• 교육시설 논문지
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• 제20권3호
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• pp.29-39
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• 2013

This study addresses the evaluation on the satisfaction of school illumination quality by applying SERVPERF model after extracting factors affecting school illumination quality. Three types of illumination systems (fluorescence light, general LED light and high color rendition LED light) were tested by students who have used each illumination system. Three factors such as effectiveness, esthetic sense and function were developed for evaluation. Satisfaction evaluation was performed based on applied SERVPERF model by comparing perceived levels. The differences of perceived levels of satisfaction on the illumination systems were analyzed by ANOVA. The results said respondents satisfy only the high color rendition LED light regardless of three factors. Especially, students who experienced high color rendition LED light have strong intention to recommend that illumination system to other schools. They also express their desire to use that system at home. Interestingly, there is not much satisfaction difference between fluorescence light and general LED light.

• 한국가구학회지
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• 제24권1호
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• pp.33-41
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• 2013

As professional postnatal care systems have been rapidly supplied, there is sharp increase of postnatal care centers without legal regulations for a mother and a infant. For the quick recovery of mothers, newborn infants that have weaker immune systems are being managed in group in the postnatal care centers. Recently, the attention of the postnatal care centers has been growing because the problem of pneumonia which led to result in a casuality in a infant unit was happen. So, this research analyzed the indoor environment of infant unit through measuring formaldehyde, carbon dioxide, intensity of illumination. As a result of the data, infant units showed higher concentration of formaldehyde and carbon dioxide than Indoor Air Quality Control Law. Moreover, infant units was measured higher intensity of illumination than the range of optimum illumination for infants.

• International journal of advanced smart convergence
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• 제6권4호
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• pp.88-95
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• 2017

The conventional scan methods are based on a laser scanner and a depth camera, which requires high cost and complicated post-processing. Whereas in photometric scanning method, the 3D modeling data is acquired through multi-view images. This is advantageous compared to the other methods. The quality of a photometric 3D model depends on the environmental conditions or the object characteristics, but the quality is lower as compared to other methods. Therefore, various methods for improving the quality of photometric scanning are being studied. In this paper, we aim to investigate the effect of illumination conditions on the quality of photometric scanning data. To do this, 'Moai' statue is 3D printed with a size of $600(H){\times}1,000(V){\times}600(D)$. The printed object is photographed under the hard light and soft light environments. We obtained the modeling data by photometric scanning method and compared it with the ground truth of 'Moai'. The 'Point-to-Point' method used to analyseanalyze the modeling data using open source tool 'CloudCompare'. As a result of comparison, it is confirmed that the standard deviation value of the 3D model generated under the soft light is 0.090686 and the standard deviation value of the 3D model generated under the hard light is 0.039954. This proves that the higher quality 3D modeling data can be obtained in a hard light environment. The results of this paper are expected to be applied for the acquisition of high-quality data.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.575-586
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• 2022

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.

• International Journal of Contents
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• 제4권1호
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• pp.12-15
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• 2008

In this paper, we propose a new method of background removing for digital self-adaptive acquisition in medical X-ray imaging. We analysis the construction of video digital acquisition system and main factors of acquired image quality, propose a more efficiency method to against background non-uniformly. With proposed method, non-uniform illumination back ground was well removed without image quality degradation.

• Journal of the Optical Society of Korea
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• 제18권6호
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• pp.809-817
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• 2014

The loss in contrast-discrimination ability of the human visual system under high ambient illumination level can cause image quality degradation in mobile phones. In this paper, we propose a perceived contrast compensation method by processing the original displayed image. With consideration that the perceived contrast significantly varies across the image, this method extracts the local band contrast from the original image; it then compensates these contrast components to counteract the perceived contrast degradation. Experimental results demonstrate that this method can maintain most contrast details even in high ambient illumination levels.

• ETRI Journal
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• 제37권4호
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• pp.677-684
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• 2015

Recently, orthogonal frequency-division multiplexing (OFDM) was applied to VLC systems owing to its high rate capability. On the other hand, a real-valued unipolar OFDM signal for VLC significantly reduces bandwidth efficiency. For practical implementation, channel estimation is required for data demodulation, which causes a further decrease in spectral efficiency. In addition, the large fluctuation of an OFDM signal results in poor illumination quality, such as chromaticity changes. This paper proposes a spectrally efficient method based on a hidden-pilot-aided precoding technology for VLC with less flickering than a conventional OFDM-based method. This approach can obtain channel information without any loss of bandwidth efficiency while ensuring illumination quality by reducing the flickering effect of an OFDM-based VLC. The simulation results show that the proposed method provides a 6.4% gain in bandwidth efficiency with a 4% reduction in flicker compared to a conventional OFDM-based method.

