A program of research directed toward the efficient and accurate machine recognition of human speech Final report
Evolutionary approach to speech perception theory and application to machine recognition of human speech
MACHINE RECOGNITION↗
SEARCH · Engineering Papers
Results for “machining”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Electrochemical machining - Prediction and correlation of process variables.
Electrochemical machining, discussing relationship between total current, applied potential, electrolyte flow rate, electrolyte conductivity and electrode gap
METAL WORKING↗
A programable display synthesizing system for man-machine communications research.
Programmable display synthesizing system for man- machine communications research based on electronic animation technique
COMMUNICATION SYSTEM↗
Data structure and man-machine communication for network problems.
Program providing man-machine communication for electronic circuit analyses using time sharing computer
COMPUTER METHOD↗
Man-machine design for the Apollo navigation, guidance, and control system.
Man-machine design for Apollo spacecraft navigation, guidance and control systems
APOLLO SPACECRAFT↗
The influence of the testing machine on the buckling of cylindrical shells under axial compression.
Testing machine effect on buckling load of electroformed cylindrical shells under axial compression
COMPRESSION BUCKLING↗
Alpha Seeding for Support Vector Machines
A key practical obstacle in applying support vector machines to many large-scale data mining tasks is that SVM's generally scale quadratically (or worse) in the number of examples or support vectors.
Support Vector Machines Alpha Seeding Data Mining↗
Machine Vision for Autonomous Small Body Navigation
This paper describes machine vision algorithms that enable precision guidance and hazard avoidance during small body exploration through onboard visual feature tracking and landmark recognition.
Estimation Landmark Recognition↗
A Remark on Kink and Time Machines
We describe an elementary proof that a manifold with the topology of the Politzer time machine does not admit a nonsingular, asymptotical flat Lorentz metric.
topology of the Politzer time machine Lorentz metr↗
Support vector machines
Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.
multiangle↗
Multiclass Reduced-Set Support Vector Machines
There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy. We show how reduced-set methods can be applied to multiclass SVMs made up of several binary SVMs, with significantly better results than reducing each binary SVM independently. Our approach is based on Burges' approach that constructs each reduced-set vector as the pre-image of a vector in kernel space, but we extend this by recomputing the SVM weights and bias optimally using the original SVM objective function. This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be 'shared' between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors. We also propose computing pre-images using differential evolution, which we have found to be more robust than gradient descent alone. We show experimental results on a variety of problems and find that this new approach is consistently better than previous multiclass reduced-set methods, sometimes with a dramatic difference.
reduced set methods↗
An Analysis of Heavy-Ion Single Event Effects for a Variety of Finite State-Machine Mitigation Strategies
Finite state-machines (FSMs) are used to control operational flow in application specific integrated circuits (ASICs) and field programmable gate array (FPGA) devices. Because of their ease of interpretation, FSMs simplify the design and verification process and consequently are significant components in a synchronous design.
Field Programmable Gate Array (FPGA)↗
State Event Models for the Formal Analysis of Human-Machine Interactions
The work described in this paper was motivated by our experience with applying a framework for formal analysis of human-machine interactions (HMI) to a realistic model of an autopilot. The framework is built around a formally defined conformance relation called "fullcontrol" between an actual system and the mental model according to which the system is operated. Systems are well-designed if they can be described by relatively simple, full-control, mental models for their human operators. For this reason, our framework supports automated generation of minimal full-control mental models for HMI systems, where both the system and the mental models are described as labelled transition systems (LTS). The autopilot that we analysed has been developed in the NASA Ames HMI prototyping tool ADEPT. In this paper, we describe how we extended the models that our HMI analysis framework handles to allow adequate representation of ADEPT models. We then provide a property-preserving reduction from these extended models to LTSs, to enable application of our LTS-based formal analysis algorithms. Finally, we briefly discuss the analyses we were able to perform on the autopilot model with our extended framework.
State-event Models↗
Learning Extended Finite State Machines
We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.
Register Automata↗
A Broadband Micro-Machined Far-Infrared Absorber
The experimental investigation of a broadband far-infrared meta-material absorber is described. The observed absorptance is greater than 0.95 from 1 to 20 terahertz (300-15 microns) over a temperature range spanning 5-300 degrees Kelvin. The meta-material, realized from an array of tapers approximately 100 microns in length, is largely insensitive to the detailed geometry of these elements and is cryogenically compatible with silicon-based micro-machined technologies. The electromagnetic response is in general agreement with a physically motivated transmission line model.
broadband far-infrared meta-material absorber↗
Application of Machine-Learning Algorithms for On-Board Asteroid Shape Model Determination
The Application of Machine-learning Algorithms for On-board Asteroid Shape Model Determination project will develop an innovative system for spacecraft navigation to expand the capability of small spacecraft to meet the critical challenges associated with small-body exploration. Such challenges include accurate navigation in a microgravity environment and precision targeting of particular locations on an asteroid surface for sample collection. This on-board system will cut the computational "umbilical" back to Earth-currently necessary for the generation of a global shape model that requires thousands of images with sufficient resolution and adequate variation of incidence and emission angles, processed manually by a team of experts on Earth for several months. Small satellites have limited bandwidth and are unable to downlink the data volume required for this processing, restricting their ability to perform deep-space asteroid exploration.
Machine learning algorithms↗
Probabilistic Modeling of Heavy Machinery Using Machine Learning
NASA Glenn Research Center’s facility operations seeks to leverage its extensive instrumentation and historical data with machine learning to increase system efficiencies. The ultimate goal of this effort is to probabilistically model the behavior of Glenn’s central air service compressors for optimal decision making and planning. This project is a first step in that direction. We propose a multimodel approach that uses high-dimensional models to ask simple questions about complex dependent structures, and low-dimensional models to ask complex questions about simple dependent structures. While the low-dimensional models make strong assumptions, they can be visualized and they can be insightful. We show good fits for univariate models of compressor sensors, and preliminary work on high-dimensional multivariate models.
Machine learning↗
Lessons Learned in the Application of Machine Learning Techniques to Air Traffic Management
There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in Air Traffic Management (ATM). The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large databases. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The promises and challenges in applying MLT to ATM is traced through three examples based on the authors’ experience, each separated by a decade, to show the influence of data and feature selection in the successful application of MLT to ATM. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.
Machine Learning Techniques↗