• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.518-523
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• 2002

우리는 생활 속에서의 원활한 의사소통을 위해 수 많은 관계와의 좋은 인터페이스를 여러 각도로 요구하며 살고 있다. 본 논문에서는 효율적인 사용자 중심의 인터페이스를 설계하기 위하여 문맥조사를 인터페이스 디자인 초기단계에 적용하는 프로토타입 제작기법을 고안하였다. 그 적용사례로 ATM(현금지급기)를 시뮬레이션 하여 분석한 결과, 기존의 시스템보다 개별 사용자의 현금지급기 서비스 처리 수행속도가 평균 40초 이상 단축되었다. 사용자 이용 시간의 단축은 비용절감과 만족도의 향상을 의미한다.

• Korean Journal of Logic
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• v.1
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• pp.9-63
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• 1997

• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.45-56
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• 2015

In this paper, we present a method that can detect context-sensitive word errors and generate correction candidates. Spelling error detection is one of the most widespread research topics, however, the approach proposed in this paper is adjusted for an automated English scoring system. A common strategy in context-sensitive word error detection is using a pre-defined confusion set to generate correction candidates. We automatically generate a confusion set in order to consider the characteristics of sentences written by second-language learners. We define a word error that cannot be detected by a conventional grammar checker because of part-of-speech ambiguity, and propose how to detect the error and generate correction candidates for this kind of error. An experiment is performed on the English writings composed by junior-high school students whose mother tongue is Korean. The f1 value of the proposed method is 70.48%, which shows that our method is promising comparing to the current-state-of-the art.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.492-500
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• 2014

This paper presents the performance enhancement of automatic bilingual lexicon extraction by using refinement of pivot-context vectors under the standard pivot-based approach, which is very effective method for less-resource language pairs. In this paper, we gradually improve the performance through two different refinements of pivot-context vectors: One is to filter out unhelpful elements of the pivot-context vectors and to revise the values of the vectors through bidirectional translation probabilities estimated by Anymalign and another one is to remove non-noun elements from the original vectors. In this paper, experiments have been conducted on two different language pairs that are bi-directional Korean-Spanish and Korean-French, respectively. The experimental results have demonstrated that our method for high-frequency words shows at least 48.5% at the top 1 and up to 88.5% at the top 20 and for the low-frequency words at least 43.3% at the top 1 and up to 48.9% at the top 20.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.254-262
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• 2009

As the trend of e-business activities develop, customers come into contact with products through on-line shopping sites and lots of customers refer product reviews before the purchasing on-line. However, as the volume of product reviews grow, it takes a great deal of time and effort for customers to read and evaluate voluminous product reviews. Lately, attention is being paid to Opinion Mining(OM) as one of the effective solutions to this problem. In this paper, we propose an efficient method for opinion sentiment classification of product reviews using product specific context information of words occurred in the reviews. We define the context information of words and propose the application of context for sentiment classification and we show the performance of our method through the experiments. Additionally, in case of word corpus construction, we propose the method to construct word corpus automatically using the review texts and review scores in order to prevent traditional manual process. In consequence, we can easily get exact sentiment polarities of opinion words in product reviews.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.437-442
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• 2009

For multiprocessor systems, Earliest Deadline First (EDF) based on deadline and Least Laxity First (LLF) based on laxity are not suitable for practical environment since EDF has low schedulability and LLF has high context switching overhead. As a combining of EDF and LLF to improve the performance, Earliest Deadline Zero Laxity (EDZL) was proposed. EDZL is basically the same as EDF. But if the laxity of a task becomes zero, its priority is promoted to the highest level. In this paper, we propose Least Laxity Zero Laxity (LLZL) which is based on LLF. But context switching is allowed only if the laxity of a task on rady queue becomes zero. Simulation results show that LLZL has high schedulability approaching to LLF and low context switching overhead similar to EDF. In comparison with EDZL, LLZL has better performance in both of schedulability and context switching overhead.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.553-560
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• 2006

Speech recognition has recently been investigated to produce more reliable recognition results in a noisy environment, by integrating diverse sources of information into the result derivation-level or producing new results through post-processing the prior recognition results. In this paper we propose a method which uses the user's usage patterns and the context information in speech command recognition for personal mobile devices to improve the recognition accuracy in a noisy environment. Sequential usage (or speech) patterns prior to the current command spoken are used to adjust the base recognition results. For the context information, we use the relevance between the current function of the device in use and the spoken command. Our experiment results show that the proposed method achieves about 50% of error correction rate over the base recognition system. It demonstrates the feasibility of the proposed method.

