• International Journal of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.27-30
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• 2019

Factory monitoring systems are gaining importance in wide areas of industry. Especially, there have been many efforts in implementation of vibration measurement and analysis for monitoring the status of rotating machines. In this paper, a digital signal processor (DSP) based monitoring system dedicated to the vibration monitoring and analysis on rotating machines is discussed. Vibration signals are acquired and processed for the continuous monitoring of the machine status. Time domain signals and fast Fourier transform (FFT) are used for vibration analysis. All of the signal processing procedures are done in the DSP to reduce the production and maintenance cost. The developed system could also provide remote and mobile monitoring capabilities to operator via internet connection. This paper describes the overview of the functional blocks of the implemented system. Test results based on signals from small-size single phase motors are discussed for monitoring and defect diagnosis of the machine status.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.387-396
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• 2002

Batch processes are a significant class of processes in the process industry and play an important role in the production of high quality speciality materials. Examples include the production of semiconductors, chemicals, pharmaceuticals, and biochemicals. With on-line sensors connected to most batch processes, massive amounts of data are being collected routinely during the batch on easily measured process variables such as temperatures, pressures, and flowrates. In this paper, multivariate SPC charts for on-line monitoring of the progress of new batches are developed which utilize the information in the on-line measurements in real-time. We propose the formation of statistical model which describes the normal operation of a batch at each time interval during the batch operation. An on-line monitoring scheme based on the proposed method can handle both cross-correlation among process variables at any one time and auto-correlation over time. And the control limits for the monitoring charts are established from sound statistical framework unlike previous researches which use the external reference distribution. The proposed charts perform real-time, on-line monitoring to ensure that the batch is progressing in a manner that will lead to a high-quality product or to detect and indicate faults that can be corrected prior to completion of the batch. This approach is capable of tracking the progress of new batch runs, identifying the time periods in which the fault occurred and detecting underlying cause.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.6
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• pp.115-122
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• 2010

The main purpose of centralized monitoring system is to manage factory electrical installation efficiently by on-line data acquisition and supervisory control. The existing centralized system is only able to be managed by operator whenever electrical installation's faults are detected. Therefore, it may be possible for propagating the installation's faults when operator make the unexpected mistakes. To overcome the unexpected mistakes, in this paper, the author presents a centralized monitoring system for factory electrical installation using active database. by using active database production rule, stated system can minimize unexpected mistake and can operate centralized monitoring system efficiently. Test results on the five factory electrical installations show that performance is efficient and robust.

• Korean Journal of Agricultural Science
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• v.42 no.3
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• pp.215-222
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• 2015

This paper sketches the strategies and designs for monitoring system of swine genetic improvement. The system should reflect every side of pig production. The system leads us to assess the efficiency of pig production and the scope of the system includes not only nucleus, multiplying and commercial herds, but also packing and processing sectors. For more accurate statistics, data for this monitoring system must be collected from all above mentioned areas, but not by random sampling. Futhermore, data analysis results including seedstocks and distribution information of genetic trend should be included in the system. The schema of knowledge database system could be employed in the system. The monitoring system in the final destination would unify the systems derived from various sources and provide any solution in swine industry including pig breeding.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Journal of the Korea Safety Management & Science
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• v.15 no.4
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• pp.171-178
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• 2013

Workers are avoiding production/manufacturing sites due to the poor working environment and concern over safety. Small and medium-sized businesses introduce new equipment to secure safety in the production site or ensure effective process management by introducing the real-time monitoring technique for existing equipment. The importance of real-time monitoring of equipment and process in the production site can also be found in the ANSI/ISA-195 model. Note, however, that most production sites still use paper-based work slip as a process management technique. Data reliability may deteriorate because information on the present condition of the production site cannot be collected/analyzed properly due to manual data writing by the worker. This paper introduces the monitoring and process management technique based on a direct facility interface to secure safety in the field by improving the poor working environment and enhance there liability and real-time characteristics of the production data. Since the data is collected from equipment in real-time directly through the SIB-based interface and PLC-based interface, problems associated with workers' manual data input are expected to be solved; safety can also be improved by enhancing workers' attention to work by minimizing workers' injuries and disruption.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.6
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• pp.1458-1464
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• 2007

Today, domestic and foreign manufacturing industries have to cope with obsolescence of manufacturing equipment because the shifting market trends drive the rapid changes in the production process resulting in stressful operation. Quality control process for manufacturing and production involves a familiar step - when the production process is completed, every item is subjected to various routine tests to determine that it meets the minimum quality standards. Typically, when a faulty product is found, the production line has to be stopped and the current batch is marked for further inspected and exhaustive testing. In this research, we propose a quality monitoring system for productivity enhancement. Our approach aims to reduces the operational down time in the production line of a car-component factory. The proposed monitoring system for productivity enhancement is designed to collect the data through testing at each phase of the assembly line and uses predictive methods on the collected data to achieve early detection of faults in the production process and minimize the number of products in a faulty batch thus minimizing the losses incurred from defective products. More importantly, this system aims to forecast and proactively detect faults and activate warnings when they are detected thus minimizing items in the defective batch, reducing the damage to manufacturing equipment and ultimately reducing the operational downtime or the delay in the resumption of normal factory operation.

• Journal of Welding and Joining
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• v.27 no.3
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• pp.85-91
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• 2009

A roll projection welding machine is introduced to fabricate metallic sandwich plate consisting of a structured inner sheet with projection-like shape and a pair of skin sheets. To fabricate the metallic sandwich plate of consistent and good quality, two process monitoring methods are introduced; dynamic resistance monitoring and skin sheet temperature monitoring. Dynamic resistance monitoring has no time delay but gives only averaged value over plate width. Skin sheet temperature monitoring has certain amount of time delay but is good for predicting weld quality of specified position. By the two complementary monitoring methods, the characteristics of the new welding process is successfully understood.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.27-33
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• 2012

At present, the overall water resources monitoring ability in China is weak since there is an absence of a sound monitoring system and comprehensive monitoring information. In addition to the problem of weak management ability in monitoring, measurement and information, it can hardly meet the need of implementing the strictest management system of water resource and also restricts the practice of the system to some extent. The production states the necessity of further development of water resources monitoring ability and points out the concept of "One Country, One Account" for constructing water resources information. There is an analysis on the demand on further development of water resources monitoring ability and profound discussion about the strategies for supporting "three red-line" management.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.271-277
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• 2013

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.