• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.180-188
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• 1995

A modified Kohonen's self-organizing feature map (SOFM) algorithm which has binary reinforcement function and a constant learning rate is proposed. In contrast to the time-varing adaptaion gain of the original Kohonen's SOFM algorithm, the proposed algorithm uses a constant adaptation gain, and adds a binary reinforcement function in order to compensate for the lowered learning ability of SOFM due to the constant learning rate. Since the proposed algorithm does not have the complicated multiplication, it's digital hardware implementation is much easier than that of the original SOFM.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1105-1108
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• 2003

Adaptation in dynamic environments gains a significant advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot navigations. We support our method, based on (1+1) evolutionary strategy, which produces changes during the lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on evolutionary neural controller for physical robots behaviors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1356-1362
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• 2015

Modular snake-like robots are robust for failure and have flexible locomotions for obstacle environment than of walking robot. This requires an adaptation capability which is obtained from a learning approach, but has not been analysed as well. In order to investigate the property of adaptation of locomotion for different terrains, NEAT controllers are trained for a flat terrain and tested for obstacle terrains. The input and output characteristics of the adaptation for the neural network controller are analyzed for different terrains in frequency domain.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.699-701
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• 2004

This paper proposes the algorithm of servo- writing based on feedback error learning neural networks. The controller consists of feedback controller using PID and feedforward controller using gaussian radial basis function network. Because the RBFNs are trained by on-line rule, the controller has adaptation capability. The performance of the proposed controller is compared to that of conventional PID controller. Proposed algorithm shows better performance than PID controller.

• ETRI Journal
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• v.35 no.6
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• pp.1001-1010
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• 2013

A sensible media simulation system for automobiles is introduced to open up new possibilities for an in-car entertainment system. In this paper, the system architecture is presented, which includes a virtuality-to-reality adaptation scheme. Standard data schemes for context and control information from the International Standard MPEG-V (ISO/IEC 23005) are introduced to explain the details of data formats, which are interchangeable in the system. A sensible media simulator and the implementation of a sensory device are presented to prove the effectiveness of the proposed system. Finally, a correlation between learning styles and sensory effects (that is, wind and vibration effects) is statistically analyzed using the proposed system. The experiment results show that the level of satisfaction with the sensory effects is unaffected overall by the learning styles of the test subjects. Stimulations by vibration effects, however, generate more satisfaction in people with a high tactile perception level or a low visual perception level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2590-2606
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• 2017

Generally, research of classical image classification algorithms assume that training data and testing data are derived from the same domain with the same distribution. Unfortunately, in practical applications, this assumption is rarely met. Aiming at the problem, a domain adaption image classification approach based on multi-sparse representation is proposed in this paper. The existences of intermediate domains are hypothesized between the source and target domains. And each intermediate subspace is modeled through online dictionary learning with target data updating. On the one hand, the reconstruction error of the target data is guaranteed, on the other, the transition from the source domain to the target domain is as smooth as possible. An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition. Experimental results verify the effectiveness of the proposed algorithm.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.291-300
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• 2023

The purpose of this study is to analyze the effect of teachers' motivation for choosing teaching on the relationship with career adaptability through self-directed learning in 271 pre-service early childhood teachers. As a result of the study, first, career adaptability, self-directed learning, and motivation for choosing teaching were high in order. Second, there was a positive correlation that the higher the motivation for choosing a teaching profession, the higher the self-directed learning and career adaptation, and the higher the self-directed learning, the higher the career adaptation. Third, it was found that self-directed learning of pre-service early childhood teachers partially mediated teachers' motivation for choosing teaching and career adaptability. This study is significant in that it sought various perspectives in practicing high-quality early childhood teacher education program and provided basic data on teacher education program.

• Educational Technology International
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• v.12 no.2
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• pp.135-151
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• 2011

Recently, a wide variety of studies on future learning have appeared owing to rapid advances in information and communication technology (ICT) and increased discussion about core competencies in twenty-first-century learning. These studies, though insufficient in number, cover various fields such as architecture (design of the learning space), education (learning model), and technology (adaptation of mobile devices). However, these studies focus on mainstream students and do not discuss the future situation of inclusive education with regard to both mainstream and students with physical disabilities. Hence, in order to fill this gap, the present study explores the perceptions and ideas held by special education preservice teachers on the future learning space with regard to school design and peer-to-peer feedback. For this purpose, these preservice teachers' design proposals about future school were collected and analyzed. In conclusion, special education preservice teachers perceive the future learning space as an inclusive environment in which smart technology is incorporated. Future learning environment were categorized in terms of flexible, ubiquitous technology, physical and mental health, safety, and spaces with facilities for students with physical disabilities.

• 한국지구과학회:학술대회논문집
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• 2005.09a
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• pp.315-316
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• 2005

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.816-821
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• 2003

Researchers have utilized artificial evolution techniques and learning techniques for studying the interactions between learning and evolution. Adaptation in dynamic environments gains a significant advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot navigations. We support our method, based on (1+1) evolutionary strategy which produces changes during the lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on evolutionary neural controller for physical robots behaviors. We investigate the effects of learning in evolutionary process by comparing the performance of the proposed realtime evolutionary learning method with that of evolutionary method only. Also, we investigate an interactive evolutionary algorithm to overcome the difficulties in evaluating complicated tasks.