In view of the problem that conventional incremental learning models may go through a way of performance degradation during the update stage, a kernelized incremental learning method was proposed based on objective equilibrium measurement. By setting the optimization term of "empirical risk minimization", an optimization objective function fulfilling the equilibrium measurement with respect to training data size was designed. The optimal solution was given under the condition of incremental learning training, and a lightweight incremental learning classification model was finally constructed based on the effective selection strategy of new data. Experimental results on a publicly available fall detection dataset show that, when the recognition accuracy of representative methods falls below 60%, the proposed method can still maintain the recognition accuracy more than 95%, while the computational consumption of the model update is only 3 milliseconds. In conclusion, the proposed method contributes to achieving a stable growth of recognition performance as well as efficiently decreasing the time consumptions, which can effectively realize wearable devices based intellectual applications in the cloud service platform.

According to the implicit characteristics of IPTV (Internet Protocol Television) user viewing behavior data, a novel implicit rating model was proposed. Firstly, the main features of IPTV user viewing behavior data were introduced, and a new mixed feature implicit scoring model was proposed, which combined with viewing ratio, user interest bias factor and video type influence factor. Secondly, the strategy of viewing behavior based on viewing time and viewing ratio was proposed. Finally, a distributed model architecture based on Hadoop was designed and implemented. The experimental results show that the proposed novel model effectively improves the quality of the recommended results in the IPTV system, improves the time efficiency, and has good scalability for large amounts of data.

For linear system with time-domain constraints including control input constraints, state constraints or their mixed constraints, an H∞ control scheme via Linear Matrix Inequalities (LMI) optimization was proposed in this paper. First, by assumption of initial states and the energy of external disturbance, a fixed ellipsoid containing all perturbed feasible trajectories was confirmed. Then, sufficient conditions of the closed-loop system satisfying time-domain constraints with the controller gain varying during a certain scope were derived and converted to LMI, and the derivation process was given in detail. Finally, the non-fragile H∞ controller design with time-domain constraints was led to solving an optimization problem with LMI constraints. Simulation results for application in the disturbance reject problem of mass-spring-damper system were discussed. The simulation application results show that the designed controller can improve the robustness of the closed-cloop system with controller gain variations while the time-domain constraints are respected.

The alternating direction method is used widely to deal with the problem of total variation image restoration. A correction method was proposed to solve the problem of inaccuracy in search direction of the alternating direction method, which may influence the efficiency of the algorithm and the quality of the restored images adversely. Combining Taylor expansion of energy function and properties of differentiable function, this subspace-optimization-based method corrected the current direction effectively by utilizing the previous one, and improved the accuracy of search direction. The numerical experiments expound the efficiency of this algorithm and the quality of the restored images by running time and Peak-Signal-to-Noise Ratio (PSNR), respectively.