Results 101 to 110 of about 1,776,592 (192)

SYSTEM OF SIGNAL DETECTION ON THE BACKGROUND OF MICROSEISMIC NOISE

Transactions of Kremenchuk Mykhailo Ostrohradskyi National University, 2020
openaire   +1 more source

Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring. [PDF]

Sensors (Basel)
Xin B, Huang Z, Huang S, Feng L.
europepmc   +1 more source

Curvelet-Based Stochastic Noise Suppression for Downhole DAS Microseismic Data. [PDF]

Sensors (Basel)
Zhang Y, Chen Z, Chen H, Cheng L, Dong J, Wu Z, Li Z.   +6 more
europepmc   +1 more source

Enhancing safety and early warning capabilities in mining through microseismic monitoring technology. [PDF]

Sci Rep
Jin J, Xiao Y, Wu P, Wang J, Liu J, Mo Y, Zhang L.   +6 more
europepmc   +1 more source

Microseismic monitoring with the quake neural operator. [PDF]

Nat Commun
Sun H.
europepmc   +1 more source

Séparation adaptative de sources dans l'espace signal

, 1995
Nous explorons la possibilité de coupler un algorithme adaptatif de séparation de sources avec un algorithme adaptatif de projection de projection sur l'espace signal afin de tirer parti d'un nombre de capteurs supérieur au nombre de sources.
CARDOSO, Jean-Francois
core  

Review of Research Advances in Full-Spectrum Fiber Optic Sensing Technologies for CO₂ Geological Storage. [PDF]

ACS Omega
Zhang C, Wang G, Dai P, Wu Q, Fu J, Zhang C, Fu L.   +6 more
europepmc   +1 more source

Study on rockburst mechanism and risk identification for isolated working face in extra-thick coal seam fold zone. [PDF]

Sci Rep
Xie Y, Cao A, Li G, Zhao W, Zhang C, Feng Z.   +5 more
europepmc   +1 more source

Optimization Research on Sensor Network Layout for Microseismic Monitoring Based on Location Error Analysis. [PDF]

Sensors (Basel)
Huang X, Li S, Dong L, Yang L.
europepmc   +1 more source

Le lissage H∞ : une alternative au filtrage de Kalman pour le rehaussement du signal de parole ?

, 2005
- La mise en oeuvre d'un filtre de Kalman repose sur des hypothèses fortes sur le processus générateur et le bruit d'observation du système. En effet, ils doivent être tous les deux blancs et gaussiens et leurs variances connues a priori.
GRIVEL, E., LABARRE, D., NAJIM, M., CHRISTOV, N.   +3 more
core