Earticle

Chest CT Enhanced CT검사를 시행하는 환자 영상에 TNC(True non-contrast) image(pre)와 Spectral data를 이용한 VNC(Virtual non-contrast) image를 비교분석하여 VNC가 기존의 TNC 정도의 Image quality를 보여주는 지, 혹은 나아가 VNC가 TNC를 대체할 수 있는지에 대한 기초자료를 제시하고자 한다. 2020년 10월부터 2021년 02월까지 응급의료센터에 내원하여 Chest Enhanced CT 처방으로 검사한 환자(무작위 30명)를 대상으로 후향적 영상 분석을 통해 정성적, 정량적 분석을 시행하였다. 조영제는 Bonorex 350을 사용하여 초당 2cc의 속도로 주입하였으며, 장치는 Philips IQon spectral CT를 사용하였다. 정량적 분석 방법으로는 흉부 영상에서 의의가 있는 Trachea, Bifurcation level Bronchus, Upper and lower lung field 등 4곳의 관심영역을 설정하였고, 동일한 ROI를 설정하 여 Hounsfield unit(HU)값을 비교하고 SD값을 비교하여 영상의 평가를 실시하였다. 실제 Data를 수집하여 분석한 결과 Chest Enhanced 영상에서 HU값과 SD값은 TNC 영상과 VNC영상의 수치가 상당 부분 유사한 결과 값이 도출되 었다. TNC image를 (A), VNC image를 (B)로 하여 (A) - (B) 결과값은 평균 HU값 5.12, SD값 4.40이 나왔으며 TNC와 VNC의 차가 거의 없음을 보여주었다. 표준편차 역시 HU 16.96, SD 11.48이 나와 수치상으로 상당히 유사한 값이 도출되었다. 실질적으로 ROI로 획득한 Data만 가지고 TNC image가 VNC image를 대체할 수 있다고 정리하는 것 보다 Chest 영역에서 TNC와 VNC를 비교하여 측정한 결과 값이 상당히 비슷한 수치를 나타내었다는 점에 의미를 두고 기초자료로 제시할 수 있을 것이다. 추후 추가적인 연구를 통해 판독에 있어서도 실제 두 영상의 화질분석 연구가 이어진다면 향후 VNC technique의 적용을 통해 환자의 피폭선량을 낮출 수 있을 것으로 기대한다.

In this study, TNC images (pre) and VNC images (Virtual Non-Contrast) were compared in chest CT dilated CT images. VNC represents the quality of an existing TNC or VNC. From October 2020 to February 2021, a qualitative and quantitative study was conducted on patients (30 random patients) who underwent chest augmentation CT prescription. Bonorex 350 was used as a contrast agent and injected at a rate of 2cc per second. Philips IConspectral CT is used as a device. As a quantitative method, the four areas of interest in thoracic imaging were set to the same ROI, and the Hounsfield unit (HU) value was evaluated. As a result of the collection and analysis of actual data, HU and SD values were derived from chest dilated images as a result of significant similarity between TNC and VNC images. The results of TNC images (A), VNC images (B), (A)-(B) were 5.12, SD values 4.40, indicating little difference between TNC and VNC. Standard deviations for HU16.96 and SD11.48 were also derived. Instead of summarizing that TNC images can replace VNC images only with data obtained through ROI, the comparison results of TNC and VNC in the thoracic area show very similar values and can be presented as basic data. In the future, it is expected that VNC technology will be used to further reduce the exposure dose of patients.

This letter proposes a powerful method to reconstruct the spectral image frames for increasing data transmission capability in Display Field Communication (DFC) systems. The proposed method reconstructs the spectral image iteratively using the good data symbol estimates fed back by the camera decoder. To begin with, initial image is estimated by means of known pilot symbols. Then, hard symbol decisions obtained by iterative decoding are used to improve the quality of the reconstructed image. The proposed technique selects good data pixels using hard information from the decoder and performs re-estimation of the image pixels. From numerical simulations, we show that the proposed method significantly boosts the data transmission capacity of the DFC system over the conventional DFC that uses reference frames.

