This book explores the history of mechanical engineering since the Bronze Age. Focusing on machinery inventions and the development of mechanical technology, it also discusses the machinery industry and modern mechanical education. The evolution of machinery is divided into three stages: Ancient (before the European Renaissance), Modern (mainly including the two Industrial Revolutions) and Contemporary (since the Revolution in Physics, especially post Second World War). The book not only clarifies the development of mechanical engineering, but also reveals the driving forces behind it – e.g. the economy, national defense and human scientific research activities – to highlight the links between technology and society; mechanical engineering and the natural sciences; and mechanical engineering and related technological areas. Though mainly intended as a textbook or supplemental reading for graduate students, the book also offers a unique resource for researchers and engineers in mechanical engineering who wish to broaden their horizons.
This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.
Machinery Dynamics includes recent advancements in this quickly evolving area, while also analyzing real applications, analyzing integrated systems, and including further discussions on each mechanical component. The book treats mechanisms separately, with different methods depending on the level of accuracy required. The contents of this book is made to suit the needs of MsC and PhD students, researchers and engineers in the areas of design of high speed machinery, condition monitoring of machine operation, and vibration. Addresses theoretical backgrounds on topics, including vibration and elastodynamics Introduces rigid and elastic dynamics of various mechanisms, including linkages, cams, gears and planetary gear trains Features relevant application examples
Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on.Both classic and novel algorithms are introduced in classifier design, face recognition, deep learning, time series recognition, image classification, and object detection.
Scalable and efficient distributed learning is one of the main driving forces behind the recent rapid advancement of machine learning and artificial intelligence. One prominent feature of this development is that recent progress has been made by researchers in two communities: (1) the system community such as database, data management, and distributed systems, and (2) the machine learning and mathematical optimization community. The interaction and knowledge sharing between these two communities has led to the rapid development of new distributed learning systems and theory. This monograph provides a brief introduction to three distributed learning techniques that have recently been developed: lossy communication compression, asynchronous communication, and decentralized communication. These have significant impact on the work in both the system and machine learning and mathematical optimization communities but to fully realize the potential, it is essential they understand the whole picture. This monograph provides the bridge between the two communities. The simplified introduction to the essential aspects of each community enables researchers to gain insights into the factors influencing both. The monograph provides students and researchers the groundwork for developing faster and better research results in this dynamic area of research.
Her mother had betrayed her for her own selfish desire, but she had met him by mistake.She married him for her children, he married her for his family;When he knew of the child's existence, how he would treat her.
In this volume, Prof. Ye and his coworkers propose and review the concept of nano-bio probe design for biochemical analysis on the basis of their recent published works. A unique biochemical analysis technology based on fluorescence polarization enhanced by nanoparticles is described here with successful applications in environmental monitoring, rapid and sensitive sensing protease activity and high-throughput screening of inhibitors. Furthermore, they introduce a versatile molecular beacon (MB)-like probe for the multiplex sensing of targets such as sequence-specific DNA, protein, metal ions and small molecule compounds based on the self-assembled biomolecule-graphene conjugates. Besides, some colorimetric and luminescence probes utilizing metal nanoparticles for biochemical applications are also presented, such as chiral enantiomer discrimination and separation, environmental monitoring, clinic diagnosis and etc.
Her mother had betrayed her for her own selfish desire, but she had met him by mistake.She married him for her children, he married her for his family;When he knew of the child's existence, how he would treat her.
Her mother had betrayed her for her own selfish desire, but she had met him by mistake.She married him for her children, he married her for his family;When he knew of the child's existence, how he would treat her.
Her mother had betrayed her for her own selfish desire, but she had met him by mistake.She married him for her children, he married her for his family;When he knew of the child's existence, how he would treat her.
Her mother had betrayed her for her own selfish desire, but she had met him by mistake.She married him for her children, he married her for his family;When he knew of the child's existence, how he would treat her.
Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss in classical matrix computation-based data analysis. This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis, and benefits researchers, engineers, and students with theoretical, computational, technical and experimental details. It presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, and comprehensive coverage of tensor computation based data analysis techniques. In addition, some practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering are also presented for easy understanding and implementation. These data analysis techniques may be further applied in other applications on neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc. The discussion begins with basic coverage of notations, preliminary operations in tensor computations, main tensor decompositions and their properties. Based on them, a series of tensor-based data analysis techniques are presented as the tensor extensions of their classical matrix counterparts, including tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, robust principal tensor component analysis, tensor regression, logistical tensor regression, support tensor machine, multilinear discriminate analysis, tensor subspace clustering, tensor-based deep learning, tensor graphical model and tensor sketch. The discussion also includes a number of typical applications with experimental results, such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, link prediction, environmental prediction, weather forecasting, background extraction, human pose estimation, cognitive state classification from fMRI, infrared small target detection, heterogeneous information networks clustering, multi-view image clustering, and deep neural network compression.
