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Electro hydraulic Control Theory and Its Applications under Extreme Environment not only presents an overview on the topic, but also delves into the fundamental mathematic models of electro hydraulic control and the application of key hydraulic components under extreme environments. The book contains chapters on hydraulic system design, including thermal analysis on hydraulic power systems in aircraft, power matching designs of hydraulic rudder, and flow matching control of asymmetric valves and cylinders. With additional coverage on new devices, experiments and application technologies, this book is an ideal reference on the research and development of significant equipment. - Addresses valves' application in aircrafts, including servo valves, relief valves and pressure reducing valves - Presents a qualitative and quantitative forecast of future electro-hydraulic servo systems, service performance, and mechanization in harsh environments - Provides analysis methods, mathematical models and optimization design methods of electro-hydraulic servo valves under extreme environments
This thesis deals with innovative working hydraulic systems for mobile machines. Flow control systems are studied as an alternative to load sensing. The fundamental difference is that the pump is controlled based on the operator’s command signals rather than feedback signals from the loads. This control approach enables higher energy efficiency and there is no load pressure feedback causing stability issues. Experimental results show a reduced pump pressure margin and energy saving potential for a wheel loader application. The damping contribution from the inlet and outlet orifice in directional valves is studied. Design rules are developed and verified by experiments. A novel system architecture is proposed where flow control, load sensing and open-centre are merged into a generalized system description. The proposed system is configurable and the operator can realize the characteristics of any of the standard systems without compromising energy efficiency. This can be done non-discretely on-the-fly. Experiments show that it is possible to avoid unnecessary energy losses while improving system response and increasing stability margins compared to load sensing. Static and dynamic differences between different control modes are also demonstrated experimentally.
Modern optimization approaches have attracted an increasing number of scientists, decision makers, and researchers. As new issues in this field emerge, different optimization methodologies must be developed and implemented. Exploring Critical Approaches of Evolutionary Computation is a vital scholarly publication that explores the latest developments, methods, approaches, and applications of evolutionary models in a variety of fields. It also emphasizes evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, genetic programming, and related fields such as swarm intelligence and other evolutionary computation techniques. Highlighting a range of pertinent topics such as neural networks, data mining, and data analytics, this book is designed for IT developers, IT theorists, computer engineers, researchers, practitioners, and upper-level students seeking current research on enhanced information exchange methods and practical aspects of computational systems.
This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first.
Equation-based object-oriented (EOO) modeling languages such as Modelica provide a convenient, declarative method for describing models of cyber-physical systems. Because of the ease of use of EOO languages, large and complex models can be built with limited effort. However, current state-of-the-art tools do not provide the user with enough information when errors appear or simulation results are wrong. It is of paramount importance that such tools should give the user enough information to correct errors or understand where the problems that lead to wrong simulation results are located. However, understanding the model translation process of an EOO compiler is a daunting task that not only requires knowledge of the numerical algorithms that the tool executes during simulation, but also the complex symbolic transformations being performed. As part of this work, methods have been developed and explored where the EOO tool, an enhanced Modelica compiler, records the transformations during the translation process in order to provide better diagnostics, explanations, and analysis. This information is used to generate better error-messages during translation. It is also used to provide better debugging for a simulation that produces unexpected results or where numerical methods fail. Meeting deadlines is particularly important for real-time applications. It is usually essential to identify possible bottlenecks and either simplify the model or give hints to the compiler that enable it to generate faster code. When profiling and measuring execution times of parts of the model the recorded information can also be used to find out why a particular system model executes slowly. Combined with debugging information, it is possible to find out why this system of equations is slow to solve, which helps understanding what can be done to simplify the model. A tool with a graphical user interface has been developed to make debugging and performance profiling easier. Both debugging and profiling have been combined into a single view so that performance metrics are mapped to equations, which are mapped to debugging information. The algorithmic part of Modelica was extended with meta-modeling constructs (MetaModelica) for language modeling. In this context a quite general approach to debugging and compilation from (extended) Modelica to C code was developed. That makes it possible to use the same executable format for simulation executables as for compiler bootstrapping when the compiler written in MetaModelica compiles itself. Finally, a method and tool prototype suitable for speeding up simulations has been developed. It works by partitioning the model at appropriate places and compiling a simulation executable for a suitable parallel platform.
There are a number of different system concepts that have gained much relevance in the area of embedded systems over the past couple of years. First, there is the classic concept of embedded systems where the focus is on control systems for physical processes. Secondly, the notion of pervasive computing has evolved, where the vision foresees everyday objects having some form of computation capacity and, in most cases, sensing and communication facilities. Thirdly, the notion of wireless sensor networks has arisen, where small computing devices are able to sense their environment and cooperate in order to achieve a well-defined goal. These three types of quite diverse systems share a lot of commonalities on the one hand and, on the other hand, have some complementary aspects in common that make a combination of these systems into a coherent system vision promising. In particular, the important notions of control, heterogeneity, wireless communication, dynamic and ad-hoc nature and cost are prevalent to various degrees in each of these systems. A future system concept needs to combine the strong points of all three system concepts in at least these functional aspects. It has to provide support for the control of physical processes like today’s embedded systems do, have as good support for device heterogeneity and spontaneity of usage as required by pervasive and ubiquitous computing approaches, and has to be as cost efficient and wirelessly agile as wireless sensor networks are. These new systems consist, therefore, of individual entities or objects that jointly strive to reach a common goal, which will typically be a goal in sensing or control, and are dynamically and loosely federating themselves for cooperation, taking care not to overtax their available resources. This book presents a roadmap to these concepts which are summarized as cooperating objects.
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Over 70,000 copies in print It''s the classic guide to success in the demanding world of consulting.
During the 21st century business environments have become more complex and dynamic than ever before. Companies operate in a world of change influenced by globalisation, volatile markets, legal changes and technical progress. As a result, they have to handle growing volumes of data and therefore require fast storage, reliable data access, intelligent retrieval of information and automated decision-making mechanisms, all provided at the highest level of service quality. Successful enterprises are aware of these challenges and efficiently respond to the dynamic environment in which their business operates. Business Intelligence (BI) and Performance Management (PM) offer solutions to these challenges and provide techniques to enable effective business change. The important aspects of both topics are discussed within this state-of-the-art volume. It covers the strategic support, business applications, methodologies and technologies from the field, and explores the benefits, issues and challenges of each. Issues are analysed from many different perspectives, ranging from strategic management to data technologies, and the different subjects are complimented and illustrated by numerous examples of industrial applications. Contributions are authored by leading academics and practitioners representing various universities, research centres and companies worldwide. Their experience covers multiple disciplines and industries, including finance, construction, logistics, and public services, amongst others. Business Intelligence and Performance Management is a valuable source of reference for graduates approaching MSc or PhD programs and for professionals in industry researching in the fields of BI and PM for industrial application.