• Journal of Industrial Technology
• /
• v.33 no.A
• /
• pp.31-39
• /
• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

• Journal of Embryo Transfer
• /
• v.25 no.2
• /
• pp.89-92
• /
• 2010

We report herein the successful results of estrus induction, sperm cryopreservation and kids born by transcervical insemination of frozen-thawed semen in a Saanen goat. Flugestone acetate (FGA: 60 mg) was inserted into vagina for 15 days. The goat was intramuscularly injected with 400 IU PMSG and 200 IU hCG ($PG600^{(R)}$: Intervet, Korea) a day before withdrawal of the FGA sponge. Follicles and corpora lutea were identified on both ovaries by laparoscopy. Artificial insemination was performed 46 hours after removal of FGA sponge. The concentration of frozen-thawed semen was $3.975{\times}10^8/ml$ and 0.5 ml of frozen-thawed semen was transcervically inseminated into uterine body under anesthesia. Three kids, all females, were born 144 days after artificial insemination. This is the first report producing kids by transcervical insemination of frozen-thawed semen in a Saanen goat of which the estrus was induced by FGA sponges, PMSG and hCG during non-breeding season in Korea.

• Korean Journal of Animal Reproduction
• /
• v.16 no.2
• /
• pp.109-116
• /
• 1992

The present study was carried out to establish a practical regimen for artificial parturition induction using prostaglandin F2$\alpha$(PGF2$\alpha$) and estradiol-benezoate in Korean native goats. The effect of parturition induction and the time intervals to induced parturition after injection were investigated. The birth weight and body weight of kids at 15 days of age were measured. A total of 24 pluriparous goats were offered for this experiment. The animals were divided into 4 goats per treatment by the injection time(142, 145 or 148 day of pregnancy) and dosage(5.0$\times$10 or 7.5$\times$7.5mg). The results obtained were summarized as follows : A total of 24 pregnant goats were intramusculary treated with 5.0$\times$10 or 7.5$\times$7.5mg of PGF2$\alpha$ and estradiol-benzoate for parturition induction of Day 142, 145 or 148 of gestation. Parturition was induced in all of the goats(100%) treated. The kids produced from induced parturition were all healthy. The time intervals to induced parturition after PGF2$\alpha$ and estradiol-benezoate injection of 5.0$\times$10 or 7.5$\times$7.5mg to pregnant goats on Day 148(23.22$\pm$0.51~23.40$\pm$1.26hrs) were significantly(P<.01) shorter than those of the 142 days of the gestation(26.34$\pm$2.22~28.39$\pm$3.02hrs). No significant difference was found in the time intervals between the doses(5.0$\times$10 or 7.5$\times$7.5mg) treated for parturition induction. The birth weight of kids from induced parturition was no significant difference between on Day 148 and on Day 142 of gestation. However, the birth weight of kids from parturition induced on Day 148 was found significantly(P<.01) heavier than that of the 142 days of gestation. The body weight of kids at 15 days old was also significantly(P<.01) lighter in the parturition induced on day 142 than those on Day 142. The birth weight and body weight of kids at 15 days old were not affected by 5.0$\times$10 or 7.5$\times$7.5mg injection of PGF2$\alpha$ and estradiol-benzoate for inducing parturition. From the above results, it was concluded that the parturition induction by PGF2$\alpha$ and estradiol-benezoate injection of 5.0$\times$10 or 7.5$\times$7.5mg on Day 142 of gestation, which was correspondent to 8 days before expected spontaneous parturition, was available without any significant troubles.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.25 no.4
• /
• pp.45-56
• /
• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.20 no.1
• /
• pp.57-64
• /
• 2006

This paper is proposed for a artificial neural network(ANN) sensorless control based on the vector controlled induction motor drive, or proposes a adaptive fuzzy teaming control(AFLC). The fuzzy logic principle is first utilized for the control rotor speed. AFLC scheme is then proposed in which the adaptation mechanism is executed using fuzzy logic. Also, this paper is proposed for a method of the estimation of speed of induction motor 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 coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• Proceedings of the KIPE Conference
• /
• 2004.07a
• /
• pp.38-41
• /
• 2004

Artificial intelligence control that use Fuzzy, Neural network, genetic algorithm etc. in the speed control of induction motor recently is studied much. Also, sensors such as Encoder and Resolver are used to receive the speed of induction motor and information of position. However, this control method or sensor use receives much effects in surroundings environment change and react sensitively to parameter change of electric motor and control Performance drops. Presume the speed and position of induction motor by ANN in this treatise, and because using FMRLC that is consisted of two Fuzzy Logic, can correct Fuzzy Rule Base through teaming and save good response special quality in change of condition such as change of parameter.

