Parsimonious Linear Fingerprinting for Time Series
|
|
|
- Ada Shepherd
- 5 years ago
- Views:
Transcription
1 Parsimonious Linear Fingerprinting for Time Series Lei Li, B. Aditya Prakash, Christos Faloutsos School of Computer Science Carnegie Mellon University VLDB L. Li, 2010 VLDB2010, 36 th International Conference on Very Large Data Bases
2 Motivation Answering similarity queries in Time Series Databases SELECT * FROM TSDB WHERE data LIKE TSDB VLDB 2010 L. Li,
3 Motivation Similar motions VLDB 2010 L. Li,
4 Motivation Automatic labeling of human motion sequences VLDB 2010 L. Li,
5 Motivation Summarization / Compression VLDB 2010 L. Li,
6 Outline Motivation Proposed Method: Intuition & Example Experiments & Results PLiF: Insight Details Conclusion VLDB 2010 L. Li,
7 Intuition: Goals G1 G2 G3 G4 Good features/similarity function Good compression Ability to forecast Scalability VLDB 2010 L. Li,
8 Intuition: Goals G1 G2 G3 G4 Good features/similarity function (1a) lag independent (1b) frequency proximity (1c) grouping harmonics Good compression Ability to forecast Scalability VLDB 2010 L. Li,
9 Example: synthetic signals Equations (a) (b) (c) (d) (e) sin(2πt/100) cos(2πt/100) sin(2πt/98 + π/6) sin(2πt/110) + 0.2sin(2πt/30) cos(2πt/110) + 0.2sin(2πt/30 + π/4) VLDB 2010 L. Li,
10 Intuition (1a) Equations (a) sin(2πt/100) Time shift (b) (c) (d) (e) cos(2πt/100) sin(2πt/98 + π/6) sin(2πt/110) + 0.2sin(2πt/30) cos(2πt/110) + 0.2sin(2πt/30 + π/4) e.g. left-foot-start walking v.s. right-foot-start walking VLDB 2010 L. Li,
11 Intuition (1b) Equations (a) sin(2πt/100) (b) cos(2πt/100) (c) sin(2πt/98 + π/6) nearby frequency & time shift (d) (e) sin(2πt/110) + 0.2sin(2πt/30) cos(2πt/110) + 0.2sin(2πt/30 + π/4) e.g. running v.s. fast running VLDB 2010 L. Li,
12 Intuition (1c) Equations (a) (b) (c) (d) (e) sin(2πt/100) cos(2πt/100) sin(2πt/98 + π/6) sin(2πt/110) + 0.2sin(2πt/30) cos(2πt/110) + 0.2sin(2πt/30 + π/4) groups of harmonics ~ human voices VLDB 2010 L. Li,
13 : only two numbers to represent each! Proposed PLiF VLDB 2010 L. Li,
14 Intuition: how it works find hidden variable/pattern HV1 HV2 HV3 500 VLDB 2010 L. Li, 2010 f=1/100 f=1/110 f=1/30 14
15 Intuition: how it works find hidden variable/pattern HV2 = HV2 HV3 HV2 HV3 VLDB 2010 L. Li, 2010 f=1/110 f=1/30 Co-occur 15
16 HV1 HV VLDB 2010 L. Li,
17 HV1 HV VLDB 2010 L. Li,
18 Why it works? / How to interpret? Group of harmonics 1/110 & 1/30 harmonics. 1/100 + VLDB 2010 L. Li, 2010 Proposed PLiF 18 -
19 Basic Idea pattern/harmonics 1/110 & 1/30 running projection to harmonics (aka. frequency) walking VLDB 2010 L. Li, 2010 pattern/harmonics 1/100 19
20 Why not SVD/PCA? + Confused! VLDB 2010 L. Li, 2010 PCA no clear grouping PLiF 20 -
21 Outline Motivation Proposed Method: Intuition & Example Experiments & Results PLiF: Insight Details Conclusion VLDB 2010 L. Li,
22 Experiment: Goals to Verify G1 G2 G3 G4 Good features (low dimensional) Good compression Ability to forecast Scalability VLDB 2010 L. Li,
23 Experiments Datasets: Mocap 49 * BGP: 10 * 103k Chlorine:166 * 4k VLDB 2010 L. Li,
24 Result Visualization Mocap PLiF first two fingerprints With PLiF, now able to visualize very high dimensional time sequences VLDB 2010 L. Li,
25 Result Clustering Mocap PLiF first two fingerprints PLiF + thresholding Pred. walk run walking running Accuracy = 46/49 PCA + kmeans Pred. walk run Accuracy = 25/49 VLDB 2010 L. Li,
26 Result Clustering BGP data: PLiF + hierarchical clustering VLDB 2010 L. Li,
27 Intuition: Goals G1 G2 G3 G4 Good features/similarity function Good compression Ability to forecast Scalability VLDB 2010 L. Li,
28 Result - Compression Chlorine 166 * 4k error Storing only the PLiF features & sampling of hidden variables compression ratio VLDB 2010 L. Li, Ideal
29 Result - Compression Mocap: 93 * 300 error Storing only the PLiF features & sampling of hidden variables compression ratio Ideal VLDB 2010 L. Li,
30 Intuition: Goals later G1 G2 G3 G4 Good features/similarity function Good compression Ability to forecast Scalability VLDB 2010 L. Li,
31 wall clock time (s) CMU SCS wall clock time (s) Scalability Linear ~ sequence length sequence length sequence length VLDB 2010 L. Li,
32 PLiF Scalability Optimized algorithm Details later wall clock time SLOPE=1/3 PLiF-basic VLDB 2010 L. Li,
33 Intuition: Goals later G1 G2 G3 G4 Good features/similarity function Good compression Ability to forecast Scalability VLDB 2010 L. Li,
34 Outline Motivation Proposed Method: Intuition & Example Experiments & Results PLiF: Insight Details Conclusion VLDB 2010 L. Li,
