• Current Optics and Photonics
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• v.5 no.2
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• pp.114-120
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• 2021

Free-space optical (FSO) communication is considered to be a potential solution to congestion in the radio-frequency spectrum and last-mile-access bottleneck issues in future cellular communication networks, such as 5G and beyond. However, FSO link performance may degrade significantly due to irradiance fluctuations and random temporal fluctuations from atmospheric turbulence. Therefore, in this work the main objective is to reduce the effect of the atmospheric turbulence by considering a multihop FSO communication system with amplify-and-forward relaying supported by a radio-frequency (RF) link, which form a hybrid FSO/RF communication system. The FSO link is assumed to follow the gamma-gamma fading model, which represents strong turbulence. Also, the RF link is modeled by a Rayleigh distribution. The performance of the considered system, in terms of the outage probability and average bit-error rate (BER), is investigated and analyzed under various weather conditions and pointing errors. Furthermore, the effect of the number of employed relay nodes on the performance of the system is investigated. The results indicate that the considered system reduces outage probability and average BER significantly, especially for low channel quality. Finally, the closed-form expressions derived in this work are compared to the results of Monte Carlo simulations, for verification.

• Journal of Environmental Impact Assessment
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• v.18 no.4
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• pp.219-227
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• 2009

Because of urban development and changed land cover types, It is very important to acquire pixel unit of land surface temperature(LST) information when the heat island effect(HIE) of regional area are investigated. The brightness temperature observed by satellite is very useful for assessing the pixel unit of LST distributions for the analysis of thermal environment problems of urban areas. Also, satellite land cover data are very useful to our understanding of surface conditions of study areas. In this study, brightness temperature information of Landsat TM thermal channel was analyzed and compared with land cover information of Jeon-ju city. The atmospheric correction of TM thermal channel carried out to explain for compared LST long term monitoring errors. However, simple estimation and evaluation methods to find a physical relationship between LST from satellite images and in-situ data are compared with reference channel emissivity.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.352-357
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• 2002

Some interaction characteristics, i.e., light scattering characteristics, of infrared (IR) radiation with small ice crystals are investigated systematically by using the exact T-matrix approach. Some important facts are obtained, which reveal, especially, that the combination of both the 25 and 3.979$\mu$m together has some advantages and potential applications for remote sensing of cirrus and other ice clouds. A new far-IR channel at the wavelength of 25$\mu$m is proposed.

• Journal of Environmental Science International
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• v.21 no.6
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• pp.667-676
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• 2012

This research investigated to reduce mass of heavy metals in AMD(acid mine drainage) by microbial mats formed on the channel bed. As, Cd, Cu, Fe, Mn and Zn components were monitored in water and microbial mats, at three points (AMD1, AMD2 and AMD3), in a total of six times. Average daily discharge mass of heavy metals was highest in July, Fe component contained more than 76% of total discharge mass. Discharge mass of heavy metals of AMD and heavy metal contents in microbial mats decreased with downstream at channel. Heavy metal components that average daily discharge mass is over 0.5 kg were Fe, Cu and Zn, and they were highest in July. Average removal efficiency of heavy metals in AMD was highest about 21% in Fe, this microbial mats were due to form from precipitation of Fe component in AMD by aerobic iron bacteria. Relative content for As component in microbial mats than AMD was over 16 times, this As components were due to absorb at iron oxide and iron hydroxide on the surface of microbial mats.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1631-1645
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• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.

• Atmosphere
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• v.24 no.4
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• pp.491-502
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• 2014

As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.

• Journal of Korean Society for Atmospheric Environment
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• v.16 no.3
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• pp.237-246
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• 2000

An experimental study has been carried out using a rotating water channel in order to investigate the effect of surface roughness on the vertical dispersion of plume within boundary layer. Dispersion measurements of tracers released from two sources with different height at neutral conditions over various rough terrain ranging from rural to urban have been performed. Various values of roughness length were simulated by combining of 4 stream velocities and 3 roughness element conditions. Dispersion measurements have also been made for rough terrain where high buildings are locally concentrated. Values of $\sigma$z increase with roughness and this tendency appears to apply both cases of with and without locally concentrated high buildings. The comparisons of the Bowne's nomogram on $\sigma$2 vs x relationship and the measurements of $\sigma$2 with roughness show good accordance in $\sigma$2 distribution at stability D class over rural, suburban and urban terrain. For constant roughness length the $\sigma$2 values of plumes from lower source height are smaller than those of plumes from higher source at short downwind distance, but this relationship becomes reverse as distance increases. Crossing appears to be made before about 2km. The value of constant I in McMullen's equation $\sigma$2=exp [I+J(In x) + K(In x)2] appears to increase with roughness length, however, the relationships between other constants and roughness have been confirmed. The values of $\sigma$2 for various downwind distances, estimated by using an equation which is employed in ISC (Industrial Source Complex) dispersion model for areas where high buildings are locally assembled, are in accordance with measurements from water channel experiments.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.58-65
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• 2016

This paper presents optimization techniques to reduce the processing time of the dehazing method and proposes an efficient dehazing method based on them. In the proposed techniques, the atmospheric light is estimated based on the distributed sorting of the dark channel pixels, so as to reduce the computations. The normalization process required in the transmission estimation is simplified by the assumption that the atmospheric light is monochromatic. In addition, the dark channel is modified into the median dark channel in order to eliminate the transmission refinement process while achieving a comparable dehazing quality. The proposed dehazing method based on the optimization techniques is presented and its performance is investigated by developing a prototype system. When compared to the previous method, the proposed dehazing method reduces the processing time by 65% while maintaining the dehazing quality.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.39-55
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• 2019

In this study, we have proposed an improved method to detect forest fires by correcting the reflected signals of day images using the middle-wavelength infrared (MWIR) channel. The proposed method is allowed to remove the reflected signals only using the image itself without an existing data source such as a land-cover map or atmospheric data. It includes the processing steps for calculating a solar-reflected signal such as 1) a simple correction model of the atmospheric transmittance for the MWIR channel and 2) calculating the image-based reflectance. We tested the performance of the method using the MODIS product. When compared to the conventional MODIS fire detection algorithm (MOD14 collection 6), the total number of detected fires was improved by approximately 17%. Most of all, the detection of fires improved by approximately 30% in the high reflection areas of the images. Moreover, the false alarm caused by artificial objects was clearly reduced and a confidence level analysis of the undetected fires showed that the proposed method had much better performance. The proposed method would be applicable to most satellite sensors with MWIR and thermal infrared channels. Especially for geostationary satellites such as GOES-R, HIMAWARI-8/9 and GeoKompsat-2A, the short acquisition time would greatly improve the performance of the proposed fire detection algorithm because reflected signals in the geostationary satellite images frequently vary according to solar zenith angle.

• Current Optics and Photonics
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• v.7 no.3
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• pp.237-247
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• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.