본 연구는 분광 정보를 활용한 하수관 수심 모니터링에 있어서 최적의 광원과 광도를 선정하였다. 분광 정보를 활용한 수심 모니터링 방법은 과거에서부터 지속적으로 연구 사용되고 있는 방법이다. 자연광 조건에서의 연구는 지속적으로 이루어지고 있으나 특정 광원이 존재하지 않는 하수관과 같은 환경에서의 연구는 부족한 실정이다. 분광 정보를 활용한 수심 산정은 설치되는 인공 조명의 광원과 광원의 각도 및 광도에 따라서 변동성이 크게 발생한다. 본 연구에서는 실내 수로 실험을 통해 정확한 수심 산정을 위한 최적의 광원과 광도를 선정하였다. 광원은 Halogen과 LED를 사용하였으며, 정확한 실험을 위해 암실 조건에서 다양한 광도 및 퇴적물 높이를 통한 수심에 따른 실험 케이스를 다분광 카메라를 통해서 분광 정보를 수집하였다. 수집된 다분광 이미지는 최적 밴드 탐색 기법(Optimal Band Ratio Analysis, OBRA)을 활용하여 수심을 산정하였다. 실험 케이스별 수심 산정 정확도를 비교하여 최적의 광원, 광도를 선정하였다. LED 광원의 실험 결과 R²는 최소 0.181에서 최대 0.799로 넓게 분포하고 있었다. 특히 단 2개의 케이스에서만 0.7 이상의 R²를 보여, 대부분의 케이스가 낮은 결과를 보여 LED광원은 수심 측정에서 낮은 정확도를 보였다. Halogen 광원의 실험 결과, R²는 최소 0.800에서 최대 0.984로 분포하며, 특히 전체 케이스 중 600~800 lx에서 높은 정확도를 보였다. 본 연구를 통해서 분광 정보를 활용한 수심 모니터링에 있어서 최적의 광원, 광도를 제시하였으며, 하수관 수심 모니터링 정확도 향상에 중요한 기여를 할 수 있을 것으로 사료된다.

This study aimed to determine the optimal light source and intensity for monitoring sewer water depth using spectral data. While water depth monitoring methods utilizing spectral data have been continuously researched and applied, most studies have focused on natural light conditions. There is a lack of research in environments such as sewers where specific light sources are absent. The estimation of water depth using spectral data can vary significantly depending on the installed artificial light source, its angle, and intensity. In this study, indoor flume experiments were conducted to select the optimal light source and intensity for accurate water depth estimation. Halogen and LED light sources were used, and spectral data were collected under darkroom conditions using a multispectral camera with various light intensities and sediment heights. The collected multispectral images were processed to calculate water depth using the Optimal Band Ratio Analysis (OBRA) method. The accuracy of water depth estimation was compared across experimental cases to identify the optimal light source and intensity. The experimental results using the LED light source showed that the R² values ranged widely from a minimum of 0.181 to a maximum of 0.799. Notably, only two cases exhibited an R² value above 0.7, indicating that the majority of cases demonstrated low accuracy, suggesting that LED light sources provide limited precision in depth estimation. In contrast, the experiments with halogen light sources yielded R² values ranging from a minimum of 0.800 to a maximum of 0.984, with particularly high accuracy observed in cases with illuminance levels between 600 and 800 lux. This study proposes optimal light sources and illumination levels for depth monitoring using spectral information and is expected to make significant contributions to improving the accuracy of depth monitoring in sewer systems. This translation incorporates more formal and technical language commonly used in academic papers while maintaining clarity and precision.

