• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.70-76
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• 2021

A cylindrical microlens (CML) has been widely used as an optical element for organic light-emitting diodes (OLEDs), light diffusers, image sensors, 3D imaging, etc. To fabricate high-performance optoelectronic devices, the CML with high aspect ratio is demanded. In this work, we report on facile solution-based processes (i.e., slot-die and needle coatings) to fabricate the CML using poly(methyl methacrylate) (PMMA). It is found that compared with needle coating, slot-die coating provides the CML with lower aspect ratio due to the wide spread of solution along the hydrophilic head lip. Although needle coating provides the CML with high aspect ratio, it requires a high precision needle array module. To demonstrate that the aspect ratio of CML can be enhanced using slot-die coating, we have varied the molecular weight of PMMA. We can achieve the CML with higher aspect ratio using PMMA with lower molecular weight at a fixed viscosity because of the higher concentration of PMMA solute in the solution. We have also shown that the aspect ratio of CML can be further boosted by coating it repeatedly. With this scheme, we have fabricated the CML with the width of 252 ㎛ and the thickness of 5.95 ㎛ (aspect ratio=0.024). To visualize its light diffusion property, we have irradiated a laser beam to the CML and observed that the laser beam spreads widely in the vertical direction of the CML.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.321-327
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• 2022

In this paper, a wrinkled structure was formed on the PDMS surface through UV/Ozone treatment, and the wrinkle structure formation mechanism was revealed through physicochemical characterization. A wrinkle structure was formed on the PDMS surface through UV/Ozone treatment for 30 min, and periodic wrinkle formation on the PDMS surface was confirmed by cross-sectional imaging of the scanning electron microscope. In addition, through x-ray photoelectron spectroscopy spectral analysis, it was confirmed that the silica-like-surface of SiOx on the PDMS surface was formed by UV/Ozone. The results of this study not only improve the understanding of the mechanism of wrinkle structure formation on the PDMS surface by UV/Ozone treatment, but also can be used as a basic study to adjust the amplitude and period of the wrinkle structure according to UV/Ozone irradiation conditions in the future.contact angles and the surface energies of FSAMs, it was confirmed that pretilt angles of LC molecules increased according to the alkyl chain length. High optical transparency and uniform homeotropic LC alignment characteristics of FSAMs showed the possibility of FSAMs as an LC alignment layers.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.715-722
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• 2022

If you don't regularly go to the dentist to check your teeth, it is difficult to notice cavities or various diseases of your teeth until you have pain or discomfort. Dental plaque is produced by the combination of food or foreign substances and bacteria in the mouth. Starch breaks down from the bacteria that form tartar. The acid that occurs at this time melts the enamel of the teeth and becomes a cavity. So tartar management is important. Poppyrin, the metabolism of bacteria in the mouth, reacts at 405 nm wavelengths and becomes red fluorescent, which can be seen by imaging through certain wavelength filters. By the above method, Frag and tartar are fluorescently detected and photographed with a yellow series of filters that pass wavelengths of 500 nm or more. It uses MATLAB to detect and display red fluorescence through image processing. Using the difference in voltage between normal teeth and tartar through an optical measuring circuit, it was connected to an Arduino and displayed on the LCD. This allows the user to know the presence and location of dental plaque more accurately.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.2-41.2
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• 2021

In this study, we generate He I 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He I 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single input SDO/AIA 19.3 nm image for Model I, single input 30.4 nm image for Model II, and double input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He I 1083 nm images with high correlations. Second, the model with two input images shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean squared error (RMSE). CC and RMSE between real and AI-generated ones for the model III with 4 by 4 binnings are 0.84 and 11.80, respectively. Third, AI-generated images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He I 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of chromosphere and coronal holes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.374-376
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• 2021

For the diagnosis of cancer-related diseases in clinical practice, pathological examination using biopsy is essential after basic diagnosis using imaging equipment. In order to proceed with such a biopsy, the assistance of an oncologist, clinical pathologist, etc. with specialized knowledge and the minimum required time are essential for confirmation. In recent years, research related to the establishment of a system capable of automatic classification of cancer cells using artificial intelligence is being actively conducted. However, previous studies show limitations in the type and accuracy of cells based on a limited algorithm. In this study, we propose a method to identify a total of 4 cancer cells through a convolutional neural network, a kind of deep learning. The optical images obtained through cell culture were learned through EfficientNet after performing pre-processing such as identification of the location of cells and image segmentation using OpenCV. The model used various hyper parameters based on EfficientNet, and trained InceptionV3 to compare and analyze the performance. As a result, cells were classified with a high accuracy of 96.8%, and this analysis method is expected to be helpful in confirming cancer.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.4
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• pp.55-61
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• 2022

