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Automation and computational intelligence for road maintenance and management : advances and applications /
"This book is a reference that makes a connection between developments in computer technology infrastructure management. It provides a unique form for computational techniques and state-of-the-art automation applications. It contains the fundamental emerging technologies and methods in both aut...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
2022.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | OR_on1317751158 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 220321t20222022njua ob 001 0 eng | ||
| 010 | |a 2022011361 | ||
| 040 | |a DLC |b eng |e rda |c DLC |d OCLCF |d YDX |d N$T |d ORMDA |d SFB |d IEEEE | ||
| 020 | |a 1119800668 |q electronic publication | ||
| 020 | |a 9781119800651 |q adobe electronic book | ||
| 020 | |a 111980065X |q adobe electronic book | ||
| 020 | |a 9781119800675 |q electronic book | ||
| 020 | |a 1119800676 |q electronic book | ||
| 020 | |a 9781119800668 |q (electronic bk.) | ||
| 020 | |z 9781119800644 |q hardcover | ||
| 024 | 7 | |a 10.1002/9781119800675 |2 doi | |
| 035 | |a (OCoLC)1317751158 | ||
| 037 | |a 9781119800644 |b O'Reilly Media | ||
| 037 | |a 9830653 |b IEEE | ||
| 042 | |a pcc | ||
| 050 | 0 | 4 | |a HD9717.5.R62 |b Z34 2022 |
| 082 | 0 | 0 | |a 363.12/560285 |2 23/eng/20220506 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Zakeri, Hamzeh, |e author. | |
| 245 | 1 | 0 | |a Automation and computational intelligence for road maintenance and management : |b advances and applications / |c Hamzeh Zakeri, Fereidoon Moghadas Nejad, Amir H. Gandomi. |
| 264 | 1 | |a Hoboken, NJ : |b John Wiley & Sons, Inc., |c 2022. | |
| 264 | 4 | |c Ã2022 | |
| 300 | |a 1 online resource : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "This book is a reference that makes a connection between developments in computer technology infrastructure management. It provides a unique form for computational techniques and state-of-the-art automation applications. It contains the fundamental emerging technologies and methods in both automation and computational intelligence, beginning with a widespread overview of the field followed a detailed presentation of the methodology. This book specifically focuses on very recent advances in maintenance and management of infrastructures robotics, automated inspection, remote sensing, and application of new and emerging computing like artificial intelligence, evolutionary computing, fuzzy logic, genetic algorithms, knowledge discovery and engineering, machine learning, neural network computing, optimization and search, parallel processing, vision and image processing."-- |c Provided by publisher. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on August 12, 2022). | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright Page -- Contents -- Dedication -- Preface -- Author Biography -- Chapter 1 Concepts and Foundations Automation and Emerging Technologies -- 1.1 Introduction -- 1.2 Structure and Framework of Automation and Key Performance Indexes (KPIs) -- 1.3 Advanced Image Processing Techniques -- 1.4 Fuzzy and Its Recent Advances -- 1.5 Automatic Detection and Its Applications in Infrastructure -- 1.6 Feature Extraction and Fragmentation Methods -- 1.7 Feature Prioritization and Selection Methods -- 1.8 Classification Methods and Its Applications in Infrastructure Management -- 1.9 Models of Performance Measures and Quantification in Automation -- 1.10 Nature-Inspired Optimization Algorithms (NIOAS) -- 1.11 Summary and Conclusion -- 1.12 Questions and Exercise -- Chapter 2 The Structure and Framework of Automation and Key Performance Indices (KPIs) -- 2.1 Introduction -- 2.2 Macro Plan and Architecture of Automation -- 2.2.1 Infrastructure Automation -- 2.2.2 Importance of Infrastructure Automation Evaluation -- 2.3 A General Framework and Design of Automation -- 2.4 Infrastructure Condition Index and Its Relationship with Cracking -- 2.4.1 Road Condition Index -- 2.4.2 Bridge Condition Index -- 2.4.3 Tunnel Condition Index -- 2.5 Automation, Emerging Technologies, and Futures Studies -- 2.6 Summary and Conclusion -- 2.7 Questions -- Further Reading -- Chapter 3 Advanced Images Processing Techniques -- Introduction -- 3.1 Preprocessing (PPS) -- 3.1.1 Edge Preservation Index (EPI) -- 3.1.2 Edge-Strength Similarity-Based Image Quality Metric (ESSIM) -- 3.1.3 QILV Index -- 3.1.4 Structural Content Index (SCI) -- 3.1.5 Signal-To-Noise Ratio Index (PSNR) -- 3.1.6 Computational time index (CTI) -- 3.2 Preprocessing Using Single-Level Methods -- 3.2.1 Single-Level Methods -- 3.2.2 Linear Location Filter (LLF) -- 3.2.3 Median Filter. | |
| 505 | 8 | |a 4.7.1 Examples of the Application of Fuzzy Methods in Infrastructure Management -- 4.8 Summary and Conclusion -- 4.9 Questions and Exercises -- Further Reading -- Chapter 5 Automatic Detection and Its Applications in Infrastructure -- 5.1 Introduction -- 5.1.1 Photometric Hypotheses (PH) -- 5.1.2 Geometric and Photometric Hypotheses (GPH) -- 5.1.3 Geometric Hypotheses (GH) -- 5.1.4 Transform Hypotheses (TH) -- 5.2 The Framework for Automatic Detection of Abnormalities in Infrastructure Images -- 5.2.1 Wavelet Method -- 5.2.2 High Amplitude Wavelet Coefficient Percentage (HAWCP) -- 5.2.3 High-Frequency Wavelet Energy Percentage (HFWEP) -- 5.2.4 Wavelet Standard Deviation (WSTD) -- 5.2.5 Moments of Wavelet -- 5.2.6 High Amplitude Shearlet Coefficient Percentage (HASHCP) -- 5.2.7 High-Frequency Shearlet Energy Percentage (HFSHEP) -- 5.2.8 Fractal Index -- 5.2.9 Moments of Complex Shearlet -- 5.2.10 Central Moments q -- 5.2.11 Hu Moments -- 5.2.12 Bamieh Moments -- 5.2.13 Zernike Moments -- 5.2.14 Statistic of Complex Shearlet -- 5.2.15 Contrast of Complex Shearlet -- 5.2.16 Correlation of Complex Shearlet -- 5.2.17 Uniformity of Complex Shearlet -- 5.2.18 Homogeneity of Complex Shearlet -- 5.2.19 Entropy of Complex Shearlet -- 5.2.20 Local Standard Deviation of Complex Shearlet Index (F_Local_STD) -- 5.3 Summary and Conclusion -- 5.4 Questions and Exercises -- Further Reading -- Chapter 6 Feature Extraction and Fragmentation Methods -- 6.1 Introduction -- 6.2 Low-Level Feature Extraction Methods -- 6.3 Shape-Based Feature (SBF) -- 6.3.1 Center of Gravity (COG) or Center of Area (COA) -- 6.3.2 Axis of Least Inertia (ALI) -- 6.3.3 Average Bending Energy -- 6.3.4 Eccentricity Index (ECI) -- 6.3.5 Circularity Ratio (CIR) -- 6.3.6 Ellipse Variance Feature (EVF) -- 6.3.7 Rectangularity Feature (REF) -- 6.3.8 Convexity Feature (COF). | |
| 505 | 8 | |a 6.3.9 Euler Number Feature (ENF) -- 6.3.10 Profiles Feature (PRF) -- 6.4 1D Function-Based Features for Shape Representation -- 6.4.1 Complex Coordinates Feature (CCF) -- 6.4.2 Extracting Edge Characteristics Using Complex Coordinates -- 6.4.3 Edge Detection Using Even and Odd Shearlet Symmetric Generators -- 6.4.4 Object Detection and Isolation Using the Shearlet Coefficient Feature (SCF) -- 6.5 Polygonal-Based Features (PBF) -- 6.6 Spatial Interrelation Feature (SIF) -- 6.7 Moments Features (MFE) -- 6.8 Scale Space Approaches for Feature Extraction (SSA) -- 6.9 Shape Transform Features (STF) -- 6.9.1 Radon Transform Features (RTF) -- 6.9.2 Linear Radon Transform -- 6.9.3 Translation of