• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.195-200
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• 2004

Computer games use an intelligent method called flocking for boids' group behavioral modeling. Flocking can naturally model group behavioral patterns of unpredictable forms such as birds and fishes using some computer resource. In this paper, we implemented an ecosystem which is composed of predator and prey for group behavioral modeling of real underwater ecosystem. Also fuzzy logic is applied to implement instinct desire of ecosystem elements. As the result, we confirmed that the model can overcome breakdown of ecosystem and model naturally ecosystem behavior.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.50.6-50
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• 2001

This paper deals with a design of nonlinear glide slope capture logic using dynamic model inversion in singular perturbation, which is applicable to the autolanding in ILS. Aircraft dynamics are separated into the fast time-scale variables, related with the inner-loop design, and the slow time-scale variables, related with the outer-loop design. It is assumed that the aircraft starts landing at 1000ft of altitude, -2.5deg of flight path angle, and 250ft/sec of velocity. In the outer-loop design, commands of altitude and velocity are selected and thereby the pseudo-controls of power level and pitch rate are determined. Also the elevator input to the aircraft is determined in the inner-loop design. The final design is evaluated in 6 DOF simulation model of the associated aircraft, in which the actuator models are not included. The results show the satisfactory autolanding ...

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.222-224
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• 2002

This paper investigates the adaptive control of a fuzzy logic based speed and flux controller for a vector controlled induction motor drive. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the model reference adaptive control(mAC) fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed MRAC fuzzy controller is confirmed by performance results for induction motor drive system.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.166-169
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• 2002

In this paper, implements the abstract interpretation of the imperative language While in SMV model-checker and explain how to apply the logic of CTL which example the security of information flow. And show the way to translate the abstract program of While into SMV program and explain the derive process of CTL logic to test the security of the information flow. For the various security test, it is suitable to use the model-checking than to implements the abstract interpretation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.389-394
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• 2004

This paper proposes a speed estimation method using FLC(Fuzzy Logic Controller) in order to realize the speed control of PMAM(Permanent Magnet AC Motor) with no speed sensor. This method uses FLC as a adaptive laws of MRAS(Model Reference Adaptive System) and estimates the rotor speed of PMAM with a difference between the reference model and the adjustable model. Speed control is performed by PI controller with the estimated speed. The experiment is executed to verify the propriety and the effectiveness of the proposed system.

• Science of Emotion and Sensibility
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• v.7 no.2
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• pp.149-155
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• 2004

This research focus on analyzing the quality of clothing product in view of consumer's sensibility trend with a emphasis on measuring objective quality of pruduct. The fuzzy logic-based multi attribute model has been developed in order to evaluate the quality of clothing product. The overall quality of a clothing products can be divided into two distinct terms, product quality and brand value. Those two values are further analyzed with a relation to human sensibility.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.364-369
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• 1988

In this study, a model for the simulation of the material flow not only inside a robot cell with flexible handling sequence but also between robot cells is presented. A method for the connection of special simulation programs has been developed and a logic model between a real system and a simulation system is employed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.6
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• pp.431-441
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• 2020

In this paper, we propose a new method using a feedforward disturbance observer that guarantees stability and robustness about the effects of external disturbance and model uncertainty. The method is consist of a disturbance observer, a feedforward controller, and an overshoot detecting logic. It has an advantage of reducing the excessive overshoot by external disturbance and model uncertainty. Also, it is easy to adjust the control gain due to a simple structure. In order to verify the effectiveness of a new method, simulation results are given for longitudinal model of F-16 aircraft. By reflecting a various of model uncertainties, the stability and the robustness are guaranteed. Finally, the stability and the robustness of the proposed method are verified using root locus plot and bode plot.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2681-2690
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• 2010

This paper proposes 3D facial modeling system without using 3D scanner and camera or expensive software. This system enables efficient 3D facial modeling to cost reduction and effort saving for natural facial modeling. It detects edges of component of face using edge detection based on fuzzy logic from any 2D image of front face. It was mapped fitting position with 3D standard face model by detected edge more correctly. Also this system generates 3D face model more easily through floating and flexible control and texture mapping after fitting that connection of control point on detected edge from 2D image and mesh of 3D standard face model.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.