• Title, Summary, Keyword: Light and dark adaptations

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The luring effect of the sardine bait for octopus pot in laboratory (실험실에서 문어 통발용 정어리 미끼의 유인 효과)

  • AN, Young-il
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.55 no.3
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    • pp.190-197
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    • 2019
  • This study investigated the luring effect of the sardine bait, which is used to catch octopus with pot, as the preliminary study for the development of alternative bait for octopus pot. The soaking time for bait was divided into "5 days or less" and "11 days or longer" The number of times octopus entered the pot with bait and the empty pot was investigated under dark adaptation and light adaptation processes and the distribution of tank section was investigated under light adaptation process. The case of "11 days or longer" sardine soaking time showed higher rate of distribution in the section of pot with bait compared to the case of "5 days or less" In the case of "5 days or less" soaking time, the number of times the octopus entered the pot with bait was similar to that it entered the pot without it even during dark adaptation and light adaptation. However, in the case of "11 days or longer", the octopus entered the pot with bait more quickly than the pot without bait and more frequently during dark adaptation hours. There were cases where the octopus did not enter any pot. In the case of "5 days or less", with less decomposition of baits, the octopus entered the empty pot more during light adaptation process, and it appeared that the pot was used as a hideout.

Adaptations of Estuarine and Freshwater Phytoplankton to Urea Decomposition (기수 및 담수 식물플랑크톤의 요소 분해에 대한 적응)

    • 한국해양학회지
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    • v.28 no.4
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    • pp.323-331
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    • 1993
  • The concentration-dependence of and the effect of light on urea decomposition, and the suppression of urea decomposition by ammonium were studied to understand adaptations in phytoplankton to utilization of urea in the estuarine system of the Mankyung and Dongjin rivers and a hypertrophied pond. Results of size-fractionation showed that bacterial fraction played a minor role (14%) in urea decomposition in the estuary. However, the role of bacteria in urea decomposition seemed to increase in a hypertrophic pond. Natural phytoplankton communities exhibited a monophonic or biphasic kinetics of urea decomposition over a wide range of concentration (upto 7.7 mM). the addition of high concentration of ammonium and incubation of the euphotic samples in the dark caused reductions in the urea decomposition rates. It is suggested that understanding of adaptations in phytoplankton to urea decomposition would help to study the temporal and spatial variabilities of urea decomposition rates in the field and the significance of urea in nitrogen cycle.

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Rear Vehicle Detection Method in Harsh Environment Using Improved Image Information (개선된 영상 정보를 이용한 가혹한 환경에서의 후방 차량 감지 방법)

  • Jeong, Jin-Seong;Kim, Hyun-Tae;Jang, Young-Min;Cho, Sang-Bok
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.1
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    • pp.96-110
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    • 2017
  • Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.