• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.317-324
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• 1997

This study was conducted to develope a machine vision system for a robotic transplanter for bedding plants. Specific objectives of this study were 1) to get coordinates of the healthy seedlings in high-density plug tray, and 2) to get the angle of the leaves of the healthy seedlings to avoid the damage to seedlings by gripper. Results of this study were summarized as follows. (1) The machine vision system of a robotic transplanter was developed. (2) Success rates of detecting empty cell and bad seedlings for 72-cell and 128-cell plug-trays were 98.8% and 94, 9% respectively. (3) Success rates of calculating the angle of leaves fer 72-cell and 128-cell plug-trays were 93.5% and 91.0% respectively.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1997.12a
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• pp.392-400
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• 1997

This study was conducted to develope the machine vision system of a robotic transplanter for bedding plants. Specific objectives of this study were 1) to get coordinates of the healthy seedlings except empty cells and bad seedlings in high-density plug tray, and 2) to get the angle of the leaves of the healthy seedlings to avoid damage to the seedlings by gripper. The results of this study are summarized as follows. (1) The machine vision system of a robotic transplanter was developed. (2) The success rates of detecting empty cell and bad seedlings in 72-cell and 128-cell plug trays were 98.8% and 94.9% respectively. (3) The success rates of calculating the angle of leaves in 72-cell and 128-cell plug trays were 93.5% and 91.0% respectively.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.113-114
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• 2008

For the supported high-technology, it is in need of estimation about power quality and reasonable price through machinism. The power system made much of precision digital industrial damage under sag. This study suggested electric machine under effects of power quality in the theory and test. A electric machine was simulated and experimented about sag. The test system made up IPC as voltage sag device. The test machine was magnetic contactor and PLC. The result, electric machines appeared to influence sag with CBEMA curve. It was make possible analysis of power system about a fault. This study was expected to method that investment and development of equipment on power market in the future.

• Journal of the Korean Society of Safety
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• v.26 no.4
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• pp.27-33
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• 2011

Forensic Engineering is the area covering the investigation of products, structures that fail to perform or do not function as intended, causing personal injury or damage to property. To investigate explosion accident of the centrifugal casting machine in terms of the forensic engineering, in this paper, the computing simulation using ADINA FSI has performed to investigate that the effect of the Check-Pin fracture by the flow phenomena and molten metal weight and the mechanical properties test of the accident Check-Pin has performed using the instrumented indentation technique. Through these studies, the safety accident that may occur in centrifugal casting machine can be minimized by performing specialized and systematic investigation of the accident cause in terms of the forensic engineering.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.307-312
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• 2003

The object of this study is to investigate a damage estimation of single edge cracked tensile specimens ($2_a$/W) as a function of acoustic emission (AE) according to the unidirectionally oriented carbon/epoxy composites, CFRP AE signals were analyzed and classified 3 regions by event counts, energy and amplitude for coressponding applied load. On tensile loading and using the results of the AE analysis, it was found that the event counts, cumulative counts or energy, and amplitude distributions useful for the prediction of damage failure.

• Steel and Composite Structures
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• v.17 no.2
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• pp.185-198
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• 2014

Carbon-manganese steel A42 (French standards) is used in steam generator pipes of nuclear center and subject to low cycle fatigue (LCF) loads. In order to obtain the material LCF behavior, the tests are implemented in a hydraulic fatigue machine. The LCF plastic deformation and cyclic stress in macroscope have been influenced by the accumulated low cycle fatigue damage. The constitutive kinematic and isotropic hardening modeling is modified with coupling fatigue damage to describe the fatigue behavior. The improved model seems to be good agreement with the test results.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.159-165
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• 2013

ESD damage by Charged Cable Model (CCM) is introduced. Due to its own impedance characteristic unlike Human Body Model (HBM) or Machine Model (MM) electric component can be destroyed even though it is located after typical protection circuit. Possible mechanism of ESD damage to automotive electric control unit (ECU) in vehicle environment by CCM discharge was investigated. Based on investigation, field-returned vehicle whose ECU is expected to be damaged by CCM discharge was tested to reproduce it and similar electric component destruction inside ECU was observed. Suggestions to reduce the possibility of ESD damage by CCM are introduced.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.287-294
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• 2021

The objective of this study was conducted to calculate the damage of whole crop maize in accordance with abnormal climate using the forage yield prediction model through machine learning. The forage yield prediction model was developed through 8 machine learning by processing after collecting whole crop maize and climate data, and the experimental area was selected as Gyeonggi-do. The forage yield prediction model was developed using the DeepCrossing (R²=0.5442, RMSE=0.1769) technique of the highest accuracy among machine learning techniques. The damage was calculated as the difference between the predicted dry matter yield of normal and abnormal climate. In normal climate, the predicted dry matter yield varies depending on the region, it was found in the range of 15,003~17,517 kg/ha. In abnormal temperature, precipitation, and wind speed, the predicted dry matter yield differed according to region and abnormal climate level, and ranged from 14,947 to 17,571, 14,986 to 17,525, and 14,920 to 17,557 kg/ha, respectively. In abnormal temperature, precipitation, and wind speed, the damage was in the range of -68 to 89 kg/ha, -17 to 17 kg/ha, and -112 to 121 kg/ha, respectively, which could not be judged as damage. In order to accurately calculate the damage of whole crop maize need to increase the number of abnormal climate data used in the forage yield prediction model.

• Advanced Composite Materials
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• v.18 no.1
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• pp.1-20
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• 2009

This paper describes the results of experiments and numerical simulation studies on the impact and indentation damage created by low-velocity impact subjected onto honeycomb sandwich panels for application to the BIMODAL tram. The test panels were subjected to low-velocity impact loading using an instrumented testing machine at six energy levels. Contact force histories as a function of time were evaluated and compared. The extent of the damage and depth of the permanent indentation was measured quantitatively using a 3-dimensional scanner. An explicit finite element analysis based on LS-DYNA3D was focused on the introduction of a material damage model and numerical simulation of low-velocity impact responses on honeycomb sandwich panels. Extensive material testing was conducted to determine the input parameters for the metallic and composite face-sheet materials and the effective equivalent damage model for the orthotropic honeycomb core material. Good agreement was obtained between numerical and experimental results; in particular, the numerical simulation was able to predict impact damage area and the depth of indentation of honeycomb sandwich composite panels created by the impact loading.

• Computers and Concrete
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• v.32 no.5
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.