• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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• pp.252-259
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• 2001

Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.87-91
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• 2019

In this paper, we have development of horizontal attitude monitoring system for agricultural robots. A two-axis gyro sensor and a two-axis accelerometer sensor are used to measure the horizontal attitude angle. The roll angle and pitch angle were measured through the fusion of the gyro sensor signal and the acceleration sensor signal for the horizontal attitude monitoring of the robot. This attitude monitoring system includes GPS and Bluetooth communication module for remote monitoring. The roll angle and pitch angle can be measured by the error of less than 1 degree and the linearity and the reproducibility of the output signal are excellent.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.43-52
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• 2024

Health Monitoring System is a sophisticating technology and another way to the normal/regular management of the health of the patient. This Health Monitoring Mobile Application is a contribution from our side to the public and to the overall health industry in Pakistan. With the help of Health mobile application, the users will be able to store their medical records, prescriptions and retrieve them later. The users can store and keep track of their vital readings (heart rate, blood pressure, fasting glucose, random glucose). The mobile application also shows hospitals that are nearby in case the user wants to avail of any medical help. An important feature of the application is the symptoms-based disease prediction, the user selects the symptoms which he has and then the application will name certain diseases that match those symptoms based on relevant algorithms. The major advances and issues have been discussed, and as well as potential tasks to health monitoring will be identified and evaluated.

• Annals of Clinical Neurophysiology
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• v.1 no.1
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• pp.70-75
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• 1999

Trancranial Doppler(TCD) monitoring is a new application of ultrasonography which allows the nonivasive detection of blood flow velocity in the horizontal (M1) segment of the middle cerebral artery (MCA) and detects microembolic phenomena in the cerebral circulation. Recent studies emphasized the potential of using this technique in vascular surgery (carotid endarterectomy, cardiopulmonary bypass), interventional and intensive care setting. Although the disparity between CBF and blood flow velocity and number of microemboli could be used to prevent cerebral ischemic and embolism based on clinical studies. A reduction of more than 60% of MCA can reflex hemodynamic ischemic state and acoustic feedback of high intensity transient signals(HITS) from the TCD monitoring unit has a direct influence on surgical technique. TCD monitoring can immediately provide information about thromboembolism and hemodynamic changes, which may be a useful tool in the study and prevention of stroke.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.64-67
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• 2005

A vibration monitoring system for a small size wind turbine (WIT) is established and operated. The monitoring system consists of monolithic integrated chip accelerometer for vibration monitoring, anemometers for wind data acquisition and auxiliary sensors for atmospheric data. Using the monitoring system, vibration response of a 6kW stand alone WIT generator is investigated. Acceleration data of the WIT tower under various operation condition is acquired in real time using LabVIEW and the data are remotely transferred from the test site to the laboratory in school by internet. Vibration response characteristics of the tower structure are diagnosed in the aspect of stability of W/T. Wind data and electrical power performance are also investigated with the stability problem.

• MALSORI
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• no.51
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• pp.57-69
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• 2004

Comprehension monitoring is a process of message evaluation and a very important skill of communication. Comprehension monitoring is a necessary language skill in classroom, as it is important for children to assess their own understanding of task instructions and teaching content. The present study investigated comprehension monitoring skill of children with Specific Language Impairment(SLI) compared to age-matched children and language-matched children. 18 vignettes and 6 displays were constructed. Children were asked to choose one object or 'DK' card from a display after the children listened to the vignettes. The results showed that children with SLI didn't have problem in comprehending unambiguous utterance and using prior statement. However, they had problem in monitoring comprehending ambiguous utterance compared age-matched children. Their performance were similar to language matched younger children. With respect to their performance, several possible explanations were discussed.

• 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.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.93-95
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• 2005

When a control system is designed computer simulations are important because they provide a way to test the performances of the control law and determine some design parameters of the control system. Since most real situations are different from those of the simulations, however, it is required to manage uncertainties of the plant and analyze errors fast and correctly. For this we consider a convenient monitoring system which can display real systems responses exactly and check the performances of the control systems. In this paper, an approach to design a monitoring system is presented by using C++Builder program which is based on PC. Firstly we introduce the structure and functions of the monitoring system. Then, an inverted-pendulum control system is investigated by using the monitoring system.

• Transactions of Materials Processing
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• v.15 no.1 s.82
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• pp.82-88
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• 2006

In this paper, the web-based monitoring system is developed for the effective process monitoring of mold manufacturing using web. In developed system, the cutting force for monitoring the manufacturing condition is measured using hall-sensor that is low cost and useful to be installed in a machine tool indirectly. Specially, the current of main spindle in a machine tool is converted into cutting force by various experiments. For effective web-based monitoring, the program which runs in the local computer of client is made to exchange message between a server and a client by making of ActiveX control and the result of manufacturing is shown on web-browser by Ch language. The developed system in this study is the foundation of establishing E-manufacturing in mold factory.

• 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.