Although hyperspectral sensing data have shown great potential to derive various surface information that is not usually available from conventional multispectral image, the acquisition of proper hyperspectral image data are often limited. To use ground-based hyperspectral camera image for remote sensing studies, radiometric calibration should be prerequisite. The objective of this study is to develop radiometric calibration procedure to convert image digital number (DN) value to surface reflectance for the 120 bands ground-based hyperspectral camera. Hyperspectral image and spectral measurements were simultaneously obtained from the experimental target that includes 22 different surface materials of diverse spectral characteristics at wavelength range between 400 to 900 nm. Calibration coefficients to convert image DN value to at-sensor radiance were initially derived from the regression equations between the sample image and spectral measurements using ASD spectroradiometer. Assuming that there is no atmospheric effects when the image acquisition and spectral measurements were made at very close distance in ground, we were also able to derive calibration coefficients that directly transform DN value to surface reflectance. However, these coefficients for deriving reflectance values should not be applied when the camera is used for aerial image that contains significant effect from atmosphere and further atmospheric correction procedure is required in such case.
Kim, Young-Pil;Ham, Hun-Ju;Hong, Sung-Hee;Ko, Seok-Cheol
Journal of the Korea Academia-Industrial cooperation Society
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v.20
no.10
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pp.415-424
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2019
In this paper, a minimum rotation radius was designed and fabricated to overcome the threshold so that elderly or disabled people who have difficulty moving can move and transfer safely and conveniently in a narrow room. In the indoor environment, where the sedentary culture develops, this study aimed to provide convenience for passengers with fracture diseases, geriatric diseases, and other knee and waist diseases. First, links, seats, armrests, covers, motors, batteries, chargers, controllers, etc. were attached to the frame so that they could be moved and lifted indoors. The product design and structure were designed considering the user's environment and physical characteristics, and IoT functions were added. A driving experiment was performed to confirm the operating performance of the manufactured indoor moving and lifting wheelchair. The performance tests, such as continuous running time, turning radius, maximum actuator load, maximum lift height, sound pressure level, minimum sensing distance of the driving aid sensor, interworking of server and app programs, device compatibility, and duty cycle error rate, were performed. As a result of the test, the built-in wheelchair could achieve the performance test target of each item and operate successfully.
A subminiature catadioptric omnidirectional optical system (SCOOS) with 2 mirrors, 6 plastic aspherical lenses, and an illumination system of 6 light emitting diodes, to observe the 360° panoramic image of the inner intestine, is optically designed and evaluated for a capsule endoscope. The total length, overall length, half field of view (HFOV), and F-number of the SCOOS are 14.3 mm, 8.93 mm, 51°~120°, and 3.5, respectively. The optical system has a complementary metal-oxide-semiconductor sensor with 0.1 megapixels, and an illumination system of 6 light-emitting diodes (LEDs) with 0.25 lm to illuminate on the 360° side view of the intestine along the optical axis. As a result, the spatial frequency at the modulation transfer function (MTF) of 0.3, the depth of focus, and the cumulative probability of tolerance at the Nyquist frequency of 44 lp/mm and MTF of 0.3 of the optimized optical system are obtained as 130 lp/mm, -0.097 mm to +0.076 mm, and 90.5%, respectively. Additionally, the simulated illuminance of the LED illumination system at the inner surface of the intestine within HFOV, at a distance of 15.0 mm from the optical axis, is from a minimum of 315 lx to a maximum of 725 lx, which is a sufficient illumination and visibility.
In this study, selective laser sintered 3D printing mold core and metal core were used to investigate the difference of the thickness shrinkage from the gate of the injection molded part at a constant interval. SLS 3D printing mold core was made of nylon-based PA2200 powder and the metal core was manufactured by conventional machining method. As the PA2200 powder material has low strength, thermal conductivity and high specific heat characteristics compared with metal, molding conditions were set with the consideration of molten temperature and injection pressure. Crystalline resin(PP) and amorphous resin(PS) with low melting temperature and viscosity were selected for the injection molding experiment. Cooling time for processing condition was selected by checking the temperature change of the cores with a cavity temperature sensor. The cooling time of the 3D printing core was required a longer time than that of the metal core. The thickness shrinkage of the molded part compared to the core depth was measured from the gate by a constant interval. It was shown that the thickness shrinkage of the 3D printing core was 2.02 ~ 4.34% larger than that of metal core. In additions, in the case of metal core, thickness shrinkage was increased with distance from the gate, on the contrary, in the case of polymer core showed reversed aspect.
In order to acquire an image from a gamma camera, linearity correction must be performed. To this end, digital coordinates are acquired by using a linearity map to accurately specify the location where the scintillator and gamma rays interact. In this study, a method for acquiring undistorted images and digital coordinates was developed using a lookup table and maximum likelihood position estimation without using a linearity map. The proposed method was verified by configuring a small gamma camera through DETECT2000 simulation. A gamma camera was constructed using a GAGG scintillator and a SiPM optical sensor, and a gamma-ray interaction was generated at the center of the scintillator, and a lookup table was prepared using the ratio of the signals obtained from the SiPM. Through the prepared lookup table and the maximum likelihood position estimation, the position of the signal obtained by the gamma-ray interaction was acquired as digital coordinates to compose an image. As a result, the linearity was maintained compared to the generally acquired image, the accuracy of the location where the gamma-ray interaction was generated was excellent, and the distance between the locations was uniform. Since the lookup table obtained through simulation is created using the ratio of the signal, it can be directly used in the experiment, and the position of the signal can be conveniently obtained with digital coordinates with corrected linearity without creating a linearity map.
