Research on system of ultra-flat carrying robot based on improved PSO algorithm

Ultra-flat carrying robots (UCR) are used to carry soft targets for functional safety road tests of intelligent driving vehicles and should have superior control performance. For the sake of analyzing and upgrading the motion control performance of the ultra-flat carrying robot, this paper develops the mathematical model of its motion control system on the basis of the test data and the system identification method. Aiming at ameliorating the defects of the standard particle swarm optimization (PSO) algorithm, namely, low accuracy, being susceptible to being caught in a local optimum, and slow convergence when dealing with the parameter identification problems of complex systems, this paper proposes a refined PSO algorithm with inertia weight cosine adjustment and introduction of natural selection principle (IWCNS-PSO), and verifies the superiority of the algorithm by test functions. Based on the IWCNS-PSO algorithm, the identification of transfer functions in the motion control system of the ultra-flat carrying robot was completed. In comparison with the identification results of the standard PSO and linear decreasing inertia weight (LDIW)-PSO algorithms, it indicated that the IWCNS-PSO has the optimal performance, with the number of iterations it takes to reach convergence being only 95 and the fitness value being only 0.117. The interactive simulation model was constructed in MATLAB/Simulink, and the critical proportioning method and the IWCNS-PSO algorithm were employed respectively to complete the tuning and optimization of the Proportional-Integral (PI) controller parameters. The results of simulation indicated that the PI parameters optimized by the IWCNS-PSO algorithm reduce the adjustment time to 7.99 s and the overshoot to 13.41% of the system, and the system is significantly improved with regard to the control performance, which basically meets the performance requirements of speed, stability, and accuracy for the control system. In conclusion, the IWCNS-PSO algorithm presented in this paper represents an efficient system identification method, as well as a system optimization method

Real-Time Inland CCTV Ship Tracking

The predator algorithm is a representative pioneering work that achieves state-of-the-art performance on several popular visual tracking benchmarks and with great success when commercially applied to real-time face tracking in long-term unconstrained videos. However, there are two major drawbacks of predator algorithm when applied to inland CCTV (closed-circuit television) ship tracking. First, the LK short-term tracker within predator algorithm easily tends to drift if the target ship suffers partial or even full occlusion, mainly because the corner-points-like features employed by LK tracker are very sensitive to occlusion appearance change. Second, the cascaded detector within the predator algorithm searches for candidate objects in a predefined scale set, usually including 3-5 elements, which hampers the tracker to adapt to the potential diverse scale variations of the target ship. In this paper, we design a random projection based short-term tracker which can dramatically ease the tracking drift when the ship is under occlusion. Furthermore, a forward-backward feedback mechanism is proposed to estimate the scale variation between two consecutive frames. We prove that these two strategies gain significant improvements over the predator algorithm and also show that the proposed method outperforms several other state-of-the-art trackers

A Genetic Optimization Algorithm Based on Adaptive Dimensionality Reduction

With the rise of big data in cloud computing, many optimization problems have gradually developed into high-dimensional large-scale optimization problems. In order to address the problem of dimensionality in optimization for genetic algorithms, an adaptive dimensionality reduction genetic optimization algorithm (ADRGA) is proposed. An adaptive vector angle factor is introduced in the algorithm. When the angle of an individual’s adjacent dimension is less than the angle factor, the value of the smaller dimension is marked as 0. Then, the angle between each individual dimension is calculated separately, and the number of zeros in the population is updated. When the number of zeros of all individuals in a population exceeds a given constant in a certain dimension, the dimension is considered to have no more information and deleted. Eight high-dimensional test functions are used to verify the proposed adaptive dimensionality reduction genetic optimization algorithm. The experimental results show that the convergence, accuracy, and speed of the proposed algorithm are better than those of the standard genetic algorithm (GA), the hybrid genetic and simulated annealing algorithm (HGSA), and the adaptive genetic algorithm (AGA)

Yue-bi-tang attenuates adriamycin-induced nephropathy edema through decreasing renal microvascular permeability via inhibition of the Cav-1/ eNOS pathway

Edema is one of the most typical symptoms of nephrotic syndrome. Increased vascular permeability makes a significant contribution to the progression of edema. Yue-bi-tang (YBT) is a traditional formula with excellent clinical efficacy in the treatment of edema. This study investigated the effect of YBT on renal microvascular hyperpermeability-induced edema in nephrotic syndrome and its mechanism. In our study, the content of target chemical components of YBT was identified using UHPLC-Q-Orbitrap HRMS analysis. A nephrotic syndrome model was replicated based on male Sprague-Dawley rats with Adriamycin (6.5 mg/kg) by tail vein injection. The rats were randomly divided into control, model, prednisone, and YBT (22.2 g/kg, 11.1 g/kg, and 6.6 g/kg) groups. After 14 d of treatment, the severity of renal microvascular permeability, edema, the degree of renal injury, and changes in the Cav-1/eNOS pathway were assessed. We found that YBT could regulate renal microvascular permeability, alleviate edema, and reduce renal function impairment. In the model group, the protein expression of Cav-1 was upregulated, whereas VE-cadherin was downregulated, accompanied by the suppression of p-eNOS expression and activation of the PI3K pathway. Meanwhile, an increased NO level in both serum and kidney tissues was observed, and the above situations were improved with YBT intervention. It thus indicates YBT exerts therapeutic effects on the edema of nephrotic syndrome, as it improves the hyperpermeability of renal microvasculature, and that YBT is engaged in the regulation of Cav-1/eNOS pathway-mediated endothelial function

The role of institutional investors in post-earnings announcement drift: evidence from China

We examine how institutional investors influence post-earnings announcement drift (PEAD) in China. Our findings suggest that institutional holdings are positively correlated with PEAD in China, especially when institutional investors herd strongly on earnings news. This positive relationship is more salient for institutional investors with shorter investment horizons and in firms with higher information opacity. We also find that stock prices reverse in the fourth quarter after the earnings announcement. In contrast to the well-established view that institutional investors exploit PEAD and accelerate the speed of information incorporation, our findings suggest that they may instead exacerbate PEAD and slow down price discovery in emerging markets with different institutional backgrounds