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MATLAB Machine Learning Recipes: A Problem-Solution Approach

為了解決nasa mars simulation的問題，作者Paluszek, Michael/ Thomas, Stephanie 這樣論述：

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable.

The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, a

utonomous driving, expert systems, and much more.What you'll learn: How to write code for machine learning, adaptive control and estimation using MATLABHow these three areas complement each otherHow these three areas are needed for robust machine learning applicationsHow to use MATLAB graphics and v

isualization tools for machine learningHow to code real world examples in MATLAB for major applications of machine learning in big dataWho is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learni

ng using MATLAB. Michael Paluszek is the co-author of MATLAB Recipes published by Apress. He is President of Princeton Satellite Systems, Inc. (PSS) in Plainsboro, New Jersey. Mr. Paluszek founded PSS in 1992 to provide aerospace consulting services. He used MATLAB to develop the control system an

d simulation for the Indostar-1 geosynschronous communications satellite, resulting in the launch of PSS’ first commercial MATLAB toolbox, the Spacecraft Control Toolbox, in 1995. Since then he has developed toolboxes and software packages for aircraft, submarines, robotics, and fusion propulsion, r

esulting in PSS’ current extensive product line. He is currently leading an Army research contract for precision attitude control of small satellites and working with the Princeton Plasma Physics Laboratory on a compact nuclear fusion reactor for energy generation and propulsion. Prior to founding P

SS, Mr. Paluszek was an engineer at GE Astro Space in East Windsor, NJ. At GE he designed the Global Geospace Science Polar despun platform control system and led the design of the GPS IIR attitude control system, the Inmarsat-3 attitude control systems and the Mars Observer delta-V control system,

leveraging MATLAB for control design. Mr. Paluszek also worked on the attitude determination system for the DMSP meteorological satellites. Mr. Paluszek flew communication satellites on over twelve satellite launches, including the GSTAR III recovery, the first transfer of a satellite to an operatio

nal orbit using electric thrusters. At Draper Laboratory Mr. Paluszek worked on the Space Shuttle, Space Station and submarine navigation. His Space Station work included designing of Control Moment Gyro based control systems for attitude control. Mr. Paluszek received his bachelors in Electrical En

gineering, and master’s and engineer’s degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology. He is author of numerous papers and has over a dozen U.S. Patents.Stephanie Thomas is the co-author of MATLAB Recipes, published by Apress. She received her bachelor’s and

master’s degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 1999 and 2001. Ms. Thomas was introduced to PSS’ Spacecraft Control Toolbox for MATLAB during a summer internship in 1996 and has been using MATLAB for aerospace analysis ever since. She built a simula

tion of a lunar transfer vehicle in C++, LunarPilot, during the same internship. In her nearly 20 years of MATLAB experience, she has developed many software tools including the Solar Sail Module for the Spacecraft Control Toolbox; a proximity satellite operations toolbox for the Air Force; collisio

n monitoring Simulink blocks for the Prisma satellite mission; and launch vehicle analysis tools in MATLAB and Java, to name a few. She has developed novel methods for space situation assessment such as a numeric approach to assessing the general rendezvous problem between any two satellites impleme

nted in both MATLAB and C++. Ms. Thomas has contributed to PSS’ Attitude and Orbit Control textbook, featuring examples using the Spacecraft Control Toolbox, and written many software User’s Guides. She has conducted SCT training for engineers from diverse locales such as Australia, Canada, Brazil,

and Thailand and has performed MATLAB consulting for NASA, the Air Force, and the European Space Agency.

