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Biblioteca

MARC Lista (tabellare)
Computational intelligence and modern heuristics / / edited by Ali Al-Dahoud
Computational intelligence and modern heuristics / / edited by Ali Al-Dahoud
Pubbl/distr/stampa [Place of publication not identified] : , : InTech, , [2010]
Descrizione fisica 1 online resource (350 pages) : illustrations
Disciplina 006.3
Soggetto topico Artificial intelligence - Industrial applications
ISBN 953-51-5799-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910138251803321
[Place of publication not identified] : , : InTech, , [2010]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Confluence of artificial intelligence and robotic process automation / / edited by Siddhartha Bhattacharyya, Jyoti Sekhar Banerjee, and Debashis De
Confluence of artificial intelligence and robotic process automation / / edited by Siddhartha Bhattacharyya, Jyoti Sekhar Banerjee, and Debashis De
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Singapore : , : Springer, , [2023]
Descrizione fisica 1 online resource (417 pages)
Disciplina 006.3
Collana Smart Innovation, Systems and Technologies
Soggetto topico Artificial intelligence - Industrial applications
ISBN 981-19-8296-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Intelligent Automation Framework using AI and RPA: An Introduction -- Chapter 2. Role of RPA in Intelligent Auditing -- Chapter 3. Impact of AI and RPA in Banking -- Chapter 4. Robotic Process Automation: The key to Reviving the Supply Chain Processes -- Chapter 5. Intelligent Document Processing in end-to-end RPA contexts: a systematic literature review -- Chapter 6. Challenges in Banking and Solving them using RPA -- Chapter 7. Robotic Process Automation in Healthcare -- Chapter 8. Intellectual Property Management in Healthcare Using Robotic Process Automation during Covid-19 -- Chapter 9. RPA Revolution in the Healthcare Industry during COVID-19 -- Chapter 10. Importance of Artificial Intelligence (AI) and Robotic process automation (RPA) in the Banking Industry: A study from an Indian perspective -- Chapter 11. Integration of RPA and AI in Industry 4.0 -- Chapter 12. A Comprehensive Review on Artificial Intelligence (AI) and Robotic Process Automation (RPA) for the development of Smart Cities -- Chapter 13. The Existing IT Functions and Robotics Process Automation -- Chapter 14. RPA Adoption in Healthcare Application -- Chapter 15. Cognitive IoT meets Robotic Process Automation: The Unique Convergence revolutionizing Digital Transformation in the Industry 4.0 era -- Chapter 16. Confluence of Artificial Intelligence and Robotic Process Automation: Concluding Remarks. .
Record Nr. UNINA-9910682590403321
Singapore : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Creative solutions for a sustainable development : 21st International TRIZ Future Conference, TFC 2021, Bolzano, Italy, September 22-24, 2021 : proceedings / / Yuri Borgianni [and three others] editors
Creative solutions for a sustainable development : 21st International TRIZ Future Conference, TFC 2021, Bolzano, Italy, September 22-24, 2021 : proceedings / / Yuri Borgianni [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (472 pages)
Disciplina 670.28563
Collana IFIP Advances in Information and Communication Technology
Soggetto topico Artificial intelligence - Industrial applications
Sustainable engineering
Sustainable development
ISBN 3-030-86614-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996464445203316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Creative solutions for a sustainable development : 21st International TRIZ Future Conference, TFC 2021, Bolzano, Italy, September 22-24, 2021 : proceedings / / Yuri Borgianni [and three others] editors
Creative solutions for a sustainable development : 21st International TRIZ Future Conference, TFC 2021, Bolzano, Italy, September 22-24, 2021 : proceedings / / Yuri Borgianni [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (472 pages)
Disciplina 670.28563
Collana IFIP Advances in Information and Communication Technology
Soggetto topico Artificial intelligence - Industrial applications
Sustainable engineering
Sustainable development
ISBN 3-030-86614-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910502648203321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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A Data-Driven Company : 21 Claves para Crear Valor a Través de Los Datos y la Inteligencia Artificial
A Data-Driven Company : 21 Claves para Crear Valor a Través de Los Datos y la Inteligencia Artificial
Autore Benjamins Richard
Edizione [1st ed.]
Pubbl/distr/stampa Madrid : , : Lid Editorial Empresarial S.L., , 2022
Descrizione fisica 1 online resource (221 pages)
Disciplina 658.4/038
Soggetto topico Business - Data processing - Management
Artificial intelligence - Industrial applications
Big data
ISBN 84-11-31423-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione spa
Record Nr. UNINA-9910860899803321
Benjamins Richard  
Madrid : , : Lid Editorial Empresarial S.L., , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Decision Making Using AI in Energy and Sustainability : Methods and Models for Policy and Practice / / Gülgün Kayakutlu and M. Özgür Kayalica, editors
Decision Making Using AI in Energy and Sustainability : Methods and Models for Policy and Practice / / Gülgün Kayakutlu and M. Özgür Kayalica, editors
Edizione [First edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, Springer Nature Switzerland AG, , [2023]
Descrizione fisica 1 online resource (306 pages)
Disciplina 333.79
Collana Applied Innovation and Technology Management Series
Soggetto topico Artificial intelligence - Environmental aspects
Artificial intelligence - Industrial applications
Power resources - Management - Data processing
Renewable energy sources - Data processing
ISBN 3-031-38387-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Contents -- Part I: Sustainability Policies -- Chapter 1: Climate Change - Can AI Help Understanding and More Effective Facing of Various Interrelated Impacts? -- 1.1 Introduction -- 1.2 Complexity of Climate Change: Comprehension of the Influencing Factors -- 1.2.1 Influencing Factors -- 1.2.1.1 Globalization -- 1.2.1.2 Innovation and Technology -- 1.2.2 Models, Components, Relations -- 1.3 Some Initiatives -- 1.4 Evaluation of Impacts -- 1.4.1 Simulation -- 1.4.2 Assessment -- 1.5 Conclusion and Perspective -- References -- Chapter 2: The European Green Deal and the 17 SDGs: Uncovering their Connection with a ML-based Approach -- 2.1 Introduction -- 2.1.1 The European Green Deal (EGD) -- 2.1.2 Energy-Related Policies Derived from the EGD -- 2.1.2.1 A New Industrial Strategy for Europe -- 2.1.2.2 EU Hydrogen Strategy -- 2.1.2.3 The Annual Sustainable Growth Strategy of 2021 (7 Technology Flagship Areas) -- 2.1.2.4 Chemicals Strategy for Sustainability -- 2.1.2.5 EU Strategy to Reduce Methane Emissions -- 2.1.2.6 A Renovation Wave for Europe -- 2.1.2.7 EU Commission Recommendation on Energy Poverty -- 2.1.2.8 EU Strategy to Harness the Potential of Offshore Renewable Energy for a Climate-Neutral Future -- 2.1.2.9 Smart Mobility Strategy -- 2.1.2.10 Updating the 2020 New Industrial Strategy: Building a Stronger Single Market for Europe´s Recovery -- 2.1.3 The European Green Deal and the 17 SDGs -- 2.2 Alignment Between Energy-Related Policies and the 17 SDGs -- 2.3 A Machine Learning Method to Evaluate the Connection Between Policy Documents and the 17 SDGs -- 2.3.1 Information Retrieval -- 2.4 Results 1 -- 2.4.1 Deep Learning -- 2.5 Results 2 -- 2.6 Discussion on the Results -- 2.7 Conclusions-Ideas for Further Research -- References.
