• Journal of Technology Innovation
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• v.25 no.3
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• pp.155-195
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• 2017

The goal of the present study is reviewing the literature on the scientific community and also on science, technology & society to increase interactions between innovation studies and social studies of science and technology. Up until now, various empirical studies on Korean scientists and engineers have been concentrated on researchers at universities, while they have paid inadequate attention to researchers at state-funded research institutes and private companies. In addition, these studies have tended to use concepts in Western academia to elucidate Korean cases. On the other hand, recent empirical researches on the effects of the evaluation systems in universities, PBS system, and the network of school ties suggest that these topics may reveal the unique characteristics of Korean scientific community. Empirical studies on the scientific community have also shown that Korean research institutes and researchers who are in charge of innovation in Korea have demonstrated a tendency to conform to the government's guidance due to long experiences of state-led R&D and nationalism. Research on science, technology and society has viewed the participation of citizens in science and technology as a way toward science and technology democracy, and tended to have a strong practical orientation. However, there has been a relatively small amount of research on how citizen participation influences the direction and content of technological innovation. Also, although, from the viewpoint of technological innovation, how participation of citizens in science and technology can contribute to knowledge production and innovation is a critical issue, relatively small numbers of case studies on this subject have been conducted. Therefore, as the scholars who have emphasized the democracy of science and technology have actually experimented with various ways of citizen participation, innovation researchers may have to design and implement citizen participation through which citizens' local knowledge can contribute to technological innovation.

• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.157-168
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• 2020

Advancements in segmentation methodology has made automatic segmentation of brain structures using structural images accurate and consistent. One method of automatic segmentation, which involves registering atlas information from template space to subject space, requires a high quality atlas with accurate boundaries for consistent segmentation. The Allen Mouse Brain Atlas, which has been widely accepted as a high quality reference of the mouse brain, has been used in various segmentations and can provide accurate coordinates and boundaries of mouse brain structures for tractography. Through probabilistic tractography, diffusion tensor images can be used to map comprehensive neuronal network of white matter pathways of the brain. Comparisons between neural networks of mouse and human brains showed that various clinical tests on mouse models were able to simulate disease pathology of human brains, increasing the importance of clinical mouse brain studies. However, differences between brain size of human and mouse brain has made it difficult to achieve the necessary image quality for analysis and the conditions for sufficient image quality such as a long scan time makes using live samples unrealistic. In order to secure a mouse brain image with a sufficient scan time, an Ex-vivo experiment of a mouse brain was conducted for this study. Using FSL, a tool for analyzing tensor images, we proposed a semi-automated segmentation and tractography analysis pipeline of the mouse brain and applied it to various mouse models. Also, in order to determine the useful signal-to-noise ratio of the diffusion tensor image acquired for the tractography analysis, images with various excitation numbers were compared.

• Journal of radiological science and technology
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• v.34 no.1
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• pp.17-25
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• 2011

In this study, we examined the impact caused by chronic exposure to Mn by investigating the degree of brain activation based on the data of recognition activities using fMRI (functional magnetic resonance imaging). A questionnaire survey, blood tests, and fMRI tests were carried out with respect to two groups. Group 1 was an exposure group consisting of 15 male workers who are 34 years old or older, and who worked for longer than 10 years in a shipbuilding factory as a welder. Group 2 was a control group consisting of 15 workers in manufacturing industries with the same gender and age. The results showed that blood Mn concentration of Group 1($1.3\;{\mu}g/dl$) was significantly higher than that of Group 2($0.8\;{\mu}g/dl$)(p < 0.001), and Pallidal Index (PI) of Group 1 was also significantly higher than that of Group 2 (p < 0.001). PI value of the group whose blood Mn concentration was $0.93\;{\mu}g/dl$ or higher was significantly higher than that of the group whose blood Mn concentration was less than $0.93 \;{\mu}g/dl$ (p < 0.001). As for brain activity area within the control group, the right and the left areas of occipital cortex showed significant activity and the left area of middle temporal cortex, the right area of superior inferior frontal cortex and inferior parietal cortex showed significant activity. Unlike the control group, the exposure group showed significant activity on the right area of superior inferior temporal cortex, the left of insula area. In the comparison of brain activity areas between the two groups, the exposure group showed significantly higher activation than the control group in such areas as the right inferior temporal cortex, the left area of superior parietal cortex and occipital cortex, and cerebellum including middle temporal cortex. However, in nowhere the control group showed more activated area than the exposure group. As the final outcome, chronic exposure to Mn increased brain activity during implementation of arithmetic task. In an identical task, activation increased in superior inferior temporal cortex, and insula area. And it was discovered that brain activity increase in temporal area and occipital area was more pronounced in the exposure group than in the control group. This result suggests that chronic exposure to Mn in the work environment affects brain activation neuro-network.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.1
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• pp.33-43
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• 2003

