Том 53, № 5 (2019)

This article proposes an approach to presenting the network infrastructure of cyberphysical systems to provide a more rapid identification of a suitable variant to rearranging the route on a graph that characterizes the target function. The proposed approach minimizes the number of used devices and allows neutralizing earlier detected cyberattacks against the system.

Induction motors are one of the critical industrial drivers due to its simplicity, inexpensiveness, and high resistance. Such motors have a nonlinear model divided into two electrical and mechanical equations in terms of modeling. Knowing the values of electric parameters and mechanical moment of inertia is critically important for speed controlling and induction motors’ position. In many algorithms, electric parameters can be obtained by the locked rotor and unloaded tests, conducting these methods in laboratory would probably cost a lot of time and money. In this paper electrical parameters and moment of inertia are used approximately, without doing the above test by currents, voltages, and motor speed sampling in motor normal operation. This paper applies cooperative co-evolution method to remove certain costly tests that are required for induction motors. Two identification algorithms are suggested for all electrical parameters and moment of inertia. All inductances and resistances which are the two input parameters measured in electric equations using Cooperative-Coevolution algorithm. Mechanical model estimated the moment of inertia and load torque by using a nonlinear method based on Lyapunov. Computerized numerical simulations show that electric parameters, moment of inertia, and load torque were properly estimated by integrating the two smart and classic methods. The results show that the stator inductance error is about 1% and rotor inductance error is around 20%. Rotor and stator resistance error and self-Inductance is also less than one percent.

Aiming at the actual targets coverage scene of targets and sensors in the three-dimensional physical world, in order to use the minimal sensors to cover all the targets, a new coverage-all targets algorithm based on Gravitational Search algorithm (GSA-CT) is proposed. Firstly, from the practical point of view, a 3D coverage-all targets model of WMSNs which based on the spatial position relationship of sensors and targets is established in three-dimensional space. Secondly, in order to avoid randomness of the current order method to determine the minimal number of sensors to cover all the targets, a new fitness calculation method has been proposed. Thirdly, in order to improve solution accuracy, GSA is used as the optimization method of coverage-all targets method. Experimental results show that compared with the other 7 coverage methods for the 9 actual coverage scenarios, the number of sensors required for GSA-CT proposed in this paper is the least, and the method is very stable.

Rehabilitation technology based on brain-computer interface (BCI) has become a promising approach for patients with dyskinesia to regain movement. In this paper, a novel classification algorithm is proposed based on the characteristic of electroencephalogram (EEG) signals. Specifically wavelet packet decomposition (WPD) and Extreme learning machine with kernel (ELM_Kernel) algorithm are studied. In view of the existence of cross-banding of WPD, the average energy of the wavelet packets of the corresponding frequency bands which belong to the mu and beta rhythm are used to form the feature vectors that are classified by the ELM_Kernel algorithm. Simulation results demonstrate that the proposed algorithm produces a high probability of correct classification of 97.8% and outperforms state-of-the-art algorithms such as ELM, BP and SVM in terms of both training time and classification accuracy.

High efficiency video coding (HEVC) introduces a well-designed quad-tree structure to improve coding efficiency. During quad-tree building, the encoder tries all possible coding unit (CU) partition schemes to determine the optimal one with the minimum rate distortion cost, which introduces high computation complexity. To deal with this problem, a fast algorithm based on edge information is proposed to accelerate CU splitting in this paper. Firstly, the depth and prediction mode of adjacent blocks are obtained and used to facilitate rough partition. Secondly, the edge pixel values of this CU are counted and analyzed to fine-tune the partition. Experimental results show that the proposed algorithm can reduce coding time by 35.7% on average, with an acceptable loss of Bjøntegaard delta rate of 0.6%.