35 Proposed Method: PLiF S1 S2 S3 S4 Learning Dynamics Finding Canonical Form Handling the Lag Grouping Harmonics VLDB 2010 L. Li,
36 Step 1. Learning Dynamics Use machine learning to find: Transition of Hidden Variables (HV): one timetick to other Mixing weights: HVs observed data Time series of hidden variables VLDB 2010 L. Li,
37 Underlying Model: Linear Dynamical Systems Details VLDB 2010 L. Li,
38 Dynamics/Transition in Hidden Variables transition matrix HV(t+1) HV(t) VLDB 2010 L. Li,
39 Mixing Weights + mixing/output matrix C VLDB 2010 L. Li,
40 Details Learning the Parameters Expectation-Maximization maximizing the expected log likelihood: Standard EM: expensive! Further speed optimization in our PLiF: matrix inversion using Woodbury matrix identity VLDB 2010 L. Li,
41 Step 2: Canonicalization But, hidden variables hard to interpret non-unique: many combinations are essentially the same Intuition: To make hidden variables compact and uniquely identified VLDB 2010 L. Li,
42 HV before f=1/110 f=1/100 f=1/30 Canonicalization adds Interpretability Harmonics frequency scaling (subtle) Time series of HV after canonicalization (real part) VLDB 2010 L. Li,
43 Step 2: Canonicalization Again, Estimating how each signal is composed of harmonics /patterns but, in complex space Mixing matrix (complex valued) VLDB 2010 L. Li,
44 Step 3:Handling Lag Intuition: Groups emerge.. reducing redundancy eliminating phase shift Conjugate! Mixing matrix (complex valued) VLDB 2010 L. Li,
45 Step 3:Handling Lag + Idea: only magnitude counts removing duplicates VLDB 2010 L. Li,
46 Step 3:Handling Lag + interpretability harmonics.1/100 harmonics 1/110 harmonics 1/30 VLDB 2010 L. Li,
47 Step 4:Grouping Harmonics + Intuition: Still a little redundancy harmonics.1/100 Think Minimum Description Length harmonics 1/110 harmonics 1/30 VLDB 2010 L. Li,
48 Step 4: Grouping Harmonics + Dimensional Reduction VLDB 2010 L. Li,
49 Step 4: Grouping Harmonics + harmonics.1/100 Group of harmonics 1/110 & 1/30 VLDB 2010 L. Li,
50 Parsimonious Linear Fingerprinting PLiF Goals Goals steps G1 G2 Good features/similarity function (1a) lag independent (1b) frequency proximity (1c) grouping harmonics Good compression S1 PLiF alg. steps Learning Dynamics G3 Ability to forecast S2 Canonical Form G4 Scalability S3 S4 Handling Lag Grouping Harmonics VLDB 2010 L. Li,
51 Outline Motivation Proposed Method: Intuition & Example Experiments & Results PLiF: Insight Details Conclusion VLDB 2010 L. Li,
52 Conclusion Need for finding compact representation of time series data Intuition & Insights of PLiF Interpretation of PLiF & How it works Experiments on a diverse set of data It really works! It is fast & scalable. VLDB 2010 L. Li,
53 Parsimonious Linear Fingerprinting PLiF Goals Goals steps G1 G2 Good features/similarity function (1a) lag independent (1b) frequency proximity (1c) grouping harmonics Good compression S1 PLiF alg. steps Learning Dynamics G3 Ability to forecast S2 Canonical Form G4 Scalability S3 S4 Handling Lag Grouping Harmonics VLDB 2010 L. Li,
54 Question? Thanks! B. Aditya Prakash Lei Li Christos Faloutsos VLDB 2010 L. Li,
55 appendix BACKUP VLDB 2010 L. Li,
56 Why not Fourier (DFT)? 1. FT cannot do forecasting VLDB
57 Why not Fourier (DFT)? 1. FT cannot do forecasting VLDB
58 Why not Fourier (DFT)? FT spectrum 1. FT cannot do forecasting 2. No arbitrary frequency true freq. VLDB 2010 frequency 58
59 Why not Fourier (DFT)? 1. FT cannot do forecasting 2. No arbitrary frequency 3. nearby frequency treated differently, not suited for across signals freq.=5 freq.=5.1 VLDB
60 Details for Implementation Read this only if you want to implement it VLDB 2010 L. Li,
61 Details Modelling the data: Linear Dynamical Systems VLDB 2010 L. Li,
62 Details Linear Dynamical Systems: parameters name µ 0 initial state for hidden variable meaning & example e.g. initial position, velocity & acceleration A transition matrix how the states move forward, e.g. soccer flying in the air C Q 0 transmission/ projection/ output matrix Initial covariance hidden state observation, e.g. camera taking picture of the soccer Q transition covariance how precision is the soccer motion R transmission/ projection covariance i.e. observation noise; e.g. how accurate is the camera 62
63 Details Learning the Dynamics Expectation-Maximization maximizing the expected log likelihood VLDB 2010 L. Li,
64 Details Finding Canonical Form Intuition: find the canonical dynamics taking eigenvalue decomposition of the transition matrix A compensate C with C h is a projection of the data to the dynamics but... VLDB 2010 L. Li,
