Neural computation and particle accelerators : research, technology and applications / Emmerich Chabot and Horace D'Arras, editors.

Format:

Book

Contributor:

Chabot, Emmerich.

D'Arras, Horace.

Series:

Neuroscience research progress series.

Neuroscience research progress series

Language:

English

Subjects (All):

Neural computers.

Particle accelerators.

Physical Description:

1 online resource (433 pages)

Edition:

1st ed.

Place of Publication:

New York : Nova Science Publishers, c2010.

Language Note:

English

Summary:

This work discusses neural computation, a network or circuit of biological neurons and relatedly, particle accelerators, a scientific instrument which accelerates charged particles such as protons, electrons and deuterons.

Contents:

Intro

NEURAL COMPUTATION AND PARTICLE ACCELERATORS: RESEARCH, TECHNOLOGY AND APPLICATIONS

CONTENTS

PREFACE

Chapter 1 MAGNETIC FRINGE FIELDS AND INTERFERENCE IN HIGH-INTENSITY ACCELERATORS

Abstract

I. Introduction

II. Magnet Modeling

A. Overview of Simulation Codes

B. A Modeling Example

III. 3D Field Multipole Expansion

A. Review of Theory

B. Expansion Techniques

C. On-Axis Gradients

D. A 5th-Order Representation

E. Higher-Order Effects

IV. Particle Optics in a Single Quad

A. Simulation Model and 3D Mulipole Expansion

B. Form Factor Theory on Magnetic Fringe Field

C. Linear Transfer Matrices from the Trajectory Equations

D. Lens Parameters and Hard Edge Models

E. Third-Order Aberrations

F. Particle Optics In 30Q44

V. Magnetic Interference between Two Magnets

A. Change in Linear Focusing Function

B. Magnetic Interference as a First-Order Perturbation

C. Hard Edge Models for a Perturbed Quad

VI. Particle Optics in Quad Doublet Assembly

A. Two-Dimensional Field Parameters

B. Magnetic Fringe and Interference

C. Linear Transfer Matrices and Hard Edge Models

D. Third-Order Aberrations

E. Verification of Particle Trajectories

VII. Particle Tracking in Beam Lines

A. SNS Ring Injection and Beam Losses in Its Dump Line

B. Injection Constraints

(a) Closed Orbit Bump and Good Injection

(b) Transport of Waste Beams Through IDSM

C. 3D Modeling of Injection Waste Beam Dump Line

(a) Simulation Models

(b) Magnets and Fields on Beam Line

(c) Initial Conditions of Test Particles

D. 3D Particle Trajectories through IDSM

E. Remedies

(a) H

Proton Particle Losses in the Y-Direction in IDSM

(b) H0-Proton Particle Losses in the X-Direction in IDSM.

(c) Modification of a Spare IDSM

F. Experimental Verifications

VIII. Conclusion

Acknowledgments

References

Chapter 2 TRANSPORT CALCULATIONS AND ACCELERATOR EXPERIMENTS NEEDED FOR RADIATION RISK ASSESSMENT IN SPACE

1. Introduction

2. Reduction of the Radiation Exposure

3. Particle and Heavy Ion Transport Codes

3.1. Deterministic Codes

3.2. Monte Carlo Codes

4. Particle and Heavy Ion Accelerators

5. Accelerator Experiments Needed for Validation of Transport Codes

6. Summary and Conclusions

Chapter 3 TRANSPORT OF ION-THERAPY BEAMS IN ROTATING GANTRIES

Introduction

Milestones, Current Status and Trends

Development of Ion Gantries

Summary of the Relevant Beam Transport Concepts

Equations of Motion

Single-Particle Transport Formalism

Beam-Envelopes Transport Formalism

Formulation of the Problem

Rotator-Based Matching Techniques

Principle of the Rotator-Matching

Rotator Lattices

Demonstration of the Rotator Action

Matching the Dispersion Function

Analysis of Ion-Optical Properties of the Rotator Lattices

Matching Techniques without the Rotator - The Sigma-Matrix Matching

Demonstration of the Sigma-Matrix Matching

Application Restrictions of the Sigma-Matrix Matching Technique

Conclusion

Acknowledgment

Chapter 4 INVESTIGATION OF SURFACE TREATMENTS OF NIOBIUM FLAT SAMPLES AND SRF CAVITIES BY GAS CLUSTER ION BEAM TECHNIQUE FOR PARTICLE ACCELERATORS

2. Brief History of GCIB and Its Application to Nb

3. Working Principal of GCIB

4. Suppression of Field Emission by GCIB Treatments

5. Modifications of Morphology of Nb Surfaces by GCIB

6. Modifications of Surface Oxide Layer Structure by GCIB.

7. GCIB Treatments on Nb Single Cell Cavities

8. Summary and Perspective

Chapter 5 THE ROLES OF CHLOROPLAST PROTEASES IN THE ASSEMBLY AND TURNOVER OF LIGHT-HARVESTING COMPLEX

Classification, Structure and Functions of LHC proteins

Chloroplast Import

Stromal Processing Peptidase

PreP1 and PreP2

Assembly of LHC Proteins

Two Hypotheses

EGY1

Degradation of LHC Proteins

Lysosomal-Like Vacuole

FtsHs

Clps

Perspectives

Acknowledgements

Chapter 6 DESIGN OF HIGH POWER NEUTRON SOURCES FOR NUCLEAR TECHNOLOGY APPLICATIONS BY MEANS OF PARTICLE ACCELERATORS

Application of Neutron Sources

General Classification of Neutron Sources

Low Intensity Neutron Sources

High Intensity Neutron Sources

High Power Neutron Sources

Overview of Neutron Source Design

Neutron Production

Radiological Assessments

Thermal-Hydraulics

Mechanical Analysis

Materials

Chapter 7 SUBDURAL INTERICTAL EEG ANALYSIS FOR EXTRACTING DISCRIMINATING FEATURES TOWARDS ELECTRODE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS

2. Methods

2.1. Participants

2.2. Recording System

2.3. Algorithm Development Process

Step 1- Filtering the Input EEG Data

Step 2- Extraction of Features from the EEG Data

Step 3- Implementation of Regression Lines for each Electrode and Parameter

Step 4- Implementation of an Artificial Neural Network for Linear Classification

Step 5- Selection of the Best Classifiers

3. Results

3.1. Detailed Results for Patient #1

3.2. Compilation of Results for All Cases

4. Discussion

5. Conclusion

References.

