Loading [MathJax]/jax/output/HTML-CSS/jax.js
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (186)

Search Parameters:
Keywords = slope effect correction

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
24 pages, 14940 KiB  
Article
Predicting Non-Point Source Pollution in Henan Province Using the Diffuse Pollution Estimation with Remote Sensing Model with Enhanced Sensitivity Analysis
by Weiqiang Chen, Yue Wan, Yulong Guo, Guangxing Ji and Lingfei Shi
Appl. Sci. 2025, 15(5), 2261; https://doi.org/10.3390/app15052261 - 20 Feb 2025
Abstract
Non-point source pollution (NPSP) originates from domestic agricultural pollutants and deforestation. Agricultural NPSP discharges into rivers and oceans through precipitation and soil runoff. Awareness and research regarding NPSP and its harmful effects on human health and the environment are increasing. The Diffuse Pollution [...] Read more.
Non-point source pollution (NPSP) originates from domestic agricultural pollutants and deforestation. Agricultural NPSP discharges into rivers and oceans through precipitation and soil runoff. Awareness and research regarding NPSP and its harmful effects on human health and the environment are increasing. The Diffuse Pollution Estimation with Remote Sensing (DPeRS) model, a distributed NPSP model proposed by Chinese researchers, seeks to predict agricultural NPSP and includes modules estimating nitrogen and phosphorus balance, vegetation coverage, dissolved pollution, and absorbed pollution. By applying the DPeRS model, the present work aims to predict the distribution of all nitrogen and phosphorus pollutants in Henan Province, China in 2021. We used statistical yearbook, remotely sensed, and hydrological data as input. To facilitate uncertainty characterization in pollution predictions, we performed sensitivity analysis, which identified the model input variables that contributed most to uncertainty in model output. Specifically, we used ArcGIS for processing data for nitrogen and phosphorus balance equations, an ENVI 5.3 software system for deriving vegetation cover, and the RUSLE soil erosion model for predicting absorption pollution. Dissolved pollution was estimated using a unified approach to estimating agricultural runoff, urban runoff, rural resident, and livestock pollutants. Absorbed pollution was estimated by considering the soil erosion model and precipitation. Moreover, Sobol’s method was applied for sensitivity analysis. We found that regardless of the accumulation of nitrogen or phosphorus, indicators of the dissolved pollution of Zhoukou were relatively high. Sensitivity analysis of the models for estimating dissolved pollution and absorbed pollution revealed that the top four influential variables for dissolved pollution were standard runoff coefficient ε0, natural factor correction coefficient Ni, the newly produced TN pollutants per area QiN, and runoff coefficient ε. For absorbed pollution, influential variables were rainfall erosion factor R, water and soil conservation factor P, slope degree factor S, and slope length factor L. The total discharges of Henan Province were 9546.4649 t, 1061.8940 t, 6031.4577 t, and 3587.6113 t for TN, TP, NH+4-N, and COD, respectively, in 2021. This paper provides a valuable reference for understanding the status of NPSP in Henan province. The DPeRS approach presented in this paper provides strong support for policymakers in the field of environmental management in China. This study confirmed that the DPeRS model can be feasibly applied to larger areas for NPSP prediction enhanced with sensitivity analysis due to its fast computation and reliance on accessible and simple data sources. Full article
(This article belongs to the Special Issue Advanced Studies in Land Cover Change and Geographic Data Fusion)
Show Figures