본 연구는 기존의 하수관 및 우수관 모니터링 기술이 가지는 한계를 극복하기 위한 새로운 방법론을 제시하며, 도시 홍수 대응 시스템의 실시간 모니터링 기술 향상에 기여할 수 있을 것으로 기대된다. 본 연구는 도시 홍수 방어를 위한 하수관 및 우수관의 유량 모니터링 기술 개발을 목표로 한다. 최근 급격한 기후 변화와 집중호우로 인한 도시 내 침수 피해가 증가하면서 기존 하수 및 우수 관거 시스템에 대한 모니터링의 중요성이 더욱 강조되고 있다. 기존 수위계를 통한 수위-유량 관계곡선 기반 측정 방식은 퇴적물 발생과 관 내부 흐름 변화 등으로 인해 정확도가 저하되며, 이로 인해 홍수 방어 인프라의 성능 평가에 한계가 발생한다. 본 연구에서는 이러한 한계를 극복하기 위해 비접촉식 분광 정보를 활용하여 하수관 내 수위가 아닌 수심을 모니터링할 수 있는 방안을 제시한다. 다분광 센서를 이용한 OBRA 기법을 통해 하수 및 우수관의 수심을 측정하는 방법을 개발하였으며, 모니터링 챔버를 활용하여 다양한 광원과 조도 조건 하에서 실험을 진행하여 정확한 데이터 수집과 분석을 수행하였다. 또한, 파이썬의 OpenCV 라이브러리를 이용하여 다분광 이미지 분석 중 발생할 수 있는 위치 오차를 보정하는 알고리즘을 도입하여 측정의 정확성을 향상시켰다. 추적점을 이용한 간격 보정 작업은 이미지 간 불일치를 최소화하여 신뢰성 있는 데이터 확보를 가능하게 했다. 실험 결과를 바탕으로 광원 종류와 조도 변화가 수심 측정에 미치는 영향을 분석하고, 이를 바탕으로 공급유량과 분광 정보를 이용한 수심 산정 기반 유량 측정 기법을 비교·분석하여 본 연구의 수심 측정 방법의 정확도를 평가하였다. 본 연구는 기존 하수관 및 우수관 모니터링 기술의 한계를 극복하기 위한 새로운 방법론을 제시하며, 도시 홍수 대응 시스템의 실시간 모니터링 기술 향상에 기여할 수 있을 것으로 기대된다.

This study aims to develop flow monitoring technology for urban flood defense in sewer and stormwater drainage systems. As urban flooding damage increases due to rapid climate change and intense rainfall, the importance of monitoring existing sewer and stormwater drainage systems is becoming more critical. Traditional measurement methods based on water level-discharge relationship curves through water level gauges suffer from reduced accuracy due to sediment accumulation and variations in internal pipe flow, which limits the performance evaluation of flood defense infrastructure. To overcome these limitations, this study proposes a contactless monitoring approach that measures depth rather than water level within sewer pipes by utilizing spectral data. A method to measure the depth in sewer and stormwater pipes using the OBRA technique with multispectral sensors was developed. Experiments were conducted under various light source and illumination conditions using a monitoring chamber to ensure accurate data collection and analysis. Additionally, an algorithm was implemented using Python's OpenCV library to correct positional errors that may occur during multispectral image analysis, enhancing measurement accuracy. The disparity correction using tracking points minimized discrepancies between images, enabling the collection of reliable data. Furthermore, based on experimental results, the effects of light source types and illumination changes on depth measurements were analyzed. Subsequently, the study evaluated the accuracy of the proposed depth measurement approach by comparing and analyzing flow estimation methods based on depth assessment using spectral data against supply flow data. This study presents a novel methodology to address the limitations of existing sewer and stormwater monitoring technologies and is expected to contribute to the enhancement of real-time monitoring technology in urban flood response systems.

