• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.535-544
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• 2021

Agricultural water occupies 48% of water demand, and management of agricultural reservoirs is essential for water resources management within agricultural basins. For more efficient use of agricultural water, monitoring the distribution of water resources in agricultural reservoirs and agricultural basins is required. Therefore, in this study, three threshold determination methods (i.e., fixed threshold, Otsu threshold, Kittler-Illingworth (KI) threshold) were compared to detect terrestrial water bodies using Sentinel-1 images for 3 years from 2018 to 2020. The purpose of this study was to evaluate methods for determining threshold values to more accurately estimate the reservoir area. In addition, by analyzing the relationship between the water surface and water storage at the Edong, Gosam, and Giheung reservoirs, water storage based on the SAR image was estimated and validated with observations. The thresholding method for detecting a waterbody was found to be the most accurate in the case of the KI threshold, and the water storage estimated by the KI threshold indicated a very high agreement (r = 0.9235, KGE' = 0.8691). Although the seasonal error characteristics were not observed, the problem of underestimation at high water levels may occur; the relationship between the water surface and the water storage could change rapidly. Therefore, it is necessary to understand the relationship between the water surface area and water storage through ground observation data for a more accurate estimation of water storage. If the use of SAR data through water resources satellites becomes possible in the future, based on the results of this study, it is judged that it will be beneficial for monitoring water storage and managing drought.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.497-510
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• 2022

Agricultural reservoirs are essential structures for water supplies during dry period in the Korean peninsula, where water resources are temporally unequally distributed. For efficient water management, systematic and effective monitoring of medium-small reservoirs is required. Synthetic Aperture Radar (SAR) provides a way for continuous monitoring of those, with its capability of all-weather observation. This study aims to evaluate the applicability of SAR in monitoring medium-small reservoirs using Sentinel-1 (10 m resolution) and Capella X-SAR (1 m resolution), at Chari (CR), Galjeon (GJ), Dwitgol (DG) reservoirs located in Ulsan, Korea. Water detected results applying Z fuzzy function-based threshold (Z-thresh) and Chan-vese (CV), an object detection-based segmentation algorithm, are quantitatively evaluated using UAV-detected water boundary (UWB). Accuracy metrics from Z-thresh were 0.87, 0.89, 0.77 (at CR, GJ, DG, respectively) using Sentinel-1 and 0.78, 0.72, 0.81 using Capella, and improvements were observed when CV was applied (Sentinel-1: 0.94, 0.89, 0.84, Capella: 0.92, 0.89, 0.93). Boundaries of the waterbody detected from Capella agreed relatively well with UWB; however, false- and un-detections occurred from speckle noises, due to its high resolution. When masked with optical sensor-based supplementary images, improvements up to 13% were observed. More effective water resource management is expected to be possible with continuous monitoring of available water quantity, when more accurate and precise SAR-based water detection technique is developed.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.295-304
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• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.

• Korean Journal of Environment and Ecology
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• v.36 no.6
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• pp.592-602
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• 2022

This study aimed to check habitat distribution and analyze influencing factors by analyzing the mating calls of Auritibicen intermedius inhabiting limited locations in South Korea by applying bioacoustic detection techniques. The study sites were 20 protection areas nationwide. The mating call analysis period was 4 years from 2017 to 2021, excluding 2020. The bioacoustic recording system installed at each study site collected recordings of mating calls every day for 1 minute per hour. Climate data received from the Meteorological Agency, such as temperature, humidity, rainfall, cloudiness, and sunshine, were analyzed. The results of this study identified A. intermedius habitat only in four national parks in the highlands of Gangwon Province (Mt. Seorak, Mt. Odae, Mt. Chiak, and Mt. Taebak) out of 20 study sites. During the four years of study, the mating call period of A. intermedius was between August 5 and September 28, and the duration of the mating call was 31 to 52 days. The temperature analysis during the appearance period of A. intermedius showed that A. intermedius mainly produced mating calls at temperatures between 13.1℃ and 35.3℃, and the average temperature during the circadian cycle of mating calls (09:00 to 16:00) was 24.4 to 24.9℃. The analysis of the circadian cycle of mating calls at four study sites where A. intermedius appeared in 2019 showed that A. intermedius produced mating calls from 06:00 to 16:00 and that they peaked around 11:00 to 12:00. During the appearance period of A. intermedius, four species appeared in common: Hyalessa maculaticollis, Meimuna opalifera, Graptopsaltria nigrofuscata, and Suisha coreana. A logistic regression analysis confirmed that sunlight was the environmental factor affecting the mating call of A. intermedius. Regarding interspecific influence, it was confirmed that A. intermedius exchanged interspecific influence with 4 other common species (H. maculaticollis, M. opalifera, G. nigrofuscata, and S. coreana). The above results confirmed that A. intermedius habitats were limited in the highlands of Gangwon Province highlands in Korea and produced mating calls at a lower temperature compared to other species. These results can be used as basic data for future research on A. intermedius in Korea.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.175-180
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• 2009

