Search results for ""author yunong zhang""

Introduces a revolutionary, quadratic-programming based approach to solving long-standing problems in motion planning and control of redundant manipulators This book describes a novel quadratic programming approach to solving redundancy resolutions problems with redundant manipulators. Known as ``QP-unified motion planning and control of redundant manipulators'' theory, it systematically solves difficult optimization problems of inequality-constrained motion planning and control of redundant manipulators that have plagued robotics engineers and systems designers for more than a quarter century. An example of redundancy resolution could involve a robotic limb with six joints, or degrees of freedom (DOFs), with which to position an object. As only five numbers are required to specify the position and orientation of the object, the robot can move with one remaining DOF through practically infinite poses while performing a specified task. In this case redundancy resolution refers to the process of choosing an optimal pose from among that infinite set. A critical issue in robotic systems control, the redundancy resolution problem has been widely studied for decades, and numerous solutions have been proposed. This book investigates various approaches to motion planning and control of redundant robot manipulators and describes the most successful strategy thus far developed for resolving redundancy resolution problems. Provides a fully connected, systematic, methodological, consecutive, and easy approach to solving redundancy resolution problems Describes a new approach to the time-varying Jacobian matrix pseudoinversion, applied to the redundant-manipulator kinematic control Introduces The QP-based unification of robots' redundancy resolution Illustrates the effectiveness of the methods presented using a large number of computer simulation results based on PUMA560, PA10, and planar robot manipulators Provides technical details for all schemes and solvers presented, for readers to adopt and customize them for specific industrial applications Robot Manipulator Redundancy Resolution is must-reading for advanced undergraduates and graduate students of robotics, mechatronics, mechanical engineering, tracking control, neural dynamics/neural networks, numerical algorithms, computation and optimization, simulation and modelling, analog, and digital circuits. It is also a valuable working resource for practicing robotics engineers and systems designers and industrial researchers.

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The latest in parallel EM solutions with both in-core and out-of-core solvers The solution of complex electromagnetic (EM) problems requires one to address the issues related with numerical accuracy and efficient distribution of the solution procedure over multiple computational nodes. With the advent of multicore processors and high performance computing (HPC) technology, the EM software designers need to know how to add new functionality to computational EM codes so that they can run efficiently on these new processors. Higher Order Basis Based Integral Equation Solver [HOBBIES] presents a road map for the analysis of complex material structures using the high-performance parallel simulation software known as HOBBIES. Focusing on the Method of Moments (MoM), the book features new parallel programming techniques and user-friendly code with superior capabilities for solving challenging EM radiation and scattering problems. It provides readers with complete guidance on how to extend the capability of MoM and achieve faster and more accurate EM analysis and utilize multicore CPUs on desktop computers. Complete with an academic version of the HOBBIES software, this book: Explains the unique features of the higher order basis functions in the solution of integral equations in a MoM context Shows how to generate a properly load balanced parallel computational procedure for MoM matrix filling and matrix equation solving in both in-core and out-of-core implementation Presents a professional graphical users interface (GUI) for generating the geometrical structure based on NURBS modeling Illustrates various automatic meshing procedures based on an a-priori defined error between the actual geometry and the meshed structure Outlines all the key features of the HOBBIES software, including multiple optimization procedures for EM synthesis The bottleneck of traditional MoM arises from the lack of memory in computers for solution of large problems. This is mitigated by using higher order basis functions and out-of-core solver in HOBBIES. HOBBIES has the capability to perform numerically accurate EM simulations using thousands of CPU cores in an HPC environment using a properly load balanced out-of-core solver. In this way, it provides a cost-effective choice for addressing modern engineering and scientific challenges that arise from the extremely complicated real-life applications.

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Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications introduces the outlook and extension toward deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors’ 20 years of research experience on neuronets, the book explores the models, algorithms, and applications of the WASD neuronet, and allows reader to extend the techniques in the book to solve scientific and engineering problems. The book will be of interest to engineers, senior undergraduates, postgraduates, and researchers in the fields of neuronets, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, simulation and modeling, deep learning, and data mining. Features Focuses on neuronet models, algorithms, and applications Designs, constructs, develops, analyzes, simulates and compares various WASD neuronet models, such as single-input WASD neuronet models, two-input WASD neuronet models, three-input WASD neuronet models, and general multi-input WASD neuronet models for function data approximations Includes real-world applications, such as population prediction Provides complete mathematical foundations, such as Weierstrass approximation, Bernstein polynomial approximation, Taylor polynomial approximation, and multivariate function approximation, exploring the close integration of mathematics (i.e., function approximation theories) and computers (e.g., computer algorithms) Utilizes the authors' 20 years of research on neuronets

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