• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.177-183
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• 2022

Recently, interesting of home training is getting bigger due to COVID-19. Accordingly, research on applying HAR(human activity recognition) technology to home training has been conducted. However, existing paper of HAR proposed static activity instead of dynamic activity. In this paper, the deep learning model where dynamic exercise posture can be analyzed and the accuracy of the user's exercise posture can be shown is proposed. Fitness images of AI-hub are analyzed by blaze pose. The experiment is compared with three types of deep learning model: RNN(recurrent neural network), LSTM(long short-term memory), CNN(convolution neural network). In simulation results, it was shown that the f1-score of RNN, LSTM and CNN is 0.49, 0.87 and 0.98, respectively. It was confirmed that CNN is more suitable for human activity recognition than other models from simulation results. More exercise postures can be analyzed using a variety learning data.

• The Journal of Information Technology
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• v.12 no.3
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• pp.1-6
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• 2009

This paper has focused on the development of an educational sensor network (ESN) based on wireless sensor networks(WSN) and pervasive monitoring systems for students' activity during scientific experiments. A number of WSN systems have been proposed with integrated wireless transmission, mounted sensor boards and local processing. However, there is no trail to employ WSN on the educational field. In this paper, to facilitate research and development using wireless sensor network and multi-sensor data fusion, the educational sensor network (ESN) hardware development platform is presented. The ESN project is conducted over one semester time period (Spring Semesters). It involves approximately twenty middle school students who enrolled a gifted program in Kongju National University. Though under prepared, these students are in general highly motivated to learning specially when presented with the ESN project. An ESN project such as this is expected to provide an excellent means for teaching and learning scientific and mathematical principles.

• Korean Journal of Child Studies
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• v.27 no.4
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• pp.165-184
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• 2006

This ethnography explored the construction of a social network and its function in a classroom of toddlers in a day-care center located in Vermont. The classroom activities of 9 two-year-old toddlers were observed for about two months, compiled and categorized. Then, the themes of psychological functions were reconstructed in data analysis. Results showed that toddlers constructed multiple relations with all peers beyond the dyadic. They also transmitted information to teachers as well as peers in indirect ways. These direct-multiple interactions as well as indirect interactions reflect the social network of Lewis' (2005) theory. In the construction of social networks, the toddlers developed communication skills, turn-taking skills, leadership, and imitation.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.147-154
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• 2023

Network intrusion refers to any unauthorized penetration or activity on a computer network. This upsets the confidentiality, integrity, and availability of the network system. One of the major threats to any system's availability is a Denial-of-Service (DoS) attack, which is intended to deny a legitimate user access to resources. Therefore, due to the complexity of DoS attacks, it is increasingly important to abstract and describe these attacks in a way that will be effectively detected. The automaton theory is used in this paper to implement a SYN Flood detection system based on Time-Dependent Finite Automata (TDFA).

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.817-822
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• 2004

This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• Development and Reproduction
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• v.16 no.4
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• pp.265-270
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• 2012

Ethanol actions in the amygdala formation may underlie in part the reinforcing effects of ethanol consumption. Previously a physiological phenomenon in the basolateral amygdala (BLA) that is dependent on neuronal network activity, compound postsynaptic potentials (cPSPs) were characterized. Effects of acute ethanol application on the frequency of cPSPs were subsequently investigated. Whole cell patch clamp recordings were performed from identified projection neurons in a rat brain slice preparation containing the amygdala formation. Acute ethanol exposure had complex effects on cPSP frequency, with both increases and decreases dependent on concentration, duration of exposure and age of the animal. Ethanol produces complex biphasic effects on synaptically-driven network activity in the BLA. These findings may relate to subjective effects of ethanol on arousal and anxiolysis in humans.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.3 s.180
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• pp.94-101
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• 2006

A global path planning method using self-organizing feature map which is a method among a number of neural network is presented. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector On the other hand, the modified method in this research uses a predetermined initial weight vectors of 1-dimensional string and 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05b
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• pp.725-730
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• 1996

In this work, the software behavior under memory faults in operation phase is modeled and simulated using the stochastic activity network, generalized stochastic Petri nets. This networks permit the representation of concurrency, timeliness, fault tolerance, and degradable performance of system and provide a means for determining the stochastic behavior of a complex system. We estimate the reliability of an application software in the digitized system in nuclear power plants and show the sensitivity of the software reliability to the major physical parameters which affect the software failure in normal operation phase. We found that the effects of the hardware faults on the software failure should be considered for predicting the software dependability accurately in operation phase.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1009-1021
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• 1999

This paper describes the design principles and structure of a prototype system for collaborative authoring over a wide area network. The system includes extensive support for commenting and comment review, facilities for document space navigation, and tools for controlling and monitoring work group activity, including document locking and activity recording. The operational prototype provides a testbed for the examination of human computer interaction, group interaction, group support, document structures, and the problem and the history of efforts to address it.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.4
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• pp.69-76
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• 2008

The self-organizing feature map(SOFM) among a number of neural network uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 1-dimensional string, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the method using string and the modified neural network is useful tool to mobile robot for the global path planning.