65 Details Lags and Harmonics group Handling the lag: Intuition: phase/shift should not matter step: eliminating duplicate conjugate in C h, taking magnitude, ==> C m Group harmonics taking SVD or PCA on C m resulting fingerprints H 1 VLDB 2010 L. Li,
66 3D VIEW OF HIDDEN VARIABLES VLDB 2010 L. Li,
67 frequency scaling (subtle) Example: parsimonious HV after canonicalization VLDB 2010 L. Li,
68 SPEEDUP OPTIMIZATION VLDB 2010 L. Li,
69 Scalability Speedup the computation of matrix inverse using Woodbury matrix identity VLDB 2010 L. Li,
SEPARATING A GAS MIXTURE INTO ITS CONSTITUENT ANALYTES USING FICA
SEPARATING A GAS MIXTURE INTO ITS CONSTITUENT ANALYTES USING FICA Aparna Mahadevan Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the
Biomechanics and Models of Locomotion
Physics-Based Models for People Tracking: Biomechanics and Models of Locomotion Marcus Brubaker 1 Leonid Sigal 1,2 David J Fleet 1 1 University of Toronto 2 Disney Research, Pittsburgh Biomechanics Biomechanics
Advanced Test Equipment Rentals ATEC (2832) OMS 600
Established 1981 Advanced Test Equipment Rentals www.atecorp.com 800-404-ATEC (2832) OMS 600 Continuous partial discharge monitoring system for power generators and electrical motors Condition monitoring
Autodesk Moldflow Communicator Process settings
Autodesk Moldflow Communicator 212 Process settings Revision 1, 3 March 211. Contents Chapter 1 Process settings....................................... 1 Profiles.................................................
Mixture Models & EM. Nicholas Ruozzi University of Texas at Dallas. based on the slides of Vibhav Gogate
Mixture Models & EM Nicholas Ruozzi University of Texas at Dallas based on the slides of Vibhav Gogate Previously We looked at -means and hierarchical clustering as mechanisms for unsupervised learning
Using Spatio-Temporal Data To Create A Shot Probability Model
Using Spatio-Temporal Data To Create A Shot Probability Model Eli Shayer, Ankit Goyal, Younes Bensouda Mourri June 2, 2016 1 Introduction Basketball is an invasion sport, which means that players move
Kochi University of Technology Aca Study on Dynamic Analysis and Wea Title stem for Golf Swing Author(s) LI, Zhiwei Citation 高知工科大学, 博士論文. Date of 2015-03 issue URL http://hdl.handle.net/10173/1281 Rights
THe rip currents are very fast moving narrow channels,
1 Rip Current Detection using Optical Flow Shweta Philip sphilip@ucsc.edu Abstract Rip currents are narrow currents of fast moving water that are strongest near the beach. These type of currents are dangerous
Predicting Horse Racing Results with Machine Learning
Predicting Horse Racing Results with Machine Learning LYU 1703 LIU YIDE 1155062194 Supervisor: Professor Michael R. Lyu Outline Recap of last semester Object of this semester Data Preparation Set to sequence
BASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG
BASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG GOAL OF PROJECT The goal is to predict the winners between college men s basketball teams competing in the 2018 (NCAA) s March
5.1 Introduction. Learning Objectives
Learning Objectives 5.1 Introduction Statistical Process Control (SPC): SPC is a powerful collection of problem-solving tools useful in achieving process stability and improving capability through the
FUT-K Team Description Paper 2016
FUT-K Team Description Paper 2016 Kosuke Onda and Teruya Yamanishi Department of Management Information Science, Fukui University of Technology Gakuen, Fukui 910 8505, Japan Abstract. This paper describes
Vibration of floors and footfall analysis
Webinar Autodesk Robot Structural Analysis Professional 20/04/2016 Vibration of floors and footfall analysis Artur Kosakowski Rafał Gawęda Webinar summary In this webinar we will focus on the theoretical
Time Pressure Dispensing
Time Pressure Dispensing by Doug Dixon, GDM Product Manager What is time pressure dispensing? Time pressure is a method of dispensing liquid materials (surface mount adhesives and gasketing materials)
Geometric moments for gait description
Geometric moments for gait description C. Toxqui-Quitl, V. Morales-Batalla, A. Padilla-Vivanco, and C. Camacho-Bello. Universidad Politécnica de Tulancingo, Ingenierías 100, 43629 Hidalgo, México. ctoxqui@upt.edu.mx
AN AUTONOMOUS DRIVER MODEL FOR THE OVERTAKING MANEUVER FOR USE IN MICROSCOPIC TRAFFIC SIMULATION
AN AUTONOMOUS DRIVER MODEL FOR THE OVERTAKING MANEUVER FOR USE IN MICROSCOPIC TRAFFIC SIMULATION OMAR AHMAD oahmad@nads-sc.uiowa.edu YIANNIS E. PAPELIS yiannis@nads-sc.uiowa.edu National Advanced Driving
In memory of Dr. Kevin P. Granata, my graduate advisor, who was killed protecting others on the morning of April 16, 2007.