Thermo-Magnetic Systems for Space Nuclear Reactors: An Introduction

為了解決nasa mars simulation的問題，作者Maidana, Carlos O. 這樣論述：

Introduces the reader to engineering magnetohydrodynamics applications and presents a comprehensive guide of how to approach different problems found in this multidisciplinary field.An introduction to engineering magnetohydrodynamics, this brief focuses heavily on the design of thermo-magnetic syste

ms for liquid metals, with emphasis on the design of electromagnetic annular linear induction pumps for space nuclear reactors. Alloy systems that are liquid at room temperature have a high degree of thermal conductivity far superior to ordinary non-metallic liquids. This results in their use for sp

ecific heat conducting and dissipation applications. For example, liquid metal-cooled reactors are typically very compact and can be used in space propulsion systems and in fission reactors for planetary exploration.Computer aided engineering (CAE), computational physics and mathematical methods are

introduced, as well as manufacturing and testing procedures. An overview on space nuclear systems is also included. This brief is an invaluable tool for design engineers and applied physicists as well as to graduate students in nuclear and mechanical engineering or in applied physics. Dr. Maidana

is a physicist and Research Engineer. He holds a Ph.D. in Engineering and Applied Science from Idaho State University, a M.Sc. in Physics from Michigan State University and a B.Sc. in Physics and Applied Physics from the UTN-INSPT (Argentina). He holds a post-doctoral certificate in Space Nuclear S

ystems Engineering from Washington State University and the Idaho National Laboratory as well as several other graduate level certificates. Dr. Maidana was a research designer in projects for the U.S. Departments of Energy, Defense and Homeland Security; as well as for NASA, CERN, the International

Committee for Future Accelerators, the U.S. Advanced Fuel Cycle Initiative and the Measurements and Control Engineering Research Center. He has over ten years of experience in computational engineering sciences (design, modeling and simulation - CAD/CAE/CAM) and multi-physics analysis; and more than

20 years of experience in Informatics Technology (IT). He has written over 20 papers and technical articles, and is a referee for international scientific journals and government organizations. Dr. Maidana is a senior member of the American Institute of Aeronautics and Astronautics (AIAA) serving a

t the Future & Nuclear Flight Propulsion Technical Committee, and he serves as a scientific officer for the Mars Society Switzerland as well.

應用於相對生物效應及微電子可靠度測試的輻射品質評估方法

為了解決nasa mars simulation的問題，作者江悅 這樣論述：

Table of Contents摘要 iiiAbstract ivChapter 1. Introduction 1Chapter 2. Radiation Environments and Their Quality 72.1. RADIATION QUANTITY AND QUALITY 72.2. RADIATION ENVIRONMENT IN THIS STUDY 92.2.1. Radiation for semiconductor industrial practice 102.2.2. Radia

tion for medical practice 122.3. SUMMARY 20Chapter 3. Microdosimetry and its simulation and measurement 213.1. CONCEPTS OF MICRODOSIMETRY 243.2. MONTE CARLO SIMULATION 353.3. MICRODOSIMETRY MEASUREMENT 403.4. SUMMARY 46Chapter 4. Lineal energy of proton in s

ilicon 474.1. THE DIFFERENCE BETWEEN LINEAL ENERGY AND LET 474.2. MICRODOSIMETRY SIMULATION 534.3. RESULTS AND DISCUSSIONS 574.3.1. Effect of SV thickness on y distribution 574.3.2. Lineal energy contribution from various secondary species 634.3.3. Effect of vario

us physics models on secondary yields 694.4. SUMMARY 70Chapter 5. Equivalence of Neutrons and Protons in Single Event Effects Testing 725.1. SINGLE EVENT EFFECT TESTING – METHODS AND FACILITIES 725.2. PROCESS FOR EQUIVALENCE VALIDATION 755.2.1. Monte Carlo Simulation

775.2.2. Material Structure 795.2.3. Data Analysis 815.3. RESULTS AND DISCUSSIONS 825.3.1. LET Difference between Neutrons and Protons 825.3.2. Secondary Particle Yield Difference between Neutronand Proton 885.3.3. LET Difference between Layer Structures with andwit

hout SiGe 915.3.4. Secondary Particle Yields Difference between Layer Structure with and without SiGe 935.3.5. Energy Deposition Difference between Neutronsand Protons 955.4. SUMMARY 99Chapter 6. Silicon equivalent gas in silicon equivalent proportional counter 1016.1.

