• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.17-21
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• 2022

In the case of the manufacturing industry that produces high-tech components such as semiconductors and large flat panel displays, the manufacturing space is made into a cleanroom to increase product yield and reliability, and various environmental factors have been managed to maintain the environment. Among them, airborne particle is a representative management item enough to be the standard for actual cleanroom grade, and a sequential particle monitoring system is usually used as one parts of the FMS (Fab or Facility monitoring system). However, this method has a problem in that the measurement efficiency decreases as the length of the sampling tube increases. In this study, in order to solve this problem, a multiple regression model was created. This model can correct the measurement error due to the decrease in efficiency by sampling tube length.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.1-5
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• 2023

Most of the production process is performed in a cleanroom in the case of facilities that produce semiconductor chips or display panels. Therefore, environmental management of cleanrooms is very important for product yield and quality control. Among them, airborne particles are a representative management item enough to be the standard for the actual cleanroom rating, and it is a part of the Fab or Facility monitoring system, and the sequential particle monitoring system is mainly used. However, this method has a problem in that measurement efficiency decreases as the length of the sampling tube increases. In addition, a statistically significant test of deterioration in efficiency has rarely been performed. Therefore, in this study, the statistically significant test between the number of particles measured by InSitu and the number of particles measured for each sampling tube ends(Remote). Through this, the efficiency degradation problem of the sequential particle monitoring system was confirmed by a statistical method.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.370-377
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• 2000

The red ginseng is very popular as a health food. It has been manufactured with raw ginseng by the conventional method. But, the yield of the heaven grade ginseng (the best quality red ginseng) among the whole products is around 5-7％, Therefore, the yield should be improved in order to increase economic returns. In this study, a new model of drying system was developed to improve the yield of heaven grade ginseng from 7% to 15% or more. For this system, temperature and relative humidity were controlled by the feedback control system, and a solenoid valve for steam supply and other variables were controlled by the PC. The special features of this system developed are an image processing system for monitoring the red ginseng during the drying process in the drying chamber, and a cylindrical porous tray for holding ginseng that is rotating with the speed of 0-10rpm in the drying chamber and makes uniform drying of red ginseng possible.

• Journal of the Korean Solar Energy Society
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• v.27 no.2
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• pp.29-35
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• 2007

Monitoring system is constructed for evaluating and analyzing performance of installed 50kW grid-connected PV system and have been monitored since October 2005. As climatic and irradiation conditions have been varied through long-term operation, there is necessity for evaluating numerical values of PV(Photovoltaic) system performance to observe the overall effect of environmental conditions on their operation characteristics. This paper presents performance monitoring results and analysis on component perspective(PV array and power conditioning system) and global perspective(yield, losses) of PV system for one year monitoring periods.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.4
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• pp.393-399
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• 2009

Historically, industrial condition monitoring has been performed by costly hard-wired sensors or infrequent checks by maintenance personnel equipped with hand held monitoring equipment. Self- powered wireless condition monitoring systems provides on-line monitoring of critical plant and machinery providing major operating cost benefits. A vibration energy harvester(VEH) is a device that converts kinetic energy occurred by machine vibration into useable electrical energy. Using VEHs to power wireless monitoring systems can yield significant benefits: increased reliability, lower life time costs and no battery disposal issues, etc. This paper proposes the novel prototype design and manufacturing of a VEH that can eliminate the effect by failed batteries.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.132-139
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• 2009

Yield is a very important measure that can expresses simply for productivity and performance of company. So, yield is used widely in many industries nowadays. With the development of the information technology and online based real-time process monitoring technology, many industries operate the production lines that are developed into automation system. In these production lines, the product structures are very complexity and variety. So, there are many multi-variate processes that need to be monitored with many quality characteristics and associated process variables at the same time. These situations have made it possible to obtain super-large manufacturing process data sets. However, there are many difficulties with finding the cause of process variation or useful information in the high capacity database. In order to solve this problem, neural networks technique is a favorite technique that predicts the yield of process for process control. This paper uses a neural networks technique for improvement and maintenance of yield in manufacturing process. The purpose of this paper is to model the prediction of a sub process that has much effect to improve yields in total manufacturing process and the prediction of adjustment values of this sub process. These informations feedback into the process and the process is adjusted. Also, we show that the proposed model is useful to the manufacturing process through the case study.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.1
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• pp.99-109
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• 2012

This study aimed to develop a Structural Health Monitoring System for steel beams in the manner of suggesting and verifying a theoretical formula for displacement estimation using strain gauges, and estimating the loading points and magnitude. According to the results of this study, it was found that when a load of 160kN (56% of the yield load) was applied, the error rate of the deflection obtained with a strain gauge at the point of maximum deflection compared to the deflection measured with a displacement meter was within 2%, and that the estimates of the magnitude and points of load application also showed the error rate of not more than 1%. This suggests that the displacement and load of steel beams can be measured with strain gauges and further, it will enable more cost-effective sensor designing without displacement meter or load cell. The Structural Health Monitoring System program implemented in Lab VIEW gave graded warnings whenever the measured data exceeds the specified range (strength limit state, serviceability limit state, yield strain), and both the serviceability limit state and strength limit state could be simultaneously monitored with strain gauge alone.

• Journal of IKEEE
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• v.23 no.1
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• pp.22-28
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• 2019

This paper presents the monitoring method of machining error and quality check to improve the productivity of boring manufacturing process. Machining error usually appears as the offset of spatial location of actual cutting path compared to ideal cutting path. In order to monitor an error of workpiece, multiple factors affecting quality of boring, such as distortion of workpiece, clamping error, radial rotation error of the spindle and motion error of machine tools, were took into account. To verify the productive quality, we propose the quality check system. The system based on IT convergence analyzes the process error rate and saves the analyzed data in memory. Also, these play important roles in detecting an inferior production goods and can decrease the production cost and loss of bearing.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.375-376
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• 2012

• Journal of Bio-Environment Control
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• v.8 no.4
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• pp.232-241
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• 1999

The automatic irrigation system using the stem diameter monitoring and the transpiration model for the determination, respectively, of irrigation timing and amount was designed and evaluated for its applicability in pot and field culture of greenhouse tomato. In the pot culture condition, the yield and quality of greenhouse tomato were improved when irrigation was practiced based on the stem diameter monitoring and the transpiration model as compared to the irrigation practice based on soil moisture monitoring. However, the effects were not significant in the field culture condition.