• Journal of Electrical Engineering and Technology
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• v.13 no.3
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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.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.11
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• pp.440-444
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• 1985

Voltage equations of a synchronous machine are derived from the electromagnetic field theory in order to develop a new equivalent circuit model considering core loss. The result from the new equivalent circuit model is superior to that of the conventional one in the analysis of machine performance and characteristics on optimal efficiency control of a synchronous motor.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.272-276
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• 1993

We made to develop CNC Tool Grinding Machine automating the linear motion of X,Y,Z axis of table, and thetilting and rotation of tool holding vice by using Encorder to increase the efficiency of tool grinding for the Conventional Tool Grinding Machine. We did not consider the part of static, dynamic and thermal behavior that must necessary consider when wedesign, because we used existing Conventional Tool Grinding Machine as it is. We investigated simulation to make existing Conventional Tool Grinding Machine like CNC. We also designed it, and we compared the capability of existing Conventional Tool Grinding and CNC Tool Grinding Machine, and we investigated, too.

• Journal of Magnetics
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• v.19 no.3
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• pp.309-313
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• 2014

This paper presents an eddy current loss analysis of a Mechanical Fixation (MF) made of 6061 aluminum alloy, which is used for an NS type double-rotor single-stator axial flux permanent magnet machine. The prototype MF made of aluminum alloy shows good mechanical performance, but poor electro-magnetic performance, since the machine's efficiency can decrease because of eddy current loss in the MF. In order to prevent efficiency decrease, a modification of the MF structure is also introduced. Three-dimensional finite element analysis (FEA) is used for magnetic field analysis, and eddy current losses are computed. The analysis results are compared to, and verified by the test results.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.184-189
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• 2014

This paper presents a multi-objective optimization approach to design rotor slot geometry of three-phase squirrel cage induction machine to achieve NEMA design D torque-speed (T-S) characteristics with high efficiency. The multi-objective Particle Swarm Optimization (MOPSO) algorithm combined with the adaptive response surface method and Latin hypercube sampling strategy is applied to obtain the Pareto optimal designs. In order to demonstrate the validity of the suggested optimal algorithm, an application to rotor slot design of three-phase induction motor is presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3200-3206
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• 2009

This paper describes the problem of existing maintenance system for mail sorting machnies, and presents to-be considerations of maintenance system based on AHP (Analytic Hierarchy Process) analysis with efficiency and economic aspects. There are standard procedure for diagnosis and repair of mail sorting machine as preventive and operational maintenance, roles and missions of maintenance technical support center for upgrade of maintenance skill in this paper. The proposed maintenance system will be adapted for more safety and efficiency maintenance of mail sorting machine.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.47
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• pp.35-45
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• 1998

Scheduling problem in Flexible Manufacturing Systems(FMS) is complex because of various situation of Manufacturing Systems. Especially, in case of short-term scheduling demanding high efficiency, low cost at short-period, efficient scheduling is a serious problem. To solve this problem, many dispatching rules are developed. But, it leave much to be desired, because real situation in shop floor is complex and real-time scheduling is needed in real manufacturing shop floor. In this paper, search algorithm that allocate different dispatching rules to each machine is presented to complement lack of dispatching rule and develop practical real-time scheduling system. The search algorithm is described in detail. First, algorithm detect machine breakdown, evaluate each dispatching rule. dispatching rules for each machine meeting performance criteria are ranked. The algorithm selects new dispatching nile for bottleneck machine. The effectivenes and efficiency of the mixed dispatching rule and search algorithm is demonstrated.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.36-38
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• 2005

In home appliances, electric energy is optimally controlled by using power electronics technology, creating a comfortable environment in terms of energy saving, low sound generation, and reduced time consumption. Usually simplicity and robustness make the three phase induction motor attractive for use in domestic appliance, including washing machines. Two main types of domestic washing machine have evolved. We focus on the front loading machine favored in Europe, which has a horizontal drum axis. The efficiency improvement in home appliances is very important for customers. Induction motor efficiency can be improved by means of loss reduction, which can be realized by motor selection and design, improvement of the waveforms supplied by power Inverter, utilizing a suitable control method. So this paper describes the architecture and feature of washing machine fed induction motor drive under minimizing losses vector control.

• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.50-56
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• 2021

Due to the recent increase in the mobile streaming market, mobile traffic is increasing exponentially. IMT-2020, named as the next generation mobile communication standard by ITU, is called the 5th generation mobile communication (5G), and is a technology that satisfies the data traffic capacity, low latency, high energy efficiency, and economic efficiency compared to the existing LTE (Long Term Evolution) system. 5G implements this technology by utilizing a high frequency band, but there is a problem of path loss due to the use of a high frequency band, which is greatly affected by system performance. In this paper, small cell technology was presented as a solution to the high frequency utilization of 5G mobile communication system, and furthermore, the system performance was improved by applying machine learning technology to macro communication and small cell communication method decision. It was found that the system performance was improved due to the technical application and the application of machine learning techniques.

• Computers and Concrete
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• v.30 no.1
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• pp.33-42
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• 2022

Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a time-consuming and laborious process. The present study aims to propose surrogate models based on Support Vector Machine (SVM) and Gaussian Process Regression (GPR) machine learning techniques, which can predict the CS of concrete containing nano-silica. Content of cement, aggregates, nano-silica and its fineness, water-binder ratio, and the days at which strength has to be predicted are the input variables. The efficiency of the models is compared in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE), Variance Account For (VAF), Nash-Sutcliffe Efficiency (NSE), and RMSE to observation's standard deviation ratio (RSR). It has been observed that the SVM outperforms GPR in predicting the CS of the concrete containing nano-silica.