• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.749-753
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• 2008

Proprioception defined it as the ability to detect, the spatial position or movement of joints using balance, power of the muscle, agility in the internal parts of the body. In existing study for improvement of proprioception, reaching task training provided a feedback; the assessment was not provided a feedback. But, this has problem that it can not guide a proprioception from situation with visual feedback. Virtual reality technique can solve the problem of way providing feedback during training. In this study, we developed proprioception training program using virtual reality and pilot study is performed. VR task were composed three modes. In mode 1, real-time movement of the body was provided using visual feedback. In mode 2, body position was provided using visual feedback when participant have specific response. And in mode 3, body position was not provided. VR task is performed five sessions at each mode and one session performed one by one a three target. In the result of this study, the moving time toward the target from mode 3 was smaller than the moving time toward the target from mode 1 (p= 0.001). The correlation was statistically significant between mode 2 and mode 3 while be offering visual feedback position of mode 2 1session. But, the correlation was not statistically significant between mode 2 and mode 3 after be offered visual feedback position of mode2 1session (p = 0.012). Training environment of mode 1 shows which training used visual feedback than proprioception. Mode2 can execute training of proprioception because first session acquires visual feedback by proprioception. The next study will be verification of the system for training or assessment by clinical experiment.

• Journal of the korean academy of Pediatric Dentistry
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• v.45 no.3
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• pp.271-279
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• 2018

Previous studies to elucidate the etiology of cyclosporine(Cs)-induced gingival overgrowth in children have not completely excluded all factors that may cause differences among individuals. This study examined the effect of cyclosporine on the metabolism of type 1 collagen(CoL-I) in experimental models that controlled the effects of biological variations on individuals. Five 5-week-old male Sprague-Dawley rats were administered Cs by gastric feeding for 6 weeks. Gingival specimens were harvested from the mandibular posterior area before beginning Cs administration and at 2, 4, and 6 weeks thereafter. Gingival fibroblasts were cultured from all the 20 biopsies collected from the gingiva. Half of the fibroblasts collected prior to the Cs administration were designated as Control. The other half of the fibroblasts were treated with Cs in vitro and called in vitro test group(Tt). The fibroblasts collected 2, 4, and 6 weeks after the Cs administration were called in vivo test groups : T2, T4, T6, respectively. Immunofluorescence microscopy was used to detect CoL-I in all the fibroblasts. CoL-I was analyzed at both the gene and protein expression levels by real-time polymerase chain reaction and western blotting. Changes in CoL-I before and after Cs treatment were evaluated from the gingiva of each rat. There was no significant difference in gene expression of CoL-I in the control and test groups. CoL-I protein expression levels of fibroblasts increased in in vitro Cs treatment for each individual, and also increased in in vivo Cs treatment. In this study, the experimental method that control biological variations that can occur due to differences among individuals was useful. Subsequent studies on other factors besides CoL-I and in-depth studies in humans are needed.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.24-30
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• 2009

Purpose: Today, Prostate cancer has been gradually increasing, according to the change of internal incidence rate of cancer. Generally, prostate cancer has lead to dead over 90%, in case of metastasis of lymph node and bone. So, innovative development of new radiopharmaceutical and imaging modality is progressed for detection of that metastasis, in nuclear medicine, now. Therefore, this study shows the usefulness of $^{18}F$-Fluoride PET/CT improved diagnosability on bone metastasis of prostate cancer. Materials and Methods: In this study, 33 male patients with prostate cancer were examined (The mean age: $67.8{\pm}10.2$ years old). Every patient was done each whole body bone scan (WBBS) and $^{18}F$-Fluoride positron emission tomography/computed tomography ($^{18}F$-Fluoride PET/CT). And then, using Receiver Operating Characteristic Curve (ROC curve), each sensitivity and specificity of two modalities was measured and compared with. Results: In 22 patients (66.6%) of all, bone metastasis was detected. And, in WBBS, sensitivity was 63.6%, specificity, 81.8%; in $^{18}F$-Fluoride PET/CT, sensitivity was 100% and specificity was 90.9%. As a result of ROC curve, AUROC (The Area under an ROC) of WBBS was 0.778, and that of $^{18}F$-Fluoride PET/CT, 0.942. Conclusions: $^{18}F$-Fluoride PET/CT was higher both sensitivity and specificity than WBBS, and it was valuable to detect bone metastasis of prostate cancer more definitely, with 3D imaging realization. Also, in $^{18}F$-Fluoride PET/CT, physiological images were acquired in more short time than WBBS, so, it was possible to reduce patient's waiting time and complaint. Therefore, it is considered that $^{18}F$-Fluoride PET/CT is able to improve diagnosability by offering more accurate images, as cuts in a share of high cost.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.1
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• pp.42-46
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• 2016

