• Journal of Breast Cancer
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• v.21 no.4
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• pp.363-370
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• 2018

Purpose: Breast cancer is the most commonly occurring cancer among women worldwide, and therefore, improved approaches for its early detection are urgently needed. As microRNAs (miRNAs) are increasingly recognized as critical regulators in tumorigenesis and possess excellent stability in plasma, this study focused on using miRNAs to develop a method for identifying noninvasive biomarkers. Methods: To discover critical candidates, differential expression analysis was performed on tissue-originated miRNA profiles of 409 early breast cancer patients and 87 healthy controls from The Cancer Genome Atlas database. We selected candidates from the differentially expressed miRNAs and then evaluated every possible molecular signature formed by the candidates. The best signature was validated in independent serum samples from 113 early breast cancer patients and 47 healthy controls using reverse transcription quantitative real-time polymerase chain reaction. Results: The miRNA candidates in our method were revealed to be associated with breast cancer according to previous studies and showed potential as useful biomarkers. When validated in independent serum samples, the area under curve of the final miRNA signature (miR-21-3p, miR-21-5p, and miR-99a-5p) was 0.895. Diagnostic sensitivity and specificity were 97.9% and 73.5%, respectively. Conclusion: The present study established a novel and effective method to identify biomarkers for early breast cancer. And the method, is also suitable for other cancer types. Furthermore, a combination of three miRNAs was identified as a prospective biomarker for breast cancer early detection.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.457-467
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• 2018

Currently, the regulation and rules for public surveying and the UCPs (Unified Control Points) adapts those of the triangulated traverse surveying. In addition, such regulations do not take account of the unique characteristics of GNSS (Global Navigation Satellite System) surveying, thus there are difficulties in field work and data processing afterwards. A detailed procesure of GNSS processing has not yet been described either, and the verification of accuracy does not follow the generic standards. In order to propose an appropriate procedure for field surveys, we processed a short session (30 minutes) based on the scenarios similar to actual situations. The reference network in Seoul was used to process the same data span for 3 days. The temporal variation during the day was evaluated as well. We analyzed the accuracy of the estimated coordinates depending on the parameterization of tropospheric delay, which was compared with the 24-hr static processing results. Estimating the tropospheric delay is advantageous for the accuracy and stability of the coordinates, resulting in about 5 mm and 10 mm of RMSE (Root Mean Squared Error) for horizontal and vertical components, respectively. Based on the test results, we propose a procedure to estimate the daily solution and then combine them to estimate the final solution by applying the minimum constraints (no-net-translation condition). It is necessary to develop a web-based processing system using a high-end softwares. Additionally, it is also required to standardize the ID of the public control points and the UCPs for the automatic GNSS processing.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.439 -447
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• 2023

This paper aims to develop a map for optimized class space using ZEP among the metaverse platforms. As a research method, the classroom space was organized so that the subject of learning became a learner, and the classroom space was modified and supplemented to optimize while being applied to elementary school computer classes. The contents of the study investigated learners' prior perception of metaverse, and compared and analyzed the advantages and disadvantages of the metaverse platform. In addition, the map was designed by reflecting the results of the survey, and after applying the map to the class, necessary APIs and apps were installed to supplement it. As a result, the learner became the subject of learning in the metaverse space, freely identified the space, and actively participated in the class. In particular, we found that students who were passive offline and those who had a low participation rate due to lack of skills participated more actively. In particular, students who were passive offline or whose participation was low due to lack of skills participated more actively. If API and JavaScript programs are added to collect log data of learners for learning analysis, real-time feedback is possible for learners, and learner feedback is possible for instructors with statistical data. If this is possible, the metaverse space can fully expect the role of a learning assistant for learners and a teaching assistant for instructors.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.97-108
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• 2024

This research delves into the escalating concerns of accidents and fatalities in the construction industry over the recent five-year period, focusing on the development of a Safety Perception Model to augment safety measures. Given the rising percentage of elderly workers and the concurrent drop in productivity within the sector, there is a pronounced need for leveraging Fourth Industrial Revolution technologies to bolster safety protocols. The study comprises an in-depth analysis of statistical data regarding construction-related fatalities, aiming to shed light on prevailing safety challenges. Central to this investigation is the formulation of a Safety Perception Model tailored for small-scale construction projects. This model facilitates the quantification of safety risks by evaluating safety grades across construction sites. Utilizing the DWM1000 module, among an array of wireless communication technologies, the model enables the real-time tracking of worker locations and the assessment of safety levels on-site. Furthermore, the deployment of a safety management system allows for the evaluation of risk levels associated with individual workers. Aggregating these data points, the Safety Climate Index(SCLI) is calculated to depict the daily, weekly, and monthly safety climate of the site, thereby offering insights into the effectiveness of implemented safety measures and identifying areas for continuous improvement. This study is anticipated to significantly contribute to the systematic enhancement of safety and the prevention of accidents on construction sites, fostering an environment of improved productivity and strengthened safety culture through the application of the Safety Perception Model.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.1
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• pp.31-38
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• 2020