Figure 1

19 pages, 2621 KiB  
Article
Multi-Scale Debris Flow Warning Technology Combining GNSS and InSAR Technology
by Xiang Zhao, Linju He, Hai Li, Ling He and Shuaihong Liu
Water 2025, 17(4), 577; https://doi.org/10.3390/w17040577 - 17 Feb 2025
Abstract
The dynamic loads of fluid impact and static loads, such as the gravity of a rock mass during the formation of debris flows, exhibit a coupled effect of mutual influence. Under this coupling effect, surface monitoring points in disaster areas experience displacement. However, [...] Read more.
The dynamic loads of fluid impact and static loads, such as the gravity of a rock mass during the formation of debris flows, exhibit a coupled effect of mutual influence. Under this coupling effect, surface monitoring points in disaster areas experience displacement. However, existing methods do not consider the dynamic–static coupling effects of debris flows on the surface. Instead, they rely on GNSS or InSAR technology for dynamic or static single-scale monitoring, leading to high Mean Absolute Percentage Error (MAPE) values and low warning accuracy. To address these limitations and improve debris flow warning accuracy, a multi-scale warning method was proposed based on Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) technology. GNSS technology was utilized to correct coordinate errors at monitoring points, thereby enhancing the accuracy of monitoring data. Surface deformation images were generated using InSAR and Small Baseline Subset (SBAS) technology, with time series calculations applied to obtain multi-scale deformation data of the surface in debris flow disaster areas. A debris flow disaster morphology classification model was developed using a support vector mechanism. The actual types of debris flow disasters were employed as training labels. Digital Elevation Model (DEM) files were utilized to extract datasets, including plane curvature, profile curvature, slope, and elevation of the monitoring area, which were then input into the training model for classification training. The model outputted the classification results of the hidden danger areas of debris flow disasters. Finally, the dynamic and static coupling variables of surface deformation were decomposed into valley-type internal factors (rock mass static load) and slope-type triggering factors (fluid impact dynamic load) using the moving average method. Time series prediction models for the variable of the dynamic–static coupling effects on surface deformation were constructed using polynomial regression and particle swarm optimization (PSO)–support vector regression (SVR) algorithms, achieving multi-scale early warning of debris flows. The experimental results showed that the error between the predicted surface deformation results using this method and the actual values is less than 5 mm. The predicted MAPE value reached 6.622%, the RMSE value reached 8.462 mm, the overall warning accuracy reached 85.9%, and the warning time was under 30 ms, indicating that the proposed method delivered high warning accuracy and real-time warning. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
Show Figures

Figure 1

19 pages, 5153 KiB  
Article
Aluminum Reservoir Welding Surface Defect Detection Method Based on Three-Dimensional Vision
by Hanjie Huang, Bin Zhou, Songxiao Cao, Tao Song, Zhipeng Xu and Qing Jiang
Sensors 2025, 25(3), 664; https://doi.org/10.3390/s25030664 - 23 Jan 2025
Viewed by 287
Abstract
Welding is an important process in the production of aluminum reservoirs for motor vehicles. The welding quality affects product performance. However, rapid and accurate detection of weld surface defects remains a huge challenge in the field of industrial automation. To address this problem, [...] Read more.
Welding is an important process in the production of aluminum reservoirs for motor vehicles. The welding quality affects product performance. However, rapid and accurate detection of weld surface defects remains a huge challenge in the field of industrial automation. To address this problem, we proposed a 3D vision-based aluminum reservoir welding surface defect detection method. First of all, a scanning system based on laser line scanning camera was constructed to acquire the point cloud data of weld seams on the aluminum reservoir surface. Next, a planar correction algorithm was used to adjust the slope of the contour line according to the slope of the contour line in order to minimize the effect of systematic disturbances when acquiring weld data. Then, the surface features of the weld, including curvature and normal vector direction, were extracted to extract holes, craters, and undercut defects. For better extraction of the defect, a double-aligned template matching method was used to ensure comprehensive extraction and measurement of defect areas. Finally, the detected defects were categorized according to their morphology. Experimental results show that the proposed method using 3D laser scanning data can detect and classify typical welding defects with an accuracy of more than 97.1%. Furthermore, different types of defects, including holes, undercuts, and craters, can also be accurately detected with precision 98.9%. Full article
(This article belongs to the Collection 3D Imaging and Sensing System)
Show Figures

