• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.4
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• pp.148-153
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• 2010

Concrete used as structural materials in construction fields is supplied as a type of carry and placement by ready-mixed concrete (RMC) truck after proportioning in batch plant. However, during conveying of concrete to the field, due to traffic jam, weather, etc., it is not easy to maintain adequate slump. In this case, we think that the insert of an admixture to concrete has no problem in concrete. For RMC, when the slump is not sufficient, the truck driver insert water additionally without any considerations. After that, concrete is placed after re-mixing and this leads to serious reasons such as strength reduction less than design strength considered in the structural design. Accordingly, in this study, to solve the problem to insert water without realistic reasons in RMC, basic experimental studies were performed. Admixtures used frequently in fields were selected and addition's repeated time and elapsed time interval after initial addition of the admixture were selected as main variables. Authors want that the results of this study is used as basic data to resolve the question.

• Korean Journal of Food Science and Technology
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• v.10 no.1
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• pp.57-62
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• 1978

The morphology and chemical properties of bracken (Pteridium aquilinum) root starch were investigated. The starch granules were mainly sphere and cocoon with the diameter of $5-12\mu$. Polarized micrograph indicated that the starch granule had a hilum at the center of granule, showing a crossed-birefringence. X-ray diffraction pattern demonstrated that the granules showed B-type. The density of the starch was 1.49 and the amylose content was 22%. The ferricyanide number and alkali number were 0.292 and 11.03, respectively. Proximate analysis showed that the starch contained 0.52% lipid, 0.63% ash and 150ppm phosphorus of which over 80% were found in the amylopectin fraction. The iodine affinity and molecular weight of amylose were 16.1 and 83,000 respectively. The degree of branching and glucose units per segment of amylopectin were 3.7% and 27, respectively.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.317-325
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• 2018

With the aim of creating new economic values and strengthening national competitiveness, governments and major private companies around the world are continuing their interest in big data and making bold investments. In order to collect objective data, such as news, securing data integrity and quality should be a prerequisite. For researchers or practitioners who wish to make decisions or trend analyses based on objective and massive data, such as portal news, the problem of using the existing Crawler method is that data collection itself is blocked. In this study, we implemented a method of collecting web data by addressing existing crawler-style problems using the cloud service platform provided by Amazon Web Services (AWS). In addition, we collected 'gas safety' articles and analyzed issues related to gas safety. In order to ensure gas safety, the research confirmed that strategies for gas safety should be established and systematically operated based on five categories: accident/occurrence, prevention, maintenance/management, government/policy and target.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Research in Plant Disease
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• v.28 no.3
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• pp.113-121
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• 2022

Rice blast is a major plant disease that occurs worldwide and significantly reduces rice yields. Rice blast disease occurs periodically in Korea, causing significant socio-economic damage due to the unique status of rice as a major staple crop. A disease outbreak prediction system is required for preventing rice blast disease. Epidemiological investigations of disease outbreaks can aid in decision-making for plant disease management. Currently, plant disease prediction and epidemiological investigations are mainly based on quantitatively measurable, structured data such as crop growth and damage, weather, and other environmental factors. On the other hand, text data related to the occurrence of plant diseases are accumulated along with the structured data. However, epidemiological investigations using these unstructured data have not been conducted. The useful information extracted using unstructured data can be used for more effective plant disease management. This study analyzed news articles related to the rice blast disease through text mining to investigate the years and provinces where rice blast disease occurred most in Korea. Moreover, the average temperature, total precipitation, sunshine hours, and supplied rice varieties in the regions were also analyzed. Through these data, it was estimated that the primary causes of the nationwide outbreak in 2020 and the major outbreak in Jeonbuk region in 2021 were meteorological factors. These results obtained through text mining can be combined with deep learning technology to be used as a tool to investigate the epidemiology of rice blast disease in the future.

