• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.83-105
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• 2016

Measuring an individual's subjective wellbeing in an accurate, unobtrusive, and cost-effective manner is a core success factor of the wellbeing support system, which is a type of medical IT service. However, measurements with a self-report questionnaire and wearable sensors are cost-intensive and obtrusive when the wellbeing support system should be running in real-time, despite being very accurate. Recently, reasoning the state of subjective wellbeing with conventional sentiment analysis and unstructured data has been proposed as an alternative to resolve the drawbacks of the self-report questionnaire and wearable sensors. However, this approach does not consider contextual polarity, which results in lower measurement accuracy. Moreover, there is no sentimental word net or ontology for the subjective wellbeing area. Hence, this paper proposes a method to extract keywords and their contextual polarity representing the subjective wellbeing state from the unstructured text in online websites in order to improve the reasoning accuracy of the sentiment analysis. The proposed method is as follows. First, a set of general sentimental words is proposed. SentiWordNet was adopted; this is the most widely used dictionary and contains about 100,000 words such as nouns, verbs, adjectives, and adverbs with polarities from -1.0 (extremely negative) to 1.0 (extremely positive). Second, corpora on subjective wellbeing (SWB corpora) were obtained by crawling online text. A survey was conducted to prepare a learning dataset that includes an individual's opinion and the level of self-report wellness, such as stress and depression. The participants were asked to respond with their feelings about online news on two topics. Next, three data sources were extracted from the SWB corpora: demographic information, psychographic information, and the structural characteristics of the text (e.g., the number of words used in the text, simple statistics on the special characters used). These were considered to adjust the level of a specific SWB. Finally, a set of reasoning rules was generated for each wellbeing factor to estimate the SWB of an individual based on the text written by the individual. The experimental results suggested that using contextual polarity for each SWB factor (e.g., stress, depression) significantly improved the estimation accuracy compared to conventional sentiment analysis methods incorporating SentiWordNet. Even though literature is available on Korean sentiment analysis, such studies only used only a limited set of sentimental words. Due to the small number of words, many sentences are overlooked and ignored when estimating the level of sentiment. However, the proposed method can identify multiple sentiment-neutral words as sentiment words in the context of a specific SWB factor. The results also suggest that a specific type of senti-word dictionary containing contextual polarity needs to be constructed along with a dictionary based on common sense such as SenticNet. These efforts will enrich and enlarge the application area of sentic computing. The study is helpful to practitioners and managers of wellness services in that a couple of characteristics of unstructured text have been identified for improving SWB. Consistent with the literature, the results showed that the gender and age affect the SWB state when the individual is exposed to an identical queue from the online text. In addition, the length of the textual response and usage pattern of special characters were found to indicate the individual's SWB. These imply that better SWB measurement should involve collecting the textual structure and the individual's demographic conditions. In the future, the proposed method should be improved by automated identification of the contextual polarity in order to enlarge the vocabulary in a cost-effective manner.

• Journal of forensic and investigative science
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• v.2 no.2
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• pp.21-29
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• 2007

News from the media on sexual assaults to children committed by natural fathers doesn't attract social attention any more. The number of crimes related to Child Sexual Assault(CSA) is increasing every year in spite of the "Special Act on Prevention of Sexual Assault in Korea". The YoungNam District Sunflower Center for prevention of Child Sexual Assaults(SC-CSA) was established in Daegu, June 2005. The YoungNam District SC-CSA provides forensic evaluation of physical evidence, medical and psychological treatment for the victims less than 13 years of sexual assaults simultaneously. This study carried out 36 cases of CSA by kin reported to YoungNam District SC-CSA, among 180 cases in total until December 2006 since its opening. Most of the victims were girls (32 cases). 28 cases (78%) were indecent assaults (78%) and 8 cases (22%) were rapes. The assailants were overwhelmingly males (35 cases). The assailants of 21 cases (58.3%) were identified as the victims' natural fathers. The incident locations were victim's residence (31 cases, 86.1%) and the victims had been sexually assaulted regularly for many years (25 cases, 69.4%). Considering the above research, we can conclude that CSA committed by kin has specific characteristics. CSA is not a one-time incident, but consistently occurring crime. However, in 22 cases (61.1%), the victim's guardian didn't want to report about it or punish the assailants. As the assailants were natural fathers or relatives of the victims, the other family members probably thought it might be shameful to reveal their wrong doings and would lead to defamation of their family's reputation. The SC-CSA provides the counseling and medical treatment to the victims with the consent of the parents. Due to the guardians' misjudgment, the incident is sometimes not reported to the police. By not reporting the incident to the police, the assailant freely commits other crimes, which multiplies victims. The legal Act of supporting the management of the SC-CSA is still not regulated, so the stability of the SC-CSA is not guaranteed, yet. Even though it is obligatory to report incidents to the police, some cases are still not reported. Currently, there are three SC-CSA centers : in Seoul, in Daegu, and in Gwangju. More centers need to be established to diminish CSA cases in Korea.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.159-168
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• 2012

