In this paper, an in-wheel vibration absorber for In-wheel-motor electric vehicle (IWM EV) is designed, and a comprehensive control strategy of in-wheel absorber and vehicle suspension is proposed to improve vehicle ride comfort. The proposed in-wheel vibration absorber, designed for suppressing the motor vibration, is composed of a spring and a controllable damper. The values of in-wheel spring stiffness and damper initial coefficient are determined by using the improved particle swarm optimization (IPSO) algorithm, which is carried on the typical driving condition. To deal with the negative interaction effects between vehicle suspension and in-wheel absorber, the linear quadratic regulator (LQR) algorithm is utilized to control suspension damper, and the fuzzy PID method is utilized to control in-wheel damper. Based on the four evaluation indexes including vehicle body vertical acceleration, suspension dynamic deflection, wheel dynamic load and motor wallop, the simulation results show that, the proposed LQR control of suspension effectively improves vehicle ride comfort, and the fuzzy PID control of in-wheel damper exhibits superior performance of motor vibration suppressing in comparison to conventional electric wheel.

In order to understand the response mechanism between plant stress, physiological indicators and hyperspectral indices, pot experiments were conducted on Suaeda salsa seedlings collected from a coastal wetland area to reveal the effects of salt stress on the physiological indicators and reflectance spectra of Suaeda salsa at the canopy and leaf level. The Suaeda salsa seedlings were exposed to seven salt treatments of different concentrations (0 mmol/L (control), 50 mmol/L, 100 mmol/L, 200 mmol/L, 300 mmol/L, 400 mmol/L, and 600 mmol/L) in natural conditions. The physiological indicators of plant height, fresh weight, dry weight, leaf succulence, chlorophyll content, and carotenoid content were measured, in addition to the reflectance spectra of Suaeda salsa at both the canopy and leaf level. Firstly, the effects of salt stress on the physiological indicators and reflectance spectra were analyzed by the qualitative and quantitative methods. Then, physiological indicators sensitive to salt stress were further retrieved. Afterwards hyperspectral indices such as a/b and ((a-b)/(a+b) ) sensitive to salt stress were also extracted by one-way analysis of variance (ANOVA) and Student-Newman-Keuls (S-N-K) comparison test. Our results showed that plant height, root length, leaf succulence, biomass, Chl-a, and Chl-b were sensitive to salt stress, while carotenoids (Car) and relative water content on the root were not significantly affected by salt stress. At the salt concentration of 200 mmol/L, plant height, biomass, relative water content, leaf succulence peaked. With enhanced salt stress, physiological indicators decreased. The first-order derivative spectral reflectance has the highest correlation with salt stress, compared to the control. The spectral index most sensitive to the salt stress at the canopy level is (D₉₀₃−D₈₅₁)/(D₉₀₃+D₈₅₁), for which the multiple determination coefficient (r²) is 0.9216. While the most sensitive spectral index to the salt stress is (D₄₄₂−D₆₆₇)/(D₄₄₂+D₆₆₇) at the leaf level, for which the r² is −0.898. In summary, the results indicated that there exists the quantitative relationship between the physiological indicators and spectra reflectance under salt stress and hyperspectral plant indices can effectively estimate the degree of salt stress. The inconsistency between the diagnostic hyperspectral plant indices at the canopy and leaf levels may be caused by the observation conditions, canopy structure.

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitation, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method