There is a major interest in diagnostic technology for multiple cancers worldwide. In order to reduce the difficulty of cancer diagnosis, a liquid biopsy technology based on a microfluidic device using trace amounts of biofluids such as blood is being studied. And optical biosensing, which measures the concentration of analytes through fluorescence imaging using biofluids, requires various strategies to improve sensitivity, and specialists and equipment are needed to carry out these strategies. This leads to an increase in diagnostic and production costs, and it is necessary to develop a technology to solve this problem. In this paper, we design and propose a fluorescent multi-cancer diagnostic sensing platform structure that implements passive self-separation technology and molecular recognition activation functions by fluid mixing, only with the geometry and microfluidic phenomena of microchannels based on self-driven flow by capillary force. In order to check the parameters affecting the performance of the plasma separation part of the designed sensor, the hydrodynamic diameter of the channel and the viscosity of the fluid were set as variables to confirm the formation of plasma separation flow through simulation. And finally, we propose an optimal sensor platform structure.

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.1-8
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• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.919-932
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• 2019

Fine particulate matter (PM2.5) is not only affected by anthropogenic emissions, but also intensifies, migrates, decreases by hydrometeorological factors. Therefore, it is essential to understand relationships between the hydrometeorological factors and PM2.5 concentration. In Korea, PM2.5 concentration is measured at the ground observatories and estimated data are given to locations where observatories are not present. In this way, the data is not suitable to represent an area, hence it is impossible to know accurate concentration at such locations. In addition, it is hard to trace migration, intensification, reduction of PM2.5. In this study, we analyzed the relationships between hydrometeorological factors, acquired from Global Land Data Assimilation System (GLDAS), and PM2.5 by means of Bayesian Model Averaging (BMA). By BMA, we also selected factors that have meaningful relationship with the variation of PM2.5 concentration. 4 PM2.5 concentration models for different seasons were developed using those selected factors, with Aerosol Optical Depth (AOD) from MODerate resolution Imaging Spectroradiometer (MODIS). Finally, we mapped the result of the model, to show spatial distribution of PM2.5. The model correlated well with the observed PM2.5 concentration (R ~0.7; IOA ~0.78; RMSE ~7.66 ㎍/㎥). When the models were compared with the observed PM2.5 concentrations at different locations, the correlation coefficients differed (R: 0.32-0.82), although there were similarities in data distribution. The developed concentration map using the models showed its capability in representing temporal, spatial variation of PM2.5 concentration. The result of this study is expected to be able to facilitate researches that aim to analyze sources and movements of PM2.5, if the study area is extended to East Asia.

• Journal of The Korean Ophthalmological Society
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• v.58 no.8
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• pp.960-967
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• 2017

Purpose: To compare the macular choroidal thickness in patients with thyroid-associated ophthalmopathy (TAO) with those with normal tension glaucoma (NTG). Methods: A total of 70 normal eyes, 74 eyes with TAO and 60 eyes with NTG were enrolled in this study. All patients underwent spectral-domain optical coherence tomography (SD-OCT) (Cirrus HD-OCT, Carl Zeiss Meditec Inc., Dublin, CA, USA). Macular choroidal thickness was assessed using enhanced depth imaging. The average macular choroidal thickness was defined as the average value of three measurements: at the fovea and at the points located 1.5 mm in the nasal and temporal directions from the fovea. Generalized estimating equations were used to uncover factors affecting the average macular choroidal thickness. Results: The average, superior and inferior quadrant retinal nerve fiber layer thicknesses were significantly thinner in the NTG group compared with the TAO and control groups (p < 0.001). The average macular choroidal thickness of the TAO group, NTG group and controls was $281.01{\pm}60.06{\mu}m$, $241.66{\pm}55.00{\mu}m$ and $252.07{\pm}55.05{\mu}m$, respectively, which were significantly different (p = 0.013). The subfoveal, nasal and temporal side choroidal thicknesses were significantly thinner in the NTG group compared with the TAO group (p = 0.014, 0.012 and 0.034, respectively). Subjects with TAO were associated with a thicker average macular choroidal thickness compared with the NTG group after adjusting for age, sex, spherical equivalent and intraocular pressure (${\beta}=32.61$, p = 0.017). Conclusions: Macular choroidal thickness was significantly thicker in patients with TAO compared with those with NTG. Further evaluation is required to determine if a thick choroid in subjects with TAO has any role in glaucomatous optic neuropathy.