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    الصورة الرمزية حسن هادي
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    نظريات المكائن في عدة مشاركات !!!!!!!!!!!نظريات المكائن في عدة مشاركات

    Information Theory and
    Machine Learning
    T-61.182 Special Course in Computer and
    Information Science II, Spring 2004 (4 cr)
    Prof. Juha Karhunen
    Helsinki University of Technology
    1
    Information Theory and
    Machine Learning
    T-61.182 Special Course in Computer and
    Information Science II, Spring 2004 (4 cr)
    Prof. Juha Karhunen
    Helsinki University of Technology
    1
    Problems have been classified in the book: 1= simple,
    2=medium, 3=moderately hard, 4=hard, 5=research project.
    You should select only problems having degrees 1, 2, or 3.
    Problems are useful because they force people to read the
    corresponding parts of the book.
    It is preferable but not necessary to return your solutions to the
    problems given within 2 weeks.
    Deadline for returning solved problems: May 15th, 2004.
    Somewhat open issue: replacing some problems by computer
    assignment(s) giving hands-on experience!?
    9


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    حسن هادي
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    الصورة الرمزية حسن هادي


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    TENTH WORLD CONGRESS ON THE THEORY OF
    MACHINE AND MECHANISMS
    Oulu, Finland, June 20 – 24, 1999
    A FORCE-UPDATED KINEMATIC VIRTUAL VIEWING SYSTEM WITH
    APPLICATION TO NUCLEAR POWER PLANT MAINTENANCE
    Marco A. Meggiolaro, Peter C. L. Jaffe, Karl Iagnemma and Steven Dubowsky
    Department of Mechanical Engineering
    Massachusetts Institute of Technology
    Cambridge, MA 02139
    Abstract
    Important nuclear maintenance tasks, such as the steam generator nozzle dam placement, could
    be most effectively done by robotic systems. However, restricted teleoperator visibility and lack
    of absolute accuracy make such tasks very difficult to perform using conventional robotic control
    technology. In this paper, successful nozzle dam placement is achieved through a 3-D virtual
    viewing system which includes contact force information.
    Keywords: contact forces, Base Sensor Control, virtual viewing, nuclear maintenance, robotics
    1 Introduction
    Most nuclear power plant maintenance tasks are currently performed by humans. Some of these
    tasks, including the important placement of the steam generator nozzle dam, require workers to
    be exposed to dangerously high levels of radiation [Zezza 1985]. To avoid human exposure,
    robotic systems have been proposed and evaluated for such tasks. However, in a number of
    cases, the robotic technology was not adequate. In this paper, a robotic system for successfully
    placing a nuclear power plant steam generator nozzle dam is presented. Nozzle dam placement
    is a challenging and typical nuclear maintenance robotic task. Restricted teleoperator visibility
    and tight geometric tolerances between the nozzle dam and its receptacle make the task very
    difficult to perform using conventional robotic control technology. Our approach consists of a
    virtual viewing system based on 3-D kinematic models combined with real-time contact force
    measurements. Laboratory experiments show that successful nozzle dam placements could be
    performed using the visualization system with a conventional Schilling hydraulic manipulator.
    2 Task and Experimental System
    2.1 A Representative Task
    The control system presented in this paper has been developed for a nuclear maintenance task.
    In order for workers to inspect and repair a nuclear power plant’s steam generator, two large
    pipes (1 meter in diameter) must be sealed with a device called a nozzle dam. The nozzle dam
    weighs 60 kg and must be inserted into a nozzle ring with clearances of approximately one
    millimeter. Workers enter the steam generator through a 0.4 meter in diameter portal and receive
    high doses of radiation while securing the nozzle dam. Hence, performing this task with a
    robotic manipulator would be very desirable. A simulated robotic nozzle dam placement can be
    seen in Figure 1a, where the manipulator is moving the nozzle dam side plate into its position in
    the nozzle ring. The center plate is then inserted within the side plate as shown in Figure 1b.
    Past attempts to place the nozzle dam with a teleoperated manipulator have taken too long