Chapter 3: Single-Valued Neutrosophic CRITIC-Based ARAS Method for the Assessment of Sustainable Circular Supplier Selection -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Preliminaries -- 3.4 SVN-CRITIC-ARAS Method -- 3.5 Case Study: Evaluation of ``Sustainable Circular Supplier Selection (SCSS)´´ -- 3.5.1 Comparison and Discussion -- 3.5.1.1 SVN-TOPSIS Model -- 3.5.1.2 SVN-VIKOR Method -- 3.5.2 Managerial Implication -- 3.6 Conclusions -- References -- Part II: Climate Change -- Chapter 4: Linguistic-Based MCDM Approach for Climate Change Risk Evaluation Methodology -- 4.1 Introduction -- 4.2 Theoretical Backgrounds -- 4.2.1 Climate Change and Supply Chain Management in Academic Literature -- 4.2.2 Climate Change and Supply Chain Management in Industrial Reports -- 4.2.3 Climate Change and Supply Chain Risks -- 4.3 Suggested Methodology -- 4.4 Case Study -- 4.4.1 Results and Analysis -- 4.5 Managerial Implications -- 4.6 Concluding Remarks -- References -- Chapter 5: Creating a Net-Zero Carbon Emission Scenario Using OSeMOSYS for the Power Sector of Turkey -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Methodology -- 5.4 Proposed Model -- 5.5 Conclusion -- References -- Chapter 6: Prediction of Downward Surface Solar Radiation Using Particle Swarm Optimization and Neural Networks -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Methodology and Data -- 6.3.1 Methodology -- 6.3.2 Data -- 6.4 Results and Discussion -- 6.5 Conclusion -- References -- Part III: Sustainability Energy Markets -- Chapter 7: Electricity Demand Prediction: Case of Turkey -- 7.1 Introduction -- 7.2 Methods -- 7.2.1 Artificial Neural Networks (ANNs) -- 7.2.2 Multiple Linear Regression (MLR) -- 7.2.3 Autoregressive Integrated Moving Average Exogenous Variable Models (ARIMAX) -- 7.3 Case of Turkey -- 7.4 Comparison of Prediction Methods -- 7.5 Summary and Conclusion.
References -- Chapter 8: The Impact of the Wind Energy Power Forecast Accuracy on the Price of Electricity -- 8.1 Introduction -- 8.1.1 Literature Review -- 8.1.2 Data -- 8.2 Methodology -- 8.3 Results and Discussion -- 8.4 Conclusion -- References -- Chapter 9: The Power of Combination Models in Energy Demand Forecasting -- 9.1 Introduction -- 9.2 Methodology -- 9.2.1 Model Selection -- 9.2.1.1 Performance Metrics -- 9.2.1.2 Hypothesis Testing -- 9.2.1.3 Graphical Inspections -- 9.2.2 Combining Time Series Forecasts -- 9.3 Results and Discussion -- 9.4 Conclusion -- References -- Part IV: Energy Efficiency -- Chapter 10: Data-Driven State Classification for Energy Modeling of Machine Tools Using Power Signals and Part-Program Informa... -- 10.1 Introduction and Contribution -- 10.2 Energy Monitoring of Machine Tools and State Identification -- 10.2.1 Related Literature on Machine Energy Models -- 10.2.2 Challenges as in the Literature -- 10.3 Data-Driven Approach for State Classification -- 10.3.1 Data Pre-processing -- 10.3.2 MLAs for State Classification -- 10.4 Real Case Application -- 10.4.1 Case Description -- 10.4.2 Data Preparation and Pre-processing -- 10.4.3 MLA Classification Performance -- 10.4.4 Sensitivity Analysis -- 10.5 Conclusive Remarks -- References -- Chapter 11: Energy Efficiency Optimization Application in Food Production Using IIOT Based Machine Learning -- 11.1 Introduction -- 11.1.1 Challenges in Production -- 11.1.2 Need of Analytic in Manufacturing -- 11.1.3 Type of Analytic -- 11.2 Literature Review -- 11.3 Methodology -- 11.4 Problem Statement -- 11.5 Industrial Case Study -- 11.5.1 Overview -- 11.5.2 Data Operations -- 11.5.2.1 Linear Regression -- 11.5.2.2 XGBoost -- 11.5.2.3 Random Forest -- 11.5.3 Ensemble Model -- 11.5.4 Results -- 11.6 Conclusion -- References -- Part V: Smart Cities.