Purpose: Detection of TSH-binding inhibitor immunoglobulin (TBII) in patients with hyperthyroidism is an important result of Graves' disease (GD) and hyperthyroidism treatment. This has been made out an inspection by commercial radio-receptor assays. To increase the sensitivity and the specificity of the assay, many results of the assay were reported. In this study we evaluated the clinical usetulness of TBII assays by the Comparative method. Material and Methods: We were measured by using healthy control group (n=30, male=20, female=10) of Seoul National University Hospital Healthcare System Gangnam Center from January to March in 2009. Similarly, We were measured by using hyperthyroid (TSH<$0.05\;{\mu}IU/mL$, FT4>1.80 ng/dL) experimental group (n=58, male=14, female=44) of division of endocrinology and metabolism department of internal medicine Seoul National University Hospital from January to March in 2009. We made a comparative study of each two assays from the first generation to the third generation. We were used of TSAb assay as a measurement of GD diagnostic technique. Results: The specificity of healthy control group was 100% according to the generation. (Specificity=100%, n=30) The sensitivity of hyperthyroid experimental group were the first generation RSR<%> (79.3%, n=58), RSR (51.7%, n=58), the second generation RSR-CT (93.1%, n=58), BRAHMSCT (98.3%, n=58), the third generation ELISA (94.6%, n=56), ECLIA (97.7%, n=58) and TS-Ab<%> (93.5%, n=46). Conclusion: We were used of TSAb assay as a measurement of GD diagnostic technique, The result of data showed a high correlation between the third generation TBII assay and the second generation TBII assay ($R^2$=0.923). Instead of the first generation assay, the second generation assay can be more useful in clincal diagnosis.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.131-150
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• 2015

Omission of noun phrases for obligatory cases is a common phenomenon in sentences of Korean and Japanese, which is not observed in English. When an argument of a predicate can be filled with a noun phrase co-referential with the title, the argument is more easily omitted in Encyclopedia texts. The omitted noun phrase is called a zero anaphor or zero pronoun. Encyclopedias like Wikipedia are major source for information extraction by intelligent application systems such as information retrieval and question answering systems. However, omission of noun phrases makes the quality of information extraction poor. This paper deals with the problem of developing a system that can restore omitted noun phrases in encyclopedia documents. The problem that our system deals with is almost similar to zero anaphora resolution which is one of the important problems in natural language processing. A noun phrase existing in the text that can be used for restoration is called an antecedent. An antecedent must be co-referential with the zero anaphor. While the candidates for the antecedent are only noun phrases in the same text in case of zero anaphora resolution, the title is also a candidate in our problem. In our system, the first stage is in charge of detecting the zero anaphor. In the second stage, antecedent search is carried out by considering the candidates. If antecedent search fails, an attempt made, in the third stage, to use the title as the antecedent. The main characteristic of our system is to make use of a structural SVM for finding the antecedent. The noun phrases in the text that appear before the position of zero anaphor comprise the search space. The main technique used in the methods proposed in previous research works is to perform binary classification for all the noun phrases in the search space. The noun phrase classified to be an antecedent with highest confidence is selected as the antecedent. However, we propose in this paper that antecedent search is viewed as the problem of assigning the antecedent indicator labels to a sequence of noun phrases. In other words, sequence labeling is employed in antecedent search in the text. We are the first to suggest this idea. To perform sequence labeling, we suggest to use a structural SVM which receives a sequence of noun phrases as input and returns the sequence of labels as output. An output label takes one of two values: one indicating that the corresponding noun phrase is the antecedent and the other indicating that it is not. The structural SVM we used is based on the modified Pegasos algorithm which exploits a subgradient descent methodology used for optimization problems. To train and test our system we selected a set of Wikipedia texts and constructed the annotated corpus in which gold-standard answers are provided such as zero anaphors and their possible antecedents. Training examples are prepared using the annotated corpus and used to train the SVMs and test the system. For zero anaphor detection, sentences are parsed by a syntactic analyzer and subject or object cases omitted are identified. Thus performance of our system is dependent on that of the syntactic analyzer, which is a limitation of our system. When an antecedent is not found in the text, our system tries to use the title to restore the zero anaphor. This is based on binary classification using the regular SVM. The experiment showed that our system's performance is F1 = 68.58%. This means that state-of-the-art system can be developed with our technique. It is expected that future work that enables the system to utilize semantic information can lead to a significant performance improvement.