The characteristics of ship traffic routes and the long term fluctuation in marine traf ic volume of the incoming and outgoing routes of the Yeosu Gwangyang Port were analyzed using vessel traffic data from the past 22 years and a real-time vessel traffic volume survey performed for 72 hours per year, for three years, between 2015 and 2017. As of 2017, the number of vessels passing through Yeosu Gwangyang Port was about 66,000 and the total tonnage of these ships was about 804,564 thousand tons, which is a 400 % increase from the 189,906 thousand tons shipped in 1996. Specifically, the dangerous cargo volume was 140,000 thousand tons, which is a 250 % increase compared to 1996. According to the real-time vessel traffic volume survey, the average daily number of vessels was 357, and traf ic route utilization rates were 28.1 % in the Nakpo sea area, 43.8 % in the specified sea area, and the coastal area traf ic route, Dolsan coastal area, and Kumhodo sea area showed the same rate of 6.8 %. Many routes meet in the Nakpo sea area and, parallel and cross passing were frequent. Many small work vessels entered the specific sea area from the neighboring coastal area traffic route and frequently intersected the path of larger vessels. The anchorage waiting rate for cargo ships was about 24 %, and the nightly passing rate for dangerous cargo ships such as chemical vessels and tankers was about 20 %. Although the vessel traffic volume of Yeosu Gwangyang Port increases every year, the vessel traffic routes remain the same. Therefore, the risk of accidents is constantly increasing. The route conditions must be improved by dredging and expanding the available routes to reduce the high risk of ship accidents due to overlapping routes, by removing reefs, and by reinforcing navigational aids. In addition, the entry and exit time for dangerous cargo ships at high-risk ports must be strictly regulated. Advancements in the VTS system can help to actively manage the traffic of small vessels using the coastal area traffic route.

• The Journal of Pediatrics of Korean Medicine
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• v.26 no.4
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• pp.10-23
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• 2012

Objectives: Gagamyanggyeoksan (G-YGS) has been used to suppress allergic reaction, however, the cellular target of G-YGS and its mode of action remain unclear. The present study was designed to investigate the effect of extracted G-YGS on the PMA and lonomycin (PI)-induced activation of RBL-2H3. Methods: For this investigation, We examined IL-4, IL-13 mRNA expression by Real-Time PCR, IL-4, IL-13 production by ELISA analysis and manifestations of GATA-1, GATA-2, NF-AT1, NF-AT2, AP-1 and NF-${\kappa}B$ p65 transcription factors by western blotting, OVA-specific IgE, IL-4, IL-13 by mouse be sensitive to OVA. Results: Here we showed that treatment of RBL-2H3 mast cells with G-YGS, suppressed PI-induced production of Th2 cytokines including IL-4 and IL-13 in a dose dependent manner. The mRNA expression of IL-4 were completely abolished by G-YGS at the concentration of $100{\mu}g/ml$. Data from a stable cell lines consistently expressing IL-4. And the mRNA expression of IL-13 were abolished by G-YGS at the $200{\mu}g/ml$. But there is no difference between the $50{\mu}g/ml$, the $100{\mu}g/ml$ and the comparison. Results from the western blot analysis of transcription factors involving IL-4 and IL-13 expression indicated that it prominently decreased the expression of mast cell specific transcricption factors including GATA-1, GATA-2, NF-AT2, c-Jun, NF-${\kappa}B$ p65 but not c-Fos. And G-YGS suppressed IgE, IL-4, IL-13 in mouse be sensitive to OVA. Conclusions We suggested the anti-allergic activities of G-YGS might be mediated by down-regulation of Th2 cytokines such as IL-4 and IL-13 through the regulation of transcription factors as GATA-1, GATA-2, NF-AT2, c-Jun, NF-${\kappa}B$ p65.

• Journal of Korean Academy of Nursing
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• v.30 no.5
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• pp.1279-1291
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• 2000

The main objectives of this study were to analyze the concept of hope, so to provide basic data to develop a valid instrument to measure hope, and to develop hope enhancing nursing intervention a program for cancer patients. The hybrid model approach was applied in three phases, the theoretical phase, the empirical phase, and the analytic phase. The study was developed on universal attributes explaining generalized hope and specific hope, which were revealed in a comprehensive review of the literature. In the empirical phase, eight cancer patients undergoing chemotherapy were interviewed to reveal causes, motivation, and their resource of hope according to The Hope Assessment Guide (Farren, Herth, & Popovich, 1995). In the analytical phase, the results of the two previous stages of the study were compared. The results were as follows : In the theoretical phase, six dimensions of hope emerged; affective, cognitive, behavioral, affiliative, temporal and contextual dimension. The antecedent of hope was loss, crisis, uncertainity, and stress. The consequences were renewal, development of new methods, safety, peace and transcendental competence. In the empirical phase, these six dimensions emerged as theoretical phases were verified and specified as these descriptive terms: feeling, intention, expectation, activity, relation, future- orientation, reality and goal-setting. The antecedent factor of hope was occurrence or recurrence of cancer. The consequence of hope was ability to cope with real condition, feeling of safety and comfort, peace, development of new strategy and recovery of disease. The major content of hope in this phase was related to specific hope, but it was also influenced on by general hope. In the analytic phase, general and specific hope was renamed as trait and state hope. All attributes emerged at the empirical phases, and also emerged at the theoretical phase. However, cognitive and contextual dimensions were revised and specified. In conclusion, the concept of hope is divided into trait hope and state hope, and state hope is an anticipatory expectation that occurs at the time of a stressful stimulus, such as being diagnosed with cancer. Hope is a multidimensional dynamic energized mental state which has the dimensions of affective, cognitive, behavioral, affiliative, temporal and contextual. There should be further studies to develope the state and trait hope scale according to definition and attributes of hope investigated in this study. In addition, considering results of the empirical phase, the family is very a important factor as a resource of hope, so it is necessary to consider family in implementing a nursing intervention program to enhance hope.