• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.573-584
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• 2016

Recent application range of sensor networks is becoming diverse. It means collected sensor data types are becoming diverse too. These sensor data have their own characteristics. Thus achieving energy efficiency, existing sensor network management policy consider their own characteristics. However, it is inefficient to apply the existing network management schemes for controlling such kind of data at the same time. Because, existing network management schemes considered one type of data only. Therefore, we propose a novel routing scheme that is able to efficient energy conservation through effective data controlling on multi-sensor application environment.

• Asia-Pacific Journal of Business
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• v.3 no.1
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• pp.29-38
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• 2012

This study aims at evaluating the impact of network formation variables and found to have positive impact on the economic performance and growth of the enterprises. The calucation of the weighted scores of networking statements brought some affirmative results to influence the performance of the enterprises. Through multiple regression and logistic regression models it is identified that network formation variables like service receiving status, consultation of the family, other business dummy and attendance in fair have some significant positive impact both on the growth and performance of the enterprises. In addition to above variables, from the set of enterprise characteristics natural logarithm of the market value of total assets and from the entrepreneurs' characteristics set of variables, schooling year and squared value of the experience have been found to have significant positive impact. Finally, it is concluded in the study that to enhance the performance and growth of the enterprises, government and policy rlated organizations need to consider important variables that have positive impact in supplying the entrepreneurial resources especially, developing the net-working relationship.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.119.3-119
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• 2001

When building a network-based real-time control system, a network-induced delay time should be surly considered for real time schedulability to be guaranteed. The network delay time on end-to-end communication has been analyzed theoretically and modeled mathematically from many previous works. There also exist any other delay element not considered before. In this paper, the remote feedback control system using the CAN protocol is proposed to control three axes´ manipulator arm and the application layer of CAN is modeled to analyze the delay elements defined by three types of time delay: Software delay time, Controller delay time, and Access delay time, in details. The analyzed results are used as an important component to determine PID gains of the proposed system. The effect of the delay time on the control performance is evaluated by com paring the response characteristics of the control system through simulation.

• KSTLE International Journal
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• v.3 no.1
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• pp.54-59
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• 2002

It can be effective on failure diagnosis of oil-lubricated tribological system to analyze operating conditions with morphological characteristics of wear debris in a lubricated machine. And it can be recognized that results are processed threshold images of wear debris. But it is needed to analyse and identify a morphology of wear debris in order to predict and estimate a operating condition of the lubricated machine. If the morphological characteristics of wear debris are identified by the computer image analysis and the neural network, it is possible to recognize the friction condition. In this study, wear debris in the lubricating oil are extracted from membrane filter (0.45 ${\mu}m$) and the quantitative value fur shape parameters of wear debris was calculated through the computer image processing. Four shape parameters were investigated and friction condition was recognized very well by the neural network.

• Journal of Welding and Joining
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• v.18 no.4
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• pp.48-54
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• 2000

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor information about weld characteristics and process parameters as well; as t modify those parameters to hold weld. The objectives of this paper are to realize the mapping characteristics of bead width through the neural network and multiple regression method as well as to select the most accurate model in order to control the weld quality(bead width0. The experimental results show that the proposed neural network estimator can predict bead width with reasonable accuracy, and guarantee the uniform weld quality.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.551-560
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• 1998

This paper presents a neural network model representing complex hydro-thermo-dynamic characteristics of a steam generator in nuclear power plants. The key modeling processes include training data gathering process, analysis of system dynamics and determining of the neural network structure, training process, and the final process for validation of the trained model. In this paper, we suggest a training data gathering method from an unstable steam generator so that the data sufficiently represent the dynamic characteristics of the plant over a wide operating range. In addition, we define the inputs and outputs of neural network model by analyzing the system dimension, relative degree, and inputs/outputs of the plant. Several types of neural networks are applied to the modeling and training process. The trained networks are verified by using a class of test data, and their performances are discussed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.6
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• pp.284-289
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• 2002

The transmission networks do not consist of perfect conductors and a percentage of the power generated is therefore lost before it reaches the loads. Since this network loss contributes to the cost of suppling power to consumers, it must be considered that the most efficient dispatch and location of generators and loads are to be achieved. In this paper, marginal loss factors are calculated for 12 load levels that represent the impact of marginal network losses on nodal prices at the transmission network connection points at which generators are located. Based on comparison analysis of marginal loss factors on 12 load levels, we found the MLF characteristics in KOREA.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1996.05a
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• pp.202-205
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• 1996

In this paper it is researched a relation between network structures and electrical properties - especially dielectric characteristics with changing of network structure. It is resulted that the specimens which have single network structures have smaller dielectric loss than SIN specimens but have relatively larger dependency to variation of temperature and frequency. For that reason formation of structures is attained by introducing of SIN to insulating materials. therefore it is counted that introduction of multiple structure including SIN is necessary to improve heat proof and electrical properties.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.305-307
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• 2001

In this paper, the household AC power line network is characterized for the low bit rate power line communication (PLC) in the frequency range from 10kHz to 450kHz. Various types of electric apparatus and the power lines constitute the network topology, and the PLC channel transfer function and the channel impedance are derived based on the constructed network topology. The channel characteristics derived with the lumped circuit model and the distributed circuit model are compared using the computer simulations. The effect of the wave reflection and signal distortions are also investigated.

• Applied Microscopy
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• v.46 no.4
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• pp.171-175
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• 2016

A distinctive neuronal network in the brain is believed to make us unique individuals. Electron microscopy is a valuable tool for examining ultrastructural characteristics of neurons, synapses, and subcellular organelles. A recent technological breakthrough in volume electron microscopy allows large-scale circuit reconstruction of the nervous system with unprecedented detail. Serial-section electron microscopy-previously the domain of specialists-became automated with the advent of innovative systems such as the focused ion beam and serial block-face scanning electron microscopes and the automated tape-collecting ultramicrotome. Further advances in microscopic design and instrumentation are also available, which allow the reconstruction of unprecedentedly large volumes of brain tissue at high speed. The recent introduction of correlative light and electron microscopy will help to identify specific neural circuits associated with behavioral characteristics and revolutionize our understanding of how the brain works.