휴대용 라만분광기와 벤치탑 라만분광기의 분석데이터를 서로 비교하여 문화 유산의 채색층 분석 시 나타날 수 있는 특성 차이를 확인하고자 하였다. 전통 안 료 7종과 근ㆍ현대 안료 3종 및 바탕재를 선정하여 채색 시편을 제작하였으며, 안료별 특성 피크를 파악하고 벤치탑 라만 스펙트럼을 기준으로 비교 분석하였 다. 또한, 표면분석과 결정구조분석 결과를 활용하여 열화에 따른 라만 스펙트럼 변화를 파악하였다. 연구 결과, 휴대용 라만 스펙트럼을 통해 전반적인 안료의 주요 피크를 확인할 수 있었다. 벤치탑 라만 스펙트럼을 기준으로 비교한 결과, 호분과 연백, 진사, 연단, 황연, 인공군청의 주요 피크는 ±1.6cm-1 이내에서 일치하였으며, 장비 간의 성능 차이에 의해 석청과 양록, 하엽, 황토 순으로 피크의 위치와 반치폭의 차이 가 크게 나타났다. 황토는 +5.8cm-1로 가장 큰 차이를 보였고, 하엽과 양록은 상 대적으로 높게 형성된 노이즈로 인해 스펙트럼 품질의 차이를 보였다. 열화에 따 른 두 장비 간의 데이터 변화 특성은 전반적으로 유사하였으며, 휴대용 라만 스 펙트럼에서 하엽과 양록은 노이즈가 증가하는 양상을 보였다. 결론적으로 국내 채색문화유산에 주로 사용되는 안료 10종에 대한 특성 분석 결과, 하엽과 양록을 제외한 안료의 특성 피크에 대해서는 명확히 해석할 수 있 어, 휴대용 라만분광기는 현장 분석기술로 유용하게 이용될 수 있을 것으로 판단 된다.

A study was conducted to compare the analytical data obtained from portable Raman spectrometers and benchtop Raman spectrometers to identify differences in their performance when analyzing painted layers in cultural heritage. Seven traditional pigments, three modern pigments, and a substrate material were selected to produce painted specimens. The characteristic peaks of each pigment were identified and compared using benchtop Raman spectra as the reference. Additionally, surface analysis and crystallographic studies were employed to investigate changes in Raman spectra resulting from material degradation. The results showed that the portable Raman spectra could successfully identify the major peaks of the pigments. Upon comparing with the benchtop Raman spectra, the main peaks of white lead, calcite, cinnabar, red lead, chrome yellow, and ultramarine matched within ±1.6 cm-1. However, due to performance differences between the instruments, significant differences in peak positions and full width at half maximum (FWHM) were observed for azurite, atacamatie, emerald green, and yellow ochre. Yellow ochre exhibited the largest discrepancy with a peak shift of +5.8 cm-1, while atacamite and emerald green showed decreased spectral quality due to relatively higher noise levels. Despite these differences, the aging trends between the two instruments were generally consistent, with increased noise observed in the portable Raman spectra of atacamite and emerald green after artificial aging. In conclusion, the characteristic peak analysis of ten pigments commonly used in Korean painted cultural heritage indicated that, excep.for atacamite and emerald green, the portable Raman spectrometer could effectively interpret the characteristic peaks of the pigments. Thus, the portable Raman spectrometer can be considered a valuable tool for on-site analytical applications.

This study explores advanced machine learning techniques for improving crop yield prediction in smart farming, utilizing multi-temporal spectral data from drone-based multispectral imagery. Conducted in garlic orchards in Andong, Gyeongbuk Province, South Korea, the research examines the effectiveness of various vegetation indices and cutting-edge models, including LSTM, CNN, Random Forest, and XGBoost. By integrating these models with the Analytic Hierarchy Process (AHP), the study systematically evaluates the factors that influence prediction accuracy. The integrated approach significantly outperforms single models, offering a more comprehensive and adaptable framework for yield prediction. This research contributes to precision agriculture by providing a robust, AI-driven methodology that enhances the sustainability and efficiency of farming practices.

A single channel land surface temperature (LST) retrieval algorithm named Single Channel Water Vapor Dependent (SCWVD) method was presented for Medium Resolution Spectral Imager (MERSI) thermal infrared band aboard FengYun-3A (FY-3A) satellite. Water Vapor Content (WVC) is the only input parameter in the algorithm assuming the surface emissivity is known. NCEP reanalysis monthly mean datasets were used to develop the SCWVD algorithm. Some tests, including global numerical simulations and validations with both in-situ measurements and MODIS LST product at Lake Tahoe, USA, were carried out to evaluate the algorithm performance. The simulation results showed that the LST could be estimated by the SCWVD algorithm with the root mean square error (RMSE) less than 0.8 K. In the inversion test, MERSI Level 2 water vapor product was employed, and the MERSI band emissivity was calculated using the MODIS band 31 and 32 emissivity by an empirical expression. The results show that the difference between the retrieved MERSI LST and the in-situ measurements is less than 1 K in most situations. The comparison with the MODIS LST products (V5) shows that the RMSE is about 2.3 K.