• Progress in Medical Physics
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• v.26 no.4
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• pp.258-266
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• 2015

The modern radiotherapy technique which delivers a large amount of dose to patients asks to confirm the positions of patients or tumors more accurately by using X-ray projection images of high-definition. However, a rapid increase in patient's exposure and image information for CT image acquisition may be additional burden on the patient. In this study, by introducing structural similarity (SSIM) index that can effectively extract the structural information of the image, we analyze the differences between daily acquired x-ray images of a patient to verify the accuracy of patient positioning. First, for simulating a moving target, the spherical computational phantoms changing the sizes and positions were created to acquire projected images. Differences between the images were automatically detected and analyzed by extracting their SSIM values. In addition, as a clinical test, differences between daily acquired x-ray images of a patient for 12 days were detected in the same way. As a result, we confirmed that the SSIM index was changed in the range of 0.85~1 (0.006~1 when a region of interest (ROI) was applied) as the sizes or positions of the phantom changed. The SSIM was more sensitive to the change of the phantom when the ROI was limited to the phantom itself. In the clinical test, the daily change of patient positions was 0.799~0.853 in SSIM values, those well described differences among images. Therefore, we expect that SSIM index can provide an objective and quantitative technique to verify the patient position using simple x-ray images, instead of time and cost intensive three-dimensional x-ray images.

• The Journal of Korean Society for Radiation Therapy
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• v.17 no.1
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• pp.57-71
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• 2005

Purpose : To evaluate radiation dose and accuracy with TLD and diode detector when treat total skin with electron beam. Materials and Methods : Using Stanford Technique, we treated patient with Mycosis Fungoides. 6 MeV electron beam of LINAC was used and the SSD was 300 cm. Also, acrylic speller(0.8 cm) was used. The patient position was 6 types and the gantry angle was 64, 90 and $116^{\circ}$. The patient's skin dose and the output were detected 5 to 6 times with TLD and diode. Result : The deviations of dose detected with TLD from tumor dose were CA $+\;6\%$, thigh $+\;8\%$, umbilicus $+\;4\%$, calf $-\;8\%$, vertex $-\;74.4\%$, deep axillae $-\;10.2\%$, anus and testis $-\;87\%$, sole $-\;86\%$ and nails shielded with 4mm lead $+4\%$. The deviations of dose detected with diode were $-4.5\%{\sim}+5\%$ at the patient center and $-1.1\%{\sim}+1\%$ at the speller. Conclusion : The deviation of total skin dose was $+\;8\%{\sim}-\;8\%$ and that deviation was within the acceptable range(${\pm}\;10\%$). The boost dose was irradiated for the low dose areas(vertex, anus, sole). The electron beam output detected at the sootier was stable. It is thought that the deviation of dose at patient center detected with diode was induced by detection point and patient position.

• Journal of radiological science and technology
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• v.32 no.3
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• pp.313-323
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• 2009

Diffusion-weighted imaging (DWI) has been demonstrated to be a practical method for the diagnosis of various brain diseases such as acute infarction, brain tumor, and white matter disease. In this study, we used two techniques to examine the average signal intensity (SI) and apparent diffusion coefficient (ADC) of the brains of patients who ranged in age from 10 to 60 years. Our results indicated that the average SI was the highest in amygdala (as derived from DWI), whereas that in the cerebrospinal fluid was the lowest. The average ADC was the highest in the cerebrospinal fluid, whereas the lowest measurement was derived from the pons. The average SI and ADC were higher in $T_2^*$-DW-EPI than in FLAIR-DW-EPI. The higher the b-value, the smaller the average difference in both imaging techniques; the lower the b-value, the greater the average difference. Also, comparative analysis of the brains of patients who had experienced cerebral infarction showed no distinct lesion in the general MR image over time. However, there was a high SI in apparent weighted images. Analysis of other brain diseases (e.g., bleeding, acute, subacute, chronic infarction) indicated SI variance in accordance with characteristics of the two techniques. The higher the SI, the lower the ADC. Taken together, the value of SI and ADC in accordance with frequently occurring areas and various brain disease varies based on the b-value and imaging technique. Because they provide additional useful information in the diagnosis and treatment of patients with various brain diseases through signal recognition, the proper imaging technique and b-value are important for the detection and interpretation of subacute stroke and other brain diseases.