• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5447-5451
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• 2012

This paper presents a pattern-switchable microstrip patch antenna with loop structure. The loop structure for switchable radiation beam pattern is connected with feeding line of the microstrip patch antenna. As changing switch on/off state, the radiation beam pattern can be changed. The target frequency is 2.4 GHz and maximum radiation gain is 3.2dBi. The proposed antenna is useful for diversity antenna and smart antenna in modern wireless communication including MIMO (Multi-Input Multi-Output) and WLAN system. The sizes of the rectangular patch and the ground plane are $28mm{\times}28mm$ and $40mm{\times}50mm$, respectively. The simulation and experimental results show that the antenna radiation pattern can be changed with switch on/off configuration.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.3
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• pp.263-272
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• 1990

In the variable speed driving system of a three phase induction motor controlled by a PWM inverter, the output terminal contains considerable amount of harmonic components of the voltage waveform due to the switching action of semiconductor devices, causing torque ripples, acoustic noise and oscillation of the motor. This paper describes a new algorithm which eliminates the harmonics and controls the fundamental voltage in three phase PWM inverter output waveform. The new algorithm utilizes the technique of particular harmonics elimination (PHE) by walsh series in three phase PWM inverter output waveform. A microprocessor (8086 CPU)-controlled three phase induction motor system is described to realize this algorithm. The system is designed for 3 phase output voltage in the 1-60Hz interval where 5th and 7th harmonics, and 5th, 7th, 11th, and 13th harmonics are eliminated. Also, the fundamental wave amplitude is designed to be proportional to the output frequency. The performance of the proposed method shows sufficient elimination of the harmonics and also reduction of computation time which determines switching pattern. The proposed PWM pattern by Walsh series, is effective not only to induction motors but also to other electromagetic equipments such as voltage regulators and UPS.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.63-69
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• 1998

A new current controlled PWM technique with NPC structure is proposed in this paper. A current controlled PWM technique with neutral-point-clamped pulse-width modulation inverter composed of main switching devices which operates as switch for PWM and auxiliary switching devices to clamp the output terminal potential to the neutral point potential is described. The proposed current controller has a first and second current band. The switching pattern will be made by the first current band. According to the second current band, the output state of the switching pattern is changed into positive and negative state. This inverter output contains less harmonic content and lower switching frequency than that of conventional current controlled PWM technique at the same current limit. Two inverters are compared analytically and the performance is investigated by the computer simulation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.133-141
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• 1994

Conventional competitive learning algorithms compute the Euclidien distance to determine the winner neuron out of all predetermined output neurons. In such cases, there is a drawback that the performence of the learning algorithm depends on the initial reference(=weight) vectors. In this paper, we propose a new competitive learning algorithm that dynamically generates output neurons. The proposed method generates output neurons by dynamically changing the class thresholds for all output neurons. We compute the similarity between the input vector and the reference vector of each output neuron generated. If the two are similar, the reference vector is adjusted to make it still more like the input vector. Otherwise, the input vector is designated as the reference vector of a new outputneuron. Since the reference vectors of output neurons are dynamically assigned according to input pattern distribution, the proposed method gets around the phenomenon that learning is early determined due to redundant output neurons. Experiments using speech data have shown the proposed method to be superior to existint methods.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.11
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• pp.128-131
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• 1993

In order to get the basic data for the study of the heat stimulation of burning moxa, the pattern of combustion temperature, which is one of the important factors of thermal characteristics, was measured by density of cone moxa along the time procedure. The following results have been obtained 1) The pattern of combustion temperature by moxa burning was classified into input period which means the infiltration of heat into the area and output period which means the radiation of heat from the area. The input period consists of preheating and heating periods, while the output period consists of heat retaining and cooling periods. 2) The pattern of combustion temperature showed the same type or curve, which was not influenced by the moxa weight. However, Its pattern gradient are varied by density. It is considered that the pattern of combution temperature is primarily influenced by the rate of combustion temperature, gradient temperature and duration of combustion.

• Journal of radiological science and technology
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• v.30 no.4
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• pp.419-426
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• 2007

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize taste(bitter, sweet, sour and salty) pattern vectors. The signal intensity of taste are used to compose the input pattern vectors. The SOM(Self Organizing Maps) algorithm for taste pattern recognition is used to learn initial reference vectors and the ot-star learning algorithm is used to determine the class of the output neurons of the sunclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ(Learning Vector Quantization) algorithm. The pattern vectors are classified into subclasses by neurons in the subclass layer, and the weights between subclass layer and output layer are learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors, the proposed algorithm is simulated with ones of the conventional LVQ, and it is confirmed that the proposed learning method is more successful classification than the conventional LVQ.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.95-103
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• 2006

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize EEG pattern vectors. The frequency and amplitude of alpha rhythms and beta rhythms are used to compose the input pattern vectors. And the algorithm for EEG pattern recognition is used SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of the subclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights between subclass layer and output layer is learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors of EEG, the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.4
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• pp.337-344
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• 2012

A matrix converter is used for converting AC/AC power directly. In order to generate sinusoidal input/output waveform in matrix converter, it uses nine bidirectional switches and PWM modulation. This paper presents an analytical averaged loss model of matrix converter with DDPWM(direct duty ratio PWM) and proposes a new switching method for reducing switching losses. A Mathematical loss models with average magnitude of voltage/current are classed as conduction and switching loss. The proposed switching pattern is improved with existing DDPWM. To prove improved efficiency with proposed DDPWM, this paper compares losses between suggested switching pattern and conventional switching pattern using mathematical and simulation method. Each loss types are influenced by environmental factors such as temperature, switching frequency, output current and modulation method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.2
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• pp.63-67
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• 2004

We know that Neural Network is in use in many control fields. In time of using as controller, Neural Network controller is needed to learning by Input-output pattern. But in many times of control field. we can not get Input-output pattern of Neural Network controller. As a method solving this problem, in this paper, we try New control method that output node of Neural Network bringing control object. Such a New control method application, we can solve the data taking problem to Neural Network controller Input-output. The effectiveness of proposed control algorithm is verified by simulation results of DC servo motor.

• Journal of Korea Trade
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• v.24 no.8
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• pp.1-18
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• 2020

Purpose - This paper examines how differently output and input tariffs affect domestic employment across industrial characteristics of comparative advantage such as labor quality and capital intensity. Design/methodology - The paper focuses on 453 Korean industries from 2007 to 2014 because Korea is a typical example of a natural resource-scarce open economy and experienced the transition of the export pattern from labor intensity to technology intensity during this period. Findings - The results show that input tariff reduction stimulated total employment, focusing on the early 2010s, while the effects of output tariff reduction were statistically insignificant in general. However, the stimulation effects of output tariff reduction on employment were found in comparative advantage industries with greater labor quality and capital intensity. As for input tariff reduction, its stimulation effects on employment were more prominent in comparative disadvantage industries with lower labor quality and capital intensity. Originality/value - These results provide significant implications for natural resource-scarce open economies which are experiencing the transition of the export pattern from labor intensity to technology intensity and the unequal distribution of income after trade liberalization: imported intermediate inputs has become increasing important, leading to trade effects on employment and alleviation of income inequality.