• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.10a
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• pp.287-292
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• 1998

In particle or short-fiber reinforced composites. cracking of the reinforcements is a significant damage mode because the broken reinforcements lose load carrying capacity. This paper deals with the load carrying capacity of intact and broken ellipsoidal inhomogeneities embedded in an infinite body and a damage theory of particle or short-fiber reinforce composites. The average stress in the inhomogeneity represents its load carrying capacity. and the difference between the average stresses of the intact t and broken inhomogeneities indicates the loss of load carrying capacity due to cracking damage. The composite in damage process contains intact and broken reinforcements in a matrix. An incremental constitutive relation of particle or short-fiber reinforced composites including the progressive cracking damage of the reinforcements have been developed based on the Eshelby's equivalent inclusion method and Mori and Tanaka's mean field concept. Influence of the cracking damage on the stress-strain response of the composites is demonstrated.

• Smart Structures and Systems
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• v.22 no.2
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• pp.231-237
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• 2018

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.1-7
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• 2022

In this paper, a study was conducted to predict natural disasters in Afghanistan based on machine learning. Natural disasters need to be prepared not only in Korea but also in other vulnerable countries. Every year in Afghanistan, natural disasters(snow, earthquake, drought, flood) cause property and casualties. We decided to conduct research on this phenomenon because we thought that the damage would be small if we were to prepare for it. The Azure Machine Learning Studio used in the study has the advantage of being more visible and easier to use than other Machine Learning tools. Decision Forest is a model for classifying into decision tree types. Decision forest enables intuitive analysis as a model that is easy to analyze results and presents key variables and separation criteria. Also, since it is a nonparametric model, it is free to assume (normality, independence, equal dispersion) required by the statistical model. Finally, linear/non-linear relationships can be searched considering interactions between variables. Therefore, the study used decision forest. The study found that overall accuracy was 89 percent and average accuracy was 97 percent. Although the results of the experiment showed a little high accuracy, items with low natural disaster frequency were less accurate due to lack of learning. By learning and complementing more data, overall accuracy can be improved, and damage can be reduced by predicting natural disasters.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.2
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• pp.127-136
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• 2022

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMY_normal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 m/s). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.1
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• pp.11-21
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• 2023

This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMY_normal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.495-508
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• 2021

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.4
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• pp.106-112
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• 2004

In particle or short-fiber reinforced composites, cracking or debonding of the reinforcements cause a significant damage mode because the damaged reinforcements lose load carrying capacity. The average stress in the inhomogeneity represents its load carrying capacity, and the difference between the average stresses of the intact and broken inhomogeneities indicates the loss of load carrying capacity due to cracking damage. The composite in damage process contains intact and broken reinforcements in a matrix. An incremental constitutive relation of discontinuously-reinforced composites including the progressive cracking damage of the reinforcements have been developed based on the Eshelby's equivalent inclusion method and Mori-Tanaka's mean field concept. Influence of the cracking damage on the stress-strain response of the composites is demonstrated.

• Journal of Aerospace System Engineering
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• v.7 no.1
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• pp.39-43
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• 2013

In this study, damage assesment and repair technique of aircraft adopted on Sandwich composite structure were performed. The sandwich composite structure were damaged by drop weight type impact test machine. The damaged sandwich composite structure was repaired using external patch repair method after removing damaged area. This study presents comparison results of the experimental investigation between the impact damaged and the repaired specimen.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.1
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• pp.29-39
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• 2013

Fishing vessels have been causing more than 70% of marine casualty in Korea. As a view of the occurring number of marine casualty, it is obvious for fishing vessel to account for the absolute high portion of that in comparison with the non-fishing vessels. That is a natural outcome because fishing vessels have occupied more than 90% of all registered Korean vessels. If we consider it not occurring number, but occurring ratio, we could find out that fishing vessels accounted for 5 times lower than non-fishing vessels in marine casualties. Nevertheless, fishing vessels have not immunity from responsibility for marine casualties at all, because the tendency of it in fishing vessel has been dominating the whole marine casualties in Korea. So for reduction of them, it is indispensable to decrease the casualties of fishing vessel. In this study, the authors tried to carry out many items of them to compare the occurring number with the occurring ratio, and dealt with the casualties of collision and machine damage in detail, because those have not only been occurring most frequently in casualties in Korea, but also led to the death and injury of lives. To reduce the collision and the machine damage, the operator have to keep the watch more strictly and check and keep the machine in good order. And it is necessary for the operator to take more education and training intended to decrease those systematically and continuously, especially for the crews of fishing vessels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.164-169
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• 1997