Figure 1

26 pages, 39396 KiB  
Article
Using a Neural Network to Model the Incidence Angle Dependency of Backscatter to Produce Seamless, Analysis-Ready Backscatter Composites over Land
by Claudio Navacchi, Felix Reuß and Wolfgang Wagner
Remote Sens. 2025, 17(3), 361; https://doi.org/10.3390/rs17030361 - 22 Jan 2025
Viewed by 313
Abstract
In order to improve the current standard of analysis-ready Synthetic Aperture Radar (SAR) backscatter data, we introduce a machine learning-based approach to estimate the slope of the backscatter–incidence angle relationship from several backscatter statistics. The method requires information from radiometric terrain-corrected gamma nought [...] Read more.
In order to improve the current standard of analysis-ready Synthetic Aperture Radar (SAR) backscatter data, we introduce a machine learning-based approach to estimate the slope of the backscatter–incidence angle relationship from several backscatter statistics. The method requires information from radiometric terrain-corrected gamma nought time series and overcomes the constraints of a limited orbital coverage, as exemplified with the Sentinel-1 constellation. The derived slope estimates contain valuable information on scattering characteristics of different land cover types, allowing for the correction of strong forward-scattering effects over water bodies and wetlands, as well as moderate surface scattering effects over bare soil and sparsely vegetated areas. Comparison of the estimated and computed slope values in areas with adequate orbital coverage shows good overall agreement, with an average RMSE value of 0.1 dB/° and an MAE of 0.05 dB/°. The discrepancy between RMSE and MAE indicates the presence of outliers in the computed slope, which are attributed to speckle and backscatter fluctuations over time. In contrast, the estimated slope excels with a smooth spatial appearance. After correcting backscatter values by normalising them to a certain reference incidence angle, orbital artefacts are significantly reduced. This becomes evident with differences up to 5 dB when aggregating the normalised backscatter measurements over certain time periods to create spatially seamless radar backscatter composites. Without being impacted by systematic differences in the illumination and physical properties of the terrain, these composites constitute a valuable foundation for land cover and land use mapping, as well as bio-geophysical parameter retrieval. Full article
(This article belongs to the Special Issue Calibration and Validation of SAR Data and Derived Products)
Show Figures

Figure 1

31 pages, 12097 KiB  
Article
Analysis and Verification of a Slope Steering Model of TRVs in Hilly and Mountainous Environments
by Luojia Duan, Kaibo Kang, Shiying Chen, Zixing Du, Longhai Zhang, Zhijie Liu, Fuzeng Yang and Zheng Wang
Agronomy 2025, 15(1), 147; https://doi.org/10.3390/agronomy15010147 - 9 Jan 2025
Viewed by 379
Abstract
Compared to wheeled vehicles, tracked robotic vehicles have less ground pressure, greater traction adhesion, and stronger climbing and obstacle crossing capabilities, making them suitable for agricultural production in hilly areas. Good steering performance directly relates to the mobility performance and operating efficiency of [...] Read more.
Compared to wheeled vehicles, tracked robotic vehicles have less ground pressure, greater traction adhesion, and stronger climbing and obstacle crossing capabilities, making them suitable for agricultural production in hilly areas. Good steering performance directly relates to the mobility performance and operating efficiency of tracked robotic vehicles. Affected by the ground slope, the ground pressure distribution of the vehicle’s two tracks is uneven, leading to changes in its steering performance. Therefore, analyzing and researching the steering performance of a tracked robotic vehicle under sloped conditions is of great significance. This study establishes a slope steering model for tracked robotic vehicles based on a ground pressure model of the multi-peak varying amplitude cosine distribution and the shearing displacement relationship between the track and the ground, and analyzes the impact of vehicle structural parameters, road surface parameters, and steering parameters on steering performance. To verify the proposed theoretical model, multi-body dynamics software is utilized for simulation modeling and analysis. Turning tests on different slopes are conducted on a “soil–machine–crop” integrated experimental platform. The relative error between the numerical analysis results and the virtual simulation software’s results is less than 12%, and the relative error between the numerical analysis results and the experimental results is less than 10.3%; the good consistency between the theoretical results and the simulation’s results and the experimental results indicates that the model is, indeed, correct and effective. The established steering model can provide a theoretical basis for the design and control of new steering mechanisms for agricultural tracked robotic vehicles. Full article
(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)
Show Figures

Figure 1

23 pages, 43085 KiB  
Article
Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
by Guolin Ma, Linlin Tian, Yilei Song and Ning Zhao
Appl. Sci. 2024, 14(23), 11438; https://doi.org/10.3390/app142311438 - 9 Dec 2024
Viewed by 769
Abstract
The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants [...] Read more.
The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants of the conventional SST k-ω model and the linear Reynolds stress model (RSM) featuring optimized closure constants. Then, these two modified models and their origin models are applied to compare and analyze wind flows from a 3D hill wind tunnel experiment and two field measurements over typical complex terrain, including Askervein hill and Bolund island, with the aim of analyzing the sensitivity of wind flows to different RANS turbulence models. The study focuses on analyzing the effects of different turbulence models on the self-sustainability of wind speed and turbulent kinetic energy upstream of the computational domain and on the accuracy of wind flow prediction over complex terrain. The results show that our modified RSM model shows better agreement with the available experimental data on the upstream and leeward sides of all simulated hills. The wind speed on the leeward slope is particularly sensitive to the turbulence model, with a maximum difference in the relative root mean square error (RRMSE) that can reach 11% among the four models. The accuracy of the turbulent kinetic energy depends on the self-sustainability of the upstream turbulent kinetic energy and the predictive ability of the turbulence model for separated flows, and the maximum difference in the RRMSE of the four models can reach 47%. In addition, the advantages and disadvantages of the tested models are discussed to provide guidance for model selection during wind flow simulations in complex terrain. Full article
(This article belongs to the Special Issue Recent Advances in Wind Engineering and Applied Aerodynamics)
Show Figures

