• Journal of the Korean Society for Precision Engineering
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• v.28 no.6
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• pp.709-715
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• 2011

Chattering in cutting operations are usually a cumbersome part of the manufacturing process in mechanical. Particular, machining performance such as that of the boring process is limited by cutting condition at the movable components. Among various sources of chatter vibration, detrimental point in cutting condition is found a mechanical condition on overhang. It limits cutting speed, depth, surface roughness and tool wear failure as result because the all properties are varying with the metal removal process. In this case, we have to observe the resonance frequencies of a boring bar for continuous cutting. In the established research, boring bar vibration of cutting system has been measured with the aid of accelerometer. However, the inherent parameters of internal turning operations are severely limit for the real time monitoring on accelerometers. At this point, this paper is proposed other method for real time monitoring during continuous cutting with optical fiber at the inside of boring bar. This method has been used a plastic fiber in the special jig on boring bar by based on experimental modal analysis. In this study, improvement of monitoring system on continuous internal cutting was attempted using optical fiber sensor of inside type because usually chattering is investigated experimentally measuring the variation in chip thickness. It is demonstrated that the optical fiber sensor is possibility to measure of chattering with real time in boring process.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.303-317
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• 2010

In this paper, we propose a novel approach to fault prediction for real-time business process monitoring method using extended KNN imputation based LOF prediction. Existing rule-based approaches to process monitoring has some limitations like late alarm for fault occurrence or no indicators about real-time progress, since there exist unobserved attributes according to the monitoring phase during process executions. To improve these limitations, we propose an algorithm for LOF prediction by adopting the imputation method to assume unobserved attributes. LOF of ongoing instance is calculated by assuming next probable progresses after the monitoring phase, which is conducted during entire monitoring phases so that we can predict the abnormal termination of the ongoing instance. By visualizing the real-time progress in terms of the probability on abnormal termination, we can provide more proactive operations to opportunities or risks during the real-time monitoring.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.448-452
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• 2007

Many industrial operations require continuous or nearly-continuous operation of machines, which if interrupted can result in significant financial loss. The condition monitoring of these machines has received considerable attention recent years. Rapid developments in semiconductor, computing, and communication with a remote site have led to a new generation of sensor called "smart" sensors which are capable of wireless communication with a remote site. The purpose of this research is the development of smart sensor using which can on-line perform condition monitoring. This system is addressed to detect conditions that may lead to equipment failure when it is running. Moreover it will reduce condition monitoring expense using low cost MEMS accelerometer. This sensor can receive data in real-time or periodic time from MEMS accelerometer. Furthermore, this system is capable for signal preprocessing task (High Pass Filter, Low Pass Filter and Gain Amplifier) and analog to digital converter (A/D) which is controlled by CPU. A/D converter that converts 10bit digital data is used. This sensor communicates with a remote site PC using TCP/IP protocols. Wireless LAN contain IEEE 802.11i-PSK or WPA (PSK, TKIP) encryption. Developed sensor executes performance tests for data acquisition accuracy estimations.

• Journal of the Korean Solar Energy Society
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• v.36 no.5
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• pp.91-100
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• 2016

This paper proposes the PV module fault diagnosis sensor which is applied to Zigbee wireless network, and monitoring system using the developed sensor. It is designed with embedded sensor in junction box. The diagnosis elements for algorithm were voltage and temperature. For that reason, It is able to reduce the price and separate the fault of bypass diode from shading differently from other monitoring systems. This fault diagnosis algorithm verified through the Field-installed operations of PV module.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.598-601
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• 2007

Urban growth management is essential for sustainable urban growth. Monitoring physical urban built-up area is a task of great significance to manage urban growth. Detecting urbanized area is essential for monitoring urbanized area. Although image classifications using satellite imagery are among the conventional methods for detecting urbanized area, they requires very tedious and hard work, especially if time-series remote sensing data have to be processed. In this paper, we propose an effective urbanized area detecting method based on normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). To verify the proposed method, we extract urbanized area using two methods; one is conventional supervised classification method and the other is the proposed method. Experiments shows that two methods are consistent with 98% in 1998, 99.3% in 2000, namely the consistency of two methods is very high. Because the proposed method requires no more process without band operations, it can reduce time and effort. Compared with the supervised classification method, the proposed method using vegetation indices can serve as quick and efficient alternatives for detecting urbanized area.

