• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.257-265
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• 2021

Home networking and its security issues are directly related. Previous studies have shown that home-network and understanding the security of it is a problem for non-technical users. The existing network management tools or ISP adapter tools are far too technical and difficult to be understood by ordinary home-network users. Its interface is not non-technical user-directed and does not address the home user's needs in securing their network. This paper presents an interactive security monitoring tool, which emphasizes support features for home-network users. The tool combines an interactive visual appearance with a persuasive approach that supports sustainability. It is not only an easy-to-use tool for all categories of home-network users but also acts as a monitoring feature for the user to secure their home-network.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.4
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• pp.458-464
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• 2008

The structural health monitoring has been gaining more importance in civil engineering areas such as earthquake and wind engineering. The use of health monitoring system can also provide tools for the validation of structural analytical model. However, only few structures such as historical buildings and some important long bridges have been instrumented with structural monitoring system due to high cost of installation, long and complicated installation of system wires. In this paper, the structural monitoring system based on cheap and wireless monitoring system is investigated. The use of advanced technology of micro-electro-mechanical system(MEMS) and wireless communication can reduce system cost and simplify the installation. Further the application of wireless MEMS system can provide enhanced system functionality and due to low noise densities. Identification results are compared to ones using data measured from traditional accelerometers and results indicate that the system identification using wireless MEMS system estimates system parameters accurately.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.4
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• pp.20-29
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• 2004

A Tool wear monitoring system is indispensable for better machining productivity with guarantee of machining safety by informing the tool changing time in automated and unmanned CNC machining. Different from monitoring using other signals, the monitoring of spindle current has been used without requiring additional sensors on machine tools. For the reliable tool wear monitoring, current signal only of tool wear should be extracted from other parameters to avoid exhaustive analyses on signals in which all parameters are fused. In this paper, influences of force components of parameters on measured spindle current are investigated and a hybrid approach to cutting force regulation is employed for tool wear signal extraction in the spindle current. Finally, wear levels are verified with experimental results by means of real-time feedrate aspects changed to regulate the force component of tool wear.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.839-841
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• 2014

The Nuclear Steam Supply System(NSSS) is designed to provide an integrated approach that includes areas of monitoring relevant to the integrity of the NSSS. LPMS is designed to function as an alarm system by providing sensor channel alarms for the associated subsystems. LPMS is equipped to provide analysis tools for new alarm events, historical events and for historical periodically stored channel data (e.g. waveforms) for most channels. This paper is intended to introduce the diagnosis principle and abnormal symptom of loose parts monitoring system as a monitoring tool in Nuclear Steam Supply System. And also, we are going to introduce signal analysis program in order to perform the actual diagnosis in power plants.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.197-205
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• 2022

The Cyber Physical System(CPS) is an important concept in achieving SMSs(Smart Manufacturing Systems). Generally, CPS consists of physical and virtual elements. The former involves manufacturing devices in the field space, whereas the latter includes the technologies such as network, data collection and analysis, security, and monitoring and control technologies in the cyber space. Currently, all these elements are being integrated for achieving SMSs in which we can control and analyze various kinds of producing and diagnostic issues in the cyber space without the need for human intervention. In this study, we focus on implementing a production equipment monitoring system related to building a SMS. First, we describe the development of a fog-based gateway system that links physical manufacturing devices with virtual elements. This system also interacts with the cloud server in a multimedia network environment. Second, we explain the proposed network infrastructure to implement a monitoring system operating on a cloud server. Then, we discuss our monitoring applications, and explain the experience of how to apply the ML(Machine Learning) method for predictive diagnostics.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.17-27
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• 2006

Recent progress in the development of non-invasive imaging technologies continues to strengthen the role of molecular imaging biological research. These tools have been validated recently in variety of research models, and have been shown to provide continuous quantitative monitoring of the location(s), magnitude, and time-variation of gene delivery and/or expression. This article reviews the use of radionuclide, magnetic resonance, and optical imaging technologies as they have been used in imaging gene delivery and gene expression for molecular imaging applications. The studies published to date demonstrate that noninvasive imaging tools will help to accelerate pre-clinical model validation as well as allow for clinical monitoring of human diseases.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.19-29
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• 1995

The basic technology for a production system represented by design, machining, assembly, and inspection, is machining technology such as CNC machine tools. etc. Direct Numerical Control, that effeciently manages NC programs is developing into Distributed Numerical Control that increases the utilization of the machining cell. It has the ability of monitoring and control, in real time, for CNC and periperial equipment. In this study, we develop a Distributed Numerical Control system that has real time and multitasking operation capability for the machining cell with various CNC's. With the consideration of economy, generalization and extension, the system is interfaced with CNC machine tools and periperial device using RS-485 network and RS-232C communication methods.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.1
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• pp.19-25
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• 1998

Remote fault diagnosis and maintenance system using general telecommunication network is necessary for an effective fault diagnosis and higher productivity of NC machine tools. In order to monitor machine tool condition and diagnose alarm states due to electrical and mechanical faults, a remote data communication system for monitoring of NC machine fault diagnosis and status is developed. The developed system consists of (1) remote communication module among NC's and host PC using PSTN. (2) 8 channels analog data sensing module, (3) digital I/O module for control or NC machine, (4) communication module between NC machine and remote data communication system via RS-232C, and (5) software man-machine interface. Diagnostic monitoring results generated through a successive type inference engine are displayed in user-friendly graphics. The validity and reliability of the developed system is verified to be a powerful commercial version on a vertical machining center through a series of experiments.