• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.5
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• pp.13-29
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• 2022

According to the revision of the Convenience Act for Persons with Disabilities, parks and park facilities where the first park development plan is established after December 4, 2021 are mandatory, and parks must be equipped with convenience facilities for the disabled. Hence, this study aims to derive the improvements of the park evaluation index by analyzing the park certification evaluation index, the building certification evaluation index of park facilities, and the evaluation reports of the current certification status cases. As a research method, first, the certification of parks and park facilities were compared and reviewed with the Park Green Act, and differences in the certification process and certification performance were compared and analyzed. Second, differences and common items were derived by analyzing barrier free (BF)-certification evaluation indicators for parks and buildings. Third, improvement plans were derived after analyzing differences and problems in 4 BF-certified parks and four building certification cases of park facilities in certified parks, focusing on the self-evaluation report and examination results. As a result of analyzing the park and building evaluation indicators, the items for which the evaluation purpose, evaluation method, and evaluation items were commonly applied to 7 access roads for each facility, 5 parking areas for the disabled, 2 guide facilities for information facilities, 14 in 5 categories of sanitation facilities, and 1 for other facilities. In the case of sanitation facilities, there is no case where it was evaluated as a park. If the park does not have an attached toilet, the park is certified as a building. Hence, it would be essential to establish the concept of an attached toilet and discuss the application of the evaluation index on the park sanitation facility. The score of buildings in parks and park facilities was lower than that of the self-evaluation results, and the certification grades of buildings declined in three cases. The items with the highest standard deviation were BF walking continuity for parks and the path to the main entrance among access roads for buildings. As a result of analyzing the park and building evaluation results of 19 common evaluation items except for sanitary facilities, the difference in the grades of the evaluation items for each case site except for one item appeared. Therefore, applying common detailed calculation criteria for items evaluated in common with parks and buildings is needed. Since sanitation facilities have no cases of park certification and are not certified as buildings, it is essential to establish the concept of attached toilets and discuss the application of park sanitation evaluation indicators. It is necessary to develop an evaluation index suitable for the characteristics of the park, such as adjusting the items that are not evaluated in parks and establishing an evaluation index considering the ones of parks. It expects that this study would be used as primary data for improving park certification indicators.

• Fashion & Textile Research Journal
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• v.24 no.6
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• pp.667-685
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• 2022

This paper aims to investigate 4D printing materials for soft robots. 4D printing is a targeted evolution of the 3D printed structure in shape, property, and functionality. It is capable of self-assembly, multi-functionality, and self-repair. In addition, it is time-dependent, printer-independent, and predictable. The shape-shifting behaviors considered in 4D printing include folding, bending, twisting, linear or nonlinear expansion/contraction, surface curling, and generating surface topographical features. The shapes can shift from 1D to 1D, 1D to 2D, 2D to 2D, 1D to 3D, 2D to 3D, and 3D to 3D. In the 4D printing auxetic structure, the kinetiX is a cellular-based material design composed of rigid plates and elastic hinges. In pneumatic auxetics based on the kirigami structure, an inverse optimization method for designing and fabricating morphs three-dimensional shapes out of patterns laid out flat. When 4D printing material is molded into a deformable 3D structure, it can be applied to the exoskeleton material of soft robots such as upper and lower limbs, fingers, hands, toes, and feet. Research on 4D printing materials for soft robots is essential in developing smart clothing for healthcare in the textile and fashion industry.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.265-273
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• 2022

