• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.1001-1010
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• 2021

Normal people are not deeply aware of their dependence on sight when eating. However, since the visually impaired do not know what kind of food is on the table, the assistant next to them holds the blind spoon and explains the position of the food in a clockwise direction, front and rear, left and right, etc. In this paper, we describe the development of a meal assistance system that recognizes each food image and announces the name of the food by voice when a visually impaired person looks at their table using a smartphone camera. This system extracts the food on which the spoon is placed through the YOLO model that has learned the image of food and tableware (spoon), recognizes what the food is, and notifies it by voice. Through this system, it is expected that the visually impaired will be able to eat without the help of a meal assistant, thereby increasing their self-reliance and satisfaction.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.21-31
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• 2023

In this paper, we introduce a case study of cinematography using digital human in virtual production. This case study deals with the system overview of virtual production using LEDs and an efficient filming pipeline using digital human. Unlike virtual production using LEDs, which mainly project the background on LEDs, in this case, we use digital human as a virtual actor to film scenes communicating with a real actor. In addition, to film the dialogue scene between the real actor and the digital human using a real-time engine, we automatically generated speech animation of the digital human in advance by applying our Korean lip-sync technology based on audio and text. We verified this filming case by using a real-time engine to produce short drama content using real actor and digital human in an LED-based virtual production environment.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.7
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• pp.221-228
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• 2020

Part-of-Speech (POS) tagged corpus is a collection of electronic text in which each word is annotated with a tag as the corresponding POS and is widely used for various training data for natural language processing. The training data generally assumes that there are no errors, but in reality they include various types of errors, which cause performance degradation of systems trained using the data. To alleviate this problem, we propose a novel method for detecting errors in the existing POS tagged corpus using the classifier of XGBoost and cross-validation as evaluation techniques. We first train a classifier of a POS tagger using the POS-tagged corpus with some errors and then detect errors from the POS-tagged corpus using cross-validation, but the classifier cannot detect errors because there is no training data for detecting POS tagged errors. We thus detect errors by comparing the outputs (probabilities of POS) of the classifier, adjusting hyperparameters. The hyperparameters is estimated by a small scale error-tagged corpus, in which text is sampled from a POS-tagged corpus and which is marked up POS errors by experts. In this paper, we use recall and precision as evaluation metrics which are widely used in information retrieval. We have shown that the proposed method is valid by comparing two distributions of the sample (the error-tagged corpus) and the population (the POS-tagged corpus) because all detected errors cannot be checked. In the near future, we will apply the proposed method to a dependency tree-tagged corpus and a semantic role tagged corpus.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3
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• pp.242-247
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• 2004

Conventional keyword spotting systems use the connected word recognition network consisted by keyword models and filler models in keyword spotting. This is why the system can not construct the language models of word appearance effectively for detecting keywords in large vocabulary continuous speech recognition system with large text data. In this paper to solve this problem, we propose a keyword spotting system using pseudo N-gram language model for detecting key-words and investigate the performance of the system upon the changes of the frequencies of appearances of both keywords and filler models. As the results, when the Unigram probability of keywords and filler models were set to 0.2, 0.8, the experimental results showed that CA (Correctly Accept for In-Vocabulary) and CR (Correctly Reject for Out-Of-Vocabulary) were 91.1% and 91.7% respectively, which means that our proposed system can get 14% of improved average CA-CR performance than conventional methods in ERR (Error Reduction Rate).

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of the Korean housing association
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• v.21 no.1
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• pp.67-77
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• 2010

Citizen participation in local redevelopment has recently been regarded as essential, since progress in democracy and diversified public interests have contributed to more importance being placed on citizen participation in the implementation of public policies. While the importance of resident participation has been increasingly emphasized in principle, in reality more effort is still required in its application. We need to develop practical strategies of collecting community opinion in order to reflect it in public policy, if we are to achieve a resident and citizen-centered society. The purpose of this study is to develop an image map construction tool that can be applied to the "Maul-Mandulgi" projects as a visualized method to facilitate the exchange of opinions and work toward agreements. The tool is intended to assist public discussion by visualizing policies and plans and reducing the possibility of misunderstanding, so that residents can properly respond to the plans. Second, this study will verify the effectiveness of the tool in the application to local community workshops. The main research method is participant observation method and field study. Major findings are as follows, First, every resident who had participated in previous workshops gathered together, used the tool and represented their opinions unusually more than once. Each resident tried to make sure that other participants appropriately understood his or her opinion. The workshop finished when all participants agreed and produced a consensus. The workshop took much less time, which is in stark contrast to previous workshops in which it took significantly more time to collect opinions. Second, it proved that residents in the redevelopment area can strike a broad agreement by themselves on a method and direction for residential improvement. In previous workshops, conflicts between residents developed over the choice between the two methods, of local improvement and total demolition prior to multi-housing construction. In this study, opinions of residents were not limited to the two methods by finding a winwin solution. Third, the use of the tool kit for image map became efficient for inactive residents to develop their own opinions in regard to the direction and orientations of the residential improvement process. In addition, for those who have either no or a slight understanding of the residential improvement projects, the tool can provide access to information and knowledge. This study concludes that the developed tool for imaging of the redevelopment projection like a design game, rather than using forms of text and speech, can be a useful tool in collecting opinions and forming an agreed opinion for forthcoming residential improvement plans.

• Journal of The Korean Association For Science Education
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• v.43 no.4
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• pp.369-387
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• 2023

This study aims to interpret the experience of science research in a graduate school laboratory from the perspective of Gilles Deleuze's concepts of "agencement" and "becoming". The research was conducted as an autobiographical narrative inquiry. The research text is written in a way that tells the story of my science research experience and retells it from the perspective of Gilles Deleuze. In Deleuze's view, science research is a constantly flowing agencement. The science research agencement is composed of a mechanical agencement of various experimental tools-machines and researcher-machines as well as a collective agencement of speech acts such as biological knowledge, experiment protocols, and laboratory rules. Furthermore, science research agencement is fluid as events occur all over the agencement. Data, as a change occurring in the material dimension, is an event and sign that raises problems. It has the agency to influence agencement through an intersubjective relationship with researchers, and the meaning of data is generated in this process. The change of agencement compelled me to perform science practice. I have performed repeated science practice, meaning that my body has constantly been connected to other machines. As a result of this connection, my body has been affected, and the capacity of my body that constitutes the agencement has been augmented. In addition, I was able to be deterritorialized from the existing science research agencement and reterritorialized in a new science research agencement with data. This process of differentiation allowed me to becoming-scientist. In sum, this study provides implications for science practice-oriented education by exploring the process of becoming-scientist based on my science research experience.