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Distributed Anomaly Detection using 1-class SVM for Vertically Partitioned Data
There has been a tremendous increase in the volume of sensor data collected over the last decade for different monitoring tasks. For example, petabytes of earth science data are... -
Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation
This paper describes how damage propagation can be modeled within the modules of aircraft gas turbine engines. To that end, response surfaces of all sensors are generated via a... -
Towards Modeling the Effects of Lightning Injection on Power MOSFETs
Power electronics are widely used in critical roles in modern day aircrafts and hence their health management is of great interest. An important part of prognostics and health... -
First International Diagnosis Competition – DXC’09
A framework to compare and evaluate diagnosis algorithms (DAs) has been created jointly by NASA Ames Research Center and PARC. In this paper, we present the first concrete... -
Discriminative Mixed-Membership Models
Although mixed-membership models have achieved great success in unsupervised learning, they have not been widely applied to classification problems. In this paper, we propose a... -
nu-Anomica algorithm
One-class nu-Support Vector machine (SVMs) learning technique maps the input data into a much higher dimensional space and then uses a small portion of the training data... -
Mining Distance-Based Outliers in Near Linear Time
Full title: Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule Abstract: Defining outliers by their distance to neighboring examples... -
Theoretically Optimal Distributed Anomaly Detection
A novel general framework for distributed anomaly detection with theoretical performance guarantees is proposed. Our algorithmic approach combines existing anomaly detection... -
SAR Image Enhancement using Particle Filters
In this paper, we propose a novel approach to reduce the noise in Synthetic Aperture Radar (SAR) images using particle filters. Interpretation of SAR images is a difficult... -
New Approaches To Photometric Redshift Prediction
Expanding upon the work of Way & Srivastava (2006) we demonstrate how the use of training sets of comparable size continue to make Gaussian Process Regression a competitive... -
Qualitative Event-based Diagnosis with Possible Conflicts Applied to Spacecraft Power Distribution Systems
Model-based diagnosis enables efficient and safe operation of engineered systems. In this paper, we describe two algorithms based on a qualitative event-based fault isolation... -
Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques
Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically... -
Replication of Electric Aircraft Powertrain Dynamics and Inner-Loop Control for V&V of System Health Management Routines
Software-in-the-loop and hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective... -
Machine Learning for Earth Observation Flight Planning Optimization
This paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science... -
Bayesian Separation of Non-Stationary Mixtures of Dependent Gaus
In this work, we propose a novel approach to perform Dependent Component Analysis (DCA). DCA can be thought as the separation of latent, dependent sources from their observed... -
A Distributed Approach to System-Level Prognostics
Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key tech- nology for systems health management that leads to improved... -
Exploring the Model Design Space for Battery Health Management
Battery Health Management (BHM) is a core enabling technology for the success and widespread adoption of the emerging electric vehicles of today. Although battery chemistries...
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