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2 edition of Seismic classification of military vehicles using statistical pattern recognition found in the catalog.

Seismic classification of military vehicles using statistical pattern recognition

Damian Lopez

Seismic classification of military vehicles using statistical pattern recognition

by Damian Lopez

  • 284 Want to read
  • 25 Currently reading

Published by UVB Universitätsverlag Dr. N. Brockmeyer in Bochum .
Written in English

    Subjects:
  • Arms control -- Verification.,
  • Vehicles, Military -- Classification.,
  • Pattern recognition systems.,
  • Neural networks (Computer science)

  • Edition Notes

    Includes bibliographical references.

    Other titlesAcoustic classification of military vehicles using neural networks
    StatementDamian Lopez. Acoustic classification of military vehicles using neural networks / Roland Peffer.
    SeriesBochumer Schriften zur Friedssicherung und zum humanitären Völkerrecht., Vol. 7
    ContributionsPeffer, Roland.
    Classifications
    LC ClassificationsUA12.5 L67 1995
    The Physical Object
    Pagination92 p. :
    Number of Pages92
    ID Numbers
    Open LibraryOL603371M
    ISBN 103819604189
    LC Control Number96197581
    OCLC/WorldCa35810898

    Seismic Radiation from Moment-Tensor Sources in the Spectral Domain 69 Seismic Energy Radiated by Point Moment-Tensor Sources 70 More Realistic Radiation Model 70 Finite Source Models 71 The Kinematic Dislocation Model 71 Haskell’s model 72 The Circular Fault Model 73File Size: KB. NEHRP* Seismic site classification based on shear-velocity (Vs) ranges. * National Earthquake Hazard Reduction Program () Color Code. S-Velocity (Vs) (m/sec) S-Velocity (Vs) (ft/sec) Title: Slide 1 Author: Choon Park Created Date.

    Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks [Timothy Masters] on *FREE* shipping on qualifying offers. Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and /5(7). between the commentary of SEAOC's Blue Book, which explained the basis for the code provisions, and everyday structural engineering design practice. The JBC Structural/ Seismic Design Manual illustrates how the provisions of the code are used. Volume 1: Code Application Examples, provides step-by-step examples for using individual code.

    A seismic hazard is the probability that an earthquake will occur in a given geographic area, within a given window of time, and with ground motion intensity exceeding a given threshold. With a hazard thus estimated, risk can be assessed and included in such areas as building codes for standard buildings, designing larger buildings and infrastructure projects, land use planning and determining. 3. The statistical pattern recognition paradigm. There are many ways by which one can organize a discussion of SHM. The authors have chosen to follow the one described in a previous Phil. Trans. R. Soc. A article (Farrar et al. ) that defines the SHM process in terms of a four-step statistical pattern recognition paradigm. This following Cited by:


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Seismic classification of military vehicles using statistical pattern recognition by Damian Lopez Download PDF EPUB FB2

It is a difficult and important task to classify the types of military vehicles using the acoustic and seismic signals generated by military vehicles. For improving the classification accuracy and reducing the computing time and memory size, we investigated different pre-processing technology, feature extraction and selection by: 4.

The book ends with an overview of how seismic attributes aid data interpretation and discusses bright spots, frequency shadows, faults, channels, diapirs, and data reconnaissance. A glossary provides definitions of seismic attributes and methods, and appendices provide background by: To address this problem, several seismic facies classification algorithms including k-means, self-organizing maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural networks have been successfully used to extract features of geologic interest from multiple volumes.

This third edition of Automatic Target Recognition provides a roadmap for breakthrough ATR designs―with increased intelligence, performance, and distinctions are made between military problems and comparable commercial deep-learning problems.

These considerations need to be understood by ATR engineers working in the defense industry as well as by their government customers. A seismic attribute is any measurable property of seismic data, such as amplitude, dip, phase, frequency, and polarity that can be measured at one instant in time/depth over a time/depth window, on a single trace, on a set of traces, or on a surface interpreted from the seismic data (Schlumberger Oilfield Glossary, ).

The classification of seismic events requires the integration of physical and statistical techniques. The task is challenging in low-seismicity areas where natural and anthropogenic seismicity often overlap in magnitude, space and time.

A sparse coverage of the monitoring network further complicates event by: A description of the pattern structure is useful for recognizing entities when a simple classification isn’t possible. Can also describe aspects that cause a pattern to not be assigned to a particular class. In complex cases, recognition can only be achieved through a description for each pattern rather than through Size: KB.

