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Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the "air-land-water" integrated model system and multi-level collaborative architecture of the basin, this paper proposes a technological system for a non-point source pollution forecasting and warning platform for the Pengxi River basin, and a non-point source pollution forecasting and warning platform for the Pengxi River basin has been established. By automatically collectting and processsing meteorological data, and invoking the meteorological model, land model, hydrodynamic model, and water quality model driven by the control center, the platform can make hourly simulated forecasts of land surface runoff and the flow of pollutants such as TN, TP and NH3-N for the next 3 to 15 days, and warn about the risk of pollutant fluxes, providing technical support for the intelligent basin management and comprehensive control of non-point source pollution. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Foggy weather is an important factor affecting highway traffic safety. Research on the automatic recognition method of highway fog visibility from surveillance images can provide technical support for the intelligent management and decision-making of the traffic management department. This paper analyzes multiple physical factors related to fog density based on the atmospheric scattering model and proposes a multi-channel fusion network that integrates these physical factors. Specifically, the method jointly exploits three streams to learn deep visual feature, transmission matrix feature and scene depth feature, and designs an attention fusion module to adaptively fuse these three streams for the final visibility level recognition, which is very beneficial for improving the recognition accuracy. Meanwhile, this paper constructs a synthetic dataset and a real-scene dataset for network parameters learning and performance evaluation. The images in the real-scene dataset are collected from surveillance videos of multiple highways in China. Experiments on these two datasets show that this method can identify visibility level more accurately than existing methods [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Global navigation satellite system (GNSS) technology is widely used in deformation monitoring such as dams, bridges, and landslides. In traditional monitoring methods, the reference stations need to be built, which leads to the increased monitoring cost. Methods: This paper proposes a GNSS deformation monitoring method based on virtual reference station (VRS), which uses the existing continuously operating reference stations (CORS) observation data to generate VRS instead of physical reference station to achieve high accuracy deformation monitoring. Firstly, the existing CORS observation data are used to obtain the atmospheric error correction models and generate VRS. Secondly, the baselines between VRS and each monitoring point are calculated. In order to further improve the accuracy of data processing, Delaunay triangulation networks are formed, and the baseline vector solution results are checked according to the criterion that the triangle closure error is zero. Finally, the checked baseline solution results are adjusted to obtain the final three-dimensional position coordinates of the monitoring points. Results: This paper uses about 30 monitoring points in Pingbian Miao Autonomous County, Yunnan Province for experimental analysis. The results show that the interpolation accuracis of the double-difference tropospheric and ionospheric delay are better than 10 mm if the distance between CORS reference stations is less than 40 km. The observation data longer than 8 hours can realize deformation monitoring with horizontal accuracy better than 5 mm and vertical accuracy better than 10 mm. Conclusions: The proposed method can realize the practical application of landslide monitoring. In the future, we can study how to improve the atmospheric error model and further improve the accuracy of data processing [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the high computational complexity of optimization algorithm for estimating transmission and the problem of dim brightness of dehazed image, this paper proposed a new algorithm based on pixel quality function and boundary constraint to estimate the transmission. Firstly, the algorithm estimated optimal transmission by maximizing pixel quality function which included pixel hazy removal rate and pixel signal-to-noise ratio indicators. The boundary constraint of scene radiance modified the optimal transmission to produce rough transmission. The guided filter smooth the rough transmission to obtain fine transmission. Finally, the algorithm improved the visibility of hazy image and brightness of dehazed image using atmospheric scattering model. The visual effect shows that the image became more natural and bright after two-stage dehazing. The objective data shows that the average gradient ratio, signal-to-noise ratio and running time of the proposed algorithm are greatly improved. The average value of the average gradient ratio of proposed algorithm is at least 1.34 times higher than that of bench-mark algorithms, the signal-to-noise ratio is at least 1.07 times higher, and the running speed is at least 1. 28 times faster. Therefore, the proposed algorithm is effective in enhancing the clarity and brightness of hazy images, and is suitable for real-time projects. [ABSTRACT FROM AUTHOR]

