• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.1009-1011
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• 2012

In this paper, a wavelength tunable light source based on mutually injected locking with two F-P LDs, has been constructed and then analyzed for wavelength shift and RIN (Relative Intensity noise) according to temperature. We have measured maximum about 2 nm for the wavelength shift and minimum -110dB/hz for the RIN. Also, the RIN and beating noise in eye patterns are increased by changing temperature high.

• Journal of the Optical Society of Korea
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• v.16 no.2
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• pp.101-106
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• 2012

We demonstrate a cost effective broadcast signal transmission at 1.25-Gb/s with 100 GHz channel spacing based on a broadband light source (BLS) for a wavelength division multiplexing-passive optical network (WDM-PON). The BLS is implemented by using mutually injected Fabry-Perot laser diodes (MI F-P LDs). The error-free transmission without a forward error correction (FEC) is achieved by its low relative intensity noise (RIN). The number of usable modes is determined by RIN and/or extinction ratio (ER) in the spectrum sliced light output.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.257-262
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• 2001

This paper describes a specialized CAD system for injection mold design, which has been developed using the application procedure interfaces of Unigraphics. The system consists of modeling modules that are mutually independent and can be accessed without any predefined sequence. In addition, the design process modeling capability proposed in this paper facilitate mold redesign process caused by modification of part shape.

• Design & Manufacturing
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• v.17 no.4
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.5
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• pp.359-367
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• 2011

The Process of Pressurized water diffusion is mixing process by pressurized water injection with coagulate and chlorine water in the water treatment system. The objectives of this research were to evaluate the mixing length and diameter of diffusion plate and distance from injection pipe for complete mixing by using computational fluid dynamics. From the results of CFD simulation, when diameter of injection pipe is 50 mm, 100 mm and injection pressure is $5kg/cm^2$ and the diameter of inlet pipe is 2,200 mm, the complete mixing length is 4D (D: Length as diameter of inlet pipe). When diameter of injection pipe is 50 mm, the diameter of the diffusion plate in o.1D and distance from injection pipe is 0.2D, the complete mixing length is 3D that is the most short mixing length. But when diameter of injection pipe is 100 mm and mutually related the diameter, distance of diffusion plate, the complete mixing length is 4D over. Therefore, as the diameter of inlet pipe is 2,200 mm, the injection pipe 50 mm is more efficient than 100 mm.