Chapter 8 A NEW METHOD FOR PREPARING SUBMICRON AND NANO-SIZED AROMATIC POLYAMIDE PARTICLES WITH VARIOUS MORPHOLOGIES AND CHARACTERISTIC FEATURES

2. Experiment

2.1. Materials

2.2. Preparation

2.3. Characterization

3. Results and Discussion

3.1. Volume of Water Added

3.2. Ultrasonic Frequency

3.3. Mixing Manner

3.4. Characteristics of Aromatic Polyamide Particles

4. Conclusion

Chapter 9 SURFACE TREATMENTS OF NIOBIUM SUPERCONDUTING RADIO FREQUENCY CAVITIES BY ELECTROPOLISHING FOR PARTICLE ACCELERATORS*

Reference

Chapter10FRICTIONALCOOLINGOFAPARTICLEBEAM

1.Introduction

2.PhaseSpaceTransformationbyFrictionalForces

3.SelectedEnergyRegimes

4.MultipleScattering

5.TransformationwithSimultaneousAcceleratingForce

6.TheFirstFrictional-CoolingExperimentwithMuons

6.1.ExperimentalArrangement

6.2.Results

Chapter11MODELSELECTIONFORGAUSSIANMIXTUREMODELINATWO-PHASEPROCEDURE:AFURTHERCOMPARATIVESTUDY

2.GaussianMixtureModelandMLLearning

3.Two-phaseProcedureandTypicalModelSelectionCriteria

3.1.Two-phaseProcedure

3.2.SeveralTypicalModelSelectionCriteria

3.3.BYYModelSelectionCriterionforSmallSampleSize

3.4.ComparativeExperimentsonCriteriainTwo-PhaseImplementation

3.4.1.OnSimulatedData

3.4.2.OnRealWorldData

4.ThreeDataSmoothingScaleUpdatingFormulaeforBYY-S

4.1.ThreeFormulaetoUpdatetheDataSmoothingScaleh2

4.2.ComparativeExperimentsonThreeUpdatingFormulae

4.2.1.OnSimulatedData

4.2.2.OnRealWorldData

5.ConcludingRemarks

Chapter12PRINCIPLEOFALASER-DRIVENCHARGED-PARTICLEACCELERATOR

Chapter13TOPOLOGICALOPTIMIZATIONOFARTIFICIALNEURALNETWORKSUSINGAPATTERNSEARCHMETHOD

1.Introduction.

2.ArtificialNeuralNetworks

3.TopologicalOptimization

3.1.GeneralizedPatternSearchMethod

3.2.EvolutionaryStrategy

4.FormulationofModelProblem

5.OptimalNetworkTopology

6.DynamicApplication

6.1.MathematicalModel

7.Conclusion

Chapter14APPROXIMATEJOINTMATRIXDIAGONALIZATIONBYRIEMANNIAN-GRADIENT-BASEDOPTIMIZATIONOVERTHEUNITARYGROUP(WITHAPPLICATIONTONEURALMULTICHANNELBLINDDECONVOLUTION)

2.JointDiagonalizationbyRiemannian-Gradient-BasedOpti-mizationovertheUnitaryGroupofMatrices

2.1.JointComplex-ValuedMatrixDiagonalizationbyUnitaryTransformCastasanOptimizationProblem

2.2.OptimizationovertheUnitaryGroupofMatricesbyaRiemannian-Gradient-BasedSteppingMethod

2.3.OptimalStepsizeScheduleSelection

3.MultichannelBlindDeconvolutionbyNeuralBlindSepara-tionintheTime/Frequency-Domain

3.1.MultichannelBlindDeconvolutionandBlindSignalSeparationintheTime/Frequency-Domain

3.2.NeuralBlindSignalSeparationintheComplexDomainbyJointEigen-matricesDiagonalization

3.3.ApproximateJointDiagonalizationofScaledEigenmatrices

4.NumericalResultsandDiscussions

4.1.ExperimentalSettingandNumericalIssues

4.2.ExperimentsonTwoSourceSignals

4.3.ExperimentsonFourSourceSignals

5.Conclusion

A.Appendix:CalculationoftheEuclideanGradientoftheCostFunction(2)

B.Appendix:CalculationoftheCoefficientsinExpansion(9)

Chapter15SPIKETIMINGDEPENDENTPLASTICITY:AROUTETOROBUSTNESSINHARDWAREANDALGORITHMS

1.1.HebbianLearningandSpikeTimingDependentPlasticity

1.2.Depth-from-MotionAlgorithm

1.3.Summary

2.AnEarlyVisualDepth-from-MotionModelMediatedbySTDP

2.1.Introduction

2.2.Model

2.2.1.SpikingNeuronalModel

2.2.2.AVisionAlgorithmUsingSpikes

2.2.3.Adaptation

2.3.SimulationResults.

2.4.Conclusion.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references and index.