• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.319-341
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• 2024

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications.An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40-70%, affecting reflectance in the red (-0.01 to 0.02) and near-infrared (NIR) bands (-0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown,reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10-20% with changes in aerosol optical thickness, 15-30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

• Imaging Science in Dentistry
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• v.54 no.3
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• pp.271-275
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• 2024

Purpose: Recent advancements in artificial intelligence (AI), particularly tools such as ChatGPT developed by OpenAI, a U.S.-based AI research organization, have transformed the healthcare and education sectors. This study investigated the effectiveness of ChatGPT in answering dentistry exam questions, demonstrating its potential to enhance professional practice and patient care. Materials and Methods: This study assessed the performance of ChatGPT 3.5 and 4 on U.S. dental exams - specifically, the Integrated National Board Dental Examination (INBDE), Dental Admission Test (DAT), and Advanced Dental Admission Test (ADAT) - excluding image-based questions. Using customized prompts, ChatGPT's answers were evaluated against official answer sheets. Results: ChatGPT 3.5 and 4 were tested with 253 questions from the INBDE, ADAT, and DAT exams. For the INBDE, both versions achieved 80% accuracy in knowledge-based questions and 66-69% in case history questions. In ADAT, they scored 66-83% in knowledge-based and 76% in case history questions. ChatGPT 4 excelled on the DAT, with 94% accuracy in knowledge-based questions, 57% in mathematical analysis items, and 100% in comprehension questions, surpassing ChatGPT 3.5's rates of 83%, 31%, and 82%, respectively. The difference was significant for knowledge-based questions(P=0.009). Both versions showed similar patterns in incorrect responses. Conclusion: Both ChatGPT 3.5 and 4 effectively handled knowledge-based, case history, and comprehension questions, with ChatGPT 4 being more reliable and surpassing the performance of 3.5. ChatGPT 4's perfect score in comprehension questions underscores its trainability in specific subjects. However, both versions exhibited weaker performance in mathematical analysis, suggesting this as an area for improvement.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.69-79
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• 2024

Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.

• Journal of Bio-Environment Control
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• v.33 no.1
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• pp.63-70
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• 2024

This study explored computer vision methods using the OpenCV open-source library to characterize the phenotypes of various horticultural crops. In the case of tomatoes, image color was examined to assess ripeness, while support vector machine (SVM) and histogram of oriented gradients (HOG) methods effectively identified ripe tomatoes. For sweet pepper, we visualized the color distribution and used the Gaussian mixture model for clustering to analyze its post-harvest color characteristics. For the quality assessment of netted melons, the LAB (lightness, a, b) color space, binary images, and depth mapping were used to measure the net patterns of the melon. In addition, a combination of depth and color data proved successful in identifying flowers of different sizes and distances in cucumber greenhouses. This study highlights the effectiveness of these computer vision strategies in monitoring the growth and development, ripening, and quality assessment of fruits and vegetables. For broader applications in agriculture, future researchers and developers should enhance these techniques with plant physiological indicators to promote their adoption in both research and practical agricultural settings.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.3
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• pp.51-63
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• 2011

The purpose of this study is to create a landscape image that considers the selection of techniques that can enhance landscape reproduction in streetscape evaluation using 3 dimensional simulations and to evaluate ways to verify similarities and the psychological changes on the part of users by season. In the comparison of technique, the Low(apply normal map) technique was selected for the natural representation of trees in a near and middle view and the Plane technique was selected for the distant view. As the result of the verification, all indicators of physical similarity were evaluated over 4.50 points and most indicators of psychological similarity were found to have no difference except for indicators of 'disordered orderly' and 'dirty - clean'. According to the results of analyzing the landscape simulation by season, images of 'bright', 'beautiful', and 'static', etc., were evaluated high for the spring streetscape. The images of 'open', 'refresh', and 'animate' appeared high in summer and images of 'warm' and 'dark' were found to be high in fall. On the other hand, all images were evaluated as low except for the 'orderly' image. In the preference of streetscape by season, summer and spring were highly preferred at 5.01 and 4.98 with winter as the lowest at 3.48. As the results of the analysis of preference factor, the spring streetscape was found to be a major influence in preference by 0.540 in 'aesthetics'. In the case of summer, 'order' was found to be high at 0.417 while influences in preference included 'variety' and 'aesthetics' in fall and 'variety', 'aesthetics', and 'order' in winter. A determination of suitable spatial planning using a comparative analysis of various city streets will be enabled through the methods of this study.