Acknowledgement In memory of Dr. Kevin P. Granata, my graduate advisor, who was killed protecting others on the morning of April 16, 2007. There are many others without whom I could not have completed
Engineering Note. Algorithms. Overview. Detailed Algorithm Description. NeoFox Calibration and Measurement. Products Affected: NeoFox
Engineering Note Topic: NeoFox Calibration and Measurement Products Affected: NeoFox Date Issued: 04/18/2011 Algorithms Overview NeoFox is a dynamic measurement system that has been designed to work with
Introduction to Pattern Recognition
Introduction to Pattern Recognition Jason Corso SUNY at Buffalo 12 January 2009 J. Corso (SUNY at Buffalo) Introduction to Pattern Recognition 12 January 2009 1 / 28 Pattern Recognition By Example Example:
INSTRUMENT INSTRUMENTAL ERROR (of full scale) INSTRUMENTAL RESOLUTION. Tutorial simulation. Tutorial simulation
Lab 1 Standing Waves on a String Learning Goals: To distinguish between traveling and standing waves To recognize how the wavelength of a standing wave is measured To recognize the necessary conditions
RICK FAUSEL, BUSINESS DEVELOPMENT ENGINEER TURBOMACHINERY CONTROL SYSTEM DESIGN OBJECTIVES
RICK FAUL, BUSINESS DEVELOPMENT ENGINEER TURBOMACHINERY CONTROL SYSTEM DESIGN OBJECTIVES The primary design objective for any turbomachinery control system should be to maintain or maximize machine and
Citation for published version (APA): Canudas Romo, V. (2003). Decomposition Methods in Demography Groningen: s.n.
University of Groningen Decomposition Methods in Demography Canudas Romo, Vladimir IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please
Relative Vulnerability Matrix for Evaluating Multimodal Traffic Safety. O. Grembek 1
337 Relative Vulnerability Matrix for Evaluating Multimodal Traffic Safety O. Grembek 1 1 Safe Transportation Research and Education Center, Institute of Transportation Studies, University of California,
PUV Wave Directional Spectra How PUV Wave Analysis Works
PUV Wave Directional Spectra How PUV Wave Analysis Works Introduction The PUV method works by comparing velocity and pressure time series. Figure 1 shows that pressure and velocity (in the direction of
Modelling and Simulation of Environmental Disturbances
Modelling and Simulation of Environmental Disturbances (Module 5) Dr Tristan Perez Centre for Complex Dynamic Systems and Control (CDSC) Prof. Thor I Fossen Department of Engineering Cybernetics 18/09/2007
ISOLATION OF NON-HYDROSTATIC REGIONS WITHIN A BASIN
ISOLATION OF NON-HYDROSTATIC REGIONS WITHIN A BASIN Bridget M. Wadzuk 1 (Member, ASCE) and Ben R. Hodges 2 (Member, ASCE) ABSTRACT Modeling of dynamic pressure appears necessary to achieve a more robust
Optimal Football Pressure as a Function of a Footballer s Physical Abilities
Proceedings Optimal Football Pressure as a Function of a Footballer s Physical Abilities Andrew Christenson 1, *, Pei Cao 2 and Jiong Tang 2 1 Edwin O. Smith High School, 1235 Storrs Road, Storrs, CT 06268,
Investigating the Problems of Ship Propulsion on a Supercomputer
Ship Elbrus powered by What I See is What I Understand. Так мы его задумали Investigating the Problems of Ship Propulsion on a Supercomputer A.A. Aksenov, S.V. Zhluktov, D.P. Silaev, S.A. Kharchenko, E.A.