Figure 1

33 pages, 22496 KiB  
Article
The Stability of Slopes and Building Structures Using an Energy Visualization Procedure
by Yi Yao, Jianjun Zhang, Xiaoyong Li, Yiliang Tu and Zuliang Zhong
Buildings 2024, 14(12), 3705; https://doi.org/10.3390/buildings14123705 - 21 Nov 2024
Viewed by 481
Abstract
Many building structures in the southwest of China are constructed on slopes due to its mountainous terrain characteristics. Therefore, it is crucial to accurately study the stability of slopes and building structures during the construction and operation stages. Traditional numerical simulation methods for [...] Read more.
Many building structures in the southwest of China are constructed on slopes due to its mountainous terrain characteristics. Therefore, it is crucial to accurately study the stability of slopes and building structures during the construction and operation stages. Traditional numerical simulation methods for slope stability often analyze from the perspectives of stress and strain. However, due to the complex changes in stress and strain inside the slope, the traditional methods are not only complex but also result in some errors. The slope failure is essentially a procedure of energy transformation, dissipation, and mutation. Therefore, the slope stability can be analyzed more effectively from the perspective of energy changes. In this paper, an energy field visualization procedure is developed and applied to analyze the failure mechanism of slopes. First, the energy calculation principle of slopes was derived based on the principle of thermodynamics. Then, FLAC3D7.0 was used to develop the energy visualization procedure for slope. It was applied to a classical two-dimensional slope to calculate the safety factor of slopes and then compared with the traditional methods. Finally, the procedure was applied to two practical slopes and building structure engineering cases to study their stability and provide suggestions for practical construction. The research results show that the energy visualization procedure can correctly simulate the energy evolution principle in the procedure of slope failure. The sudden change of energy can be used to determine the safety factor and sliding surface of slopes. The error of the slope safety factor calculated by this procedure is only 0.02, indicating that the procedure is correct. The deformation and failure of slopes are essentially driven by energy. There are corresponding relationships between the energy stability stage and the slope equilibrium stage, the energy dissipation stage and the slope deformation stage, and the energy mutation stage and the slope failure stage. The preferred backfill scheme of high-fill slope engineering is one with less variation in gravitational potential energy and a greater increase in elastic strain energy. Pile foundation and building structure are effective methods to increase slope stability. Therefore, the energy visualization procedure developed in this paper can more intuitively and accurately analyze the stability of slopes and building structures. Full article
Show Figures

Figure 1

24 pages, 21738 KiB  
Article
New Method to Correct Vegetation Bias in a Copernicus Digital Elevation Model to Improve Flow Path Delineation
by Gabriel Thomé Brochado and Camilo Daleles Rennó
Remote Sens. 2024, 16(22), 4332; https://doi.org/10.3390/rs16224332 - 20 Nov 2024
Viewed by 759
Abstract
Digital elevation models (DEM) are widely used in many hydrologic applications, providing key information about the topography, which is a major driver of water flow in a landscape. Several open access DEMs with near-global coverage are currently available, however, they represent the elevation [...] Read more.
Digital elevation models (DEM) are widely used in many hydrologic applications, providing key information about the topography, which is a major driver of water flow in a landscape. Several open access DEMs with near-global coverage are currently available, however, they represent the elevation of the earth’s surface including all its elements, such as vegetation cover and buildings. These features introduce a positive elevation bias that can skew the water flow paths, impacting the extraction of hydrological features and the accuracy of hydrodynamic models. Many attempts have been made to reduce the effects of this bias over the years, leading to the generation of improved datasets based on the original global DEMs, such as MERIT DEM and, more recently, FABDEM. However, even after these corrections, the remaining bias still affects flow path delineation in a significant way. Aiming to improve on this aspect, a new vegetation bias correction method is proposed in this work. The method consists of subtracting from the Copernicus DEM elevations their respective forest height but adjusted by correction factors to compensate for the partial penetration of the SAR pulses into the vegetation cover during the Copernicus DEM acquisition process. These factors were calculated by a new approach where the slope around the pixels at the borders of each vegetation patch were analyzed. The forest height was obtained from a global dataset developed for the year 2019. Moreover, to avoid temporal vegetation cover mismatch between the DEM and the forest height dataset, we introduced a process where the latter is automatically adjusted to best match the Copernicus acquisition year. The correction method was applied for regions with different forest cover percentages and topographic characteristics, and the result was compared to the original Copernicus DEM and FABDEM, which was used as a benchmark for vegetation bias correction. The comparison method was hydrology-based, using drainage networks obtained from topographic maps as reference. The new corrected DEM showed significant improvements over both the Copernicus DEM and FABDEM in all tested scenarios. Moreover, a qualitative comparison of these DEMs was also performed through exhaustive visual analysis, corroborating these findings. These results suggest that the use of this new vegetation bias correction method has the potential to improve DEM-based hydrological applications worldwide. Full article
Show Figures

