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Revolutionizing Pressure Sensors for Wild Migratory Bird Tracking

Scientists from the Aerospace Information Research Institute (AIR) at the Chinese Academy of Sciences (CAS) have introduced an enhanced algorithm known as Dynamic Quantum Particle Swarm Optimization (DQPSO) to enhance the precision and dependability of pressure sensors utilized in the tracking and monitoring of wild migratory birds. ....

Jessica Rolletta Shutterstock , Megan Craig , Chinese Academy Of Sciences , Aerospace Information Research Institute , Chinese Academy , Dynamic Quantum Particle Swarm Optimization , Industry Focus , Radial Basis Function , Dynamic Temperature Compensation , Pressure Sensors , Migratory Bird Biologging ,

New Algorithm Boosts Precision of Bird-Tracking Pressure Sensors

New Algorithm Boosts Precision of Bird-Tracking Pressure Sensors
miragenews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from miragenews.com Daily Mail and Mail on Sunday newspapers.

Aerospace Information Research Institute , Chinese Academy Of Sciences , Electronics On Oct , Chinese Academy , Dynamic Quantum Particle Swarm Optimization , Radial Basis Function ,

Improved algorithm enhances precision of pressure sensors for wild bird tracking

Improved algorithm enhances precision of pressure sensors for wild bird tracking
phys.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from phys.org Daily Mail and Mail on Sunday newspapers.

Jinlu Xie , Chinese Academy Of Sciences , Electronics On Oct , Aerospace Information Research Institute , Chinese Academy , Dynamic Quantum Particle Swarm Optimization , Radial Basis Function , Dynamic Temperature Compensation , Pressure Sensors , Migratory Bird Biologging Applications ,

"Adaptive radial basis function sliding mode control for platoons under" by Xiaofei Zhang, Haiping Du et al.

This paper proposes an adaptive sliding mode control based on an auxiliary distance sensor for nonlinear platoons over vehicular ad-hoc networks under Denial-of-Services (DoS) attacks and external disturbances. DoS attacks can cause packet dropping for the wireless network and will lead to the collision or degradation to adaptive cruise control of platoons. To solve this problem, an adaptive Radial Basis Function (RBF) sliding mode control method is proposed for the attacked platoons. RBF neural networks are used to approximate the unknown nonlinear part and external disturbances contained in the vehicle models. Then the adaptive sliding mode controllers are designed to achieve the control goals: the spacing error converges to zero, and the platoon maintains the string stability. The system's internal stability and string stability are proved. Numerical examples are applied to show the effectiveness of the proposed method. Compared with the existing method, the spacing error is de ....

Radial Basis Function ,