金属相变超塑性的研究进展

综述了国内外金属相变超塑性的研究现状,包括金属相变超塑性的实现条件、影响因素和太合金变形机制,提出了相变超塑性的研究方向.     

基于多核支持向量回归的浓香型白酒风味成分逐步预测模型研究

目的 探究白酒感官品评与白酒风味成分之间的关系, 实现通过感官品评对风味成分进行预测。方法 采用变分自编码器(variational auto encoder, VAE)对原始数据进行增强, 以多核支持向量回归(multi-kernel support vector regression, MKSVR)结合遗传算法(genetic algorithm, GA)建立单预测模型, 再采取逐步预测的方式按照酸、酯、醇、醛类物质的顺序进行预测, 从而构建最终模型。结果 在经过VAE对数据进行增强的条件下, 多元线性回归(mixed logistic regression, MLR)对酸、酯、醇、醛类物质预测的拟合优度分别为0.9660、0.9106、0.8767、0.8686, 随机森林(random forests, RF)对酸、酯、醇、醛类物质预测的拟合优度分别为0.9663、0.9186、0.8805、0.8708, GA-MKSVR对酸、酯、醇、醛类物质预测的拟合优度分别为0.9715、0.9423、0.9072、0.8809, GA-MKSVR逐步预测对酸、酯、醇、醛类物质预测的拟合优度分别为0.9715、0.9447、0.9102、0.8851, GA-MKSVR逐步预测的效果均为最优。结论 GA-MKSVR逐步预测方法相较于传统的机器学习方法, 具有更好的性能, 对数据具有更高的适应性, 能更好地构建白酒感官与风味成分之间的关系模型。     

金属材料超塑性的研究进展

综述了金属超塑性的研究进展。在系统总结金属超塑性的实现条件、影响因素和变形机制的基础上,对金属超塑性的研究进行了展望。     

Command-filtered sensor-based backstepping controller for small unmanned aerial vehicles with actuator dynamics

In this study, a command-filtered sensor-based backstepping controller is proposed for small unmanned aerial vehicles (UAVs) with actuator dynamics. The command filter is introduced to prompt the virtual control law to be limited in a certain range and the corresponding state to subsequently be restricted to a certain area. When using the sensor-based backstepping recursive method, precise models of the UAVs are not required because the controller is not sensitive to the external disturbance. The actuator dynamics are compensated without prior knowledge of the mathematical model of the executing agency. Besides, a robust compensator is developed for the virtual control law of the first subsystem of the UAV, which shows strong robustness against the uncertainties of the aerodynamic coefficients and external disturbances. Moreover, the closed-loop system is proven stable in the sense that the signals are bounded. A numerical simulation is carried out to verify the effectiveness of the developed controller.     

Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures

We present a new, generic method/model for multi-objective design optimization of laminated composite components using a novel multi-objective optimization algorithm developed on the basis of the Quantum behaved Particle Swarm Optimization (QPSO) paradigm. QPSO is a co-variant of the popular Particle Swarm Optimization (PSO) and has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are – the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; Failure Mechanism based Failure criteria, Maximum stress failure criteria and the Tsai–Wu Failure criteria. The optimization method is validated for a number of different loading configurations – uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences as well as fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Also, the performance of QPSO is compared with the conventional PSO.