• International conference on construction engineering and project management
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• 2017.10a
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• pp.291-296
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• 2017

Infrastructure development as national project suffers from social conflict which is one of main risk to be managed. Social conflicts have a negative impact on not only the social integration but also the national economy as they require enormous social costs to be solved. Against this backdrop, this study analyzes social conflict using articles published by online news media based on web-crawling and natural language processing (NLP) techniques. As an illustrative case, the Jeju Naval Base (JNB) project which is one of representative conflict case in South Korea is analyzed. Total of 21,788 articles and representative keywords are identified annually. Additionally, comparative analysis is conducted between the extracted keywords and actual events occurred during the project. The authors explain actual events in the JNB project based on the extracted words by the year. This study contributes to analyze social conflict and to extract meaningful information from unstructured data.

• Smart Media Journal
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• v.11 no.8
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• pp.47-54
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• 2022

It is necessary to discover Korea's diverse traditional culture and publicize it to the world to continue the Korean Wave and develop it in a more positive direction. The present study proposes methods for publicizing little-known 'traditional Korean ceramic tableware' by focusing on Hansik, which is the most frequently published in the British Oxford Dictionary among Korean traditional cultures and can best represent Korean food. To this end, the present study measured cultural recipients' emotions regarding traditional Korean ceramic tableware to derive the 'representative emotions felt regarding traditional Korean ceramic tableware' as a method to reflect it in the design. First, the Delphi Technique was carried out based on 182 emotional vocabulary items collected from existing studies to create 33 groups of emotional vocabularies with similar concepts. In addition, among the emotional vocabularies included in each of the 33 groups, those of overlapping concepts were regrouped based on the characteristics of traditional Korean ceramic tableware, and the most appropriate emotional vocabularies were extracted and reduced to 75. A survey was carried out with 135 cultural recipients experienced with traditional Korean ceramic tableware to derive 32 representative emotions felt regarding traditional Korean ceramic tableware. Finally, from the results of a factor analysis of 32 representative emotions, this study classified vocabulary into six emotion categories including 'aesthetic, pleasure, freshness, ownership, satisfaction, and comfort'. The six emotion categories and 32 representative emotions derived from this study's results can be utilized to measure emotional levels felt by cultural recipients while using traditional Korean ceramic tableware.

• Science of Emotion and Sensibility
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• v.7 no.1
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• pp.41-49
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• 2004

The affective words on photographic images were analyzed and a model was structured. Based on this model, the effects of color on the affections were studied. In study 1, the photographic images with various materials and techniques were presented and the affective responses are collected. The factor analysis using principal axing method showed that the variance of the affective words could be explained about 42％ by the three factors. These are named positive-negative, dynamic-static, light-heavy, respectively. In study 2, the effects of color on the affections were evaluated on three basic dimensions. Ninety representative color images were converged black-and-white images, and each of 180 images was rated on the three affective scales. The t-test showed that the effects of color are statistically significant on the three affective scales, respectively. The achromatic images were felt more negative, more static, and heavier than chromatic images.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.261-266
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• 2022

The full-fledged digital age derived from the fourth industrial revolution and the impact of COVID-19 lead to changes in various fields, including companies. In other words, the importance of cloud computing is being emphasized in the rapidly changing digital environment due to the rapid growth of the cloud market due to the rapid increase in digital services. The cloud may be one of the representative strategies for sustainable growth and survival in various fields as well as related industries. Although there have been a variety of studies on the cloud, the tendency of them has been not been adequately examined. This paper, therefore, analyzed the tendency of studies on the cloud computing. by using SCOPUS, the database of overseas academic journals using both R and LAD technique. The findings showed that many studies with high interest in the cloud computing have been conducted, the cloud computing were most often drawn from an analysis on key words. Moreover, various key words, including cloud, cloud and computing, data and computing were drawn, except for the theme of cloud computing. It is expected that could be used as a basic data, in that they provide the foundation for activating the related industries in terms of practice of the cloud computing.

• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.620-629
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• 2019

An automated minutes summary system is required to objectively summarize and classify the contents of discussions or discussions for decision making. This paper designs and implements a minutes summary system using word2vec model to complement the existing minutes summary system. The proposed system is further implemented with word2vec model to remove index words during morpheme analysis and to extract representative sentences with common opinions from documents. The proposed system automatically classifies documents collected during the meeting process and extracts representative sentences representing the agenda among various opinions. The conference host can quickly identify and manage all the agendas discussed at the meeting through the proposal system. The proposed system analyzes various agendas of large-scale debates or discussions and summarizes sentences that can be representative opinions to support fast and accurate decision making.