• Proceedings of the KIEE Conference
• /
• 2003.07b
• /
• pp.1111-1113
• /
• 2003

This paper proposes an effective self-turning algorithm based on Artificial Neural Network (ANN) for fuzzy speed control of the indirect vector controlled induction motor. Indirect vector control method divides and controls stator current by the flux and the torque producing current so that the dynamic characteristic of induction motor may be superior. However, if motor parameter changes, the flux current and the torque producing one's coupling happens and deteriorates the dynamic characteristic. The fuzzy speed controller of an induction motor has the robustness over the effect of this parameter variation than a conventional PI speed controller in some degree. This paper improves its adaptability by adding the self-tuning mechanism to the fuzzy controller. For tracking the speed command, its membership functions are adjusted using ANN adaptation mechanism. This adaptability could be embodied by moving the center positions of the membership functions. Proposed self-tuning method has wide adaptability than existent fuzzy controller or PI controller and is proved robust about parameter variation through Matlab/Simulink simulation.

• Proceedings of the KIEE Conference
• /
• 2009.04b
• /
• pp.32-34
• /
• 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 model reference adaptive fuzzy control(MFC) 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 adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Journal of Embryo Transfer
• /
• v.26 no.4
• /
• pp.271-276
• /
• 2011

This study was focused on improvement of milk production in Mongolian dairy industry by artificial insemination (AI) technology, supported by ODA of KOICA in Republic of Korea. This program was started in January 2009 and it is in $3^{rd}$ years. This manuscript summarized the data especially on estrus synchronization and pregnancy establishment in dairy cows (Holstein) this year. A total of 81 dairy cows from 4 private farms (38 from Undarmal milk and that of 30, 8 and 5 dairy cows from Onjin (Enkhbayer), Jargalant, and BRM School farms respectively) were synchronized with 5 ml Lutalyse (i.m.) in the dump of dairy cows and then estrus was detected 2 to 3 days after $PGF_{2{\alpha}}$ injection. The synchronized dairy cows were inseminated with 0.5 ml dairy frozen semen by conventional artificial insemination (AI) techniques. Pregnancy was diagnosed about 60 days after AI by palpation method. About 96.3% (78/81) of synchronized cows were responded to single $PGF_{2{\alpha}}$ injection. Total 75 over 78 dairy cows (90.1%) inseminated were diagnosed as pregnant. The estrus induction and pregnancy rates were very effective using Lutalyse injection and conventional AI techniques in Mongolian dairy cow. The present results indicated that AI after estrus induction in Mongolian dairy cows could be applied to dairy breeding technology for improving breeding efficiency and milk production of the country.

• Journal of Life Science
• /
• v.29 no.2
• /
• pp.272-278
• /
• 2019

The silkworm performs sexual reproduction for the production of its healthy offsprings from generations to generations. Parthenogenesis in the silkworm, Bombyx mori acquires immense use in the development of outstanding homozygouse lines with higher viability, hybrid vigour, combining ability and less phenotypic variability, and it can serve as a powerful tool in controlling sex of the offsprings as well as a useful tool in selection of breeding schemes. However, naturally occuring parthenogenesis in silkworm could not be found so far. Fortunately, artificial induction of parthenogenesis is possible in silkworm. So, it is very important to find out novel methods for induction of parthenogenesis. We investigated to attempt to get a novel parthenogenetic method. Accordingly, parthenogenetic studies on between unfertilized in vivo ovarian eggs of live silkworm moth(novel) and unfertilized in vitro ovarian eggs(conventional) taken out from live silkworm moth were investigated by hot water ($46^{\circ}C$), hot air ($46^{\circ}C$) and low temperature ($0^{\circ}C$ and $-20^{\circ}C$) treatments. The best ratio of parthenogenetic eggs was obtained with in vivo ovarian eggs of live silkworm moth rather than with in vitro ovarian eggs taken out from live silkworm moth in all the treatments. The optimum exposure time absolutely depended upon the temperatures of treatments and the forms of in vivo or in vitro ovarian eggs. From these results, we expect that in vivo artificial parthenogenetic treatments on live silkworm moth will be useful for the higher induction of parthenogenesis in the silkworm, B. mori.