• Title/Summary/Keyword: Luminous Intensity Distribution

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Simulation of undewater irradiance distribution in coastal squid jigging vessel using the LED and metal halide fishing lamp combination (LED와 메탈헬라이드 집어등을 겸용한 연안 오징어채낚기 어선의 수중 방사조도 분포 시뮬레이션)

  • Bae, Jae-Hyun;An, Heui-Chun;Kim, Mi-Kyung;Park, Hae-Hun;Jung, Mee-Suk
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.50 no.4
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    • pp.511-519
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    • 2014
  • This study is aimed to analyze the three-dimensional underwater irradiance using an optical simulation software and to clarify the propriety and operation method under considering luminous intensity distribution of the luring lamp and penetrability in the seawater, when we use the light diffuser type 300W high powered LED and the metal halide lamp (MHL) on a coastal squid jigging vessel in the 10-ton class, simultaneously. For their attenuation characteristics of each wavelength in relation to the sea, LED lamp was to be effective in the 1.9-fold at 50 m depth and 2.1-fold at 80 m for underwater irradiance more than MHL according to the power consumption. In addition, the underwater irradiance distribution using the LED and MHL combination was rather increased even when reducing total power usage up to 20% depending on the simulation with changing the configuration and lighting angle of the lamp. These results can be utilized as an evaluation method of the operation and performance of the LED lamp according to adjusting its arrangement and lighting angle.

LED Source Optimization for the LED Chip Array of the LED Luminaires (LED 조명기구에서 LED 칩 배치에 따른 광원 최적화)

  • Yoon, Seok-Beom;Chang, Eun-Young
    • Journal of Digital Convergence
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    • v.14 no.4
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    • pp.419-424
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    • 2016
  • In this paper, we studied a light distribution for the LED chips arrangement using an optical design software. The structures of the edge type LED luminaires are reflector plane, LGP(lighting guide plane) and diffuse plane. The reflector plane is on the middle of the overall structure. We had simulation that placing LED chips on the reflector center of the reflector edge by changing the position of LED chips above the reflector center at 1mm, 2mm, and 3mm respectively. In the case, when LED chips are on the center of the reflector, it shows the light distribution of the general diffuse illumination, the semi-direct distribution with 0.56 efficiency and the direct distribution with 0.31 efficiency. And the wedge type LGP shows more efficiency than the flat type. Gradually increasing shape of semi-spherical type by 0.015mm has power of 1.02W, efficiency of 0.25, and maximum luminous intensity of 0.104W/sr, it also and shows the better optical characteristics than the reflector plane that have no patterns. This semi-spherical type shows the better optical characteristics than the reflector plane that have no patterns.

A Study on Face Recognition using Neural Networks and Characteristics Extraction based on Differential Image and DCT (차영상과 DCT 기반 특징 추출과 신경망을 이용한 얼굴 인식에 관한 연구)

  • 임춘환;고낙용;박종안
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.24 no.8B
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    • pp.1549-1557
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    • 1999
  • In this paper, we propose a face recognition algorithm based on the differential image method-DCT This algorithm uses neural networks which is flexible for noise. Using the same condition (same luminous intensity and same distance from the fixed CCD camera to human face), we have captured two images. One doesn't contain human face. The other contains human face. Differential image method is used to separate the second image into face region and background region. After that, we have extracted square area from the face region, which is based on the edge distribution. This square region is used as the characteristics region of human face. It contains the eye bows, the eyes, the nose, and the mouth. After executing DCT for this square region, we have extracted the feature vectors. The feature vectors were normalized and used as the input vectors of the neural network. Simulation results show 100% recognition rate when face images were learned and 92.25% recognition rate when face images weren't learned for 30 persons.

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