The midwater pair trawl which is being used at present in Korea have several problems. Firstly, it is difficult to control the net height on high towing speed. Secondly, net breaking often occurs owing to floats and thirdly, the volume of net on the net drum is so large. This study is aiming for examining the possibility of application for the Korean midwater pair trawl through the model experiment of non-float midwater pair trawl. The model of non-float midwater pair trawl was manufactured as 1/100 of the full scale net which is being used in bottom pair trawl for 850ps class considering the Tauti's Similarity law. The model experiment was carried out to analyze the opening efficiency according to the variation of lower warp length and the opening efficiency was investigated between th proto type and non-float type. The results obtained can be summarized as follows ; 1. The hydrodynamic resistance of non-float type was about 10~20% smaller than that of the proto type and it increased about 1ton according to the increase of dL at the condition of the same flow speed. The resistance acting on the lower warp decreased about 5% but that of the upper warp increased according to the increase of lower warp length (dL) at the condition of the same flow speed. 2. The net height of the non-float type decreased almost linearly according as the increased of flow speed and it increased in a logarithmic functional form with the increase of the lower warp length at the condition of the same flow speed. On the decreasing rate of the net height, the non-float type was lower than the proto type and the difference of the decreasing rate was about 12% at 3.0 knot, 25% at 4.0 knot, 25% at 4.0 knot respectively when dL was 30m. 3. The net width of non-float type was not varied so much as only 2m range and was larger than that of proto type. 4. The mouth area of non-float type decreased in a exponential functional form. On the decreasing rate of the mouth area, the non-float type was lower than the proto type. The filtering volume increased in a logarithmic functional form with increasing flow speed and the filtering volume of proto type decreased steeply over 3.0knot, but that of non-float type increased until 4.0knot. 5. The optimal length of lower warp was when the value of dL was about 30m and the optimal position of front weight was at the connection point of four net pendants.

• Journal of Korean Society of Transportation
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• v.36 no.2
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• pp.155-168
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• 2018

In this study, we studied the directional pattern of entering the intersection from the intersection upstream link prior to predicting short future (such as 5 or 10 minutes) intersection direction traffic volume on the interrupted flow, and examined the possibility of traffic volume prediction using traffic assignment model. The analysis method of this study is to investigate the similarity of patterns by performing cluster analysis with the ratio of traffic volume by intersection direction divided by 2 hours using taxi DTG (Digital Tachograph) data (1 week). Also, for linking with the result of the traffic assignment model, this study compares the impact area of 5 minutes or 10 minutes from the center of the intersection with the analysis result of taxi DTG data. To do this, we have developed an algorithm to set the impact area of intersection, using the taxi DTG data and traffic assignment model. As a result of the analysis, the intersection entry pattern of the taxi is grouped into 12, and the Cubic Clustering Criterion indicating the confidence level of clustering is 6.92. As a result of correlation analysis with the impact area of the traffic assignment model, the correlation coefficient for the impact area of 5 minutes was analyzed as 0.86, and significant results were obtained. However, it was analyzed that the correlation coefficient is slightly lowered to 0.69 in the impact area of 10 minutes from the center of the intersection, but this was due to insufficient accuracy of O/D (Origin/Destination) travel and network data. In future, if accuracy of traffic network and accuracy of O/D traffic by time are improved, it is expected that it will be able to utilize traffic volume data calculated from traffic assignment model when controlling traffic signals at intersections.