Chapter 12: Hype: A Data-Driven Tool for Smart City Profile (SCP) Discrimination -- 12.1 Introduction -- 12.2 Methodology -- 12.2.1 Modeling Smart City Profiles (SCP) -- 12.2.2 Computing Smart City Profiles (SCP) -- 12.2.2.1 Simplicial Complexes to Study Connectivity -- 12.2.2.2 Hype, a Tool to Compute Simplicial Complexes -- 12.3 Application -- 12.4 Conclusion -- References -- Chapter 13: An Integrated Hesitant Fuzzy Linguistic MCDM Methods to Assess Smart City Solutions -- 13.1 Introduction -- 13.2 The Research Subject: Smart City Concept and Smart City Solutions -- 13.2.1 Smart City Concept -- 13.2.2 The Proposed Smart City Model and Solutions -- 13.3 The Proposed Integrated Research Methodology -- 13.4 Application -- 13.5 Conclusion -- References -- Chapter 14: Presence of Renewable Resources in a Smart City for Supplying Clean and Sustainable Energy -- 14.1 Introduction -- 14.2 Renewable Resources and Sustainable Development -- 14.2.1 Energy Security -- 14.2.2 Socioeconomic Development -- 14.2.3 Energy Access -- 14.2.4 Climate Change -- 14.3 Smart Energy System -- 14.3.1 Smart Power Grid -- 14.3.2 Smart Thermal Grid -- 14.3.3 Smart Gas Grid -- 14.4 Smart Energy Network for Smart City -- 14.4.1 Solar Energy -- 14.4.1.1 Solar Water Heating -- 14.4.1.2 Seasonal Thermal Energy Storage (STES) System -- 14.4.2 Wind -- 14.4.3 Geothermal Energy -- 14.5 Conclusion -- References -- Chapter 15: Syrian Household Energy Consumption Behavior Analysis in Turkey: Bayesian Belief Network -- 15.1 Introduction -- 15.2 Literature Review -- 15.2.1 Main Drivers Shaping Energy Consumption Behavior -- 15.2.2 Studies Concerning Migrants -- 15.2.3 Bayesian Belief Network Applications on the Energy Consumption -- 15.3 Methods -- 15.3.1 Survey on Migrated Households -- 15.3.2 Bayesian Belief Network -- 15.4 Results and Discussions -- 15.5 Conclusions -- References.
Part VI: Modelling the Sustainable Future -- Chapter 16: Informativeness in Twitter Textual Contents for Farmer-Centric Pest Monitoring -- 16.1 Introduction -- 16.2 Related Works -- 16.2.1 Crowdsensing for Agriculture -- 16.2.2 NLP for Twitter-Based Crowdsensing -- 16.3 Use Cases and Methodology -- 16.3.1 Data Collection -- 16.3.2 Histogram by Mention of Keywords -- 16.3.3 Topic Detection Based on Bag of Word Models -- 16.3.4 Text Classification Based on Pre-trained Language Models -- 16.4 Conclusion -- References -- Chapter 17: A Multi-criteria Decision-Making Model for Technology Selection in Renewable-Based Residential Microgrids -- 17.1 Introduction -- 17.2 Literature Review: Renewable Energy Technology Selection from Sustainability Perspective -- 17.3 Methodology: AHP- and TOPSIS-Based Decision Support System for Technology Selection in Renewable-Based Residential Microg... -- 17.4 Application: A Renewable-Based Residential Microgrid in Antalya, Turkey -- 17.5 Analysis -- 17.6 Conclusion -- References -- Chapter 18: Energy Management in Power-Split Hybrid Electric Vehicles Using Fuzzy Logic Controller -- 18.1 Introduction -- 18.2 Energy Management and Control Strategy in Power-Split HEV Configuration -- 18.3 Fuzzy Controller Design for Energy Management -- 18.3.1 Fuzzification of Inputs -- 18.3.2 Fuzzy Inference System -- 18.3.3 Defuzzification of Output -- 18.4 Implementation of Fuzzy Controller in HEV Model Using AVL CRUISE -- 18.5 Simulation Results and Discussion -- 18.5.1 Eighty Percent Initial SOC Without Fuzzy Logic Controller (Case A) -- 18.5.2 Eighty Percent Initial SOC with Fuzzy Logic Controller (Case B) -- 18.5.3 Forty-Five Percent Initial SOC Without Fuzzy Logic Controller (Case C) -- 18.5.4 Forty-Five Percent Initial SOC With Fuzzy Logic Controller (Case D) -- 18.6 Conclusions -- References.
Record Nr. UNINA-9910751386403321
Cham, Switzerland : , : Springer, Springer Nature Switzerland AG, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Deep Dive into Power Automate : Learn by Example / / by Goloknath Mishra
Deep Dive into Power Automate : Learn by Example / / by Goloknath Mishra
Autore Mishra Goloknath
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023
Descrizione fisica 1 online resource (486 pages)
Disciplina 005.1
Soggetto topico Process control - Automation
Artificial intelligence - Industrial applications
ISBN 1-4842-9732-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: Introduction-. Chapter 2: How to Kick-start Using Power Automate -- Chapter 3: Cloud Flow. -- Chapter 4: Desktop Flow -- Chapter 5: Business Process Flow -- Chapter 6: Process Advisor -- Chapter 7: AI Builder -- Chapter 8: Licensing Considerations -- Chapter 9: Mini Project.
Record Nr. UNINA-9910770257203321
Mishra Goloknath  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Design and development of efficient energy systems / / editors, Suman Lata Tripathi [et al.]
Design and development of efficient energy systems / / editors, Suman Lata Tripathi [et al.]
Pubbl/distr/stampa Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Descrizione fisica 1 online resource (384 pages)
Disciplina 621.317
Collana Artificial Intelligence and Soft Computing for Industrial Transformation
Soggetto topico Artificial intelligence - Industrial applications
Electric power supplies to apparatus - Energy conservation - Data processing
Soggetto genere / forma Electronic books.
ISBN 1-5231-4332-0
1-119-76178-6
1-119-76177-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Design of Low Power Junction Less Double-Gate MOSFET -- 1.1 Introduction -- 1.2 MOSFET Performance Parameters -- 1.3 Comparison of Existing MOSFET Architectures -- 1.4 Proposed Heavily Doped Junction-Less Double Gate MOSFET (AJ-DGMOSFET) -- 1.5 Heavily Doped JL-DG MOSFET for Biomedical Application -- 1.6 Conclusion -- References -- 2 VLSI Implementation of Vedic Multiplier -- 2.1 Introduction -- 2.2 8x8 Vedic Multiplier -- 2.3 The Architecture of 8x8 Vedic Multiplier (VM) -- 2.3.1 Compressor Architecture -- 2.4 Results and Discussion -- 2.4.1 Instance Power -- 2.4.2 Net Power -- 2.4.3 8-Bit Multiplier -- 2.4.4 16-Bit Multiplier -- 2.4.5 Applications of Multiplier -- 2.5 Conclusion -- References -- 3 Gas Leakage Detection from Drainage to Offer Safety for Sanitary Workers -- 3.1 Introduction -- 3.1.1 IOT-Based Sewer Gas Detection -- 3.1.2 Objective -- 3.1.3 Contribution of this Chapter -- 3.1.4 Outline of the Chapter -- 3.2 Related Works -- 3.2.1 Sewer Gas Leakage Detection -- 3.2.2 Crack Detection -- 3.3 Methodology -- 3.3.1 Sewer Gas Detection -- 3.3.2 Crack Detection -- 3.3.3 Experimental Setup -- 3.4 Experimental Results -- 3.5 Conclusion -- References -- 4 Machine Learning for Smart Healthcare Energy-Efficient System -- 4.1 Introduction -- 4.1.1 IoT in the Digital Age -- 4.1.2 Using IoT to Enhance Healthcare Services -- 4.1.3 Edge Computing -- 4.1.4 Machine Learning -- 4.1.5 Application in Healthcare -- 4.2 Related Works -- 4.3 Edge Computing -- 4.3.1 Architecture -- 4.3.2 Advantages of Edge Computing over Cloud Computing -- 4.3.3 Applications of Edge Computing in Healthcare -- 4.3.4 Edge Computing Advantages -- 4.3.5 Challenges -- 4.4 Smart Healthcare System -- 4.4.1 Methodology -- 4.4.2 Data Acquisition and IoT End Device.
