• Korean Journal of Agricultural Science
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• v.46 no.3
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• pp.661-670
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• 2019

In this study, we constructed a model of an area where the production and production amount of wild vegetables which are designated as short term income forest products for the whole country are self-sufficient for the representative Eastern Braken fern(Pteridium aquilinum)and Edible aster(Aster scaber). The difference between the existing cultivation site and the model result was examined, and the distribution of the cultivable area was simulated according to the near future climate change by the 2050s. The degree of agreement between the cultivated area and the actual native area was very low at 14.5% for Eastern Braken fern and 12.9% for Edible aster. Using the Maxent model, which has already been proven by many research examples, the cultivation maps through the model can guarantee statistical accuracy by considering many variables. To analyze future location changes, the RCP 4.5 scenario and the RCP 8.5 scenario were applie Edible aster d to predict potential future cultivable areas and compare them to the present. There was no decrease in the cultivable area due to climate change nationwide. However, in the RCP 8.5 scenario for Eastern Braken fern and the RCP 4.5 scenario for Edible aster, declining areas such as Gangwon-do, Jeollabuk-do and Gyeongsangbuk-do showed prominence according to the scenarios. The result of this study suggests that various models can be used for the production of short-term forest productivity maps and it will be used as a climate change impact assessment data for competitive forest products considering the influence of future climate change.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Health Policy and Management
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• v.33 no.1
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• pp.40-54
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• 2023

Background: Based on the increase in the needs for convalescent rehabilitation medical services in Korea, this study aims to calculate the needs for rehabilitation services and examine its determinants for 229 regions. Methods: Claim data from the Health Insurance Review and Assessment Service were used to estimate patients who need to receive rehabilitation services, and data from various sources were also used for analysis. The number of cases and incidence rates of hospitalization related to convalescent rehabilitation were calculated to estimate the needs for services by region, and the results were visualized via a map. Multivariate regression and fixed effects regression using panel data were performed to identify the determinants of regional variation of the incidence rate. Results: First, the incidence rate of rural areas such as Jeolla-do, Gyeongsang-do, and Chungcheong-do was higher than urban areas (metropolitan cities). Second, the population, proportion of the elder, medical aid recipients, financial independence, traffic deaths, smoking, diabetes rate, and medical infrastructure correlated significantly with the incidence rate. Third, 'rho' values which mean the fraction of variance due to individual terms in panel data regression models were 0.965 and 0.976, respectively. Conclusion: The incidence rate of hospitalizations was correlated with most independent variables in this study and there is a gap between urban and rural areas. These regional disparities are fixed in our society. An improved regional convalescent rehabilitation system is suggested to cover the entire area including rural areas with a high rate of aging.

• Journal of the Korean Geotechnical Society
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• v.23 no.2
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• pp.5-17
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• 2007

Various field setup and filtering criteria have been suggested to avoid the near field effects in surface wave methods. Unlike other surface wave methods HWAW method uses the near field component positively. It is possible by using maximum energy point based on time-frequency map and inversion method to consider receiver locations from the source point and body wave component. To verify the HWAW method in the near field numerical study was performed and the wave propagation in the stratified soil media was simulated due to a surface point load. All of five representative soil models were used. The experimental dispersion curves, determined by HWAW method at the various receiver distances in the region of near field, all coincided well with the theoretical dispersion curves determined by 3D forward modeling (Kausel's method). Consequently, it was considered that the HWAW method can provide reliable $V_s$ profiles effectively in the near field.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.510-519
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• 2020

Within an intelligent automated cyber-physical system, the realization of the autonomous mechanism for data collection, data integration, and data analysis plays a critical role in the design, development, operation, and maintenance of such a system. This construct is particularly vital for fault-tolerant route-finding systems that rely on the imprecise GPS location of the vehicles to properly operate, timely plan, and continuously produce informative feedback to the user. More essentially, the integration of digital twins with cyber-physical route-finding systems has been overlooked in intelligent transportation services with the capacity to construct the network routes solely from the locations of the operating vehicles. To address this limitation, the present study proposes a conceptual architecture that employs digital twin to autonomously maintain, update, and manage intelligent transportation systems. This virtual management simulation can improve the accuracy of time-of-arrival prediction based on auto-generated routes on which the vehicle's real-time location is mapped. To that end, first, an intelligent transportation system was developed based on two primary mechanisms: 1) an automated route finding process in which predictive data-driven models (i.e., regularized least-squares regression) can elicit the geometry and direction of the routes of the transportation network from the cloud of geotagged data points of the operating vehicles and 2) an intelligent mapping process capable of accurately locating the vehicles on the map whereby their arrival times to any point on the route can be estimated. Afterward, the digital representations of the physical entities (i.e., vehicles and routes) were simulated based on the auto-generated routes and the vehicles' locations in near-real-time. Finally, the feasibility and usability of the presented conceptual framework were evaluated through the comparison between the primary characteristics of the physical entities with their digital representations. The proposed architecture can be used by the vehicle-tracking applications dependent on geotagged data for digital mapping and location tracking of vehicles under a systematic comparison and simulation cyber-physical system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.35-38
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• 2023

