LEADER 13089 am 22005773u 450 001 9910321054003321 005 20221206102409.0 010 $a1-000-43906-2 010 $a0-429-32077-9 024 7 $a10.1201/9780429320774 035 $a(CKB)4100000008104157 035 $a(OAPEN)1004924 035 $a(OCoLC)1103441013 035 $a(OCoLC-P)1103441013 035 $a(FlBoTFG)9780429320774 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/39202 035 $a(EXLCZ)994100000008104157 100 $a20190604d2019 ky 0 101 0 $aeng 135 $aurcnu|||unuuu 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMining goes digital $eproceedings of the 39th International Symposium 'Application of Computers and Operations Research in the Mineral Industry' (APCOM 2019), June 4-6, 2019, Wroclaw, Poland /$feditors: Christoph Mueller [and 6 more] 210 $cTaylor & Francis$d2019 210 1$aLeiden, Netherlands :$cCRC Press,$d2019. 215 $a1 online resource (xx, 759 pages) $cdigital, PDF file(s) 225 0 $aProceedings in Earth and geosciences series ;$v3 300 $a"A Balkema book." 311 08$aPrint version: 9780367336042 320 $aIncludes bibliographical references and index. 327 $aGeneral aspects of digital transformation in mining The so-called "Green Paradox" A.B. Bendiek & M.P. Bendiek Digital technology trends and their implementation in the mining industry L. Barnewold Virtual reality mine: A vision for digitalised mining engineering education R. Suppes, Y. Feldmann, A. Abdelrazeq & L. Daling Investing in engineering, research and education in Africa to derive a roadmap for ensuring local digital mining success W. Assibey-Bonsu The application of correlation models for the analysis of market risk factors in KGHM capital group ?. Bielak, P. Mis?ta, A. Michalak & A. Wy?oman?ska Improvement of investment processes in mining company by implementation of project management system M. Wach & I. Chomiak-Orsa Resource estimation and geostatistics An approach for drilling pattern simulation G. Usero, S. Misk & A. Saldanha Application of Locally Varying Anisotropy (LVA) kriging at the Grasberg porphyry Cu-Au-Ag deposit, Papua, Indonesia A. Issel, A. Schwarz, K. Moss & R. Rossi Multivariate geostatistical simulation using principal component analysis M. Bolgkoranou & J.M. Ortiz Application of ASTER multispectral data and hyperspectral spectroscopy for phosphate exploration N. Mezned, A. Fatnassi & S. Abdeljaouad Multivariate Gaussian process for distinguishing geological units using measure while drilling data K.L. Silversides & A. Melkumyan Machine learning classification of geochemical and geophysical data L. Huang, M. Balamurali & K.L. Silversides Covariance table and PPMT: Spatial continuity mapping of multiple variables J. Kloeckner, C.Z. da Silva & J.F.C.L. Costa Optimal drill hole spacing for resource classification M. Nowak & O. Leuangthong Geostatistical simulation with heterotopic soft data without the LMC C.P. Arau?jo, M.A.A. Bassani & J.F.C.L. Costa Application of localized multivariate uniform conditioning and conditional simulation for a stockwork niobium deposit L. Bertossi, D. Raposo, J. Watanabe, S. Silva & G. Usero MILP framework for open pit and underground mining transitions evaluation B.O. Afum & E. Ben-Awuah Multi-collocated cokriging: An application to grade estimation in the mining industry N. Madani Recursive convolutional neural networks in a multiple-point statistics framework S. Avalos & J.M. Ortiz Grade estimation in a tabular deposit using unstructured grids M.A.A. Bassani, C.P. Arau?jo & J.F.C.L. Costa Influence of drilling spacing on the mineral resources uncertainty C.J.E. Silva, M.A.A. Bassani & J.F.C.L. Costa Evolving estimation techniques for an evolving world class stratiform copper deposit at Kamoa-Kakula, Democratic Republic of the Congo G. Gilchrist Critical review of mineral resource classification techniques in the gold mining industry S.K.A. Owusu & K. Dagdelen Transforming exploration data through machine learning I.W.S. Whitehouse & W. Slabik Rock mass characterization using MWD data and photogrammetry