4.4.3 IoT End Device and Backend Server -- 4.5 Conclusion and Future Directions -- References -- 5 Review of Machine Learning Techniques Used for Intrusion and Malware Detection in WSNs and IoT Devices -- 5.1 Introduction -- 5.2 Types of Attacks -- 5.3 Some Countermeasures for the Attacks -- 5.4 Machine Learning Solutions -- 5.5 Machine Learning Algorithms -- 5.6 Authentication Process Based on Machine Learning -- 5.7 Internet of Things (IoT) -- 5.8 IoT-Based Attacks -- 5.8.1 Botnets -- 5.8.2 Man-in-the-Middle -- 5.9 Information and Identity Theft -- 5.10 Social Engineering -- 5.11 Denial of Service -- 5.12 Concerns -- 5.13 Conclusion -- References -- 6 Smart Energy-Efficient Techniques for Large-Scale Process Industries -- 6.1 Pumps Operation -- 6.1.1 Parts in a Centrifugal Pump -- 6.1.2 Pump Efficiency -- 6.1.3 VFD -- 6.1.4 VFD and Pump Motor -- 6.1.5 Large HT Motors -- 6.1.6 Smart Pumps -- 6.2 Vapour Absorption Refrigeration System -- 6.2.1 Vapour Compression Refrigeration -- 6.2.2 Vapour Absorption Refrigeration -- 6.3 Heat Recovery Equipment -- 6.3.1 Case Study -- 6.3.2 Advantages of Heat Recovery -- 6.4 Lighting System -- 6.4.1 Technical Terms -- 6.4.2 Introduction -- 6.4.3 LED Lighting -- 6.4.4 Energy-Efficiency Techniques -- 6.4.5 Light Control with IoT -- 6.4.6 EU Practices -- 6.5 Air Conditioners -- 6.5.1 Technical Terms -- 6.5.2 Types of Air Conditioners -- 6.5.3 Star Rating of BEE -- 6.5.4 EU Practices -- 6.5.5 Energy-Efficiency Tips -- 6.5.6 Inverter Air Conditioners -- 6.5.7 IoT-Based Air Conditioners -- 6.6 Fans and Other Smart Appliances -- 6.6.1 BLDC Fan Motors -- 6.6.2 Star Ratings -- 6.6.3 Group Drive of Fans -- 6.6.4 Other Smart Appliances -- 6.7 Motors -- 6.7.1 Motor Efficiency -- 6.7.2 Underrated Operation -- 6.7.3 Energy-Efficient Motors -- 6.7.4 Retrofit of Standard Motors with Energy-Efficient Motors.
6.7.5 Other Salient Points -- 6.7.6 Use of Star-Delta Starter Motor -- 6.8 Energy-Efficient Transformers -- 6.8.1 IEC Recommendation -- 6.8.2 Super Conducting Transformers -- References -- 7 Link Restoration and Relay Node Placement in Partitioned Wireless Sensor Network -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 Existing Techniques -- 7.3 Proposed K-Means Clustering Algorithm -- 7.3.1 Homogenous and Heterogeneous Network Clustering Algorithms -- 7.3.2 Dynamic and Static Clustering -- 7.3.3 Flow Diagram -- 7.3.4 Objective Function -- 7.4 System Model and Assumption -- 7.4.1 Simulation Parameters -- 7.5 Results and Discussion -- 7.6 Conclusions -- References -- 8 Frequency Modulated PV Powered MLI Fed Induction Motor Drive for Water Pumping Applications -- 8.1 Introduction -- 8.2 PV Panel as Energy Source -- 8.2.1 Solar Cell -- 8.3 Multi-Level Inverter Topologies -- 8.3.1 Types of Inverters Used for Drives -- 8.3.2 Multi-Level Inverters -- 8.4 Experimental Results and Discussion -- 8.4.1 PV Powered H Bridge Inverter-Fed Drive -- 8.4.2 PV Powered DCMLI Fed Drive -- 8.5 Conclusion and Future Scope -- References -- 9 Analysis and Design of Bidirectional Circuits for Energy Storage Application -- 9.1 Introduction -- 9.2 Modes of Operation Based on Main Converters -- 9.2.1 Single-Stage Rectification -- 9.2.2 Single-Stage Inversion -- 9.2.3 Double-Stage Rectification -- 9.2.4 Double-Stage Inversion -- 9.3 Proposed Methodology for Three-Phase System -- 9.3.1 Control Block of Overall System -- 9.3.2 Proposed Carrier-Based PWM Strategy -- 9.3.3 Experiment Results -- 9.4 Conclusion -- References -- 10 Low-Power IOT-Enabled Energy Systems -- 10.1 Overview -- 10.1.1 Conceptions -- 10.1.2 Motivation -- 10.1.3 Methodology -- 10.2 Empowering Tools -- 10.2.1 Sensing Components -- 10.2.2 Movers -- 10.2.3 Telecommunication Technology.