Her mother had betrayed her for her own selfish desire, but she had met him by mistake.She married him for her children, he married her for his family;When he knew of the child's existence, how he would treat her.
New Polymeric Products: Fundamentals, Forming Methods and Applications introduces applications of polymer materials in different fields, including new products and processing methods. This book is rich in content and creativity and introduces the development, history, characteristics and existing processing methods of polymer materials in a comprehensive and systematic manner. Sections include the latest achievements from future travel, energy problems, special environment, lens materials and biomedicine, which are the most concerning areas of human society today. The book also introduces forming principles, methods, achievements and development prospects from shallow to profound. It will benefit researchers and new academic participants and broaden their vision. Sections cover the development history and prospect of polymer materials, introduce polymer materials, including new materials, characteristics, synthesis, naming and functionality, and delve into new processing and forming methods which are introduced in three parts: plastic, rubber and fiber according to different product types. Composed of relevant research results from the author's team, including general basic knowledge and the latest research in relevant fields Introduces basic knowledge such as polymer development history, material characteristics and forming principles Arranges trivial contents such as polymer development history in tables to make it clearer and easier to understand Gives readers a clearer understanding of products, processing equipment and processes
Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss in classical matrix computation-based data analysis. This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis, and benefits researchers, engineers, and students with theoretical, computational, technical and experimental details. It presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, and comprehensive coverage of tensor computation based data analysis techniques. In addition, some practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering are also presented for easy understanding and implementation. These data analysis techniques may be further applied in other applications on neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc. The discussion begins with basic coverage of notations, preliminary operations in tensor computations, main tensor decompositions and their properties. Based on them, a series of tensor-based data analysis techniques are presented as the tensor extensions of their classical matrix counterparts, including tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, robust principal tensor component analysis, tensor regression, logistical tensor regression, support tensor machine, multilinear discriminate analysis, tensor subspace clustering, tensor-based deep learning, tensor graphical model and tensor sketch. The discussion also includes a number of typical applications with experimental results, such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, link prediction, environmental prediction, weather forecasting, background extraction, human pose estimation, cognitive state classification from fMRI, infrared small target detection, heterogeneous information networks clustering, multi-view image clustering, and deep neural network compression.
One-Dimensional Nanostructures: Electrospinning Technique and Unique Nanofibers is a comprehensive book depicting the electrospinning technique and related 1D unique electrospun nanofibers. The first part of the book focuses on electrospinning technique, with chapters describing Electrospinning setup, electrospinning theories, and related working parameter. The second part of the book describes in detail specific topics on how to control the electrospun fiber properties such as how to control the fiber direction, how to control the fiber surface morphology, how to control the fiber structure, and how to construct 3D structures by electrospun fibers. The final part of the book depicts the applications of the electrospun nanofibers, with sections describing in detail specific fields such as electrospun nanofiber reinforcement, filtration, electronic devices, lithium-ion batteries, fuel cells, biomedical field, and so on. One-Dimensional Nanostructures: Electrospinning Technique and Unique Nanofibers is designed to bring state-of-the-art on electrospinning together into a single book and will be valuable resource for scientists in the electrospinning field and other scientists involved in biomedical field, mechanical field, materials, and energy field. Dr. Zhenyu Li is an associate professor at the Dept. of Chemistry, Jilin University, Changchun, P. R. China. Currently, he also holds the position in Australian Future Fibres Research & Innovation Centre, Institute for Frontier Materials, Deakin University, Geelong, Victoria, Australia. Dr. Ce Wang is a professor at the Dept. of Chemistry, Jilin University, Changchun, P. R. China.
One-Dimensional Nanostructures: Electrospinning Technique and Unique Nanofibers is a comprehensive book depicting the electrospinning technique and related 1D unique electrospun nanofibers. The first part of the book focuses on electrospinning technique, with chapters describing Electrospinning setup, electrospinning theories, and related working parameter. The second part of the book describes in detail specific topics on how to control the electrospun fiber properties such as how to control the fiber direction, how to control the fiber surface morphology, how to control the fiber structure, and how to construct 3D structures by electrospun fibers. The final part of the book depicts the applications of the electrospun nanofibers, with sections describing in detail specific fields such as electrospun nanofiber reinforcement, filtration, electronic devices, lithium-ion batteries, fuel cells, biomedical field, and so on. One-Dimensional Nanostructures: Electrospinning Technique and Unique Nanofibers is designed to bring state-of-the-art on electrospinning together into a single book and will be valuable resource for scientists in the electrospinning field and other scientists involved in biomedical field, mechanical field, materials, and energy field. Dr. Zhenyu Li is an associate professor at the Dept. of Chemistry, Jilin University, Changchun, P. R. China. Currently, he also holds the position in Australian Future Fibres Research & Innovation Centre, Institute for Frontier Materials, Deakin University, Geelong, Victoria, Australia. Dr. Ce Wang is a professor at the Dept. of Chemistry, Jilin University, Changchun, P. R. China.
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