• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.1-7
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• 2021

With the advent of industry 4.0, big data and machine learning techniques are being widely adopted in the maintenance domain. Inverters are widely used in many engineering applications. However, overloading and complex operation conditions may lead to various failures in inverters. In this study, failure mode effect analysis was performed on inverters and voltages collected to investigate the over-voltage effect on capacitors. Several features were extracted from the collected sensor data, which indicated the health state of the inverter. Based on this correlation, the best features were selected for classification. Moreover, random forest classifiers were used to classify the healthy and faulty states of inverters. Different performance metrics were computed, and the classifiers' performance was evaluated in terms of various health features.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.5
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• pp.314-322
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• 2022

During the shipbuilding process, many blocks are moved between shipyard workshops by block carrying vehicles called a transporter. Because block logistics management is one of the essential factors in enhancing productivity, it is necessary to manage block information with the transporter that moves it. Currently, because a large amount of data per day are collected from sensors attached to blocks and transporters via IoT infrastructure installed in shipyards, automated methods are needed to analyze them. Therefore, in this study, we developed an algorithm that can automatically match the transporter and the working block based on the GPS sensor data. By comparing the distance between the transporter and the blocks calculated from the Haversine formula, the block is found which is moved by the transporter. In this process, since the time of the measured data of moving objects is different, the time standard for calculating the distance must be determined. The developed algorithm was verified using actual data provided by the shipyard, and the correct result was confirmed with the distance based on the moving time of the transporter.

• The KIPS Transactions:PartC
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• v.14C no.3 s.113
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• pp.229-238
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• 2007

Sensor networks consist of many tiny sensor nodes that collaborate among themselves to collect, process, analyze, and disseminate data. In sensor networks, sensor nodes are typically powered by batteries, and have limited computing resources. Moreover, the redeployment of nodes by energy exhaustion or their movement makes network topology change dynamically. These features incur problems that do not appear in traditional, wired networks. Security in sensor networks is challenging problem due to the nature of wireless communication and the lack of resources. Several efforts are underway to provide security services in sensor networks, but most of them are preventive approaches based on cryptography. However, sensor nodes are extremely vulnerable to capture or key compromise. To ensure the security of the network, it is critical to develop suity mechanisms that can survive malicious attacks from "insiders" who have access to the keying materials or the full control of some nodes. In order to protect against insider attacks, it is necessary to understand how an insider can attack a sensor network. Several attacks have been discussed in the literature. However, insider attacks in general have not been thoroughly studied and verified. In this paper, we study the insider attacks against routing protocols in sensor networks using the Ad-hoc On-Demand Distance Vector (AODV) protocol. We identify the goals of attack, and then study how to achieve these goals by modifying of the routing messages. Finally, with the simulation we study how an attacker affects the sensor networks. After we understand the features of inside attacker, we propose a detect mechanism using hop count information.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.71-76
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• 2001

In general, it is known that the wear rate of the abrasive and the removal rate of the metal bond of the grinding wheel should be balanced to maintain the depth of the insulating surface layer to an appropriate level. In order to accomplish, the high quality ELID grinding, therefore, it is necessary to measure the depth of the insulating layer in real-time and then to control the electrolytic conditions to keep the depth to a certain level. In this study, an in-process measurement system of the insulated layer using two gap sensors - a capacitor type and an eddy current type - developed and the change of the status of the insulated layer during ELID grinding is detected. And from the experimental data, we have chosen the best mathematical model to predict the depth of the insulating layer.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.2
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• pp.34-39
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• 2012

In this study, the friction welding experiment was performed by using the design of experiment. And the signal data acquired by acoustic emission sensor were analyzed to predict the tensile strength of friction welding part at friction welding process for AZ31 magnesium alloy. A dimensionless coefficient($\phi_{AE}$), which consisted in the square of AE rms and variance, was defined as the characteristic of friction welding and the prediction equation was obtained by using linear regression. As the result of analysis, it was seen that the correlation between predicted and measured values became very close and on-line prediction of the ensile strength was possible in friction welding part.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.1
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• pp.13-18
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• 2007

In this paper, authors deal with an application of power quality monitoring using the Sampled Value which is described in the IEC61850 International Standard for substation communication. Firstly, while Merging Unit is designed as a process level device transmitting sensor data, the practical problems such as time delay compensation and optical fiber communication are issued. Secondly, the Sampled Value message which is proper to a power quality monitoring system is presented. Because the power quality monitoring system requests non time critical service comparing to protection and control applications, the Sampled Value service message structure is introduced to improve efficiency. At last, the power quality monitoring server having various power quality analysis functions is suggested to verify the performance of Merging Unit. With the diverse experiments, it is proved that the process bus distributed solution is flexible and economic for the power quality monitoring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.72-77
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• 2004

The purpose of this study is to develop temperature measurement system with Rf sensors on shoes dry process. The experimental results showed that the developed system successfully could be applied on real dry process to reduce defective shoes caused from insufficient dry with scientific data management.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.57-63
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• 1996

Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.

• Transactions of Materials Processing
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• v.32 no.6
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• pp.376-383
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• 2023

The torsion bar, which is a steering torque sensor, is mounted between the steering pinion and the input shaft in the IPA(input pinion assembly). Accurate torque measurement is important to improve the sense of operation, and the straightness of the torsion bar can affect torque measurement. In this study, the amount of bending was measured and the exact shape was analyzed regarding the bending phenomenon in the press-fitting process for torsion bars. The effect of alignment error was analyzed through finite element forming analysis. Process data analysis was conducted for the double-end press fit model. If there is an alignment error of about 10% of the serration tooth height, the indentation load is reduced by about 10%. If there is an alignment error, the torsion bar is rotated.

• Proceedings of the KWS Conference
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• 2000.10a
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• pp.40-42
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• 2000

This paper presents a new method of segmenting a one-dimensional signal into a set of features of type(line, Vee-groove, Lap-joint and etc.), A set of requirements for the segmentation process result from the application area, which in this case are laser welding, GMAW(Gas Metal Arc Welding), SAW(Submerged Arc Welding) and high speed tack welding. The algorithm is able to detect an exact welding position in the presence of noise and missing data, yet is reasonably economical to implement