Tags: Sys IS, Optimal, Subtraction, Adaptive…. List of useful documentation.

Slides:



Advertisements
Similar presentations
SacMan Control Tuning Bert Clemmens Agricultural Research Service.
Advertisements

Neural Simulation and Control.. Simulation Input/Output models Proces u(k) y(k+d) d(k) The NARMA model:
Multiuser Detection for CDMA Systems
Figures for Chapter 7 Advanced signal processing Dillon (2001) Hearing Aids.
Robust Speech recognition V. Barreaud LORIA. Mismatch Between Training and Testing n mismatch influences scores n causes of mismatch u Speech Variation.
Bicoherence studies on LLO data Vijay Chickarmane, Gabriela Gonzalez LSU Offline analysis of bicoherence of 6hrs of data, July 12, 2002 using functions.
VSMC MIMO: A Spectral Efficient Scheme for Cooperative Relay in Cognitive Radio Networks 1.
For the Collaboration GWDAW 2005 Status of inspiral search in C6 and C7 Virgo data Frédérique MARION.
Image classification Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them?
(t,x) domain, pattern-based ground roll removal Morgan P. Brown* and Robert G. Clapp Stanford Exploration Project Stanford University.
LIGO- G Z Optimally Combining the Hanford Interferometer Strain Channels Albert Lazzarini LIGO Laboratory Caltech S. Bose, P. Fritschel, M. McHugh,
Extended Kalman Filter (EKF) And some other useful Kalman stuff!
LIGO- G Z March 22, 2006March 2006 LSC Meeting - DetChar 1 S5 Spectral Line Catalogue Status Keith Thorne (PSU) for the Spectral Line Catalogue.
| October 12, 2011 A Multiple Signal Classification Method for Directional Gravitational-wave Burst Search Junwei.
Kalman’s Beautiful Filter (an introduction) George Kantor presented to Sensor Based Planning Lab Carnegie Mellon University December 8, 2000.
280 SYSTEM IDENTIFICATION The System Identification Problem is to estimate a model of a system based on input-output data. Basic Configuration continuous.
Paper Title Your Name CMSC 838 Presentation. CMSC 838T – Presentation Motivation u Problem paper is trying to solve  Characteristics of problem  … u.
Single-Channel Speech Enhancement in Both White and Colored Noise Xin Lei Xiao Li Han Yan June 5, 2002.
Optimization of Signal Significance by Bagging Decision Trees Ilya Narsky, Caltech presented by Harrison Prosper.
Tracking with Linear Dynamic Models. Introduction Tracking is the problem of generating an inference about the motion of an object given a sequence of.
Computational aspects of motor control and motor learning Michael I. Jordan* Mark J. Buller (mbuller) 21 February 2007 *In H. Heuer & S. Keele, (Eds.),
Adaptive Signal Processing
ECE 8443 – Pattern Recognition ECE 8423 – Adaptive Signal Processing Objectives: Adaptive Noise Cancellation ANC W/O External Reference Adaptive Line Enhancement.
For 3-G Systems Tara Larzelere EE 497A Semester Project.
What is R By: Wase Siddiqui. Introduction R is a programming language which is used for statistical computing and graphics. “R is a language and environment.
Suspension Control with Thoughts on Modern Control Brett Shapiro 19 May May GWADW- G – v3.
Particle Filter & Search
ECE 8443 – Pattern Recognition ECE 8423 – Adaptive Signal Processing Objectives: Introduction SNR Gain Patterns Beam Steering Shading Resources: Wiki:
LIGO-G Z Detector characterization for LIGO burst searches Shourov K. Chatterji for the LIGO Scientific Collaboration 10 th Gravitational Wave.
RUP Implementation and Testing
1/25 Current results and future scenarios for gravitational wave’s stochastic background G. Cella – INFN sez. Pisa.
Eigenstructure Methods for Noise Covariance Estimation Olawoye Oyeyele AICIP Group Presentation April 29th, 2003.
GWADW Controls Sessions we make it look easy, but … G v1 GWADW Controls Sessions introduction.