포트홀은 주행하는 자동차와 접촉이 이뤄지면 차체나 운전자에게 충격을 주고 제어를 잃게 하여 도로 위 안전을 위협할 수 있다. 포트홀의 검출을 위한 국내 동향으로는 진동을 이용한 방식과 신고시스템 이용한 방식과 영상 인식을 기반한 방식이 있다. 이 중 영상 인식 기반 방식은 보급이 쉽고 비용이 저렴하나, 컴퓨터 비전 알고리즘은 영상의 품질에 따라 정확도가 달라지는 문제가 있었다. 이를 보완하기 위해 영상 인식 기반의 딥러닝 모델을 사용한다. 따라서, 본 논문에서는 사전 학습된 딥러닝 모델의 정확도 향상을 위한 Feature Level Ensemble 기법을 제안한다. 제안된 기법은 사전 학습된 CNN 모델 중 Test 데이터의 정확도 기준 Top-3 모델을 선정하여 각 딥러닝 모델의 Feature Map을 Concatenate하고 이를 Fully-Connected(FC) Layer로 입력하여 구현한다. Feature Level Ensemble 기법이 적용된 딥러닝 모델은 평균 대비 3.76%의 정확도 향상을 보였으며, Top-1 모델인 ShuffleNet보다 0.94%의 정확도 향상을 보였다. 결론적으로 본 논문에서 제안된 기법은 사전 학습된 모델들을 이용하여 각 모델의 다양한 특징을 통해 기존 모델 대비 정확도의 향상을 이룰 수 있었다.

• Journal of Technologic Dentistry
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• v.45 no.3
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• pp.67-73
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• 2023

Purpose: This in vitro study aimed to evaluate the clinical acceptability of precision of fit of the support thickness of Co-Cr alloy copings fabricated using selective laser melting (SLM). Methods: Thirty dental stone models of maxillary left molar abutments were manufactured, images were taken using a scanner, and a computer-aided design program was used to design the form of a conventional metal ceramic crown coping. Overall, 30 single copings were made from Co-Cr alloy using SLM and divided into three support radius groups (0.1, 0.25, and 0.35 mm) of 10 for each. Digitized data were superimposed with three-dimensional inspection software to quantitatively obtain the machinability of a ceramic crown coping, and visual differences were confirmed using a color map. The root mean square values of the ceramic crown coping group were statistically analyzed using one-way analysis of variance (α=0.05). Results: The precision of fit was superior with 0.25 mm compared with 0.1 mm and 0.35 mm, and the results exhibited significant differences (p<0.05). All specimens showed that various support thicknesses did not exceed the clinically permitted value of 120 ㎛, which mean that more than 0.1 mm and 0.35 mm of support radius for SLM was adequate. Conclusion: The support thickness of Co-Cr alloy restoration fabricated using SLM is shown to affect the adaptation.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.771-783
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• 2023

This study aims to compare the performance of each machine learning model for preparing a grid-based disaster risk map related to flooding in Jung-gu, Ulsan, for Typhoon Chaba which occurred in 2016. Dynamic data such as rainfall and river height, and static data such as building, population, and land cover data were used to conduct a risk analysis of flooding disasters. The data were constructed as 10 m-sized grid data based on the national point number, and a sample dataset was constructed using the risk value calculated for each grid as a dependent variable and the value of five influencing factors as an independent variable. The total number of sample datasets is 15,910, and the training, verification, and test datasets are randomly extracted at a 6:2:2 ratio to build a machine-learning model. Machine learning used random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) techniques, and prediction accuracy by the model was found to be excellent in the order of SVM (91.05%), RF (83.08%), and KNN (76.52%). As a result of deriving the priority of influencing factors through the RF model, it was confirmed that rainfall and river water levels greatly influenced the risk.

• Restorative Dentistry and Endodontics
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• v.33 no.4
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• pp.352-368
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• 2008

This study was done to evaluate the reliability of the digital color analysis system (ShadeScan, CYNOVAD, Montreal. Canada) for dentistry. Sixteen tooth models were made by injecting the A2 shade chemical cured resin for temporary crown into the impression acquired from 16 adults. Surfaces of the model teeth were polished with resin polishing cloth. The window of the ShadeScan handpiece was placed on the labial surface of tooth and tooth images were captured, and each tooth shade was analyzed with the ShadeScan software. Captured images were selected in groups, and compared one another. Two models were selected to evaluate repeatability of ShadeScan, and shade analysis was performed 10 times for each tooth. And, to ascertain the color difference of same shade code analyzed by ShadeScan, CIE $L^*a^*b^*$values of shade guide of Gradia Direct (GC, Tokyo, Japan) were measured on the white and black background using the Spectrolino (GretagMacbeth, USA), and Shade map of each shade guide was captured using the ShadeScan. There were no teeth that were analyzed as A2 shade and unique shade. And shade mapping analyses of the same tooth revealed similar shade and distribution except incisal third. Color difference (${\Delta}E^*$) among the Shade map which analyzed as same shade by ShadeScan were above 3. Within the limits of this study, digital color analysis instrument for dentistry has relatively high repeatability, but has controversial in accuracy.