• Korean Journal of Remote Sensing
• /
• v.32 no.2
• /
• pp.119-131
• /
• 2016

Descriptions are provided of the automated aerosol-type classification and mass concentration calculation algorithm for real-time data processing and aerosol products in Korea Aerosol Lidar Observation Network (KALION, http://www.kalion.kr). The KALION algorithm provides aerosol-cloud classification and three aerosol types (clean continental, dust, and polluted continental/urban pollution aerosols). It also generates vertically resolved distributions of aerosol extinction coefficient and mass concentration. An extinction-to-backscatter ratio (lidar ratio) of 63.31 sr and aerosol mass extinction efficiency of $3.36m^2g^{-1}$ ($1.39m^2g^{-1}$ for dust), determined from co-located sky radiometer and $PM_{10}$ mass concentration measurements in Seoul from June 2006 to December 2015, are deployed in the algorithm. To assess the robustness of the algorithm, we investigate the pollution and dust events in Seoul on 28-30 March, 2015. The aerosol-type identification, especially for dust particles, is agreed with the official Asian dust report by Korean Meteorological Administration. The lidar-derived mass concentrations also well match with $PM_{10}$ mass concentrations. Mean bias difference between $PM_{10}$ and lidar-derived mass concentrations estimated from June 2006 to December 2015 in Seoul is about $3{\mu}g\;m^{-3}$. Lidar ratio and aerosol mass extinction efficiency for each aerosol types will be developed and implemented into the KALION algorithm. More products, such as ice and water-droplet cloud discrimination, cloud base height, and boundary layer height will be produced by the KALION algorithm.

• Korean Journal of Remote Sensing
• /
• v.26 no.2
• /
• pp.65-73
• /
• 2010

To observe and analyze the characteristics of cloud and precipitation properties, the Cloud physics Observation System (CPOS) has been operated from December 2003 at Daegwallyeong ($37.4^{\circ}N$, $128.4^{\circ}E$, 842 m) in the Taebaek Mountains. The major instruments of CPOS are follows: Forward Scattering Spectrometer Probe (FSSP), Optical Particle Counter (OPC), Visibility Sensor (VS), PARSIVEL disdrometer, Microwave Radiometer (MWR), and Micro Rain Radar (MRR). The former four instruments (FSSP, OPC, visibility sensor, and PARSIVEL) are for the observation and analysis of characteristics of the ground cloud (fog) and precipitation, and the others are for the vertical cloud characteristics (http://weamod.metri.re.kr) in real time. For verification of CPOS products, the comparison between the instrumental products has been conducted: the qualitative size distributions of FSSP and OPC during the hygroscopic seeding experiments, the precipitable water vapors of MWR and radiosonde, and the rainfall rates of the PARSIVEL(or MRR) and rain gauge. Most of comparisons show a good agreement with the correlation coefficient more than 0.7. These reliable CPOS products will be useful for the cloud-related studies such as the cloud-aerosol indirect effect or cloud seeding. The visibility value is derived from the droplet size distribution of FSSP. The derived FSSP visibility shows the constant overestimation by 1.7 to 1.9 times compared with the values of two visibility sensors (SVS (Sentry Visibility Sensor) and PWD22 (Present Weather Detect 22)). We believe this bias is come from the limitation of the droplet size range ($2{\sim}47\;{\mu}m$) measured by FSSP. Further studies are needed after introducing new instruments with other ranges.

• The Journal of Korean Academy of Prosthodontics
• /
• v.49 no.3
• /
• pp.254-262
• /
• 2011

Purpose: This study aims to evaluate and prospect for current research trend and developmental perspectives via analyzing recent biomaterial coated-implants study. Materials and methods: To investigate each subject respectively, several biomaterials that are using for implant surface coating were set as 'keywords'. By these keywords, major research groups in each subject were chosen, and research trend of them was analyzed. Trend of In vivo studies that examined selected biomaterials were analyzed to evaluate commercial potential. Results: The collagen research accounted for 40% of total implant study, which was the highest, and fibronectin, BMPs (bone morphogenetic proteins) and RGD (Arg-Gly-Asp) peptides followed, which were ranked in descending order. Furthermore, figures of all four research subjects were also increased with time, especially a sharp increase in RGD research. According to the results of major research group, collagen that was combined with other organic and inorganic biomaterials was mostly examined, rather than using collagen only. Major research groups investigating BMPs mostly focused on rhBMP-2. In animal studies, collagen was used as resorbable membrane in guided bone regeneration (GBR) or drug carrier, while BMPs were used with bone graft materials or coating material for titanium implant surface. Conclusion: There is not consistency of results even in identical subjects research field. Many studies are ongoing to optimize combination between mechanical surface treatment and biomaterials such as extracellular matrix component and growth factors.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.69-92
• /
• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Korean Medicine Rehabilitation
• /
• v.23 no.4
• /
• pp.95-115
• /
• 2013

