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Tensor network simulation of dynamics and finite-temperature quantum system
物理系colloquium:重离子加速器发展前沿和重大应用
Large N theory of critical Fermi surface
Temporal Entanglement in Dual-Unitary Clifford CircuitswithProbabilistic Mea...
报告题目:
Integrated Sensing and Sensor Networks for Health Monitoring and Diagnosis in Mechanical Systems
 报告人:
Professor Robert X. Gao
Director, Electromechanical Systems Laboratory
 Department of Mechanical and Industrial Engineering
University of Massachusetts
报告时间:
2007-12-27 14:00
报告地点:
精仪系制造所会议室(2501室)
主办单位:
精仪系
  简介:

The last decade of the 20th century has witnessed an increasing convergence of the information technology with communication and system miniaturization. This convergence has accelerated the long standing development of the sensor technology, placing it at the verge of significant growth in the 21st century. Miniaturization has made it possible to decrease the size, weight, and cost of sensors by orders of magnitude, while increasing their accuracy and resolution. As a result, a large number of sensors can be networked and integrated into mechanical systems to improve the observability of their working conditions and performance. The ubiquitous presence of sensors in civil and mechanical structures, transportation
vehicles, warehouses, kitchen appliances and various industrial and commercial fields have in many ways quietly transformed the society and the way of life in many parts of the world. This seminar presents an overview of research activities in the Electromechanical Systems Laboratory at the University of Massachusetts, in the areas of embedded sensing, sensor signal processing, and sensor networks for the health monitoring and diagnosis of mechanical systems. Issues concerning sensing methods are first presented, in the context of high quality data acquisition and remote data retrieval from constrained environment. Next, techniques for non-stationary data analysis in sensor signal processing
are illustrated. Finally, issues on energy efficiency and data communication in distributed sensor networks are discussed, which have potential applications in various industrial and commercial fields.

 
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