• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.712-717
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• 2008

In swarm robot systems, each robot must act by itself according to the its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, reinforcement learning method with SVM based on structural risk minimization and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. By distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning that basis of SVM is adopted in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.312-317
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• 1992

This paper proposes a new framework of an autonomous and distributed flexible manufacturing system - Multi Client Robot Groups(MCR) - and describes a stochastic learning scheme applied to managerial problems of the system. The MCR is composed of groups of manufacturing robots, named Client Robots (CRs), which are capable of both versatility and independence in their performances. The MCR is expected to have high performance because the MCR can perform concurrent and corporative processing. However, the system performance is determined by the organizations of the CR groups. Therefore the treatment of the managerial problems and organizations of the system are important problems. In this paper, it is assumed that CR groups being able to processing tasks are selected stochastically based on the strengths of the robot groups. The learning scheme adjusting the strength is introduced to organize the groups in the system and control the each performance of the groups according to the total system performance. Finally, some experimental results of the learning scheme are shown.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.3
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• pp.1-8
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• 1998

In this paper, we propose a Neural Call Admission Control (CAC) method using a Sparse Distributed Memory(SDM). CAC is a key technology of TM network traffic control. It should be adaptable to the rapid and various changes of the ATM network environment. conventional approach to the ATM CAC requires network analysis in all cases. So, the optimal implementation is said to be very difficult. Therefore, neural approach have recently been employed. However, it does not mett the adaptability requirements. because it requires additional learning data tables and learning phase during CAC operation. We have proposed a neural network CAC method based on SDM that is more actural than conventioal approach to apply it to CAC. We compared it with previous neural network CAC method. It provides CAC with good adaptability to manage changes. Experimenatal results show that it has rapid adaptability and stability without additional learning table or learning phase.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.139-148
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• 2022

In this paper, we implement a distributed FMCW MIMO radar system to obtain Micro Doppler signatures of target motions. In addition, we also develop federated learning based motion recognition algorithm based on the Micro-Doppler radar signature collected by the implemented FMCW MIMO radar system. Through the experiment, we have verified that the proposed federated learning based algorithm can improve the motion recognition accuracy up to 90%.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1107-1116
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• 2016

Recent astronomical survey observations have produced substantial amounts of data as well as completely changed conventional methods of analyzing astronomical data. Both classical statistical inference and modern machine learning methods have been used in every step of data analysis that range from data calibration to inferences of physical models. We are seeing the growing popularity of using machine learning methods in classical problems of astronomical data analysis due to low-cost data acquisition using cheap large-scale detectors and fast computer networks that enable us to share large volumes of data. It is common to consider the effects of inhomogeneous spatial and temporal coverage in the analysis of big astronomical data. The growing size of the data requires us to use parallel distributed computing environments as well as machine learning algorithms. Distributed data analysis systems have not been adopted widely for the general analysis of massive astronomical data. Gathering adequate training data is expensive in observation and learning data are generally collected from multiple data sources in astronomy; therefore, semi-supervised and ensemble machine learning methods will become important for the analysis of big astronomical data.

• Journal of the Korean School Mathematics Society
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• v.9 no.3
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• pp.265-286
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• 2006

Given that less attention has been paid to teachers than students in mathematics education, this study attempted to provide theoretical foundations to understand better mathematics teacher learning and teaching practice in transition. First, this paper summarized three conceptions of teacher learning on the basis of the relationships of knowledge and practice followed by several implications to mathematics teacher education. Second, this paper provided a brief overview of cognition as situated, social, and distributed. This paper then explored new implications and issues about mathematics teacher learning that the overview brought to light. It is expected for teacher educators and researchers to participate in rich discussion of many implicit issues about teacher learning that this paper begins to raise.

• Journal of the Korean Society of Earth Science Education
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• v.13 no.1
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• pp.110-120
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• 2020

It is generally reported that field learning and a class using regional materials motivate learning and give a positive effect on learning of geology unit, science subject. The purpose of this study is to develop and to suggest regional materials for learning of geology unit, science subject at Jaeundo. The results of this study are as follows. Regional materials were developed at three locations (namjin dockyard area, yangsan beach area and dunjang beach area) of the study area. Namjin dockyard area (A site) is composed of terrain of sea cliff, sand beach and mud flat. Sedimentary rocks, weathering phenomenon of rocks and strata of various shape are distributed in sea cliff of A site. Yangsan beach area (B site) is composed of coastal terrain as sea cliff and sand beach about 1.5km long. Sedimentary rocks and rhyolite are distributed in sea cliff of B site. Tafoni formed by weathering process of rocks are developed on sedimentary rock outcrop of B site. Dunjang beach area (C site) is composed of coastal terrain of sea cliff, sand beach about 2km long and sea stack. Stratified sedimentary rocks are distributed in sea cliff of C site. Sea stack located in near halmi island on the west side of dunjang beach area is a good sample showing erosion process of sea cave for a long time. Unique geomorphology and geology phenomena distributed in 3 sites at Jaeundo can be used as regional materials for learning of geology unit, science subject. And, Regional materials shall be used in conjunction with the text book data of geology units. These 3 sites of the study area are worth using as field learning course for elementary and middle school students.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.24-30
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• 2022

Distributed fiber-optic acoustic·vibration sensing technology is becoming increasingly popular in many industrial and academic areas such as in securing large edifices, exploring underground seismic activity, monitoring oil well/reservoir, etc. Long-range perimeter intrusion detection exemplifies an application that not only detects intrusion, but also pinpoints where it happens and recognizes kinds of threats made along the perimeter where a single fiber cable was installed. In this study, we developed a distributed fiber-optic sensing device that measures a distributed acoustic·vibration signature (pattern) for intrusion detection. In addition, we demontrate the proposed deep learning algorithm and how it classifies various intrusion events. We evaluated the sensing device and deep learning algorithm in a practical testbed setup. The evaluation results confirm that the developed system is a promising intrusion detection system for long-distance and seamless recognition requirements.

• Journal of The Korean Association of Information Education
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• v.9 no.1
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• pp.79-86
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• 2005

With the rapid advance of computer and communication technology, the amount of data transferred is also increasing more than ever. The recent trend of education systems is connecting related information semantically in different systems in order to improve the utilization of computerized information Therefore, Web-based teaching-learning is developing in the ubiquitous learning direction that learners select and organize the contents, time and order of learning by themselves. That is, it is evolving to provide teaching-learning environment adaptive to individual learners' characteristics (their level of knowledge, pattern of study, areas of interest). This paper proposes the efficient evaluation method of learning contents in a mediator for the integration of heterogeneous information resources. This means that the autonomy of a remote server can be preserved to the highest degree. In addition, this paper proposes the adaptive optimization of learning contents such that available storage in a mediator can be highly utilized at any time. In order to differentiate the recent usage of a learning content from the past, the accumulated usage frequency of a learning content decays as time goes by.