• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.39 no.1 s.119
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• pp.38-47
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• 2007

This study was carried out to develop a new application field and obtain the basic data of citrus peel as waste in Jeju island and traditional Hanji for producing functional Hanji. The results measuring physical and optical properties, water vapor permeance and antibacterial activity are as follows. It was revealed that apparent density go as down but bulk raise up in the structural view of Hanji with increasing of the addition various Korean citrus peel (citrus unshiu, cheonggyun and hanrabong peel, and citrus unshiu peel powder) percentages, and that the density of Hanji added citrus unshiu peel was higher, but bulk was lower in compared with Hanji added other kinds of peel. Those Hanji added citrus unshiu peel, cheonggyun peel, hanrabong peel and citrus unshiu powder were very great not only in the strength (breaking length, burst index, tear index and folding endurance) but also in water vapor permeant rate in comparison with Hanji. The pHs of Hanji were neutrality (7 to 8). The brightness of the Hanji added various citrus peel percentages was low in compared to Hanji, and the 40% addition of hanrabong peel was the lowest. When 40% hanrabong peel was added to Hanji, it was very yellow in the color degree. When cheonggyun peel was added to Hanji manufacture, water vapor permeant rate was highly effective. It is known that vacant space of intrafiber was reduced by image analysis of Hanji and the additions of peel of citrus unshiu, cheonggyun and hanrabong were distributed equally in the interior of Hanji. The antibacterial activity of Hanji added citrus unshiu peel is more than 98%. After all, it would be able to increase utilization of Hanji, extensively. Namely, production of high quality Hanji added functional materials is expected for new valuable industry of citrus peel and Hanji.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.6
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• pp.1678-1727
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• 2006

This study was perfromed to develop the assessment guideline and endpoints for clinical trial with anticancer herbal medicine. The botanical products used to humans for long time may be applied to phase 3 clinical trial after submitting the evidences for safety and efficacy of them or completion of basic requirement of phase 1 and phase 2 for safety confirmation and dose determination. Syndrome improvement was chiefly evaluated by Zubrod and karnofsky(%) methods. We suggest the general clinical trial assessment with botanical products, by following assessment points, that is, tumor size for 50 points, survival fate for 10 points, major syndromes for 40 points. It is recommendable that the each symptom of Qi deficiency syndrome, blood deficiency syndrome and Qi stagnation syndrome was allocated by assessment points, Similarly, the each symptom was given the assessment points according to the severity of symptom, for example, slight for 3 points, moderate for 2 points and severe for 1 point in hepatocelluar carcinoma and lung cancer. Then, the efficacy of botanical products was evaluated by the difference between pre-treatment and post-treatment. Asking the neoplastic patients of questionnaire on physical, emotional, cognitive, social and role subjects availability, three more syndromes (Fatigue, Pain and Nausea/Vomit), quality of life(QOL) will be evaluated by GLM statistics. In addition, in case of lung cancer, 13 questions will be asked by the EORTC QLQ-C13 forms. As the assessment of endpoints for efficacy to reduce side effects induced by chemotherapy and radiotherapy, the data of image scanning and hemato-urinalysis can be usefully applied on immune response, weight loss, indigestion, hemopoietic damage and injury of liver and kidney, while the changes of syndromes of side effect can be evaluated by differentiation methods of Qi and blood and five viscera. However, it is still necessary to determine the ratio between scientific analytical method and Oriental differentiation method as well as confirm the Oriental assessment endpoints by clinical trial. In addition, we suggest the continuous development of assessment endpoints on other carcinomas except of hepatocelluar carcinoma and lung cancer in future.

• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.289-305
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• 2004

Atmospheric correction of Landsat Visible and Near Infrared imagery (VIS/NIR) over aquatic environment is more demanding than over land because the signal from the water column is small and it carries immense information about biogeochemical variables in the ocean. This paper introduces two methods, a modified dark-pixel substraction technique (path--extraction) and our spectral shape matching method (SSMM), for the correction of the atmospheric effects in the Landsat VIS/NIR imagery in relation to the retrieval of meaningful information about the ocean color, especially from Case-2 waters (Morel and Prieur, 1977) around Korean peninsula. The results of these methods are compared with the classical atmospheric correction approaches based on the 6S radiative transfer model and standard SeaWiFS atmospheric algorithm. The atmospheric correction scheme using 6S radiative transfer code assumes a standard atmosphere with constant aerosol loading and a uniform, Lambertian surface, while the path-extraction assumes that the total radiance (L/sub TOA/) of a pixel of the black ocean (referred by Antoine and Morel, 1999) in a given image is considered as the path signal, which remains constant over, at least, the sub scene of Landsat VIS/NIR imagery. The assumption of SSMM is nearly similar, but it extracts the path signal from the L/sub TOA/ by matching-up the in-situ data of water-leaving radiance, for typical clear and turbid waters, and extrapolate it to be the spatially homogeneous contribution of the scattered signal after complex interaction of light with atmospheric aerosols and Raleigh particles, and direct reflection of light on the sea surface. The overall shape and magnitude of radiance or reflectance spectra of the atmospherically corrected Landsat VIS/NIR imagery by SSMM appears to have good agreement with the in-situ spectra collected for clear and turbid waters, while path-extraction over turbid waters though often reproduces in-situ spectra, but yields significant errors for clear waters due to the invalid assumption of zero water-leaving radiance for the black ocean pixels. Because of the standard atmosphere with constant aerosols and models adopted in 6S radiative transfer code, a large error is possible between the retrieved and in-situ spectra. The efficiency of spectral shape matching has also been explored, using SeaWiFS imagery for turbid waters and compared with that of the standard SeaWiFS atmospheric correction algorithm, which falls in highly turbid waters, due to the assumption that values of water-leaving radiance in the two NIR bands are negligible to enable retrieval of aerosol reflectance in the correction of ocean color imagery. Validation suggests that accurate the retrieval of water-leaving radiance is not feasible with the invalid assumption of the classical algorithms, but is feasible with SSMM.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.65-74
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• 2021