arxiv: v1 [stat.ap] 18 Nov 2018
![arxiv: v1 [stat.ap] 18 Nov 2018](/thumbs/90/103502770.jpg "arxiv: v1 [stat.ap] 18 Nov 2018")
Modeling Baseball Outcomes as Higher-Order Markov Chains Jun Hee Kim junheek1@andrew.cmu.edu Department of Statistics & Data Science, Carnegie Mellon University arxiv:1811.07259v1 [stat.ap] 18 Nov 2018
by Michael Young Human Performance Consulting
by Michael Young Human Performance Consulting The high performance division of USATF commissioned research to determine what variables were most critical to success in the shot put The objective of the
Bézier Curves and Splines
CS-C3100 Computer Graphics Bézier Curves and Splines Majority of slides from Frédo Durand vectorportal.com CS-C3100 Fall 2016 Lehtinen Before We Begin Anything on your mind concerning Assignment 1? Linux
A COURSE OUTLINE (September 2001)
189-265A COURSE OUTLINE (September 2001) 1 Topic I. Line integrals: 2 1 2 weeks 1.1 Parametric curves Review of parametrization for lines and circles. Paths and curves. Differentiation and integration
Ron Gibson, Senior Engineer Gary McCargar, Senior Engineer ONEOK Partners
New Developments to Improve Natural Gas Custody Transfer Applications with Coriolis Meters Including Application of Multi-Point Piecewise Linear Interpolation (PWL) Marc Buttler, Midstream O&G Marketing
Introduction to Pattern Recognition
Introduction to Pattern Recognition Jason Corso SUNY at Buffalo 19 January 2011 J. Corso (SUNY at Buffalo) Introduction to Pattern Recognition 19 January 2011 1 / 32 Examples of Pattern Recognition in
Incorporating 3D Suction or Discharge Plenum Geometry into a 1D Compressor Simulation Program to Calculate Compressor Pulsations
Purdue University Purdue e-pubs International Compressor Engineering Conference School of Mechanical Engineering 2012 Incorporating 3D Suction or Discharge Plenum Geometry into a 1D Compressor Simulation
The Evolution of Transport Planning
The Evolution of Transport Planning On Proportionality and Uniqueness in Equilibrium Assignment Michael Florian Calin D. Morosan Background Several bush-based algorithms for computing equilibrium assignments
EXPERIMENTAL RESULTS OF GUIDED WAVE TRAVEL TIME TOMOGRAPHY
18 th World Conference on Non destructive Testing, 16-20 April 2012, Durban, South Africa EXPERIMENTAL RESULTS OF GUIDED WAVE TRAVEL TIME TOMOGRAPHY Arno VOLKER 1 and Hendrik VOS 1 TNO, Stieltjesweg 1,
Prediction of Basketball Free Throw Shooting by OpenPose
Prediction of Basketball Free Throw Shooting by OpenPose Masato Nakai,Yoshihiko Tsunoda, Hisashi Hayashi, Hideki Murakoshi School of Industrial Technology, Advanced Institute of Industrial Technology 1-10-40,
Exploring the relationship between the pressure of the ball and coefficient of restitution.
Exploring the relationship between the pressure of the ball and coefficient of restitution. When I started thinking about possible investigations I knew I wanted to create a lab that was related to sports.
Numerical Simulation of Wave Loads on Static Offshore Structures
Numerical Simulation of Wave Loads on Static Offshore Structures Hrvoje Jasak, Inno Gatin, Vuko Vukčević Wikki Ltd, United Kingdom Faculty of Mechanical Engineering and Naval Architecture University of
67. Sectional normalization and recognization on the PV-Diagram of reciprocating compressor
67. Sectional normalization and recognization on the PV-Diagram of reciprocating compressor Jin-dong Wang 1, Yi-qi Gao 2, Hai-yang Zhao 3, Rui Cong 4 School of Mechanical Science and Engineering, Northeast
UNIT IV: SOUND AND LIGHT Chapter 25-31
IMPORTANT TERMS: Amplitude Antinodes Blue shift Bow wave Constructive interference Crest Destructive interference Doppler effect Frequency Hertz In phase Interference pattern Longitudinal wave Node Out
THE FUNDAMENTALS OF THE AIR SAMPLER CALIBRATION-VERIFICATION PROCESS
THE FUNDAMENTALS OF THE AIR SAMPLER CALIBRATION-VERIFICATION PROCESS Frank M. Gavila F&J SPECIALTY PRODUCTS, INC. 404 Cypress Road Ocala, Florida, USA 3447 fandj@fjspecialty.com INTRODUCTION The calibration
Projecting Three-Point Percentages for the NBA Draft
Projecting Three-Point Percentages for the NBA Draft Hilary Sun hsun3@stanford.edu Jerold Yu jeroldyu@stanford.edu December 16, 2017 Roland Centeno rcenteno@stanford.edu 1 Introduction As NBA teams have
Acoustical Modeling of Reciprocating Compressors With Stepless Valve Unloaders
Acoustical Modeling of Reciprocating Compressors With Stepless Valve Unloaders Kelly Eberle, P.Eng. Principal Engineer keberle@betamachinery.com Brian C. Howes, M.Sc., P.Eng. Chief Engineer bhowes@betamachinery.com
Motion Control of a Bipedal Walking Robot
Motion Control of a Bipedal Walking Robot Lai Wei Ying, Tang Howe Hing, Mohamed bin Hussein Faculty of Mechanical Engineering Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia. Wylai2@live.my
Trouble With The Curve: Improving MLB Pitch Classification
Trouble With The Curve: Improving MLB Pitch Classification Michael A. Pane Samuel L. Ventura Rebecca C. Steorts A.C. Thomas arxiv:134.1756v1 [stat.ap] 5 Apr 213 April 8, 213 Abstract The PITCHf/x database
6. EXPERIMENTAL METHOD. A primary result of the current research effort is the design of an experimental