초분광 영상은 적외선 영역의 전자기파 대역을 수백 개의 파장으로 나누어 영상화한 데이터로 다양한 분야에서 물체를 찾거나 분류하는 것에 활용된다. 최근에는 딥러닝을 사용하여 분류하는 방법이 주목받고 있지만 초분광 영상 데 이터의 특성으로 인해 초분광 영상을 학습 데이터로 사용하기 위해서는 기존의 가시광 영상과는 다른 처리 기법이 필요 하다. 이를 위해 초분광 큐브에서 특정 파장의 영상을 선택하여 Ground Truth 작업을 수행하고 환경정보를 포함하여 데이터를 관리하는 소프트웨어를 개발하였다. 본 논문에서는 해당 소프트웨어의 구성과 기능에 대하여 설명한다.

The hyper-spectral image is data obtained by dividing the electromagnetic wave band in the infrared region into hundreds of wavelengths. It is used to find or classify objects in various fields. Recently, deep learning classification method has been attracting attention. In order to use hyper-spectral image data as deep learning training data, a processing technique is required compared to conventional visible light image data. To solve this problem, we developed a software that selects specific wavelength images from the hyper-spectral data cube and performs the ground truth task. We also developed software to manage data including environmental information. This paper describes the configuration and function of the software.

The spectral data were collected from the two 600 MHz spectrometers from the two major manufacturers, Broker and Varian. The samples were prepared to create standard curves for quantitative measurements of metabolite concentrations. Instead of employing one-dimensional $^1H$ experiments, the two-dimensional $^1H-^{13}C$ HSQC experiments were performed for better separation of resonances. For some resonances, the high salt condition hindered the linear correlation between the intensity and actual metabolite concentration. Excluding overlapped ones, most resonances showed good linearity. Although the Varian spectrometer showed better linearity, both spectrometers were able to generate acceptable standard curves. From this data, we could identify resonances that could be used to better quantify the concentrations of the particular metabolites. With these standard curves, the quantitative measurements of the metabolites from the real samples will be facilitated.

The regrowth of OQPSK power spectral sidelobes from AM/AM and AM/PM amplifier nonlinearity is analyzed. The time-domain expression for amplifier output shows how spectral re-growth will depend on the cubic coefficient of the Taylor's series of the amplifier nonlinearity as well as input amplitude ripple. Closed form spectrum calculations show that the spectral sidelobes produced by AM/PM take the same form as those produced by AM/AM. The rate of growth of AM/PM sidelobes is, however, not as great as for AM/AM.

In this paper the inverse problems for the Dirac Operator are studied. A set of values of eigenfunctions in some internal point and spectrum are taken as a data. Uniqueness theorems are obtained. The approach that was used in the investigation of inverse problems for interior spectral data of the Sturm-Liouville operator is employed.

Ridge regression is compared with multiple linear regression (MLR) for determination of Research Octane Number (RON) when the baseline and signal-to-noise ratio are varied. MLR analysis of near-infrared (NIR) spectroscopic data usually encounters a collinearity problem, which adversely affects long-term prediction performance. The collinearity problem can be eliminated or greatly improved by using ridge regression, which is a biased estimation method. To evaluate the robustness of each calibration, the calibration models developed by both calibration methods were used to predict RONs of gasoline spectra in which the baseline and signal-to-noise ratio were varied. The prediction results of a ridge calibration model showed more stable prediction performance as compared to that of MLR, especially when the spectral baselines were varied. . In conclusion, ridge regression is shown to be a viable method for calibration of RON with the NIR data when only a few wavelengths are available such as hand-carry device using a few diodes.

