• Journal of Environmental Health Sciences
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• v.38 no.2
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• pp.118-127
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• 2012

Objectives: This study was undertaken to assess the sanitary conditions in the kitchens of food court/cafeterias and determine seasonal variations. Methods: We measured environmental factors (air temperature, relative humidity, illumination intensity, noise level), and dropping airborne microbes (bacteria and fungi) in the kitchens of eight food court/cafeterias in four seasons (January, April, July, and October). Air temperature and relative humidity were measured with in/out thermo-hygrometers at 1.2-1.5 m above floor level. Illuminance measurement was performed through the multiple point method of Korean Standards (KS). Noise level was measured by the standard methods for the examination of environmental pollution (noise and vibration) of Korea. The estimation of dropping airborne bacteria and fungi was performed through use of Koch's method. Results: The highest kitchen air temperature was in July, and the lowest in January. The average temperature surpassed $21^{\circ}C$ throughout the seasons, suggesting a higher temperature than required for the safe handling of food. Humidity in all the kitchens was measured in the range of 50-60%. Half of the kitchens showed illumination intensities below 300 Lux in April. It was found that the sound pressure level of noise in almost all of the kitchens was higher than 85 dB (A). The highest levels of dropping airborne bacteria and fungi were noted in July. The numbers of airborne bacteria were higher than those of fungi. The levels of dropping airborne bacteria and fungi were affected by air temperature, relative humidity, season, and place. Conclusions: This study indicates that the kitchen environments were unqualified to supply safe food. The hygiene level of the kitchens should be improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.93-96
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• 2015

The aim of the estimation of disparity map is to find the corresponding pixels from similar two or more images. However, it is a difficult problem to get precise and consistent disparity under a variety of real world situations. In other words, the color values of stereo images are easily influenced by radiometric properties such as illumination direction, illumination color, and camera exposure. Therefore, conventional stereo matching methods can have low performances under radiometric conditions. In this paper, we propose an approaching of disparity map estimation that is reliable in controlling various radiometric variations close to the real environment. This method is motivated by following constancy. Even though each other has different radiometric property in stereo images, intensity of pixels of object have general constancy in specific block. Experimental results show that the proposed method has better performances compared to the comparison group under different radiometric conditions between stereo images. Consequentially, the proposed method is able to estimate the disparity map in stable under various radiometric variations.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• JSTS:Journal of Semiconductor Technology and Science
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• v.4 no.2
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• pp.100-105
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• 2004

The influencing factors on the OPC (optical proximity correction) results are quantitatively analyzed using OPCed L/S patterns. ${\sigma}$ values of proximity variations are measured to be 9.3 nm and 15.2 nm for PR-A and PR-B, respectively. The effect of post exposure bake condition is assessed. 16.2 nm and 13.8 nm of variations are observed. Proximity variations of 11.6 nm and 15.2 nm are measured by changing the illumination condition. In order not to seriously deteriorate the OPC, these factors should be fixed after the OPC rules are extracted. Proximity variations of 11.4, 13.9, and 15.2 nm are observed for the mask mean-to-targets of 0, 2 and 4 nm, respectively. The decrease the OPC grid size from 1 nm to 0.5 nm enhances the correction resolution and the OCV is reduced from 14.6 nm to 11.4 nm. The enhancement amount of proximity variations are 9.2 nm corresponding to 39% improvement. The critical dimension (CD) uniformity improvement for adopting the small grid size is confirmed by measuring the CD uniformity on real SRAM pattern. CD uniformities are measured 9.9 nm and 8.7 nm for grid size of 1 nm and 0.5 nm, respectively. 22% improvement of the CD uniformity is achieved. The decrease of OPC grid size is shown to improve not only the proximity correction, but also the uniformity.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1024-1033
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• 2016

In this study, a system is proposed for analyzing whether driver's eyes are open or closed on the basis of images to determine driver's drowsiness. The proposed system converts eye areas detected by a camera to a color space area to effectively detect eyes in a dark situation, for example, tunnels, and a bright situation due to a backlight. In addition, the system used a thickness distribution of a detected eye area as a feature value to analyze whether eyes are open or closed through the Support Vector Machine(SVM), representing 90.09% of accuracy. In the experiment for the images of driver wearing glasses, 83.83% of accuracy was obtained. In addition, in a comparative experiment with the existing PCA method by using Eigen-eye and Pupil Measuring System the detection rate is shown improved. After the experiment, driver's drowsiness was identified accurately by using the method of summing up the state of driver's eyes open and closes over time and the method of detecting driver's eyes that continue to be closed to examine drowsy driving.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.9
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• pp.424-433
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• 2014