• Korean Journal of Clinical Laboratory Science
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• v.44 no.4
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• pp.184-198
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• 2012

The purpose of intra-operative neurological monitoring (INM) is to minimize surgically induced nerve damage, sensory nerves and motor neurons without affecting the operations to proceed during surgery such as evoked potentials (EP), electromyography (EMG), electroencephalography (EEG), transcranial doppler (TCD), etc. During the course of checking a patient's condition, surveillance of ambulatory patients is a very different thing to check if the test is done under general anesthesia. INM can be possible or impossible depending on the type of drugs used and their concentrations because the monitoring is performed under anesthesia. Therefore, it is emphasized on the necessity of reviewing anesthesia which influences on INM.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.2
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• pp.145-155
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• 2002

It is essential for health monitoring of a cable-stayed bridge to provide more accurate and enough information from the sensors. In experimental modal testing, the chosen measurement locations and the number of measurements have a major influence on the quality of the results. The choice is often difficult for complex structures like a cable-stayed bridge. It is extremely important a cable-stayed bridge to minimize the number of sensing operations required to monitor the structural system. In order to obtain the desired accuracy for the structural test, several issues must take into consideration. Two important issues are the number and location of response sensors. There are usually several alternative locations where different sensors can be located. On the other hand, the number of sensors might be limited due to economic constraints. Therefore, techniques such as methodologies, algorithms etc., which address the issue of limited instrumentation and its effects on resolution and accuracy in health monitoring systems are paramount to a damage diagnosis approach. This paper discusses an optimum sensor placement criterion suitable to the identification of structural damage for continuous health monitoring. A Kinetic Energy optimization technique and an Effective Independence Method are analyzed and numerical and theoretical issues are addressed for a cable-stayed bridge. Its application to a cable-stayed bridge is discussed to optimize the sensor placement for identification and control purposes.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.375-380
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• 2021

Internal control is important for monitoring operations of organizations. In order to achieve the quality of internal control, organizations need to recognize different perspectives in which the components of internal control play a decisive role. Internal control is a process designed by the manager and it is applied within the organization to provide reasonable assurance of the reliability of financial information and to comply with policies, procedures, rules, regulations and laws. The article uses the ordinary least squares method and the seven-point Likert scale to test the variables affecting the quality of internal control in 18 Vietnamese listed banks. The article surveyed 179 leaders of listed banks. The results show that there are three variables out of a total of five variables that positively affect the quality of internal control, including the control environment, control activities, and monitoring. Accordingly, the managers of Vietnamese listed banks need to pay attention to building a corporate culture environment, improve the quality of control activities, and periodically and regularly conduct the monitoring. It contributes to improving the quality of internal control and is also an opportunity to increase economic benefits for Vietnamese listed banks in the context of international economic integration.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.275-279
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• 2023

Throughout the 20th century, the transition of pig farms from extensive to intensive commercial operations amplified the risk of disease transmission, particularly involving African swine fever (ASF). Real-time temperature monitoring systems have emerged as essential tools for early ASF diagnosis. In this paper, we introduce new real-time temperature monitoring ear tags (RTMEs) modeled after existing ear tag designs. Our crafted Pig-Temp platforms have three primary advantages. First, they can be effortlessly attached to pig ears, ensuring superior compatibility. Second, they enable real-time temperature detection, and the data can be displayed on a personal computer or smartphone application. Furthermore, they demonstrate excellent measurement accuracy, ranging from 98.9% to 99.8% at temperatures between 2.2 and 360℃. A linear regression approach enables fever symptoms associated with ASF to be identified within 3 min using RTMEs. The communication range extends to approximately 12 m (452 m²), enabling measurements from an estimated 75 to 2,260 pigs per gateway. These newly developed Pig-Temp platforms offer singifcant enhancement of early ASF detection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.374-379
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• 2016

On-line process monitoring schemes are designed to give early warnings of process faults. In the artificial intelligence and machine learning fields, reliable approaches have been utilized, such as kernel-based nonlinear techniques. This work presents a kernel-based empirical monitoring scheme with a small sample problem. The measurement data of normal operations are easy to collect, whereas special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing the process monitoring performance. This can be achieved by the preprocessing of raw process data and eliminating unwanted variations of data. In this work, the performance of several monitoring schemes was demonstrated using three-dimensional batch process data. The results showed that the monitoring performance was improved significantly in terms of the detection success rate.