• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.39-48
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• 2024

In this study, we propose a method to augment the provided reasoning paths to improve the answer performance and explanatory power of KGQA. In the proposed method, we utilize LLMs and GNNs to retrieve reasoning paths related to the question from the knowledge graph and evaluate reasoning paths. Then, we retrieve the external information related to the question and then converted into triples to answer the question and explain the reason. Our method evaluates the reasoning path by checking inference results and semantically by itself. In addition, we find related texts to the question based on their similarity and converting them into triples of knowledge graph. We evaluated the performance of the proposed method using the WebQuestion Semantic Parsing dataset, and found that it provides correct answers with higher accuracy and more questions with explanations than the reasoning paths by the previous research.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.5
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• pp.51-68
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• 2024

To safely operate lithium-ion batteries that power mobile electronic devices, it is crucial to accurately predict the remaining useful life (RUL) of the battery. Recently, with the advancement of machine learning technologies, artificial intelligence (AI)-based RUL prediction models for batteries have been actively researched. However, existing models have limitations as the reasoning process within the models is not transparent, making it difficult to fully trust and utilize the predicted values derived from machine learning. To address this issue, various explainable AI techniques have been proposed, but these techniques typically visualize results in the form of graphs, requiring users to manually analyze the graphs. In this paper, we propose an explainable RUL prediction method for lithium-ion batteries that interprets the reasoning process of the prediction model in textual form using SHAP analysis based on large language models (LLMs). Experimental results using publicly available lithium-ion battery datasets demonstrated that the LLM-based SHAP analysis enabled us to concretely understand the model's prediction rationale in textual form.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.453-459
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• 2024

Recent advances in generative AI technologies using natural language processing have critically impacted text, image, and video production. Despite these innovations, we still need to improve the consistency and reusability of AI-generated outputs. These issues are critical in cartoon creation, where the inability to consistently replicate characters and specific objects can degrade the work's quality. We propose an integrated adaption of language analysis-based requirement engineering and cartoon engineering to solve this. The proposed method applies the linguistic frameworks of Chomsky and Fillmore to analyze natural language and utilizes UML sequence models for generating consistent 3D representations of object interactions. It systematically interprets the creator's intentions from textual inputs, ensuring that each character or object, once conceptualized, is accurately replicated across various panels and episodes to preserve visual and contextual integrity. This technique enhances the accuracy and consistency of character portrayals in animated contexts, aligning closely with the initial specifications. Consequently, this method holds potential applicability in other domains requiring the translation of complex textual descriptions into visual representations.

• Journal of the Korean Society of Radiology
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• v.85 no.5
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• pp.848-860
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• 2024

The recent advent of large language models (LLMs), such as ChatGPT, has drawn attention to generative artificial intelligence (AI) in a number of fields. Generative AI can produce different types of data including text, images, and voice, depending on the training methods and datasets used. Additionally, recent advancements in multimodal techniques, which can simultaneously process multiple data types like text and images, have expanded the potential of using multimodal generative AI in the medical environment where various types of clinical and imaging information are used together. This review summarizes the concepts and types of LLMs, image generative AI, and multimodal AI, and it examines the status and future possibilities of generative AI in the field of radiology.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.367-368
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• 2021

본 연구의 목적은 빅데이터 분석을 통해 QR 코드에 관한 연구 동향을 살펴보고 향후 활용 방안을 수립하는 데 그 방향성을 제시하는 것이다. 먼저 QR 코드에 관한 주제 분야별, 연도별 연구 동향을 살펴보고, 텍스트 분석을 실시한다. 아울러 이 결과를 데이터 시각화하여 분석결과를 살펴본다. 구체적으로 본 연구는 데이터 scraping 및 수집을 하였으며, R x64 4.0.2 프로그램 패키지를 활용 전처리 활동과 빅데이터 분석을 하였다. 본 연구의 결과는 다음과 같다. 첫째, 전반적으로 QR 코드 관련 연구가 지속적으로 증가하는 추세가 발견되었다. 둘째, 빈출키워드를 분석한 결과 주제 분야별, 연도별로 다소 차이가 있으나 전반적으로 모든 분야에서 QR 코드 사용이 유사한 형태로 나타났다. 본 연구는 QR 코드에 관한 연구가 다양한 분야에서 활용되고 있으며, 향후에도 같은 추세로 활용가능성이 높음을 확인하였다. 본 연구의 결과는 QR 코드가 사회문화적 현상을 반영하고 있으며, 우리는 이를 정보의 수단 및 활용의 관점으로 접근할 필요가 있음을 시사한다. 본 연구의 결과는 QR 코드에 관한 정부지원 및 활성화 방안을 마련하는데 유용한 기초자료로 활용될 수 있을 것으로 기대된다.