SILICON EQUIVALENT GAS 1016.2. SIMULATION AND ANALYZATION METHODS FOR SE GAS SELECTION 1036.3. RESULTS AND DISCUSSION 1046.3.1. LET spectra 1046.3.2. Secondary particle yields 1056.4. SUMMARY 112Chapter 7. High Z material enhanced RBE 1147.1. RADIATION SENSI

TIZERS IN RADIATION THERAPY 1147.2. RBE SIMULATION AND CALCULATION METHODS 1177.2.1. MKM simulation 1177.2.2. DSB simulation 1207.3. RESULTS AND DISCUSSION 1217.3.1. Verification for microdosimetry simulation 1217.3.2. Microdosimetry spectra and RBE 1237.3.3.

Secondary electron spectra 1307.3.4. Correlation of DSB with electron energy 1327.3.5. Spectra of DSB 1337.4. SUMMARY 134Chapter 8. Conclusion 136References 138 List of FiguresFigure 1 1 LET threshold of SEEs vs. Feature size [6] 4Figure 1 2 (a) mechanism of total

ionization effect, (b) ΔVtm vs. time diagram due to TID [4] 4Figure 1 3 (a) Ionizing radiation generates charge, (b) Negative charge moves to the positive electrode to generate current, (c) Potential difference generates current, and (d) Current vs. time diagram due to a single event under revers

e bias[7] 5Figure 2 1 Example of a CMOS structure and mechanism of single event effect. (A) is the event from the heavy ions. (B) from the natural particle or proton. 12Figure 2 2 Schematic comparison of the local dose distributions (left) and corresponding spatial DSB distributions (right) fo

r low energetic (top) and high energetic (bottom) carbon ions. Assumed DSB yields are 50 DSB and 0.5 DSB for the low energetic and high energetic ions, respectively [33] 18Figure 2 3 Representation of a 10mGy dose delivered from gamma 60Co (left) and the same dose delivered by 1 MeV neutrons (rig

ht) in a cell volume of 150 cell of 5 µm diameter [37] 19Figure 2 4　The explanation of domain in microdosimetry kinetic model 19Figure 3 1 Specific energy (dE/dm) deposited by radiation in matter as a function of mass with the macroscopic dose being constant. 23Figure 3 2 lineal energy dist

ribution of tissue irradiated by 250 kVp X-ray. Linear scale. 31Figure 3 3 lineal energy distribution of tissue irradiated by 250 kVp X-ray. Log scale. 32Figure 3 4 lineal energy distribution of tissue irradiated by 250 kVp X-ray. Semi-log scale. 33Figure 3 5 dose weighted lineal energy dis

tribution of tissue irradiated by 250 kVp X-ray. Semi-log scale. 34Figure 3 6 Lineal energy spectra of different sensitive volumes in silicon irradiated by a 200 MeV proton beam. 35Figure 3 7 Block diagram of basic process of Monte Carlo method in radiation transportation code 39Figure 3 8

A sketch of the cross-sectional view of SEPC with its component 43Figure 3 9 block diagram of the SEPC measurement system 43Figure 3 10 Simulated lineal energy spectra for SEPC irradiated by 50 kVp and 150 kVp X-ray 45Figure 4 1 The geometry setup in this study. The silicon is with natural

isotope abudence, density is 2,330 mg/cm3 and mean excitation potential I = 173 eV. 57Figure 4 2 Lineal energy spectra of different sensitive volumes in silicon irradiated by a 200 MeV proton beam. 61Figure 4 3 Cumulative distribution function of kinetic energy of secondary particles generate

d by 200 protons irradiated on silicon 62Figure 4 4 Lineal energy spectra of different sensitive volumes in silicon irradiated by a 200 MeV proton beam (log y scale). 63Figure 4 5 y spectra in 100 nm silicon irradiated by a 200 MeV proton beam 67Figure 4 6 Secondary particle yields in 100 n

m silicon irradiated by a 200 MeV proton beam using various physics models. BIC represents the Binary cascade model. BERT represents Bertini cascade model. HP represents high precision add-on 70Figure 5 1 The Los Alamos Neutron Science Centre (LANSCE) broad band neutron spectrum used in this stud

y [112]. 76Figure 5 2 The layer structure (a) without SiGe and (b) with SiGe used in this simulation (not to scale). 80Figure 5 3 Linear energy transfer (LET) spectra in a structure without silicon-germanium (SiGe) irradiated by 63, 105, 150, 200, and 230 MeV proton and LANSCE neutron. 84Fi

