm) was applied because it can consider correlationship and statistical characteristics between data at a time. Subsequently, to alleviate complexity of the proposed prediction system, a system selection method was used. Finally, this paper analyzed and compared the performances between the Type-1 prediction system and the Interval Type-2 prediction system using simulations of three typical time series examples.