S. Manzoor, S. Liaghat, A. Gustafson, D. Johansson & H. Schunnesson Ore grade prediction using informative features of MWD data S. Liaghat, A. Gustafson, D. Johansson & H. Schunnesson Recoverable resource estimation mixing different quality of data C.R.O. Mariz, A. Prior & J. Benndorf Declustering weights as a measure of average sample spacing, applications in mineral resource classification D.E. Hulse & R.C. Bryan Mine planning in digital transformation Multi stage dumping sequence--a new approach for waste disposal B.T. Kuckartz & R.L. Peroni Parametric analysis of the optimal depth of an open-pit gold mine R. Motta, C. Porto, D. Machado & O.C. Souto A procedure to generate optimized ramp designs using mathematical programming N. Espejo, P. Nancel-Penard & N. Morales Break line and shotpile surfaces modeling in design of large-scale blasts S.V. Lukichev, O.V. Nagovitsyn & A.S. Shishkin Incorporation of mineralisation risk into underground mine planning R.C. Rosado, J.F.C.L. Costa & A.A. Saldanha Economic optimization of rib pillars placement in underground mines A.B. Andrade, A.R.C. Faria & P.C.B. Rampazzo Performance assessment of antithetic random fields in a stochastic mine planning model G. Nelis, N. Morales & J.M. Ortiz A data science model on production level pillar stability at El Teniente mine R.J. Quevedo, R.F. Quezada, R.A. Zepeda, S.A. Balboa, J.P. Vargas & S.A. Pe?rez Incorporating grade uncertainty into sublevel stope sequencing Y.A. Sari & M. Kumral A spatial clustering algorithm for orebody classification and boundary setting S. Li, Y.A. Sari & M. Kumral Underground mine planning optimization process to improve values and reduce risks H.H. Wang Application of a digital model of deposit in Polish hard coal mines on the example of Polish Mining Group Ltd. V. Soko?a-Szewio?a & M. Poniewiera Scheduling and dispatch Improvements in plan-driven truck dispatching systems for surface mining M. Samavati, A.W. Palmer, A.J. Hill & K.M. Seiler Short-term production scheduling of multiple mines using genetic algorithms P. Pathak & B. Samanta A two-stage solution approach for a shift scheduling problem with a simultaneous assignment of machines and workers C. Seifi, M. Schulze & J. Zimmermann Understanding plan's priorities: Short term scheduling optimization A.B. Andrade & P.C.B. Rampazzo Simulation and optimization framework for evaluating the mining operations A. Moradi Afrapoli & H. Askari-Nasab Framework of optimal operational indices for the open pit mines production scheduling problems M.R. Moghaddam & E. Moosavi Mine schedule optimization and mine operational realities: Bridging the gap A. Chowdu, M. Goycoolea & A. Brickey Optimization model for rostering and crew assignment for train transportation J. Amaya, E. Molina, N. Morales & P. Uribe Generating pushbacks using direct block mine production scheduling algorithm C. Aras, K. Dagdelen & T. Johnson Industrial internet of things and gamification applied to fleet and personnel management S. Dessureault Short-term open-pit mine production scheduling with hierarchical objectives F. Manri?quez, H. Gonza?lez & N. Morales Mine operation and equipment A deep learning approach for automated quality control of iron ores A.K. Gorai, B.C. Balusa & U. Sameer Comparison between regression models and neural networks applied to forecast geometallurgical variables F.G.F. Niquini & J.F.C.L. Costa The simulation of the excavation sites of coal mines K.N. Kopylov, S.S. Kubrin & D.I. Blokhin An operational data based framework for longwall shearer performance measurement E. Yilmaz & M. Erkayaoglu The digital mine eco-system W.A.S. Fourie Application of DEM-FEM methods in tests of loads on idlers B. Doroszuk, R. Kro?l & L. G?adysiewicz Comprehensive, experimental verification of the effects of the lock-up function implementation in LHD haul trucks in the deep underground mine T. Kaniewski, P. S?liwin?ski, J. Hebda-Sobkowicz & R. Zimroz Analysis of dynamic external loads to haul truck machine subsystems during operation in a deep underground mine P. S?liwin?ski, T. Kaniewski, J. Hebda-Sobkowicz, R. Zimroz & A. Wyloman?ska Selection of variables acquired by the on-board monitoring system to determine operational cycles for haul truck vehicle P. S?liwin?ski, M. Andrzejewski, T. Kaniewski, J. Hebda-Sobkowicz & R. Zimroz An integrated simulation model for opportunistic maintenance O. Golbasi, M. Olmez Turan & C. Karpuz Approach of mining equipment performance with simulation of the use of autonomous trucks W.S. Felsch Jr., A.F. Oliveira & C.E.A. Ortiz Modelling and forecasting geometallurgical recovery at a phosphate mine L.B. Andrade, I.E. Cabral & J.F.C.L. Costa Artificial intelligence using real-time data L.-P. Campeau & M. Dubois LTE, 4G & 5G - Broadband mobile communications in mining applications W. Santos Development assumptions of a data and service management centre at KGHM S.A. P. Pyda, P. Stefaniak & H. Dudycz Mineberry--remote monitoring of abandoned shaft openings B. vom Berg, F. Schmachtenberger, B. von Gruchalla, F. Wollnik, S. Klaß, A. Koschare, S. Schnell & J. Schliebs Optimization of material logistics by using leading edge electronic Information and Communication Technologies (ICT) in underground coalmine M.T. Sto?ttner Mine safety in digital transformation Development of blast-induced ground vibration wireless monitoring system R. Prashanth & D.S. Nimaje Increased safety in deep mining with IoT and autonomous robots F. Gu?nther, H. Mischo, R. Lo?sch, S. Grehl & F. Gu?th Coupled CFD-DEM modelling of mine dust dispersion in underground roadway L. Tan & T. Ren Proximity detection of explosive methane clouds in longwall mines J.F. Brune, H.S. Du?zgu?n, G.E. Bogin Jr., A. Juganda, C. Strebinger, T. Nguyen, E. Isleyen & C. Demirkanr Evaluation of trackless mobile machine collision management systems H.A. Hamersma, P.S. Els & C.E. Doran A sensitive carbon monoxide monitoring system for forecasting coal spontaneous combustion Z.W. Wang, Y.F. Li, T.T. Zhang, Y.B. Wei & T.Y. Liu Application of laser methane sensor in on-line monitoring of gas pipeline G.X. Jin, H. Meng, G.H. Jia, W.W. Wang, H. Zhang, Z.D. Shi, T.Y. Liu, C.X. Song & Y.N. Ning Fibre optic sensor for coal mine combustion detection T.Y. Liu, X.J. Meng, F.Q. Wang, R.C. Li, M.Y. Hou, Z.W. Wang, J. Hu, Y.F. Li, L.Z. Ma, Y.B. Wei & S.X. Zhang IoT and robotics Lithological hyperspectral characterization for UAV sensor selection F.S. Beretta, A.L. Rodrigues, R.L. Peroni, S.B. Rolim & J.F. Costa High-resolution modeling of open-pit slopes using UAV and photogrammetry R. Battulwar, J. Valencia, G. Winkelmaier, B. Parvin & J. Sattarvand Enhancement of explosive energy distribution using UAVs and machine learning J. Valencia, R. Battulwar, M. Zare Naghadehi & J. Sattarvand The concept of walking robot for mining industry B. De?bogo?rski, P. Sperzyn?ski, M. Fieden?, T. Ursel & A. Muraszkowski State-of-the-art mechatronic systems for mining developed in Poland D. Jasiulek, M. Malec, B. Po 330 $aThe conferences on 'Applications for Computers and Operations Research in the Minerals Industry' (APCOM) initially focused on the optimization of geostatistics and resource estimation. Several standard methods used in these fields were presented in the early days of APCOM. While geostatistics remains an important part, information technology has emerged, and nowadays APCOM not only focuses on geostatistics and resource estimation, but has broadened its horizon to Information and Communication Technology (ICT) in the mineral industry. Mining Goes Digital is a collection of 90 high quality, peer reviewed papers covering recent ICT-related developments in: - Geostatistics and Resource Estimation- Mine Planning- Scheduling and Dispatch - Mine Safety and Mine Operation- Internet of Things, Robotics- Emerging Technologies - Synergies from other industries - General aspects of Digital Transformation in Mining Mining Goes Digital will be of interest to professionals and academics involved or interested in the above-mentioned areas. 