Freedom of speech is indispensable in Democracy. It is a rink among government agencies. Mass media as institutionalized means which forms public opinion impacts quite a few to a society. Mass media as a life media in our daily lives has characteristics of speed and prompt report. It is difficult to measure the effect on a society. Mass media is a lifeline in democracy because it has freedom of opinion for seeing, listening, speaking, and criticizing about the people's right to know in an information society. Our Constitution also guarantees freedom of the press, information(peoples's right to know), report, the collection of news, and edition. Because an unnecessary thing about a privacy is reported by mass media, it can violate defamation. This study seeks to be unbiased in reporting and what the principles of the Constitution for minimizing an invasion of a person's privacy is. This study also seeks freedom of speech and the right to know. In case that a personal honor is invaded by a mass media and a publication, this study provides the Constitution basis, Criminal Law basis, and Civic Law basis for remedy violation. A report for apology on newspaper and by television was widely used as "a proper punishment for honor recovery in the past". The constitutional court had decided that including the report of apology for "a proper punishment of honor recovery" in the article 764 of the Civic Law as a reason of freedom of conscience and the violation of personal rights was against the Constitution. Therefore, this study examples what is a legal remedy in practical?, where is legal basis of special remedy in the Civic Law, and what is a method by the Press Arbitration Law compared with the examples of other countries. On the other hand, because a mass media may injure a person's honor and infringe a person's privacy, if the report is categorized as a malicious press, the true role which mass media has to do may not demonstrated. In conclusion, this study was to minimalize infringement of mass media to a person and to seek a realistic alternative of a legal remedy.

• The Journal of Society for e-Business Studies
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• v.25 no.3
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• pp.41-61
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• 2020

China's rapid development and commercialization of high-tech technologies in the fourth industrial revolution has led to effective technology exchanges between Korean and Chinese firms becoming more important to Korea's mid-term and long-term industrial development. However, there is still a lack of empirical research on how technology exchanges between Korean and Chinese firms proceed and their effectiveness. In response, this study conducted a social network analysis based on text mining data of Korea-China business technology exchange and cooperation articles introduced in the news from 2018 to March 2020 on the current status and effects of Korea-China technology exchanges related to the fourth industrial revolution, and conducted a regression analysis how network centrality effect on the firm performance. According to the results, most of the Korean major electronic firms are actively networking with Chinese firms and institutions, showing high centrality in the centrality index. Korean telecommunication firms showed high betweenness centrality and subgraph centrality, and Korean Internet service providers and broadcasting contents firms showed high eigenvector centrality. In addition, Chinese firms showed higher betweenness centrality than Korean firms, and Chinese service firms showed higher closeness centrality than manufacturing firms. As a result of regression analysis, this network centrality had a positive effect on firm performance. To the best of our knowledge, this is the first to analyze the impact of the technical cooperation between Korean and Chinese firms under the fourth industrial revolution context. This study has theoretical implications that suggested the direction of social network analysis-based empirical research in global firm cooperation. Also, this study has practical implications that the guidelines for network analysis in setting the direction of technical cooperation between Korea and China by firms or governments.

