• Proceedings of the KSRS Conference
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• v.1
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• pp.200-203
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• 2006

Cloud detection algorithm is being developed as major one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-1R and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithm and preliminary test result of both algorithms.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.566-569
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• 2005

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using artificial intelligent(AI) controller. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also. this paper is proposed the experimental results to verify the effectiveness of AI controller.

• Smart Structures and Systems
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• v.3 no.4
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• pp.439-454
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• 2007

In this paper a damage imaging technique using pre-stack migration is developed using Lamb (guided) wave propagation in composite structures for imaging multi damages by both numerical simulations and experimental studies. In particular, the paper focuses on the experimental study using a finite number of sensors for future practical applications. A composite laminate with a surface-mounted linear piezoelectric ceramic (PZT) disk array is illustrated as an example. Two types of damages, one straight-crack damage and two simulated circular-shaped delamination damage, have been studied. First, Mindlin plate theory is used to model Lamb waves propagating in laminates. The group velocities of flexural waves in the composite laminate are also derived from dispersion relations and validated by experiments. Then the pre-stack migration technique is performed by using a two-dimensional explicit finite difference algorithm to back-propagate the scattered energy to the damages and damages are imaged together with the excitation-time imaging conditions. Stacking these images together deduces the resulting image of damages. Both simulations and experimental results show that the pre-stack migration method is a promising method for damage identification in composite structures.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.110-114
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.4
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• pp.587-595
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• 2002

Recent trend changes a criterion for evaluation of humors that environmental problems are raised as a global issue. Burners with higher thermal efficiency and lower oxygen in the exhaust gas, evaluated better. To comply with environmental regulations, burners must satisfy the NO/sub x/ and CO regulation. Consequently, 'good burner'means one whose thermal efficiency is high under the constraint of NO/sub x/ and CO consistency. To make existing burner satisfy recent criterion, it is highly recommended to develop a feedback control scheme whose output is the consistency of NO/sub x/ and CO. This paper describes the development of a real time flame diagnosis technique that evaluate and diagnose the combustion states, such as consistency of components in exhaust gas, stability of flame in the quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using Neuro-Fuzzy algorithm. This study focuses on the relation of the color of the flame and the state of combustion. Neuro-Fuzzy loaming algorithm is used in obtaining the fuzzy membership function and rules. Using the constructed inference algorithm, the amount of NO/sub x/ and CO of the combustion gas was successfully inferred.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.367-372
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• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

• Tunnel and Underground Space
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• v.16 no.1 s.60
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• pp.85-94
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• 2006

In this study, scaled model tests were carried out to decide the optimal critical velocity, to prevent back layering in the case of fire in a long traffic tunnel. Realistic estimates were made for the time required for people to escape ken the tunnel and far the time required by the ventilation operator to increase the system speed to full capacity. The analysis, predicts that the emergency ventilation will start about 240 seconds after the tunnel fire. It was also found that prevention of back layering would occur within 4 minutes after fan operation. To find out optimal critical velocity, a 1/50 scaled model tunnel(diameter : 0.2 m and length : 20 m) based on the Froude similarity technique was constructed. Changing $\beta$ values in the Tetzner's equation, smoke propagation was observed. From the experiment, it was concluded that using a $\beta$ value of 0.5 to prevent back layering successfully allowed time for safe evacuation.

• Journal of IKEEE
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• v.12 no.3
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• pp.158-166
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• 2008

Medical ultrasound has benefits in mobility and safety than any other medical techniques such as X-ray, CT and MRI but has speckle noise which decrease the ability of an observer to distinguish the fine details in diagnostic examination. But simple removing of speckle often causes losing boundary information. Then, in this paper, we presented a novel neuro-fuzzy method which could remove speckle efficiently without loss of boundary information. Proposed method consists of image clustering by fuzzy algorithm and image processingby neural networks which was learned by back propagation. From the experiments for simulation image and real ultrasound image, we could verify the proposed method.