    because of the combination of poor operator visibility and the lack of manipulator repeatability.
    Tens of thousands of dollars of revenue are lost each hour the nuclear power plant is offline. In
    this paper, both the operator interface and manipulator repeatability are improved to make
    automated nozzle dam insertion practical.
    Side Plate
    Center Plate
    Nozzle Ring
    Manipulator
    (a)
    Nozzle Dam
    Center Plate
    Nozzle Dam
    Side Plate
    (b)
    Figure 1 - Simulated Robotic Nozzle Dam Task
    2.2 Experimental System
    Figure 2 shows the laboratory system used to emulate one of the critical parts of the nozzle dam
    placement task. Figure 3 shows an 18 kg nozzle dam center plate and a variable tolerance
    receptacle used to emulate the insertion of the nozzle dam center plate into the side plate. The
    receptacle is mounted in the workspace so that the manipulator configurations are representative
    of the actual task. A handle on the center plate provides a repeatable grip for the manipulator.
    The manipulator chosen for this task is a Schilling Titan II six DOF hydraulic robot capable of
    handling payloads in excess of 100 kg. This manipulator is widely used in nuclear maintenance.
    Figure 2 - System Hardware Configuration
    (a) (b)
    Figure 3 – Nozzle Dam Center Plate (a) and Variable Tolerance Receptacle (b)
    Joint resolver signals, standard on the Schilling, are converted to quadrature encoder waveforms
    using a Delta Tau Data/PMAC controller design. Both base and wrist force/torque sensors are
    sampled by a Data Translation 16-bit ADC. A 300 MHz PC handles the control loop
    computations and graphical user interface.
    In order to perform the nozzle dam placement task, a custom teleoperator software package has
    been developed. The system contains 3-D kinematic models of the manipulator and the
    workspace, reflecting the actual system configuration based on the joint resolvers. The interface
    provides improved operator visibility by allowing “virtual viewing” of physically obscured
    regions using “virtual cameras” [Cho, 1998]. The virtual cameras also allow for magnifying the
    mating edges in order to aid in teleoperated insertion. A Cartesian end-point controller is
    embedded in the software to provide full teleoperation functionality. Figure 4 shows the
    manipulator and experimental testbed for both real and simulated systems.
    (a) (b)
    Figure 4 – Real (a) and Simulated (b) Experimental System
    The tight tolerances of the task require the teleoperator to command fine position adjustments.
    However, the strong manipulators required for such tasks lack repeatability, mainly due to high
    friction in their joints and actuators. Base Sensor Control (BSC) [Morel et al. 1996] is
    implemented to improve the manipulator repeatability through accurate joint torque control.
    Furthermore, manipulator end-point errors due to geometric distortions of the system and elastic
    deformations degrade the manipulator’s accuracy. Here a method called Geometric and Elastic
    Error Compensation (GEC) is used to greatly reduce these errors [Meggiolaro et al. 1998].
    However, even with these key enabling technologies, some geometric uncertainty still exists
    between the modeled and real environments making teleoperation difficult to perform. To
    overcome this, the contact forces between the center plate and its receptacle are estimated from
    wrist sensor and task geometry and displayed to the teleoperator.
    3 Contact Force Estimation
    Contact force information between the manipulator end-effector and the environment is
    fundamental for placement tasks with small tolerances [Bicchi 1993]. However, a wrist
    force/torque sensor provides limited information, namely 3 force and 3 torque components, while
    each contact point is associated with 9 unknowns: the coordinates of the contact point location
    and the contact wrench components. In the case where there is only one contact point with the
    environment and where the contact torque is zero, it is possible to calculate the contact
    information required for control. This can be obtained from wrist force torque information
    combined with knowledge of the geometry of the mating parts.
    Figure 5 shows a plate attached to the end-effector of a manipulator. The force exerted by the
    environment on the plate,
    Fc, and the contact location with respect to the wrist force/torque
    sensor,
    rc, can be calculated from:

    s
    s
    s
    s s
    s s
    c s c
    F
    F
    M
    F M
    F M
    F F r
    + a
    ´
    ´
    =
    , = (1)
    where
    Fs and Ms are respectively the forces and moments measured from the wrist sensor, and a

    is an arbitrary constant. Note that Equation (1) has an infinite number of solutions, since two
    equal forces along the same line of action result in the same wrist sensor reading, see Figure 5.
    Figure 5 - Contact Force and Wrist Force/Torque Sensor Readings
    In order to obtain a unique solution to Equation (1), the plate geometry must be considered.
    Defining
    G as a vector function representing the plate surface in the wrist sensor coordinates,
    then
    a is determined by calculating the intersection between the line of action of the contact
    force and the plate surface
    G,

    s
    s
    s s
    s s
    s
    F
    M
    F M
    F M
    F

    ´
    ´
    a º
    G - (2)
    Due to the nature of contact forces, which are directed toward the interior of the plate, the
    calculated values of
    rc must also satisfy

    n
    (rc ) ×Fc £ 0 (3)
    where
    n(rc) is the normal vector to the plate surface at the point rc.
    If
    G represents a convex surface, then the solution to Equations (2) and (3) is either unique or
    non-existent. Otherwise, multiple solutions exist for certain configurations. For the particular
    case shown in Figure 5,
    G is not convex, but it can be represented by a set of simple equations of
    the planes of the plate. Frequently, as in the case of the nozzle dam insertion plate, a single
    solution for the contact point can be determined by considering the contact friction as well as the
    geometry of the mating parts.
    Based on Equations (2) and (3) and models of the plate and receptacle, a force vector and contact
    point is calculated from the measured wrist wrench and displayed to the teleoperator. Figure 6
    summarizes how a force-updated operator interface is combined with the high accuracy BSC
    position controller to perform the nozzle dam placement task.