Graphical abstract

25 pages, 89520 KiB  
Article
A Fuzzy Logic Control-Based Adaptive Gear-Shifting Considering Load Variation and Slope Gradient for Multi-Speed Automated Manual Transmission (AMT) Electric Heavy-Duty Commercial Vehicles
by Shanglin Wang, Xiaodong Liu, Xuening Zhang, Yulong Zhao and Yanfeng Xiong
Electronics 2024, 13(22), 4458; https://doi.org/10.3390/electronics13224458 - 14 Nov 2024
Viewed by 781
Abstract
The current trend in pure electric heavy-duty commercial vehicles (PEHCVs) is the increasing utilization of automated manual transmission (AMT) to optimize driveline efficiency. However, the existing gear-shift schedule of AMT fails to account for crucial factors such as vehicle load and slope gradient, [...] Read more.
The current trend in pure electric heavy-duty commercial vehicles (PEHCVs) is the increasing utilization of automated manual transmission (AMT) to optimize driveline efficiency. However, the existing gear-shift schedule of AMT fails to account for crucial factors such as vehicle load and slope gradient, leading to frequent gear position changes during uphill driving, compromising driving comfort. This study proposes a novel approach incorporating the vehicle’s load and slope gradient to develop an enhanced gear-shift strategy based on fuzzy logic control to address this issue more effectively. Initially, a dynamic gear-shift schedule was formulated for a 6-speed AMT-equipped PEHCV, followed by an analysis of the impact of vehicle load and slope gradient on the gear-shift schedule. Subsequently, an adaptive gear-shift design framework was developed using fuzzy logic control, considering inputs such as acceleration pedal opening, vehicle load, and slope gradient. Simultaneously, the velocity correction factor was designed as an output to adjust the velocity of gear-shift points based on the dynamic gear-shift schedule. Finally, simulations were conducted under various operating scenarios, including different slope gradients, varying vehicle loads, changing pedal openings, and random scenarios to compare and validate the proposed gear-shift schedule against its predecessor—the previous dynamic gear-shift schedule. The results demonstrate that the proposed gear-shift schedule exhibits exceptional adaptability to various driving scenarios. The average acceleration time can be reduced by over 20%, while the gear-shift frequency within 200 s can be decreased by more than 30 times. Full article
Show Figures

Figure 1

22 pages, 2141 KiB  
Article
Performance Evaluation of CMIP6 Climate Model Projections for Precipitation and Temperature in the Upper Blue Nile Basin, Ethiopia
by Fekadie Bazie Enyew, Dejene Sahlu, Gashaw Bimrew Tarekegn, Sarkawt Hama and Sisay E. Debele
Climate 2024, 12(11), 169; https://doi.org/10.3390/cli12110169 - 22 Oct 2024
Cited by 1 | Viewed by 1719
Abstract
The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias [...] Read more.
The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias in the CMIP6 model data was adjusted using data from meteorological stations. Additionally, this study uses daily CMIP6 precipitation and temperature data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the near (2015–2044), mid (2045–2074), and far (2075–2100) periods. Power transformation and distribution mapping bias correction techniques were used to adjust biases in precipitation and temperature data from seven CMIP6 models. To validate the model data against observed data, statistical evaluation techniques were employed. Mann–Kendall (MK) and Sen’s slope estimator were also performed to identify trends and magnitudes of variations in rainfall and temperature, respectively. The performance evaluation revealed that the INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. The precipitation projections in all agro-climatic zones under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show a significant (p < 0.01) positive trend. The mean annual maximum temperature over UBNB is estimated to increase by 1.8 °C, 2.1 °C, and 2.8 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5 between 2015 and 2100, respectively. Similarly, the mean annually minimum temperature is estimated to increase by 1.5 °C, 2.1 °C, and 3.1 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. These significant changes in climate variables are anticipated to alter the incidence and severity of extremes. Hence, communities should adopt various adaptation practices to mitigate the effects of rising temperatures. Full article
(This article belongs to the Section Climate and Environment)
Show Figures