Seismic Design Categories. The NEHRP Recommended Seismic Provisions. recognizes that, independent of the quality of their design and construction, not all buildings pose the same seis-mic risk. Factors that affect a structure’s seismic risk include: • The intensity of ground shaking and other earthquake effects the structureFile Size: 1MB.

Seismic attributes are the components of the seismic data, which are obtained by measurement, computation, and other methods from the seismic data. Seismic attribute analysis can extract information from seismic data that is, otherwise, hidden in the data and have been used to identify prospects, ascertain depositional Size: 2MB.

Search the world's most comprehensive index of full-text books. My library. The four fea- tures that are used to distinguish the species of iris are sepal length, sepal width, petal length and petal width. The next step in the pattern recognition process is to find many flowers from each species and measure the corre- sponding sepal length, sepal width, petal length, and petal width.

A comparison of classification techniques for seismic facies recognition 1. A comparison of classification techniques for seismic facies recognition Tao Zhao1, Vikram Jayaram2, Atish Roy3, and Kurt J.

Marfurt1 Abstract During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have in- creased to the extent that it is difficult, if not impossible.

Statistical pattern recognition methods have been extensively applied in the field of artificial intelligence. Successful applications of these methods in the field of computer vision include extraction of low-level visual information from visual images, edge detection, extracting shape information from shading information, object segmentation, and object labeling (e.g., Chellapa and JainDuda.

SUPPORT VECTOR MACHINE CLASSIFICATION OF PHYSICAL AND BIOLOGICAL DATASETS. CONG-ZHONG CAI MILITARY VEHICLE CLASSIFICATION VIA ACOUSTIC AND SEISMIC SIGNALS USING STATISTICAL LEARNING METHODS.

A Comparative Study of Feature Extraction and Classification Methods for Military Vehicle Type Recognition Using Acoustic and Seismic Cited by: Pattern recognition is the process of recognizing patterns by using machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation.

1. Introduction. As shown on reference datasets such as ImageNet [], convolutional neural networks (CNNs) have become the state-of-the-art approaches for object classification in the introduction of networks capable of dealing with both detection and classification [2,3,4,5], neural networks can provide an all-in-one solution for target detection and by: 3.

Military Vehicles Subcategories. Air Force Vehicles. Amphibious Vehicles. Transport Vehicles. M Stryker Combat Vehicle.

High Mobility Multipurpose Wheeled Vehicle (HMMWV) M1A2. Chapter 6 Seismic Design Page Geotechnical Design Manual M July associated design requirements. See GDM Section for requirements to assess the hazard level.

Bridge approach embankments and fills through which cut-and-cover tunnels are constructed should be designed to remain stable during the design seismic event because. @article{osti_, title = {Processing of seismic signals for pattern recognition}, author = {Brolley, J E}, abstractNote = {The study describes the preparation of seismic signals in order to identify the best technique for input to a pattern recognition scheme.

The signal is first filtered with zero phase shift high and low pass Butterworth filters. Comparison of the USA, China and Japan Seismic Design Procedures Guangren Yu1, M.

ASCE and Gary Y.K. Chock2, F. ASCE 1Structural Engineer, Martin & Chock, Inc., Bishop Street, SuiteHonolulu, HI 2President, Martin & Chock, Inc., Bishop Street, SuiteHonolulu, HI ABSTRACT This paper presents a comparison of the current seismic design procedures in theFile Size: KB.

Multi-Layer perceptron is used in seismic interpretation for pattern recognition, approximation and classification. [2] Probabilistic neural network: An important method in seismic interpretation used for pattern recognition and classification that computes the distances between the input vector and the training input vector and then produces a vector that shows how close the input data is to the training .Seismic Site Classification via the average shear wave velocity over the top 30m (Vs30 or Vs) D 40 Vs = Vs30 = m/s m) over the top 30m (Vs30 or Vs) • Vs profile also needed for more advanced ground motion 60 50 advanced ground motion prediction via site response analysis 0 Shear Wave Velocity (ft/sec).

The problem is I am a new graduate and the other problem is a new structure is going to be placed on a site with a Class D seismic site classification. After running through the ASCE 7 classification, using data from our soil boring and SPT testing, it is a Class D and therefore would incur several hundred thousand dollars worth of structural.