scholarly journals Evaluation algorithm of alhagi sparsifolia desertification control under different irrigation amounts

2021 ◽  
Vol 24 (4) ◽  
pp. 449-457
Author(s):  
Juan Li ◽  
Wei Liu ◽  
Xinxin Zhang
Keyword(s):  
Remote Sensing ◽  
Environmental Parameters ◽  
Modern Society ◽  
True Positive Rate ◽  
Control Effect ◽  
Analytic Hierarchy ◽  
Irrigation Amount ◽  
Alhagi Sparsifolia ◽  
Desertification Control ◽  
Evaluation Algorithm

Desertification control is an important issue that must be considered in modern society. In order to effectively improve the accuracy and practicability of the evaluation algorithm of desertification control effect, the Alhagi sparsifolia index under different irrigation amount was taken as the research object, and the evaluation algorithm of desertification control effect was proposed. In the “vegetation-sandstorm-soil” index system, a number of indexes were selected according to the core environmental parameters of Alhagi sparsifolia and grassland desertification. And the analytic hierarchy process, remote sensing, geographic information system, and landscape technology were used to assign index weights of desertification control capacity, which were calculated by multiple discriminant matrices. Finally, the data regression analysis was performed based on remote sensing and computer image information screening and processing to determine the final evaluation results. The experimental data show that the true positive rate of the algorithm in this paper is between 160 and 200, which is within a large range of advantages, indicating that the overall evaluation accuracy of the algorithm is high and the evaluation effect is perfect.

Dynamic monitoring feedback iteration control for damping-adjustable semi-active suspension systems

2021 ◽  
pp. 1-9
Author(s):  
Chongchong Li ◽  
Jiangyong Xiong ◽  
Tingshan Liu ◽  
Ziang Zhang
Keyword(s):  
Root Mean Square ◽  
Working Conditions ◽  
Control Algorithm ◽  
Active Suspension ◽  
Dynamic Monitoring ◽  
Iteration Algorithm ◽  
Mean Square ◽  
Control Effect ◽  
Analytic Hierarchy ◽  
Vehicle Simulation

In order to further improve vehicle ride performance, a dynamic monitoring feedback iteration control algorithm is proposed by combining the features of a variable-damping semi-active suspension system and applying them to the system. A seven-degree-of-freedom finished vehicle simulation model is built based on MATLAB/Simulink. The root-mean-square values of the acceleration of the sprung mass, the dynamic travel of the suspension and the dynamic tire load are taken as evaluation indicators of vehicle ride performance. An analytic hierarchy process (AHP) is used to determine the weighting coefficients of the evaluation indicators, and a genetic algorithm is utilized to determine the optimal damping of the suspension under various typical working conditions. Suspension damping is controlled with a dynamic monitoring feedback iteration algorithm. The correction coefficients of the control algorithm are determined according to the deviation between the obtained damping and the optimized damping so that the control parameters will agree with the optimal result under typical working conditions, and the control effect under other working conditions is verified. The simulation results indicate that the proposed dynamic monitoring feedback iteration control algorithm can effectively reduce the root-mean-square value of the acceleration of the sprung mass by 10.56% and the root-mean-square value of the acceleration of the dynamic travel of the suspension by 11.98% under mixed working conditions, thus improving vehicle ride performance. The study in this paper provides a new attempt for damping control of semi-active suspension and lays a theoretical foundation for its application in engineering.

scholarly journals Efficacy of using radar-derived factors in landslide susceptibility analysis: case study of Koslanda, Sri Lanka

2019 ◽  
Vol 19 (8) ◽  
pp. 1881-1893 ◽  
Author(s):  
Ahangama Kankanamge Rasika Nishamanie Ranasinghe ◽  
Ranmalee Bandara ◽  
Udeni Gnanapriya Anuruddha Puswewala ◽  
Thilantha Lakmal Dammalage
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Prediction Models ◽  
Low Cost ◽  
Information Value ◽  
Spatial Data Analysis ◽  
Optical Remote Sensing ◽  
Analytic Hierarchy ◽  
Technological Developments

Abstract. Through the recent technological developments of radar and optical remote sensing in (i) the areas of temporal, spectral, spatial, and global coverage; (ii) the availability of such images either at a low cost or free of charge; and (iii) the advancement of tools developed in image analysis techniques and GIS for spatial data analysis, there is a vast potential for landslide studies using remote sensing and GIS as tools. Hence, this study aimed to assess the efficacy of using radar-derived factors (RDFs) in identifying landslide susceptibility using the bivariate information value method (InfoVal method) and the multivariate multi-criteria decision analysis based on the analytic hierarchy process statistical analysis. Using identified landslide causative factors, four landslide prediction models – bivariate with and without RDFs as well as multivariate with and without RDFs – were generated. Twelve factors such as topographical, hydrological, geological, land cover and soil plus three RDFs are considered. The weight of index for landslide susceptibility is calculated by using the landslide failure map, and susceptibility regions are categorized into four classes as very low, low, moderate, and high susceptibility to landslides. With the integration of RDFs, boundary detection between high- and very-low-susceptibility regions are increased by 7 % and 4 % respectively.

