• The Research Journal of the Costume Culture
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• v.30 no.3
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• pp.414-428
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• 2022

The goal of this study was to develop a curriculum based on practice and job competency, reflecting opinions on the required job competence of nail practitioners and professionals related to nail beauty. Through in-depth interviews with nail experts, the research focuses on developing nail beauty competency-based curriculum and curriculum profiles that reflect practitioners' needs of job competence in the field. In-depth interviews with 11 field experts and surveys of 154 people were conducted to develop a competency-based curriculum for beginner nail hairdressers. The results of this study show that the existing 38 National Competency Standards (NCS) job competencies were reduced to 21 job competencies. In addition, based on the common opinions of experts who reflect the current trend, two tasks on "eyelashes" and "waxing" were added, and they were modified and supplemented with 23 core competencies. The development of a competency-based curriculum and educational programs for nail beauty was performed based on the requirements of the core competencies investigated and the development of a systematic map for the core competencies of beginner nail technicians and hairdressers. In conclusion, the need for professional education and training for nail hairdressers is growing, and it can be seen that a curriculum building multi-faceted abilities is needed for their qualifications as experts. This study found that it is necessary to develop interpersonal communication skills that include marketing elements other than practical skills such as personality and customer response methods in the nail beauty curriculum.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.891-898
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• 2022

Purpose: There is not much time left for mandatory BIM implementation for all sectors and stages of the construction industry. Therefore, it is necessary to find a way to secure technology to substantially improve the productivity of BIM work. In the research, we proposed a method to automatically verify related construction standards for major objects produced by BIM modeling procedures so that engineers can verify construction standards in the BIM-based design process. Method: We defined a modeling work procedure for BIM-based road design work and prepared a method for constructing related design standards in a database. In addition, a process map for developing a BIM-based design basis review automation system was also presented. Result: A BIM-based design standard review automation module was developed using Civil3D and Dynamo. And it was confirmed by the test application that it is possible to quickly judge whether the BIM object manufactured in the design process conforms to the construction design standard. Conclusion: BIM-based design standard review automation technology can improve the productivity of BIM model production work and secure the quality of the BIM model.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.1
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• pp.17-34
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• 2023

This study aimed to develop a fitted torso pattern with an improved fit for inverted-triangular males in their twenties. For this study, six torso patterns were collected, compared and evaluated, and a fitted torso pattern was developed using virtual fittings. The research results are as follows. First, the fitted torso pattern received a good rating as a result of the virtual fitting evaluation: waist dart set 2 of the front; the amount of comfort is set at 5 cm around the chest, 4 cm around the waist and 10 cm around the hips. Second, the evaluation of virtual fitting of the development pattern showed that fit evaluation was 4.11/5 points, ease evaluation was 6.53/7 points, and that the stress map and airgap were suitable for the human body. Third, the actual fit evaluation of the development pattern was 4.25/5 points, 6.35/7 points for ease evaluation, and 4.81/5 points for motion evaluation. Fourth, there was no significant difference between the results of the virtual and actual fitting evaluation with the objectivity test. It is therefore possible to apply a pattern developed through a virtual fitting to an actual human body and to confirm the objectivity of the pattern.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.732-737
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• 2009

Due to various project delivery methods and the complexity of construction projects in the construction industry, developing the framework of construction management for critical, highly complex projects in the construction industry has become problematic. Currently, a set of construction manuals play a pivotal role in planning and managing construction projects as subcontractors try to complete their scope of work according to the instructions of a general contractor. It is challenging for general contractors to write a construction management procedure manual to cover various types of project delivery methods and construction projects. In construction, the construction procedure manuals describe specific actions to be taken through the project. In reality a few contactors own such manuals and their construction schedules include more construction operation activities. Thus, it is hard to estimate the workload and productivity of construction managers because the manual and the schedule do not present the amount of management efforts required to complete a project. This paper proposes a framework to present construction management tasks according to project delivery methods which can be applied to various construction projects. Actions for management tasks were mapped and were integrated with construction activities throughout the project life cycle. The framework can then be used to give specific instructions to project participants, collect management actions, and replicate management actions throughout the project life cycle. The framework can also be can used to visualize complete construction project to analyze and manage construction management activities in each phase of a project in order to enhance productivity and efficiency. The studies of existing construction manuals were carried out to identify construction managers' responsibilities. An artificial intelligence program, CLIPS (C-Language Integrated Production System) was used to search for appropriate actions for impending tasks from a set of predefined actions to be performed for a given situation. The framework would significantly help construction managers to understand interrelations among management tasks or actions within a project. Furthermore, the framework can be embedded into Building Information Modeling (BIM) or Facility Management Systems (FMS) so that designers and constructors would execute constructability review before construction begins.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.651-657
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• 2023

Railroad alignment improvements and operating speed increase occupy considerable portion in recent railroad market. Developing countries have limitations to construction of new high speed railroads due to the burden of budgets and the lack of demands, and the projects of operating speed increase arepracticallyrecommended. Thisstudy developed the methodologyto provide the railroad alignment design alternatives and the costs by upgrading the "Intelligent Railroad Alignment Design Program (ei-Rail)" which has been used to obtain the alignment plans and construction costs for railroad construction projects. The program provides the cost for alignment improvement, design drafts and the effects of operating speed increase with the input of target improvement speed and the prevailing railroad alignment on the numerical map. It is then expected for the ei-Rail program to be used for the supporting tool for the railroad alignment improvement projects.

• Journal of Drive and Control
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• v.20 no.2
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• pp.15-23
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• 2023

In this study, two-dimensional location of crops for auto weeding was detected using deep learning. To construct a dataset for soybean detection, an image-capturing system was developed using a mono camera and single-board computer and the system was mounted on a weeding robot to collect soybean images. A dataset was constructed by extracting RoI (region of interest) from the raw image and each sample was labeled with soybean and the background for classification learning. The deep learning model consisted of four convolutional layers and was trained with a weakly supervised learning method that can provide object localization only using image-level labeling. Localization of the soybean area can be visualized via CAM and the two-dimensional position of the soybean was estimated by clustering the pixels associated with the soybean area and transforming the pixel coordinates to world coordinates. The actual position, which is determined manually as pixel coordinates in the image was evaluated and performances were 6.6(X-axis), 5.1(Y-axis) and 1.2(X-axis), 2.2(Y-axis) for MSE and RMSE about world coordinates, respectively. From the results, we confirmed that the center position of the soybean area derived through deep learning was sufficient for use in automatic weeding systems.

• Plant Breeding and Biotechnology
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• v.7 no.3
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• pp.272-286
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• 2019

Developing elite hybrid rice varieties is one important objective of rice breeding programs. Several genes related to male sterilities, restores, and pollinators have been identified through map-based gene cloning within natural variations of rice. These identified genes are good targets for introducing genetic traits in molecular breeding. This study was conducted to breed elite hybrid lines with major genes related to hybrid traits and disease/insect resistance in 240 genetic resources and F₁ hybrid combinations of rice. Molecular markers were reset for three major hybrid genes (S5, Rf3, Rf4) and thirteen disease/insect resistant genes (rice bacterial blight resistance genes Xa3, Xa4, xa5, Xa7, xa13, Xa21; blast resistance genes Pita, Pib, Pi5, Pii; brown planthopper resistant genes Bph18(t) and tungro virus resistance gene tsv1). Genotypes were then analyzed using molecular marker-assisted selection (MAS). Biological assay was then performed at the Red River Delta region in Vietnam using eleven F₁ hybrid combinations and two control vatieties. Results showed that nine F₁ hybrid combinations were highly resistant to rice bacterial blight and blast. Finally, eight F₁ hybrid rice varieties with resistance to disease/insect were selected from eleven F₁ hybrid combinations. Their characteristics such as agricultural traits and yields were then investigated. These F₁ hybrid rice varieties developed with major genes related to hybrid traits and disease/insect resistant genes could be useful for hybrid breeding programs to achieve high yield with biotic and abiotic resistance.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.201-201
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• 2022

Rice bakanae disease is a serious global threat in major rice-cultivating regions worldwide causing high yield loss. It is caused by the fungal pathogen Fusarium fujikuroi. Varying degree of resistance or susceptibility to bakanae disease had been reported among Korean japonica rice varieties. We developed a modified in vitro bakanae disease bioassay method and tested 31 Korean japonica rice varieties. Nampyeong and Samgwang varieties showed highest resistance while 14 varieties including Junam and Hopum were highly susceptible with 100% mortality rate. We carried out mapping QTLs for bakanae disease resistance with four F2:F3 populations derived from the crosses between Korean japonica rice varieties. The Kompetitive Allele-Specific PCR (KASP) markers developed in our laboratory based on the SNPs detected in Korean japonica rice varieties were used in genotyping F2 plants in the populations. We found four major QTLs on chromosome 1, 4, 6, and 9 with LOD scores of 21.4, 6.9, 6.0, and 60.3, respectively. In addition, we are doing map-based cloning of the QTLs on chromosome 1 and 9 which were found with Junam/Nampyeong F2:F3 population and Junam/Samgwang F2:F3 population, respectively. These QTLs will be very useful in developing bakanae disease resistant high quality rice varieties.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• Journal of Environmental Health Sciences
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• v.50 no.2
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• pp.113-124
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• 2024

Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.