• 한국재료학회지
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• 제14권9호
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• pp.655-658
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• 2004

Lithium ferrites are a low-cost material which have been prominent in the high frequency core industry because of their excellent temperature performance and high squareness ratio. In order to develope the lithium ferrites with the high squareness and low coercive force, the ferrites of $Li_{0.48}Bi_{0.02}Ni_{0.04}Fe_{2.46-x}O_4$ were investigated the by varying composition, temperature and frequency. Electric loss of the Li-ferrite was lowered with the substitution of $Mn_{2}O_3$. The addition of $Mn_{2}O_3$ increased the magnetic induction (Bm&Br) but decreased the coercive force (Hc) and the squareness ratio (R=Br/Bm). Also, the Br value was stable at environmental temperature variation.

• 전기의세계
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• 제14권5호
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• pp.20-27
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• 1965

There are several methods of speed control in induction motors. One of which is to change the frequency of source but was not used frequently because of the difficulty of frequency-change. Then, the development of frequency changer using semiconductor enables the method useful. In this paper the speed characteristics of mechanical output, secondary input torque and secondary power factor are described when the frequency of source and voltage which is proportional to the frequency of source in order to make the flux in the air gap in constant, are changed. The above characteristics are searched on the basis of the rated characteristics of the motor. Because the ratio of these is proportional to that of the secondary current or the square value of it. To get the ratio of the secondary current, a current circle diagram is introduced, and the magnitude of the ratio from this diagram is accurate and simple. Experimental results are in good agreement with theoretical predictions.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2009년도 춘계학술대회 논문집
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• pp.40-43
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• 2009

This study is for investigating the effect of induction heating with various alloy elements to manufacture the pre-heat treated steels of 100kgf/$mm^2$ for cold heading. For four kinds of steels, the condition of induction heating (especially, induction tempering) were observed, and their microstructure and tensile and compressive properties were investigated. The middle carbon steel and the low carbon Cr-Mo steel are needed the higher Grange-Baughman tempering parameter than that of the low carbon Cr steel to obtain 100kgf/$mm^2$ tensile strength. For accomplishing the pre-heat treated steel of 100kgf/$mm^2$ tensile strength having advanced cold heading. It is needed that the pre-heat treated steel is manufactured by induction quenching and tempering with the low carbon alloy steel to have the high ratio of ferrite and the fine globular cementite simultaneously.

• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1149-1160
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• 2021

Lack of sleep time increases risks of fatigue, hypomnesis, decreased emotional stability, indigestion, and dementia. The risks can be reduced by providing eyelid-warming, inducing sleep and improving sleep quality. However, effective warming temperature to an person varies depending on physical condition and the individual. The various types of frequencies can be identified in brain wave from a person and amount of frequencies is also changed continuously before and after sleep. Therefore we can identify the user's sleep stage with brain wave, namely EEG. Effective sleep induction is possible if warming temperature to a person is controlled based on EEG. In this paper, we propose customized warming control techniques based on EEG for a efficient and effective sleep induction. As an experiment, sleep induction effects of standard sleep mask and customized temperature control techniques sleep mask are compared. EEG data and warming temperature were measured in 100 experiments. At customized warming control techniques, experiments showed that the ratio of alpha and theta waves increased by 3.21%p and the time to sleep decreased by 85 seconds. It will contribute to effective sleep induction and performance verification methods in customized sleep mask systems.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제55권7호
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• pp.373-379
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• 2006

The low-voltage induction motors have been widely driven by IGBT PWM inverters, ever since it was used to apply variable speed drives. Recently, the insulation failures of the stator windings become critical problems due to the high ratio of dv/dt in IGBT PWM inverters. In this paper, the detailed insulation tests on the IGBT PWM inverter fed induction motor are carried out. Five different types of insulation techniques are used to ti induction motors. The change of the insulation characteristics such as partial discharge, AC current, capacitance, and dissipation factor are compared. respectively In addition, insulation breakdown tests using the high voltage pulse are performed, and corresponding breakdown voltages are analyzed.

• 전기학회논문지P
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• 제55권4호
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• pp.155-160
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• 2006

In this paper, deduced suitable optimization to request output condition after taking closely characteristic data of single phase induction motor(SIM) which is the possibility becoming economic is coming to be demanded. Motor proper move connection data took advantage of result of existing data and iron loss and copper loss, mechanical loss took advantage of statistical data, and decide motor move laking advantage of saving data and secondary resistance and optimum purpose of method that is proposed through single phase induction motor and comparison performance evaluation having on the same output parameter. That decide material factor, electric power damage ratio, and coefficient of utilization for optimum function by method that search request output and optimum values of efficiency case by case and decided is proper that is saved after take magnetizing reactance relationship. This research result which it sees against a material expense with use coefficient of utilization which is included in loss expense decides the same plan variable back the place efficiency is useful and will be applied.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 A
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• pp.27-29
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• 1995

This paper presents the design and characteristics analysis of induction machine for diesel-electric hybrid vehicle powertrain. Diesel-electric hybrid vehicles are very efficacious in reduction of consumption energy, environment pollution and saftly yields with high dynamic of machines. However, their sophisticated construction requires from designers both complex made-technology and control strategy which would be able to put into practice this requirements. These problems are still considered in processing the design and analysis of induction machine. The requirements for a given volume, input power to weight ratio, high efficiency and wide speed range are met by the induction machine.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• 전력전자학회논문지
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• 제7권4호
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• pp.377-383
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• 2002

유도전동기의 고성능 벡터제어를 수행하기 위해서는 회전자 자속의 정확한 추정이 필요하다. 유도전동기의 회전자 자속관측기들에서는 자속추정시에 전동기 상수들이 사용되므로 이들의 변동에 따라 자속추정에 영향을 받는다. 본 논문에서는 파라미터 민감도를 이용하여 파라미터 변동에 의한 자속관측기들의 추정자속 오차를 해석하였다. 파라미터 민감도는 시스템 파라미터의 변동분에 대한 시스템 전달함수의 변동분의 비로 정의되므로 실제 자속과 추정 자속의 비를 전달함수로 정의하고 해석을 수행하였다. 제안된 해석방법의 타당성을 확인하기 위해 시뮬레이션과 실험을 행하였다.