• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.21-27
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• 1997

Neuro-fuzzy network that combined advantages of the neural network in learning and fuzzy system in inferencing can be used to establish a system model in the design of a controller. In this paper, we presented the neuro-fuzzy system that can be able to generated a linguistic fuzzy model which results in a similar input/output response to the original system. The network was used to model a system. We tested the performance ot the neuro-fuzzy network through computer simulations.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1250-1260
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• 2020

This study proposes a novel deep neural network model that can accurately detect objects and their relationships in an image and represent them as a scene graph. The proposed model utilizes several multimodal features, including linguistic features and visual context features, to accurately detect objects and relationships. In addition, in the proposed model, context features are embedded using graph neural networks to depict the dependencies between two related objects in the context feature vector. This study demonstrates the effectiveness of the proposed model through comparative experiments using the Visual Genome benchmark dataset.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.198-205
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• 2011

GPS, ARPA, AIS, NAVTEX, VHF as modern aids-to-navigation equipments improve the safe navigation and help to reach a reduction in marine accidents by providing images, numeric values, texts, audio-based information for mates, However, we also noticed that it's complicate and difficult for a mate to acquire and analyze such information from these devices while he should devote himself to bridge watchkeeping especially in the urgent situation. Language is another way to get information and free the eyes and hands, so, to solve the problem above, we are trying to propose a new aids-to-navigation system, which can understand and merge multimedia marine safety information, analyze the situation and provide the necessary information in language. In this paper, we try to suggest a meaning interpretation model for supporting linguistic navigation safety information.

• Language and Information
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• v.20 no.1
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• pp.25-49
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• 2016

Honorific forms express the speaker's social attitude to others and also indicate the social ranks and level of intimacy of the participants in the discourse. In a cross-linguistic perspective of grammar engineering, modelling honorification has been regarded as a key strategy for improving language processing applications. Using the HPSG and MRS formalism, this article provides a multilingual grammar model of honorification. The present study incorporates the honorific information into the Meaning Representation System (MRS) via Individual Constraints (ICONS), and then conducts an evaluation to see if the model contributes to semantics-based language processing.

• Journal of Radiation Protection and Research
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• v.19 no.2
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• pp.109-120
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• 1994

A method for estimating the effectiveness of each protective action against a nuclear accident has been proposed using the fuzzy set theory. In most of the existing countermeasure models in actions under radiological emergencies, the large variety of possible features is simplified by a number of rough assumptions. During this simplification procedure, a lot of information is lost which results in much uncertainty concerning the output of the countermeasure model. Furthermore, different assumptions should be used for different sites to consider the site specific conditions. Tn this study, the diversity of each variable related to protective action has been modelled by the linguistic variable. The effectiveness of sheltering and evacuation has been estimated using the proposed method. The potential advantage of the proposed method is in reducing the loss of information by incorporating the opinions of experts and by introducing the linguistic variables which represent the site specific conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1639-1658
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• 2019

Named entity recognition (NER) is an important technique for improving the performance of data mining and big data analytics. In previous studies, NER systems have been employed to identify named-entities using statistical methods based on prior information or linguistic features; however, such methods are limited in that they are unable to recognize unregistered or unlearned objects. In this paper, a method is proposed to extract objects, such as technologies, theories, or person names, by analyzing the collocation relationship between certain words that simultaneously appear around specific words in the abstracts of academic journals. The method is executed as follows. First, the data is preprocessed using data cleaning and sentence detection to separate the text into single sentences. Then, part-of-speech (POS) tagging is applied to the individual sentences. After this, the appearance and collocation information of the other POS tags is analyzed, excluding the entity candidates, such as nouns. Finally, an entity recognition model is created based on analyzing and classifying the information in the sentences.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.173-177
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• 2003

In the first part an overview on fuzzy sets and fuzzy numbers is given. A detailed treatment of these notions is introduced in [1,2,3]. This sintetically presentation is useful in understanding and in developping the applications in context problems. In the second part, fuzzy context model is given as an application of fuzzy sets and the fuzzy equilibrium equation is solved [4,5].

• The Journal of the Acoustical Society of Korea
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• v.18 no.1
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• pp.53-60
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• 1999

This paper is a study on the korean broadcasting speech recognition. Here we present the methods for the large vocabuary continuous speech recognition. Our main concerns are the language modeling and the search algorithm. The used acoustic model is the uni-phone semi-continuous hidden markov model and the used linguistic model is the N-gram model. The search algorithm consist of three phases in order to utilize all available acoustic and linguistic information. First, we use the forward Viterbi beam search to find word end frames and to estimate related scores. Second, we use the backword Viterbi beam search to find word begin frames and to estimate related scores. Finally, we use A/sup */ search to combine the above two results with the N-grams language model and to get recognition results. Using these methods maximum 96.0% word recognition rate and 99.2% syllable recognition rate are achieved for the speaker-independent continuous speech recognition problem with about 12,000 vocabulary size.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.79-88
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• 2017

The purpose of this study was to identify the difference of creativity according to the type of brain dominance for deveoping pre-service early childhood teachers's creativity teacher education program. The subjects of this study were 210 pre-service early childhood teachers. The tests were conducted by using the Herrmann' BDI and TTCT: verbal. The study have applied Pearson product-moment correlation to find out relation between the type of brain dominance and creativity, and used multi-variate analysis to find out the difference of creativity according to the type of brain dominance. The results of the study are as follow; first, the upper left brain, lower left limb, and right brains had no relation to fluency, flexibility, originality and overall linguistic creativity. The lower right limb showed a positive correlation with fluency, flexibility, originality, and overall linguistic creativity. Second, the lower left, upper right lower, and lower right limb dominant teachers showed higher fluency, flexibility, originality and overall linguistic creativity than upper left neural dominant teachers. The result of analyzing the language creativity according to the type of brain dominance of the pre-service early childhood teachers will be used as a suggestion to develop the brain-based creativity teacher education program.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.