• Journal of the Ergonomics Society of Korea
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• v.23 no.3
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• pp.73-88
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• 2004

This study was contrived Hierarchical Information Search (HIS) model. HIS model is based on a “cognitive process” in which model, comprising basic human information processing mechanize and information interaction. Its process include 3 semantic cognitive processes: Schema-Association LTM, Form Domain, and Alternative Selection. Design methodology consists to elicitate memory, thinking and cognitive response variables. In case study, menu structure of mobile phone was applied. In result, a correlation between predictive error rate and real error rate was .892. and a correlation between selective and real reaction time was .697. This present to suggest a model of how the methodology could be applied to real system design effectively when this was used. HIS model could become one of the most important factors for success of product design. In the perspective, the systemic methodology would contribute to design a quantitative and predictive system.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.159-162
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• 2017

Long Short-Term Memory Recurrent Neural Network(LSTM RNN)는 순차 데이터 모델링에 적합한 딥러닝 모델이다. Bidirectional LSTM RNN(BiLSTM RNN)은 RNN의 그래디언트 소멸 문제(vanishing gradient problem)를 해결한 LSTM RNN을 입력 데이터의 양 방향에 적용시킨 것으로 입력 열의 모든 정보를 볼 수 있는 장점이 있어 자연어처리를 비롯한 다양한 분야에서 많이 사용되고 있다. Highway Network는 비선형 변환을 거치지 않은 입력 정보를 히든레이어에서 직접 사용할 수 있게 LSTM 유닛에 게이트를 추가한 딥러닝 모델이다. 본 논문에서는 Highway Network를 한국어 의미역 결정에 적용하여 기존 연구 보다 더 높은 성능을 얻을 수 있음을 보인다.

• Korean Journal of Biological Psychiatry
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• v.23 no.3
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• pp.89-101
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• 2016

Transcranial magnetic stimulation (TMS) is a safe, noninvasive and useful technique for exploring brain function. Especially, for the study of cognition, the technique can modulate a cognitive performance if the targeted area is engaged, because TMS has an effect on cortical network. The effect of TMS can vary depending on the frequency, intensity, and timing of stimulation. In this paper, we review the studies with TMS targeting various regions for evaluation of cognitive function. Cognitive functions, such as attention, working memory, semantic decision, discrimination and social cognition can be improved or deteriorated according to TMS stimulation protocols. Furthermore, potential therapeutic applications of TMS, including therapy in a variety of illness and research into cortical localization, are discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.33-39
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• 2010

The interaction of emotion is an important factor in Human-Robot Interaction(HRI). This requires a contextual appraisal of emotion extracting the emotional information according to the events happened from past to present. In this paper an emotion appraisal system based on the cognitive context is presented. Firstly, a conventional emotion appraisal model is simplified to model a contextual emotion appraisal which defines the types of emotion appraisal, the target of the emotion induced from analyzing emotional verbs, and the transition of emotions in the context. We employ a language based cognitive system and its sentential memory and object descriptor to define the type and target of emotion and to evaluate the emotion varying with the process of time with the a priori emotional evaluation of targets. In a experimentation, we simulate the proposed emotion appraisal system with a scenario and show the feasibility of the system to HRI.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.194-197
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• 2015

Long Short-term Memory Network(LSTM) 기반 Recurrent Neural Network(RNN)는 순차 데이터를 모델링 할 수 있는 딥 러닝 모델이다. 기존 RNN의 그래디언트 소멸 문제(vanishing gradient problem)를 해결한 LSTM RNN은 멀리 떨어져 있는 이전의 입력 정보를 볼 수 있다는 장점이 있어 음성 인식 및 필기체 인식 등의 분야에서 좋은 성능을 보이고 있다. 또한 LSTM RNN 모델에 의존성(전이 확률)을 추가한 LSTM CRF모델이 자연어처리의 한 분야인 개체명 인식에서 우수한 성능을 보이고 있다. 본 논문에서는 한국어 문장의 지배소가 문장 후위에 나타나는 점에 착안하여 Backward 방식의 LSTM CRF 모델을 제안하고 이를 한국어 의미역 결정에 적용하여 기존 연구보다 더 높은 성능을 얻을 수 있음을 보인다.