606 $aMines and mineral resources$xData processing$vCongresses 606 $aMining engineering$xData processing$vCongresses 606 $aMining engineering$xTechnological innovations$vCongresses 610 $aComputers 610 $amineral industry 610 $amining 615 0$aMines and mineral resources$xData processing 615 0$aMining engineering$xData processing 615 0$aMining engineering$xTechnological innovations 676 $a622.0285 700 $aMueller$b Christoph$4edt$01357075 702 $aMueller$b Christoph 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910321054003321 996 $aMining goes digital$93362412 997 $aUNINA LEADER 11026nam 2200529 450 001 996495560303316 005 20230315144919.0 010 $a981-19-5053-9 035 $a(MiAaPQ)EBC7127058 035 $a(Au-PeEL)EBL7127058 035 $a(CKB)25208276000041 035 $a(PPN)265862728 035 $a(EXLCZ)9925208276000041 100 $a20230315d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvanced driver assistance systems and autonomous vehicles $efrom fundamentals to applications /$fYan Li and Hualiang Shi, editors 210 1$aSingapore :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (628 pages) 311 08$aPrint version: Li, Yan Advanced Driver Assistance Systems and Autonomous Vehicles Singapore : Springer,c2022 9789811950520 320 $aIncludes bibliographical references. 327 $aIntro -- Contents -- Introduction -- 1 Reshape the Future of Transportation -- 2 Challenges -- 2.1 New Technologies -- 2.2 Requalification of Non-Auto-Grade Components -- 2.3 New Mission Profiles for Existing Auto-Grade Components -- 3 Overview of Chapters -- 4 Summary -- References -- Basics and Applications of AI in ADAS and Autonomous Vehicles -- 1 Introduction -- 1.1 Advanced Driver-Assistance Systems (ADASs) -- 1.2 Autonomous Vehicles (AVs) and Automation Levels -- 1.3 Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) -- 2 Applications of AI in ADAS -- 2.1 Supervised Learning -- 2.2 Unsupervised Learning -- 2.3 Reinforcement Learning -- 2.4 Deep Learning (DL) -- 3 Safety in ADAS and AV Based on AI -- 3.1 Safety Standards and Methodologies -- 3.2 AI Safety Challenges: Edge Cases and Heavy Tail Distribution -- 3.3 Safety in AI System Design, Validation, Testing and Implementation -- 4 Datasets, Simulators, and Infrastructures for AI Systems -- 4.1 Publicly Available Training and Testing Datasets -- 4.2 Open-source Simulators -- 4.3 Infrastructures for AI Systems -- 5 Summary -- References -- Computing Technology in Autonomous Vehicle -- 1 Introduction -- 2 Compute and ADAS Technology -- 2.1 Levels of Autonomous Driving -- 2.2 Platform for Autonomous Driving System -- 2.3 Perception and Localization -- 2.4 Prediction, Planning, and Control -- 2.5 Functional Safety -- 3 Advanced Computer System -- 3.1 Architecture Solution and Comparisons -- 3.2 Environment Perception Sensors -- 3.3 System on Chip (SoC) -- 3.4 Memory -- 3.5 Storage -- 3.6 Network -- 3.7 Real-Time Operating System -- 3.8 Management, Failure Detection, and Diagnostics -- 3.9 Security and Middleware -- 4 Electrical Functional and Reliability Validation -- 4.1 Automotive Level EE Functional Tests. 327 $a4.2 Reliability Validation Tests Based on AV Mission Profiles -- 4.3 EMC/ESD Validation -- 5 Challenges to Safe Deployment at Scale -- 5.1 Artificial Intelligence: Perception and Prediction -- 5.2 Power Consumption -- 5.3 Thermal Management -- 5.4 Manufacturing, Assembly, and Quality Control -- 5.5 Size and Cost -- 5.6 Quality and Reliability -- 5.7 Security and Safety -- 6 Summary -- References -- Overview of Packaging Technologies and Cooling Solutions in ADAS Market -- 1 Introduction -- 1.1 Market Opportunity and Trends -- 1.2 Road to Autonomy: ADAS Architecture -- 2 Package Technology and AD Requirements -- 2.1 Role of Advantage Packaging Technology in AD Market -- 2.2 Smaller System