1,056 research outputs found
Hadron Production in Proton-Proton Collisions
No full textOnTEAM metadata: GDSID: DOC-2008-Sep-154; Attribute ID: LIBRARY-thesis_ma-2008-001; Title: [GSI Master 2008-01] Hadron Production in Proton-Proton Collisions [30.7.2008]; Author(s): Fasel, Markus; Corporate author(s): ; Publication date: 20080929; Creator: manton; Creation date: 29.09.2008 15:09:47; Change date: 30.09.2010 16:05:30; Access: Welt; Attribute type: Text.Thesis.MA; Directory path: ['GSI Publications', 'GSI as Publisher']; Attribute path: ['Infrastructure', 'Library and Documentation', 'thesis_ma', 'Added in 2008']; File name(s): ['DOC-2008-Sep-154-1.pdf']; File title(s): ['']; File access: ['GSI-intern'
Damage detection using frequency domain ARX models and extreme value statistics
No full textThe author acknowledges Tim Johnson and Seth Gregg
and the Los Alamos Dynamic Summer School for providing
the test structure as well as helping with the set-up,
instrumentation and acquisition of data from the test
structure. Funding for the summer school was provided by
the Engineering Sciences and Application Division at Los
Alamos National Laboratory and the Department of
Energy’s Education Program Office
Application of frequency domain ARX models and extreme value statistics to impedance-based damage detection
No full textFunding for this project was provided by the Department of Energy through the internal funding program at Los Alamos National Laboratory known as Laboratory Directed Research and Development. The author acknowledges Tim Johnson and Seth Gregg and the Los Alamos Dynamic Summer School for providing the test structure as well as helping with the set-up, instrumentation and acquisition of data from the test structure. Funding for the summer school was provided by the Engineering Sciences and Application Division at Los Alamos National Laboratory and the Department of Energy’s Education Program Office
Teaching by touching: an intuitivemethod for development of humanoid robot motions
No full textThis paper investigates touching as a natural way for humans to communicate with robots. In particular we developed a system to edit motions of a small humanoid robot by touching its body parts. This interface has two purposes: it allows the user to develop robot motions in a very intuitive way, and it allows us to collect data useful for studying the characteristics of touching as a means of communication. Experimental results confirm the interface's ease of use for inexpert users, and analysis of the data collected during human-robot teaching episodes has yielded several useful insights
Recommended from our members
A Framework for Recognizing and Executing Verb Phrases
No full textToday, the physical capabilities of robots enable them to perform a wide variety of useful tasks for humans, making the need for simple and intuitive interaction between humans and robots readily apparent. Taking natural language as a key element of this interaction, we present a novel framework that enables robots to learn qualitative models of the semantics of an important class of verb phrases, such as "follow me to the kitchen," and leverage these verb models to perform two tasks: Executing verb phrase commands, and recognizing when another agent has performed a given verb. This framework is based on a qualitative, relational model of verb semantics called the Verb Finite State Machine, or VFSM. We describe the VFSM in detail, motivating its design and providing a characterization of the class of verbs it can represent. The VFSM supports the recognition task natively, and we show how to combine it with modern planning techniques to support verb execution in complex environments. Grounded natural language semantics must be learned through interaction with humans, so we describe methods from learning VFSM verb models through natural interaction with a human teacher in the apprenticeship learning paradigm. To demonstrate the efficacy of our framework, we present empirical results showing rapid learning and high performance on both the recognition and execution tasks. In these experiments, the VFSM is able to consistently outperform a baseline method based on recent work in the verb learning literature. We close with a discussion of some of the current limitations of the framework, and a roadmap for future work in this area
Recommended from our members
Learning 3-D Models of Object Structure from Images
No full textRecognizing objects in images is an effortless task for most people.Automating this task with computers, however, presents a difficult challengeattributable to large variations in object appearance, shape, and pose. The problemis further compounded by ambiguity from projecting 3-D objects into a 2-D image.In this thesis we present an approach to resolve these issues by modeling objectstructure with a collection of connected 3-D geometric primitives and a separatemodel for the camera. From sets of images we simultaneously learn a generative,statistical model for the object representation and parameters of the imagingsystem. By learning 3-D structure models we are going beyond recognitiontowards quantifying object shape and understanding its variation.We explore our approach in the context of microscopic images of biologicalstructure and single view images of man-made objects composed of block-likeparts, such as furniture. We express detected features from both domains asstatistically generated by an image likelihood conditioned on models for theobject structure and imaging system. Our representation of biological structurefocuses on Alternaria, a genus of fungus comprising ellipsoid and cylindershaped substructures. In the case of man-made furniture objects, we representstructure with spatially contiguous assemblages of blocks arbitrarilyconstructed according to a small set of design constraints.We learn the models with Bayesian statistical inference over structure andcamera parameters per image, and for man-made objects, across categories, suchas chairs. We develop a reversible-jump MCMC sampling algorithm to exploretopology hypotheses, and a hybrid of Metropolis-Hastings and stochastic dynamicsto search within topologies. Our results demonstrate that we can infer both 3-Dobject and camera parameters simultaneously from images, and that doing soimproves understanding of structure in images. We further show how 3-D structuremodels can be inferred from single view images, and that learned categoryparameters capture structure variation that is useful for recognition
- …
