• The Journal of Engineering Geology
• /
• v.7 no.2
• /
• pp.113-126
• /
• 1997

Electrical resistivity methods of dipole - dipole array profiling and Schiumberger array sounding were tested on a segment of the Woraksan granitic batholith for the research into the imaging of irregular attitudes of fracture zones in the crystaaline rock in terms of processing and interpretation schemes. By the dipole - dipole array method, inhomogeneities such as small scale of fracture zones were properly delineated down at some depth even within hard rock environment. Fracture zones were interpreted to be at the boundaries between the high amplitude zone and very low amplitude zone in the resistivity plot and they were also successfully outlined in two - dimensional layer and pseudo - three - dimensional volume constructed by the incorporation of vertical sounding data. The surface location of the fracture zones was correlated by the zero - crossing point in the VLF(very low frequency) electromagnetic data. Pseudo - three - dimensional attitudes of fracture zones were efficiently illuminated by optimum projection angle. The mean of bulk resistivity for the Woraksan granite and the near fracture zones is estimated to be approximately of 4,000 ohm - m which is much higher than the value of 700 ohm - m for the Rwachunri limesilicate environment. This difference is due to both the rock type, i.e., biotite granite vs limesilicate, and the occurrence of secondary openings of fold and fault associated with the intrusion of granite. In this study statistical analyses on the resistivity color plot were performed in terms of three representative statistical moments, i.e., standard deviation, skewness, and kurtosis. The fracture zones in the standard deviation plot were characterized by the higher value, compared to the value of homogeneous portion. The upper boundary of the high resistivity zone was also successfully delineated in the skewness and kurtosis plots.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.29 no.12 s.148
• /
• pp.1619-1626
• /
• 2005

The durability of a stretch fabric is mainly related to the change in the dimension and mechanical properties of elastomeric fibers during wearing and washing. In this study, we examined the effects of washing temperature, presoaking time and the number of washing cycles on the change in length, tenacity, elongation at break, and permanent elongation after six repeated cycles of $100\%$ extension and relaxation of spandex yams with varying fineness and with a different rate of extension during heat-set. The spandex yarns continued to shrink as the wash temperature and the number of wash cycles increased. In addition, the finer spandex yams decreased in length more than the thicker yams. The increase in temperature and presoaking time tended to cause a slight decrease in the tenacity and elongation at break of the spandex yarns. Permanent elongation of the spandex yams also increased as the temperature, presoaking time and the number of washing cycles increased. Moreover, an extended presoaking time followed by washing at $40\%$ like repeated washing cycles showed the great increase in the permanent elongation of spandex yams. The thinner spandex yin had a better elasticity than the thicker one, since the former had a lower permanent elongation percentage than the latter. Based on the DSC thermograms, the melting points of the spandex yarns after washing were almost the same as those of the spandex yarns before washing.

• Journal of Korea Technology Innovation Society
• /
• v.20 no.4
• /
• pp.939-969
• /
• 2017

The interest in social regulation and its influence on innovation are increasing as the society concerns more for environment and safety. There have been plenty of literature about the impact of social regulation on innovation and its mechanism. Majority of research have been influenced by or based on the famous Porter's hypothesis. However, majority of the literature focus on internal factors such as expected benefits from change of regulations, and it is hard to find one studying social regulation's influence on innovation through external factors such as market or industrial structure. This study addresses this issue of the impact of social regulation on innovation by analyzing the case of fire-retardant coating material for steel structure industry in Korea. It scrutinizes the impact of social regulation which affects competition and innovation on substitute competing market, and tries to reveal that there might exist the other path to innovation, besides the way that the expected benefit from compliance of regulation directly drives innovation. As a result of the case study, we have found that changes in social regulation may act like economic regulation and restructure the market segment and this effect may lead to innovation. It can be explained by the fact that expected benefits from compliance of regulation can be a direct source of innovation, as Porter suggested, but the change of industry structure and competitive strength caused by the change in social regulation can also act as a driving force of innovation.

• Journal of the Korean Institute of Gas
• /
• v.2 no.4
• /
• pp.18-24
• /
• 1998

Polyurethane (PU) synthesized from 4,4'-diphenylmethane diisocyanate having high functionality (f=2.9) and polyester polyol have been investigated by differential scanning calorimeter (DSC), dynamic mechanical thermal analyzer (DMTA), and Fourier transform infrared spectroscope (FT-IR). From the DSC measurement of polyurethane, a single transition temperature ($T_g$) was observed. This result indicates that polyurethanes synthesized in this work have homogeneous network structure due to high functionality of diisocyanate. It was also found that the $T_g$ of polyurethane was increased as hard segment content was increased. The results from DMTA measurement are consistent with DSC results. In order to investigate the effect of thermal annealing on the $T_g$ of polyurethane, the samples were annealed at various annealing conditions. $T_gs$ of polyurethanes were found to increased with annealing temperature. From swelling experiment and FT-IR studies, it was found that the $T_g$ was increased as crosslinking density of polyurethane was increased.

• Journal of Intelligence and Information Systems
• /
• v.19 no.4
• /
• pp.39-53
• /
• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• 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.