gure 5 4 The LET contribution from He, Mg, and Al generated by the 200 MeV proton and the LANSCE neutron. In parentheses, the first symbol represents incident particles, and the second symbol represents particles that contribute to the LET. 85Figure 5 5 LET spectra in a structure without SiGe fro

m 10, 30, 50, 63, and 200 MeV protons and LANSCE neutron. 86Figure 5 6 The secondary particle yields in structure without SiGe irradiated by 63, 105, 150, 200, and 230 MeV protons and LANSCE neutron. 89Figure 5 7 LET spectra of the structure with and without SiGe irradiated by 63 and 230 MeV p

rotons and LANSCE neutron. The plot is in log-log scale. 92Figure 5 8 The secondary particle yields in the structure with and without SiGe irradiated by 63 and 230 MeV protons and LANSCE neutron. 94Figure 6 1 Simulated LET spectra in the SEPC cavity for proton irradiations of (a) 63 MeV and (b

) 230 MeV. Results of cavity gas Si, CCl4, propane, Ne and Ar are plotted. 107Figure 6 2 Simulated LET spectra in the SEPC cavity for neutron irradiations of (a) 4.44 MeV and (b) 750 MeV. Results of cavity gas Si, CCl4, propane, Ne and Ar are plotted. 108Figure 6 3 Evaluation index, EI, of LET

spectra for different SEPC cavity gases under proton and neutron irradiations 109Figure 6 4 Simulated secondary particle yields in the SEPC cavity for proton irradiations of (a) 63 MeV and (b) 230 MeV. Results of cavity gas Air, Ar, CCl4, CO2, He, Kr, Ne, propane, Si and Xe are plotted 110Fig

ure 6 5 Simulated secondary particle yields in the SEPC cavity for neutron irradiations of (a) 4.44 MeV and (b) 750 MeV. Results of cavity gas Air, Ar, CCl4, CO2, He, Kr, Ne, propane, Si and Xe are plotted. 111Figure 6 6 Evaluation index, EI, of secondary particle yields for different SEPC cavity

gases under proton and neutron irradiations 112Figure 7 1 Input spectra for Monte Carlo simulation. The spectra are measured by INER and modified for Geant4 GPS input format. 119Figure 7 2 Comparison of simulation data with measurement data. Dots represent the simulation data with 20 points p

er decade. The continuous line shows the measurement data in INER’s medium energy X-ray air kerma rate calibration system. 123Figure 7 3 Microdosimetry spectra of 80 kVp La transmission X-ray w/ and w/o iodine 125Figure 7 4 Microdosimetry spectra of 250 kVp X-ray w/ and w/o iodine 126Figure

7 5 Secondary electron spectra. (A) The secondary electron of 80 kVp La Fluorescence X-ray. (B) The secondary electron of 250 kVp X-ray 131Figure 7 6 The yields of DSB for different electron. The energy step is set 20 energies per decade in log scale in simulation. The cubic spline method is app

lied to do the interpolation. The fitting curve is shown in 10 eV per step. 133Figure 7 7 DSB yield. The DSB yield is the product of secondary electron and DSB cross-section. 134 List of TablesTable 4 1 The calculated LET using mean energy of secondary particles generated by 200 MeV proton irr

adiate on silicon 68Table 5 1 Evaluation index (EI) for LET in layer structure without SiGe. 88Table 5 2 EI for secondary particle yields in layer structure without SiGe. 90Table 5 3 EI for LET in layer structure with SiGe. 94Table 5 4 EI for secondary particle yields in layer structure

with SiGe. 95Table 5 5 Energy deposition analysis results for the layer structure without SiGe for 1010 neutron/proton incident 97Table 5 6 Energy deposition analysis results for the layer structure with SiGe for 1010 neutron/proton incident 98Table 7 1 Frequency mean lineal energy, dose me

an lineal energy and calculated RBE for each irradiation condition 127Table 7 2 Relative dose in cavity and wall for each irradiation condition in same fluence 128Table 7 3 Relative number of secondary electrons generated by unit dose and overall RBE 129