    Desired joint
    variables
    Actual joint positions
    Base Force/
    Torque Sensor
    BSC Robot
    -
    +
    q
    q
    des
    X
    Inverse
    kinematics
    Desired
    End-Effector
    Position
    des
    Contact Force
    Estimation
    Plate Geometry
    Visualization
    Software
    Teleoperator
    Wrist Force/
    Torque Sensor
    Figure 6 - Base Sensor Control and Contact Force Estimation Scheme
    4 Experimental Verification
    Representative nozzle dam placements were conducted to demonstrate the effectiveness of the
    force-updated virtual viewing system. Figure 7 shows a sequence of screenshots from the
    teleoperator display during a typical placement. Each figure shows the center plate contacting
    the mating receptacle as well as visual feedback of the estimated contact force. The contact
    vector identifies misalignments in the insertion process, providing the necessary information to
    command small corrective motions.
    Figure 7a suggests translational motions are necessary to align the plate. The next four
    screenshots shown in Figure 7 indicate rotational alignment errors. Finally, the contact force in
    Figure 7f suggests that successful placement was achieved.
    (a) (b) (c)
    (d) (e) (f)
    Figure 7 – Typical Placement Steps Using Contact Force Visualization
    The experimental insertions show that the force-updated virtual viewing system outlined in
    Figure 6 allows a conventional hydraulic manipulator to successfully perform the nozzle dam
    placement task. This approach is made practical by the means of BSC and GEC Control.
    5. Conclusions
    In this paper, a robotic visualization system for successfully placing a nuclear power plant steam
    generator nozzle dam is presented. A teleoperator software package has been developed
    containing 3-D kinematic models of a Schilling Titan II hydraulic manipulator and the
    workspace. Contact force information between the center plate and its receptacle is obtained
    from wrist sensor wrench measurements and geometric models of the mating geometries. The
    contact force vector is displayed to the teleoperator and allows for real-time recognition of
    misalignments in the insertion process. This aids in successfully achieving insertion using a
    position control algorithm. Experiments demonstrated that the nozzle dam placement task can be
    achieved by combining a high repeatability position controller, such as BSC and GEC, and a
    force-updated operator interface.
    Acknowledgments:
    The assistance and encouragement of Dr. Byung-Hak Cho of the Korean Electric Power
    Research Institute (KEPRI) and Mr. Jacque Pot of the Electricité de France (EDF) in this
    research is most appreciated, as the financial support of KEPRI and EDF.
    References:
    Bicchi, A., Salisbury, J., Brock, D., Contact Sensing from Force Measurements, The
    International Journal of Robotics Research, Vol. 12, No. 3, 1993, pp 249-262.
    Cho, B., Simulation Studies on Robot System Applied to Nozzle Dam Installation, KEPRI
    Technical Memo, 1998.
    Iagnemma, K., Morel, G. and Dubowsky, S., A Model-Free Fine Position Control System Using
    the Base-Sensor: With Application to a Hydraulic Manipulator, Symposium on Robot Control,
    SYROCO ‘97, Vol. 2, 1997, pp 359-365.
    Meggiolaro, M., Mavroidis, C. and Dubowsky, S., Identification and Compensation of
    Geometric and Elastic Errors in Large Manipulators: Application to a High Accuracy Medical
    Robot, Proceedings of the 1998 ASME Design Engineering Technical Conference, Atlanta,
    1998.
    Meggiolaro, M., Jaffe, P. and Dubowsky, S., "Achieving Fine Absolute Positioning Accuracy in
    Large Powerful Manipulators", submitted to the IEEE International Conference on Robotics and
    Automation (ICRA'99), Detroit, 1999.
    Morel, G. and Dubowsky, S., The Precise Control of Manipulators with Joint Friction: A Base
    Force/Torque Sensor Method, Proceedings of the IEEE International Conference on Robotics
    and Automation, Vol. 1, 1996, pp 360-365.
    Zezza, L., Steam Generator Nozzle Dam System”, Transactions of the American Nuclear
    Society, Vol. 50, 1985, pp 412-413.

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    حسن هادي
    حسن هادي غير متواجد حالياً
    عضو متميز
    الصورة الرمزية حسن هادي


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    http://robots.mit.edu/people/Karl/iftomm99.PDF
    استخدم الرابط للحصول على معلومات افضل

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    حسن هادي
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    عضو متميز
    الصورة الرمزية حسن هادي


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    Chapter category: Nanomedicine

    CHAPTER 2 Classical Theory of Machine Replication

    This chapter appears in the following book:
    Kinematic Self-Replicating Machines

    Edited by: Robert A. Freitas, Jr. and Ralph C. Merkle
    ISBN: 1-57059-690-5
    » Get more information about this book at landesbioscience.com «
    Chapter authors:
    Robert A. Freitas Jr. and Ralph C. Merkle


    [+] view image
    The early history of machine replication theory is largely the record of von Neumann’s thinking on the matter during the 1940s and 1950s, particularly his kinematic and cellular models, described below. Von Neumann did not finish or publish most of his work on this subject prior to his untimely death in 1957, but Arthur Burks, a colleague of von Neumann, extensively edited and completed many of von Neumann’s manuscripts on the subject. Automata theory has advanced and been refined in the decades since, with many alternative models of machine replication having been proposed and discussed as will be described later. By 1980, a detailed technical study co-edited by Freitas2 concluded that “there appear to be no fundamental inconsistencies or insoluble paradoxes associated with the concept of self-replicating machines.” Physics professor Jeremy Bernstein concurred:1040 “I believe, on the basis of the history of technology, that human nature is such that whatever can be constructed, in theory, will, eventually, be constructed. Since self-replicating automata are possible in principle, they will, I think, eventually be built. When, by whom, and what for, I do not have the foggiest idea.”
    » Add chapter to custom book
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    Additional chapters from this book:


    CHAPTER 1 The Concept of Self-Replicating Machines

    Robert A. Freitas Jr. and Ralph C. Merkle
    For most of human history, man’s tools and machines bore no resemblance to living organisms and gave no hint of any commonality between the living and the artificial.150 In Paleolithic times,151-15...</B>

    CHAPTER 2 Classical Theory of Machine Replication

    Robert A. Freitas Jr. and Ralph C. Merkle
    The early history of machine replication theory is largely the record of von Neumann’s thinking on the matter during the 1940s and 1950s, particularly his kinematic and cellular models, described b...</B>

    CHAPTER 3 Macroscale Kinematic Machine Replicators

    Robert A. Freitas Jr. and Ralph C. Merkle
    Specific proposals and realizations of von Neumann’s kinematic replicators and related physical implementations of macroscale machine replicators or self-replicating factory systems are of the grea...</B>

    CHAPTER 6 Motivations for Molecular-Scale Machine Replicator Design

    Robert A. Freitas Jr. and Ralph C. Merkle
    In 1959, Feynman2182 proposed that we could arrange atoms in most of the ways permitted by physical law. Von Neumann3 analyzed a few basic architectures for self-replicating systems in the 1940s an...</B>

    APPENDIX A Data for Replication Time and Replicator Mass

    Robert A. Freitas Jr. and Ralph C. Merkle
    Data for replication time (τ) as a function of replicator mass (M) for 126 biological species,2600 1 chemical species,1372 and 9 actual or proposed artificial kinematic replicating systems acr...</B>

    APPENDIX B Design Notes on Some Aspects of the Merkle Freitas Molecular Assembler

    Robert A. Freitas Jr. and Ralph C. Merkle
    Geometrical Derivation of Assembler Dimensions A preliminary design iteration revealed that the physical dimensions of the proposed molecular assembler are constrained by the choice of 4 box-specif...</B>



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    حسن هادي
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    حسن هادي
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    Bayesian Decision Theory and Machine Learning


    Kathryn Blackmond Laskey
    Department of Systems Engineering
    George Mason University


    This talk presents the Bayesian approach to machine learning. The talk begins with an overview the basic philosophy and approach of Bayesian decision theory. Next, application of the decision theoretic approach to machine learning is discussed. In decision theory, learning is viewed as a problem of inference, in which a prior distribution and data are used to infer a posterior distribution for parameters of interest. Problems in machine learning may be contrasted with more traditional statistical inference problems. Machine learning problems are characterized by very high dimensional parameter spaces and by models that are not "identifiable" -- that is, it may not be possible to distinguish on the basis of available training data which of several candidate representations is the "correct" one. This suggests an alternative characterization of the machine learning problem in decision theoretic terms. The learning task is viewed as acquiring a problem representation that has high utility, where utility depends both on accuracy (i.e., projected performance on problems not in the training set) and computational complexity. Theoretical and pragmatic arguments for Bayesian methods are presented. A summary of recent research in knowledge representations and learning methods is presented. A few applications of Bayesian learning are discussed.
    View Presentation Download Presentation
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    Bayesian Decision Theory
    Decision Theory and
    Decision Theory
    Bayesian Inference
    A Caricature of a Contrast
    Machine Learning
    Graphical Models
    Learning for High-Dimensional
    Structural Uncertainty
    Approaches to Structural Uncertainty
    Higher Order Uncertainty
    Learning about Structure
    Some Examples
    Advantages to Model Averaging
    More Advantages
    Criticisms
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    Decision Theory
    Occam's Razor
    Occam's Razor (cont.)
    Decision Theory and Occam's Razor
    Summary

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    Thoughtfully reflect on the new video: "God, Mind, Truth" - Part One
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