• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.98-105
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• 2008

In this paper, the system for monitoring process of aluminum laser welding was developed using the light signal emitted from the plasma which comes from interaction between material and laser. Photodiode for monitoring system was selected based on the spectrum analysis of light from plasma and keyhole. Behavior of plasma and keyhole was analyzed through the sensor signals. Value of sensor signal represented the light intensity and fluctuation of signal indicated the stability of plasma and keyhole. For the relation between welding condition and sensor signals, the input power and weld geometry greatly effected on the average of each sensor signals. Using the feature values of signals, estimation model for tensile strength of weld was formulated with neural network algorithm. Performance of this model was verified through coefficient of determination and average error rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.9-15
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• 2013

Recently, Intrusion detection system is an important technology in computer network system because of has seen a dramatic increase in the number of attacks. The most of intrusion detection methods do not detect intrusion on real-time because difficult to analyze an auditing data for intrusions. A network intrusion detection system is used to monitors the activities of individual users, groups, remote hosts and entire systems, and detects suspected security violations, by both insider and outsiders, as they occur. It is learns user's behavior patterns over time and detects behavior that deviates from these patterns. In this paper has rule-based component that can be used to encode information about known system vulnerabilities and intrusion scenarios. Integrating the two approaches makes Intrusion Detection System a comprehensive system for detecting intrusions as well as misuse by authorized users or Anomaly users (unauthorized users) using RFM analysis methodology and monitoring collect data from sensor Intrusion Detection System(IDS).

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.37-43
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• 2005

Real time monitoring of exit burr formation is critical in manufacturing automation. In this paper, acoustic emission (AE) was used to detect the burr formation during drilling. By using wavelet transform (WT), AE data were compressed without unnecessary details. Then the transformed data were used as selected features (inputs) of a back-propagation artificial neural net (ANN). In order to validate the in process AE monitoring system, both WT-based ANN and cutting condition (cutting speed, feed, drill diameter, etc.) based ANN outputs were compared with experimental data.

• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.70-76
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• 2006

The objective of this paper is to propose a reference model of a fire detection and monitoring system using MS/TP protocol. The reference model is designed to satisfy the requirements of response time and flexibility. The reference model is operated on the basis of BACnet, a standard communication protocol for building automation systems. Validity of the reference model was examined using a simulation model. This study also evaluated the performance of the BACnet-based fire detection and monitoring system in terms of network-induced delay. Simulation results show that the reference model satisfies the requirements of the fire detection and monitoring system.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.420_421
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• 2009

The digital TRS(Trunked Radio System) is an excellent alternative for the transmission medium for the DAS(Distribution Automation System). KEPCO(Korea Electric Power Corporation) made the power IT network with digital TRS system. The main function of the radio network is transmitting and receiving information for the DAS. KEPCO's digital TRS is based on TETRA standard. The digital TRS supports data communication for the electric power control and monitoring systems in various ways.

• Journal of Wind Energy
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• v.9 no.4
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• pp.16-23
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• 2018

A damage detection method for the tripod support structure of offshore wind turbines is presented for structural health monitoring. A finite element model of a prototype tripod support structure is established and the modal properties are calculated. The degree and location of the damage are estimated based on the neural network technique using the changes of natural frequencies and mode shape due to the damage. The stress distribution occurring in the support structure is obtained by a dynamic analysis for the wind turbine system to select the output data of the neural network. The natural frequencies and mode shapes for 36 possible damage scenarios were used for the input data of the learned neural network for damage assessment. The estimated damages agreed reasonably well with the accurate ones. The presented method could be effectively applied for damage detection and structural health monitoring of various types of support structures of offshore wind turbines.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.2
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• pp.96-102
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• 2011

There are many problems with the fire detection devices being used in currently, because it is difficult to find location of the source of fire and determine where devices are working or not. In this paper, we proposed fire detection and rescue system using wireless sensor network that can be real-time monitoring and determine safe exit. Fire detection and rescue system based on ubiquitous sensor network can know exactly source of fire and help determine rescue tactics using sensing data from wireless sensor nodes. Transmitted wirelessly in real-time thermal sensor and gas sensor information to analyze the GUI to monitor the status information output to the screen by use of a system implemented in everyday life, looked at the possibility.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.282-288
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• 1997

The purpose of this paper is to predict the situation of leak in closed space using an Artificial Neural Network (ANN). The existing system can't monitor the whole He situations with on/off signals. Especially the first stage of data determines the leak spot and intensity is disregarded in gas accidents. To complement these faults, a new prototype of monitoring system is proposed. Ihe system is composed of'sensing systenL data acquisition system computer, and ANN implemented in software and is capable of identifying the leak spot and intensity in closed space. The concentration of gas is measured at the 4 different places. The network has 3 layers that are composed of 4 input Processing Element (PE),24 hidden PEs, md 4 output PEs. The ANN has optimum condition through several experiments and as a consequence the recognition rate of93.75% is achieved finally

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.4
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• pp.67-73
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• 2010

A Wireless Sensor Network(WSN) is a promising infrastructure for the future U-healthcare system. In a WSN for the U-healthcare system, both biometric data and location data are transferred hierarchically from lots of mobile patients to the base station server and some countermeasures are made in real time if necessary. In this process, we encounter the load-balancing problem when many patients gather in a specific area. We also encounter the data duplication problem when each patient moves into an area monitored by several supervisors. The second problem is closely related to the first one. In this paper, we propose a mobile patient monitoring system with priority-based policy in load-balancing to solve the previous two problems and perform a DEVS Java-based system simulation to verify the system efficiency.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.103-112
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• 2008

In this paper we present two databases for two different systems each having novel implementation ideas: a u-Healthcare system with a mobile gateway and MAGI network monitoring system. u-Healthcare system is capturing and monitoring vital sign data without restrictions of location and time using body sensor network and a mobile gateway. MAGI is a system which has a dynamic firewall function and solves problems of existing traffic monitoring tools. In addition to presenting the design principles behind these two systems, we describe the respective database schemas together with detailed information of the data stored within the databases. We will also show and discuss performance measurements and calculations for both systems. Based on the performance data we will discuss the systems' suitability for their intended uses.