• Cartoon and Animation Studies
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• s.37
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• pp.197-219
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• 2014

, one of the Disney's animations, hit the 10 million audience mark for the first time in the history of animations released in Korea. not only raised the fever with its theme song, 'Let it go', as well as Elsa, Anna, and Olaf's character products but caused sensations in many ways. If so, we need to think about what kind of meaning did create in Korea to be so sensational. This study examines the value that Frozen intended to deliver and the meaning it generated by using Greimas actant model and semiotic square. From the actant model analysis on Anna and Elsa from , it was identified that Anna desired to recover her relationship with Elsa and to take summer back in Arendelle. Her desires can be interpreted as her love toward Elsa and people in Arendelle. Meanwhile, Elsa always desired freedom although she confined herself because of her ability to freeze. In other words, Elsa desired to free herself from her freezing ability by finding out how to control her ability. Such desires of Anna and Elsa were achieved by their actions of true love, and the solution of all the conflicts in was an action of true love. From the semiotic square analysis on the meaning of , it was found out that created past-oriented value with which characters tried to change their abnormal lives of the present into their normal lives of the past. The characters tried to change their present lives where freezing winter comes in the middle of summer, communication between the sisters is cut off, and people try to take advantage of the abnormal state deliberately, into the past when the sisters had a good relationship and the natural season of summer in Arendelle. The past-oriented value that tried to tell us is similar to our reality. In our reality with a lot of unbelievable news and unstable circumstances, we desire to go back to the past when we were filled with affection and hope even though our lives were tough and difficult. This sentiment must have contributed to the huge success of in Korea.

• Information Systems Review
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• v.15 no.3
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• pp.1-37
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• 2013

This study is conducted to explore the possibility of service convergence to promote mobile learning. This study has attempted to identify how mobile learning service is provided, which services among them are considered most popular, and which services are highly demanded by users. This study has also investigated the potential opportunities for service convergence of mobile service and e-learning. This research is then extended to examine the possibility of active convergence of common services in mobile services and e-learning. Important variables have been identified from related web pages of portal sites using social network analysis (SNA) and association rules. Due to the differences in number and type of variables on different web pages, SNA was used to deal with the difficulties of identifying the degree of complex connection. Association analysis has been used to identify association rules among variables. The study has revealed that most frequent services among common services of mobile services and e-learning were Games and SNS followed by Payment, Advertising, Mail, Event, Animation, Cloud, e-Book, Augmented Reality and Jobs. This study has also found that Search, News, GPS in mobile services were turned out to be very highly demanded while Simulation, Culture, Public Education were highly demanded in e-learning. In addition, It has been found that variables involving with high service convergence based on common variables of mobile and e-learning services were Games and SNS, Games and Sports, SNS and Advertising, Games and Event, SNS and e-Book, Games and Community in mobile services while Games, Animation, Counseling, e-Book, being preceding services Simulation, Speaking, Public Education, Attendance Management were turned out be highly convergent in e-learning services. Finally, this study has attempted to predict possibility of active service convergence focusing on Games, SNS, e-Book which were highly demanded common services in mobile and e-learning services. It is expected that this study can be used to suggest a strategic direction to promote mobile learning by converging mobile services and e-learning.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.2
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• pp.39-47
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• 2014

Purpose As breast cancer patients continue to increase every year, cases of BSGI are on the rise with a heavier reliance on it. However, BSGI protocol in hospitals was not studied enough despite it was covered by hospital's condition and recommendation of manufacturers. The objective of the study was an examination of methods to be applicable to BSGI protocols, putting the self-development phantom to use in quality assessment of the images. Materials and Methods Dilon 6800 (Dilon Technologies Inc, Newport News, USA) was used in the study and five different sizes of sphere were distinctively produced in the phantom. The study used $^{99m}TcO_4$. The cases were classified in to three categories that background radioactivity to region of interest as ratio of 2: 4: 8, They were acquired images for 5, 7, 10mins. The acquired image was set region of interest according to the size of sphere, and We analyzed quantitative and qualitative analysis. The acquired data statistically analyzed with SPSS ver.18.0. Results As the result of quantitative and qualitative analysis, count rate of each sphere in accordance with difference of injection dose showed that higher count rate as injection dose and sphere size increased (P<0.005). Count rate of each sphere in accordance with difference of acquisition time showed that higher count rate as acquisition time and sphere size increased (P<0.005). Contrast noise ratio of each sphere in accordance with difference of injection dose showed that higher contrast noise ratio as injection dose increased. Particularly, Contrast noise ratio of eight times ratio images was the highest among. Contrast noise ratio of each sphere in accordance with difference of acquisition time showed that higher contrast noise ratio as acquisition time increased. And, Contrast noise ratio of seven minute image was the highest among (P<0.005). Conclusion There was significant change of Contrast noise ratio through quantitative and qualitative analysis. Moreover, We found usefulness of phantom. If Institutions identified image through the phantom study and they made BSGI protocol, We expected to help the improvement of diagnostic value of the images.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.