Background: The coronavirus disease (COVID-19) has caused changes in human activity, and these changes may possibly increase or decrease exposure to fine dust (PM2.5). Therefore, it is necessary to evaluate the exposure to PM2.5 in relation to the outbreak of COVID-19. Objectives: The purpose of this study was to compare and evaluate the exposure to PM2.5 concentrations by the variation of dynamic populations before and after the outbreak of COVID-19. Methods: This study evaluated exposure to PM2.5 concentrations by changes in the dynamic population distribution in Guro-gu, Seoul, before and after the outbreak of COVID-19 between Jan and Feb, 2020. Gurogu was divided into 2,204 scale standard grids of 100 m×100 m. Hourly PM2.5 concentrations were modeled by the inverse distance weight method using 24 sensor-based air monitoring instruments. Hourly dynamic population distribution was evaluated according to gender and age using mobile phone network data and time-activity patterns. Results: Compared to before, the population exposure to PM2.5 decreased after the outbreak of COVID-19. The concentration of PM2.5 after the outbreak of COVID-19 decreased by about 41% on average. The variation of dynamic population before and after the outbreak of COVID-19 decreased by about 18% on average. Conclusions: Comparing before and after the outbreak of COVID-19, the population exposures to PM2.5 decreased by about 40%. This can be explained to suggest that changes in people's activity patterns due to the outbreak of COVID-19 resulted in a decrease in exposure to PM2.5.
Journal of the Korean Association of Geographic Information Studies
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v.25
no.2
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pp.1-17
/
2022
This study analyzed the PM2.5 pattern by using data measured for one year from June 2020 to May 2021 by 21 low-cost sensors installed near the Changwon National Industrial Complex in Changwon, Gyeongsangnam-do. For the PM2.5 pattern, the land use types around the measuring points and meteorological factors such as air temperature and wind speed were considered. The PM2.5 concentration was high from November to March in winter, and from 1 to 9 in the morning and early in the morning by time zone. The concentration of PM2.5 was higher as it got closer to the industrial area, but the concentration was lower in the residential area and public facility area. In terms of meteorological factors, the higher the air temperature and wind speed, the lower the concentration of PM2.5. As a result of this study, it was possible to identify the PM2.5 patter near Changwon National Industrial Complex. This result will be useful data that can be used in urban and environmental planning to improve air quality including PM2.5 in urban area in the future.
The 2-dimensional arrangement method of nodes has been used in most of RF (Radio Frequency) based communication network simulations. However, this method is not useful for the an none-obstacle 3-dimensional space networks in which the propagation delay speed in communication is very slow and, moreover, the values of performance factors such as the communication speed and the error rate change on the depth of node. Such a typical example is an underwater communication network. The 2-dimensional arrangement method is also not useful for the RF based network like some WSNs (Wireless Sensor Networks), IBSs (Intelligent Building Systems), or smart homes, in which the distance between nodes is short or some of nodes can be arranged overlapping with their different heights in similar planar location. In such cases, the 2-dimensional network simulation results are highly inaccurate and unbelievable so that they lead to user's erroneous predictions and judgments. For these reasons, in this paper, we propose a method to place uniformly and randomly communication nodes in 3-dimensional network space, making the wireless link with neighbor node possible. In this method, based on the communication rage of the node, blocks are generated to construct the 3-dimensional network and a node per one block is generated and placed within a block area. In this paper, we also introduce an algorithm based on this method and we show the performance results and evaluations on the average time in a node generation and arrangement, and the arrangement time and scatter-plotted visualization time of all nodes according to the number of them. In addition, comparison with previous studies is conducted. As a result of evaluating the performance of the algorithm, it was found that the processing time of the algorithm was proportional to the number of nodes to be created, and the average generation time of one node was between 0.238 and 0.28 us. ultimately, There is no problem even if a simulation network with a large number of nodes is created, so it can be sufficiently introduced at the time of simulation.
Jaemin, Woo;Gihong, Min;Dongjun, Kim;Mansu, Cho;Kyeonghwa, Sung;Jungil, Won;Chaekwan, Lee;Jihun, Shin;Wonho, Yang
Journal of Environmental Health Sciences
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v.48
no.6
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pp.298-305
/
2022
Background: The concentration of air pollutants as measured by the Air Quality Monitoring System (AQMS) is not an accurate population exposure level since actual human activities and temporal and spatial variability need to be considered. Therefore, to increase the accuracy of exposure assessment, the population should be considered. However, it is difficult to obtain population data due to limitations such as personal information. Objectives: The existing population defined in this study is the number of people in each region's grid. The purpose is to provide a methodology for evaluating exposure to PM2.5 through existing population data provided by the National Geographic Information Institute. Methods: The selected study period was from October 26 to October 28, 2021. Using PM2.5 concentration data measured at the Sensor-based Air Monitoring Station (SAMS) installed in Guro-gu and Wonju-si, the concentration for each grid was estimated by applying inverse distance weights through QGIS version 3.22. Considering the existing population, population-weighted average concentration (PWAC) was calculated and the exposure level of the population was compared by region. Results: The outdoor PM2.5 concentration as measured through the SAMS was high in Wonju-si on all three days. Wonju-si showed an average 22% higher PWAC than Guro-gu. As a result of comparing the PWAC and outdoor PM2.5 concentration by region, the PWAC in Guro-gu was 1~2% higher than the observed value, but it was almost the same. Conversely, observations of Wonju-si were 10.1%, 11.3%, and 8.2% higher than PWAC. Conclusions: It is expected that the Geographic Information System (GIS) method and the existing population will be used to evaluate the exposure level of a population with a narrow activity radius in further research. In addition, based on this study, it is judged that research on exposure to environmental pollutants and risk assessment methods should be expanded.
Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.
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