• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2256-2262
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• 2010

The requirements for automated handing of business process and its monitoring usually have a proprietary form for each enterprise. Unlike the conventional database transaction, business process takes long time for its completion and incorporates very complex handling logics along with business situations. Since those handling logics are frequently changing in accordance with the business policies or environment, enterprises want to integrally capture the whole business semantics while monitoring those process instances. In this paper, we adopted GMA(Grid Monitoring Architecture) for the integrated monitoring of business processes. The GMA(Grid Monitoring Architecture) is a very scalable architecture to effectively monitor and manage monitoring information under the heterogeneous environment. By introducing complex event handling features into GMA to support various processing logics, we could implement a system that enables automated execution and high-level monitoring of business processes.

• Laser Solutions
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• v.6 no.2
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• pp.27-33
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• 2003

A laser shock cleaning technology is a new dry cleaning methodology for the effective removal of small particles from the surface. This technique uses a plasma shock wave produced by a breakdown of air due to an intense laser pulse. In order to optimize the laser shock cleaning process, it needs to evaluate the cleaning performance quantitatively by using a monitoring technique. In this paper, an acoustic monitoring technique was attempted to investigate the laser shock cleaning process with an aim to optimize the cleaning process. A wide-band microphone with high sensitivity was utilized to detect acoustic signals during the cleaning process. It was found that the intensity of the shock wave was strongly dependent on the power density of laser beam and the gas species at the laser beam focus. As a power density was larger, the shock wave became stronger. It was also seen that the shock wave became stronger in the case of Ar gas compared with air and N$_2$ gas.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1082-1086
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• 2007

This research has a purpose of constructing the data monitoring system that makes two-tier work state in the brick production factory to unification by reusing cinder. Monitoring system automatically manages data by using data managing processes such as a state managing process, a location managing process, a badness managing process, a circumstances managing process. In this research, the data management monitoring system manufactures state information of each processes received from RFID and transmits them to data monitoring system. Analyzed data through this system reuses the cinder, so it can effectively manage the production process of the factory which produces bricks through processing automation, faulty-ratio minimization, real-time monitoring and loading managing.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.149-155
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• 1996

Recent researches in the trouble monitoring system of grinding process have emphasized the use of deep knowledge. Such works include the monitoring and diagnostic systems for cylindrical grinding using sensors on chatter vibration and grinding burn during the process. But, since grinding operations are especially related with a lalrge amount of ambique parameters, it is effectively difficult to detect the grinding troubles occuring during the grinding process. In this paper, monitoring system for grinding utilizes the neural networks based on grinding power signatures. The monitoring system of grinding operations, which makes use of PDP neural networks, is presented. Then, the implementation results by computer simulations and experimental data with respect to chatter vibration and grinding burn are compared.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.773-782
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• 2007

The statistical process control charts are very extensively used for monitoring of process mean, deviation, defect rate or reject rate. In this paper we consider a control chart to monitor the process reject rate in the high yield process, which is based on the observed cumulative probability of the number of items inspected until r defective items are observed. We first propose selection of the optimal value of r in the CPC-r charts, and also consider the usefulness of the chart in high yield process such as semiconductor or TFT-LCD manufacturing process.

• YAKHAK HOEJI
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• v.60 no.2
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• pp.85-91
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• 2016

Drying of granules for tablet formulation is one of the important unit operations. The loss on drying method is traditionally used for this purpose. However, it is a time-consuming method, requiring at least 1 h. Moreover, it is ineffective in monitoring the moisture content of granules during the drying process. In this study, online real-time monitoring of moisture content during the drying process was successfully performed using near-infrared (NIR) spectroscopy. NIR spectra were collected during 15 different drying batches for developing a reliable NIR spectroscopic method. Such a large number of batches were used to develop a more robust partial least squares (PLS) model. NIR spectra collected from 12 batches were used for developing the model that was validated by predicting the moisture content of the samples in the remaining 3 batches. The standard errors of predictions (SEPs) in the measurement of batch 1, batch 2, and batch 3 were 0.52%, 0.57%, and 0.56%, respectively. The online NIR spectroscopic method developed in this study was reliable and accurate in monitoring the moisture content during the drying process.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.6
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• pp.89-97
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• 2022

Ultrasonic metal welding has been widely used for joining lithium-ion battery tabs. Weld quality monitoring has been an important issue in lithium-ion battery manufacturing. This study focuses on the weld quality monitoring in ultrasonic metal welding with the longitudinal-torsional vibration mode horn developed newly. As the quality of ultrasonic welding depends on welding parameters like pressure, time, and amplitude, the suitable values of these parameters were selected for experimentation. The welds were tested via tensile testing machine and weld strengths were investigated. The dataset collected for performance test was used to train the multi-layer perceptron neural network. The three layer neural network was used for the study and the optimum number of neurons in the first and second hidden layers were selected based on performances of each models. The best models were selected for the horn and then tested to see their performances on an unseen dataset. The neural network models for the longitudinal-torsional mode horn attained test accuracy of 90%. This result implies that proposed models has potential for the weld quality monitoring.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.83-92
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• 2020

Recently, many companies and organizations are interested in predictive process monitoring for the efficient operation of business process models. Traditional process monitoring focused on the elapsed execution state of a particular process instance. On the other hand, predictive process monitoring focuses on predicting the future execution status of a particular process instance. In this paper, we implement the function of the business process remaining time prediction, which is one of the predictive process monitoring functions. In order to effectively model the remaining time, normalization by activity is proposed and applied to the predictive model by taking into account the difference in the distribution of time feature values according to the properties of each activity. In order to demonstrate the superiority of the predictive performance of the proposed model in this paper, it is compared with previous studies through event log data of actual companies provided by 4TU.Centre for Research Data.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.387-390
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• 1997

Grinding process takes a long time that grinding machine is setted properly. It is difficult for user to judge correctly the abnormal states generated in grinding process. Air grinding has to be reduced for the improvement of productivity. In addition, it is important to monitor the dressing and the grinding process so that the grinding working maintains optimal grinding conditions. In this study, the monitoring system using the acoustic emission is developed to monitor these processes continuously. This system was able to reduce the preparation as well as the machine setting time in grinding operation.