6. EXPERIMENTAL METHOD 6.1 Introduction A primary result of the current research effort is the design of an experimental setup that can simulate the interaction of a windmill with a vortex wake and record
A Novel Approach to Predicting the Results of NBA Matches
A Novel Approach to Predicting the Results of NBA Matches Omid Aryan Stanford University aryano@stanford.edu Ali Reza Sharafat Stanford University sharafat@stanford.edu Abstract The current paper presents
Shot-by-shot directional source deghosting and directional designature using near-gun measurements
H1-1-3 Shot-by-shot directional source deghosting and directional designature using near-gun measurements Neil Hargreaves, Rob Telling, Sergio Grion Dolphin Geophysical, London, UK Introduction In this
Kinematics & Dynamics
Kinematics & Dynamics Thomas Funkhouser Princeton University C0S 46, Fall 000 Overview Kinematics Considers only motion Determined by positions, velocities, accelerations Dynamics Considers underlying
ME 8843-Advanced Mechatronics. Project Proposal-Automatic Bike Transmission
ME 8843-Advanced Mechatronics Project Proposal-Automatic Bike Transmission 1/21/09 Razid Ahmad Brandon Borm Todd Sifleet Project Proposal: Our goal for the semester long project is to create and automatic
A PHASE-AMPLITUDE ITERATION SCHEME FOR THE OPTIMIZATION OF DETERMINISTIC WAVE SEQUENCES
Proceedings of the ASME 29 28th International Conference on Ocean, Offshore and Arctic Engineering OMAE29 May 31 - June, 29, Honolulu, Hawaii, USA Proceedings of the ASME 28th International Conference
Geophysical Model Functions for the Retrieval of Ocean Surface Winds
Geophysical Model Functions for the Retrieval of Ocean Surface Winds Donald R. Thompson and Frank M. Monaldo Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road, Laurel, MD 20708
AN31E Application Note
Balancing Theory Aim of balancing How an unbalance evolves An unbalance exists when the principle mass axis of a rotating body, the so-called axis of inertia, does not coincide with the rotational axis.
Safety Assessment of Installing Traffic Signals at High-Speed Expressway Intersections
Safety Assessment of Installing Traffic Signals at High-Speed Expressway Intersections Todd Knox Center for Transportation Research and Education Iowa State University 2901 South Loop Drive, Suite 3100
Cycling Volume Estimation Methods for Safety Analysis
Cycling Volume Estimation Methods for Safety Analysis XI ICTCT extra Workshop in Vancouver, Canada Session: Methods and Simulation Date: March, 01 The Highway Safety Manual (HSM) documents many safety
Wind Regimes 1. 1 Wind Regimes
Wind Regimes 1 1 Wind Regimes The proper design of a wind turbine for a site requires an accurate characterization of the wind at the site where it will operate. This requires an understanding of the sources
Bayesian Optimized Random Forest for Movement Classification with Smartphones
Bayesian Optimized Random Forest for Movement Classification with Smartphones 1 2 3 4 Anonymous Author(s) Affiliation Address email 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Delta Compressed and Deduplicated Storage Using Stream-Informed Locality
Delta Compressed and Deduplicated Storage Using Stream-Informed Locality Philip Shilane, Grant Wallace, Mark Huang, & Windsor Hsu Backup Recovery Systems Division EMC Corporation Motivation and Approach
Exploring the relationship between the pressure of the ball and coefficient of restitution.
Exploring the relationship between the pressure of the ball and coefficient of restitution. When I started thinking about possible investigations I knew I wanted to create a lab that was related to sports.
Putting Report Details: Key and Diagrams: This section provides a visual diagram of the. information is saved in the client s database
Quintic Putting Report Information Guide Putting Report Details: Enter personal details of the client or individual who is being analysed; name, email address, date, mass, height and handicap. This information
The Intrinsic Value of a Batted Ball Technical Details
The Intrinsic Value of a Batted Ball Technical Details Glenn Healey, EECS Department University of California, Irvine, CA 9617 Given a set of observed batted balls and their outcomes, we develop a method
Calibration and validation of an air quality finite element model around an electric power plant in Gran Canaria island
Calibration and validation of an air quality finite element model around an electric power plant in Gran Canaria island J. Ramírez (1), A. Oliver (2), J.E. González (3), R. Montenegro (1) (1) Instituto
Decentralized Autonomous Control of a Myriapod Locomotion Robot
Decentralized utonomous Control of a Myriapod Locomotion Robot hmet Onat Sabanci University, Turkey onat@sabanciuniv.edu Kazuo Tsuchiya Kyoto University, Japan tsuchiya@kuaero.kyoto-u.ac.jp Katsuyoshi
Research on Small Wind Power System Based on H-type Vertical Wind Turbine Rong-Qiang GUAN a, Jing YU b
06 International Conference on Mechanics Design, Manufacturing and Automation (MDM 06) ISBN: 978--60595-354-0 Research on Small Wind Power System Based on H-type Vertical Wind Turbine Rong-Qiang GUAN a,