10.2.4 Internet of Things Information and Evaluation -- 10.3 Internet of Things within Power Region -- 10.3.1 Internet of Things along with Vitality Production -- 10.3.2 Smart Metropolises -- 10.3.3 Intelligent Lattice Network -- 10.3.4 Smart Buildings Structures -- 10.3.5 Powerful Usage of Vitality in Production -- 10.3.6 Insightful Transport -- 10.4 Difficulties Relating Internet of Things -- 10.4.1 Vitality Ingestion -- 10.4.2 Synchronization via Internet of Things through Sub-Units -- 10.4.3 Client Confidentiality -- 10.4.4 Safety Challenges -- 10.4.5 IoT Standardization and Architectural Concept -- 10.5 Upcoming Developments -- 10.5.1 IoT and Block Chain -- 10.5.2 Artificial Intelligence and IoT -- 10.5.3 Green IoT -- 10.6 Conclusion -- References -- 11 Efficient Renewable Energy Systems -- Introduction -- 11.1 Renewable-Based Available Technologies -- 11.1.1 Wind Power -- 11.1.2 Solar Power -- 11.1.3 Tidal Energy -- 11.1.4 Battery Storage System -- 11.1.5 Solid Oxide Energy Units for Enhancing Power Life -- 11.2 Adaptability Frameworks -- 11.2.1 Distributed Energy Resources (DER) -- 11.2.2 New Age Grid Connection -- 11.3 Conclusion -- References -- 12 Efficient Renewable Energy Systems -- 12.1 Introduction -- 12.1.1 World Energy Scenario -- 12.2 Sources of Energy: Classification -- 12.3 Renewable Energy Systems -- 12.3.1 Solar Energy -- 12.3.2 Wind -- 12.3.3 Geothermal -- 12.3.4 Biomass -- 12.3.5 Ocean -- 12.3.6 Hydrogen -- 12.4 Solar Energy -- 12.5 Wind Energy -- 12.6 Geothermal Energy -- 12.7 Biomass -- 12.7.1 Forms of Biomass -- 12.8 Ocean Power -- 12.9 Hydrogen -- 12.10 Hydro Power -- 12.11 Conclusion -- References -- 13 Agriculture-IoT-Based Sprinkler System for Water and Fertilizer Conservation and Management -- 13.1 Introduction -- 13.1.1 Novelty of the Work -- 13.1.2 Benefit to Society -- 13.2 Development of the Proposed System.
13.3 System Description -- 13.3.1 Study of the Crop Under Experiment -- 13.3.2 Hardware of the System -- 13.3.3 Software of the System -- 13.4 Layers of the System Architecture -- 13.4.1 Application Layer -- 13.4.2 Cloud Layer -- 13.4.3 Network Layer -- 13.4.4 Physical Layer -- 13.5 Calibration -- 13.6 Layout of the Sprinkler System -- 13.7 Testing -- 13.8 Results and Discussion -- 13.9 Conclusion -- References -- 14 A Behaviour-Based Authentication to Internet of Things Using Machine Learning -- 14.1 Introduction -- 14.2 Basics of Internet of Things (IoT) -- 14.2.1 The IoT Reference Model -- 14.2.2 Working of IoT -- 14.2.3 Utilization of Internet of Things (IoT) -- 14.3 Authentication in IoT -- 14.3.1 Methods of Authentication -- 14.4 User Authentication Based on Behavioral-Biometric -- 14.4.1 Machine Learning -- 14.4.2 Machine Learning Algorithms -- 14.5 Threats and Challenges in the Current Security Solution for IoT -- 14.6 Proposed Methodology -- 14.6.1 Collection of Gait Dataset -- 14.6.2 Gait Data Preprocessing -- 14.6.3 Reduction in Data Size -- 14.6.4 Gaits Feature -- 14.6.5 Classification -- 14.7 Conclusion and Future Work -- References -- 15 A Fuzzy Goal Programming Model for Quality Monitoring of Fruits during Shipment Overseas -- 15.1 Introduction -- 15.2 Proposed System -- 15.2.1 Problem Statement -- 15.2.2 Overview -- 15.2.3 System Components -- 15.3 Work Process -- 15.3.1 System Hardware -- 15.3.2 Connections and Circuitry -- 15.4 Optimization Framework -- 15.4.1 Fuzzy Goal Description -- 15.4.2 Characterizing Fuzzy Membership Function -- 15.4.3 Construction of FGP Model -- 15.4.4 Definition of Variables and Parameters -- 15.4.5 Fuzzy Goal Description -- 15.5 Creation of Database and Website -- 15.5.1 Hosting PHP Application and Creation of MySQL Database -- 15.5.2 Creation of API (Application Programming Interfaces) Key.
15.6 Libraries Used and Code Snipped.
Record Nr. UNINA-9910555117803321
Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Design and development of efficient energy systems / / editors, Suman Lata Tripathi [et al.]
Design and development of efficient energy systems / / editors, Suman Lata Tripathi [et al.]
Pubbl/distr/stampa Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Descrizione fisica 1 online resource (384 pages)
Disciplina 621.317
Collana Artificial Intelligence and Soft Computing for Industrial Transformation
Soggetto topico Artificial intelligence - Industrial applications
Electric power supplies to apparatus - Energy conservation - Data processing
ISBN 1-5231-4332-0
1-119-76178-6
1-119-76177-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Design of Low Power Junction Less Double-Gate MOSFET -- 1.1 Introduction -- 1.2 MOSFET Performance Parameters -- 1.3 Comparison of Existing MOSFET Architectures -- 1.4 Proposed Heavily Doped Junction-Less Double Gate MOSFET (AJ-DGMOSFET) -- 1.5 Heavily Doped JL-DG MOSFET for Biomedical Application -- 1.6 Conclusion -- References -- 2 VLSI Implementation of Vedic Multiplier -- 2.1 Introduction -- 2.2 8x8 Vedic Multiplier -- 2.3 The Architecture of 8x8 Vedic Multiplier (VM) -- 2.3.1 Compressor Architecture -- 2.4 Results and Discussion -- 2.4.1 Instance Power -- 2.4.2 Net Power -- 2.4.3 8-Bit Multiplier -- 2.4.4 16-Bit Multiplier -- 2.4.5 Applications of Multiplier -- 2.5 Conclusion -- References -- 3 Gas Leakage Detection from Drainage to Offer Safety for Sanitary Workers -- 3.1 Introduction -- 3.1.1 IOT-Based Sewer Gas Detection -- 3.1.2 Objective -- 3.1.3 Contribution of this Chapter -- 3.1.4 Outline of the Chapter -- 3.2 Related Works -- 3.2.1 Sewer Gas Leakage Detection -- 3.2.2 Crack Detection -- 3.3 Methodology -- 3.3.1 Sewer Gas Detection -- 3.3.2 Crack Detection -- 3.3.3 Experimental Setup -- 3.4 Experimental Results -- 3.5 Conclusion -- References -- 4 Machine Learning for Smart Healthcare Energy-Efficient System -- 4.1 Introduction -- 4.1.1 IoT in the Digital Age -- 4.1.2 Using IoT to Enhance Healthcare Services -- 4.1.3 Edge Computing -- 4.1.4 Machine Learning -- 4.1.5 Application in Healthcare -- 4.2 Related Works -- 4.3 Edge Computing -- 4.3.1 Architecture -- 4.3.2 Advantages of Edge Computing over Cloud Computing -- 4.3.3 Applications of Edge Computing in Healthcare -- 4.3.4 Edge Computing Advantages -- 4.3.5 Challenges -- 4.4 Smart Healthcare System -- 4.4.1 Methodology -- 4.4.2 Data Acquisition and IoT End Device.
