• 공학교육연구
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• 제11권4호
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• pp.109-114
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• 2008

본 연구는 팀 중심의 과제 수행이 요구되는 종합설계과목의 협동학습에서 스스로에게 영향력을 행사함으로써 자율성과 책임감을 강조하는 셀프 리더십이 팀웍과 어떤 관계를 갖고 있는지를 조사하기 위해 수행되었다. 그 결과, 셀프 리더십은 팀웍과 유의미한 상관관계를 나타냈고, 셀프 리더십이 높은 학생들의 팀웍이 셀프 리더십이 낮은 학생들의 팀웍보다 더 높았다. 또한 셀프 리더십의 여러 전략들 중에서 특히 인지적 전략(자연보상을 주는 활동을 일에 도입하기, 일의 좋은 면에 초점 맞추기, 자연보상의 분별)이 팀웍에 가장 큰 영향을 미치는 요인으로 확인되었다. 이러한 결과를 종합해볼 때, 공학교육과정에 셀프 리더십의 인지적 전략들을 학습할 수 있는 프로그램을 도입하는 것이 도움이 될 것으로 판단된다.

• Journal of Information Processing Systems
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• 제17권5호
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• pp.867-878
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• 2021

In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multi-channel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multi-channel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

• 대한인간공학회지
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• 제27권2호
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• pp.59-65
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• 2008

Product usability consists of many attributes such as learnability, efficiency, memorability, and so on. In particular, learnability is one of the most important attributes in product usability. Therefore, many people consider the primary criterion for a good user interface to be the degree to which it is easy to learn. Learnability represents the degree of how much can easily learn the usage of a product. It concerns the features of the interactive system that allow novice users to understand how to use it initially and then how to attain a maximal level of performance. In this study, we studied on the application of learning curve to evaluate product learnability. In order to validate the applicability, we carried out simple experiment using mobile phone. We got task completion times through the experiment and predicted the times using learning curve model. And then, we compared prediction times to task completion times. Finally, we identified that learning curve could apply to predict and compare product learnability.

• 영어어문교육
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• 제11권4호
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• pp.335-363
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• 2005

One of the most important features in the 7th National Curriculum of English is the introduction of the middle school multi-purpose English classes. Despite the importance of the classes, there doesn't seem to be enough studies of developing learning materials for them. The purpose of the current study is, therefore, to develop English learning materials for the multi-purpose English classes based on the Task-Based Learning framework. To do so, various tasks were collected and adapted for the classes, and different teaching techniques suitable for the tasks were designed. It is hoped that this research will help teachers prepare for teaching materials for the classes, and students recognize their interests in English and to improve their English abilities.

• 한국음향학회지
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• 제31권2호
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• pp.87-92
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• 2012

In this paper, a robust feature compensation method to deal with the environmental mismatch is proposed. The proposed method applies energy based weights according to the degree of speech presence to the Mean subtraction, Variance normalization, and ARMA filtering (MVA) processing. The weights are further smoothed by the moving average and maximum filters. The proposed feature compensation algorithm is evaluated on AURORA 2 task and distant talking experiment using the robot platform, and we obtain error rate reduction of 14.4 % and 44.9 % by using the proposed algorithm comparing with MVA processing on AURORA 2 task and distant talking experiment, respectively.

• 대한인간공학회지
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• 제26권4호
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• pp.17-24
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• 2007

Analyses of eye tracking and think-aloud protocol data were performed to examine novice-expert differences in perceptual and cognitive aspects of air traffic controllers' situation awareness. In Experiment 1, three groups of field air traffic controllers (experts, intermediates, novices) were asked to perceive situations that were manipulated by situation complexity. In Experiment 2, protocol analysis for previous situation awareness tasks was performed to extract different task models and strategy models as a function of expertise. Then delayed-recall task and interviews about air control plans for the recalled situations were also executed. Results showed that expert controllers concentrate only on several critical features and have their own strategies to reduce mental workloads.

• 로봇학회논문지
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• 제3권2호
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• pp.106-111
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• 2008

This paper presents a decentralized coordination for a small-scale mobile robot teams performing a task through cooperation. Robot teams are required to generate and maintain various geometric patterns adapting to an environment and/or a task in many cooperative applications. In particular, all robots must continue to strive toward achieving the team's mission even if some members fail to perform their role. Toward this end, given the number of robots in a team, an effective coordination is investigated for decentralized formation control strategies. Specifically, all members are required first to reach agreement on their coordinate system and have an identifier (ID) for role assignment in a self-organizing way. Then, employing IDs on individual robots within a common coordinate system, a decentralized neighbor-referenced formation control is realized to generate, keep, and switch between different geometric shapes. This approach is verified using an in-house simulator and physical mobile robots. We detail and evaluate the formation control approach, whose common features include self-organization, robustness, and flexibility.

• 정보과학회 논문지
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• 제43권8호
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• pp.878-882
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• 2016

이미지의 내용을 설명하는 캡션을 자동으로 생성하는 기술은 이미지 인식과 자연어처리 기술을 필요로 하는 매우 어려운 기술이지만, 유아 교육이나 이미지 검색, 맹인들을 위한 네비게이션 등에 사용될 수 있는 중요한 기술이다. 본 논문에서는 이미지 캡션 생성을 위해 Convolutional Neural Network(CNN)으로 인코딩된 이미지 정보를 입력으로 갖는 이미지 캡션 생성에 최적화된 Recurrent Neural Network(RNN) 모델을 제안하고, 실험을 통해 본 논문에서 제안한 모델이 Flickr 8K와 Flickr 30K, MS COCO 데이터 셋에서 기존의 연구들보다 높은 성능을 얻음을 보인다.

• ETRI Journal
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• 제40권5호
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• pp.643-652
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• 2018

Dysarthria is a degenerative disorder of the central nervous system that affects the control of articulation and pitch; therefore, it affects the uniqueness of sound produced by the speaker. Hence, dysarthric speaker recognition is a challenging task. In this paper, a feature-extraction method based on deep belief networks is presented for the task of identifying a speaker suffering from dysarthria. The effectiveness of the proposed method is demonstrated and compared with well-known Mel-frequency cepstral coefficient features. For classification purposes, the use of a multi-layer perceptron neural network is proposed with two structures. Our evaluations using the universal access speech database produced promising results and outperformed other baseline methods. In addition, speaker identification under both text-dependent and text-independent conditions are explored. The highest accuracy achieved using the proposed system is 97.3%.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.20-25
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• 1998

This paper proposes a new compliant contact control strategy for the robot manipulators accidentally interacting with an unknown environment. The main features of the proposed method are summarized as follows: First, each entry in the diagonal stiffness matrix corresponding to the task coordinate in Cartesian space is adaptively adjusted during con-tact along the corresponding axis based on the contact force with its environment. Second, it can be used for both unconstrained and constrained motions without any switching mechanism which often causes undesirable instability and/or vibrational motion of the end effector. Third, the adjusted stiffness gains are automatically recovered to initially specified stiffness gains when the task is changed from constrained motion to unconstrained motion. The simulation results show the effectiveness of the proposed method by employing a two-link direct drive manipulator interacting with an unknown environment.