Objectives The aim of this review is to provide fundamental data for low back pain scales which can be used in clinical trial. Methods We investigated the latest studies on chronic low back pain via PubMed. And we also investigated domestic studies through "http://oasis.kiom.re.kr". 95 research papers were analyzed. Scales were classified into pain scale, function scale, generic health status scale and psychological scale. Results 1) According to foreign clinical studies, Visual Analog Scale (VAS) and Numerical Rating Scale (NRS) were used 18 times as pain scale. Oswestry Disability Index (ODI) was used 20 times as function scale, Roland-Morris Disability Questionnaire (RMDQ) was 17, and Hannover Functional Ability Questionnaire (HFAQ) was used 3 times. 36-item Short Form Health Survey (SF-36) was used 13 times as generic health status scale, Euroqol-5 Dimentions Questionnaire (EQ-5D) was 11, and 12-item Short Form Health Survey (SF-12) was used 3 times. Fear-Avoidance Beliefs Questionnaire (FABQ) was used 9 times as psychological scale, Pain Catastrophizing Scale (PCS) and Tampa Scale for Kinesiophobia (TSK-R) both were used 3 times. 2) According to domestic clinical studies, VAS was used 37 times as pain scale, NRS was 11, and Short Form McGill Pain Questionnaire (SF-MPQ) was used 6 times. ODI was used 30 times as function scale, RMDQ was 2 times only. SF-36 was used once as generic health status scale and Beck's Depression Inventory (BDI) was used 3 times as psychological scale. Conclusions We recommend VAS or NRS as a measure to evaluate pain, and ODI as a measure to evaluate functional disability. And we also recommend SF-36 or SF-12 and EQ-5D as a measure to evaluate generic health status. Finally, we recommend FABQ for use in measuring psychological scale.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.38 no.2
• /
• pp.42-59
• /
• 2020

The purpose of this study is to examine the actual cases of crane breeding at private homes in the Joseon Dynasty period, thereby identifying the universal meaning and characteristics of this act reflected in these cases. This study is likely to help understand the true nature of garden culture during the period. The study' temporal and spatial scope was limited to the Joseon Dynasty and private homes. As references for the study, translated versions of classical literature were selected from the Database of Korean Classics(http://db.itkc.or.kr). To complement for the data, related researchers' translated materials were also used in part. The study's results are summed up as follows: First, Individuals from various social classes including royal families, noblemen, noble families in countryside, and commoners kept cranes at their homes. These crane breeders included those who left a significant mark in the Joseon Dynasty politically and academically as well as 'cheosa(處士)' that refers to scholars living in seclusion without entering the government throughout their lifetime. Second, Crane breeders were spread all over the country. Notably, various cases of crane breeding were found within the Hanyang Wall and its vicinity. Third, The act of crane breeding was highly associated with blood ties and academic lineages such as friendships and teacher-student relations. In this regard, crane breeding was not just a simple taste or appreciation for the arts, but rather reflective of a person's life attitude and orientation. Forth, The consciousness of Confucian origins based on an ancient story of Limpo (林逋) appears to have a large impact on the act of crane breeding. In addition, some cases exhibited the reflection of Taoistic tastes. Fifth, Some individuals tamed cranes for a living. This proves the presence of steady demand for cranes in this period. The present study's limitation is its reliance on translated materials, which hindered research into various cases. Therefore, the future discovery of additional data and the accumulation of their translations will enable the investigation of a wealth of cases.

