基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

Effects on the crystallite growth behavior of LaPO4 nanopowders and its mechanical properties

The effects of pH of the reaction solution and the concentration of phosphoric acid on the crystal growth behavior of LaPO₄ crystallites were investigated and the mechanical properties of rare-earth phosphates were compared. As a result, the concentration of phosphoric acid of 10% was beneficial to the crystal growth of LaPO₄ nanocrystalline. When the pH value of the reaction solution was 2, the size of LaPO₄ crystallites increased gradually with the increasing reaction temperature, and the smallest crystallite size of 43.27 nm was obtained after heat-treatment at 1000 °C. Simultaneously, the activation energy for crystal growth of LaPO₄ nanocrystalline was relatively lower (26.82 kJ mol⁻¹). With the decreasing radii of rare-earth ions, the hardness, Young's modulus and fracture toughness of the bulk rare-earth phosphates exhibited a reduced tendency, resulted from the increase of porosity under the same preparation process.     

Epitaxial Growth of 2D Materials on High-Index Substrate Surfaces

Recently, the successful synthesis of wafer-scale single-crystal graphene, hexagonal boron nitride (hBN), and MoS₂ on transition metal surfaces with step edges boosted the research interests in synthesizing wafer-scale 2D single crystals on high-index substrate surfaces. Here, using hBN growth on high-index Cu surfaces as an example, a systematic theoretical study to understand the epitaxial growth of 2D materials on various high-index surfaces is performed. It is revealed that hBN orientation on a high-index surface is highly dependent on the alignment of the step edges of the surface as well as the surface roughness. On an ideal high-index surface, well-aligned hBN islands can be easily achieved, whereas curved step edges on a rough surface can lead to the alignment of hBN along with different directions. This study shows that high-index surfaces with a large step density are robust for templating the epitaxial growth of 2D single crystals due to their large tolerance for surface roughness and provides a general guideline for the epitaxial growth of various 2D single crystals.     

Biocompatibility and electron microscopy studies of epitaxial nanolaminate (Al0·5Ti0.5)N/ZrN coatings deposited by Arc-PVD technique

The ability to combine layers with high mechanical strength and additional physicochemical properties, such as biocompatibility, makes the use of multilayer coatings attractive for various applications. The transition from single layer to nanolaminate architecture can improve the mechanical performance of the coatings by increasing the number of interfaces and decreasing the modulation period of the layers. The microstructural study of the nanolaminate (Al_0·5Ti_0.5)N/ZrN coating with a modulation period λ of ≃ 20 nm was carried out using the TEM-HRTEM method. It was found that the coatings of (Al_0·5Ti_0.5)N/ZrN series consisted of two phases: the fcc-(Ti,Al)N solid solution obtained by isomorphic substitution of Ti atoms with Al ones in the TiN crystal lattice and the cubic ZrN phase. ZrN layers had a high texture structure with [111]-preferred growth texture and made a dominant effect on the nucleation and growth of (Al_0·5Ti_0.5)N layers. The epitaxial growth process was the most pronounced for fcc-(Ti,Al)N (111)||fcc-(ZrN) (111) and fcc-(Ti,Al)N (200)||fcc-(ZrN) (200) grains. Finally, the new coating demonstrated high biocompatibility, failure to toxicity and supported U2OS osteogenic cells proliferation within 7 days of cultivation.     

基于改进SOA-VMD的磁声发射信号去噪算法

磁声发射（MAE）是铁磁性材料磁化过程中产生的声发射信号，在构件应力检测和微观损伤检测中有着广泛的应用。针对MAE信号非稳态、复杂性、衰减性等特点，提出海鸥算法结合变分模态分解（SOA-VMD）的去噪方法，为克服海鸥算法求解过程中易陷入局部最优解问题，利用柯西变异算子产生随机迭代过程，使改进算法即柯西变异海欧算法（CVSOA）跳出早熟收敛。采用以幅值谱熵为适应度函数，优化VMD算法中分解模态个数K和二次惩戒因子α两个参数，将含噪声的MAE信号进行VMD分解重构。经仿真信号和实际检测信号分析表明，改进后的CVSOA-VMD算法全局寻优能力和去噪性能优于传统的SOA-VMD算法，降噪后的MAE信号特征值对于不同应力下均方根、偏斜度特征值的重复性更好，可靠性更高。     

A thermal perspective of flash sintering: The effect of AC current ramp rate on microstructure evolution

In flash sintering experiments, the thermal history of the sample is key to understanding the mechanisms underlying densification rate and final properties. By combining robust temperature measurements with current-ramp-rate control, this study examined the effects of the thermal profile on the flash sintering of yttria-stabilized zirconia, with experiments ranging from a few seconds to several hours. The final density was maximized at slower heating rates, although processes slower than a certain threshold led to grain growth. The amount of grain growth observed was comparable to a similar conventional thermal process. The bulk electrical conductivity correlated with the maximum temperature and cooling rate. The only property that exhibited behavior that could not be attributed to solely the thermal profile was the grain boundary conductivity, which was consistently higher than conventional in flash sintered samples. These results suggest that, during flash sintering, athermal electric field effects are relegated to the grain boundary.     

Nitrogen-doped cobalt sulfide as an efficient electrocatalyst for hydrogen evolution reaction in alkaline and acidic media

The enhancement in intrinsic catalytic activity and material conductivity of an electrocatalyst can leads to promoting HER activity. Herein, a successful nitrogenation of CoS₂ (N–CoS₂) catalyst has been investigated through the facile hydrothermal process followed by N₂ annealing treatment. An optimized N–CoS₂ catalyst reveals an outstanding hydrogen evolution reaction (HER) performance in alkaline as well as acidic electrolyte media, exhibiting an infinitesimal overpotential of ?0.137 and ?0.097 V at a current density of ?10 mA/cm² (?0.309 and ?0.275 V at ?300 mA/cm²), corresponding respectively, with a modest Tafel slope of 117 and 101 mV/dec. Moreover, a static voltage response was observed at low and high current rates (?10 to ?100 mA/cm²) along with an excellent endurance up to 50 h even at ?100 mA/cm². The excellent catalytic HER performance is ascribed to improved electronic conductivity and enhanced electrochemically active sites, which is aroused from the synergy and mutual interaction between heteroatoms that might have varied the surface chemistry of an active catalyst.