Maximum likelihood-based adaptive differential evolution identification algorithm for multivariable systems in the state-space form

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.     

Comparative study of Fourier transform infrared spectroscopy (FTIR) analysis of natural fibres treated with chemical,physical and biological methods

Polymer Bulletin - Kenaf and luffa fibres are hydrophilic due to the presence of water sensitive constituents, which tend to form a poor compatibility when binding with polymers. Thus, the surface...     

Mesoporous g-C3N4 decorated by Ni2P nanoparticles and CdS nanorods together for enhancing photocatalytic hydrogen evolution

Ni₂P nanoparticles and CdS nanorods were grew together on a mesoporous g-C₃N₄ through a facile in-situ solvothermal approach. Under visible light (λ > 400 nm), the as-prepared ternary PCN–CdS-5% Ni₂P composite displays a high H₂ evolution rate with 2905.86 μmol g^?1 h^?1, which is about 14, 18 and 279 times that of PCN–CdS, PCN–Ni₂P and PCN, respectively. The enhanced photocatalytic activity is mainly attributed to the improved separation efficiency of the photocarriers by the type II PCN–CdS heterojunction and the effective extraction of photogenerated electrons by Ni₂P. Meanwhile, Ni₂P acts as co-catalyst to provide the photocatalytic active site for hydrogen reduction. In addition, PCN–CdS-5% Ni₂P composite exerts good stability in 12-h cycles.     

基于DSP的智能电源控制单元设计与实现

本文利用TI公司TMS320F28335芯片高效的浮点运算能力,结合片上丰富的外设,设计并实现了一种具有高可靠性的智能电源控制单元。该控制单元周期性地对各片上外设进行自检维护,完成多路负载通道控制、电压、电流的实时监控,并对故障进行指示、处理和上报,同时提供人机交互界面更新状态信息。经过验证,该控制单元工作稳定,具备良好的工程应用价值。     

Viscoplastic behavior of AA7075 aluminum alloy at high strain rate

Journal of Mechanical Science and Technology - The flow stress increases with the increase in strain rate. This phenomenon is the strain rate effect of plastic deformation. Hopkinson experiment...     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Core–shell NTC materials with low thermal constant and high resistivity for wide-temperature thermistor ceramics

Core–shell structures have been proposed to improve the electrical properties of negative-temperature coefficient (NTC) thermistor ceramics. In this work, Al₂O₃-modified Co_1.5Mn_1.2Ni_0.3O₄ NTC thermistor ceramics with adjustable electrical properties were prepared through citrate-chelation followed by conventional sintering. Co_1.5Mn_1.2Ni_0.3O₄ powder was coated with a thin Al₂O₃ shell layer to form a core–shell structure. Resistivity (ρ) increased rapidly with increasing thickness of the Al₂O₃ layer, and the thermal constant (B) varied moderately between 3706 and 3846 K. In particular, Co_1.5Mn_1.2Ni_0.3O₄@Al₂O₃ ceramic with 0.08 wt% Al₂O₃ showed the increase of ρ double, and the change in its B was less than 140 K. The Co_1.5Mn_1.2Ni_0.3O₄@Al₂O₃ NTC ceramics showed high stability, and their grain size was relatively uniform due to the protection offered by the shell. The aging coefficient of the ceramic was less than 0.2% after aging for 500 hours at 125°C. Taken together, the results indicate that as-prepared Co_1.5Mn_1.2Ni_0.3O₄@Al₂O₃ NTC ceramics with a core–shell structure may be promising candidates for application as wide-temperature NTC thermistor ceramics.