Chao Zhang – författare

Multi-scale Mechanics of 2D Triaxially Braided Composites: Characterization, Mechanics and Machine-Learning Based Modelling presents the latest advances in this important research field. The book begins with a brief introduction to these materials including their mechanical features, damage and failure mechanisms and typical applications. The contents are then divided into three main sections on experimental characterization; mechanics-based modeling and machine-learning based modeling approaches. By taking a multi-scale modeling approach, that includes progressive damage and impact simulation, as well as theoretical modelling, machine-learning and multi-scale mechanics aspects, the author presents key findings in this important field. A systematic introduction is given to the multi-scale and machine-learning based modeling approach, along with their corresponding source codes for the progressive damage model. Then an up-to-date theoretical model is also presented, as well as, high-efficient finite element mesh of the unit cell, for conducting multi-scale analysis and design of these materials and structures. Numerical examples are also presented to illustrate the application of presented methods for quasi-static and impact problems. To enhance the reader’s understanding numerous engineering case studies are also included, together with examples of material/structure optimization. The book provides the latest knowledge and methodology for the design and analysis of aerospace structures and other materials technologies, guiding the researcher to understand this cutting-edge research framework.Presents experimental, analytical, and numerical studies of two-dimensional triaxially braided compositesProvides cutting-edge knowledge on progressive failure simulationIncludes clarification of failure mechanisms under various loading conditionsPresents the latest advances on multi-scale modeling methodsDiscusses challenges and future prospectives on multi-scale mechanics of textile composites

Game Theory and Deep Learning: Fundamentals and Applications is the first book to establish a clear and comprehensive link between game theory and deep learning, demonstrating the critical importance of this interplay for solving key technological challenges such as those found in future wireless and energy networks. It delves into the latest connections between game theory and generative AI, including large language models, showcasing how these advanced concepts can be harnessed to address complex real-world problems. Readers will gain a deep understanding of how these two powerful fields intersect and the practical applications of this knowledge.The book teaches readers how to develop a solid foundation in game theory and understand interactive scenarios in both engineering and everyday life, effectively apply their knowledge to practical problems, including resource allocation, security, and influence maximization, and finally, design strategies that optimally exploit available information through successive optimization, reinforcement learning, deep learning, and generative AI techniques.Gives applications of game theory and deep learning across various domains, such as future wireless and energy networksExplores the burgeoning connections between game theory and generative AI, including large language models, providing readers with insights into the most current technological advancementsGives practical methodologies for studying games and to develop design strategies that are supported by numerous examples and detailed case studiesProvides an in-depth overview of outstanding research challenges and potential future directions in the interplay between game theory and deep learning, helping readers remain current and identify new areas for exploration and innovationIncludes guidance on implementation with code snippets and a detailed presentation of methods

Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task.This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making.The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people''s information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task.

This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making.

The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

This book constitutes the thoroughly refereed post-conference proceedings of the 6th International ICST Conference on Wireless Internet (WICON 2011) held in Xi’an, Shaanxi, China in October 2011. The 55 revised full papers were carefully selected from numerous submissions and focus on research in telecommunication and networking and development in information and communication technologies. The papers are presented in 10 technical sessions and cover topics as video streaming over OFDMA downlink systems, cognitive radio networks, distributed antenna systems, joint source channel coding, multiuser MIMO systems, signal detection, frequency synchronization in 3GPP LTE systems, visual cognitive radio, wireless relay networks, and network flow identifying methods.

This book constitutes the thoroughly refereed post-conference proceedings of the 6th International ICST Conference on Wireless Internet (WICON 2011) held in Xi’an, Shaanxi, China in October 2011. The 55 revised full papers were carefully selected from numerous submissions and focus on research in telecommunication and networking and development in information and communication technologies. The papers are presented in 10 technical sessions and cover topics as video streaming over OFDMA downlink systems, cognitive radio networks, distributed antenna systems, joint source channel coding, multiuser MIMO systems, signal detection, frequency synchronization in 3GPP LTE systems, visual cognitive radio, wireless relay networks, and network flow identifying methods.

This thesis studied the effect of aging of intumescent coatings (ICs) on the reliability of protected steel columns in fire condition and developed a probabilistic approach to assess the service life of ICs applied on steel columns. In the study, Monte Carlo simulations were conducted to obtain the reliability index or failure probability of steel columns protected by ICs subjected to compartment fires.  The effect of aging of intumescent coatings on the failure probability of protected steel columns was investigated by using variable insulation property of intumescent coatings in the simulation. The test data on aging effect on insulation property of intumescent coatings from literature was used. Based on the reliability analysis, a probabilistic approach is given to determine the service life of intumescent coatings for steel columns. In that approach, the failure probability of the protected steel columns is compared with the target probability of the structural fire design. The approach can also be used for probabilistic analysis of steel columns protected by conventional inert fire protection materials.

This thesis studied the effect of aging of intumescent coatings (ICs) on the reliability of protected steel columns in fire condition and developed a probabilistic approach to assess the service life of ICs applied on steel columns. In the study, Monte Carlo simulations were conducted to obtain the reliability index or failure probability of steel columns protected by ICs subjected to compartment fires.  The effect of aging of intumescent coatings on the failure probability of protected steel columns was investigated by using variable insulation property of intumescent coatings in the simulation. The test data on aging effect on insulation property of intumescent coatings from literature was used. Based on the reliability analysis, a probabilistic approach is given to determine the service life of intumescent coatings for steel columns. In that approach, the failure probability of the protected steel columns is compared with the target probability of the structural fire design. The approach can also be used for probabilistic analysis of steel columns protected by conventional inert fire protection materials.

This thesis studied the effect of aging of intumescent coatings (ICs) on the reliability of protected steel columns in fire condition and developed a probabilistic approach to assess the service life of ICs applied on steel columns. In the study, Monte Carlo simulations were conducted to obtain the reliability index or failure probability of steel columns protected by ICs subjected to compartment fires.  The effect of aging of intumescent coatings on the failure probability of protected steel columns was investigated by using variable insulation property of intumescent coatings in the simulation. The test data on aging effect on insulation property of intumescent coatings from literature was used. Based on the reliability analysis, a probabilistic approach is given to determine the service life of intumescent coatings for steel columns. In that approach, the failure probability of the protected steel columns is compared with the target probability of the structural fire design. The approach can also be used for probabilistic analysis of steel columns protected by conventional inert fire protection materials.

This book mainly reviews the progress, effectiveness, and transformation mode of China's current practice of "Lucid Waters and Lush Mountains Are Invaluable Assets" Theory (abbreviated as the "Two Mountain Theory"). A total of 10 typical cases of ecological restoration, ecological industry, ecological market and green finance have been formed based on the theory. In the future, the Center for Ecological Civilization will continue to strengthen the practical exploration and theoretical innovation of the "Two Mountains Theory" to provide support for achieving new progress in ecological civilization construction and building a beautiful China.