Remote sensing of environmental parameters

1982 ◽  
Vol 72 (S1) ◽  
pp. S19-S19
Author(s):  
Paul G. Nadeau ◽  
Richard Nowak
Keyword(s):  
Remote Sensing ◽  
Environmental Parameters

scholarly journals Investigation on linear quadratic Gaussian control of semi-active suspension for three-axle vehicle

2020 ◽  
pp. 146134842096092
Author(s):  
Shaohua Li ◽  
Junwu Zhao ◽  
Zhida Zhang
Keyword(s):  
Active Suspension ◽  
Travel Speed ◽  
Vertical Vibration ◽  
Control Effect ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Analytic Hierarchy ◽  
Passive Suspension ◽  
On The Road ◽  
Weight Coefficients

An 8-DOF three-axle vehicle model with semi-active suspension is built in this paper, of which the accuracy is verified through simulations and experiments. Based on the optimal control theory, the linear quadratic Gaussian controller for semi-active suspension is designed with 10 evaluation indicators. Considering the deficiency of linear quadratic Gaussian control weight coefficients based on experience, analytic hierarchy process is employed to determine the weight coefficients of each indicator. The control effect is analyzed through MATLAB/Simulink. The adaptability of proposed control strategy under 25 driving conditions is analyzed with different road grades and speeds. The driving condition of “70 km/h travel speed on the road of grade B” is selected, under which the comparison of vehicle responses between semi-active suspension and passive suspension is made. Results show that the vertical vibration is effectively diminished by using semi-active suspension with linear quadratic Gaussian controller. Compared with passive suspension, the riding comfort is improved and the adverse effect on handling stability is eliminated. The three-axle vehicle with semi-active suspension has good adaptability to various working conditions.

Spatial variability of evapotranspiration of old-growth cypress forest using remote sensing — a case study of Chilan Mountain cypress forest in Taiwan

2012 ◽  
Vol 42 (6) ◽  
pp. 1060-1071
Author(s):  
Chih-Da Wu ◽  
Chi-Chuan Cheng ◽  
Yung-Chung Chuang
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Variability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Environmental Parameters ◽  
Habitat Characteristics ◽  
Old Growth ◽  
Mountain Area ◽  
Watershed Divisions

The Chilan Mountain cypress forest, northeastern Taiwan, is the only one where the genus Chamaecyparis is situated in a subtropical region. The health of a forest ecosystem is closely tied to the evapotranspiration (ET) of water through forests. This study focused on estimating the ET of old-growth cypress in the Chilan Mountain area and investigated its spatial variability in different watershed divisions using remote sensing. Our methods included applying hybrid image classification to generate land cover maps using Landsat-5 images, calculating habitat characteristics of old-growth using the Surface Energy Balance Algorithm for Land (SEBAL), investigating spatial variability of ET in relation to environmental parameters, and examining the gap-snag effect on old-growth cypress ET. The results indicated that the study area was classified into three land cover types (i.e., old-growth, non-old growth, and others). Old-growth had lower values in net radiance, the normalized difference vegetation index (NDVI), and daily ET than did non-old-growth. Watershed divisions at various scales did cause the variation on old-growth ET characteristics according to the selected parameters and the number of parameters for predicting the value of ET. Finally, ET between gap-snag and non-gap-snag habitats was statistically different. A higher proportion in gap-snag composition would lead to a lower value in daily ET and the NDVI.

scholarly journals Automatic Classification of Remote Sensing Images Using Multiple Classifier Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Yang ◽  
Chunxiang Cao ◽  
Ying Xing ◽  
Xiaowen Li
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Ground Truth ◽  
Support Vector ◽  
Accuracy Evaluation ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Remote Sensing Images ◽  
Analytic Hierarchy ◽  
Multiple Classifier

It is a challenge to obtain accurate result in remote sensing images classification, which is affected by many factors. In this paper, aiming at correctly identifying land use types reflec ted in remote sensing images, support vector machine, maximum likelihood classifier, backpropagation neural network, fuzzy c-means, and minimum distance classifier were combined to construct three multiple classifier systems (MCSs). Two MCSs were implemented, namely, comparative major voting (CMV) and Bayesian average (BA). One method called WA-AHP was proposed, which introduced analytic hierarchy process into MCS. Classification results of base classifiers and MCSs were compared with the ground truth map. Accuracy indicators were computed and receiver operating characteristic curves were illustrated, so as to evaluate the performance of MCSs. Experimental results show that employing MCSs can increase classification accuracy significantly, compared with base classifiers. From the accuracy evaluation result and visual check, the best MCS is WA-AHP with overall accuracy of 94.2%, which overmatches BA and rivals CMV in this paper. The producer’s accuracy of each land use type proves the good performance of WA-AHP. Therefore, we can draw the conclusion that MCS is superior to base classifiers in remote sensing image classification, and WA-AHP is an efficient MCS.

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