SIDT 2017 XXII SEMINARIO SCIENTIFICO DELLA SOCIETÀ ITALIANA DOCENTI DI TRASPORTI
SIDT 2017 XXII SEMINARIO SCIENTIFICO DELLA SOCIETÀ ITALIANA DOCENTI DI TRASPORTI POLITECNICO DI BARI TOWARDS SUSTAINABLE CITIES: INNOVATIVE SOLUTIONS FOR MOBILITY AND LOGISTICS SEPTEMBER 14-15, 2017 SESSION
EVALUATION OF ENVISAT ASAR WAVE MODE RETRIEVAL ALGORITHMS FOR SEA-STATE FORECASTING AND WAVE CLIMATE ASSESSMENT
EVALUATION OF ENVISAT ASAR WAVE MODE RETRIEVAL ALGORITHMS FOR SEA-STATE FORECASTING AND WAVE CLIMATE ASSESSMENT F.J. Melger ARGOSS, P.O. Box 61, 8335 ZH Vollenhove, the Netherlands, Email: info@argoss.nl
Sensing and Modeling of Terrain Features using Crawling Robots
Czech Technical University in Prague Sensing and Modeling of Terrain Features using Crawling Robots Jakub Mrva 1 Faculty of Electrical Engineering Agent Technology Center Computational Robotics Laboratory
DETRMINATION OF A PLUNGER TYPE WAVE MAKER CHARACTERISTICE IN A TOWING TANK
The 9 th International Conference on Coasts, Ports and Marine Structures (ICOPMAS 2010) 29 Nov.-1 Dec. 2010 (Tehran) DETRMINATION OF A PLUNGER TYPE WAVE MAKER CHARACTERISTICE IN A TOWING TANK sayed mohammad
A CO 2 Waveform Simulator For Evaluation and Testing of Respiratory Gas Analyzers
2011 ROCKY MOUNTAIN NASA SPACE GRANT CONSORTIUM 1 A CO 2 Waveform Simulator For Evaluation and Testing of Respiratory Gas Analyzers Christina Long, and Joseph Orr, Ph.D. Department of Bioengineering, University
CALCULATING THE SPEED OF SOUND IN NATURAL GAS USING AGA REPORT NO Walnut Lake Rd th Street Houston TX Garner, IA 50438
CALCULATING THE SPEED OF SOUND IN NATURAL GAS USING AGA REPORT NO. 10 Jerry Paul Smith Joel Clancy JPS Measurement Consultants, Inc Colorado Engineering Experiment Station, Inc (CEESI) 13002 Walnut Lake
Reliability. Introduction, 163 Quantifying Reliability, 163. Finding the Probability of Functioning When Activated, 163
ste41912_ch04_123-175 3:16:06 01.29pm Page 163 SUPPLEMENT TO CHAPTER 4 Reliability LEARNING OBJECTIVES SUPPLEMENT OUTLINE After completing this supplement, you should be able to: 1 Define reliability.
Anabela Brandão and Doug S. Butterworth
Obtaining a standardised CPUE series for toothfish (Dissostichus eleginoides) in the Prince Edward Islands EEZ calibrated to incorporate both longline and trotline data over the period 1997-2013 Anabela
COMPLETING THE RESULTS OF THE 2013 BOSTON MARATHON
COMPLETING THE RESULTS OF THE 2013 BOSTON MARATHON Dorit Hammerling 1, Matthew Cefalu 2, Jessi Cisewski 3, Francesca Dominici 2, Giovanni Parmigiani 2,4, Charles Paulson 5, Richard Smith 1,6 1 Statistical
An investigation of kinematic and kinetic variables for the description of prosthetic gait using the ENOCH system
An investigation of kinematic and kinetic variables for the description of prosthetic gait using the ENOCH system K. OBERG and H. LANSHAMMAR* Amputee Training and Research Unit, University Hospital, Fack,
Rock Ramp Design Guidelines. David Mooney MS Chris Holmquist-Johnson MS Drew Baird Ph.D. P.E. Kent Collins P.E.
Rock Ramp Design Guidelines David Mooney MS Chris Holmquist-Johnson MS Drew Baird Ph.D. P.E. Kent Collins P.E. Rock Ramp Design Guidelines OUTLINE Local and System Interactions with Rock Ramps Ramp Geometry
Modal Analysis of Propulsion Shafting of a 48,000 tons Bulk Carrier
Modal Analysis of Propulsion Shafting of a 48,000 tons Bulk Carrier Zixin Wang a Dalian Scientific Test and Control Technology Institute. 16 Binhai Street. Dalian, 116013, China. Abstract a wangzixin_1@163.com
UNIVERSITY OF WATERLOO
UNIVERSITY OF WATERLOO Department of Chemical Engineering ChE 524 Process Control Laboratory Instruction Manual January, 2001 Revised: May, 2009 1 Experiment # 2 - Double Pipe Heat Exchanger Experimental
A Performanced Based Angle of Attack Display
A Performanced Based Angle of Attack Display David F. Rogers, Phd, ATP www.nar-associates.com The Problem The current angle of attack displays basically warn you about the approach to stall with yellow
Modal Analysis of Barrels for Assault Rifles
PROBLEMS OF MECHATRONICS ARMAMENT, AVIATION, SAFETY ENGINEERING ISSN 2081-5891 2 (8), 2012, 7-16 Modal Analysis of Barrels for Assault Rifles Peter LISÝ, Mário ŠTIAVNICKÝ Academy of the Armed Forces of
PUBLISHED PROJECT REPORT PPR850. Optimisation of water flow depth for SCRIM. S Brittain, P Sanders and H Viner
PUBLISHED PROJECT REPORT PPR850 Optimisation of water flow depth for SCRIM S Brittain, P Sanders and H Viner Report details Report prepared for: Project/customer reference: Copyright: Highways England,
#19 MONITORING AND PREDICTING PEDESTRIAN BEHAVIOR USING TRAFFIC CAMERAS
#19 MONITORING AND PREDICTING PEDESTRIAN BEHAVIOR USING TRAFFIC CAMERAS Final Research Report Luis E. Navarro-Serment, Ph.D. The Robotics Institute Carnegie Mellon University November 25, 2018. Disclaimer
Unsteady Wave-Driven Circulation Cells Relevant to Rip Currents and Coastal Engineering
Unsteady Wave-Driven Circulation Cells Relevant to Rip Currents and Coastal Engineering Andrew Kennedy Dept of Civil and Coastal Engineering 365 Weil Hall University of Florida Gainesville, FL 32611 phone:
If you need to reinstall FastBreak Pro you will need to do a complete reinstallation and then install the update.
