Radar Working State Recognition Based on Improved HPSO-BP

In this paper, a recognition model based on the improved hybrid particle swarm optimisation (HPSO) optimised backpropagation network (BP) is proposed to improve the efficiency of radar working state recognition. First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. Then, the BP neural network’s initial weights and thresholds are optimised to overcome the shortcomings of slow convergence rate and falling into local optima. In the simulation experiment, improved HPSO-BP recognition models were established based on the datasets for three radar types, and these models were subsequently compared to other recognition models. The results reveal that the improved HPSO-BP recognition model has better prediction accuracy and convergence rate. The recognition accuracy of different radar types exceeded 97%, which demonstrates the feasibility and generalisation of the model applied to radar working state recognition.

RESEARCH OF COMPUTING EFFICIENCY IN MODULAR MULTIPROCESSOR SYSTEMS

The article is devoted to the research of efficiency of a multiprocessor computing system in solving problems aimed at expanding the computing area. The basic regularities concerning the time of solving the problem are revealed, depending on the change in the multiprocessor system calculations area. The research is aimed at determining the deceleration factor associated with the increase of the computing area of a multiprocessor system when compared with the computer version with an unlimited computing area. The analytical ratios are derived for determining the calculations deceleration coefficient. A stage of simulation for calculations of the deceleration factor was carried out to determine the regularities of its change, depending on the application of a particular computing platform. The revealed tendencies of such a change point to the need to reconcile the components of the network interface and computing capabilities of the chosen computing platform. The derived analytical relations were aimed at determining the optimal number of nodes of a multiprocessor system which allow the minimum delay of calculations.

Sensitivity Analysis of Entrance Design Parameters of a Backward-Inclined Centrifugal Fan Using DOE Method and CFD Calculations

Centrifugal fans with an electric motor included in the hub are commonly used in HVAC (heating, ventilation, and air conditioning) systems. A design of experiments (DOE) has been performed to study the effect of the entrance conditions of a backward-inclined centrifugal fan on its efficiency. The parameters involved are the base radius of the motor hub, the radius of the fan entry section, the deceleration factor throughout the entry zone (from the entry of the fan to the entry of the blade), and the solidity factor. Numerical simulation coupled with the DOE has been used for the sensitivity analysis of the entrance parameters. Initially, a complete factorial plan (24) was performed to screen the most influent parameters and interactions. This has shown that the motor’s cap radius, as well as its interactions with other parameters, is not significant. A second DOE, using composite central design (CCD which has a second order of accuracy) has then been performed on the remaining parameters (radius of the fan entry section, deceleration factor, and the solidity factor). The effects of these parameters and their interactions on the fan efficiency are now presented. A linear regression with three parameters has been performed to establish the efficiency distribution map. The methodology employed is validated by comparing the predicted results from the DOE and those from the numerical simulation of the corresponding fan.