4.4.3 IoT End Device and Backend Server -- 4.5 Conclusion and Future Directions -- References -- 5 Review of Machine Learning Techniques Used for Intrusion and Malware Detection in WSNs and IoT Devices -- 5.1 Introduction -- 5.2 Types of Attacks -- 5.3 Some Countermeasures for the Attacks -- 5.4 Machine Learning Solutions -- 5.5 Machine Learning Algorithms -- 5.6 Authentication Process Based on Machine Learning -- 5.7 Internet of Things (IoT) -- 5.8 IoT-Based Attacks -- 5.8.1 Botnets -- 5.8.2 Man-in-the-Middle -- 5.9 Information and Identity Theft -- 5.10 Social Engineering -- 5.11 Denial of Service -- 5.12 Concerns -- 5.13 Conclusion -- References -- 6 Smart Energy-Efficient Techniques for Large-Scale Process Industries -- 6.1 Pumps Operation -- 6.1.1 Parts in a Centrifugal Pump -- 6.1.2 Pump Efficiency -- 6.1.3 VFD -- 6.1.4 VFD and Pump Motor -- 6.1.5 Large HT Motors -- 6.1.6 Smart Pumps -- 6.2 Vapour Absorption Refrigeration System -- 6.2.1 Vapour Compression Refrigeration -- 6.2.2 Vapour Absorption Refrigeration -- 6.3 Heat Recovery Equipment -- 6.3.1 Case Study -- 6.3.2 Advantages of Heat Recovery -- 6.4 Lighting System -- 6.4.1 Technical Terms -- 6.4.2 Introduction -- 6.4.3 LED Lighting -- 6.4.4 Energy-Efficiency Techniques -- 6.4.5 Light Control with IoT -- 6.4.6 EU Practices -- 6.5 Air Conditioners -- 6.5.1 Technical Terms -- 6.5.2 Types of Air Conditioners -- 6.5.3 Star Rating of BEE -- 6.5.4 EU Practices -- 6.5.5 Energy-Efficiency Tips -- 6.5.6 Inverter Air Conditioners -- 6.5.7 IoT-Based Air Conditioners -- 6.6 Fans and Other Smart Appliances -- 6.6.1 BLDC Fan Motors -- 6.6.2 Star Ratings -- 6.6.3 Group Drive of Fans -- 6.6.4 Other Smart Appliances -- 6.7 Motors -- 6.7.1 Motor Efficiency -- 6.7.2 Underrated Operation -- 6.7.3 Energy-Efficient Motors -- 6.7.4 Retrofit of Standard Motors with Energy-Efficient Motors.
6.7.5 Other Salient Points -- 6.7.6 Use of Star-Delta Starter Motor -- 6.8 Energy-Efficient Transformers -- 6.8.1 IEC Recommendation -- 6.8.2 Super Conducting Transformers -- References -- 7 Link Restoration and Relay Node Placement in Partitioned Wireless Sensor Network -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 Existing Techniques -- 7.3 Proposed K-Means Clustering Algorithm -- 7.3.1 Homogenous and Heterogeneous Network Clustering Algorithms -- 7.3.2 Dynamic and Static Clustering -- 7.3.3 Flow Diagram -- 7.3.4 Objective Function -- 7.4 System Model and Assumption -- 7.4.1 Simulation Parameters -- 7.5 Results and Discussion -- 7.6 Conclusions -- References -- 8 Frequency Modulated PV Powered MLI Fed Induction Motor Drive for Water Pumping Applications -- 8.1 Introduction -- 8.2 PV Panel as Energy Source -- 8.2.1 Solar Cell -- 8.3 Multi-Level Inverter Topologies -- 8.3.1 Types of Inverters Used for Drives -- 8.3.2 Multi-Level Inverters -- 8.4 Experimental Results and Discussion -- 8.4.1 PV Powered H Bridge Inverter-Fed Drive -- 8.4.2 PV Powered DCMLI Fed Drive -- 8.5 Conclusion and Future Scope -- References -- 9 Analysis and Design of Bidirectional Circuits for Energy Storage Application -- 9.1 Introduction -- 9.2 Modes of Operation Based on Main Converters -- 9.2.1 Single-Stage Rectification -- 9.2.2 Single-Stage Inversion -- 9.2.3 Double-Stage Rectification -- 9.2.4 Double-Stage Inversion -- 9.3 Proposed Methodology for Three-Phase System -- 9.3.1 Control Block of Overall System -- 9.3.2 Proposed Carrier-Based PWM Strategy -- 9.3.3 Experiment Results -- 9.4 Conclusion -- References -- 10 Low-Power IOT-Enabled Energy Systems -- 10.1 Overview -- 10.1.1 Conceptions -- 10.1.2 Motivation -- 10.1.3 Methodology -- 10.2 Empowering Tools -- 10.2.1 Sensing Components -- 10.2.2 Movers -- 10.2.3 Telecommunication Technology.