Figure 1

22 pages, 12339 KiB  
Article
Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline
by Oliver Gutiérrez-Hernández and Luis V. García
Remote Sens. 2024, 16(20), 3886; https://doi.org/10.3390/rs16203886 - 19 Oct 2024
Cited by 3 | Viewed by 1011
Abstract
We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false [...] Read more.
We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management. Full article
Show Figures

Figure 1

18 pages, 1713 KiB  
Review
Empirical Predictions on Wave Overtopping for Overtopping Wave Energy Converters: A Systematic Review
by Deping Cao, Jie He and Hao Chen
Processes 2024, 12(9), 1940; https://doi.org/10.3390/pr12091940 - 10 Sep 2024
Cited by 1 | Viewed by 1286
Abstract
Over the past three decades, the development and testing of various overtopping wave energy converters (OWECs) have highlighted the importance of accurate wave run-up and overtopping predictions on those devices. This study systematically reviews the empirical formulas traditionally used for predicting overtopping across [...] Read more.
Over the past three decades, the development and testing of various overtopping wave energy converters (OWECs) have highlighted the importance of accurate wave run-up and overtopping predictions on those devices. This study systematically reviews the empirical formulas traditionally used for predicting overtopping across different types of breakwaters by assessing their strengths, limitations, and applicability to OWECs. This provides a foundation for future research and development in OWECs. Key findings reveal that empirical formulas for conventional breakwaters can be categorized as mild or steep slopes and vertical structures based on the angle of the slope. For the same relative crest freeboards, the dimensionless average overtopping discharge of mild slopes is larger than that of vertical structures. However, the formula features predictions within a similar range for small relative crest freeboards. The empirical formulas for predicting overtopping in fixed and floating OWECs are modified from the predictors developed for conventional breakwaters with smooth, impermeable and linear slopes. Different correction coefficients are introduced to account for the effects of limited draft, inclination angle, and low relative freeboard. The empirical models for floating OWECs, particularly the Wave Dragon model, have been refined through prototype testing to account for the unique 3D structural reflector’s influence and dynamic wave interactions. Full article
(This article belongs to the Special Issue Design and Utilization of Wind Turbines/Wave Energy Convertors)
Show Figures

Figure 1

19 pages, 11934 KiB  
Article
The Characteristics of Long-Wave Irregularities in High-Speed Railway Vertical Curves and Method for Mitigation
by Laiwei Jiang, Yangtenglong Li, Yuyuan Zhao and Minyi Cen
Sensors 2024, 24(13), 4403; https://doi.org/10.3390/s24134403 - 7 Jul 2024
Cited by 2 | Viewed by 1007
Abstract
Track geometry measurements (TGMs) are a critical methodology for assessing the quality of track regularities and, thus, are essential for ensuring the safety and comfort of high-speed railway (HSR) operations. TGMs also serve as foundational datasets for engineering departments to devise daily maintenance [...] Read more.
Track geometry measurements (TGMs) are a critical methodology for assessing the quality of track regularities and, thus, are essential for ensuring the safety and comfort of high-speed railway (HSR) operations. TGMs also serve as foundational datasets for engineering departments to devise daily maintenance and repair strategies. During routine maintenance, S-shaped long-wave irregularities (SLIs) were found to be present in the vertical direction from track geometry cars (TGCs) at the beginning and end of a vertical curve (VC). In this paper, we conduct a comprehensive analysis and comparison of the characteristics of these SLIs and design a long-wave filter for simulating inertial measurement systems (IMSs). This simulation experiment conclusively demonstrates that SLIs are not attributed to track geometric deformation from the design reference. Instead, imperfections in the longitudinal profile’s design are what cause abrupt changes in the vehicle’s acceleration, resulting in the measurement output of SLIs. Expanding upon this foundation, an additional investigation concerning the quantitative relationship between SLIs and longitudinal profiles is pursued. Finally, a method that involves the addition of a third-degree parabolic transition curve (TDPTC) or a full-wave sinusoidal transition curve (FSTC) is proposed for a smooth transition between the slope and the circular curve, designed to eliminate the abrupt changes in vertical acceleration and to mitigate SLIs. The correctness and effectiveness of this method are validated through filtering simulation experiments. These experiments indicate that the proposed method not only eliminates abrupt changes in vertical acceleration, but also significantly mitigates SLIs. Full article
(This article belongs to the Section Intelligent Sensors)
Show Figures

