• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.1
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• pp.43-50
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• 1996

From a manufacturing standpoint it would be desirable to monitor the degradation of drawing die, that is essential for the maintenance of die quality, the evaluation of product integrity and the reducing scrap. Acoustic emission is powerful method in monitoring fine wire drawing process, especially in detecting the die fracture at early stage. Experiments also suggested that acoustic emission sigals contained valuable information regarding the stage of a drawing process such as the surface appearance of products and the condition of lubrication. These informations are AE monitoring techniques a possible tool in monitoring the drawing process operation. In order to approach this, this paper discusses the nature of acoustic emission signal presented which illustrate the effects of wire and die material, lubricants, and drawing speed on the generation and the mean voltage level of acoustic emission signal. From these experimental, results, we understanded controlling factors of acoustic emission generation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.472-477
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• 2003

Spindle state monitoring is getting more and more important according to the technology trend of spindle that is accurate and automated. Spindle state monitoring is to measure the state of rotation vibrations. The spindle rotation error motion detected by sensing device includes rotation object's unbalance, external forced vibrations, shape error of spindle, as well as measuring error of monitoring device. In this paper, we have inspected the runout characteristics. Also, we introduce the way to exclude the runout element that appear while you monitor a spindle state.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.178-186
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• 2009

Most of automotive plastic parts are injection molding products. Inspection of total product is impossible, because number of product to inspect is too many and various. Condition-based Monitoring was proposed to decrease cost and time for inspecting. In this research, a system that predicts quality of part at fabrication point of time, and confirms informations through the internet was developed. Cavity sensors were installed inside of mold, and gathered signals as measuring, and through this process Sensor-based Monitoring system can be observed manufacturing of a part. Monitoring system transmits signals to client through the internet, and finally developed system provides manufacturing informations and predictions of quality as web-based monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.10 s.175
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• pp.90-98
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• 2005

A monitoring system is necessary to make the polishing process more reliable in order to ensure the high quality and performance of the final products. Generally, AE (Acoustic Emission) is known to be closely related to the material removal rate (MRR). As the surface becomes rougher, the MRR and AE increase. Therefore, the surface roughness can be indirectly estimated using the AE signal measured during the polishing. In this study, an AE sensor-based monitoring system was fabricated to detect the very small AE signal resulting from the friction between a tool and a workpiece during polishing. The performance of this monitoring system was estimated according to polishing conditions, the relation between the level of the AE RMS and the surface roughness during the polishing was investigated.

• Journal of Information Technology and Architecture
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• v.11 no.2
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• pp.131-141
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• 2014

Refining the monitoring and evaluation reporting is an important thing in e-Government. Indonesia which has had e-Government roadmap since 2000, has not had the systematic mechanism to monitor and evaluate e-Government in which the report can be easily accessed by the public and used to be the best practice to improve other e-Government implementation. Enterprise architecture (EA) has the major objective to straighten an enterprise to its necessary requirements. It can be used to propose the required system and that was the purpose of this paper. It was started by reviewing the literature about e-Government monitoring and evaluation, using quality of service as the means, and understanding TOGAF as one of EA framework. The second step was comparing EA and the evaluation of e-Government in Korea and Indonesia to get the best practices. And the last step was creating EA for the monitoring and evaluation reporting system based on Korea and the literature reviewed. It is expected that the formulized EA can be a tool to improve e-Government implementation in Indonesia.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.99-106
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• 2003

The shoreline changes were observed and analysed using the video image by a remote unmanned automatic camera monitoring system installed at Haeundae beach of Busan City. In order to analyse quantitatively the shoreline changes caused by waves and tides, the image averaging technique and the rectification technique for obliquely acquired image were applied to the video image during the typhoon Bart in September, 1999. The results showed that the camera monitoring system can be used as a very cost effective and efficient tool for monitoring shorelines which change continuously due to waves and tides.

• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.487-498
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• 2022

Social network analysis (SNA) techniques have recently been developed to monitor and detect abnormal behaviors in social networks. As a useful tool for process monitoring, control charts are also useful for network monitoring. In this paper, the degree and closeness centrality measures, in which each has global and local perspectives, respectively, are applied to an exponentially weighted moving average (EWMA) chart and a multinomial cumulative sum (CUSUM) chart for monitoring undirected weighted networks. In general, EWMA charts monitor only one variable in a single chart, whereas multinomial CUSUM charts can monitor a categorical variable, in which several variables are transformed through classification rules, in a single chart. To monitor both degree centrality and closeness centrality simultaneously, we categorize them based on the average of each measure and then apply to the multinomial CUSUM chart. In this case, the global and local attributes of the network can be monitored simultaneously with a single chart. We also evaluate the performance of the proposed procedure through a simulation study.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.25-32
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• 1995

In highly automated milling process, in-process monitoring of the malfunction is indispensable to ensure efficient cutting operation. Among many malfunctions in milling process, chatter vibration deteriorates surface finish, tool life and productivity. In this study, the monitoring system of chatter vibration for face milling process is proposed and experimentally estimated. The monitoring system employs two types of sensor such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are extracted in time domain for the input patterns of neural network to reduce time delay in signal processing state. The resultes of experimental evaluation show that the system works well over a wide range of cutting conditions.

• Structural Engineering and Mechanics
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• v.31 no.1
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.269-273
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• 1997

In order to maker unmanned machining systems with satisfactory performances, it is necessary to incorporate appropriate condition monitoring systems in the machining workstations to provide the required intelligence of the expert. This paper deals with condition monitoring for chatter, tool wear and fracture during turning operation. To develop economic sensing and identification methods for turning processes, sound pressure measurement and digital signal processing technique were proposed. We suppressed chatter by stability control methodology, which was studied through manipulation of spindle speeds regarding to chatter frequencies. It was shown that tool wear and fracture were identified and to be estimated by using the wear indices. The validity of the proposed system was confirmed through the large number of cutting tests.