• 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 the Korea Convergence Society
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• v.11 no.12
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• pp.253-259
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• 2020

This paper is to understanding the recent research trend of leadership. To this end, it is intended to grasp the research trend of leadership by using foreign academic databases such as SCOPUS and SPRINGER. As a result of the analysis, it was found that a lot of research has been conducted in management, human resource management, organization, etc., which have traditionally been studied. However, in addition to this field, research in the fields of Nursing, Engineering, Computer Science, and Education has been steadily increasing. This shows that the study of leadership is becoming important not only in companies and specific organizations, but also in various disciplines. In particular, as leadership is becoming more important than ever, it is expected that research on leadership will continue to be carried out with interest in many fields.

• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.3
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• pp.63-74
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• 2008

With the awareness that Sinyeongwol Sipgyeong(ten scenic spots in Yeongwol) were designed too hastily and only for PR purposes after the change in the tourism environment, this paper indicates that most tourism and culture sources in Yeongwol are related to King Danjong, the sixth king of the Joseon Dynasty. This study proposes a 'Storytelling Plan' for the landscape content called 'Cultural Landscapes - Yeongwol Palgyeong(eight scenic spots in Yeongwol)' after reviewing types and content of Yeongwol Palgyeong through the halo effect of the well-known sad history of King Danjong and the cultural value of Yeongwol. The significance of the unity of the historic site and neighboring landscape is focused on by investigating the anaphoric relations between cultural landscape texts('Yeongwol Palgyeong') and historic content(the sad history of King Danjong). For this, the cultural lnddscape of Yeongwol has been framed and layered to make spatial texts. To emphasize the 'Telling' as well as the 'Story,' interesting episodes have been reviewed to discover a motive. To diversify the 'Telling' methods, absorptive landscape factors have been classified as 'Place,' 'Object' and 'Visual Point.' In addition the storytelling of Yeongwol Palgyeong was examined in consideration of the story and background of 'Yeongwol Palgyeong - Sad Story of King Danjong' and the interaction of a variety of cultural content by suggesting micro-content such as infotainment and edutainment as absorptive landscape factors. In order to make the storytelling plan available in practice as an alternative plan for Yeongwol Tourism, a visual point should be properly set to make the landscape look sufficiently dynamic. In addition, real landscape routes and narration scenarios should be prepared as well. Professional landscape interpreters who are well informed of the natural features of Yeongwol and the history of King Danjong should be brought into the project, and Internet and digital technology-based strategies should be developed.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.21-44
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• 2018

In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.

• Cross-Cultural Studies
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• v.48
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• pp.289-323
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• 2017

L'Espoir, a novel by Andre Malraux, contains traits of the genre of literacy reportage that depicts the full account of the Spanish Civil War as non-fiction based on his personal experience of participating in war; the novel has been dramatized into a semi-documentary film that corresponds to reportage literature. A semi-documentary film is the genre of film that pursues realistic illustration of social incidents or phenomenon. Despite difference in types of genre of the novel and the film L'Espoir, such creative activities deserve close relevance and considerable narrative connectivity. Therefore, $G{\acute{e}}rard$ Genette's narrative discourse of novel and film based on narrative theory carries value of research. Every kind of story, in a narrative message, has duplicate times in which story time and discourse time are different. This is because, in a narrative message, one event may occur before or later than another, told lengthily or concisely, and aroused once or repeatedly. Accordingly, analyzing differing timeliness of the actual event occurring and of recording that event is in terms of order, duration, and frequency. Since timeliness of order, duration, and frequency indicates dramatic pace that controls the passage of a story, it appears as an editorial notion in the novel and the film L'Espoir. It is an aesthetic discourse raising curiosity and shock, the correspondence of time in arranging, summarizing, deleting the story. In addition, Genette mentions notions of speech and voice to clearly distinguish position and focalization of a narrator or a speaker in text. The necessity to discriminate 'who speaks' and 'who sees' comes from difference in views of the narrator of text and the text. The matter of 'who speaks' is about who portrays narrator of the story. However, 'who sees' is related to from whose stance the story is being narrated. In the novel L'Espoir, change of focalization was ushered through zero focalization and internal focalization, and pertains to the multicamera in the film. Also, the frame story was commonly taken as metadiegetic type of voice in both film and novel of L'Espoir. In sum, narrative discourse in the novel and the film L'Espoir is the dimension of story communication among text, the narrator, and recipient.