• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 추계학술대회
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• pp.1009-1011
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• 2012

본 논문에서는 두개의 Unpolarized F-P LD를 이용한 상호주입 잠김 기반의 파장 가변 광원을 구현하였고, 온도 변화에 따른 특성을 분석하였다. 온도 변화에 따라 파장의 가변 범위가 최대 약 2nm이고, 상대적 밀도 잡음(RIN)는 최저 -110dB/Hz임을 확인하였다. 또한 온도가 높을수록 상대적 밀도 잡음(RIN)는 높아지고, Eye Pattern에 Beating Noise가 발생하는 것을 알 수 있었다.

• Journal of the Optical Society of Korea
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• 제16권2호
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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.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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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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• 제17권4호
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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.

• 대한환경공학회지
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• 제33권5호
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• pp.359-367
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• 2011

압력수 확산공정은 정수공정에서 응집제나 염소용해수를 고압의 압력수로 분사하여 혼합하는 공정이다. 본 연구의 목적은 압력수 확산공정에 대한 전산유체역학적(Computational Fluid Dynamics) 진단을 통해 투입한 약품의 완전 혼합거리 및 혼합 거리를 줄이기 위한 확산판의 크기와 설치거리를 도출하는 것이다. 진단결과 2,200 mm 대형관에 $5kg/cm^2$ 압력수를 50mm, 100 mm 분사관으로 분사할 경우 혼합이 완료되는 혼합거리는 4D였다. 혼합거리를 줄이기 위해 분사관 전방에 확산판을 설치할 경우 분사관이 50 mm일 때 0.1D 직경의 확산판을 분사관 전방 0.2D 거리에 설치하면 혼합거리를 3D로 줄일 수있다. 그러나 분사관이 100 mm인 경우는 확산판의 크기와 설치 거리와는 상관없이 확산판이 없는 4D보다 확산거리를 줄일 수 없는 것으로 진단되었다. 따라서 2,200 mm 관에 압력수를 분사하는 경우는 50 mm 분사관을 설치하는 것이 100 mm보다 훨씬 효율적인 것으로 나타났다.