Virgo Commissioning update Gabriele Vajente for the Virgo Collaboration LSC/VIRGO Meeting – MIT 2007, July LIGO-G Z.
Status of stochastic background’s joint data analysis by Virgo and INFN resonant bars G. Cella (INFN Pisa) For Auriga-ROG-Virgo collaborations Prepared.
Sensitivity Analysis of Mesoscale Forecasts from Large Ensembles of Randomly and Non-Randomly Perturbed Model Runs William Martin November 10, 2005.
Estimation of Sound Source Direction Using Parabolic Reflection Board 2008 RISP International Workshop on Nonlinear Circuits and Signal Processing (NCSP’08)
LSC-March  LIGO End to End simulation  Lock acquisition design »How to increase the threshold velocity under realistic condition »Hanford 2k simulation.
Study of Broadband Postbeamformer Interference Canceler Antenna Array Processor using Orthogonal Interference Beamformer Lal C. Godara and Presila Israt.
Robotics Research Laboratory 1 Chapter 7 Multivariable and Optimal Control.
EE 460 Advanced Control and Sys Integration Monday, August 24 EE 460 Advanced Control and System Integration Slide 1 of 13.
Lecture 25: Implementation Complicating factors Control design without a model Implementation of control algorithms ME 431, Lecture 25.
Pairwise Preference Regression for Cold-start Recommendation Speaker: Yuanshuai Sun
HACR at Virgo: implementation and results Gabriele Vajente 12 th ILIAS WG1 meeting Geneva, March 29 th -30 th 2007.
Adaptive Control Loops for Advanced LIGO
M1G Introduction to Programming 2 3. Creating Classes: Room and Item.
Team Members Ming-Chun Chang Lungisa Matshoba Steven Preston Supervisors Dr James Gain Dr Patrick Marais.
Current Works Corrected unit conversions in code Found an error in calculating offset (to zero sensors) – Fixed error, but still not accurately integrating.
Software Engineering Introduction.
LSC Meeting at LHO LIGO-G E 1August. 21, 2002 SimLIGO : A New LIGO Simulation Package 1. e2e : overview 2. SimLIGO 3. software, documentations.
Matlab Tutorial for State Space Analysis and System Identification
Dongxu Yang, Meng Cao Supervisor: Prabin.  Review of the Beamformer  Realization of the Beamforming Data Independent Beamforming Statistically Optimum.
1 Espen Åkervik 7 th ERCOFTAC SIG33 WORKSHOP Low dimensional model for control of the Blasius boundary layer by balanced truncation Espen Åkervik in collaboration.
Gabriele Vajente ILIAS WG1 meeting - Frascati Noise Analysis Tools at Virgo.
LIGO-G Z Adaptive FIRs taking place at Lasti Richard Mittleman Laurent Ruet Brett Shapiro April 2006.
The Mechanical Simulation Engine library An Introduction and a Tutorial G. Cella.
Robust Localization Kalman Filter & LADAR Scans
Anders Nielsen Technical University of Denmark, DTU-Aqua Mark Maunder Inter-American Tropical Tuna Commission An Introduction.
LIGO-G E Lazzarini - 60Hz CorrelationsLIGO Laboratory at Caltech 1 60 Hz Mains Correlations for the U.S. Power Grids The Aspen Winter Conference.
ARENA08 Roma June 2008 Francesco Simeone (Francesco Simeone INFN Roma) Beam-forming and matched filter techniques.
Channel Equalization Techniques
LQR Linear Quadratic Regulator
Unscented Kalman Filter for a coal run-of-mine bin
Nergis Mavalvala Aspen January 2005
Generation of squeezed states using radiation pressure effects
Discussion of Challenges & Opportunities Brainstorming Stacy Kowalczyk
Systems Analysis and Design in a Changing World, 6th Edition
Kalman’s Beautiful Filter (an introduction)
QUANSER Flight Control Systems Design 2DOF Helicopter 3DOF Helicopter 3DOF Hover 3DOF Gyroscope Quanser Education Solutions Powered by.
Improved Signal detection in Direct Imaging of Exoplanets
Presentation transcript:

Tags: Sys IS, Optimal, Subtraction, Adaptive…. List of useful documentation

Session B: Optimal Control -SISO filtering versus Optimal Control -(i) Optimal controllers (ii) Observers, SNR (iii) Modal control -LQR: Cost function, weight… -Kalman filter Goals: -Already investigated, good outcome, still not used, why -Prospects, application -Working groups, collaboration Half step done…

Introduction talk (review of modern control tools, state space methods, their pros and cons) Den Martynov Very detailed introduction: -State Space methods -Linear quadratic -Weighting -Augmenting the state -Kalman -H Infinity Went other one hour, great interaction. G v1 State-Space Methods for Feedback Control

T v3 Report from the Commissioning Workshop of Winter 2014 Summary of the Caltech WorkshopGabriele G A summary of the Modern Controls Caltech

Summary of the Caltech WorkshopGabriele Time domain versus frequency domain tools Open question on minimizing signals with optimal feedback tools

Suspension Controls, including thoughts on applicability of modern controls Brett Shapiro G v2Suspension Control with Thoughts on Modern Control

G v2Suspension Control with Thoughts on Modern Control

G v2Suspension Control with Thoughts on Modern Control

Kalman filtering for vibration isolation Jo van den Brand

Discussion and comparison of classical and modern control techniques Christophe Collette LIGO-G : Discussion and comparison of classical and modern control techniquesDiscussion and comparison of classical and modern control techniques

Understanding the tools Their pros and cons It’s not a magic solution But we can see the potentials: -Filter blends -Handling cross couplings -Keeping the momentum Modern control, general outcome:

Session C: Feedforward and noise subtraction Already made the step… -The standard way (rely on intuition, identify the sensor path, identify the correction path, implement) -The optimal way (array of sensors, linear regression, least square minimization, Wiener…) -Results already obtained Goals: -Where do we stand (Past results, current work) -Prospects, applications -Instrumentalist and DetChar -Automating -Adaptive

Some questions: 1) How to make the best use of our array of sensors? 2) How do make used of our array of witness sensors, vertical axis as rotation sensors, tilt sensors? 3) Are we using the good target sensors? 4) How do we search for coherence? 5) Toolbox for the linear regression, Wiener implementation? 6) Going adaptive for non stationary processes? Sensor correction in seismic and suspensionsFabrice

Data mining tools: hot to find channels that can be used for subtraction; how to tackle non stationary noises and couplings? Gabriele Vajente G v1Data mining: how to find channels to feed-forward BruCo: Brute Force Coherence (LIGO-G )

Feed forward of auxilairy degrees of freedomBas Swinkels

Online subtraction pipeline, and bilinear noise coupling Keita Kawabe

Feedforward at LLO and 40m, adaptive feed forward Denis Martynov

DetChar & Commissioning Tools Goal: Collect a set of vetted tools in one place so they get used Plan: Create an Approved/Complete Tools wiki Organized by Category Organizers: Ryan Fisher, Fabrice Matichard, Gabriele Vajented, Dennis Coyne, Bas Swinkels Ryan Fisher

“Vetting” Requirements SVN or GIT checkout of code available A set of instructions that allows a user to start from nothing and end up with an expected output A set of instructions on how to change the inputs or configurations to get different desired outputs Description of what the tool is useful for and what you get out of it Any limitations (location, software) or package requirements on running it The tool should be vetted by a volunteer from DetChar or elsewhere Ryan Fisher

What Kinds of Tools? First set: BruCo - Brute force correlation code Excavator - correlate auxiliary channels with glitches wDQ - used to simplify generating Omega Scans for glitch follow-up Desired: Non-stationary noise Bi-Linear and Non-Linear coupling analysis Weiner statistic/adaptive analysis calculation Spectral tools Ryan Fisher

Feedforward/Subtration, general outcome -Need to generalize the search for coherence -Auxiliary degrees of feedom (using similar tools…) -Solving non stationarity -Search for non linearity, bi-linear couplings -Adaptive tools -Making the tools available -Wonderful collaborative effort -To do list -Things are in motion