• Korean Journal of Animal Reproduction
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• v.15 no.2
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• pp.109-115
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• 1991

This experiment was conducted to find out the time to farrowing induction after PGF2$\alpha$ treatment, the rate of farrowing induction, the effect of kids born by the induced farrowing on birth weight and development with study on artificial control of parturition in Korean native goats. A total of 24 pluriparous goats were offered for this experiment. The animals were divided into 4 goats per treatment by administration time(142, 145 or 148 day of pregnancy) and dosage (5 or 7.5mg). The results obtained in this experiments were as follows : 1. The effect of synchronization of estrus was induced 23 goats(95.1%), 16 goats(66.6%) in the 1st treatment and 7 goats(28.5%) in the 2nd treatment PGF2$\alpha$ and exhibited estrus at a mean interval of 75$\pm$5 hours after PGF2$\alpha$ injection. 2. The time to farrowing induction after PGF2$\alpha$ treatment on day 145 or 148 was significantly(P<0.01) shorter than that of the 5mg treatment, but there was no significant difference(P>0.01). 3. Parturition was induced in all goats(100%) treated 5 or 7.5mg of PGF2$\alpha$ on day 148 and 7.5mg of PGF2$\alpha$ on day 145, but was induced 75% in treatment with 5mg of PGF2$\alpha$ on day 145 and 5 or 7.5mg of F2$\alpha$ on day 142. 4. The birth weight and development of kids induced farrowing was heaviest (P<0.01) on days 148 among treatment, but there was no significant difference in dosage.

• Journal of Embryo Transfer
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• v.26 no.1
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• pp.1-7
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• 2011

The purpose of this study was to determine the relationship between conception rate and other parameters (body condition score; BCS, progesterone concentrations and follicle size) before estrus induction with CIDR(intravaginal progesterone-releasing controlled internal drug release). The conception rate in cows with < 2.75, 2.75 to 3.25, and 3.25 <, BCS regardless of AI (artificial insemination) time was 46.6%, 63.3%, and 46.6% at CIDR insertion, respectively. The conception rate regardless of BCS was 54.9% in cows inseminated based on detected estrus, and 48.7% in cows inseminated at 72 to 80 hours (timed artificial insemination, TAI) after removal of CIDR. The conception rate regardless of AI time was 40.0% in cows with low progesterone concentrations (less than 1.0 ng/ml), and 56.6% in cows with high progesterone concentrations (more than 1.0 ng/ml) at CIDR injection. The conception rate regardless of progesterone concentrations was 53.8% in cows inseminated based on detected estrus, and 38.0% in cows of TAI after removal of CIDR. The conception rate regardless of AI time was 43.3% in cows with small follicle (less than 5 mm), 53.3% in cows between 5 mm to 10 mm of follicle, and 63.3% in cows with large folliclc (more than 10 mm) at CIDR injection, respectively. The conception rate regardless of follicle size was 58.4% in cows inseminated based on detected estrus, and 45.9% in cows of TAI after removal of CIDR. These results indicated that if the cows with BCS 2.75 to 3.25, active corpus luteum, and/or large dominant follicle (more than 10 mm) are used for estrus induction, the conception rate will be greater.

• Agricultural and Biosystems Engineering
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• v.3 no.1
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• pp.29-34
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• 2002

Two charging methods of electrostatic nozzle, i.e. induction and ionized field corona charging, were designed and evaluated for orchard sprayer application. An artificial (metallic) target was constructed and used in this experiment. The charge-to-mass ratio for the induction electrode was measured by using the Faraday cage. Two conventional pressure-swirl nozzles have been employed with different orifice diameters under the same experimental operating conditions. A commercial pressure-swirl nozzle with orifice diameter of 1.0 was used for the conventional spray. The diameter of the electrostatic was 0.59 mm. The experiment was carried out for individual nozzle sprays at $0^{\circ}$, $20^{\circ}$ and $50^{\circ}$ oriented angles and three nozzles, sprayed simultaneously at a distance of 1.0 and 2.0 m from the nozzle tip to the target. The nozzles were mounted on a carriage with constant speed of 1.26 km/h with a blower attached. The weighing method was employed to evaluate for the spray deposition, ground loss and estimated drift. The results show more promising for the induction charging method, especially at $20^{\circ}$oriented angle at a distance of 1.0 m from the target for a single nozzle and when all three nozzles were operated simultaneously for spray deposition. The results of the induction charging method show promising with the developed electrostatic technique.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.3
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• pp.224-232
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• 2006

The paper proposes the artificial neural network(ANN) sensorless control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper proposes the speed control of induction motor using AFC 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 error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled AFLC and him controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.248-252
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• 2003

This paper has a control method proposed for the effective self-tuning fuzzy speed control based on neural network of the induction motor indirect vector control. The vector control of an induction motor provides the decoupled control of the rotor flux magnitude and the torque producing current to performance is desirable. But, the drive performance often degrades for the machine parameter variations and its condition give rise to coupling of flux and torque current. The fuzzy speed control of an induction motor has the robustness about machine parameter variations compared with conventional PID speed control in a way. That proved to be some waf from the true. The purpose of this paper is to improve the adaptation by offering self-turning function to fuzzy speed controller. In this paper, the adaptive mechanism of fuzzy speed control in used ANN(Artificial Neural Network) technique is applied in an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve desired speed. In this paper proved the self-turning method of fuzzy controller has the robustness about parameter variation and the wide range of adaptation by simulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.10
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• pp.27-34
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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.

• Korean Journal of Animal Reproduction
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• v.16 no.3
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• pp.231-237
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• 1992

The effects to the corticosteroid dexamethasone(DEX) and(or) estradiol-benzoate(E-B) treated to induction of parturition and birth weights of the young was conducted in 48 pregnant Korean native goats. The animals were divided into 4 goats per each treatment by the time(142, 145 and 148 day of pregnancy) and dosage(DEX 15, 20mg, DEX 8mg＋E-B 10mg and DEX 13mg＋E-B 7.5mg per goat). The results obtained were summarized as follows: 1 The time for induction of parturtion after DEX(15, 20mg) treatment on day 148 of pregnancy was significantly(P<0.01) shorter than 142 and 145 days. There was no significant difference each dosage of DEX on same day of pregnancy. 2. The addition of E-B to DEX treatment compared with DEX alone group at day 142 and 145 of pregnancy were significantly(P<0.01) shorter the time for induction of parturition. However, treatment with DEX along or with E-B on 148 day of pregnancy did not affect the time to induction(about 27 hrs) of parturition between each treatment. 3. The birth weight of kids after parturition was heaviest(P<0.01) on day 148 of pregnancy. However, development and vigor of kids were not significant different between DEX alone treatment and with E-B.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.22-33
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• 2011

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.404-407
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation even under ideal field oriented conditions. This paper is proposed adaptive fuzzy controller(AFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaptation mechanism(FAM), AFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, AFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Journal of Power Electronics
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• v.8 no.2
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.