• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.369-381
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• 2021

Artificial Intelligence(AI) technology can be used in arts exhibitions ranging from planning exhibitions, filed progress, and evaluation. AI has been expanded its scope from planning exhibition and guidance services to tools for creating arts. This paper focuses on chatbots that utilize exhibition and AI technology convergence to provide information and services. To study more specifically, I developed a chatbot for exhibition services using the Naver Clova chatbot tool and information from the National Museum of Modern and Contemporary Art(MMCA), Korea. In this study, information was limited to viewing and exhibition rather than all information of the MMCA, and the chatbot was developed which provides a scenario type to get an answering user want to gain through a button and a text question and answer(Q&A) type to directly input a question. As a result of evaluating the chatbot with six items according to ELIZA's chatbot evaluation scale, a score of 4.2 out of 5 was derived by completing the development of a chatbot to be used to deliver viewing and exhibition information. The future research task is to create a perfect chatbot model that can be used in an actual arts exhibition space by connecting the developed chatbot with continuous scenario answers, resolving text Q&A-type answer failures and errors, and expanding additional services.

• Journal of the Korea Computer Graphics Society
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• v.23 no.2
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• pp.11-22
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• 2017

This study proposes IMToon(IMage-based carToon) which is an image-based cartoon authoring system using an image processing algorithm. The proposed IMToon allows general users to easily and efficiently produce frames comprising cartoons based on image. The authoring system is designed largely with two functions: cartoon effector and interactive story editor. Cartoon effector automatically converts input images into a cartoon-style image, which consists of image-based cartoon shading and outline drawing steps. Image-based cartoon shading is to receive images of the desired scenes from users, separate brightness information from the color model of the input images, simplify them to a shading range of desired steps, and recreate them as cartoon-style images. Then, the final cartoon style images are created through the outline drawing step in which the outlines of the shaded images are applied through edge detection. Interactive story editor is used to enter text balloons and subtitles in a dialog structure to create one scene of the completed cartoon that delivers a story such as web-toon or comic book. In addition, the cartoon effector, which converts images into cartoon style, is expanded to videos so that it can be applied to videos as well as still images. Finally, various experiments are conducted to verify the possibility of easy and efficient production of cartoons that users want based on images with the proposed IMToon system.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.49-59
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• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.

• Water Engineering Research
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• v.2 no.4
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• pp.219-229
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• 2001

Synthetic unit hydrograph equations for rainfall run-off characteristics analysis and estimation of design flood have long and quite frequently been presented, the Snyder and SCS synthetic unit hydrograph. The major inputs to the Snyder and SCS synthetic unit hydrograph are lag time and peak coefficient. In this study, the methods for estimating lag time and peak coefficient for small watersheds proposed by Zhao and McEnroe(1999) were applied to the Kum river basin in Korea. We investigated lag times of relatively small watersheds in the Kum river basin in Korea. For this investigation the recent rainfall and stream flow data for 10 relatively small watersheds with drainage areas ranging from 134 to 902 square kilometers were gathered and used. 250 flood flow events were identified along the way, and the lag time for the flood events was determined by using the rainfall and stream flow data. Lag time is closely related with the basin characteristics of a given drainage area such as channel length, channel slope, and drainage area. A regression analysis was conducted to relate lag time to the watershed characteristics. The resulting regression model is as shown below: ※ see full text (equations) In the model, Tlag is the lag time in hours, Lc is the length of the main river in kilometers and Se is the equivalent channel slope of the main channel. The coefficient of determinations (r$^2$)expressed in the regression equation is 0.846. The peak coefficient is not correlated significantly with any of the watershed characteristics. We recommend a peak coefficient of 0.60 as input to the Snyder unit-hydrograph model for the ungauged Kum river watersheds

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.5
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• pp.99-106
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• 2012