• Journal of the Korean Society for information Management
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• v.36 no.2
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• pp.175-199
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• 2019

Uncertainty means incomplete stages of knowledge of propositions due to the lack of consensus of information and existing knowledge. As the amount of academic literature increases exponentially over time, new knowledge is discovered as research develops. Although the flow of time may be an important factor to identify patterns of uncertainty in scientific knowledge, existing studies have only identified the nature of uncertainty based on the frequency in a particular discipline, and they did not take into consideration of the flow of time. Therefore, in this study, we identify and analyze the uncertainty words that indicate uncertainty in the scientific literature and investigate the stream of knowledge. We examine the pattern of biomedical knowledge such as representative entity pairs, predicate types, and entities over time. We also perform the significance testing using linear regression analysis. Seven pairs out of 17 entity pairs show the significant decrease pattern statistically and all 10 representative predicates decrease significantly over time. We analyze the relative importance of representative entities by year and identify entities that display a significant rising and falling pattern.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

• Journal of the Korean Institute of Landscape Architecture
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• v.35 no.6
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• pp.84-96
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• 2008

This research investigated the landscape characteristics of the skyline and the cognitive characteristics of Mt. Mudeung (1,186m) from various viewpoints. Mt. Mudeung, the representative landscape of Gwangju City, has been recognized as a natural landmark and theme of paintings. By analyzing the perspective from 32 points with a digital terrain model, some landscape indices of the skyline were derived and the relationships are discussed. Assessment of the semantic differential scale with 21 adjective variables and representativeness to 15 landscape photographs of the mountain were accomplished. 1. Through regression analysis of the skyline indices, significant relationships were found between them the angle from the visual axis and number of skyline jumps, the vertical angle fluctuation and number of jumps per degree, the visual depth fluctuation and vertical angle fluctuation of skyline, and between the vertical angle mean and number of jumps per degree. Meaningful relations were found between the number of jumps of skyline to number of jumps per degree and the angle from visual axis to visual distance. However, in the representative assessment no difference was found on the angle from visual axis of viewpoints. On the other hand, it seemed to relate representativeness with visual clarity based on visual distance. 2. We found 4 factors "familiarity", "fluctuation of skylines", "openness", and "feeling of texture" in the results of factor analysis of semantic differential assessment. When considering the results of assessment for representativeness, adjective words for familiarity and openness seemed to have a close assessment. Specifically, the research showed that the landscape representation was highly assessed in a view which could be seen from the higher parts to the lower part of hills. This result indicates that the management of viewpoints which could get a scene from intermediate to distant, and locating a high elevation is important. 3. In the picturesque expression of Mt. Mudeung, various impressions from the different points, a skyline based on the top of Mt. Mudeung and a mono structure by overlapping hills were common characteristics. These common characteristics were also partially found through the analysis of topographical landscape indices and landscape images. Therefore, the viewpoints for the representative landscape management should be selected in natural or open spaces.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.193-221
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• 2017

Grouping episodes into semantically related categories is necessary for better mnemonic structure. However, the effect of grouping on memory of subordinate details was not clearly understood. In an fMRI study, we tested whether attending superordinate during semantic association disrupts or enhances subordinate episodic details. In each cycle of the experiment, five cue words were presented sequentially with two related detail words placed underneath for each cue. Participants were asked whether they could imagine a category that includes the previously shown cue words in each cycle, and their confidence on retrieval was rated. Participants were asked to perform cued recall tests on presented detail words after the session. Behavioral data showed that reaction times for categorization tasks decreased and confidence levels increased in the third trial of each cycle, thus this trial was considered to be an important insight where a semantic category was believed to be successfully established. Critically, the accuracy of recalling detail words presented immediately prior to third trials was lower than those of followed trials, indicating that subordinate details were disrupted during categorization. General linear model analysis of the trial immediately prior to the completion of categorization, specifically the second trial, revealed significant activation in the temporal gyrus and inferior frontal gyrus, areas of semantic memory networks. Representative Similarity Analysis revealed that the activation patterns of the third trials were more consistent than those of the second trials in the temporal gyrus, inferior frontal gyrus, and hippocampus. Our research demonstrates that semantic grouping can cause memories of subordinate details to fade, suggesting that semantic retrieval during categorization affects the quality of related episodic memory.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.90-94
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• 2020

Artificial intelligence (AI) is software that learns large amounts of data and provides the desired results for certain patterns. In other words, learning a large amount of data is very important, and the role of memory in terms of computing systems is important. Massive data means wider bandwidth, and the design of the memory system that can provide it becomes even more important. Providing wide bandwidth in AI systems is also related to power consumption. AlphaGo, for example, consumes 170 kW of power using 1202 CPUs and 176 GPUs. Since more than 50% of the consumption of memory is usually used by system chips, a lot of investment is being made in memory technology for AI chips. MRAM, PRAM, ReRAM and Hybrid RAM are mainly studied. This study presents various memory technologies that are being studied in artificial intelligence chip design. Especially, MRAM and PRAM are commerciallized for the next generation memory. They have two significant advantages that are ultra low power consumption and nearly zero leakage power. This paper describes a comparative analysis of the four representative new memory technologies.