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Advances in Water Science / Shuikexue Jinzhan is the property of Editorial Board of Advances in Water Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The mean volumetric diameter (MVD) and liquid water content (LWC) are two important parameters that affect aircraft icing, but they are difficult to be measured accurately in practice. If these two parameters can be accurately obtained in real time, they can provide some guidance for icing prediction and the establishment of aircraft airworthiness certification standards. In this paper, a prediction model of icing meteorological parameters based on the genetic‑algorithm‑optimized neural network is proposed. We use the ice thickness and icing rate of different combinations of measuring points, ambient temperature, flight speed and wing angle of attack as input parameters, and the icing meteorological parameters MVD and LWC as output parameters, and develop a prediction model of icing meteorological parameters optimized by the genetic algorithm, This model predicts the icing meteorological parameters of the numerical calculation test group data and the icing wind tunnel experiment data. The results show that the relative error of the prediction model for numerical calculation of icing meteorological parameters of the test group is within 10%, and the relative error of the experiment data is within 20%. This method is feasible. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rain streaks and fog brought by rain can degrade the quality of images taken outdoors. In order to remove the in-fluence of rain and fog on images, this paper proposed an image derain method based on rain and fog separation processing and multi-scale convolutional neural network. Firstly, it extracted the rain lines and image details to the high frequency layer using guided filtering, while separated the fog and background information to the low frequency layer. Then it constructed a multi-scale convolutional neural network to remove the rain lines in the high frequency layer, and incorporated multiple dense connection modules into the network to improve the accuracy of feature extraction. Secondly, it constructed a lightweight defogging network with multi-layer feature fusion to remove the fog in the low frequency layer, and used the parameter integration structure to avoid estimation of multiple atmospheric scattering model parameters resulting in sub-optimal solutions. Finally, it combined the processed high and low frequency results to restore a clear image. Tested on several synthetic rain and fog datasets as well as on real natural scene images, the qualitative and quantitative results show that the proposed method removes rain and fog while retaining color information well, and improves the image structure similarity by 0. 02 to 0. 08 and the image peak signal-to-noise ratio by 0. 2 to 3. 5 dB compared to recent algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the phenomenon of the edges of the image produce light bands in the de-hazing process and the whole image ' s color becomes dim after de-hazing, this paper proposed a novel algorithm based on improved transmission and semi-inverse algorithm. Aiming at the dark channel prior failure at the edge of the image, a transmission correction mechanism is proposed in this paper to eliminate the band phenomenon at the edge and improve the application of the dark channel prior. Secondly, based on the atmospheric scattering model, the atmospheric light is obtained by using the semi-inverse algorithm. Then for the situation that the color of the whole image become dim after de-hazing, the image contrast algorithm based on wavelet transform is used to enhance the stretching treatment and improve the visual effect of image. The experimental results show that the algorithm can remove fog more effectively, and the ability to de-haze is better than the original algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective Variation in crop yield is the consequence of many natural and anthropogenic factors, and disentangling their impacts is important for improving agricultural management but difficult. The purpose of this paper is to propose and compared different methods to isolate the impacts of meteorological change on crop yield based on long time series of maize yield in Yellow River-water irrigated region in Ninxia province of China. Method The analysis is based on maize yield measured from1988 to 2019 in 6 counties located in the Yellow River-watered irrigation areas. We compared three methods for the separation: five -year moving average method, quadratic exponential smoothing method, and five-point quadratic smoothing method. The consistent correlation coefficient, trend coincidence conformity analysis method, consistency of climate change characteristics, which lead to the same rise-fall in meteorological yield, were used as the evaluation criteria. Their applicability and rationality were compared and analyzed. All methods were calibrated based on the relationship between meteorological factors and maize yield. Result All methods can fit the yield trend well. Compared with the average yield trend, the consistency correlation coefficients of all three methods were >0.5, suggesting that there was no significant difference between these methods for fitting the yield trend. The advantage of the quadratic exponential smoothing method and the five-point quadratic smoothing method is that they accurately describe the change in the yield as affected by national productivity and national policy. The change in the yield due to meteorological factors estimated by the five-point quadratic smoothing method described the effect of inter-annual meteorological factors better, and its associated meteorological yield model is able to describe the relationship between the meteorological factors and the maize yield. Conclusion omprehensive analysis showed that the five-point quadratic smoothing method modeled the yield change due to meteorological factors better than the other two methods. [ABSTRACT FROM AUTHOR]

With the continuous warming of the global climate, the ecological environment and water resources in the Yangtze River Basin are apt to be affected. The analysis and study of the future precipitation change trend in the region will help to provide a theoretical basis for water resources management and ecological protection in the Yangtze River Basin. Based on the observed precipitation data and precipitation data output from 13 global climate models of the Sixth International Coupled Model Comparison Program (CMIP6) with bias correction, this paper evaluates the precipitation simulation ability of climate models in the Yangtze River Basin from 1995 to 2014. The temporal and spatial variation trends of precipitation in 2021-2040 (near term), 2041-2070 (middle term), and 2071-2100 (long term) are analyzed under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results show that: ① The model data with bias correction can simulate the characteristics of precipitation well in the Yangtze River Basin on the spatial and temporal scale, which is close to the observed values. ② The annual precipitation in the Yangtze River Basin will increase significantly with