• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.24-29
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• 2023

In this paper, we propose a real-time bus arrival notification system using deep learning to guarantee movement rights for the visually impaired. In modern society, by using location information of public transportation, users can quickly obtain information about public transportation and use public transportation easily. However, since the existing public transportation information system is a visual system, the visually impaired cannot use it. In Korea, various laws have been amended since the 'Act on the Promotion of Transportation for the Vulnerable' was enacted in June 2012 as the Act on the Movement Rights of the Blind, but the visually impaired are experiencing inconvenience in using public transportation. In particular, from the standpoint of the visually impaired, it is impossible to determine whether the bus is coming soon, is coming now, or has already arrived with the current system. In this paper, we use deep learning technology to learn bus numbers and identify upcoming bus numbers. Finally, we propose a method to notify the visually impaired by voice that the bus is coming by using TTS technology.

• Journal of Korea Game Society
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• v.10 no.1
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• pp.65-78
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• 2010

In Video Software Dealers Association v. Arnold Schwarzenegger, the federal 9th Circuit Court decided that a California law imposing restrictions and a labeling requirement on the sale or rental of violent video games to minors (the "Act") violated rights guaranteed by the First and Fourteenth Amendments to the United States Constitution because: (1) the state introduced insufficient evidence to support a compelling interest that video games created psychological or neurological harm, (2) the Act was not the least-restrictive alternative to negate the harm, and (3) the lower, rational basis standard applicable to commercial speech did not apply to the Act's labeling requirements because the required label did not convey factual information. On the contrary, Korean Constitutional Court decided that "Harmful Medium to Youth" and "Preliminary Rate Classification" would be constitutional. However, under the least-restrictive method rule of the U. S. Court and Korean Court, overlap application of "Harmful Medium to Youth" and "Preliminary Rate Classification" could be a problem and it would be possible that stronger regulation among these would be found as unconstitutional.

• Journal of International Area Studies (JIAS)
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• v.22 no.1
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• pp.67-90
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• 2018

France has allowed a deprivation of nationality for terrorism since 1996 Law. After then, a series of law revision (1998, 2006 law and 2010, 2015-2015 reform tries) have not only linked an act of terror with nationality deprivation but also put the dual national and their deprivation at the centre of dispute. Both 2010 Immigration Law by Sarkozy and 2015-2016 constitutional amendment by Hollande had been not adopted but have combined immigrants and security issue together by firmly solidifying the relationship between dual national and terrorist. Especially in 2015-2016, the constitutional amendment that failed to be adopted has extended a state of emergency and left the nationality deprivation of dual national as controversial issue. After over 4 months of discussion, the amendment that contains a clause of a state of emergency and extension plan for nationality deprivation was abolished but the dual national issues which had been non-politicized has now become politicized, defined as security issues and finally securitized. It shows very two-stage process of securitization argued by Copenhagen school including Buzan. The importance of securitization notion by Copenhagen school lies on the fact that it scrutinizes the constructive elements of the process of securitization. The concept of securitization allows us to see the problem of the process that defines a non-politicized issue as security one by making it political matter. Applying this process of securitization into the controversy of nationality deprivation of dual nationals in France, we can find out the development of non-controversial and non-politicized dual national issues into social agenda by politicization through speech-act or announcement effect.

• Archives of design research
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• v.20 no.1 s.69
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• pp.55-64
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• 2007

Non-verbal communications are important because they support and replace verbal communication. Body language of various non-verbal communications is the communication using the body. In animation, expression of body language is very important because characters play an important role in communicating the scenario. Animation has a dual communication structure, different from general communication. One is the communication between the speaker character and the hearer character, the other is the image and the audience, which includes the communication between the speaker character and the hearer character. In this study, we divide the body language from the characters into the discourse-in act and discourse-out act according to this dual structure and classify it into adaptors, emblem, illustrator, regulator, affect display by a functional approach method. Especially, the illustrator is subdivided into pragmatic speech act. Finally, this study analyzes the features of body language in animation and represents animation character's body language for an effective expression of the communications in animation.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.17-22
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• 2017

화행이란 의사소통 과정에서 발화자가 가지는 발화 의도를 말한다. 성공적인 의사소통을 위해서는 발화자의 화행을 정확하게 파악하는 것이 매우 중요하다. 본 논문에서는 한국어 대화체 문장의 화행 자동분류를 위해, 화행을 결정짓는 요인이 무엇인지 언어학적으로 분석하고자 하였다. 한국어 수업 대화를 분석하여 화행 분류 체계를 새롭게 자체 정립하였고, 언어학적 근거를 바탕으로 10개의 화행 분류 자질을 제안하였다. 또한 제안하는 화행 분류 자질을 검증하고자 웨카(Weka)를 이용하여 정확률 실험을 진행하였다.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.119-121
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• 2016

