13,146 research outputs found
Rules of Thumb for Information Acquisition from Large and Redundant Data
We develop an abstract model of information acquisition from redundant data.
We assume a random sampling process from data which provide information with
bias and are interested in the fraction of information we expect to learn as
function of (i) the sampled fraction (recall) and (ii) varying bias of
information (redundancy distributions). We develop two rules of thumb with
varying robustness. We first show that, when information bias follows a Zipf
distribution, the 80-20 rule or Pareto principle does surprisingly not hold,
and we rather expect to learn less than 40% of the information when randomly
sampling 20% of the overall data. We then analytically prove that for large
data sets, randomized sampling from power-law distributions leads to "truncated
distributions" with the same power-law exponent. This second rule is very
robust and also holds for distributions that deviate substantially from a
strict power law. We further give one particular family of powerlaw functions
that remain completely invariant under sampling. Finally, we validate our model
with two large Web data sets: link distributions to domains and tag
distributions on delicious.com.Comment: 40 pages, 17 figures; for details see the project page:
http://uniquerecall.co
The Galileo PPS expert monitoring and diagnostic prototype
The Galileo PPS Expert Monitoring Module (EMM) is a prototype system implemented on the SUN workstation that will demonstrate a knowledge-based approach to monitoring and diagnosis for the Galileo spacecraft Power/Pyro subsystems. The prototype will simulate an analysis module functioning within the SFOC Engineering Analysis Subsystem Environment (EASE). This document describes the implementation of a prototype EMM for the Galileo spacecraft Power Pyro Subsystem. Section 2 of this document provides an overview of the issues in monitoring and diagnosis and comparison between traditional and knowledge-based solutions to this problem. Section 3 describes various tradeoffs which must be considered when designing a knowledge-based approach to monitoring and diagnosis, and section 4 discusses how these issues were resolved in constructing the prototype. Section 5 presents conclusions and recommendations for constructing a full-scale demonstration of the EMM. A Glossary provides definitions of terms used in this text
Reusable rocket engine turbopump health monitoring system, part 3
Degradation mechanisms and sensor identification/selection resulted in a list of degradation modes and a list of sensors that are utilized in the diagnosis of these degradation modes. The sensor list is divided into primary and secondary indicators of the corresponding degradation modes. The signal conditioning requirements are discussed, describing the methods of producing the Space Shuttle Main Engine (SSME) post-hot-fire test data to be utilized by the Health Monitoring System. Development of the diagnostic logic and algorithms is also presented. The knowledge engineering approach, as utilized, includes the knowledge acquisition effort, characterization of the expert's problem solving strategy, conceptually defining the form of the applicable knowledge base, and rule base, and identifying an appropriate inferencing mechanism for the problem domain. The resulting logic flow graphs detail the diagnosis/prognosis procedure as followed by the experts. The nature and content of required support data and databases is also presented. The distinction between deep and shallow types of knowledge is identified. Computer coding of the Health Monitoring System is shown to follow the logical inferencing of the logic flow graphs/algorithms
Synergy-Based Hand Pose Sensing: Optimal Glove Design
In this paper we study the problem of improving human hand pose sensing
device performance by exploiting the knowledge on how humans most frequently
use their hands in grasping tasks. In a companion paper we studied the problem
of maximizing the reconstruction accuracy of the hand pose from partial and
noisy data provided by any given pose sensing device (a sensorized "glove")
taking into account statistical a priori information. In this paper we consider
the dual problem of how to design pose sensing devices, i.e. how and where to
place sensors on a glove, to get maximum information about the actual hand
posture. We study the continuous case, whereas individual sensing elements in
the glove measure a linear combination of joint angles, the discrete case,
whereas each measure corresponds to a single joint angle, and the most general
hybrid case, whereas both continuous and discrete sensing elements are
available. The objective is to provide, for given a priori information and
fixed number of measurements, the optimal design minimizing in average the
reconstruction error. Solutions relying on the geometrical synergy definition
as well as gradient flow-based techniques are provided. Simulations of
reconstruction performance show the effectiveness of the proposed optimal
design.Comment: Submitted to International Journal of Robotics Research 201
Guidance, navigation, and control subsystem equipment selection algorithm using expert system methods
Enhanced engineering tools can be obtained through the integration of expert system methodologies and existing design software. The application of these methodologies to the spacecraft design and cost model (SDCM) software provides an improved technique for the selection of hardware for unmanned spacecraft subsystem design. The knowledge engineering system (KES) expert system development tool was used to implement a smarter equipment section algorithm than that which is currently achievable through the use of a standard data base system. The guidance, navigation, and control subsystems of the SDCM software was chosen as the initial subsystem for implementation. The portions of the SDCM code which compute the selection criteria and constraints remain intact, and the expert system equipment selection algorithm is embedded within this existing code. The architecture of this new methodology is described and its implementation is reported. The project background and a brief overview of the expert system is described, and once the details of the design are characterized, an example of its implementation is demonstrated
Fault tolerant data management system
Described in detail are: (1) results obtained in modifying the onboard data management system software to a multiprocessor fault tolerant system; (2) a functional description of the prototype buffer I/O units; (3) description of modification to the ACADC and stimuli generating unit of the DTS; and (4) summaries and conclusions on techniques implemented in the rack and prototype buffers. Also documented is the work done in investigating techniques of high speed (5 Mbps) digital data transmission in the data bus environment. The application considered is a multiport data bus operating with the following constraints: no preferred stations; random bus access by all stations; all stations equally likely to source or sink data; no limit to the number of stations along the bus; no branching of the bus; and no restriction on station placement along the bus
Ground Robotic Hand Applications for the Space Program study (GRASP)
This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time
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