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Articles by Long-Hua Ma
Total Records ( 6 ) for Long-Hua Ma
  Long-Hua Ma , Yu Zhang , Chun-Ning Yang and Hui Li
  For signal processing and process control, the minimax problem is a crucial point in research subjects. But efficient solutions to equality and inequality constrained nonlinear general minimax problems are relatively scarce. A minimax neural network model was proposed to solve the general minimax problem based on penalty function. In this model, the unique requirement is that the objective function and constraint functions should be first-order differentiable. In addition to the global stability analysis based on the Lyapunov function, the proposed model was simulated and its validity was evaluated with numerical results. Experimental results demonstrated that the proposed minimax neural network model can solve the problem in seconds which is more efficient than the conventional genetic algorithm and simplex genetic algorithms.
  Chun-Ning Yang , Long-Hua Ma , Ya-Zhou Yue and Hui Li
  In this study, a general scheme for implementation of attitude and velocity algorithm in Strapdown Inertial Navigation System (SINS) under real-time environment was presented. This real-time implementation scheme was formulated in a recursive form using two time-rate executions and utilized integer arithmetic only. The coding of the attitude and velocity algorithm was conducted by a navigation computer (such as DSP or FPGA). It is shown that the general real-time implementation of strapdown attitude and velocity algorithm can be used in strapdown inertial navigation system for any combinations of gyro incremental angle sample of mth minor interval and (m-1)th minor interval.
  Long-Hua Ma , Yu Zhang , Zhe-Ming Lu and Hui Li
  Particle degradation, as a main limitation of particle filter, can be resolved by making use of common re-sampling method, but it always bring about the problem of sample dilution. The Immune Particle Swarm Optimization (IMPSO) was introduced into particle filter and a new kind of particle filter named IMPSO-based particle filter was proposed. In the IMPSO-based particle filter algorithm, particles are driven to the area with a higher posterior probability density and maintain big particle diversity at the same time. Simulation results show that IMPSO-based particle filter can eliminates the degeneracy phenomenon, avoid the sample dilution problem and guarantee the effectiveness.
  Long-Hua Ma , Kai-Li Wang and Hui Li
  Land combat vehicles are inevitably subject to the vibration disturbance by wind gust or engine idling, etc. in the stationary initial alignment process of the Strapdown Inertial Navigation System (SINS). Obviously, it’s necessary to consider the impact of vibration disturbance during the alignment process to achieve better performance. In order to guarantee the alignment accuracy on the rocking base and shorten the convergence time of alignment, a gyrocompass alignment method of SINS based on Kalman filter pretreatment and dynamic gain adjustment was proposed. The output of gyros and accelerometers was firstly pre-filtered by Kalman filter to remove the impact of high-frequency small-amplitude rocking interference. The low-frequency large-amplitude rocking interference on vehicle was tracked through dynamic gain adjustment of gyrocompass alignment. The vehicle test of a ring laser SINS showed that the new gyrocompass alignment method can suppress high-frequency disturbances when the vehicle underwent low-frequency large-amplitude rocking interference. And the alignment process can track the attitude change of vehicle caused by low-frequency large-amplitude rocking interference. Comparing with traditional gyrocompass alignment algorithm and Kalman filter alignment method, the performance of the new gyrocompass alignment method is much improved by filtering random noise caused by vibration disturbance of vehicle effectively.
  Long-Hua Ma , Ming Xu , Meng Shao and Zhe-Ming Lu
  Randomness and parameter selection in the Particle Swarm Optimization (PSO) algorithm had great influence on its performance. This study presented a formal convergence and spectral radius analysis of the standard PSO algorithm model, where some of the parameters were stochastic. Based on the analysis of the relationship of {ω, c1, c2}, a sufficient condition was given to guarantee that the PSO algorithm was mean-square convergent, using the stochastic process theory. Then, the mean spectral radius was constructed. According to the relationship between the spectral radius and the convergent speed, it was shown that, a small spectral radius lead to a faster convergent speed than a big one. By optimizing the mean spectral radius of the PSO algorithm in the mean-square convergent region, a minimum spectral radius and corresponding parameter selection guidelines were derived to guarantee that the PSO algorithm was mean-square convergent and had a fast convergent speed in the stochastic sense. Finally, one parameter selection {c1 = c2 = 2, ω = 0.4222} was proposed. with the parameter, the study gave examples whose performance on benchmark functions were superior to previously published results.
  Meng Shao , Zheng-Liang Huang , Long-Hua Ma , Zhe-Ming Lu and Xiao-Long Shi
  Security issues are increasingly obvious. An automated real-time online alarm system to ensure the safety of property and personality while considering numerous smart Terminal Equipments (TE) becomes a major challenge. At the same time, this is representative of novel and emerging alarm system for assisted living in the daily life. Two problems of current alarm system are identified. A smart Cyber-Physical Alarm System (CPAS) based approach is proposed to address these problems. A prototype system installed in a house to assist living has been running stably and shows quite promising performance.
 
 
 
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