Combustion Optimization Based On Computational Intelligence
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| Author | : Hao Zhou |
| Publisher | : Springer |
| Total Pages | : 291 |
| Release | : 2018-02-02 |
| Genre | : Technology & Engineering |
| ISBN | : 9811078750 |
This book presents the latest findings on the subject of combustion optimization based on computational intelligence. It covers a broad range of topics, including the modeling of coal combustion characteristics based on artificial neural networks and support vector machines. It also describes the optimization of combustion parameters using genetic algorithms or ant colony algorithms, an online coal optimization system, etc. Accordingly, the book offers a unique guide for researchers in the areas of combustion optimization, NOx emission control, energy and power engineering, and chemical engineering.
| Author | : Jihad Badra |
| Publisher | : Elsevier |
| Total Pages | : 260 |
| Release | : 2022-01-28 |
| Genre | : Technology & Engineering |
| ISBN | : 0323884571 |
Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines summarizes recent developments in Artificial Intelligence (AI)/Machine Learning (ML) and data driven optimization and calibration techniques for internal combustion engines. The book covers AI/ML and data driven methods to optimize fuel formulations and engine combustion systems, predict cycle to cycle variations, and optimize after-treatment systems and experimental engine calibration. It contains all the details of the latest optimization techniques along with their application to ICE, making it ideal for automotive engineers, mechanical engineers, OEMs and R&D centers involved in engine design. Provides AI/ML and data driven optimization techniques in combination with Computational Fluid Dynamics (CFD) to optimize engine combustion systems Features a comprehensive overview of how AI/ML techniques are used in conjunction with simulations and experiments Discusses data driven optimization techniques for fuel formulations and vehicle control calibration
| Author | : Jihad Badra |
| Publisher | : Elsevier |
| Total Pages | : 262 |
| Release | : 2022-01-05 |
| Genre | : Technology & Engineering |
| ISBN | : 032388458X |
Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines summarizes recent developments in Artificial Intelligence (AI)/Machine Learning (ML) and data driven optimization and calibration techniques for internal combustion engines. The book covers AI/ML and data driven methods to optimize fuel formulations and engine combustion systems, predict cycle to cycle variations, and optimize after-treatment systems and experimental engine calibration. It contains all the details of the latest optimization techniques along with their application to ICE, making it ideal for automotive engineers, mechanical engineers, OEMs and R&D centers involved in engine design. - Provides AI/ML and data driven optimization techniques in combination with Computational Fluid Dynamics (CFD) to optimize engine combustion systems - Features a comprehensive overview of how AI/ML techniques are used in conjunction with simulations and experiments - Discusses data driven optimization techniques for fuel formulations and vehicle control calibration
| Author | : Vassilis S. Kodogiannis |
| Publisher | : MDPI |
| Total Pages | : 116 |
| Release | : 2019-11-08 |
| Genre | : Technology & Engineering |
| ISBN | : 3039217607 |
Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.
