• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.195-201
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• 2016

Eddy current testing (ECT) is commonly applied for the inspection of automated production lines of metallic products, because it has a high inspection speed and a reasonable price. When ECT is applied for the inspection of a metallic object having an uneven target surface, such as the spline gear of a spline shaft, it is difficult to distinguish between the original signal obtained from the sensor and the signal generated by a defect because of the relatively large surface signals having similar frequency distributions. To facilitate the detection of defect signals from the spline gear, implementation of high-order filters is essential, so that the fault signals can be distinguished from the surrounding noise signals, and simultaneously, the pass-band of the filter can be adjusted according to the status of each production line and the object to be inspected. We will examine the infinite impulse filters (IIR filters) available for implementing an advanced filter for ECT, and attempt to detect the flaw signals through optimization of system design parameters for detecting the signals at the system level.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3792-3799
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• 2012

The dedusting characteristics of pulse air jet type dedusting system which is widely applied in the industries were identified by utilizing the computational fluid dynamics (CFD) and the dedusting performance in modified shape of dedusting unit was compared in this study. The review on the dedusting air volume, air stream distribution and inflow velocity distribution on each shape of dedusting part showed that the case of installing the nozzle on the blow tube (Case-3) and the case of installing the double intaking tube to the venturi (Case-4 and Case-5) were more excellent than the structure (Case-1) which is widely applied in the field in its amplification effect on the air volume and extension of stream width. The specification of venturi was decided to apply the selected Case-5 for the option of the commercial back filter. It is considered that the dedusting air volume will be maintained in maximum in the case of 50 mm and 90 mm for the diameter of internal and external intaking pipe respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.469-486
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• 2017

Economy-based urban redevelopment is the main priority in Malaysia, but has resulted in social problems such as gentrification, loss of heritage and identity, inequity, etc. Hence, it is crucial for the government to seek other alternatives rather than being solely reliant on urban redevelopment. Neighborhood renewal is a strategy involving the integration of redevelopment, rehabilitation, revitalization and preservation that aims to improve deprived areas using a more holistic approach. The aim of this paper is to review the neighborhood renewal policies in developed countries and to identify those elements that can be adopted in Malaysia. This study is conducted via a literature review. It was found that neighborhood renewal which integrates people-based, place-based and system-based policies highlights the importance of diversity, thereby aiming for resource optimization, community engagement and urban governance with the focal point of the fair, equity and systematic provision of resources. This paper concluded that neighborhood renewal in Malaysia should be initiated by locals with an emphasis on real local participation and a sustainable funding system. The government and local authorities should be observers rather than implementers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.7-14
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• 2021

Tomato crops are highly affected by tomato diseases, and if not prevented, a disease can cause severe losses for the agricultural economy. Therefore, there is a need for a system that quickly and accurately diagnoses various tomato diseases. In this paper, we propose a system that classifies nine diseases as well as healthy tomato plants by applying various pretrained deep learning-based CNN models trained on an ImageNet dataset. The tomato leaf image dataset obtained from PlantVillage is provided as input to ResNet, Xception, and DenseNet, which have deep learning-based CNN architectures. The proposed models were constructed by adding a top-level classifier to the basic CNN model, and they were trained by applying a 5-fold cross-validation strategy. All three of the proposed models were trained in two stages: transfer learning (which freezes the layers of the basic CNN model and then trains only the top-level classifiers), and fine-tuned learning (which sets the learning rate to a very small number and trains after unfreezing basic CNN layers). SGD, RMSprop, and Adam were applied as optimization algorithms. The experimental results show that the DenseNet CNN model to which the RMSprop algorithm was applied output the best results, with 98.63% accuracy.

• Journal of Aerospace System Engineering
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• v.13 no.3
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• pp.78-86
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• 2019

This paper describes the stiffness optimization of the torsion spring hinge of the large SAR antenna considering the deployment performance. A large SAR antenna is folded in a launch environment and then unfolded when performing a mission in orbit. Under these conditions, it is very important to find the proper stiffness of the torsion spring hinge so that the antenna panels can be deployed with minimal impact in a given time. If the torsion spring stiffness is high, a large impact load at the time of full deployment damages the structure. If it is weak, it cannot guarantee full deployment due to the deployment resistance. A multi-body dynamics analysis model was developed to solve this problem using RecurDyn and the development performance were predicted in terms of: development time, latching force, and torque margin through deployment analysis. In order to find the optimum torsion spring stiffness, the deployment performance was approximated by the response surface method (RSM) and the optimal design was performed to derive the appropriate stiffness value of the rotating springs.

