• Korean Journal of Cognitive Science
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• v.21 no.4
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• pp.459-480
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• 2010

A dialogue system includes various morphological analyses for recognizing a user's intention from the user's utterances. However, a user can represent various intentions via emotional states in addition to morphological expressions. Thus, a user's emotion recognition can analyze a user's intention in various manners. This paper presents a new method to automatically recognize a user's emotion for a dialogue system. For general emotions, we define nine categories using a psychological approach. For an optimal feature set, we organize a combination of sentential, a priori, and context features. Then, we employ a support vector machine (SVM) that has been widely used in various learning tasks to automatically classify a user's emotions. The experiment results show that our method has a 62.8% F-measure, 15% higher than the reference system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.81-93
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• 2006

A file system is an evidence resource of cyber crime in computer forensics. Therefore the methods of recovering the file system and searching important information have been offered. However, the methods for finding a malicious fie in free blocks or slack spaces have not been suggested. In this paper, we propose an investigation method to find a maliciously executable fragmented file. After estimating if a file is executable with a machine code rate, we conclude it could be malicious by comparing a similarity of instruction sequences. To examine instruction sequences, we also propose a method of profiling malicious files using file and a method of comparing the continued scores. As the results, we could exactly pick out the malicious execution files, such as buffer overflow attack program, at fitting threshold level.

• Journal of agricultural medicine and community health
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• v.28 no.2
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• pp.61-70
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• 2003

Objectives: Traffic accidents often occur to power tillers without reflective light in the dawn, evening and night. Because of this reason, there has been a 'campaign to attach reflective lights' to power-tillers in recent years. Therefore, the authors investigated the relationship between reflective light and traffic accidents involving power-tillers. Methods: We defined traffic accidents of power tillers as those cases of rear-end collision by a car in the dawn, evening or night. According to our definition, four cases were confirmed in Hyungok-myeon, Gyeongju and five cases in Gigye-myeon, Pohang. We selected a control group from people in the same village with similar age, sex, driving history and education. Results: The study group contained 9 accidents and 36 non-accidents. Power tillers with reflective light were 32 cases (72.7%) of 44 cases (excluded one case due to death). Of those, the status of reflective light was 'clean' in 18 cases (56.3%). The recognition that reflective light can prevent accidents was 'Yes' in 26 cases of 44 cases (59.1%). The recognition of the 'campaign to attach reflective lights' to power tillers was 'Yes' in 38 cases of 44 cases (86.4%). The recognition about the safety regulation of driving power-tillers was 'Yes' in 32 cases of 44 cases (72.7%). Odds ratio of traffic accidents for no reflective light was 7.00 (95% CI: 1.06-58.37). Conclusions: Although the 'campaign to attach reflective lights' to power tillers are going on, its effectiveness may unknown. Therefore, more extensive epidemiologic study is needed into the relationship between reflective light and power tiller traffic accidents, with effective administration of the government and the attention of medical persons.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• Journal of the Korean Geographical Society
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• v.44 no.3
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• pp.301-322
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• 2009

This paper is an attempt to explore semiotic representations of subway space as the urban machine space of local mobility in terms of space, time and place. For this, the second section of the paper reviews the contours of the urban space of mobility in terms of 'machine space', 'non-place' and 'cognitive map'. The third section interprets the sings of 'spatial' and 'temporal' representations of subway space in Daegu, and suggests the implications of the semiotic representations. It is uncovered that various sign-scapes which coexist in the subway space in coordinated or contradictory ways product the space into multiple and complex techno-social spaces. That is, the spatio-temporal representations of the subway space form the space of 'non-place' on the one hand and the space of 'place' on the other hand, and involve the spatialization of 'memory' on the one hand and the spatialization of 'forgetting' on the other hand. Thus, the subway space should be regarded to be not only the space of 'mobility' which people move in and through, but also the space of 'identity' which has effects on the ways for them to see the machine space and its urban space.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.479-488
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• 2022

In recent years, with the rapid development of machine learning technology, research utilizing machine learning has been actively conducted in fields such as cognition, reasoning and judgment, and action among various technologies constituting intelligent systems. In order to utilize this machine learning, it is indispensable to collect data for learning. However, the types of data generated vary according to the environment in which the data is generated, and the types and forms of data required are different depending on the learning model to be used for machine learning. Due to this, there is a problem that the existing data collection method cannot be reused in a new environment, and a specialized data collection module must be developed each time. In this paper, we propose a specification-based methology for data collection in artificial intelligence system to solve the above problems, ensure the reusability of the data collection method according to the data collection environment, and automate the implementation of the data collection function.

• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.323-336
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• 2006

In this paper, we present a pilot system that can extract paraphrases from a parallel corpus using to-training method. Paraphrases are useful for the applications that should rreate a varied ind fluent text, such as machine translation, question-answering system, and multidocument summarization system. One of the difficulties in extracting paraphrases is to find a rich source from which we can extract paraphrases. The bible is one of the good sources fur extracting paraphrases as it has several Korean versions in which every sentence can be easily aligned by the chapter and the verse. We ran extract not only the lexical-level paraphrases but also the phrasal-level paraphrases from the parallel corpus which consists of the bibles using co-training method.

• Information and Communications Magazine
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• v.26 no.1
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• pp.25-30
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• 2009

IT산업은 '97년 외환위기를 기점으로 고성장을 거듭하였고, 세계 10위권 규모의 경제 강국으로 성장하는데 있어 국내 산업의 대표적인 성장동력으로서 자리 매김하였다. 하지만, 최근 성장률이 예전만 못하다는 우려의 목소리와 함께 IT가 한 단계 더 업그레이드하기 위해서는 자신의 허물을 과감히 벗는 용기가 필요하다는 것이 일반적인 시각이다. 이러한 흐름속에서 기존 IT전략과 차별화된 New IT 발전방안을 수립하게 되었다. 지금까지의 IT전략이 IT 고도화 추진을 위한 IT 중심 발전 전략이었다면, New IT전략은 IT기반 융합산업, IT융합 신산업, Next IT산업 등3대 성장축간 시너지 창출이 가능한 IT로의 수렴과 확산이 핵심이라고 할 수 있다. IT기반 융합산업의 최종 목표는 전통산업과 IT가 만나 자동차, 조선, 의료, 국방, 건설, 섬유, 기계항공 등 7개 주력기간산업의 고부가가치화 및 초일류화를 우선 실현하는 것이다. IT융합 신산업의 최종 목표는 나노(NT), 바이오(BT), 인지기술(CT) 등 비(非)IT와 교감을 통하여 IT가 에너지, 환경, 건강 등 사회적 문제를 해결하고, 녹색성장 추진의 핵심인 5대 신산업을 창출하는데 있다. 5대 신산업으로는 Green IT산업, Welfare-Infra산업, 감성조명산업, 인지단말산업, THz응용산업 등을 꼽을 수 있다. Next IT산업은 기존 14대 IT 분야를 'ETRI 비전 2020' 등 미래 청사진을 바탕으로 장기적 관점에서 재설계한 것이며, 최종 목표는 TDX, 4M D램, CDMA, 와이브로, DMB, NoLA 등 IT 강국 계보를 이어갈 4G, 미래인터넷, Smart Radio, 실감미디어, 웹3.0, 투명전자소자 등 미래 유망 핵심원천기술을 확보하고, IT 경쟁력을 강화하는 것이다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.210-213
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• 2011

기억 인출 단서는 학습을 통해 장기기억 공간에 저장된 정보를 인출하는 과정에서 중요하며, 서로 다른 종류의 기억 인출 단서에 따른 기억 인출 결과 및 이에 대한 인지 학습적 특성 규명은 교육, 범죄 수사, 그리고 인간의 뇌 기능을 모방한 기계학습 연구 등에서 중요하게 다루어져야 할 문제이다. 본 논문에서는 비디오 데이터를 이용하여 학습한 내용을 인출하는 과정에서 텍스트와 이미지가 각각 인출 단서로서 기억인출 결과에 미치는 영향을 분석하고, 기억 정보 및 시각 정보 처리와 관련된 뇌 영역에서의 뇌전도 분석을 이용하여 이를 해석하였다. 실험 결과를 통해 기억 인출을 위해 이미지-텍스트를 제시할 경우 전전두엽의 기억인출 관련 부위와 시각 피질이 위치한 후두엽의 인터랙션이 높게 이루어지면서 암묵적인 시각적기억 표상의 인출이 발생하는 것을 알 수 있었다.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.83-90
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• 2018

Recently, many people have suffered from insomnia, labor loss, cognitive decline, and mental illness. The solution to this problem is almost entirely cognitive therapy or medication, but it is not recommended in the long term due to side effects and dependency problems. Therefore, in this paper, we propose a neuro feedback system based on portable EEG that helps induce sleeping. We design and evaluate the EEG classifier, which is the most important function to implement the system, and propose an optimized classifier modeling method for various factors that can affect performance. When using the proposed classifier, we could distinguish 97.9% of awakening and sleep phase in portable EEG.