Using this Beta Version of FastBreak Pro First, this new beta version (Version 6.X) will only work with users who have version 5.X of FastBreak Pro. We recommend you read this entire addendum before trying
THE SAFE ZONE FOR PAIRED CLOSELY SPACED PARALLEL APPROACHES: IMPLICATIONS FOR PROCEDURES AND AUTOMATION
THE SAFE ZONE FOR PAIRED CLOSELY SPACED PARALLEL APPROACHES: IMPLICATIONS FOR PROCEDURES AND AUTOMATION Steven Landry and Amy R. Pritchett Georgia Institute of Technology Abstract Changes to air traffic
Two Machine Learning Approaches to Understand the NBA Data
Two Machine Learning Approaches to Understand the NBA Data Panagiotis Lolas December 14, 2017 1 Introduction In this project, I consider applications of machine learning in the analysis of nba data. To
Chapter 6. Analysis of the framework with FARS Dataset
Chapter 6 Analysis of the framework with FARS Dataset. Having represented each phase of the structure with the aid of different data sets at each stage, to support the efficacy of the proposed framework
Policy Management: How data and information impacts the ability to make policy decisions:
Policy Management: How data and information impacts the ability to make policy decisions: Louis Cripps Regional Transportation District, Asset Management Denver, Colorado Quick exercise... What do these
Design Review Agenda
Design Review Agenda 1) Introduction, Motivation, and Previous Work a. Previous Work and Accomplishments i. Platform Launches ii. Successful Test Firings 2) More In-Depth Design Overview of the Existing
Smart-Walk: An Intelligent Physiological Monitoring System for Smart Families
Smart-Walk: An Intelligent Physiological Monitoring System for Smart Families P. Sundaravadivel 1, S. P. Mohanty 2, E. Kougianos 3, V. P. Yanambaka 4, and M. K. Ganapathiraju 5 University of North Texas,
2600T Series Pressure Transmitters Plugged Impulse Line Detection Diagnostic. Pressure Measurement Engineered solutions for all applications
Application Description AG/266PILD-EN Rev. C 2600T Series Pressure Transmitters Plugged Impulse Line Detection Diagnostic Pressure Measurement Engineered solutions for all applications Increase plant productivity
Spatio-temporal analysis of team sports Joachim Gudmundsson
Spatio-temporal analysis of team sports Joachim Gudmundsson The University of Sydney Page 1 Team sport analysis Talk is partly based on: Joachim Gudmundsson and Michael Horton Spatio-Temporal Analysis
Environmental Science: An Indian Journal
Environmental Science: An Indian Journal Research Vol 14 Iss 1 Flow Pattern and Liquid Holdup Prediction in Multiphase Flow by Machine Learning Approach Chandrasekaran S *, Kumar S Petroleum Engineering
Gait Analysis for Recognition and Classification
Gait Analysis for Recognition and Classification L. Lee W. E. L. Grimson MIT Artificial Intelligence Lab Cambridge, MA 0239 Abstract This paper describes a representation of gait appearance for the purpose
International Journal of Technical Research and Applications e-issn: , Volume 4, Issue 3 (May-June, 2016), PP.
DESIGN AND ANALYSIS OF FEED CHECK VALVE AS CONTROL VALVE USING CFD SOFTWARE R.Nikhil M.Tech Student Industrial & Production Engineering National Institute of Engineering Mysuru, Karnataka, India -570008
Course 495: Advanced Statistical Machine Learning/Pattern Recognition
Course 495: Advanced Statistical Machine Learning/Pattern Recognition Lectures: Stefanos Zafeiriou Goal (Lectures): To present modern statistical machine learning/pattern recognition algorithms. The course
Finding the optimal trajectory for your driver
Finding the optimal trajectory for your driver One of the more discussed topics involving Clubfitting is about what launch angle is best for any given golfer. With the increased awareness in golf periodicals
Simulating Major League Baseball Games
ABSTRACT Paper 2875-2018 Simulating Major League Baseball Games Justin Long, Slippery Rock University; Brad Schweitzer, Slippery Rock University; Christy Crute Ph.D, Slippery Rock University The game of
Traffic circles. February 9, 2009
Traffic circles February 9, 2009 Abstract The use of a traffic circle is a relatively common means of controlling traffic in an intersection. Smaller Traffic circles can be especially effective in routing