• Journal of Wetlands Research
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• v.19 no.3
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• pp.345-352
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• 2017

The average global temperature on Earth has increased by about $0.85^{\circ}C$ since 1880 due to the global warming. The temperature increase affects hydrologic phenomenon and so the world has been suffered from natural disasters such as floods and droughts. Therefore, especially, in the aspect of water deficit, we may require the accurate prediction of water demand considering the uncertainty of climate in order to establish water resources planning and to ensure safe water supply for the future. To do this, the study evaluated future water balance and water deficit under the climate change for Anseong river basin in Korea. The future rainfall was simulated using RCP 8.5 climate change scenario and the runoff was estimated through the SLURP model which is a semi-distributed rainfall-runoff model for the basin. Scenario and network for the water balance analysis in sub-basins of Anseong river basin were established through K-WEAP model. And the water demand for the future was estimated by the linear regression equation using amounts of water uses(domestic water use, industrial water use, and agricultural water use) calculated by historical data (1965 to 2011). As the result of water balance analysis, we confirmed that the domestic and industrial water uses will be increased in the future because of population growth, rapid urbanization, and climate change due to global warming. However, the agricultural water use will be gradually decreased. Totally, we had shown that the water deficit problem will be critical in the future in Anseong river basin. Therefore, as the case study, we suggested two alternatives of pumping station construction and restriction of water use for solving the water deficit problem in the basin.

• Journal of Environmental Impact Assessment
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• v.29 no.1
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• pp.26-36
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• 2020

This study aimed at investigating present research and knowledge-base on climate change adaptation in ecosystem sector and analyzed the current status of basic information on ecosystem that functions as evidence-base of climate change adaptation to deduce the suggestions for the future development for knowledge and information in biodiversity. In this perspective, a questionary survey titled as "the ecological knowledge-base and information needs for climate change adaptation" with the researchers who were engaged with adaptation studies for biodiversity in the ecosystem related-research institutes including national and 17 regional local governments-affiliated agencies in Korea. The results are as follows; current status of utilizing ecological information which supports climate change adaptation strategy, future needs for adaptation knowledge and ecological information, and activation of utilizing ecological information. The majority of respondents (90.7%) replied that the ecological information has high relevance when conducting research on climate change adaptation. However, only half of all respondents (53.2%) agreed with the real viability of current information to the adaptation research. Particularly, urgent priority for researchers was deduced as intensifying knowledge-base and constructing related information on 'ecosystem change from climate change (productivity, community structure, food chain, phenology, range distribution, and number of individuals) with the overall improvement of information contents and its quality. The respondents emphasized with the necessity of conducting field surveys of local ecosystem and constructing ecosystem inventories, advancing monitoring designs for climate change in ecosystem, and case studies for regional ecosystem changes with the guidance or guidelines for monitoring ecosystem change to enhance the quality of adaptation research and produce related information. In terms of activation for ecological information usage, national and local adaptation network should be working based on the integrated ecological platform necessary to support exchanges of knowledge and information and to expand ecosystem types in time and spatial dimension.

