• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.313-318
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• 2007

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 fuzzy neural network controller and artificial neural network(ANN). This control method is applicable over the entire speed range which 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 Adaptive-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 reposes speed control of IPMSM using Adaptive-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 a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, 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-FNN and ANN controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.9
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• pp.1609-1613
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• 2007

(Ba,Sr,Ca)$TiO_3$ powders, which were prepared by sol-gel method using a solution of Ba-, Sr- and Ca-acetate and Ti iso-propoxide, were mixed with organic vehicle and the BSCT thick films were fabricated by the screen-printing techniques on high purity alumina substrates. The structural and dielectirc properties were investigated for various $Dy_2O_3$ doping contents. As a result of thermal analysis, the exothermic peak was observed at around 670^{\circ}C $ due to the formation of the polycrystalline perovskite phase. All BSCT thick films, sintered at $1420^{\circ}C$ for 2h, showed the typical XRD patterns of perovskite polycrystalline structure and no pyrochlore phase was observed. The average grain size of the specimens decreased with increasing amount of $Dy_2O_3$. The average grain size and thickness of the BSCT specimens doped with 0.1 mol% $Dy_2O_3$ were approximately $1.9{\mu}m$ and $70{\mu}m$, respectively. The relative dielectric constant decreased and dielectric loss increased with increasing amount of $Dy_2O_3$, the values of the BSCT thick films doped with 0.1 mol% $Dy_2O_3$ were 3697 and 0.4% at 1 kHz, respectively. The leakage current densities in all BSCT thick films were less than $10^{-9}A/cm^2$ at the applied electric field range of 0-20 kV/cm.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.10a
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• pp.25.2-25.2
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• 2011

Recently, thin film capacitors used for vehicle inverters are small size, high capacitance, fast response, and large capacitance. But its applications were made up of liquid as electrolyte, so its capacitors are limited to low operating temperature range and the polarity. This research proposes using Ni-P alloys by electroless plating as the electrode instead of liquid electrode. Our substrate has a high aspect ratio and complicated shape because of anodic aluminum oxide (AAO). We used AAO because film thickness and effective surface area are depended on for high capacitance. As the metal electrode instead of electrolyte is injected into AAO, the film capacitor has advantages high voltage, wide operating temperature, and excellent frequency property. However, thin film capacitor made by electroless-plated Ni on AAO for full-filling into etched tunnel was limited from optimizing the deposition process so as to prevent open-through pore structures at the electroless plating owing to complicated morphological structure. In this paper, the electroless plating parameters are controlled by temperature in electroless Ni plating for reducing reaction rate. The Electrical properties with I-V and capacitance density were measured. By using nickel electrode, the capacitance density for the etched and Ni electroless plated films was 100 nFcm-2 while that for a film without any etch tunnel was 12.5 nFcm-2. Breakdown voltage and leakage current are improved, as the properties of metal deposition by electroless plating. The synthesized final nanostructures were characterized by scanning electron microscopy (SEM).

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.32-43
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• 2007

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 and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning 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 learning 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 learning fuzzy neural network and artificial neural network.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.602-611
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• 2015

Even though GNSS provides highly accurate navigation information all over the world, it is vulnerable to jamming in the electronic warfare due to its weak signal power. The United States and Korea have plans to use terrestrial navigation systems as back-up systems during outage of GNSS. In order to develop back-up systems of GNSS, an M&S software platform is necessary for performance evaluation of various vehicle trajectories and integrated navigation systems. In this paper a design method of an M&S software is proposed for evaluation of multiple radio positioning integration systems. The proposed M&S software consists of a navigation environment generation part, a navigation algorithm part, a GUI part and a coverage analysis part. Effectiveness of the proposed design method is shown by implementing an M&S software for the GPS, DME and eLoran navigation systems.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1424-1430
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• 2013

This paper presents design procedure of the magnetic bearings used for high-speed electric machines and flywheel energy storage systems. Magnetic bearing can be categorized by inner-rotor type and outer-rotor type according to the position of the rotary disc. These two types are applicable based on application environments such as application space, required attraction force, and controllability. Magnetic bearing is generally designed based on the ratio (geometrical coefficient or geometrical efficiency) of pole width to rotor journal radius but proper ratio is only decided by the analysis. This is the difficulty of the magnetic bearing design. In this paper, proper design technology of the inner-rotor type and outer-rotor-type eight pole magnetic bearings is introduced and compared with the FEM analysis results, which verifies the proposed design procedure is suitable to be applied to the design of the magnetic bearings for the industrial applications and flywheel energy storage system.

• Journal of Advanced Navigation Technology
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• v.20 no.3
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• pp.232-237
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• 2016

As a possible standardization of wireless local area network (WLAN), IEEE 802.11 LRLP is under discussion in order to support long range and low power (LRLP) communication for internet of things (IoT) including drones and many other IoT devices. In this paper, an efficient adaptive resource unit allocation scheme for uplink multiuser transmission in IEEE 802.11 LRLP networks is proposed. In the proposed scheme, which adopts OFDMA random access based transmission scheme of IEEE 802.11ax, in order to enhance the efficiency of the slotted OFDMA random access, access point (AP) traces the history of the sizes of successfully transmitted uplink data, and adjusts the sizes of resource units for the next uplink multiuser transmission adaptively. Our simulation results corroborate that the proposed scheme significantly improves the system throughput.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.1
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• pp.148-156
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• 2007

This paper explains stocker system that is operated in the long distance and linear area. The power system of stocker system uses Contactless Power Supply(CPS) to reduce problems of particle generation. The circuit configuration of CPS is simplified than the conventional ones, and the prototype is designed for commercial product. To transfer output data(information) from crane(secondary vehicle system) of the CPS to Primary system optical modem and TMS320F243(DSP, TI) are used between primary and secondary. power system are used, and the output voltage is controlled by operating frequency modulation. This paper is applied to stocker system controlled as one to one communication between the moving part(secondary system) and fixed part(primary system) of crane using optical modem. The study makes one to multi communication between fixed part and moving part for the purpose of multi crane operation of stocker system and must be done in the future.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.5
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• pp.29-37
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• 2009

This paper describes a vision-based and real-time system for detecting road signs from within a moving vehicle. The proposed system has the standard architecture with adaboost algorithm to detect road signs in real time. And it uses the value of vortical plane in the process of extracting candidate areas in view of fact that there are vertically most of signs on roads. Although being useful for detecting objects in real time, the conventional adaboost algorithm deteriorates the performance of detection rate in complex circumstance by reason of using only integral images as features. To overcome this problem, this paper proposes the method that improves the reliability of candidates as using the value of vertical plane for extracting candidate area and improves the performance of the detection rate as using integral images to which we add the kind of feature prototype. The experiments of this paper show that the detection rate of the proposed method has higher than that of the conventional adaboost algorithm under the real complex circumstance of roads.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.