• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.2
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• pp.89-103
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• 2013

National demands for the ocean forecasting system have been increased to support economic activity and national safety including search and rescue, maritime defense, fisheries, port management, leisure activities and marine transportation. Further, the ocean forecasting has been regarded as one of the key components to improve the weather and climate forecasting. Due to the national demands as well as improvement of the technology, the ocean forecasting systems have been established among advanced countries since late 1990. Global Ocean Data Assimilation Experiment (GODAE) significantly contributed to the achievement and world-wide spreading of ocean forecasting systems. Four stages of GODAE were summarized. Goal, vision, development history and research on ocean forecasting system of the advanced countries such as USA, France, UK, Italy, Norway, Australia, Japan, China, who operationally use the systems, were examined and compared. Strategies of the successfully established ocean forecasting systems can be summarized as follows: First, concentration of the national ability is required to establish successful operational ocean forecasting system. Second, newly developed technologies were shared with other countries and they achieved mutual and cooperative development through the international program. Third, each participating organization has devoted to its own task according to its role. In Korean society, demands on the ocean forecasting system have been also extended. Present status on development of the ocean forecasting system and long-term plan of KMA (Korea Meteorological Administration), KHOA (Korea Hydrographic and Oceanographic Administration), NFRDI (National Fisheries Research & Development Institute), ADD (Agency for Defense Development) were surveyed. From the history of the pre-established systems in other countries, the cooperation among the relevant Korean organizations is essential to establish the accurate and successful ocean forecasting system, and they can form a consortium. Through the cooperation, we can (1) set up high-quality ocean forecasting models and systems, (2) efficiently invest and distribute financial resources without duplicate investment, (3) overcome lack of manpower for the development. At present stage, it is strongly requested to concentrate national resources on developing a large-scale operational Korea Ocean Forecasting System which can produce open boundary and initial conditions for local ocean and climate forecasting models. Once the system is established, each organization can modify the system for its own specialized purpose. In addition, we can contribute to the international ocean prediction community.

• The Korean Journal of Emergency Medical Services
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• v.9 no.2
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• pp.111-128
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• 2005

Background: Cardiac arrest is one of the most critical diseases which can likely lead to severe cerebral disability or brain death when the cases can not recover their circulation within 10 minutes. Saving out-of-hospital cardiac arrest cases is a recent concern in Korea. Resuscitation has become an important multidisciplinary branch of medicine, demanding a spectrum of skills and attracting a plethora of specialities and organizations. The best survival can be achieved if all the following links have been optimized : rapid access, and early CPR, defibrillation and ACLS, Since the "Utstein Style" was advocated in 1991, many reports about out-of-hospital cardiac arrest have been published based on this guideline. These differences prevent valid inter-hospital and international comparisons. However, it is not known how effective resuscitation has become to the patients. In other words, there are no guidelines for reviewing, reporting, and conducting research on resuscitation in Korea. This dissertation aims to provide the basic data for a unified reporting guideline of resuscitation in Korea and evaluating the out-of-hospital factors associated with survival discharge of out-of-hospital cardiac arrest. Methods: As for this study, uses the collected data about Out-of-hospital cardiac arrests at 4 area, from January, 2005 to April. 2005. With a retrospective study, 174 cases were analyzed. The data was recorded based on the Out-of-Hospital Utstein Style. Results: Resuscitation was performed on 174 out-of-hospital cardiac arrest cases at the 4 area 14 patients(8.1%) recovered their spontaneous circulation. Overall, the ROSC of the out-of-hospital cardiac arrest patients was 8.1%, which was poorer than that of western countries. Gender distribution was 50 females(28.7%) and 124 males(71.3%), approximately twice as many males as females. ROSC of witnessed arrests was found out to be 97.7%. The ratio of the witnessed arrest groups showed higher results than that of unwitnessed arrest groups in the above-examined cases. Cardiac etiology consisted of cardiac(33.5%), non-cardiac(45.7%), trauma(20.1%), and unknown(6.0%). Cardiac was the best performance. Initial rhythm showed Ventricular Tachycardia/pulseless Ventricular Fibrillation in 8 patients(6.0%), asystole in 100(75.2%) and unknown in 25(18.8%). The results of the Ventricular Tachycardia/pulseless Ventricular Fibrillation showed higher results than the others cases, The proportion of the cardiogenic cause was 33.5%, which was only half of western countries. Ventricular Tachycardia/pulseless Ventricular Fibrillation is relatively rare. These differences were due to the prevalent pattern of Out-of-hospital cardiac arrest as well as prematurity of the EMSS. Bystander CPR was practiced on 13 patients(7.52%). ROSC was shown in 46.2% cases. CPR by EMT was carried out on 167 cases(96.5%). ACLS by EMf was rare. From collapse, 4 cases(2.6%) arrived to ED within 6 minutes. 13 (8.6%) within 10 minutes, and 49(32.5%) over 31 minutes. The sooner the patients arrived, the greater the ratio of ROSC and discharged alive became, and the same with collapse time to ROSC. As the results of the logistic regression analysis, ROSC was found out to be highly influenced by the time of ED arrival from collapse and Ventricular Tachycardia/pulseless Ventricular Fibrillation. Therefore, the ratio of ROSC depends on not any single factor but various intervention factors. Conclusion: This dissertation presents the following suggestions and directions of the study hereafter. First, the first step for a chain of survival should be taken to activate EMSS early with a phone as soon as cardiac arrests are witnessed. Second, it is keenly needed that emergency medical technicians should be increased through emergency education for living. Third, it is necessary to establish the emergency transportation system. Fourth, most of the Koreans have little understanding of EMT and the present operation systems have many problems, which should be fundamentally changed. Fifth, it is required to have an active medical control over Out-of-hospital CPR, And proper psychological supports should be given not only to patients themselves and their family but also individuals who are engaged in emergency situation. Finally, through studies hereafter on nationwide, comprehensive, and standard forms, it is needed to examine into the biological figures of human body, causes and trends of cardiac arrests, and then, to enhance the survival rate of Out-of-hospital cardiac arrests. Korean guidelines for Cardiopulmonary resuscitation need to be made.

