• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.109-117
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• 2005

In case of fine machining processes, the cutting state monitoring by a skilled operator is impossible because the physical changes generated during fine machining are very weak. To realize the high efficient and precise fine machining, it is necessary to develop the sensor based monitoring system which is able to detect the fine changes of cutting state. In this paper, the fine acoustic emission monitoring system is developed to monitor the state of the fine machining process. The developed system consists of the AE sensor and the AE signal processing unit. And this has the high-sensitivity and bandwidth which can detect fine AE signal generated during fine machining process. In order to investigate the feasibility of the developed system, evaluation experiments were performed in the fine fixed-abrasive machining processes such as polishing and glass ferrule slicing. Experimental results show that the developed monitoring system possesses an excellent real-time monitoring capability at fine machining processes.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.679-690
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• 2011

Two widely used approaches for improving the quality of the output of a process are statistical process control (SPC) and automatic process control (APC). In recent hybrid processes that combine aspects of the process and parts industries, process variations due to both the inherent wandering and special causes occur commonly, and thus simultaneous application of APC and SPC schemes is needed to effectively keep such processes close to target. The simultaneous implementation of APC and SPC schemes is called integrated process control (IPC). In the IPC procedure, the output variables are monitored during the process where adjustments are repeatedly done by its controller. For monitoring the APC-controlled process, control charts can be generally applied to the output variable. However, as an alternative, some authors suggested that monitoring the input variable may improve the chance of detection. In this paper, we evaluate the performance of several monitoring statistics, such as the output variable, the input variable, and the difference variable, for efficiently monitoring the APC-controlled process when we assume IMA(1,1) noise model with a minimum mean squared error adjustment policy.

• Journal of Biosystems Engineering
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• v.33 no.4
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• pp.276-281
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• 2008

A flow injection analysis method (FIA), which analyzes sample conditions after injecting a sample and reagents into a continuous stream, are recognized as the most adequate analyzing method according to the increase of sampling frequency, the decrease of measuring time and the diversity of measuring targets. Specially, the FIA is considered to be used effectively for the control of a fermentation process to produce fermentation food and useful microbial production by activation of a fermentation industry for development of biological materials. In this study, a flow injection analysis sensor unit was developed for on-line monitoring of the fermentation process. The performance was verified by on-line measuring the concentration of glucose of the fermentation process of wine. The glucose concentrations of the samples were measured every 12 hours during the whole fermentation process and compared with those by a HPLC. The concentration relative errors of glucose on the fermentation process of wine showed below 30% within 72 hours and over 50% after the 72 hours. The sensor unit had potential to on-line monitoring of the fermentation process but some problems to overcome for an commercial application.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.311-315
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• 2000

In thls paper, a remote machine monitoring system for a vimal machine is proposed. The monltonng system is one of the core functmns of a vimd machne that provides a modeling and simulation environment for machining processes and management of the machine life cycle. The proposed system contains the modules for investigating tool wear using neural network and web-based real time process monitoring. An example implementation for tool wear and machining status monitoring is illustrated

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.97-106
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• 2007

In this paper, the business process was easily modeled by distinguishing between the business process and work logic. Based on this model, B2Bi collaboration Workflow modeling tool, which facilitates collaboration, was designed and implemented. The collaboration workflow modeling tool consists of 3 components; business process modeling tool, execution engine and monitoring tool. First, a business process modeling tool is used to build a process map that reflects the business logic of an application in a quick and accurate manner. Second an execution engine provides a real-time execution environment for business process instance. Third, a monitoring tool provides a real-time monitoring function for the business process that is in operation at the time. In addition to this, it supports flexibility and expandability based on XML and J2EE for the linkage with the legacy system that was used previously, and suggests a solution for a new corporate strategy and operation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.719-720
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• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.721-722
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.307-308
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• 2009

• Journal of Environmental Impact Assessment
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• v.3 no.2
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• pp.69-75
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• 1994

Environmental Impact Assessment is composed of screening, scoping, inventory survey, prediction, assessment, alternative assessment, mitigation measure, and post management. Environmental monitoring data is applied to EIA process such as prediction and post management. It must he collected and managed systematically for effective applying in EIA process. This article explains factors such as air quality, water quality, soil, ocean, odor, noise & vibration, ecosystem, etc. and organizations of environmental monitoring managed by Ministry of Environment.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2612-2616
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• 2003

Method for performance monitoring and diagnosis of a MIMO control system has been studied aiming at application to model predictive control (MPC) for industrial processes. The performance monitoring part is designed on the basis of the traditional SPC/SQC method. To meet the underlying premise of Schwart chart observation that the observed variable should be univariate and independent, the process variables are decorrelated temporally as well as spatially before monitoring. The diagnosis part was designed to identify the root of performance degradation among the controller, process, and disturbance. For this, a method to estimate the model-error and disturbance signal has been devised. The proposed methods were evaluated through numerical examples.