10.2.4 Internet of Things Information and Evaluation -- 10.3 Internet of Things within Power Region -- 10.3.1 Internet of Things along with Vitality Production -- 10.3.2 Smart Metropolises -- 10.3.3 Intelligent Lattice Network -- 10.3.4 Smart Buildings Structures -- 10.3.5 Powerful Usage of Vitality in Production -- 10.3.6 Insightful Transport -- 10.4 Difficulties Relating Internet of Things -- 10.4.1 Vitality Ingestion -- 10.4.2 Synchronization via Internet of Things through Sub-Units -- 10.4.3 Client Confidentiality -- 10.4.4 Safety Challenges -- 10.4.5 IoT Standardization and Architectural Concept -- 10.5 Upcoming Developments -- 10.5.1 IoT and Block Chain -- 10.5.2 Artificial Intelligence and IoT -- 10.5.3 Green IoT -- 10.6 Conclusion -- References -- 11 Efficient Renewable Energy Systems -- Introduction -- 11.1 Renewable-Based Available Technologies -- 11.1.1 Wind Power -- 11.1.2 Solar Power -- 11.1.3 Tidal Energy -- 11.1.4 Battery Storage System -- 11.1.5 Solid Oxide Energy Units for Enhancing Power Life -- 11.2 Adaptability Frameworks -- 11.2.1 Distributed Energy Resources (DER) -- 11.2.2 New Age Grid Connection -- 11.3 Conclusion -- References -- 12 Efficient Renewable Energy Systems -- 12.1 Introduction -- 12.1.1 World Energy Scenario -- 12.2 Sources of Energy: Classification -- 12.3 Renewable Energy Systems -- 12.3.1 Solar Energy -- 12.3.2 Wind -- 12.3.3 Geothermal -- 12.3.4 Biomass -- 12.3.5 Ocean -- 12.3.6 Hydrogen -- 12.4 Solar Energy -- 12.5 Wind Energy -- 12.6 Geothermal Energy -- 12.7 Biomass -- 12.7.1 Forms of Biomass -- 12.8 Ocean Power -- 12.9 Hydrogen -- 12.10 Hydro Power -- 12.11 Conclusion -- References -- 13 Agriculture-IoT-Based Sprinkler System for Water and Fertilizer Conservation and Management -- 13.1 Introduction -- 13.1.1 Novelty of the Work -- 13.1.2 Benefit to Society -- 13.2 Development of the Proposed System.
13.3 System Description -- 13.3.1 Study of the Crop Under Experiment -- 13.3.2 Hardware of the System -- 13.3.3 Software of the System -- 13.4 Layers of the System Architecture -- 13.4.1 Application Layer -- 13.4.2 Cloud Layer -- 13.4.3 Network Layer -- 13.4.4 Physical Layer -- 13.5 Calibration -- 13.6 Layout of the Sprinkler System -- 13.7 Testing -- 13.8 Results and Discussion -- 13.9 Conclusion -- References -- 14 A Behaviour-Based Authentication to Internet of Things Using Machine Learning -- 14.1 Introduction -- 14.2 Basics of Internet of Things (IoT) -- 14.2.1 The IoT Reference Model -- 14.2.2 Working of IoT -- 14.2.3 Utilization of Internet of Things (IoT) -- 14.3 Authentication in IoT -- 14.3.1 Methods of Authentication -- 14.4 User Authentication Based on Behavioral-Biometric -- 14.4.1 Machine Learning -- 14.4.2 Machine Learning Algorithms -- 14.5 Threats and Challenges in the Current Security Solution for IoT -- 14.6 Proposed Methodology -- 14.6.1 Collection of Gait Dataset -- 14.6.2 Gait Data Preprocessing -- 14.6.3 Reduction in Data Size -- 14.6.4 Gaits Feature -- 14.6.5 Classification -- 14.7 Conclusion and Future Work -- References -- 15 A Fuzzy Goal Programming Model for Quality Monitoring of Fruits during Shipment Overseas -- 15.1 Introduction -- 15.2 Proposed System -- 15.2.1 Problem Statement -- 15.2.2 Overview -- 15.2.3 System Components -- 15.3 Work Process -- 15.3.1 System Hardware -- 15.3.2 Connections and Circuitry -- 15.4 Optimization Framework -- 15.4.1 Fuzzy Goal Description -- 15.4.2 Characterizing Fuzzy Membership Function -- 15.4.3 Construction of FGP Model -- 15.4.4 Definition of Variables and Parameters -- 15.4.5 Fuzzy Goal Description -- 15.5 Creation of Database and Website -- 15.5.1 Hosting PHP Application and Creation of MySQL Database -- 15.5.2 Creation of API (Application Programming Interfaces) Key.
15.6 Libraries Used and Code Snipped.
Record Nr. UNINA-9910830773703321
Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Designing intelligent construction projects / / Michael Frahm and Carola Roll
Designing intelligent construction projects / / Michael Frahm and Carola Roll
Autore Frahm Michael
Pubbl/distr/stampa Hoboken, New Jersey ; ; Chichester, West Sussex : , : Wiley Blackwell, , [2022]
Descrizione fisica 1 online resource (259 pages)
Disciplina 006.3
Soggetto topico Artificial intelligence - Industrial applications
Construction industry - Technological innovations
Cybernetics
ISBN 1-119-69069-2
1-119-69084-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgements -- About the Authors -- Chapter 1 Complexity, Cybernetics, and Dynamics -- 1.1 Complexity -- 1.1.1 Complexity in the Mathematical Sciences -- 1.1.2 Complexity in Sociology -- 1.1.3 Complexity in Management -- 1.1.4 Complexity in Construction Management -- 1.1.5 How to Cope with Complexity -- 1.1.6 Interaction and Autopoiesis -- 1.2 Viable System Model -- 1.2.1 The Static Perspective on the VSM -- 1.2.1.1 System 1: Operation -- 1.2.1.2 System 2: Coordination -- 1.2.1.3 System 3: Operational Management -- 1.2.1.4 System 3*: Monitoring/Audit -- 1.2.1.5 System 4: Strategic Management -- 1.2.1.6 System 5: Policy -- 1.2.2 Ashby's Variety -- 1.2.2.1 The Variety Number -- 1.2.2.2 The Degree of Variety -- 1.2.3 The Dynamic Perspective on the VSM -- 1.2.3.1 Variety Balance 1: Workload -- 1.2.3.2 Variety Balance 2: Line Balancing -- 1.2.3.3 Variety Balance 3: Autonomy vs. Cohesion -- 1.2.3.4 Variety Balance 4: Change Rate -- 1.2.3.5 Variety Balance 5: Change vs. Status Quo -- 1.3 Modelling with the Viable System Model -- 1.3.1 Modelling Steps -- 1.3.2 Create a VSM Model Using an Example -- 1.4 System Dynamics -- 1.4.1 Systemic Archetypes -- 1.4.2 Modelling with System Dynamics -- 1.4.3 Example: Managing Risks with System Dynamics -- 1.5 Findings, Criticism, and Reflective Questions -- 1.5.1 Findings -- 1.5.2 Criticism -- 1.5.3 Reflective Questions -- Chapter 2 Lean Management and Lean Construction -- 2.1 Pioneers of Lean Management -- 2.2 Toyota Production System and Tools -- 2.2.1 Waste, Kanban, and Just‐in‐time Principle -- 2.2.2 Jidoka and Related Elements -- 2.2.3 Heijunka -- 2.2.4 Single Minute Exchange of Die (SMED) -- 2.2.5 Kaizen and Standards -- 2.3 Lean Management and Its Principles -- 2.3.1 Resource and Flow Efficiency -- 2.3.2 Examples for Resource and Flow Efficiency.