• Journal of dental hygiene science
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• v.15 no.2
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• pp.106-112
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• 2015

This study was conducted to provide with baseline data with the purpose of increasing the values of medical services. Self-administered questionnaire survey was conducted on 236 patients at a dental clinic follow-up visit in dentist clinic Gyeongnam area from June 2013 to August 2013. All statistical analyses were performed using SPSS. The motivation visiting the dental clinic is that the first one is introduction from the family and friends, the second is accessibility, the third is conspicuity and the last one is awareness of the dentist. The main variables in the process of treatment are service system, kindness, satisfaction of service, efficient of re-call system. The relief of discomfort at revisit show the highest score in the process of implant treatment and intention of revisit hereafter do in the prostheses process. In the correlation between main variables, service system and relief of discomfort at revisit (r=0.440, p<0.001), kindness and satisfaction of medical service (r=0.675, p<0.001), revisit hereafter and satisfaction of service (r=0.387, p<0.001) and efficiency of re-call system and revisit showed the highest correlation. The influence on satisfaction of dental service show meaningful level in kindness (p<0.001) and efficiency of re-call system (p<0.05). The intention of revisit is affected meaningfully by relief of uncomfort (p<0.05), service system (p<0.05), kindness (p<0.01) and efficiency of re-call system (p<0.01). In summary, the personal network of patients is most important variable at intention for revisit of dental clinic. As satisfaction of kindness and efficiency of re-call system is higher, satisfaction of medical service and intention for revisit are shown higher. Therefore further research for improvement of satisfaction for medical service and of intention of revisit at the dental clinic should be carried out.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.81-96
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• 2013

Video information retrieval has become a very important issue because of the explosive increase in video data from Web content development. Meanwhile, content-based video analysis using visual features has been the main source for video information retrieval and browsing. Content in video can be represented with content-based analysis techniques, which can extract various features from audio-visual data such as frames, shots, colors, texture, or shape. Moreover, similarity between videos can be measured through content-based analysis. However, a movie that is one of typical types of video data is organized by story as well as audio-visual data. This causes a semantic gap between significant information recognized by people and information resulting from content-based analysis, when content-based video analysis using only audio-visual data of low level is applied to information retrieval of movie. The reason for this semantic gap is that the story line for a movie is high level information, with relationships in the content that changes as the movie progresses. Information retrieval related to the story line of a movie cannot be executed by only content-based analysis techniques. A formal model is needed, which can determine relationships among movie contents, or track meaning changes, in order to accurately retrieve the story information. Recently, story-based video analysis techniques have emerged using a social network concept for story information retrieval. These approaches represent a story by using the relationships between characters in a movie, but these approaches have problems. First, they do not express dynamic changes in relationships between characters according to story development. Second, they miss profound information, such as emotions indicating the identities and psychological states of the characters. Emotion is essential to understanding a character's motivation, conflict, and resolution. Third, they do not take account of events and background that contribute to the story. As a result, this paper reviews the importance and weaknesses of previous video analysis methods ranging from content-based approaches to story analysis based on social network. Also, we suggest necessary elements, such as character, background, and events, based on narrative structures introduced in the literature. We extract characters' emotional words from the script of the movie Pretty Woman by using the hierarchical attribute of WordNet, which is an extensive English thesaurus. WordNet offers relationships between words (e.g., synonyms, hypernyms, hyponyms, antonyms). We present a method to visualize the emotional pattern of a character over time. Second, a character's inner nature must be predetermined in order to model a character arc that can depict the character's growth and development. To this end, we analyze the amount of the character's dialogue in the script and track the character's inner nature using social network concepts, such as in-degree (incoming links) and out-degree (outgoing links). Additionally, we propose a method that can track a character's inner nature by tracing indices such as degree, in-degree, and out-degree of the character network in a movie through its progression. Finally, the spatial background where characters meet and where events take place is an important element in the story. We take advantage of the movie script to extracting significant spatial background and suggest a scene map describing spatial arrangements and distances in the movie. Important places where main characters first meet or where they stay during long periods of time can be extracted through this scene map. In view of the aforementioned three elements (character, event, background), we extract a variety of information related to the story and evaluate the performance of the proposed method. We can track story information extracted over time and detect a change in the character's emotion or inner nature, spatial movement, and conflicts and resolutions in the story.