Korea navy has been using the voice-oriented ICS to raise a efficiency of naval ship operation. Recently a multimedia network which are included voice, video and text is under consideration by korean navy. As a basic research to establish the integrated multimedia network of a naval ship, this paper classify the networks in order to apply to an integrated network among the various networks within a naval ship. We also consider the sort and characteristic of the multimedia traffic which is using within the classified networks. To predict the link capacity of switch from number of traffic input source, we suggest a traffic aggregation model. Then we calculate the link capacity of aggregated traffic and analyze a aggregated traffic of Korea major naval ship.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.75-84
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• 2005

Bulletin board systems have evolved to support diverse multimedia data as well as text. However, current board systems have an weakness : it takes much time and efforts for users to figure out contents of articles. Most board systems provide a searching function with lexical level data access for solving that problem, however it fails to serve users' intented searching results. Moreover, it is nearly impossible to search proper articles if they contain multimedia data. This paper proposed a bulletin board system adopting the Semantic Web to solve this issue. The proposed system provides users with new ontology which is used for describing articles' domain knowledge and multimedia features. Users can describe their own board ontology using the proposed ontology. To support semantic-based searching for diverse domain knowledge without modification of the system, the system dynamically generated input/query interface and RDF data access module according to the board ontology written by administrators. The proposed board system shows that semantic-based searching is feasible and effective for users to find their intended articles.

• Journal of KIISE
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• v.44 no.8
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• pp.774-781
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• 2017

Mention detection systems use nouns or noun phrases as a head and construct a chunk of text that defines any meaning, including a modifier. The term "mention detection" relates to the extraction of mentions in a document. In the mentions, a coreference resolution pertains to finding out if various mentions have the same meaning to each other. A pointer network is a model based on a recurrent neural network (RNN) encoder-decoder, and outputs a list of elements that correspond to input sequence. In this paper, we propose the use of mention detection using pointer networks. Our proposed model can solve the problem of overlapped mention detection, an issue that could not be solved by sequence labeling when applying the pointer network to the mention detection. As a result of this experiment, performance of the proposed mention detection model showed an F1 of 80.07%, a 7.65%p higher than rule-based mention detection; a co-reference resolution performance using this mention detection model showed a CoNLL F1 of 52.67% (mention boundary), and a CoNLL F1 of 60.11% (head boundary) that is high, 7.68%p, or 1.5%p more than coreference resolution using rule-based mention detection.

• Journal of the Korean Society for information Management
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• v.30 no.3
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• pp.23-48
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• 2013

This study compared and analyzed 15 CBMR (Content-based Music Retrieval) systems accessible on the web in terms of DB size and type, query type, access point, input and output type, and search functions, with reviewing features of music information and techniques used for transforming or transcribing of music sources, extracting and segmenting melodies, extracting and indexing features of music, and matching algorithms for CBMR systems. Application of text information retrieval techniques such as inverted indexing, N-gram indexing, Boolean search, truncation, keyword and phrase search, normalization, filtering, browsing, exact matching, similarity measure using edit distance, sorting, etc. to enhancing the CBMR; effort for increasing DB size and usability; and problems in extracting melodies, deleting stop notes in queries, and using solfege as pitch information were found as the results of analysis.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.614-618
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• 2008

As the multimedia data increase a lot with the rapid development of the Internet, an efficient retrieval technique focusing on individual users is required based on the analyses of such data. However, user modeling services provided by recent web sites have the limitation of text-based page configurations and recommendation retrieval. In this paper, we construct the user preference model with a Bayesian network to apply the user modeling to video retrieval, and suggest a method which utilizes probability reasoning. To do this, context information is defined in a real office environment and the video scripts acquired from established cameras and annotated the context information manually are used. Personal information of the user, obtained from user input, is adopted for the evidence value of the constructed Bayesian Network, and user preference is inferred. The probability value, which is produced from the result of Bayesian Network reasoning, is used for retrieval, making the system return the retrieval result suitable for each user's preference. The usability test indicates that the satisfaction level of the selected results based on the proposed model is higher than general retrieval method.