• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.131-146
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• 1999

The satellite visible data have been successfully applied to study the ocean color. Another ocean color sensor, the Ocean Scanning Multi-spectral Imager (OSMI) on the Korea Multi-Purpose Satellite (KOMPSAT) will be launched in 1999. In order to understand the characteristics of future OSMI images, we have first discussed the simulation models and procedures in detail, and produced typical patterns of radiances at visible bands by using radiative transfer models. The various simulated images of full satellite passes and Korean local areas for different seasons, water types, and the satellite crossing equator time (CET) are presented to illustrate the distribution of each component of radiance (i.e., aerosol scattering, Rayleigh scattering, sun glitter, water-leaving radiance, and total radiance). A method to evaluate the image quality and availability is then developed by using the characteristics of image defined as the Complex Signal Noise Ratio (CSNR). Meanwhile, a series of CSNR images are generated from the simulated radiance components for different cases, which can be used to evaluate the quality and availability of OSMI images before the KOMPSAT will be placed in orbit. Finally, the quality and availability of OSMI images are quantitatively analyzed by the simulated CSNR image. It is hoped that the results would be useful to all scientists who are in charge of OSMI mission and to those who plan to use the data from OSMI.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.48-63
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• 2015

In this study, we introduce various hyperspectral data processing techniques for the monitoring of shallow and coastal waters to enlarge the application range and to improve the accuracy of the end results in Korea. Unlike land, more accurate atmospheric correction is needed in coastal region showing relatively low reflectance in visible wavelengths. Sun-glint which occurs due to a geometry of sun-sea surface-sensor is another issue for the data processing in the ocean application of hyperspectal imagery. After the preprocessing of the hyperspectral data, a semi-analytical algorithm based on a radiative transfer model and a spectral library can be used for bathymetry mapping in coastal area, type classification and status monitoring of benthos or substrate classification. In general, semi-analytical algorithms using spectral information obtained from hyperspectral imagey shows higher accuracy than an empirical method using multispectral data. The water depth and quality are constraint factors in the ocean application of optical data. Although a radiative transfer model suggests the theoretical limit of about 25m in depth for bathymetry and bottom classification, hyperspectral data have been used practically at depths of up to 10 m in shallow and coastal waters. It means we have to focus on the maximum depth of water and water quality conditions that affect the coastal applicability of hyperspectral data, and to define the spectral library of coastal waters to classify the types of benthos and substrates.

• Journal of Biosystems Engineering
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• v.33 no.4
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• pp.260-268
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• 2008

Optical diffuse reflectance sensing has potential for rapid and reliable on-site estimation of soil properties. For good results, proper calibration to measured soil properties is required. One issue is whether it is necessary to develop calibrations using samples from the specific area or areas (e.g., field, soil series) in which the sensor will be applied, or whether a general "factory" calibration is sufficient. A further question is if specific calibration is required, how many sample points are needed. In this study, these issues were addressed using data from 42 paddy fields representing 14 distinct soil series accounting for 74% of the total Korean paddy field area. Partial least squares (PLS) regression was used to develop calibrations between soil properties and reflectance spectra. Model evaluation was based on coefficient of determination ($R^2$) root mean square error of prediction (RMSEP), and RPD, the ratio of standard deviation to RMSEP. When sample data from a soil series were included in the calibration stage (full information calibration), RPD values of prediction models were increased by 0.03 to 3.32, compared with results from calibration models not including data from the test soil series (calibration without site-specific information). Higher $R^2$ values were also obtained in most cases. Including some samples from the test soil series (hybrid calibration) generally increased RPD rapidly up to a certain number of sample points. A large portion of the potential improvement could be obtained by adding about 8 to 22 points, depending on the soil properties to be estimated, where the numbers were 10 to 18 for pH, 18-22 for EC, and 8 to 22 for total C. These results provide guidance on sampling and calibration requirements for NIR soil property estimation.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.334-343
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• 1996

Quality evaluation for food and agricultural products have always been one of the most elusive problems associated with the handling , processing and marketing in a food plant production. In order to detect physical foreign materials in food and agricultural products, non-destructive techniques have been developed for many years. Application of X-ray system to detect physical foreign materials in food and agricultural products could be considered to be a high potential method. Especially , it is impossible to detect internal physical foreign materials by visual inspections. In this study, it was tried to be applied for two different X-ray devices. Soft X-ray system with CdTe sensor and X-ray CT scanner were evaluated for advantage of the detection of non-meltallic foreign materials in food and agricultural products . Though the soft X-ray is not a high energy radiation, it is possible to detect small different density in a material. The CdTe sensor has a high resolution for t e soft X-ray energy region. The density characteristics of foods and foreign material were expressed region. The density characteristics of foods and foreign materials were expressed as a soft X-ray energy spectrum. The energy spectrum was analyzed by a personal computer with a multi-channel analyzer. X-ray CT scanner can provide visual image and analyze by three dimensional information inside food and agricultural products. The X-ray CT scanner using as a medical equipment was used to detect a foreign material. The density characteristics of food and foreign materials in food were tried to be detected by the threshold value on the basis of the CT numbers. The soft X-ray absorption characteristics for acrylin plates and distilled water were obtained and could be found the possibility of detecting a small physical foreign materials such as a plastic wrapping film , a stone and grasshopper in food and agricultural products.