Waxy corn (Zea mays L.), which contains homozygous mutant alleles for the waxy1 (wx1) gene, is widely consumed as a snack food in Asia. This study evaluated sixteen agronomic characteristics of inbred Korean waxy corn lines to aid development of high-quality waxy corn cultivars. The plant materials studied were 177 inbred waxy corn lines developed by the National Institute of Crop Science, Rural Development Administration, Republic of Korea. For the tested lines, days to tasseling and silking averaged 77.69±2.22 days (with a range of 56-97 days), and 81.12±7.56 days (66-99 days), respectively. Plant length ranged from 88 to 237 cm (averaged 164.88±22.67 cm), ear length averaged 11.75±2.52 cm (5.0-18.5 cm), and ear width averaged 2.94±0.68 cm (1.4-4.5 cm). The number of rows on each ear of corn averaged 12.22±2.22 (7-32 rows) and the kernel number averaged 24.30±4.22 (9-37 kernels) per row. The crude protein content was 12.05±1.53% (8.90-21.80%) and total starch content was 69.27±5.74% (49.5-83.9%). Principal component analysis revealed that ear width, grain length, ear length, days to tasseling, days to silking, percentage of ear setting height, and total starch are features that allow distinction between the 177 waxy inbred corn lines. Hierarchical cluster analysis identified twelve waxy inbred lines that produce tall plants and have a short silking period. These lines may improve yield among quickly growing corn varieties.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Korean journal of applied entomology
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• v.61 no.3
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• pp.399-408
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• 2022

There has been much debate on the morphometric divergence between the recently identified Apis cerana koreana and Apis cerana honey bees. The aim of this study was to obtain phenotypic information that can be used to compare A. c. koreana data with other A. cerana subspecies data from open resources and determine breeding results on the basis of morphometric traits. To differentiate A. c. koreana, we investigated 22 classic morphological characteristics; royal jelly secretion; and the weight of workers, queens, and drones of A. c. koreana bred in Korea. To define the selection results, we used the geometric morphometric method. The artificially selected A. c. koreana secreted significantly more royal jelly (1.18 times) than the naturally selected A. c. koreana, which positively influenced the health of the colonies. These honey bees were identified more clearly with the geometric morphometric method than with the classic morphometric method, which is traditionally used to determine the subspecies. Large trends were noted for A. c. koreana on the basis of our results and literature from the 1980s regarding A. cerana sizes in Korea (tarsal index, length of forewing, and cubital index were measured). The cluster analysis revealed the proximity of A. c. koreana, A. cerana in China, and A. c. indica on the basis of eight classic characters, which, perhaps, relay the origin of the honey bees. The results of this study defined the morphometric responses of A. c. koreana honey bees to geographic isolation, climate change, and selection, which are important to identify, protect, and preserve honey bee stock in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Journal of Food Hygiene and Safety
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• v.38 no.4
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• pp.211-216
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• 2023

A total of 100 commercially available olive oil products were analyzed for 179 pesticide residues using gas chromatography-tandem mass spectrometry (GC/MS/MS). The olive oil samples were mixed with organic solvents, centrifuged and frozen to remove fat, and pesticide residues were analyzed using the "quick, easy, cheap, effective, rugged, and safe" (QuEChERS) method. The determination coefficient (R²) of the analysis method used in this study was ≥0.998. The detection limit of the method ranged 0.004-0.006 mg/kg and its quantitative limit ranged 0.012-0.017 mg/kg. The recovery rate (n=5) measured at the level ranging 0.01-0.02, 0.1, and 0.5 mg/kg ranged 66.8-119.5%. The relative standard deviation (RSD) was determined to be ≤5.7%, confirming that this method was suitable for the "Guidelines for Standard Procedures for Preparing Food Test Methods". The results showed that a total of 151 pesticides (including difenoconazole, deltamethrin, oxyfluorfen, kresoxim-methyl, phosmet, pyrimethanil, tebuconazole, and trifloxystrobin) were detected in 64 of the 100 olive oil products. The detection range of these pesticide residues was 0.01-0.30 mg/kg. The percentage acceptable daily intake (%ADI) of the pesticides calculated using ADI and estimated daily intake (EDI) was 0.0001-0.1346, indicating that the detected pesticides were present at safe levels. This study provides basic data for securing the safety of olive oil products by monitoring pesticide residues in commercially available oilve oil products. Collectively, the analysis method used in this study can be used as a method to analyze residual pesticides in edible oils.