RT -- 6.9.4 Scaling of RT -- 6.9.5 Point and Line Transform Using RT -- 6.9.6 RT in Sparse Objects -- 6.9.7 Point and Line in RT -- 6.10 Various Case-Based Examples in Infrastructures Management -- 6.10.1 Case 1: Feature Extraction from Polypropylene Modified Bitumen Optical Microscopy Images -- 6.10.2 Ratio of Number of Black Pixels to the Number of Total Pixels (RBT) -- 6.10.3 Ratio of Number of Black Pixels to the Number of Total Pixels in Watershed Segmentation (RWS) -- 6.10.4 Number and Average Area of the White Circular Objects in the Binary Image (The number of circular objects [NCO] & -- ACO) -- 6.10.5 Entropy of the Image -- 6.10.6 Radon Transform Maximum Value (RTMV) -- 6.10.7 Entropy of Radon Transform (ERT) -- 6.10.8 High Amplitude Radon Percentage (HARP) -- 6.10.9 High-Energy Radon Percentage (HERP) -- 6.10.10 Standard Deviation of Radon Transform (STDR) -- 6.10.11 Qth-Moment of Radon Transform (QMRT) -- 6.10.12 Case 2: Image-Based Feature Extraction for Pavement Skid Evaluation -- 6.10.13 Case 3: Image-Based Feature Extraction for Pavement Texture Drainage Capability Evaluation. | |
| 505 | 8 | |a 6.10.14 Case 4: Image-Based Features Extraction in Pavement Cracking Evaluation -- 6.10.15 Automatic Extraction of Crack Features -- 6.10.16 Extraction of Crack Skeleton Using Shearlet Complex Method -- 6.10.17 Calculate Crack Width Feature Using External Multiplication Method -- 6.10.18 Detection of Crack Starting Feature (Crack Core) Using EPA Emperor Penguin Metaheuristic Algorithm -- 6.10.19 Selection of Crack Root Feature Based on Geodetic Distance -- 6.10.20 Determining Coordinates of the Crack Core as the Optimal Center at the Failure Level using EPA Method -- 6.10.21 Development of New Features for Crack Evaluation Based on Graph Energy -- 6.10.22 Crack Homogeneity Feature Based on Graph Energy Theory -- 6.10.23 Spall Type 1 Feature: Crack Based on Graph Energy Theory in Crack Width Mode -- 6.10.24 General Crack Index Based on Graph Energy Theory -- 6.11 Summary and Conclusion -- 6.12 Questions and Exercises -- Further Reading -- Chapter 7 Feature Prioritization and Selection Methods -- 7.1 Introduction -- 7.2 A Variety of Features Selection Methods -- 7.2.1 Filter Methods -- 7.2.2 Correlation Criteria -- 7.2.3 Mutual Information (MI) -- 7.2.4 Wrapper Methods -- 7.2.5 Sequential Feature Selection (SFS) Algorithm -- 7.2.6 Heuristic Search Algorithm (HAS) -- 7.2.7 Embedded Methods -- 7.2.8 Hybrid Methods -- 7.2.9 Feature Selection Using the Fuzzy Entropy Method -- 7.2.10 Hybrid-Based Feature Selection Using the Hierarchical Fuzzy Entropy Method -- 7.2.11 Step 1: Measure Similarity Index and Evaluate Features -- 7.2.12 Step 2: Final Feature Vector -- 7.3 Classification Algorithm Based on Modified Support Vectors for Feature Selection -- CDFESVM -- 7.3.1 Methods for Determining the Fuzzy Membership Function in Feature Selection -- 7.4 Summary and Conclusion -- 7.5Questions and Exercises -- Further Reading. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Road construction industry |x Automation. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 7 | |a Computational intelligence. |2 fast |0 (OCoLC)fst00871995 | |
| 700 | 1 | |a Nejad, Fereidoon Moghadas, |e author. | |
| 700 | 1 | |a Gandomi, Amir Hossein, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Zakeri, Hamzeh. |t Automation and computational intelligence for road maintenance and management |d Hoboken, NJ : Wiley, 2022 |z 9781119800644 |w (DLC) 2022011360 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781119800644/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a EBSCOhost |b EBSC |n 3313264 | ||
| 994 | |a 92 |b IZTAP | ||