the increase in the radiation forcing level. Compared with the historical period (1995-2014), the annual precipitation growth in each scenario is relatively flat in the near term, and the precipitation growth is the largest in the long term. In general, seasonal precipitation shows the largest increase in winter, and the increase in spring and summer precipitation is relatively gentle. In autumn, except for the negative value of the precipitation change rate in the near term under the scenario SSP3-7.0, the other scenarios and periods are slowly increasing with lower change rates. ③ Spatially, the regions with larger annual precipitation increase are concentrated on the source region of the Yangtze River and the middle and upper reaches of the Yangtze River with relatively less precipitation. The high value center of spring precipitation increase is in the source area and the north of the middle and upper reaches, while the precipitation change rate is low in the south of the basin. The precipitation increment in summer and autumn is relatively low, and the near term and middle-term precipitation in the northern part of the middle and upper reaches is lower than that in the historical period. In winter, the precipitation in the whole basin has increased, which shows that the precipitation change rate in the north of the Yangtze River Basin is the largest, and it is smaller in the south. [ABSTRACT FROM AUTHOR]

Objectives: The gravity variation caused by hydrometeorological factors is an important factor affecting the interpretation of seismic gravity variation results. However, quantitative analysis of gravity field changes caused by hydrometeorological factors is rarely carried out. Methods: This paper takes the northern Xinjiang, China as the research area and uses the global hydrological model data of global land data assimilation system (GLDAS) and an atmospheric model to calculate the gravity influence of hydrometeorological factors on the research area from January 2016 to December 2017. Results: The results show that the annual change of terrestrial water influence is 1.3 μGal, and the difference in spatial distribution of terrestrial water influence between two adjacent periods is lower than 1 μGal. The annual change of atmospheric influence is as high as 8 μGal, and the difference in spatial distribution of atmospheric influence between two adjacent periods is up to 6 μGal. Conclusions: The gravity variations before and after deducting hydrometeorological factors were compared using the mobile gravity measurement data of three periods, i. e., April 2016, August 2016 and June 2017. The comparison results show that in the processing of mobile gravity data in the northern Xinjiang, the large⁃scale hydrological factor can be ignored and the meteorological factor should be taken into account. Meanwhile, for a better analysis of the mobile gravity variation, soil moisture and atmospheric pressure near the measuring point should be observed during the mobile gravity measurement. [ABSTRACT FROM AUTHOR]

Objectives: Sea ice concentration (SIC), which is defined as the proportion of a given area of ocean that is covered by ice, is a significant parameter for Arctic sea ice and climate change, an important input for regional climate model and numerical weather prediction model. The passive microwave sensors are able to penetrate the atmosphere and clouds, regardless of observation time, and their data is commonly used to retrieve SIC. Passive microwave obtained from the microwave radiation imager (MWRI) aboard on the Chinese Fengyun‑3 (FY ‑3) series satellites can be used to extract polar SIC. However, the study on its accuracy is limited. Methods: This paper focuses on the application of MWRI to Arctic SIC estimation accuracy, and compares the SIC obtained using special sensor microwave imager sounder (SSMIS), MWRI and advanced microwave scanning radiometer 2 (AMSR2) data, and utilizes Bootstrap, NASA Team (NT), fully constrained least squares (FCLS), Enhanced NASA Team (NT2) and Arctic radiation and turbulence interaction study (ARTIST) sea ice (ASI) methods with the in‑situ data. Results: Validation results show that MWRI has the smallest root mean square error (RMSE) at different spatial resolution (20.4%- 24.4%). FCLS performed better than the other five algorithms for three passive microwave data because the error was considered during the SIC retrieve, the SIC was constrained with non‑negative constrain, and numerical optimization was used to solved the SICs. In addition, MWRI performs well in summer (17.9%-23.0%) and winter (11.2%-17.8%).Conclusions: MWRI has relatively stable performance and a great potential for sea ice monitoring. Uncertainty also exists in evaluation, such as the uncertainty of accuracy of in ‑situ data in Arctic and tie points. Furthermore, scale difference between multi ‑source data could also induce the uncertainty of validation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Resources / Ziran Ziyuan Xuebao is the property of Journal of Natural Resources and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the existing image defogging algorithm based on atmospheric scattering model, the defogging process would inevitablely have a halo effect and details were lost, this paper proposed a fast defogging algorithm for eliminating halo effect and keep the detail information of the image. The algorithm first used space division level method based on four tree sub to get more accurate atmospheric light value. Through analysis of the atmospheric dispersion consumption function, it used the improved gradient information fusion filter to get the accurate estimation of atmospheric dispersion consumption function, and adaptively got the threshold of image minimum and average value of atmospheric optical, and calculated the transmission map. Finally it inversion recovered no fog image, and adjusted the brightness of the restored image. Experiment results show the algorithm can effectively suppress the halo effect in fog removal, and can preserve more details and reduce the running time by about twice. The improved guide filter by fusion gradient information can better eliminate the halo effect effectively, and has better robustness and time complexity. This algorithm is suitable for transportation and other outdoor scenes to defog. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)