• The Mathematical Education
• /
• v.63 no.2
• /
• pp.123-138
• /
• 2024

This study described the utilization of ChatGPT in teaching and students' learning processes for the course "Introductory Mathematics for Artificial Intelligence (Math4AI)" at 'S' University. We developed a customized ChatGPT and presented a learning model in which students supplement their knowledge of the topic at hand by utilizing this model. More specifically, first, students learn the concepts and questions of the course textbook by themselves. Then, for any question they are unsure of, students may submit any questions (keywords or open problem numbers from the textbook) to our own ChatGPT at https://math4ai.solgitmath.com/ to get help. Notably, we optimized ChatGPT and minimized inaccurate information by fully utilizing various types of data related to the subject, such as textbooks, labs, discussion records, and codes at http://matrix.skku.ac.kr/Math4AI-ChatGPT/. In this model, when students have questions while studying the textbook by themselves, they can ask mathematical concepts, keywords, theorems, examples, and problems in natural language through the ChatGPT interface. Our customized ChatGPT then provides the relevant terms, concepts, and sample answers based on previous students' discussions and/or samples of Python or R code that have been used in the discussion. Furthermore, by providing students with real-time, optimized advice based on their level, we can provide personalized education not only for the Math4AI course, but also for any other courses in college math education. The present study, which incorporates our ChatGPT model into the teaching and learning process in the course, shows promising applicability of AI technology to other college math courses (for instance, calculus, linear algebra, discrete mathematics, engineering mathematics, and basic statistics) and in K-12 math education as well as the Lifespan Learning and Continuing Education.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.1-23
• /
• 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
• /
• v.19 no.3
• /
• pp.153-163
• /
• 2017

The objective of this study is to enhance the model's speed of estimating weather variables (e.g., minimum/maximum temperature, sunshine hour, PRISM (Parameter-elevation Regression on Independent Slopes Model) based precipitation), which are applied to the Agrometeorological Early Warning System (http://www.agmet.kr). The current process of weather estimation is operated on high-performance multi-core CPUs that have 8 physical cores and 16 logical threads. Nonetheless, the server is not even dedicated to the handling of a single county, indicating that very high overhead is involved in calculating the 10 counties of the Seomjin River Basin. In order to reduce such overhead, several cache and parallelization techniques were used to measure the performance and to check the applicability. Results are as follows: (1) for simple calculations such as Growing Degree Days accumulation, the time required for Input and Output (I/O) is significantly greater than that for calculation, suggesting the need of a technique which reduces disk I/O bottlenecks; (2) when there are many I/O, it is advantageous to distribute them on several servers. However, each server must have a cache for input data so that it does not compete for the same resource; and (3) GPU-based parallel processing method is most suitable for models such as PRISM with large computation loads.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.3
• /
• pp.430-438
• /
• 2018

This study was conducted to develop and evaluate a web-based infection prevention education program for children, parents and teachers. Research for development of the web-based education program was completed in four phases (analysis, design, development, and evaluation) from 1 February 2015 to 5 October 2015, and the completed website was named CHILD4HEALTH (http://uwcms.pusan.ac.kr). Educational contents pertaining to infection prevention were composed of three sections, children, parents and teachers. Subjects were divided into nine categories, animation, children's dictionary, with mom, music, games, quizzes, educational contents for parents, educational contents for teachers, school newsletters, and handouts. Six characters were developed to increase interest and educational effect. Program evaluation items comprised the website, reliability, and satisfaction. Website evaluation by parents revealed that ease of use was $3.77{\pm}0.70$, entertainment value was $4.07{\pm}0.27$, childproof was $3.82{\pm}0.67$, education value was $4.02{\pm}0.75$, and design features were rated $3.65{\pm}0.53$. According to teachers, ease of use was $3.98{\pm}0.37$, entertainment value was $4.00{\pm}0.17$, childproof was $4.34{\pm}0.60$, education value was $4.00{\pm}0.20$, and design features were $3.81{\pm}0.56$. Parents scored reliability and satisfaction as $8.33{\pm}0.62$ and $7.80{\pm}0.77$, respectively, while they were scored as $8.50{\pm}0.73$ and $8.10{\pm}0.74$ by teachers. Based on the results of this study, the developed web-based education program will help prevent infectious disease and facilitate development of future education programs regarding such diseases.