Blind reconstruction of convolutional code based on segmented Walsh-Hadamard transform

Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.

Blind reconstruction of linear scrambler

An algorithm based on eigenanalysis technique and Walsh-Hadamard transform （WriT） is proposed. The algorithm contains two steps. Firstly, the received sequence is divided into temporal windows, and a covariance matrix is computed. The linear feedback shift register （LFSR） sequence is reconstructed from the first eigenvector of this matrix. Secondly, equations according to the recovered LFSR sequence are constructed, and the Walsh spectrum corresponding to the equations is computed. The feedback polynomial of LFSR is estimated from the Walsh spectrum. The validity of the algorithm is verified by the simulation result. Finally, case studies are presented to illustrate the performance of the blind reconstruction method.

Optimization of virtual machine placement based on constrained immune memory and immunodominance clone in IaaS cloud mode equipment training

In infrastructure as a service(IaaS)cloud mode equipment simulated training,to keep the resource utilization ratio in a rational high level,improve the training effect and reduce the system running cost,the problem of training virtual machine(TVM)placement needs to be resolved first.We make analysis to the problem and give the mathematical formulation to the problem.Then,we figure out the principle and target of the TVM placement.Based on above analysis,we propose a constrained immune memory and immunodominance clone(CIMIC)TVM placement optimization algorithm.By reverse optimization of the initial antibody population,the searching range is reduced.The common antibody population and the immunodominance antibody population evolve simultaneously,which realizes the simultaneous progressing of global searching and local searching of solutions.Further,local optimal is avoided by this means.Memory antibody makes ful use of the unfeasible solutions and the diversity of antibody population is maintained.The constraint information of the problem is utilized to improve the optimization effect.Experiment results show that the CIMIC algorithm improves the overall optimization effect of TVM placement,reduces the server number and improves the resource utilization and system stability.

Equipment remaining useful life prediction oriented symbiotic simulation driven by real-time degradation data

As an emerging simulation technology in the field of system modeling and simulation,the equipment symbiotic simulation has become research emphasis.In the field of equipment maintenance support,the outstanding problem of equipment remaining useful life(RUL)prediction is analyzed,i.e.,the stable model parameters without self-evolution ability,which has become the primary factor that hinders self-adaptive prediction of equipment RUL.Combined with parallel systems theory,the equipment RUL prediction oriented symbiotic simulation framework is proposed on the basis of modeling analysis and Wiener state space model(SSM)is taken as the basic simulation model in the framework.Driven by the dynamic injected equipment degradation observation data,the model parameters are updated online by using expectation maximum(EM)algorithm and the data assimilation between simulation outputs and observation data is executed by using Kalman filter,so as to realize dynamic evolution of the simulation model.The simulation model evolution which makes the simulation outputs close to equipment real degradation state provides high fidelity model and data for predicting equipment RUL accurately.The framework is verified by the performance degradation data of a bearing.The simulation results show that the symbiotic simulation method can accurately simulate the equipment performance degradation process and the self-adaptive prediction of equipment RUL is realized on the basis of improving prediction accuracy,proving the feasibility and effectiveness of symbiotic simulation method.