• 현장농수산연구지
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• 제25권4호
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• pp.111-117
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• 2024

Reproductive research such as artificial insemination and embryo transfer is necessary to produce high-quality riding horses. In this study, we investigated the effects of estrus synchronization and ovulation induction methods, which can be considered the basis of artificial insemination in horses, on the hormone concentration and artificial insemination pregnancy rate of mares. For the purpose of synchronization of estrus in horses, Cidr-plus insertion method, Regumate feeding method, and 150mg progesterone + 10mg estradiol mixed administration method were used. In the Cidr-plus insertion method and the Regumate feeding method, the progesterone concentration reached the appropriate level for ovulation induction on the 8th day of administration. The mixed administration method of 150mg progesterone + 10mg estradiol maintained the progesterone concentration at an appropriate level immediately after administration. With the administration of PGF2a and hCG, progesterone concentration decreased rapidly, making ovulation induction possible. As a result of comparing the pregnancy rate between natural estrus and estrus synchronization, the pregnancy rate was found to be higher in estrus synchronization and ovulation induction. From the results of this study, it is insufficient to judge the effect of the pregnancy rate due to the small number of tests, but in terms of usability, estrus synchronization and ovulation induction were useful. Therefore, it is expected to contribute to improving the efficiency of future roadster production.

• Journal of Advanced Marine Engineering and Technology
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• 제30권1호
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• pp.169-175
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• 2006

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 후기학술대회논문집
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• pp.47-48
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• 2005

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.

• 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.

• 한국발생생물학회지:발생과생식
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• 제16권3호
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• pp.205-217
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• 2012

The ovarian cycle, the biological minimum size, and artificial spawning frequency by artificial spawning induction of the female hard clam, Meretrix petechialis, were investigated by histological observations and morphometric data. The ovarian cycle of this species can be classified into five successive stages: early active stage, late active stage, ripe stage, partially spawned stage, and spent/inactive stage. The spawning period was from June to September, and the main spawning occurred between July and August when the seawater temperature exceeds over $20^{\circ}C$. The biological minimum size (shell length at 50% of first sexual maturity) in females were 40.39 mm in shell length (considered to be two years of age), and all clams over 50.1 mm in shell length sexually matured. In this study, the mean number of the spawned eggs by spawning induction increased with the increase of size (shell length) classes. In case of artificial spawning induction for the clams > 40.39 mm, the number of spawned eggs from the clams of a sized class was gradually decreased with the increase of the number of the spawning frequencies (the first, second, and third spawning). In the experiments of artificial spawning induction during the spawning season, the interval of each spawning of this species was estimated to be 15-18 days (approximately 17 days).

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2219-2226
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• 2017

This paper deals with artificial neural network approach for automatic detection of severity level of stator winding fault in induction motor. The problem is faced through modelling and simulation of induction motor with inter coil shorting in stator winding. The sum of the absolute values of difference in the peak values of phase currents from each half cycle has been chosen as the main input to the classifier. Sample values from workspace of Simulink model, which are verified with experiment setup practically, have been imported to neural network architecture. Consideration of a single input extracted from time domain simplifies and advances the fault detection technique. The output of the feed forward back propagation neural network classifies the short circuit fault level of the stator winding.

• 한국수학사학회지
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• 제35권2호
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• pp.43-56
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• 2022

The 21st century world has experienced all-around changes from the 4th industrial revolution. In this developmental changes, artificial intelligence is at the heart, with data science adopting certain scientific methods and tools on data. It is necessary to investigate on the logic lying underneath the methods and tools. We look at the origins of logic, deduction and induction, and scientific methods, together with mathematical induction, probabilistic method and data science, and their meaning.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.50-52
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• 2006

This paper Proposes a new method of on-line estimation for rotor resistance of the induction motor in the indirect vector controlled drive, using artificial neural network (ANN). The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and actual state variable of a neural network model is back propagated to adjust the weight of a neural network model, so that the actual state variable tracks the desired value. The performance of rotor resistance estimator and torque and flux responses of drive, together with these estimators, are investigated variations rotor resistance from their nominal values. The rotor resistance are estimated analytically, using the proposed ANN in a vector controlled induction motor drive.

• Journal of Electrical Engineering and Technology
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• 제1권4호
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• pp.482-489
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• 2006

This paper presents the generalized dynamic modeling of self-excited induction generator (SEIG) using state-space approach. The proposed dynamic model consists of induction generator; self-excitation capacitance and load model are expressed in stationary d-q reference frame with the actual saturation curve of the machine. An artificial neural network model is implemented to estimate the machine magnetizing inductance based on the knowledge of magnetizing current. The dynamic performance of SEIG is investigated under no load, with the load, perturbation of load, short circuit at stator terminals, and variation of prime mover speed, variation of capacitance value by considering the effect of main and cross-flux saturation. During voltage buildup the variation in magnetizing inductance is taken into consideration. The performance of SEIG system under various conditions as mentioned above is simulated using MATLAB/SIMULINK and the simulation results demonstrates the feasibility of the proposed system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2222-2224
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• 2002

Induction motors are critical components of many industrial machines and are frequently integrated in commercial equipment. The heavy economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motors supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the artificial neural network, and the diagnosis system is programmed by using LabVIEW and MATLAB.