In order to efficiently inspect rapidly increasing old tunnels in many well-developed countries, many inspection methodologies have been proposed using imaging equipment and image processing. However, most of the existing methodologies evaluated their performance on a clean concrete surface with a limited area where other objects do not exist. Therefore, this paper proposes a 6-step framework for tunnel crack detection deep learning model development. The proposed method is mainly based on negative sample (non-crack object) training and Cascade Mask R-CNN. The proposed framework consists of six steps: searching for cracks in images captured from real tunnels, labeling cracks in pixel level, training a deep learning model, collecting non-crack objects, retraining the deep learning model with the collected non-crack objects, and constructing final training dataset. To implement the proposed framework, Cascade Mask R-CNN, an instance segmentation model, was trained with 1561 general crack images and 206 non-crack images. In order to examine the applicability of the trained model to the real-world tunnel crack detection, field testing is conducted on tunnel spans with a length of about 200m where electric wires and lights are prevalent. In the experimental result, the trained model showed 99% precision and 92% recall, which shows the excellent field applicability of the proposed framework.

• Journal of the Korea Institute of Building Construction
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• v.18 no.6
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• pp.611-619
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• 2018

The researches have only conducted regarding construction safety management and risk on interior construction site(workers) and is insufficient about the exterior construction site(third party). As a result, ordinary people who were near construction sites have injured and hold a negative view when they think about the construction industry because construction industry have been exposed to them having a high accidents rate through media. In addition, the importance of industrial disaster prevention is emphasized at this point in time, the overall safety management system should be constructed with considering construction site external(third human) for improving the negative image of the construction industry among ordinary people. Therefore, the objective of this study is to develop the quantification model of human accident utilizing the insurance claim payout occurred construction site exterior(third party). In the future, it can be used as a reference for developing the safety management checklist in construction site interior exterior and development for forecasting control system of human accident.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.3
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• pp.71-80
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• 2019

The Fourth Industrial Revolution, centered on intelligence and information, began to take hold in 2016. This study uses virtual reality technology, which is the most popular technology of the Fourth Industrial Revolution. The purpose of this study is to explore a Virtual Walk-through method, which can be easily applied to landscape architecture. At present, virtual reality technology is widely used in the fields of games, emergency training, and architectural design. However, in the field of landscape architecture, it is still in the development stage. In addition, most of the traditional ways to display virtual reality use 2D images, but such methods have some limitations. Therefore, this research addresses the three stages of "design-exhibition-experience" and puts forward a new simple method called 'Virtual Walk-through' that breaks from traditional landscape design exhibitions. The results show that compared with traditional methods, virtual reality has many advantages, such as the freedom of experience, a diversity of viewing angles, information supply, interaction, etc. It can show high quality images and effects, which are suitable for landscape design. It provides an evaluation method for garden design that can be utilized in the future. It is simple and has value as it can reflect the method and the expected effects. Virtual reality technology can bring an infinite number of prospects to the development of landscape architecture.

• Journal of Korean Home Economics Education Association
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• v.33 no.3
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• pp.159-171
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• 2021

In order for young people to be interested in tradition, a specialized experience program that can be frequently encountered is needed. We have developed an after-school traditional gift-wrapping design program in relation to the subject of home economics for the purpose of enabling students to become interested in tradition and deepen their traditional knowledge. The research process was comprised of analysis, development, and evaluation. We analyzed the home economics curriculum, authentic designs from blogs and department stores along with books published by gift-wrapping associations, and interviews with three gift wrapping specialists. Contemporary traditional packaging design is not limited to the reproduction of the traditional design but also creates designs that strongly express unique Korean identity using traditional symbol patterns, colors, traditional decorations, and small items along with modern materials. A 16-week after-school program was developed based on the analysis results. After the implementation of the after-school program in a middle school, survey results showed students who were indifferent toward tradition showed interest and acquired a positive image towards tradition.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.

• Journal of the Korean Institute of Rural Architecture
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• v.24 no.1
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• pp.9-17
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• 2022

Rural spaces are increasingly valuable as areas for introducing renewable energy infrastructure to achieve carbon neutrality. Rural areas are the living grounds of rural residents, and the balance of conservation and development for rural areas is important for the introduction of reasonable facilities. In order to maintain a balance between development and preservation and to introduce reasonable renewable energy facilities, it is necessary to develop a current status survey and an effective survey method to utilize a space capable of introducing renewable energy facilities such as idle land and vacant houses. Therefore, this study was conducted to verify the readability using an unmanned aerial vehicle, and the main results are as follows. The detection of photovoltaic power generation facilities using unmanned aerial vehicles was effective in analyzing the location and area of photovoltaic panels located on the roofs of buildings, and it was possible to calculate the expected power generation by region through the area calculation of photovoltaic panels. The vacant house detection can be used to select an investigation target for an vacant house condition survey as it can identify damage to buildings that are expected to be empty houses, management status, and electricity supply facilities through aerial photos. It is judged that the unmanned aerial vehicle detection capability can be utilized as a method to improve the efficiency of investigation and supplement the data related to solar power generation facilities and vacant houses provided by public institutions. Although this study detected the status of solar power generation facilities and vacant houses through high-resolution aerial image analysis, as a follow-up study, automatic measurement methods using the temperature difference of solar power generation facilities and general characteristics of vacant houses that can be read from the air were investigated. If the deriving research is carried out, it is judged that it will be possible to contribute to the improvement of the accuracy of the detection result using the unmanned aerial vehicle and the expansion of the application range.