• Journal of the Korean Society of Radiology
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• v.6 no.6
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• pp.465-471
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• 2012

In this study, three dimensional X-ray dose distribution from dental X-ray generator system was measured by ALOKA PDM-117 dosimeter. The X-ray dose distribution will be change with XCP-DS FIT in oral shot, because the distance between X-ray generator and the dosimeter. The X-ray dose change affects on patient exposure and radiograph image quality. Therefore, it is important to obtain relation between the X-ray dose and the distance. The X-ray dose at the central position was decreased with increasing the distance. Furthermore, the dose at the edge of the X-ray flux was increased with increasing the distance. The increased dose affects on the patient radiation exposure. The present results will provide for good dental radiograph image and reducing radiation over-exposure on patient.

• Korean Journal of Food Science and Technology
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• v.31 no.2
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• pp.314-321
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• 1999

This study was performed to test application possibility of electronic nose with 32 conducting polymer sensor arrays for aroma analysis of persimmon vinegar. The 20, 30, 40, 50, 60, and $70^{\circ}Bx$ persimmon vinegar concentrates were prepared by vacuum concentration at $55^{\circ}C$. The recovery yield of water soluble solid to concentrates was 55.5% on $20^{\circ}Bx$ persimmon vinegar concentrate. As the concentration of persimmon vinegar concentrates increased, pH of concentrates increased and acidity as acetic acid decreased. From sensory evaluation for persimmon vinegar concentrates, as the concentration of persimmon vinegar concentrates increased, their cooking odor and umami taste increased, sour taste and acidic odor decreased, salty odor and astringency were not changed. Aroma analysis by electronic nose (AromaScan) showed no difference in normalized pattern and odor intensity among persimmon vinegar concentrates. All quality factors among concentrates also were less than 1.042. And so the electronic nose with conducting polymer sensor was not suitable for aroma analysis of persimmon vinegar concentrate.

• Journal of information and communication convergence engineering
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• v.14 no.3
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• pp.200-206
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• 2016

Images can be obtained by collecting rays from objects. The characteristics of electromagnetic wave propagation depend on the medium. In particular, in an underwater imaging system, the interface between air and water must be considered. Further, reflection and transmission coefficients can be found by using electromagnetic theory. Because of the fact that the values of these coefficients differ according to the media, the recorded light intensities will change. A color image sensor has three different color channels. Therefore, the reflection and transmission coefficients have to be calculated individually. Thereafter, by using these coefficients, we can compensate for the color information of underwater objects. In this paper, we present a method to compensate for the color information of underwater objects by using electromagnetic wave propagation theory. To prove our method, we conducted optical experiments and evaluated the quality of the compensated image by a metric known as mean square error.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.433-436
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• 2011

스마트폰, 아이패드, 갤력시S, 탭 등 비약적인 과학기술의 발전으로 생활수준이 향상함에 따라, 한강 르네상스 및 4대강 살리기 사업, 각종 생태하천복원 등 새로운 수변문화공간 구축을 위한 공공 사업에서 첨단무선통신기술을 융합한 신개념의 서비스와 문화가 요구되고 있다. 서울시, 서울인근 지역의 강, 하천에 유량이 유입되는 주요지점에 수중환경감지 센서설치. 주변오염지역 감지 및 수중환경 생태를 실시간 감지하여 이상 징후(BOD, COD, DO, 전도, 탁도 등)발생이 예상되는 주변 오염원인 공장, 폐기처리장, 폐수처리장 등의 시설물을 D/B 관리하여 오염원 관련 피해주민에게 실시간 공지하여 대형사고 예방 및 청정하천 수중환경유지를 목표로 한다. 본 논문에서는 수중환경(하천, 호수, 강 등)대한 지능적이고, 효율적인 관리를 위해 센서수중환경에 적합한 4세대 센서 보드 설계 및 블록다이어그램 설계, 통신기술, 인터페이스 개발을 통해 수중환경에 적합한 센서 및 데이터 획득기술 개발한다.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Ocean and Polar Research
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• v.43 no.4
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• pp.229-243
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• 2021

Quality control (QC) to process observed time series has become more critical as the types and amount of observed data have increased along with the development of ocean observing sensors and communication technology. International ocean observing institutions have developed and operated automatic QC procedures for these observed time series. In this study, the performance of automated QC procedures proposed by U.S. IOOS (Integrated Ocean Observing System), NDBC (National Data Buy Center), and OOI (Ocean Observatory Initiative) were evaluated for observed time-series particularly from the Yellow and East China Seas by taking advantage of a confusion matrix. We focused on detecting additive outliers (AO) and temporary change outliers (TCO) based on ocean temperature observation from the Ieodo Ocean Research Station (I-ORS) in 2013. Our results present that the IOOS variability check procedure tends to classify normal data as AO or TCO. The NDBC variability check tracks outliers well but also tends to classify a lot of normal data as abnormal, particularly in the case of rapidly fluctuating time-series. The OOI procedure seems to detect the AO and TCO most effectively and the rate of classifying normal data as abnormal is also the lowest among the international checks. However, all three checks need additional scrutiny because they often fail to classify outliers when intermittent observations are performed or as a result of systematic errors, as well as tending to classify normal data as outliers in the case where there is abrupt change in the observed data due to a sensor being located within a sharp boundary between two water masses, which is a common feature in shallow water observations. Therefore, this study underlines the necessity of developing a new QC algorithm for time-series occurring in a shallow sea.