• 전자공학회논문지
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• 제51권2호
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• pp.114-123
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• 2014

자동차용 영상 사고기록장치(블랙박스)는 도로위의 일반적인 상황만을 촬영하게 된다. 또한, 급격한 조도변화의 상황에서는 주위의 환경을 제대로 인식하기 어렵고 렌즈 자체의 왜곡이 매우 심하기 때문에 사고 발생 시 명확한 증거로 사용하기 어렵다. 이러한 문제를 해결하기 위한 첫 번째 방법으로 정규화된 밝기 정보의 수표현자인 NLD(Normalized Luminance Descriptor)값과 정규화된 명암정보의 수표현자인 NCD(Normalized Contrast Descriptor)값을 정의하여 추출하고 두 값의 관계를 갖는 영상의 수표현자인 NIQ(Normalized Image Quality)값을 사용하여 급격한 조도변화에 대응하였다. 두 번째로, 어안렌즈가 디자인되는 방법을 기본으로 하는 FOV(Field Of View)모델을 이용하여 렌즈의 왜곡을 보정한다. 결과적으로 두 가지 영상왜곡은 각각 감마보정 및 렌즈왜곡보정의 영상처리 기법을 사용하여 병렬로 처리한 후 이를 하나의 영상으로 통합하는 알고리즘을 제안한다.

• 한국환경보건학회지
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• 제32권2호
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• pp.140-148
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• 2006

Recently, indoor air quality (IAQ) in workplace, residential environments and schools has been concerned of people, scientists and related the public, and has recognized the health effects related to indoor air pollution. Therefore, this study was performed to investigate the characteristics of IAQ in 55 kindergartens, elementary school, middle schools, and high schools from June, 2004 to May, 2005 in Korea. We measured indoor air pollutants($PM_{10},\;CO_2$, HCHO, total bacteria colony(TBC), CO, radon, TVOCs, asbestos, and $O_3$), and physical factors(noise, temperature, relative humidity, and illumination) with necessary of management for IAQ in school. We classified into 5 kinds of the school by period since building completion, <1 year, 1-3 years, 3-5 years, and 5-10 years. The concentration of pollutants and the level of physical factors compared with standards and guidelines of IAQ on the Ministry of Environment, the Ministry of Health and Welfare, and the Ministry of Education and Human Resources Development. The major results obtained from this study were as follows. Temperature, relative humidity and illumination among the physical factors did not exceed the standards, but noise exceeded it. Asbestos and $O_3$ did not detect in surveyed classrooms. CO, TBC, TVOCs, and HCHO in kindergartens, TBC in elementary schools, TBC, TVOCs dnd HCHO in middle schools, and HCHO in high schools detected the standards. This study is conducted as a part of efforts to provide a foundational data for further relative researches on management of IAQ of school. Therefore, we suggest that country plan for management of IAQ in school should be established through long-term and continuous investigation for assessment on IAQ in school and health risk assessment for students.

• Journal of Information Processing Systems
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• 제20권4호
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• pp.558-573
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• 2024

Considering factors such as illumination, camera quality variations, and background-specific variations, identifying a face using a smartphone-based facial image capture application is challenging. Face Image Quality Assessment refers to the process of taking a face image as input and producing some form of "quality" estimate as an output. Typically, quality assessment techniques use deep learning methods to categorize images. The models used in deep learning are shown as black boxes. This raises the question of the trustworthiness of the models. Several explainability techniques have gained importance in building this trust. Explainability techniques provide visual evidence of the active regions within an image on which the deep learning model makes a prediction. Here, we developed a technique for reliable prediction of facial images before medical analysis and security operations. A combination of gradient-weighted class activation mapping and local interpretable model-agnostic explanations were used to explain the model. This approach has been implemented in the preselection of facial images for skin feature extraction, which is important in critical medical science applications. We demonstrate that the use of combined explanations provides better visual explanations for the model, where both the saliency map and perturbation-based explainability techniques verify predictions.