In recent years, the superconducting technology is popular in our production and life. An increasing number of researchers and institutions participate in study of the superconducting magnetic energy storage (SMES) system. SMES is widely used in power supply system, new energy system and pulsed power supply system. However, the leakage magnetic field of traditional SMES is more serious, which limited the application in engineering. This paper proposes an improved scheme for the 1 MJ single-solenoid structure magnet by using the actively shielding theory. Moreover, by using particle swarm optimization, the structural parameters of the superconducting magnets are optimized respectively. Finally, the ANSYS modeling simulation is built by these structural parameters, all simulation data are analyzed and compared.

As a result of tremendous rise in internet usage

This paper presents an efficient Parkinson disease diagnosis system using Least Squares Twin Support Vector Machine (LSTSVM) and Particle Swarm Optimization (PSO). LSTSVM is a promising binary classifier and has shown better generalization ability and faster computational speed. PSO is used for feature selection and parameter optimization. Parkinson disease dataset is taken from UCI repository. The performance of proposed system is compared with other existing approaches in terms of accuracy, sensitivity and specificity. Experimental results validate the effectiveness of proposed Parkinson disease diagnosis system over other exiting techniques.

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