Stochastic analysis and convergence velocity estimation of genetic algorithms

Formulizations of mutation and crossover operators independent of representation of solutions are proposed. A kind of precisely quantitative Markov chain of populations of standard genetic algorithms is modeled. It is proved that inadequate parameters of mutation and crossover probabilities degenerate standard genetic algorithm to a class of random search algorithms without selection bias toward any solution based on fitness. After introducing elitist reservation, the stochastic matrix of Markov chain of the best-so-far individual with the highest fitness is derived.The average convergence velocity of genetic algorithms is defined as the mathematical expectation of the mean absorbing time steps that the best-so-far individual transfers from any initial solution to the global optimum. Using the stochastic matrix of the best-so-far individual, a theoretic method and the computing process of estimating the average convergence velocity are proposed.

Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅰ)

Previously, researchers raised the accuracy for a robot′s hand to track a specified path in Car-tesian space mainly through increasing the number of knots on the path and the number of the path′s segments, which results in the heavier online computational burden for the robot controller. Aiming at overcoming this drawback, the authors propose a new kind of real-time accurate hand path tracking and joint trajectory planning method. Through selecting some extra knots on the specified hand path by a certain rule and introducing a sinusoidal function to the joint displacement equation of each segment, this method can greatly raise the path tracking accuracy of robot′s hand and does not change the number of the path′s segments. It also does not increase markedly the computational burden of robot controller. The result of simulation indicates that this method is very effective, and has important value in increasing the application of industrial robots.

DWT-Based Robust Color Image Watermarking Scheme

A scheme of embedding an encrypted watermark into the green component of a color image is proposed. The embedding process is implemented in the discrete wavelet transformation （DWT） domain, The original binary watermark image is firstly encrypted through scrambling technique, and then spread with two orthogonal pseudo-random sequences whose mean values are equal to zero, and finally embedded into the DWT low frequency sub-band of green components, The coefficients whose energies are larger than the others are selected to hide watermark, and the hidden watermark strength is determined by the energy ratio between the selected coefficients energies and the mean energy of the subband. The experiment results demonstrate that the proposed watermarking scheme is very robust against the attacks such as additive noise, low-pass filtering, scaling, cropping image, row （or column ） deleting, and }PEG compression.

Validation concurrency control protocol in parallel real-time database systems

In parallel real-time database systems, concurrency control protocols must satisfy time constraints as well as the integrity constraints. The authors present a validation concurrency control(VCC) protocol, which can enhance the performance of real-time concurrency control mechanism by reducing the number of transactions that might miss their deadlines, and compare the performance of validation concurrency control protocol with that of HP2PL(High priority two phase locking) protocol and OCC-TI-WAIT-50(Optimistic concurrency control-time interval-wait-50) protocol under shared-disk architecture by simulation. The simulation results reveal that the protocol the author presented can effectively reduce the number of transactions restarting which might miss their deadlines and performs better than HP2PL and OCC-TI-WAIT-50. It works well when arrival rate of transaction is lesser than threshold. However, due to resource contention the percentage of missing deadline increases sharply when arrival rate is greater than the threshold.

Online battery model parameters identification approach based on bias-compensated forgetting factor recursive least squares

Accuracy of a lithium-ion battery model is pivotal in faithfully representing actual state of battery,thereby influencing safety of entire electric vehicles.Precise estimation of battery model parameters using key measured signals is essential.However,measured signals inevitably carry random noise due to complex real-world operating environments and sensor errors,potentially diminishing model estimation accuracy.Addressing the challenge of accuracy reduction caused by noise,this paper introduces a Bias-Compensated Forgetting Factor Recursive Least Squares(BCFFRLS)method.Initially,a variational error model is crafted to estimate the average weighted variance of random noise.Subsequently,an augmentation matrix is devised to calculate the bias term using augmented and extended parameter vectors,compensating for bias in the parameter estimates.To assess the proposed method's effectiveness in improving parameter identification accuracy,lithium-ion battery experiments were conducted in three test conditions—Urban Dynamometer Driving Schedule(UDDS),Dynamic Stress Test(DST),and Hybrid Pulse Power Characterization(HPPC).The proposed method,alongside two contrasting methods—the offline identification method and Forgetting Factor Recursive Least Squares(FFRLS)—was employed for battery model parameter identification.Comparative analysis reveals substantial improvements,with the mean absolute error reduced by 25%,28%,and 15%,and the root mean square error reduced by 25.1%,42.7%,and 15.9%in UDDS,HPPC,and DST operating conditions,respectively,when compared to the FFRLS method.

A NEW CONSTRAINTS IDENTIFICATION TECHNIQUE-BASED QP-FREE ALGORITHM FOR THE SOLUTION OF INEQUALITY CONSTRAINED MINIMIZATION PROBLEMS

In this paper, we propose a feasible QP-free method for solving nonlinear inequality constrained optimization problems. A new working set is proposed to estimate the active set. Specially, to determine the working set, the new method makes use of the multiplier information from the previous iteration, eliminating the need to compute a multiplier function. At each iteration, two or three reduced symmetric systems of linear equations with a common coefficient matrix involving only constraints in the working set are solved, and when the iterate is sufficiently close to a KKT point, only two of them are involved. Moreover, the new algorithm is proved to be globally convergent to a KKT point under mild conditions. Without assuming the strict complementarity, the convergence rate is superlinear under a condition weaker than the strong second-order sufficiency condition. Numerical experiments illustrate the efficiency of the algorithm.