• 한국HCI학회:학술대회논문집
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• 2007.02b
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• pp.7-14
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• 2007

디지털 컨버전스가 가속화됨에 따라 휴대폰의 기능은 나날이 복잡해져 가고 있으며, 이러한 경향은 사람들에게 자신에게 불필요한 기능까지 익혀야 하는 불편함을 초래하고 있다, 특히, 단순한 통화방식에 익숙하고 인지적으로 새로운 것을 유연하게 받아들이지 못하는 고령 사용자들에게도 이미 생활필수품이 된 휴대폰은 사용하는데 어렵고 불편한 부분이 많이 있다. 본 연구에서는 한국의 고령사용자들을 대상으로 휴대폰 사용에 있어 문맥적, 정성적 조사(Contextual & Qualitative research)기법을 적용하여 평소 그들이 일상생활에서 느끼는 휴대폰 관련 요구사항을 도출하고자 하였다. 조사는 세 단계로 이루어졌다. 먼저, 휴대폰을 사용 중인 60대 이상 고령사용자 83명을 대상으로 사용 현황 및 수준을 파악하고 적절한 사용자를 섭외하기 위한 설문을 진행하였으며, 둘째, 초보사용자 8명, 중급사용자 3명을 대상으로 휴대폰 사용 기록을 기반으로 하는 심층인터뷰를 진행하였으며, 초보사용자들을 대상으로는 문자메세지, 전화번호부, 카메라 폰 기능 교육 세션을 통한 행동 관찰법을 시행하였다. 마지막으로, 교육 세션 한달 후, 사용성 평가와 추가 인터뷰를 진행하였다. 이상과 같이 장기간에 걸친 사용자 조사를 통해, 고령사용자들의 휴대폰 사용에 있어 심리, 문화적 특이사항과 인터랙션 상의 불편요소들을 발견할 수 있었으며, 수집된 동영상 데이터를 분석하여 고령사용자의 휴대폰 관련 요구사항을 신체적 요인, 인지적요인, 심리적 요인의 세가지로 구분하여 제시하였다. 본 연구에서는 문맥적, 정성적 사용자 조사를 통해 고령사용자들의 일상생활에서의 휴대폰 관련 요구사항을 도출함으로써 풍부하고 실증적인 경험데이터를 수집할 수 있었다. 이와 같이 도출된 요구사항 데이터는 이후 고령사용자를 위한 유니버설 디자인 휴대폰 개발 전략수립과 UI 디자인 개발에 활용되었다. 뿐만 아니라, 이 연구는 휴대폰 사용 경험 연구에 적용할 수 있는 문맥적 조사기법 프로세스의 사례를 제시하고 있다는데 의의가 있다고 하겠다.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.117-124
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• 2014

Cluttered background is a major obstacle in developing salient object detection and tracking system for mobile device captured natural scene video frames. In this paper we propose a context aware feature vector selection model to provide an efficient noise filtering by machine learning based classifiers. Since the context awareness for feature selection is achieved by searching nearest neighborhoods, known as NP hard problem, we apply a fast approximation method with complexity analysis in details. Separability enhancement in feature vector space by adding the context aware feature subsets is studied rigorously using principal component analysis (PCA). Overall performance enhancement is quantified by the statistical measures in terms of the various machine learning models including MLP, SVM, Naïve Bayesian, CART. Summary of computational costs and performance enhancement is also presented.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.369-377
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• 2010

In vocabulary recognition systems based on HMM(Hidden Markov Model)s, training process unseen model bring on show a low recognition rate. If recognition vocabulary modify and make an addition then recreated modeling of executed database collected and training sequence on account of bring on additional expenses and take more time. This study suggest efficient context dependent process modeling method using decision tree-based state tying. On study suggest method is reduce recreated of model and it's offered that robustness and accuracy of context dependent acoustic modeling. Also reduce amount of model and offered training process unseen model as concerns context dependent a likely phoneme model has been used unseen model solve the matter. System performance as a result of represent vocabulary dependence recognition rate of 98.01%, vocabulary independence recognition rate of 97.38%.