• Progress in Medical Physics
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• v.23 no.1
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• pp.62-69
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• 2012

The dose re-calculation process using Megavoltage cone-beam CT images is inevitable process to perform the Adaptive Radiation Therapy (ART). The purpose of this study is to improve dose re-calculation accuracy using MVCBCT images by applying intensity calibration method and three dimensional rigid body transform and filtering process. The three dimensional rigid body transform and Gaussian smoothing filtering process to MVCBCT Rando phantom images was applied to reduce image orientation error and the noise of the MVCBCT images. Then, to obtain the predefined modification level for intensity calibration, the cheese phantom images from kilo-voltage CT (kV CT), MVCBCT was acquired. From these cheese phantom images, the calibration table for MVCBCT images was defined from the relationship between Hounsfield Units (HUs) of kV CT and MVCBCT images at the same electron density plugs. The intensity of MVCBCT images from Rando phantom was calibrated using the predefined modification level as discussed above to have the intensity of the kV CT images to make the two images have the same intensity range as if they were obtained from the same modality. Finally, the dose calculation using kV CT, MVCBCT with/without intensity calibration was applied using radiation treatment planning system. As a result, the percentage difference of dose distributions between dose calculation based on kVCT and MVCBCT with intensity calibration was reduced comparing to the percentage difference of dose distribution between dose calculation based on kVCT and MVCBCT without intensity calibration. For head and neck, lung images, the percentage difference between kV CT and non-calibrated MVCBCT images was 1.08%, 2.44%, respectively. In summary, our method has quantitatively improved the accuracy of dose calculation and could be a useful solution to enhance the dose calculation accuracy using MVCBCT images.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.9
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• pp.117-124
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• 2015

In this paper, we introduce how to control TR, TE physical MR parameters for managing $H_1$ spin's SI(Signal Intensity) which is combined with gadolinium following administration MR agent in T1 effect for diagnostic usefulness. we used MRI phantom made with 0.5 mol Gadoteridol. This phantom was scanned by FSE sequence with different TR, TE parameters. In this study, to make T1 effect, TR was 200, 250, 300, 350, 400, 450, 500, 550, 600 msec. In addition to, TE was 6.2, 12.4, 18.6, 21.6 msec. The results were as follows ; Each RSP(Reaction Starting Point) was 100, 50, 40, 30 mmol in TE 6.2, 12.4, 18.6, 21.6 msec being irrelevant to TR. In MPSI(Max Peak Signal Intensity), 4 mmol was showed in TR 200 msec while peak signal was decreased to low concentration mol in TR 250-600 msec. In terms of RA(Reaction Area), the highest SI was TE 6.2 msec in TR 200-600msec. According to the study, we are able to recognize it is possible to control enhance rates by managing TR and TE of MR parameters; moreover, we expect that enhanced T1 image in MR clinical field can be performed in a practical way with this quantitative data.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.242-251
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• 2011

A curriculum of study demands a change as period of time and society evolve. Therefore, at this point where changes are required, this study is to analyze and evaluate the curriculums which will enhance and improve current studies as a preceding stage. The research was based on the survey by groups of education experts and 19 universities with current curriculum of study in radiologic science, and their references. The study was focused on the scope of work by radiologic technologist, change of college systems, academic research about radiologic science, and the improvement and the future of radiologic science field in perspective to globalization and the digital era. In terms of work scope, angiography and interventional radiology at 6 to 8 schools, fluoroscopy at 4 schools, ultrasound and practices at 6 schools, magnetic resonance image at 2 schools were found to be unestablished. The basic medical subjects, humuan physiology, human anatomy and practices, medical terminology courses were set up at most schools; however, pathology at 5 schools, image anatomy at 6 schools, clinical medicine at 11 schools were yet opened. Among the basic science and engineering subjects, general biology and its practices at 11 schools, general physics and its practices at 14 schools, and general chemistry and its practices at 8 schools were established which is about a half from a total number of schools. Only 4-5 schools established digital subjects such as, health computer, computer programming, PACS which are the basic major subjects. In order to provide academic improvement in radiologic science, digitalized education and globalization, and basis for future-oriented education for the field of radiologic science, including expanded scope of work, it is acknowledged that curriculums that are opened and run at each school need to be standardized. Therefore, the need for introduction of certificate for the radiologic science education courses are suggested.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.79-90
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• 2013

The altitude range of habitats in which Fairy Pitta inhabited in 1960s is different from the present in Jeju Island. We studied on the habitat environment to understand the causes of difference through the comparison of satellite image data(Landsat) between 1975 and 2002, the literature review in relation to habitats, vegetations, and forest landscapes. The area of below 600m asl.(above sea level) where is mainly Fairy Pitta inhabited at the present with a lot of forests, was massive pasture with small isolated forests nearby valley. The forests were broad-leaved evergreen forests, and second forests with poor condition in the size and forest structure. The forests around 700m asl. were also second forests with approximately 3m height trees. The forests from 800m to 1300m asl. were also disturbed by mushroom cultivation by local people. The authors believe that Fairy Pitta could not inhabited in the area above 1300m because of the poor forest conditions in the size and structure in which consist of Ilex crenata, Rhododendron mucronulatum var. ciliatum and coppice forests. Therefore it might be possible that the best forests for the Fairy Pitta habitat were located in the area of 1,000m to 1,300m above sea level in 1960s. Compared to present habitats, forests at 100m up to 800m above sea level, the authors believe that the size of habitats were smaller with less population of Fairy Pitta. Since 1960s the forest landscape of Jeju Island has been improved successfully, and because of that the population of Fairy Pitta also has been increased. To protect the Fairy Pitta and habitats in Jeju Island, it is suggested that sustainable forest management focusing on the species composition and stand structure maintain or enhance the biodiversity.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.