The radioactive iodine ($^{131}I$) presents in the environment through the excrete process of nuclear medicine patients. In the detecting of low level of $^{131}I$ in the public water, the counting uncertainty has an effect on the accuracy and reliability of detecting $^{131}I$ radioactivity concentration. In this study, the contribution of sample amount, radioactivity concentration and counting time to the uncertainty was investigated in the case of public water sample. Sampling points are public water and the effluents of a sewage treatment plant at Sapkyocheon stream, Geumgang river. In each point, 1, 10 and 20 L of liquid samples were collected and prepared by evaporation method. The HPGe (High Purity Germanium) detector was used to detect and analyze emitted gamma-ray from samples. The radioactivity concentration of $^{131}I$ were in the range of 0.03 to 1.8 Bq/L. The comparison of the counting uncertainty of the sample amount, 1 L sample is unable to verify the existence of the $^{131}I$ under 0.5 Bq/L radioactivity concentration. Considering the short half-life of $^{131}I$ (8.03 days), a method for measuring 1 L sample was used. However comparing the detecting and preparing time of 1, 10 L respectively, detecting 10 L sample would be an appropriate method to distinguish $^{131}I$ concentration in the public water.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.23
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• pp.10407-10412
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• 2015

Background: ${\beta}$-elemene, extracted from herb medicine Curcuma wenyujin has potent anti-tumor effects in various cancer cell lines. However, the activity of ${\beta}$-elemene against glioma cells remains unclear. In the present study, we assessed effects of ${\beta}$-elemene on human glioma cells and explored the underlying mechanism. Materials and Methods: Human glioma U87 cells were used. Cell proliferation was determined with MTT assay and colony formation assay to detect the effect of ${\beta}$-elemene at different doses and times. Fluorescence microscopy was used to observe cell apoptosis with Hoechst 33258 staining and change of glioma apoptosis and cell cycling were analyzed by flow cytometry. Real-time quantitative PCR and Western-blotting assay were performed to investigated the influence of ${\beta}$-elemene on expression levels of Fas/FasL, caspase-3, Bcl-2 and Bax. The experiment was divided into two groups: the blank control group and ${\beta}$-elemne treatment group. Results: With increase in the concentration of ${\beta}$-elemene, cytotoxic effects were enhanced in the glioma cell line and the concentration of inhibited cell viability ($IC_{50}$) was $48.5{\mu}g/mL$ for 24h. ${\beta}$-elemene could induce cell cycle arrest in the G0/G1 phase. With Hoechst 33258 staining, apoptotic nuclear morphological changes were observed. Activation of caspase-3,-8 and -9 was increased and the pro-apoptotic factors Fas/FasL and Bax were upregulated, while the anti-apoptotic Bcl-2 was downregulated after treatment with ${\beta}$-elemene at both mRNA and protein levels. Furthermore, proliferation and colony formation by U87 cells were inhibited by ${\beta}$-elemene in a time and does-dependent manner. Conclusions: Our results indicate that ${\beta}$-elemene inhibits growth and induces apoptosis of human glioma cells in vitro. The induction of apoptosis appears to be related with the upregulation of Fas/FasL and Bax, activation of caspase-3,-8 and -9 and downregulation of Bcl-2, which then trigger major apoptotic cascades.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.311-320
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• 2007

This paper presents vision-based 3D facial expression animation technique and system which provide the robust 3D head pose estimation and real-time facial expression control. Many researches of 3D face animation have been done for the facial expression control itself rather than focusing on 3D head motion tracking. However, the head motion tracking is one of critical issues to be solved for developing realistic facial animation. In this research, we developed an integrated animation system that includes 3D head motion tracking and facial expression control at the same time. The proposed system consists of three major phases: face detection, 3D head motion tracking, and facial expression control. For face detection, with the non-parametric HT skin color model and template matching, we can detect the facial region efficiently from video frame. For 3D head motion tracking, we exploit the cylindrical head model that is projected to the initial head motion template. Given an initial reference template of the face image and the corresponding head motion, the cylindrical head model is created and the foil head motion is traced based on the optical flow method. For the facial expression cloning we utilize the feature-based method, The major facial feature points are detected by the geometry of information of the face with template matching and traced by optical flow. Since the locations of varying feature points are composed of head motion and facial expression information, the animation parameters which describe the variation of the facial features are acquired from geometrically transformed frontal head pose image. Finally, the facial expression cloning is done by two fitting process. The control points of the 3D model are varied applying the animation parameters to the face model, and the non-feature points around the control points are changed by use of Radial Basis Function(RBF). From the experiment, we can prove that the developed vision-based animation system can create realistic facial animation with robust head pose estimation and facial variation from input video image.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• 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.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.