The purpose of this study is to evaluate the effectiveness in the lighting performance of mixed type light-shelf, by comparing and analyzing the internal light-shelf, external light-shelf, and mixed-type light-shelf. The performance of light-shelf was evaluated according to the angle of light-shelf at summer solstice, winter solstice, vernal equinox, and autumn equinox. The comparative analysis between the internal light-shelf, external light-shelf, and mixed-type light-shelf was carried out using the performance evaluation and analysis method. The result of performance evaluation is shown as follows. The mixed type light-shelf showed the highest lighting performance all at summer solstice, winter solstice, vernal equinox, and autumn equinox, followed by the external light-shelf and the internal light-shelf in the same order. The mixed type light-shelf was the most favorable for bringing daylight to indoors by adjusting the angle of light-shelf, and it also showed the highest lighting performance in terms of uniformity ratio of illumination which indicates the quality of light.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.5
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• pp.755-765
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• 2015

This paper is concerned with Interval Type-2 Radial Basis Function Neural Network classifier realized with the aid of Census Transform(CT) and (2D)2LDA methods. CT is considered to improve performance of face recognition in a variety of illumination variations. (2D)2LDA is applied to transform high dimensional image into low-dimensional image which is used as input data to the proposed pattern classifier. Receptive fields in hidden layer are formed as interval type-2 membership function. We use the coefficients of linear polynomial function as the connection weights of the proposed networks, and the coefficients and their ensuing spreads are learned through Conjugate Gradient Method(CGM). Moreover, the parameters such as fuzzification coefficient and the number of input variables are optimized by Artificial Bee Colony(ABC). In order to evaluate the performance of the proposed classifier, Yale B dataset which consists of images obtained under diverse state of illumination environment is applied. We show that the results of the proposed model have much more superb performance and robust characteristic than those reported in the previous studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.517-525
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• 2006

In this paper, the algorithm which improve the efficiency of surveillance in spite of the change of light is proposed and confirmed by virtue of the experiments. One of the problems for the implementation of visual surveillance system is the image processing technique to overcome with the variations of illumination lights. Some conventional systems are generally not considered the error due to the change of lights because the system use at indoor. In practical, the factors of bad image can be classified to the ghosts due to the reflection of lights and shadows in a scene. Especially weak images and noises at night are decreased the performance of visual surveillance system. In the paper, the filter which improve the images with some change of illumination lights is designed and the gabor filter is used for recognition and tracking of the moving objects. In the results, the system showed that the recognition and tracking were obtained $92\sim100%$ of recognition rate at daytime, but $80\sim90%$ of nighttime.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.25-36
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• 2008

In this paper, we present a robust hand recognition approach to sudden illumination changes. The proposed approach constructs a background model with respect to hue and hue gradient in HSI color space and extracts a foreground hand region from an input image using the background subtraction method. Eighteen features are defined for a hand pose and multi-class SVM(Support Vector Machine) approach is applied to learn and classify hand poses based on eighteen features. The proposed approach robustly extracts the contour of a hand with variations in illumination by applying the hue gradient into the background subtraction. A hand pose is defined by two Eigen values which are normalized by the size of OBB(Object-Oriented Bounding Box), and sixteen feature values which represent the number of hand contour points included in each subrange of OBB. We compared the RGB-based background subtraction, hue-based background subtraction and the proposed approach with sudden illumination changes and proved the robustness of the proposed approach. In the experiment, we built a hand pose training model from 2,700 sample hand images of six subjects which represent nine numerical numbers from one to nine. Our implementation result shows 92.6% of successful recognition rate for 1,620 hand images with various lighting condition using the training model.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.36.1-37
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• 2021

Six images of IRC+10216 taken by the Hubble Space Telescope at three epochs in 2001, 2011, and 2016 are compared in the rest frame of the central carbon star. An accurate astrometry has been achieved with the help of Gaia Data Release 2. The positions of the carbon star in the individual epochs are determined using its known proper motion, defining the rest frame of the star. In 2016, a local brightness peak with compact and red nature is detected at the stellar position. A comparison of the color maps between 2016 and 2011 epochs reveals that the reddest spot moved along with the star, suggesting a possibility of its being the dusty material surrounding the carbon star. Relatively red, ambient region is distributed in an Ω shape and well corresponds to the dusty disk previously suggested based on near-infrared polarization observations. In a larger scale, differential proper motion of multiple ring-like pattern in the rest frame of the star is used to derive the average expansion velocity of transverse wind components, resulting in ~12.5 km s-1 (d/123 pc), where d is the distance to IRC+10216. Three dimensional geometry is implied from its comparison with the line-of-sight wind velocity determined from half-widths of submillimeter emission line profiles of abundant molecules. Uneven temporal variations in brightness for different searchlight beams and anisotropic distribution of extended halo are revisited in the context of the stellar light illumination through a porous envelope with postulated longer-term variations for a period of 10 years.