Level Footprints -- 3 Thermal Management -- 3.1 ECU Thermal Fundamentals -- 3.2 Component Level Fundamentals -- 3.3 Vehicle Operating Environment -- 3.4 ADAS ECU Thermal Management -- 4 ADAS Product Reliability Requirements -- 4.1 Qualification Requirements -- 4.2 ADAS Performance Requirements and Implications-ADAS Mission Profile -- 4.3 Failure Regimes-Quality and Wear-Out Failures -- 4.4 Package Reliability Challenges -- 5 Summary -- References -- Flash Memory and NAND -- 1 NAND Flash-The Perfect Storage Medium -- 1.1 What is NAND Flash -- 1.2 NOR Versus NAND -- 1.3 Evolution of NAND Flash -- 2 NAND Fundamentals -- 2.1 NAND Arrays in 2D and 3D -- 2.2 Basic NAND Operations -- 2.3 Multi-Bit-Per-Cell Technologies -- 2.4 Anatomy of a NAND Product -- 2.5 3D NAND Technology Basics -- 3 3D NAND Technology and Design Challenges -- 3.1 Cost-Performance-Reliability Tradeoffs -- 3.2 3D NAND Technology Challenges -- 3.3 3D NAND Design Challenges -- 4 NAND Reliability Issues -- 4.1 Write Errors -- 4.2 Disturb Errors -- 4.3 Data Retention Errors -- 5 3D NAND Future Outlook -- References -- Interconnect -- 1 Interconnects for Applications Under the Hood. 327 $a1.1 Nanoparticle Sintering Method -- 1.2 Transient Liquid Phase Bonding Technology -- 1.3 Electrochemical Migration Phenomenon -- 2 Solder Joint Technology for Applications Under the Hood -- 2.1 Low Melting Point Solders -- 2.2 Low-Temperature Assembly -- 3 Introduction for Low-Temperature Cu to Cu Direct Bonding -- 3.1 Cu-Cu Bonding by Surface-Activated Bonding Process -- 3.2 Cu-Cu Bonding by Chemical Pretreatment -- 3.3 Cu-Cu Bonding by Thermal Compressive Bonding -- 3.4 Low-Temperature Cu-Cu Bonding by (111) Nanotwinned Structure -- 3.5 Low-Temperature Cu to Cu Bonding with Ag Passivation Under Atmosphere -- 3.6 Hybrid Bonding -- References -- Cameras in Advanced Driver-Assistance Systems and Autonomous Driving Vehicles -- 1 Introduction -- 2 Camera System Overview -- 3 Camera System Hardware -- 3.1 Image Sensor -- 3.2 Optics -- 3.3 Electronics -- 3.4 Image Signal Processor (ISP) -- 4 Image Processing -- 4.1 Image Processing Pipeline -- 4.2 Calibration -- 4.3 ISP Tuning -- 5 Camera Product Development -- 5.1 Product Definition -- 5.2 Camera Design -- 5.3 Prototype -- 5.4 Validation -- 5.5 Manufacturing -- 5.6 Implementation -- 5.7 Support -- 6 Summary -- References -- Lidar Technology -- 1 Introduction -- 2 Overview of Current Lidar Technology for Automotive Application -- 3 Important Performance Metrics for Lidar -- 3.1 Range -- 3.2 Field of View -- 3.3 Angular Resolution/Accuracy -- 3.4 Frame Rate -- 3.5 Eye Safety -- 4 Transmitter and Receiver -- 5 Distance Calculation -- 5.1 Range of Time of Flight -- 5.2 Signal-To-Noise Ratio -- 5.3 Factors that Affect Range Detection -- 6 Future Direction of Lidar Developments -- 6.1 Frequency-Modulated Continuous Wave (FMCW) -- 7 Mapping Methods -- 7.1 Mechanical Spinning Scanner -- 7.2 Opto-Mechanical Scanning -- 7.3 MEMS Scanning -- 7.4 Flash -- 7.5 Optical Phased Array (OPA) -- 8 Discussion -- References. 327 $aRadar Technology -- 1 Introduction -- 2 Radar Physical Design -- 2.1 Radar Architecture -- 2.2 Radar Categories -- 3 Waveform Design -- 3.1 Pulse Radar -- 3.2 Pulse Coded Radar -- 3.3 FMCW Radar -- 4 Link Budget Analysis for FMCW Radar -- 4.1 Radar Equation -- 4.2 Target Reflectivity -- 4.3 Processing Gain -- 5 Challenges and Solutions for Automotive Radars -- 5.1 Interference -- 5.2 Under- and Overclustering -- 5.3 Classification -- 5.4 Lack of Resolution -- 5.5 Data Fusion -- 5.6 Radar Integration -- 6 Summary -- References -- Electrochemical Power Systems for Advanced Driver-Assistant Vehicles -- 1 Introduction -- 2 Batteries -- 2.1 Introduction -- 2.2 Types of Battery Cells -- 2.3 Battery Cell Internal Structure -- 2.4 Battery Cell Manufacturing Process -- 2.5 Chemistry Choices for Li-Ion Battery for EV Applications -- 2.6 Next-Generation Li-Ion Battery for EV