• Journal of Climate Change Research
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• v.5 no.2
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• pp.153-163
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• 2014

This study analyzed directions of the energy product efficiency improvement and Carbon Tax for the domestic building sector. In order to analyze GHG reduction potential and total cost, the cost optimization model MESSAGE was used. In the case of the "efficiency improvement scenario," the cumulative potential GHG reduction amount - with respect to the "Reference scenario" - from 2010 to 2030 is forecast to be $104MtCO_2eq$, with a total projected cost of 2.706 trillion KRW. In the "carbon tax scenario," a reduction effect of $74MtCO_2eq$ in cumulative potential GHG reduction occurred, with a total projected cost of 2.776 trillion KRW. The range of per-ton GHG reduction cost for each scenario was seen to be approximately $-475{\sim}272won/tCO_2eq$, and the "efficiency improvement scenario" showed as the highest in the order of priority, in terms of the GHG reduction policy direction. Regarding policies to reduce GHG emissions in the building sector, the energy efficiency improvement is deemed to deployed first in the future.

• Progress in Medical Physics
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• v.30 no.1
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• pp.7-13
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• 2019

Purpose: To compare the dosimetrical and radiobiological parameters among various volumetric modulated arc therapy (VMAT) techniques using restricted and continuous arc beams for left-sided breast cancer. Materials and Methods: Ten patients with left-sided breast cancer without regional nodes were retrospectively selected and prescribed the dose of 42.6 Gy in 16 fractions on the planning target volume (PTV). For each patient, three plans were generated using the $Eclipse^{TM}$ system (Varian Medical System, Palo Alto, CA) with one partial arc 1pVMAT, two partial arcs 2pVMAT, and two tangential arcs 2tVMAT. All plans were calculated through anisotropic analytic algorithm and photon optimizer with 6 MV photon beam of $VitalBEAM^{TM}$. The same dose objectives for each plan were used to achieve a fair comparison during optimization. Results: For PTV, dosimetrical parameters such as Homogeneity index, conformity index, and conformal number were superior in 2pVMAT than those in both techniques. $V_{95%}$, which indicates PTV coverage, was 91.86%, 96.60%, and 96.65% for 1pVMAT, 2pVMAT, and 2tVMAT, respectively. In most organs at risk (OARs), 2pVMAT significantly reduced the delivered doses compared with the other techniques, excluding the doses to contralateral lung. For the analysis of radiobiological parameters, a significant difference in normal tissue complication probability was observed in ipsilateral lung while no difference was observed in the other OARs. Conclusions: Our study showed that 2pVMAT had better plan quality and normal tissue sparing than 1pVMAT and 2tVMAT but not for all parameters. Therefore, 2pVMAT could be considered the priority choice for the treatment planning for left breast cancer.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.155-163
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• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.203-215
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• 2022

The study is to investigate research trends and knowledge structures in the Smart Farm field. To achieve the research purpose, keywords and the relationship among keywords were analyzed targeting 104 Korean academic journals related to the Smart Farm in KCI(Korea Citation Index), and topics were analyzed using the LDA Topic Modeling technique. As a result of the analysis, the main keywords in the Korean Smart Farm-related research field were 'environment', 'system', 'use', 'technology', 'cultivation', etc. The results of Degree, Betweenness, and Eigenvector Centrality were presented. There were 7 topics, such as 'Introduction analysis of Smart Farm', 'Eco-friendly Smart Farm and economic efficiency of Smart Farm', 'Smart Farm platform design', 'Smart Farm production optimization', 'Smart Farm ecosystem', 'Smart Farm system implementation', and 'Government policy for Smart Farm' in the results of Topic Modeling. This study will be expected to serve as basic data for policy development necessary to advance Korean Smart Farm research in the future by examining research trends related to Korean Smart Farm.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.33-41
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• 2023

As carbon-free has been getting interest, renewable energy sources have been increasing. However, renewable energy is intermittent and variable so it is difficult to predict the produced electrical energy from a renewable energy source. In this study, digital-twin concept is applied to solve difficulties in predicting electrical energy from a renewable energy source. Considering that rotation of wind turbine has high correlation with produced electrical energy, a model which simulates rotation in the drivetrain of a wind turbine is developed. The base of a drivetrain simulation model is set with well-known state equation in mechanical engineering, which simulates the rotating system. Simulation based machine learning is conducted to get unknown parameters which are not provided by manufacturer. The simulation is repeated and parameters in simulation model are corrected after each simulation by optimization algorithm. The trained simulation model is validated with 27 real wind turbine operation data set. The simulation model shows 4.41% error in average compared to real wind turbine operation data set. Finally, it is assessed that the drivetrain simulation model represents the real wind turbine drivetrain system well. It is expected that wind-energy-prediction accuracy would be improved as wind turbine digital twin including the developed drivetrain simulation model is applied.