• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.427-432
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• 2005

This paper is concerned with a simultaneous design of TSK (Takagi-Sugeno-Kang)-linguistic fuzzy models with uncertain model output and the computationally efficient representation. For this purpose, we use the fundamental idea of linguistic models introduced by Pedrycz and develop their comprehensive design framework. The design process consists of several main phases such as (a) the automatic generation of the linguistic contexts by probabilistic distribution using CDF (conditional density function) and PDF (probability density function) (b) performing context-based fuzzy clustering preserving homogeneity based on the concept of fuzzy granulation (c) augment of bias term to compensate bias error (d) combination of TSK and linguistic context in the consequent part. Finally, we contrast the performance of the enhanced models with other fuzzy models for automobile MPG predication data and coagulant dosing process in a water purification plant.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1510-1515
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• 2005

This paper is concerned with a Logic-based Fuzzy Neural Networks (LFNN) with the aid of fuzzy granulation. As the underlying design tool guiding the development of the proposed LFNN, we concentrate on the context-based fuzzy clustering which builds information granules in the form of linguistic contexts as well as OR fuzzy neuron which is logic-driven processing unit realizing the composition operations of T-norm and S-norm. The design process comprises several main phases such as (a) defining context fuzzy sets in the output space, (b) completing context-based fuzzy clustering in each context, (c) aggregating OR fuzzy neuron into linguistic models, and (c) optimizing connections linking information granules and fuzzy neurons in the input and output spaces. The experimental examples are tested through two-dimensional nonlinear function. The obtained results reveal that the proposed model yields better performance in comparison with conventional linguistic model and other approaches.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.76-82
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• 2010

In this paper, we develop Linguistic Model (LM) based on information granules as a systematic approach to generating fuzzy if-then rules from a given input-output data. The LM introduced by Pedrycz is performed by fuzzy information granulation obtained from Context-based Fuzzy Clustering(CFC). This clustering estimates clusters by preserving the homogeneity of the clustered patterns associated with the input and output data. Although the effectiveness of LM has been demonstrated in the previous works, it needs to improve in the sense of performance. Therefore, we focus on the automatic generation of linguistic contexts, addition of bias term, and the transformed form of consequent parameter to improve both approximation and generalization capability of the conventional LM. The experimental results revealed that the improved LM yielded a better performance in comparison with LM and the conventional works for automobile MPG(miles per gallon) predication and Boston housing data.

• 중국학논총
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• no.70
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• pp.61-86
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• 2021

本文主要討論了語境在中韓語篇翻譯中的作用。作者把韓礼德的語境三分法, 應用于中韓語篇翻譯實踐中。概括起來設, 上下文語境(又称爲語言語境)是指一个詞, 一个短語, 乃至更長的語篇前后的内容。情景語境就是語域變量, 語域變量可分爲以下三种 : 語場, 語旨和語式, 最后, 文化語境指語篇所涉及的社會, 文化, 經濟, 宗教和政治背景等。作者把語境分爲上下文語境, 情景語境和文化語境, 深入探討了中韓翻譯中的語境問題。作者把文化語境, 還分爲文化詞和文化含義詞的影響与制約和文化詞的翻譯戰略二部分, 論述了文化詞翻譯的難点以及文化詞的翻譯技巧。通過語境分析我們可以看出, 在中韓翻譯實踐中利用語境因素能排除歧義 ; 借助語境中特定的情境意義在譯文中重构原文中用語法, 語用和語体等表現出的意義。最后, 我們在翻譯過程中依靠文化語境能判斷在原文中文化詞所含有的詞義。

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3675-3684
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• 2021

Human reliability analysis (HRA) is a proactive approach to model and evaluate human systematic errors, and has been extensively applied in various complicated systems. Dependence assessment among human errors plays a key role in the HRA, which relies heavily on the knowledge and experience of experts in real-world cases. Moreover, there are ofthen different types of uncertainty when experts use linguistic labels to evaluate the dependencies between human failure events. In this context, this paper aims to develop a new method based on linguistic hesitant fuzzy sets and the technique for human error rate prediction (THERP) technique to manage the dependence in HRA. This method handles the linguistic assessments given by experts according to the linguistic hesitant fuzzy sets, determines the weights of influential factors by an extended best-worst method, and confirms the degree of dependence between successive actions based on the THERP method. Finally, the effectiveness and practicality of the presented linguistic hesitant fuzzy THERP method are demonstrated through an empirical healthcare dependence analysis.

• International Journal of Contents
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• v.6 no.2
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• pp.33-40
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• 2010

This study aims at investigating the linguistic and educational factors affecting TOEFL scores, focusing on three OECD countries, Korea, Japan, and Finland. The data comprise document analysis on curriculums, websites, and literature. The findings reveal that the number of Korean test-takers and their TOEFL scores gradually increased year by year. Finnish test-takers consistently gained greatly high scores, and Japanese examinees showed the lowest scores. The languages Korean, Japanese, and Finnish are all far distant from English and receive little support on historical grounds from the Indo-European family tree. In Finland, however, Swedish which belongs to Indo-European languages is still used as an official language with Finnish. Korea and Finland adopt English education from Year 3 in primary school, whereas English is not an official subject in primary school at present in Japan. Finnish students are taught a foreign language in addition to English from primary school. These seem to support the result of the high TOEFL scores of Finnish test-takers. This study concludes that social context which includes linguistic and educational environments are the main factors which affect TOEFL scores.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1187-1190
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• 1993

The problem of the optimization of fuzzy relational models for dealing with (non-fuzzy) numerical data is investigated. In this context, interfaces optimization assumes particular importance, becoming a determinant factor in what concerns the overall model performance. Considering this, several scenarios for building fuzzy relational models are presented. These are: (i) optimizing I/O interfaces in advance (independently from the linguistic part of the model); (ii) optimizing I/O interfaces in advance and allowing that their optimized parameters may change during the learning of the linguistic part of the model; (iii) build simultaneously both interfaces and the linguistic subsystem; and (iv) build simultaneously both linguistic subsystem and interfaces, now subject to semantic integrity constraints. As linguistic subsystems, both a basic type and an extended versions of fuzzy relation equations are exploited in each one of these scenarios. A comparative analysis of the differ nt approaches is summarized.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.162-168
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• 2023

This article presents an examination of the major cognitive-semantic theories in linguistics (Langacker, Lakoff, Fillmore, Croft). The CST's foundations are discussed concerning the educational policy changes, which are necessary to improve the linguistic disciplines in the changing context of higher education, as well as the empowerment and development of the industry. It is relevant in the light of the linguistic specialists' quality training and the development of effective methods of language learning. Consideration of the theories content, tools, and methods of language teaching, which are an important component of quality teaching and the formation of a set of knowledge and skills of students of linguistic specialties, remains crucial. This study aims to establish the main theoretical positions and directions of cognitive-semantic theory in linguistics, determine the usefulness of teaching the basics of cognitive linguistics, the feasibility of using methods of cognitive-semantic nature in the learning process. During the research, the methods of linguistic description and observation, analysis, and synthesis were applied. The result of the study is to establish the need to study basic linguistic theories, as well as general theoretical precepts of cognitive linguistics, which remains one of the effective directions in the postmodern mainstream. It also clarifies the place of the main cognitive-semantic theories in the teaching linguistics' practice of the XXI century.

• 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].