Applications -- 2.7 Battery Management System -- 2.8 Battery Testing Methods and Industrial Standards -- 2.9 Battery Failure Mode and Effects Analysis (FMEA) -- 3 Fuel Cells -- 3.1 Major Types of Fuel Cells -- 3.2 Fuel Cells for EV Application -- 4 Capacitors -- 5 Summary -- References -- In-Vehicle Display Technology -- 1 Introduction -- 2 In-Vehicle Display Technologies and Architectures -- 2.1 LCD -- 2.2 TFT LCD -- 2.3 OLED -- 2.4 LED, Mini-/Micro-LED -- 2.5 Head-Up Display -- 2.6 Flexible and Free-Form -- 2.7 Touch Technology -- 3 In-Vehicle Display Requirements -- 3.1 Optical Performance Requirement -- 3.2 Appearance -- 3.3 Integration and Fabrication -- 3.4 Color Measurement and Characterization -- 3.5 Mura, Defect, Inspection, and Demura -- 3.6 Visibility in Bright Light and Complete Darkness -- 3.7 Improvement of Image and Touch Quality -- 3.8 Reliability and Durability -- 3.9 Functional Safety -- 4 In-Vehicle Display Challenges -- 4.1 Specification and Functionality Challenges. 327 $a4.2 Quality, Reliability, and Validation Challenges -- 4.3 EMC/EMI Challenges -- 4.4 ESD and High-Transient Voltage Challenges -- 5 Common LED LCD Reliability Testing Failure Modes and Effects Case Studies -- 5.1 FOS Spotlighting Failure Mechanism and Risk Assessment -- 5.2 BLU Film Buckling/Waving/Wrinkle Failure Mechanism Study -- 5.3 Metal Oxide TFT Panel-Level VGH and VGL Reliability Modeling -- 5.4 LCD Panel UV Irradiation Aging Reliability Modeling -- 5.5 Polarizer Edge Bleaching Failure Mechanism and Reliability Modeling -- 5.6 Free-Fall Object Impact Test and LCD Glass Crack Failure Risk Assessment -- 5.7 LED Lumen Degradation Reliability Modeling -- 6 Summary -- References -- Disk Drive for Data Center Storage -- 1 Introduction -- 2 Hard Disk Drive Application in Data Center -- 2.1 Data Storage for Autonomous Vehicle -- 2.2 Data Storage Configurations in Data Center -- 2.3 Hard Disk Drive Versus Solid-State Drive in Data Center -- 3 Hard Disk Drive Design -- 3.1 Hard Disk Drive System -- 3.2 Components in Recording Head -- 3.3 Next Generation Hard Disk Drive -- 4 Challenges in the Performance and Reliability -- 4.1 The Need for Higher Areal Data Density -- 4.2 Microwave-Assisted Magnetic Recording (MAMR) -- 4.3 Heat-Assisted Magnetic Recording (HAMR) -- 4.4 The Future of High-Volume Hard Disk Drive -- 5 Summary -- References -- Role and Responsibility of Hardware Reliability Engineer -- 1 Introduction -- 2 Risk Assessment Methodologies -- 2.1 Failure Mode and Effect Analysis (FMEA) -- 2.2 Fault Tree Analysis (FTA) -- 2.3 Stress-Strength Analysis -- 3 Accelerated Life Testing (ALT) and Highly Accelerated Life Testing (HALT) -- 3.1 Introduction -- 3.2 Identify Field Stress Factors -- 3.3 Determine Stress Levels -- 3.4 Acceleration Models and Acceleration Factor -- 3.5 Case Study -- 4 Reliability Statistics -- 4.1 Sample Size. 327 $a4.2 Life Distribution Analysis. 606 $aAutomated vehicles 606 $aDriver assistance systems 606 $aArtificial intelligence 615 0$aAutomated vehicles. 615 0$aDriver assistance systems. 615 0$aArtificial intelligence. 676 $a629.2 702 $aLi$b Yan 702 $aShi$b Hualiang 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996495560303316 996 $aAdvanced driver assistance systems and autonomous vehicles$93062554 997 $aUNISA LEADER 00860nam0 2200289 450 001 9910804888803321 005 20240216123328.0 010 $a88-238-0619-4 100 $a20240216d2000----km y0itay50 ba 101 0 $aita 102 $aIT 105 $a 001yy 200 1 $aInformation technology e gestione del cambiamento organizzativo$fRoberto Ravagnani 210 $aMilano$cEgea$d2000 215 $a290 p.$d24 cm 225 1 $aBiblioteca dell'economia d'azienda$v74 454 0$12001 610 0 $aAziende$aProcessi innovativi 676 $a658.4$v22$zita 700 1$aRavagnani,$bRoberto$0438025 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910804888803321 952 $aBON / RAV 1$b10883$fBFS 959 $aBFS 996 $aInformation technology e gestione del cambiamento organizzativo$968256 997 $aUNINA