Figure 1

21 pages, 8759 KiB  
Article
Research and Experiment on Cruise Control of a Self-Propelled Electric Sprayer Chassis
by Lingxi Zhou, Chenwei Hu, Yuxiang Chen, Peijie Guo, Liwei Zhang, Jinyi Liu and Yu Chen
Agriculture 2024, 14(6), 902; https://doi.org/10.3390/agriculture14060902 - 7 Jun 2024
Cited by 2 | Viewed by 834
Abstract
In order to address the issues of poor stability in vehicle speed and deteriorated spraying quality caused by changes in road slope and the decrease in overall mass due to liquid spraying, this study focuses on analyzing the structure and longitudinal dynamic characteristics [...] Read more.
In order to address the issues of poor stability in vehicle speed and deteriorated spraying quality caused by changes in road slope and the decrease in overall mass due to liquid spraying, this study focuses on analyzing the structure and longitudinal dynamic characteristics of a 4WID high ground clearance self-propelled electric sprayer. By utilizing MATLAB/Simulink software, three subsystems, namely, the inverse longitudinal dynamics model, torque distribution model, and motor model, are established. The model takes into account the effects of longitudinal driving resistance, slope, and vehicle roll angle on the distribution of loads among the four wheels during slope driving. A seven-degrees-of-freedom dynamic model is developed. A hierarchical control structure is designed, incorporating an upper-level PID controller and a lower-level fuzzy PID controller, to control the overall system. The control algorithms are tailored to the specific characteristics of the sprayer’s operation, and simulation experiments are conducted under the corresponding operating conditions. Building upon this, a sensor-equipped experimental platform is set up in the self-propelled sprayer manufactured by the team in the preliminary stage. Real vehicle tests are conducted in two scenarios: transition transportation and field operations, with the evaluation of the overall vehicle speed serving as the performance metric to validate the correctness of the model and the control theory. Full article
(This article belongs to the Section Agricultural Technology)
Show Figures

Figure 1

17 pages, 4885 KiB  
Article
Position Correction Control of Permanent-Magnet Brushless Motor Based on Commutation-Interval Current Symmetry
by Yongwu Guo, Yun Zhang and Xiaowei Li
World Electr. Veh. J. 2024, 15(5), 203; https://doi.org/10.3390/wevj15050203 - 7 May 2024
Viewed by 1208
Abstract
With the needs of environmental protection and the adjustment of energy structure, new energy vehicles are playing an increasingly important role in the field of transportation today. The permanent-magnet brushless direct-current motor has the characteristics of high efficiency, and can be used in [...] Read more.
With the needs of environmental protection and the adjustment of energy structure, new energy vehicles are playing an increasingly important role in the field of transportation today. The permanent-magnet brushless direct-current motor has the characteristics of high efficiency, and can be used in the drive system of new energy vehicles or other auxiliary equipment. In the control process of the permanent-magnet brushless direct-current motor, based on a three-Hall position sensor, due to various factors, there are some errors in the Hall position signal, which must be corrected by appropriate measures. In this paper, the relationship between the position deviation in the commutation interval and the non-commutation-phase current is analyzed, and the current expressions in three different states are given. A new closed-loop compensation strategy for correcting the inaccurate commutation caused by the Hall signal error is proposed. Taking the position of a 30° electrical angle before and after the phase-change point as the H point, realizing the current symmetry within the 30° interval around the H point as the target and the sum of the slopes of the tangent lines at the two points symmetrical within the β (0 < β < 30) electrical angle around the H point as the deviation, a proportional-integral regulator is designed to correct the phase error of the phase-change signal. Finally, it is verified by experiments that the closed-loop compensation strategy proposed in this paper can effectively compensate the phase deviation of the commutation signal at a speed of about 2000 r/min, which improves the working efficiency of the motor to a certain extent. Full article
Show Figures

Figure 1

Back to TopTop