• Asian-Australasian Journal of Animal Sciences
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• v.29 no.6
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• pp.793-800
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• 2016

Most hinnies (female donkey${\times}$male horse) and mules (female horse${\times}$male donkey) are sterile with few reports of equine fertile hybrids. The main cause of this sterility is thought to be a meiotic block to spermatogenesis and oogenesis. This study compared the developmental features of the testes and a histological analyses of spermatogenesis in a male hinny with those of a normal, fertile stallion and Jack donkey. Hinny testes showed a thicker tunica albuginea, fewer blood vessels and more connective tissue in the testis parenchyma than those of the stallion and Jack donkey. Although the mean number of seminiferous tubules was significantly higher in stallion and hinny than Jack donkey (p<0.01), the mean proportion of seminiferous tubules was lower in the hinny (p<0.01) which resulted in a smaller diameter of seminiferous tubules. The mean number of spermatogonia and spermatocytes per unit area were significantly lower in hinny testis (p<0.01) and no spermatids or mature spermatozoa cells were found during immunofluorescent analyses. These results indicated that defects in seminiferous tubule development and structure occur in the testis of hinnies. Furthermore, most spermatogonia and spermatocytes cease development in synapsis during mid-meiosis of spermatocytes, which results in a block to spermatogenesis that prevents the formation of spermatids and matured spermatozoa during meiosis in male hinnies.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1287-1294
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• 2004

Hub bearings not only sustain the body of a cat, but permit wheels to rotate freely. Excessive radial or axial load and many other reasons can cause defects to be created and grown in each component. Therefore, nitration and noise from unwanted defects in outer-race, inner-race or ball elements of a Hub bearing are what we want to detect as early as possible. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing signal has Periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.593-596
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• 2004

Hub bearings not only sustain the body of a car, but permit wheels to rotate freely. Excessive radial or axial load and many other reasons can cause defects to be created and grown in each component. Therefore, vibration and noise from unwanted defects in outer-race, inner-race or ball elements of a Hub bearing are what we want to detect as early as possible. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing signal has periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1995.03a
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• pp.19-30
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• 1995

The analysis had been already completed to detect forming defects for the forming processes of C/R bearing rings. But some unpredicted problems were found through the experiments. So expert redesigned new forming processes to prevent the problems and new analysis was began according to the new processes to find faults for the processes. The forming processes consist of 1 for the outer ring. 6 inner ring. The thickness of metal sheet used is changed to 1.5mm from 1.6mm. Elasto-plastric finite element method is applied to involve the effect of spring back . The most representative alteration is forming of two predents to assist later forming . Thining and distribution of high residual stress are derived from the results of simulations. It is confirmed that the industry expert agree the possiblilty of defects dervied from the new FEM results.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.6
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• pp.82-87
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• 2006

Availability of defect test algorithm that recognizes exact and standardized defect information in order to fundamentally resolve generated defects in industrial sites by giving artificial intelligence to SAT(Scanning Acoustic Tomograph), which previously depended on operator's decision, to find various defect information in a semiconductor package, to decide defect pattern, to reduce personal errors and then to standardize the test process was verified. In order to apply the algorithm to the lately emerging Neural Network theory, various weights were used to derive results for performance advancement plans of the defect test algorithm that promises excellent field applicability.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.345-350
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• 2002

In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method fur entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.356-362
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• 2009

The ELectromagnetic Core Imperfection Detector (EL-CID) test was performed on a small generator in the laboratory and a gas turbine generator in the field to assess the fault condition of generator stator core. Artificial defects with six different sizes were introduced in the small generator. The scan results on six defects show a very large increase in the magnitude of fault current compared to that obtained with a healthy core. After the stator core heats up, a thermal imaging camera was used to detect hot spot on the inner surface of the core for comparison. Several faults were found during inspection of the gas turbine generator with the EL-CID. It has been shown that the existence of a fault can be determined by monitoring the magnitude of fault current.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.80-84
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• 2002

In this study, researchers developed the est algorithm for artificial defects in the semic packages and performed to it by pattern recogn technology. For this purpose, this algorithm was I that researcher made software with matlab. The so consists of some procedures including ultrasonic acquistion, equalization filtering, self-organizing backpropagation neural network. self-organizing ma backpropagation neural network are belong to metho neural networks. And the pattern recognition tech has applied to classify three kinds of detective pa semiconductor packages. that is, crack, delaminat normal. According to the results, it was found estimative algorithm was provided the recognition r 75.7%( for crack) and 83.4%( for delamination) 87.2 % ( for normal).

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.91-96
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• 2005

In this study, researchers developed the estimative algorithm for artificial defects in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-Organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages: Crack, Delamination and Normal. According to the results, we were confirmed that estimative algorithm was provided the recognition rates of $75.7\%$ (for Crack) and $83_4\%$ (for Delamination) and $87.2\%$ (for Normal).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.721-726
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• 2005

In this study, researchers classifying the artificial flaws in semiconductor. packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method for entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.