• Radiation Oncology Journal
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• v.22 no.2
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• pp.77-90
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• 2004

Purpose: The purpose of this review Is to provide an update on novel radiation treatments for head and neck cancer Recent Findings: Despite the remarkable advances In chemotherapy and radiotherapy techniques, the management of advanced head and neck cancer remains challenging. Epidermal growth factor receptor (EGFR) Is an appealing target for novel therapies In head and neck cancer because not only EGFR activation stimulates many important signaling pathways associated with cancer development and progression, and importantly, resistance to radiation. Furthermore, EGFR overexpression Is known to be portended for a worse outcome in patients with advanced head and neck cancer. Two categories of compounds designed to abrogate EGFR signaling, such as monoclonal antibodies (Cetuxlmab) and tyrosine kinase inhibitors (ZD1839 and 051-774) have been assessed and have been most extensively studied In preclinical models and clinical trials. Additional TKIs In clinical trials include a reversible agent, Cl-1033, which blocks activation of all erbB receptors. Encouraging preclinical data for head and neck cancers resulted In rapid translation Into the clinic. Results from Initial clinical trials show rather surprisingly that only minority of patients benefited from EGFR inhibition as monotherapy or In combination with chemotherapy. In this review, we begin with a brief summary of erbB- mediated signal transduction. Subsequently, we present data on prognostic-predictive value of erbB receptor expression in HNC followed by preclinlcal and clinical data on the role of EGFR antagonists alone or in combination with radiation In the treatment of HNC. Finally, we discuss the emerging thoughts on resistance to EGFR biockade and efforts In the development of multiple-targeted therapy for combination with chemotherapy or radiation. Current challenges for investigators are to determine (1 ) who will benefit from targeted agents and which agents are most appropriate to combine with radiation and/or chemotherapy, (2) how to sequence these agents with radiation and/or cytotoxlc compounds, (3) reliable markers for patient selection and verification of effective blockade of signaling in vivo, and (4) mechanisms behind intrinsic or acquired resistance to targeted agents to facilitate rational development of multi-targeted therapy, Other molecuiar-targeted approaches In head and neck cancer were briefly described, Including angloenesis Inhibitors, farnesyl transferase inhibitors, cell cycle regulators, and gene therapy Summary: Novel targeted theraples are highly appealing in advanced head and neck cancer, and the most premising strategy to use them Is a matter of intense Investigation.

• Radiation Oncology Journal
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• v.22 no.1
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• pp.33-39
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• 2004

Purpose : A tumor registry system for the patients treated by radiotherapy at Samsung Medical Center since the opening of a hospital at 1994 was employed. In this study, the tumor registry system was introduced and the validity of the tumor registration was analyzed. Materials and Methods: The tumor registry system was composed of three parts: patient demographic, diagnostic, and treatment Information. All data were input in a screen using a mouse only. Among the 10,000 registered cases in the tumor registry system until Aug, 2002, 199 were randomly selected and their registration data were compared with the patients' medical records. Results : Total input errors were detected on 15 cases (7.5%). There were 8 error items In the part relating to diagnostic Information: tumor site 3, pathology 2, AJCC staging 2 and performance status 1. In the part relating to treatment information there were 9 mistaken items: combination treatment 4, the date of initial treatment 3 and radiation completeness 2. According to the assignment doctor, the error ratio was consequently variable. The doctors who 010 no double-checks showed higher errors than those that 010 (15.6%:3.7%). Conclusion: Our tumor registry had errors within 2% for each Item. Although the overall data qualify was high, further improvement might be achieved through promoting sincerity, continuing training, periodic validity tests and keeping double-checks. Also, some items associated with the hospital Information system will be input automatically In the next step.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used