In this research, we presented an effective clustering method based on ICA for the analysis of huge Raman hyperspectral dental data. The hyperspectral dataset captured by HR800 micro Raman spectrometer at UMKC-CRISP(University of Missouri-Kansas City Center for Research on Interfacial Structure and Properties), has 569 local points. Each point has 1,005 hyperspectal dentin data. We compared the clustering effectiveness and the clustering time for the case of using all dataset directly and the cases of using the scores after PCA and ICA. As the result of experiment, the cases of using the scores after PCA and ICA showed, not only more detailed internal dentin information in the aspect of medical analysis, but also about 7~19 times much shorter processing times for clustering. ICA based approach also presented better performance than that of PCA, in terms of the detailed internal information of dentin and the clustering time. Therefore, we could confirm the effectiveness of ICA for the analysis of Raman hyperspectral dental data.

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

Output data streams of the MSC contain flags, Headers and image data according to the established protocols and data formats. Especially the Header added to each data lines contain information of a line sync, a line counter and, ancillary data which consist of ancillary identification bit and one ancillary data byte. This information is used by ground station to calculate the geographic coordinates of the image and get the on-board time and several EOS(Electro-Optical Subsystem) parameters used at the time of imaging. Therefore, the EGSE(Electrical Ground Supporting Equipment) that is used for testing MSC has to have functions of interpreting and displaying this Header information correctly following the protocols. This paper describes the design of the header data processing module which is in EOS­EGSE. This module provides users with various test functions such as header validation, ancillary block validation, line-counter and In-line counter validation checks which allow convenient and fast test on imagery data.

The FGI is an integral component of the MSC test equipment and is situated in a slot along the ISA bus of the test equipment PC. The main function of the FGI is an interface between the MSC image data via hotlink interface and Frame Grabber. The FGI has two independent receiving channels that allow the board to receive image data arriving. The FGI also includes two transmission channels with hotlink transmitters. Additionally, the FGI is capable of generating digital video test patterns to test the NUC.

Multi-Spectral Camera(MSC) is a main payload on the KOMPSAT-2 satellite to perform the earth remote sensing. The MSC instrument has one(1) channel for panchromatic imaging and four(4) channel for multi-spectral imaging covering the spectral range from 450nm to 900nm using TDI CCD Focal Plane Array (FPA). The compression method on KOMPSAT-2 MSC was selected and used to match EOS input rate and PDTS output data rate on MSC image data chain. At once the MSC performance was carefully handled to minimize any degradation so that it was analyzed and restored in KGS(KOMPSAT Ground Station) during LEOP and Cal./Val.(Calibration and Validation) phase. In this paper, on-orbit image data chain in MSC and image data processing on KGS including general MSC description is briefly described. The influences on image performance between on-board compression algorithms and between performance restoration methods in ground station are analyzed and discussed.

The inherent trade-offs among spatial resolution, spectral richness, and revisit frequency remain a fundamental limitation of current satellite sensor systems. Spatial-spectral fusion has therefore emerged as an effective strategy to integrate complementary information from heterogeneous observations. This study presents a spatial-spectral fusion framework based on a Spatial Time-series Geostatistical Deconvolution/Fusion Model(STGDFM), with particular emphasis on spectral-profile-based reconstruction. To enable controlled quantitative evaluation, synthetic datasets were generated from Sentinel-2 imagery by simulating complementary coarse- and fine-scale observations. Two implementations were compared: (1) a resolution-matrix-only reconstruction and (2) the full STGDFM framework incorporating spectral profile extraction and residual correction. Quantitative assessment was conducted using the correlation coefficient (CC) and root mean square error (RMSE), focusing on red-edge (RE) and shortwave infrared (SWIR) bands that are not directly available in fine-scale inputs. The results show that the full STGDFM framework generally improves reconstruction consistency across most RE and SWIR bands compared with the resolution-matrix-only implementation. The magnitude of improvement is strongly band-dependent,with more pronounced gains observed in narrow RE bands. Additional experiments conducted in two regions with contrasting land-cover characteristics further indicate that the relative benefit of spectral-profile integration can vary depending on scene heterogeneity and spectral variability. Although the evaluation was conducted using synthetic datasets derived from Sentinel-2 imagery, the findings provide insight into the role of spectral dependency in spatial-spectral fusion. Overall, the proposed framework highlights the importance of jointly reconstructing spatial detail and spectral relationships when extending spectral information in high-resolution Earth observation imagery.