최근 사람과 컴퓨터가 대화를 하는 채팅시스템 연구가 활발해지고 있다. 컴퓨터가 사람의 말에 적절한 응답을 하기 위해선 그 의미를 분석할 필요가 있다. 발화에 대한 의미 분석의 기본이 되는 연구로 감정분석과 화행분석이 있다. 그러나 이 둘은 서로 밀접한 연관이 있음에도 불구하고 함께 분석하는 연구가 시도되지 않았다. 본 연구에서는 Long Short-term Memory(LSTM)를 이용하여 대화체 문장의 감정과 화행, 서술자를 동시에 분석하는 통합 대화 분석모델을 제안한다. 사랑 도메인 데이터를 사용한 실험에서 제안 모델은 감정 58.08%, 화행 82.60%, 서술자 62.74%의 정확도(Accuracy)를 보였다.

• MALSORI
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• no.56
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• pp.185-194
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• 2005

In this paper, we present POSSDM (POSTECH Situation-Based Dialogue Manager) for a spoken dialogue system using a new example and situation-based dialogue management technique for effective generation of appropriate system responses. Spoken dialogue system should generate cooperative responses to smoothly control dialogue flow with the users. We introduce a new dialogue management technique incorporating dialogue examples and situation-based rules for EPG (Electronic Program Guide) domain. For the system response inference, we automatically construct and index a dialogue example database from dialogue corpus, and the best dialogue example is retrieved for a proper system response with the query from a dialogue situation including a current user utterance, dialogue act, and discourse history. When dialogue corpus is not enough to cover the domain, we also apply manually constructed situation-based rules mainly for meta-level dialogue management.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.17-22
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• 2017

화행이란 의사소통 과정에서 발화자가 가지는 발화 의도를 말한다. 성공적인 의사소통을 위해서는 발화자의 화행을 정확하게 파악하는 것이 매우 중요하다. 본 논문에서는 한국어 대화체 문장의 화행 자동분류를 위해, 화행을 결정짓는 요인이 무엇인지 언어학적으로 분석하고자 하였다. 한국어 수업 대화를 분석하여 화행 분류 체계를 새롭게 자체 정립하였고, 언어학적 근거를 바탕으로 10개의 화행 분류 자질을 제안하였다. 또한 제안하는 화행 분류 자질을 검증하고자 웨카(Weka)를 이용하여 정확률 실험을 진행하였다.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.532-536
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• 2018

심층인공신경망을 이용한 대화 모델링 연구가 활발하게 진행되고 있다. 본 논문에서는 대화에서 발화의 감정과 화행을 분류하기 위해 멀티태스크(multitask) 학습을 이용한 End-to-End 시스템을 제안한다. 우리는 감정과 화행을 동시에 분류하는 시스템을 개발하기 위해 멀티태스크 학습을 수행한다. 또한 불균형 범주 분류를 위해 계단식분류(cascaded classification) 구조를 사용하였다. 일상대화 데이터셋을 사용하여 실험을 수행하였고 macro average precision으로 성능을 측정하여 감정 분류 60.43%, 화행 분류 74.29%를 각각 달성하였다. 이는 baseline 모델 대비 각각 29.00%, 1.54% 향상된 성능이다. 본 논문에서는 제안하는 구조를 이용하여, 발화의 감정 및 화행 분류가 End-to-End 방식으로 모델링 가능함을 보였다. 그리고, 두 분류 문제를 하나의 구조로 적절히 학습하기 위한 방법과 분류 문제에서의 범주 불균형 문제를 해결하기 위한 분류 방법을 제시하였다.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.243-246
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• 2018

최근 사용자 발화를 이해하고 그에 맞는 피드백을 생성할 수 있는 대화 시스템의 중요성이 증가하고 있다. 따라서 사용자 의도를 파악하기 위한 화행 분석은 대화 시스템의 필수적인 요소이다. 최근 많이 연구되는 심층 학습 기법은 모델이 데이터로부터 자질들을 스스로 추출한다는 장점이 있다. 발화 자체의 연속성과 화자간 상호 작용을 포착하기 위하여 CNN에 RNN을 결합한 CNN-RNN을 제안한다. 본 논문에서 제안한 계층 구조 어텐션 매커니즘 기반 CNN-RNN을 효과적으로 적용한 결과 워드 임베딩을 추가한 조건에서 가장 높은 성능인 91.72% 정확도를 얻었다.