2.3.2.1 The Machine and Plant Manufacturer -- 2.3.2.2 The Vacation Flight -- 2.3.2.3 The Healthcare System -- 2.3.2.4 The Automotive Industry -- 2.3.3 Four Important Principles -- 2.3.3.1 Flow Principle -- 2.3.3.2 Takt Principle -- 2.3.3.3 Pull Principle -- 2.3.3.4 Zero‐defect Principle -- 2.3.4 Lean Leadership -- 2.3.4.1 Excursion: Kata -- 2.4 Lean Construction and Tools -- 2.4.1 Last Planner System -- 2.4.1.1 Milestone Planning -- 2.4.1.2 Collaborative Programming -- 2.4.1.3 Making Ready -- 2.4.1.4 Production Planning -- 2.4.1.5 Production Management and Learning -- 2.4.2 Takt Planning and Control -- 2.4.2.1 Takt Planning -- 2.4.2.2 Takt Control -- 2.4.3 Last Planner System and Takt Planning and Control -- 2.4.4 Lean Construction Case Study -- 2.4.4.1 Takt Planning -- 2.4.4.2 Takt Control -- 2.5 Tools, Tools, Tools -- 2.5.1 First‐run Study -- 2.5.1.1 Phase Plan -- 2.5.1.2 Phase Do -- 2.5.1.3 Phase Study -- 2.5.1.4 Phase Adjust -- 2.5.2 Waste Walks -- 2.5.2.1 5 Why and 6W Questioning Technique -- 2.5.3 Ishikawa Diagram -- 2.5.4 A3 Method and Report -- 2.5.5 Visual Management -- 2.5.6 5S/5A -- 2.5.6.1 Seiri - Sort -- 2.5.6.2 Seiton - Set in Order -- 2.5.6.3 Seiso - Shine -- 2.5.6.4 Seiketsu - Standardise -- 2.5.6.5 Shitsuke - Sustain -- 2.5.7 Plus/Delta Review -- 2.5.8 Big Room -- 2.6 Practice Insights from Martin Jäntschke -- 2.6.1 Infrastructure Railway - Introduction of Lean Construction in Large Projects -- 2.6.2 Implementing Change in an Infrastructure Organisation -- 2.6.3 Conclusion -- 2.6.3.1 To Section -- 2.6.3.2 To Section -- 2.7 Findings, Criticism, and Reflective Questions -- 2.7.1 Findings -- 2.7.2 Criticism -- 2.7.3 Reflective Questions -- Chapter 3 Cybernetics and Lean -- 3.1 VSM and Lean (Construction) Thinking -- 3.2 Mapping the Viable System Model with Lean Construction Methods -- 3.2.1 Mapping VSM and the Last Planner System.
3.2.2 Mapping VSM and Takt Planning and Control -- 3.2.3 Mapping Information Channels and Lean Construction -- 3.3 Mapping the Viable System Model with Lean Management Methods -- 3.4 Performance Measurement -- 3.4.1 General Measurement -- 3.4.2 Lean Measurement Construction -- 3.4.3 Beers' Triple -- 3.5 Case Studies and Practice Insights -- 3.5.1 Case Study: Planning Project -- 3.5.2 Case Study: Major Project (Planning and Execution) -- 3.5.2.1 Design Phase and Approval Phase -- 3.5.2.2 Tendering and Awarding Phase -- 3.5.2.3 Construction Phase -- 3.5.3 Case Study: Megaproject (Execution)6 -- 3.5.3.1 Boundary Conditions -- 3.5.3.2 Analysis of the Megaproject -- 3.5.3.3 Section Analysis -- 3.5.4 Practice Insights from a Medium‐sized Mechanical Engineering Company -- 3.5.4.1 Challenges for the Industry -- 3.5.4.2 The Solution: The Creation of a Hybrid Corporate Form Based on the VSM -- 3.5.4.3 From Theory to Practice: The Organisational Structure -- 3.5.4.4 Levels of Complexity -- 3.5.4.5 Process Organisation -- 3.5.4.6 Role Profiles -- 3.5.4.7 Organiplastic as a Base for the Management Cockpit -- 3.5.4.8 Conclusion -- 3.5.4.9 Adaptability -- 3.6 Findings, Criticism, and Reflective Questions -- 3.6.1 Findings -- 3.6.2 Criticism -- 3.6.3 Critical Reflection to Practice Insights from a Medium‐sized Mechanical Engineering Company -- 3.6.4 Reflective Questions -- Chapter 4 Beyond Cybernetics and Lean -- 4.1 Control, Regulate, Steer -- 4.2 Self‐organisation -- 4.3 Viable, Lean, … and What About Agile? -- 4.4 Digital Transformation -- 4.5 Phases of Digital Change -- 4.6 Digitalisation in the Construction Industry -- 4.6.1 Status Quo -- 4.6.2 Phase 1: BIM, VR, AR, MR -- 4.6.3 Phase 2: Intelligent Project Management -- 4.6.4 Phase 3: Artificial Intelligence in Construction -- 4.6.5 Phase 4: Autonomous Project Management -- 4.7 Changing the Game.
4.7.1 Nudge Management -- 4.7.2 Tit for Tat -- 4.8 Partnering -- 4.9 Success Patterns in Projects -- 4.10 Findings, Criticism, and Reflective Questions -- 4.10.1 Findings -- 4.10.2 Criticism -- 4.10.3 Reflective Questions -- Chapter 5 Summary and Closing Remarks -- 5.1 Complexity, Cybernetics, and Dynamics -- 5.2 Lean Management and Lean Construction -- 5.3 Cybernetic and Lean -- 5.4 Beyond Cybernetic and Lean -- References -- Glossary -- List of Figures -- List of Tables -- List of Equations -- List of Abbreviations -- Index -- EULA.
Record Nr. UNINA-9910830959903321
Frahm Michael  
Hoboken, New Jersey ; ; Chichester, West Sussex : , : Wiley Blackwell, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui