• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.815-821
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• 2005

Among the various artificial neural networks the backpropagation network (BPN) has become a standard one. One of the components in a neural network is an activating function or a transfer function of which a representative function is a sigmoid. We have discovered that by updating the slope parameter of a sigmoid function simultaneous with the weights could improve performance of a BPN.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.363-369
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• 2010

Demonstration-based learning has the advantage that a user can easily teach his/her robot new task knowledge just by demonstrating directly how to perform the task. However, many previous demonstration-based learning techniques used a kind of attribute-value vector model to represent their state spaces and policies. Due to the limitation of this model, they suffered from both low efficiency of the learning process and low reusability of the learned policy. In this paper, we present a new demonstration-based learning method, in which the relational model is adopted in place of the attribute-value model. Applying the relational instance-based learning to the training examples extracted from the records of the user demonstrations, the method derives a relational instance-based policy which can be easily utilized for other similar tasks in the same domain. A relational policy maps a context, represented as a pair of (state, goal), to a corresponding action to be executed. In this paper, we give a detail explanation of our demonstration-based relational policy learning method, and then analyze the effectiveness of our learning method through some experiments using a robot simulator.

• Journal of Engineering Education Research
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• v.26 no.6
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• pp.19-29
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• 2023

This study aims to develop and apply a metaverse-based instructional design model for the education in science and technology. It analyzed the concept and characteristics of metaverse, existing non-contact education models, and major teaching strategies systematically. Based on the prior researches, an instructional design model using metaverse is developed that presents metaverse-related teaching strategies and design principles for the before-, during-, and after-lesson phases. Then, this model was applied to a project-based learning program, conducted a perception survey on instructors and learners, and revised the metaverse instructional design model based on the results of the survey. In the Metaverse Instructional Design Model, before-lesson phase is a physical and psychological preparation stage for class participation, which includes familiarization with the Metaverse learning environment, formation of expectations for education, and self-directed pre-learning. During the lesson, to effectively deliver the lesson content, it is necessary to build confidence in the learning environment, promote learning participation, provide reference materials, perform team projects and provide feedback, digest learning content, and transfer learning content. The after-lesson phase provides strategies for ongoing interaction between learners and mentors. This study introduces a new instructional design model that utilizes metaverse and shows the potential of metaverse-based education in science and technology. It also has important implications in that it provides practical guidelines for the effective design and implementation of metaverse-based education.

• The Journal of Information Systems
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• v.22 no.1
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• pp.89-116
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• 2013

The influence factors on the development of information technology(IT) infrastructure for knowledge management(KM) were not empirically investigated and identified. This study empirically examines the relationships among strategic performance measurement systems(SPMS), organizational learning, the strategic alignment of business strategy with information systems(IS) strategy, which is the necessary condition to provide the kinds of knowledge required for the successful realization of business strategy, and the active construction of IT infrastructure for KM. This study demonstrates that SPMS directly affects the organizational learning, with which the members of an organization acquire the types of knowledge about strategic goals or objectives and the ways attaining these goals, and indirectly influences the IS strategic alignment through organizational learning. Thus, the alignment between business strategy and IS strategy can be facilitated and activated by the adoption and implementation of SPMS. According to the results of this study, it is observed that the IS strategic alignment and organizational learning positively affect the activation of the development of KM IT infrastructure in a firm. The results of this study also shows that the construction of KM IT infrastructure, which supports the realization of KM activities, such as knowledge creation, transfer and sharing, can enhance the strategic position of a firm, and the intensified competitiveness of a firm can lead to the improvement of performance.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.22-30
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• 2019

For effective human-robot interaction, robots need to understand the current situation context well, but also the robots need to transfer its understanding to the human participant in efficient way. The most convenient way to deliver robot's understanding to the human participant is that the robot expresses its understanding using voice and natural language. Recently, the artificial intelligence for video understanding and natural language process has been developed very rapidly especially based on deep learning. Thus, this paper proposes robot vision to audio description method using deep learning. The applied deep learning model is a pipeline of two deep learning models for generating natural language sentence from robot vision and generating voice from the generated natural language sentence. Also, we conduct the real robot experiment to show the effectiveness of our method in human-robot interaction.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.184-189
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• 2020

This paper is a study on data augmentation for small dataset by using deep learning. In case of training a deep learning model for recognition and classification of non-mainstream objects, there is a limit to obtaining a large amount of training data. Therefore, this paper proposes a data augmentation method using perspective transform and image synthesis. In addition, it is necessary to save the object area for all training data to detect the object area. Thus, we devised a way to augment the data and save object regions at the same time. To verify the performance of the augmented data using the proposed method, an experiment was conducted to compare classification accuracy with the augmented data by the traditional method, and transfer learning was used in model learning. As experimental results, the model trained using the proposed method showed higher accuracy than the model trained using the traditional method.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.8-25
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• 2017

This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.135-142
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• 2024

Cu-Cu bonding, one of the key technologies in advanced packaging, enhances semiconductor chip performance, miniaturization, and energy efficiency by facilitating rapid data transfer and low power consumption. However, the quality of the interface bonding can significantly impact overall bond quality, necessitating strategies to quickly detect and classify in-process defects. This study presents a methodology for detecting defects in wafer junction areas from Scanning Acoustic Microscopy images using a ResNet-50 based deep learning model. Additionally, the use of the defect map is proposed to rapidly inspect and categorize defects occurring during the Cu-Cu bonding process, thereby improving yield and productivity in semiconductor manufacturing.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.5
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• pp.394-403
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• 1991

This paper described the development of input driven multilayer perceptron(IDMLP) neural network and it's application to the Korean spoken digit recognition. The IDMPLP neural network used here and the learning algorithm for this network was proposed newly. In this model, weight value is integer and transfer function in the neuron is hard limit function. According to the result of the network learning for the some kinds of input data, the number of network layers is one or more by the difficulties of classifying the inputs. We tested the recognition of binaried data for the spoken digit 0 to 9 by means of the proposed network. The experimental results are 100% and 96% for the learning data and test data, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.319-323
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• 1994

In this paper, we discuss a design method of iterative learning control systems for parabolic linear distributed parameter systems(DPSs). First, we discuss some aspects of boundary control of the DPS, and then propose to employ the Karhunen-Loeve procedure to reduce the infinite dimensional problem to a low-order finite dimensional problem. An iterative learning control(ILC) for non-square transfer function matrix is introduced finally for the reduced order system.