- Cary NC, US Saba Emrani - Santa Clara CA, US Jorge Manuel Gomes da Silva - Durham NC, US Ilknur Kaynar Kabul - Apex NC, US
International Classification:
G06N 7/00 G06F 17/18 G06F 17/16 G06N 99/00
Abstract:
A computing device computes a weight matrix to predict a value for a characteristic in a scoring dataset. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.
Monitoring, Detection, And Surveillance System Using Principal Component Analysis With Machine And Sensor Data
- Cary NC, US Jorge Manuel Gomes da Silva - Durham NC, US Saba Emrani - Santa Clara CA, US Arin Chaudhuri - Raleigh NC, US
International Classification:
G06F 17/16 G06F 17/18
Abstract:
A computing device detects an abnormal observation vector using a principal components decomposition. The principal components decomposition includes a sparse noise vector scomputed for the observation vector that includes a plurality of values, wherein each value is associated with a variable to define a plurality of variables. The sparse noise vector shas a dimension equal to m a number of the plurality of variables. A zero counter time series value ĉis computed using ĉ=Σs[i]. A probability value for ĉis computed using p=ΣH[i]/ΣH[i], where H[i] includes a count of a number of times each value of ĉoccurred for previous observation vectors. The probability value is compared with a predefined abnormal observation probability value. An abnormal observation indicator is set when the probability value indicates the observation vector is abnormal. The observation vector is output when the probability value indicates the observation vector is abnormal.
Monitoring, Detection, And Surveillance System Using Principal Component Analysis With Machine And Sensor Data
- Cary NC, US Jorge Manuel Gomes da Silva - Durham NC, US Saba Emrani - Santa Clara CA, US Arin Chaudhuri - Raleigh NC, US
International Classification:
G06K 9/00 G06F 17/16 G06K 9/48 G06K 9/62
Abstract:
A computing device updates an estimate of one or more principal components for a next observation vector. An initial observation matrix is defined with first observation vectors. A number of the first observation vectors is a predefined window length. Each observation vector of the first observation vectors includes a plurality of values. A principal components decomposition is computed using the initial observation matrix. The principal components decomposition includes a sparse noise vector s, a first singular value decomposition vector U, and a second singular value decomposition vector ν for each observation vector of the first observation vectors. A rank r is determined based on the principal components decomposition. A next principal components decomposition is computed for a next observation vector using the determined rank r. The next principal components decomposition is output for the next observation vector and monitored to determine a status of a physical object.
1501 Indian School Rd northeast, Albuquerque, NM 87102
Industry:
Research
Work:
Apple
Senior Machine Learning Engineer
Apple
Next-Generation Sensor Algorithm Engineer
Sas Jul 2016 - May 2017
Advanced Analytics R and D
North Carolina State University Aug 2012 - Jul 2016
Research Assistant
Nsf Assist Nanosystems Center Aug 2012 - Jun 2016
Research Assistant
Education:
North Carolina State University 1990 - 2016
Doctorates, Doctor of Philosophy, Mathematics, Electrical Engineering, Philosophy
The University of New Mexico 2011 - 2012
Master of Science, Masters, Electrical Engineering
Amirkabir University of Technology - Tehran Polytechnic 2008 - 2010
Master of Science, Masters, Electrical Engineering
Amirkabir University of Technology - Tehran Polytechnic 2004 - 2008
Bachelors, Bachelor of Science, Electrical Engineering
Skills:
Signal Processing Machine Learning Matlab Simulations Neural Networks Control Systems Design Latex Sensors Robotics Mathematical Modeling Simulink Data Mining Algorithms Labview Control Theory Statistical Data Analysis C++ Research C Pattern Recognition Programming Artificial Intelligence Numerical Analysis Python Experimentation Biomedical Engineering Pspice Digital Signal Processing Big Data Analytics Image Processing Multi Agent Systems Dynamical Systems Ros Stochastic Methods Fuzzy Logic Networked Control Systems Control System Design Wavelets Audio Processing Topological Data Analysis Internet of Things Wearables Biosensors Medical Devices Sas Programming Tensorflow Numpy
Interests:
Education Science and Technology Human Rights Animal Welfare Health
Languages:
English
Certifications:
Neural Networks and Deep Learning Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Structuring Machine Learning Projects