• The Korean Journal of Air & Space Law and Policy
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• v.19 no.2
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• pp.137-162
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• 2004

Generally the aircraft accident caused a great loss of lives, severe property damages including aircraft's total loss and thus, affect enormous emotional and economic damages to the public. We, therefore, should try every efforts to prevent the re-occurrence of aircraft accident by examining the cause of accident closely and discovering it through aircraft accident investigation. Though the object of an accident investigation is not to apportion blame but to discover a cause or causes of an accident to prevent future accidents, the cause of an accident would play a vital role in determining the liability of the carrier, legal relationship with the third party and jurisdiction, etc. in the aviation litigation. Therefore, it is very important that aircraft accident investigation are carried out by a professional and independent agency. Also, it needs for us to be careful in applying investigation results in the courts not to be deterrent to discovering the cause of accident. Korea now has the Aviation Accident Investigation Agency Board under the Korean Ministry of Construction and Transportation, but unfortunately it is often pointed out that it lacks professionalism and independency due to the bureaucratism of the Government. We, therefore, should establish a professional and independent aircraft accident investigation agency like United States' NTSB and reflect the issues mentioned-above on the new Act.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.183-187
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• 2017

Smart phone users prefer fast reading and texting. Hence, users frequently use abbreviated sequences of words and phrases. Nowadays, abbreviations are widely used from chat terms to technical terms. Therefore, gathering abbreviations would be helpful to many services, including information retrieval, recommendation system, and so on. However, manually gathering abbreviations needs to much effort and cost. This is because new abbreviations are continuously generated whenever a new material such as a TV program or a phenomenon is made. Thus it is required to generate of abbreviations automatically. To generate Korean abbreviations, the existing methods use the rule-based approach. The rule-based approach has limitations, in that it is unable to generate irregular abbreviations. Another problem is to decide the correct abbreviation among candidate abbreviations generated rules. To address the limitations, we propose a method of generating Korean abbreviations automatically using sequence-to-sequence learning in this paper. The sequence-to-sequence learning can generate irregular abbreviation and does not lead to the problem of deciding correct abbreviation among candidate abbreviations. Accordingly, it is suitable for generating Korean abbreviations. To evaluate the proposed method, we use dataset of two type. As experimental results, we prove that our method is effective for irregular abbreviations.

• Journal of Hospice and Palliative Care
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• v.19 no.4
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• pp.292-295
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• 2016

Hospice and palliative care in Taiwan has been growing continuously. The 2015 Quality of Death index, as rated by the Economist Intelligence Unit, ranked Taiwan first among Asian countries and sixth in the world. In this review article, we highlight three particular areas that might have contributed to this success; the laws and regulations, spiritual care and research network. Finally, we discuss the future challenges and prospects for Taiwanese encounters. A systemic review was conducted with the keywords "hospice palliative care Taiwan" using PubMed. The passing of the "Natural Death Act" in 2000 set the example and established a landmark for patient autonomy in Asia; it guarantees the patient's right to request that medical staff do not resuscitate (DNR) them and to reject other futile medical treatments at the end of their life, thus reflecting the importance of palliative care from the policy perspective. In 2015, Taiwan passed another pioneering law entitled the "Patient Autonomy Act". This law states that a patient may decline medical treatment according to his/her own will. Taiwanese indigenous spiritual care was launched in 2000. It requires a Buddhist Chaplain to successfully complete a training program consisting of lectures, as well as bedside practicum before applying Buddhist practices to end-of-life care. The Japan-Korea-Taiwan research network was established for the purpose of enabling collaborative research for the East-Asian collaborative cross-cultural Study to Elucidate the Dying process (EASED) cohort. With consensus from the government and society to make it a priority, hospice and palliative medicine in Taiwan has been growing steadily.

• Korean journal of communication and information
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• v.46
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• pp.75-107
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• 2009

Nevertheless reported the same events, news audience have diverse sense of sights and opinions about the events because of the different news frames. This notion was repeatedly evi nnced by several framing studies. This pa wa tried to analyse an interacting relationship between viewer’s level of elaboration likelihood and the effects of the news frames. This pa wa sfrrted with a discussion about the framing effects, then compared them with Elaboration Likelihood Ms notraming effely. And this study conducted an ex waiment selecting indivi ual dispngitions (involvement and cognitive complexity) and message characteristics(number of cues and arguments) as intermediating variables on the message framing effects. This study found out that, the more involvement about the issues the viewers had, the more their thoughts coincided with the issue's frame. On the other hand, when the viewers had low involvement about the issues and cognitive complexity, the framing effects were not found because they processed the messages through the peripheral route. Although the viewers' cognitive complexity was a factor in choosing the central route, but it was not directly connected to the framing effect. Both the number of cues and argument diversity in the messages had positive relationships with the framing effects.

• 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.