• Smart Media Journal
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• v.5 no.2
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• pp.24-32
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• 2016

Recently, the development of electronic teaching materials and the demand of digital learners have led the needs on the education contents that replace learning from character information and the change of an information design method for this. Chinese character education in the traditional schooling mainly focuses on writing and memorization (semantic memory). This way that the stories do not exist has brought the learners' recognition that Chinese character is difficult to learn. Meanwhile, for a language study such as English, cross-media development between printed materials and audio-visual materials has been actively introduced. The method that extends episode memories along with memorization through a story is widely used. Therefore, this content suggests a prototype, which is broken away from an existing way of learning Chinese character that mainly focuses on writing, one sided instruction and information cramming. This makes learners learn through a story from printed materials and animation. Furthermore, it suggests a method that extends episode memories through Chinese education contents based on IoT explaining the principle of Chinese character by combining IT technology (information and communications, IoT) and education contents on block toys.

• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.1-9
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• 1996

A key issue in spoken language processing has become the integration of speech understanding and natural language processing(NLP). This paper presents a parallel computational model for the integration of speech and NLP. The model adopts a hierarchically-structured knowledge base and memory-based parsing techniques. Processing is carried out by passing multiple markers in parallel through the knowledge base. Speech-specific problems such as insertion, deletion, and substitution have been analyzed and their parallel solutions are provided. The complete system has been implemented on the Semantic Network Array Processor(SNAP) and is operational. Results show an 80% sentence recognition rate for the Air Traffic Control domain. Moreover, a 15-fold speed-up can be obtained over an identical sequential implementation with an increasing speed advantage as the size of the knowledge base grows.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.155-168
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• 2008

This paper proposes a relational security model-based RDF Web ontology access control model. The Semantic Web is recognized as a next generation Web and RDF is a Web ontology description language to realize the Semantic Web. Much effort has been on the RDF and most research has been focused on the editor, storage, and inference engine. However, little attention has been given to the security issue, which is one of the most important requirements for information systems. Even though several researches on the RDF ontology security have been proposed, they have overhead to load all relevant data to memory and neglect the situation that most ontology storages are being developed based on relational database. This paper proposes a novel RDF Web ontology security model based on relational database to resolve the issues. The proposed security model provides high practicality and usability, and also we can easily make it stable owing to the stability of the relational database security model.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.7-13
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• 2006

The advent of Semantic Web has generated several methods that can access the data on the web. Thus, it is necessary to handle the data by accessing the current web ontology as well as the existing knowledge base system. Web ontology languages are RDF(Resource Description Framework), DAML-OIL, OWL(Web Ontology Language), and so on. This paper presents the creation and the method of the ontology object model that can access, represent, and process the web ontology and the existing knowledge base. Unlike the existing access approach of web ontology using the model on memory constructed by each parser, we divide the model of web ontology into three layers such as frame-based ontology layer, generic ontology layer, and functional ontology layer. Generic ontology layer represents the common vocabulary among several domains and functional ontology layer contains the dependent vocabulary to each ontology respectively. Our model gets rid of the redundancy of the representation and enhances the reusability. Moreover, it can provide the easy representation of knowledge and the fast access of the model in the application.

• Journal of KIISE
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• v.43 no.1
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• pp.27-33
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• 2016

Virtualization has become one of the most helpful techniques in computing systems, and today it is prevalent in several computing environments including desktops, data-centers, and enterprises. However, since I/O layers are implemented to be oblivious to the I/O behaviors on virtual machines (VM), there still exists an I/O scalability issue in virtualized systems. In particular, when a multi-queue solid state drive (SSD) is used as a secondary storage, each system reveals a semantic gap that degrades the overall performance of the VM. This is due to two key problems, accelerated lock contentions and the I/O parallelism issue. In this paper, we propose a novel approach, including the design of virtual CPU (vCPU)-dedicated queues and I/O threads, which efficiently distributes the lock contentions and addresses the parallelism issue of Virtio-blk-data-plane in virtualized environments. Our approach is based on the above principle, which allocates a dedicated queue and an I/O thread for each vCPU to reduce the semantic gap. Our experimental results with various I/O traces clearly show that our design improves the I/O operations per second (IOPS) in virtualized environments by up to 155% over existing QEMU-based systems.