Research on modeling and optimization simulation analysis of micro electric vehicle suspension

Suspension system is one of the key parts of vehicle, the performance of suspension system has great influence on vehicle handling stability and safety. In order to improve the performance of suspension system, the Macpherson suspension of a vehicle is taken as the research object, the suspension model is established by ADAMS/Car, and carried out parallel wheel travel simulation to analyze the key parameters variation of camber angle, toe angle, caster angle, kingpin inclination angle and scrub radius. Simulation results show that camber angle and scrub radius are beyond normal design range and require optimization. Wheel alignment parameters are determined by sensitivity analysis, and optimized by ADAMS/Insight. Then carried out simulation to analyze the performance of optimized suspension system. Results show that optimized suspension system satisfies the requirements of vehicle stability and safety

Paroxysmal dyskinesia and epilepsy in pseudohypoparathyroidism

Abstract Background Paroxysmal kinesigenic dyskinesia (PKD) and epilepsy share common pathogenic mechanisms but their pathophysiological connections remain unknown. Our study reports an individual with both disorders as a consequence of pseudohypoparathyroidism (PHP). This observation suggests potential shared pathophysiological mechanisms between PKD and epilepsy. Methods We report the case of a 15‐year‐old male with pre‐diagnosed PKD and symptomatic epilepsy. We recorded the symptoms and carried out comprehensive biochemical, genetic, imaging, and EEG analyses to examine the characteristics and potentially shared etiology of these conditions. Results In this case, the patient's PKD and symptomatic epilepsy were secondary to pseudohypoparathyroidism (PHP). The patient had a seven‐year history of intermittent, involuntary paroxysmal episodic movements, and a six‐year history of a loss of consciousness with convulsions. The electroencephalography results showed that the paroxysmal low and medium amplitude slow waves, isolated sharp waves, and sharp slow‐wave release occurred in the right prefrontal temporal cortex. Serum analysis indicated a calcium concentration of 1.91 mmol/L, a phosphorus concentration of 2.68 mmol/L, an alkaline phosphatase concentration of 114 IU/L, and a parathyroid hormone concentration of 109 pg/ml. Computerized tomography and magnetic resonance imaging results showed multiple calcifications in the bilateral frontal and parietal lobe cortex, bilateral thalamus, basal ganglia, and centrum semiovale. Furthermore, GNAS methylation abnormalities were discovered during methylation testing. There was no recurrence of abnormal movements or epileptic seizures, and calcium concentrations returned to healthy levels, following the pharmacological treatment of PHP. Conclusion In this case, PKD and symptomatic epilepsy were caused by PHP. This report underscores the importance of looking for biochemical abnormalities in PKD and symptomatic epilepsy patients. We suggest that all such intractable epilepsy seizure patients should be screened for PHP

Combining Estimation of Green Vegetation Fraction in an Arid Region from Landsat 7 ETM+ Data

Fractional vegetation cover (FVC), or green vegetation fraction, is an important parameter for characterizing conditions of the land surface vegetation, and also a key variable of models for simulating cycles of water, carbon and energy on the land surface. There are several types of FVC estimation models using remote sensing data, and evaluating their performance over a specific region is of great significance. Therefore, this study firstly evaluated three types of FVC estimation models using Landsat 7 ETM+ data in an agriculture region of Heihe River Basin, China, and then proposed a combination strategy from different individual models to improve the FVC estimation accuracy, which employed the multiple linear regression (MLR) and Bayesian model average (BMA) methods. The validation results indicated that the spectral mixture analysis model with three endmembers (SMA3) achieved the best FVC estimation accuracy (determination coefficient (R2) = 0.902, root mean square error (RMSE) = 0.076) among the seven individual models using Landsat 7 ETM+ data. In addition, the MLR and BMA combination methods could both improve FVC estimation accuracy (R2 = 0.913, RMSE = 0.063 and R2 = 0.904, RMSE = 0.069 for MLR and BMA, respectively). Therefore, it could be concluded that both MLR and BMA combination methods integrating FVC estimates from different models using Landsat 7 ETM+ data could effectively weaken the estimation errors of individual models and improve the final FVC estimation accuracy

Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model

A fractional vegetation cover (FVC) estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed, which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarse-resolution FVC pixel were all assumed to have the same vegetation growth model. However, this assumption does not hold over heterogeneous areas, meaning that the method cannot be applied to large regions. Therefore, this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite (GLASS) FVC product. The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data. Then, independent dynamic vegetation models were built for each finer-resolution pixel. Finally, the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale. Validation results indicated that the proposed method (R2 = 0.7757, RMSE = 0.0881) performed better than either the previous method (R2 = 0.7038, RMSE = 0.1125) or a commonly used method involving look-up table inversions of the PROSAIL model (R2 = 0.7457, RMSE = 0.1249)

Fractional vegetation cover estimation based on soil and vegetation lines in a corn-dominated area

An automatic fractional vegetation cover (FVC) estimation method based on image characteristics in an agricultural region was proposed in this study to remove the empiricism in determining the key parameters of empirical methods. The proposed method automatically determined the soil and vegetation lines in the two-dimensional space of the red and blue band reflectances, which involved an iterative soil and vegetation pixels selection procedure, and then estimated FVC of a pixel based on its distances from the soil and vegetation lines. The accuracy assessment using field survey data indicated that the performance of the proposed method (R2 = 0.69, RMSE = 0.072, Bias = 0.014) was comparable with several commonly used empirical methods. Therefore, it was indicated that the proposed method could effectively estimate FVC in the corn-dominated region

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data

Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region

Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type. The radiative transfer model was firstly used to simulate the physical relationship between canopy reflectance and LAI under different soil and vegetation conditions, and then the training sample dataset was formed. Then, neural networks (NNs) were used to develop the LAI estimation algorithm using the training sample dataset. Green, red and near-infrared band reflectances of GF-1 WFV data were used as the input variables of the NNs, as well as the corresponding LAI was the output variable. The validation results using field LAI measurements in the agriculture region indicated that the LAI estimation algorithm could achieve satisfactory results (such as R2 = 0.818, RMSE = 0.50). In addition, the developed LAI estimation algorithm had potential to operationally generate LAI datasets using GF-1 WFV land surface reflectance data, which could provide high spatial and temporal resolution LAI data for agriculture, ecosystem and environmental management researches

Mechanism Study on Transition of Cassie Droplets to Wenzel State after Meniscus Touching Substrate of Pillars

To understand the conditions and mechanism of droplet wetting transition from Cassie to Wenzel state (C-W) and furthermore to prohibit this transition are the key research contents about superhydrophobic materials. In this study, the C-W transition process after meniscus touching substrate (MTS) was divided into different stages. Then, the changes of droplet interfacial free energy (IFE) after MTS were analyzed. And the resistance on three-phase contact line (TPCL) was also investigated. Furthermore, based on the droplet IFE always changing from high to low, a criterion formula for C-W transition was derived so that the mathematical model was founded. The calculation results show that the smaller the pitch and/or diameter of pillars, the more difficult the MTS and C-W transition for an initial sessile Cassie droplet. Therefore, nanotextures can efficiently prevent the C-W transition. Besides, the MTS of droplets on short and high pillars can be realized with sag and TPCL depinning impalement, respectively. Additionally, the greater the intrinsic contact angle, the more unfavorable the droplet C-W transition. Moreover, micronano two-tier textures can effectively inhibit the C-W transition. Finally, the model results are in good agreement with the experimental measurements reported in literature, with 92% accuracy

Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover

Fractional vegetation cover (FVC) is an essential parameter for characterizing the land surface vegetation conditions and plays an important role in earth surface process simulations and global change studies. The Sentinel-2 missions carrying multi-spectral instrument (MSI) sensors with 13 multispectral bands are potentially useful for estimating FVC. However, the performance of these bands for FVC estimation is unclear. Therefore, the objective of this study was to assess the performance of Sentinel-2 MSI spectral band reflectances on FVC estimation. The samples, including the Sentinel-2 MSI canopy reflectances and corresponding FVC values, were simulated using the PROSPECT + SAIL radiative transfer model under different conditions, and random forest regression (RFR) method was then used to develop FVC estimation models and assess the performance of various band reflectances for FVC estimation. These models were finally evaluated using field survey data. The results indicate that the three most important bands of Sentinel-2 MSI data for FVC estimation are band 4 (Red), band 12 (SWIR2) and band 8a (NIR2). FVC estimation using these bands has a comparable accuracy (root mean square error (RMSE) = 0.085) with that using all bands (RMSE = 0.090). The results also demonstrate that band 12 had a better performance for FVC estimation than the green band (RMSE = 0.097). However, the newly added red-edge bands, with low scores in the RFR model, have little significance for improving FVC estimation accuracy compared with the Red, NIR2 and SWIR2 bands