• Nuclear Engineering and Technology
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• v.33 no.5
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• pp.483-497
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• 2001

In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system The PCA is used to reduce the dimension of an input space without losing a significant amount of information. The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.193-201
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• 2002

For measuring a traffic symbolic confusion Quantity and symbolic air pleasantness, we use fuzzy sensor algorithm maded by symbolic information Quantity. Hut for implementation of fuzzy sensor, we use some symbolic information item, this method cannot produce precise output because we use vague fuzzy rule method and we cannot abundance fuzzy for precision of fuzzy rule method. For this reason, this paper introduce new fuzzy sensor algorithm composed of not fuzzy rule method but using Analytic Hierachy Process. To prove that new method is good, two type of fuzzy sensor applied to traffic signal controller and through much passing vehicle, two fuzzy sensor compared each other.

• Journal of the Korea Society for Simulation
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• v.14 no.4
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• pp.95-106
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• 2005

Recent advances in wireless sensor networks have led to many routing protocols designed for energy-efficiency in wireless sensor networks. Despite that many routing protocols have been proposed in wireless sensor networks, a single routing protocol cannot be energy-efficient if the environment of the sensor network varies. This paper presents a fuzzy logic based Adaptive Routing (FAR) algorithm that provides energy-efficiency by dynamically changing protocols installed at the sensor nodes. The algorithm changes protocols based on the output of the fuzzy logic which is the fitness level of the protocols for the environment. A simulation is performed to show the usefulness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.539-542
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• 2005

This paper contains studies which are Cruise speed control which is made by PID algorithm and automated steering system for avoiding the obstacle coming from the front which is using Fuzzy algorithm. This mobile car uses DC motor whose speed is detected by encoder. Ultrasonic Waves Sensor established in the front detects the obstacle and the curve. And the sensor established in the side detects the distance of the space of the road. If the sensor detects the obstacle or the curve, the car is controlled by using Fuzzy algorithm. The Fuzzy algorithm calculates the speed and steering angle by using the value which is obtained from sensor.

• Nuclear Engineering and Technology
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• v.32 no.5
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• pp.485-494
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• 2000

In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique . Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.293-298
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• 2006

In this paper, a sensor fusion based robot navigation method for the autonomous control of a miniature human interaction robot is presented. The method of navigation blends the optimality of the Fuzzy Neural Network(FNN) based control algorithm with the capabilities in expressing knowledge and learning of the networked Intelligent Robotic Space(IRS). States of robot and IR space, for examples, the distance between the mobile robot and obstacles and the velocity of mobile robot, are used as the inputs of fuzzy logic controller. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a sensor fusion technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process. Preliminary experiment and results are shown to demonstrate the merit of the introduced navigation control algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.448-451
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• 2005

For measuring a traffic symbolic confusion quantity and symbolic air pleasantness, we use fuzzy sensor algorithm maded by symbolic information quantity. But for implementation of fuzzy sensor, we use some symbolic information item, this method cannot produce precise output because we use vague fuzzy rule method and we cannot abundance fuzzy for precision of fuzzy rule method. For this reason this paper introduce new fuzzy sensor algorithm composed of not fuzzy rule method but using Analytic Hierachy Process. To prove that new method is good, two type of fuzzy sensor applied to traffic signal controller and through much passing vehicle, two fuzzy sensor compared each other.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.277-283
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• 2014

This paper proposes robot localization using Fuzzy-Extended Kalman Filter algorithm of the mobile robots equipped with least sensors. In order to improve the accuracy of the localization, we usually add the sensors or equipment. However, it increases the simulation time and expenses. This paper solves this problem using only the odometer and ultrasonic sensors to get the localization with the Fuzzy-Extended Kalman Filter algorithm method. By inputting the robot's angular velocity, sensor data variation, and residual errors into the fuzzy algorithm, we get the sensor weight factor to decide the sensor's importance. The performance of the designed method shows by the simulation and Pioneer 3-DX mobile robot test in the indoor environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.4
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• pp.1030-1038
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• 2008

Traffic monitoring method is mainly loop detector and piezo sensor. But this method is only detecting the number of vehicle. Monitoring traffic volume is not checking the number of vehicle but checking the length of access road, width of road, number of passing people, passing vehicle, delayed vehicle. The traffic signal control cycle is not fixed by only passing vehicle number but all related traffic proposal. This paper proposed selecting common characteristic out of each unrelated traffic proposal through Analytic Hierachy Process and this characteristic is applied to compose fuzzy sensor algorithm which find out new traffic volume concept of confusion degree. The accumulated delayed vehicle time is shorter in new fuzzy sensor algorithm applied by AHP than other traffic method

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.276-285
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• 2008

Traffic monitoring method is mainly loop detector and piezo sensor. But this method is only detecting the number of vehicle. Monitoring traffic volume is not checking the number of vehicle but checking the length of access road, width of road, number of passing people,passing vehicle,delayed vehicle. The traffic signal control cycle is not fixed by only passing vehicle number but all related traffic proposal. This paper proposed selecting common characteristic out of each unrelated traffic proposal through Analytic Hierachy Process and this characteristic is applied to compose fuzzy sensor algorithm which find out new traffic volume concept of confusion degree. The accumulated delayed vehicle time is shorter in new fuzzy sensor algorithm applied by AHP than other traffic method