| Author | : Hsiang-Chuan Liu |
| Publisher | : CRC Press |
| Total Pages | : 1477 |
| Release | : 2014-03-26 |
| Genre | : Computers |
| ISBN | : 1315775565 |
This proceedings set contains selected Computer, Information and Education Technology related papers from the 2014 International Conference on Computer, Intelligent Computing and Education Technology (CICET 2014), held March 27-28, 2014 in Hong Kong. The proceedings aims to provide a platform for researchers, engineers and academics as well as indu
| Author | : Yunmook Nah |
| Publisher | : Springer Nature |
| Total Pages | : 296 |
| Release | : 2020-09-21 |
| Genre | : Computers |
| ISBN | : 3030594130 |
The LNCS 12115 constitutes the workshop papers which were held also online in conjunction with the 25th International Conference on Database Systems for Advanced Applications in September 2020. The complete conference includes 119 full papers presented together with 19 short papers plus 15 demo papers and 4 industrial papers in this volume were carefully reviewed and selected from a total of 487 submissions. DASFAA 2020 presents this year following five workshops: The 7th International Workshop on Big Data Management and Service (BDMS 2020) The 6th International Symposium on Semantic Computing and Personalization (SeCoP 2020) The 5th Big Data Quality Management (BDQM 2020) The 4th International Workshop on Graph Data Management and Analysis (GDMA 2020) The 1st International Workshop on Artificial Intelligence for Data Engineering (AIDE 2020)
| Author | : Xingrang Liu |
| Publisher | : CRC Press |
| Total Pages | : 344 |
| Release | : 2016-08-19 |
| Genre | : Science |
| ISBN | : 1315354268 |
Thermal Power Plants: Modeling, Control, and Efficiency Improvement explains how to solve highly complex industry problems regarding identification, control, and optimization through integrating conventional technologies, such as modern control technology, computational intelligence-based multiobjective identification and optimization, distributed computing, and cloud computing with computational fluid dynamics (CFD) technology. Introducing innovative methods utilized in industrial applications, explored in scientific research, and taught at leading academic universities, this book: Discusses thermal power plant processes and process modeling, energy conservation, performance audits, efficiency improvement modeling, and efficiency optimization supported by high-performance computing integrated with cloud computing Shows how to simulate fossil fuel power plant real-time processes, including boiler, turbine, and generator systems Provides downloadable source codes for use in CORBA C++, MATLAB®, Simulink®, VisSim, Comsol, ANSYS, and ANSYS Fluent modeling software Although the projects in the text focus on industry automation in electrical power engineering, the methods can be applied in other industries, such as concrete and steel production for real-time process identification, control, and optimization.
| Author | : Tinghui Ouyang |
| Publisher | : Frontiers Media SA |
| Total Pages | : 628 |
| Release | : 2022-10-14 |
| Genre | : Technology & Engineering |
| ISBN | : 2832501419 |
| Author | : Ying Tan |
| Publisher | : Springer |
| Total Pages | : 495 |
| Release | : 2015-06-01 |
| Genre | : Computers |
| ISBN | : 3319204696 |
This book and its companion volumes, LNCS volumes 9140, 9141 and 9142, constitute the proceedings of the 6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the Second BRICS Congress on Computational Intelligence, CCI 2015, held in Beijing, China in June 2015. The 161 revised full papers presented were carefully reviewed and selected from 294 submissions. The papers are organized in 28 cohesive sections covering all major topics of swarm intelligence and computational intelligence research and development, such as novel swarm-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms; evolutionary and genetic algorithms; differential evolution; brain storm optimization algorithm; biogeography based optimization; cuckoo search; hybrid methods; multi-objective optimization; multi-agent systems and swarm robotics; Neural networks and fuzzy methods; data mining approaches; information security; automation control; combinatorial optimization algorithms; scheduling and path planning; machine learning; blind sources separation; swarm interaction behavior; parameters and system optimization; neural networks; evolutionary and genetic algorithms; fuzzy systems; forecasting algorithms; classification; tracking analysis; simulation; image and texture analysis; dimension reduction; system optimization; segmentation and detection system; machine translation; virtual management and disaster analysis.
| Author | : Derong Liu |
| Publisher | : Springer |
| Total Pages | : 667 |
| Release | : 2011-05-20 |
| Genre | : Computers |
| ISBN | : 3642211054 |
The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed proceedings of the 8th International Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011. The total of 215 papers presented in all three volumes were carefully reviewed and selected from 651 submissions. The contributions are structured in topical sections on computational neuroscience and cognitive science; neurodynamics and complex systems; stability and convergence analysis; neural network models; supervised learning and unsupervised learning; kernel methods and support vector machines; mixture models and clustering; visual perception and pattern recognition; motion, tracking and object recognition; natural scene analysis and speech recognition; neuromorphic hardware, fuzzy neural networks and robotics; multi-agent systems and adaptive dynamic programming; reinforcement learning and decision making; action and motor control; adaptive and hybrid intelligent systems; neuroinformatics and bioinformatics; information retrieval; data mining and knowledge discovery; and natural language processing.
