• Journal of the Korean Wood Science and Technology
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• v.47 no.2
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• pp.229-238
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• 2019

Various wood defects occur during tree growing or wood processing. Thus, to use wood practically, it is necessary to objectively assess their quality based on the usage requirement by accurately classifying their defects. However, manual visual grading and species classification may result in differences due to subjective decisions; therefore, computer-vision-based image analysis is required for the objective evaluation of wood quality and the speeding up of wood production. In this study, the SIFT+k-NN and CNN models were used to implement a model that automatically classifies knots and analyze its accuracy. Toward this end, a total of 1,172 knot images in various shapes from five domestic conifers were used for learning and validation. For the SIFT+k-NN model, SIFT technology was used to extract properties from the knot images and k-NN was used for the classification, resulting in the classification with an accuracy of up to 60.53% when k-index was 17. The CNN model comprised 8 convolution layers and 3 hidden layers, and its maximum accuracy was 88.09% after 1205 epoch, which was higher than that of the SIFT+k-NN model. Moreover, if there is a large difference in the number of images by knot types, the SIFT+k-NN tended to show a learning biased toward the knot type with a higher number of images, whereas the CNN model did not show a drastic bias regardless of the difference in the number of images. Therefore, the CNN model showed better performance in knot classification. It is determined that the wood knot classification by the CNN model will show a sufficient accuracy in its practical applicability.

• Journal of the Korea Institute of Building Construction
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• v.19 no.2
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• pp.175-184
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• 2019

This paper presents a modeling prototype that optimizes construction processes implementing information communication technology (ICT). Using "IAMB", each and every actual steps of the construction process may be defined visually and explicitly by elaborating ICT to improve productivity and management efficiency. It contributes to identify the features on what parts of construction task are covered by ICT, and the effects of ICT on process performance. After analyzing 10 real project cases, ICT application types are classified into four categories according to ICT type, job functions that ICT applied, and the project phase in which ICT used. As a reuslt, it was confirmed that the positive outcomes on the construction process were beneficial to mostly the general contractors and mainly obtained by automated information processing, external consignment and consistent information use. Negative effects, which were occurred by mainly manual manipulation and duplication of information handling, were accrued to general contractors minimally, but to subcontractors maximally. Expert focus group commends that several important issues should be considered when implementing ICT.

• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.41-48
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• 2021

As the rural population continues to decline and aging, the improvement of agricultural productivity is becoming more important. Early prediction of crop quality can play an important role in improving agricultural productivity and profitability. Although many researches have been conducted recently to classify diseases and predict crop yield using CNN based deep learning and transfer learning technology, there are few studies which predict postharvest crop quality early in the planting stage. In this study, a early quality prediction model is proposed for sprout ginseng, which is drawing attention as a healthy functional foods. For this end, we took pictures of ginseng seedlings in the planting stage and cultivated them through hydroponic cultivation. After harvest, quality data were labeled by classifying the quality of ginseng sprout. With this data, we build early quality prediction models using several pre-trained CNN models through transfer learning technology. And we compare the prediction performance such as learning period and accuracy between each model. The results show more than 80% prediction accuracy in all proposed models, especially ResNet152V2 based model shows the highest accuracy. Through this study, it is expected that it will be able to contribute to production and profitability by automating the existing seedling screening works, which primarily rely on manpower.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.187-201
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• 2021

Recently, the demand for a 3D urban spatial information infrastructure for storing, operating, and analyzing a large number of digital data produced in cities is increasing. CityGML is a 3D spatial information data standard of OGC (Open Geospatial Consortium), which has strengths in the exchange and attribute expression of city data. Cases of constructing 3D urban spatial data in CityGML format has emerged on several cities such as Singapore and New York. However, the current ecosystem for the creation and editing of CityGML data is limited in constructing CityGML data on a large scale because of lack of completeness compared to commercial programs used to construct 3D data such as sketchup or 3d max. Therefore, in this study, a method of constructing CityGML data is proposed using commercial 3D mesh data and 2D polygons that are rapidly and automatically produced through aerial LiDAR (Light Detection and Ranging) or RGB (Red Green Blue) cameras. During the data construction process, the original 3D mesh data was geometrically transformed so that each object could be expressed in various CityGML LoD (Levels of Detail), and attribute information extracted from the 2D spatial information data was used as a supplement to increase the utilization as spatial information. The 3D city features produced in this study are CityGML building, bridge, cityFurniture, road, and tunnel. Data conversion for each feature and property construction method were presented, and visualization and validation were conducted.

• Food Science and Industry
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• v.55 no.2
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• pp.154-165
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• 2022

Humans have been breeding the mulberry silkworm for the long period of time to obtain silk fabric and nutrient-rich pupae. Currently, silkworm larvae, pupae, and silk-Fibroin hydrolysates are registered as food raw materials, while silkworm feces and Bombyx batryticatus are registered as Korean traditional medicines. Among sericulture products, individually recognized health functional food ingredients include silk-protein acid-hydrolysates for immunity enhancement, Fibroin-hydrolysates for memory improvement, and freeze-dried 5^th instar and 3^rd-day-silkworm powder for lowering-blood sugar. Recently, HongJam produced by steaming and freeze-drying mature silkworms were reported to have various health-promoting effects such as preventing the onset of Alzheimer's disease and Parkinson's disease, enhancing gastro-intestinal functions, improving skin-whitening and hair growth, and extending healthspan. By consuming silkworm products with various health-promoting effects, it is possible to increase the healthspan of human beings, thereby reducing personal and national medical expenses, resulting in increasing the individual's happiness.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.28-36
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• 2022

Currently, the automation and intelligence of the shipbuilding industry have improved its work production capacity and cost competitiveness, but the reduction rate of safety accidents among industrial site workers is still low and the damage caused by safety accidents is very serious, so there is a need for improvement according to the workplace. This research aims to demonstrate the connectivity between smart safety helmets in the demonstration area to verify the effectiveness along with the development of smart helmets for worker protection and environmental safety in shipyards. For efficient communication between workers, impact noise of over 95dB was confirmed in the workplace, and noise reduction was required. To solve this problem, the filtering performance was compared and analyzed using the Butterworth, Chebyshev, and elliptic algorithms. The connectivity test and noise reduction method between smart helmets proposed in this study will increase the usability and safety of the field through the development of advanced smart helmets tailored to the shipbuilding workplace in the future.

• Korean Journal of Construction Engineering and Management
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• v.23 no.6
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• pp.111-118
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• 2022

Recently, as underground facilities buried under roads in Korea are aging, the amount of underground facility maintenance work is rapidly increasing. For the maintenance and management of such underground facilities, the cutting work of the road pavement should be preceded. However, the conventional road pavement cutters used in Korea are not eco-friendly, and the reality is that they generate a lot of noise and cutting sludge (scattering dust). Therefore, in this study, the concept of the cutting sludge recovery device was derived, and an eco-friendly pavement cutter including this function was designed and manufactured. The developed equipment took about 20 to 30 seconds to cut 1m to a depth of 100 to 150mm. Also, the sludge suction performance was good in most sections, and the noise level of the equipment briefly measured at a distance of 2m was 82.7dB on average. However, due to the limitation that the developed equipment was at the level of the first prototype, the driving stability was somewhat low, and equipment abnormalities such as engine shutdown and sludge recovery performance decreased in some cases. The cutting performance and sludge recovery function will be more stable through tuning and improvement of the developed prototype in the future. In addition, we plan to quantitatively compare and analyze productivity by applying the improved prototype to actual field conditions.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.387-392
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• 2022

To participate in global carbon neutrality, the Korean government is also planning to carry out zero-energy building certification for all buildings by 2030 through the enforcement decree of the 'Green Building Support Act'. Accordingly, the government is providing various projects related to solar power generation, which are relatively close to life. In particular, roof-mounted photovoltaic power generation systems are attracting attention in terms of using unused space to produce energy without destroying the environment, but low power generation efficiency compared to other photovoltaic power generation facilities is pointed out as a disadvantage. Therefore, in this paper, to solve this problem, we propose an efficient solar panel angle variable system through research on the solar panel structure for single-axial solar tracking, and also consider the application environment of the roof-mounted solar power generation system. Suggests measures to prevent damage and secondary damage. In addition, it is judged that it is possible to control the solar panel based on ICT convergence and configure the accident prediction safety system to link the project-based education program.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.276-279
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• 2022

The 4th industrial revolution refers to the transition to a knowledge society in which the production speed of knowledge is fast and the proportion of the knowledge industry is greatly increased. The reorganization of the industrial structure and the change of occupations and jobs due to new technologies are bringing about changes in education, and the development of digital technology has made education that is borderless, individual and dynamic, becoming the new standard of education. With these changes, interest in nano-degrees on new technologies or micro-degrees focused on core courses rather than regular course degrees is increasing. As a representative example, Udacity in the United States has opened and operated online nanodegree courses related to jobs, and collaborates with major companies to develop and educate core training courses necessary for companies, thereby efficiently supporting companies in securing talent. With the revitalization of online vocational and job training, an environment has been prepared in which individuals can set goals for vocational competency development and continue their portfolio-based sustainable learning. However, for effective vocational education, automated and personalized educational content design should be preceded. To this end, in this paper, we propose a personalized career and course map recommendation system in the era of online learning

• Smart Media Journal
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• v.12 no.10
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• pp.71-84
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• 2023

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results.