Jun Ma – författare

Precision motion control is strongly required in many fields, such as precision engineering, micromanufacturing, biotechnology, and nanotechnology. Although great achievements have been made in control engineering, it is still challenging to fulfill the desired performance for precision motion control systems. Substantial works have been presented to reveal an increasing trend to apply optimization approaches in precision engineering to obtain the control system parameters. In this book, we present a result of several years of work in the area of advanced optimization for motion control systems.

The book is organized into two parts: Part I focuses on the model-based approaches, and Part II presents the data-based approaches. To illustrate the practical appeal of the proposed optimization techniques, theoretical results are verified with practical examples in each chapter. Industrial problems explored in the book are formulated systematically with necessary analysis of the control system synthesis.

By virtue of the design and implementation nature, this book can be used as a reference for engineers, researchers, and students who want to utilize control theories to solve the practical control problems. As the methodologies have extensive applicability in many control engineering problems, the research results in the field of optimization can be applied to full-fledged industrial processes, filling in the gap between research and application to achieve a technology frontier increment.

Precision motion control is strongly required in many fields, such as precision engineering, micromanufacturing, biotechnology, and nanotechnology. Although great achievements have been made in control engineering, it is still challenging to fulfill the desired performance for precision motion control systems. Substantial works have been presented to reveal an increasing trend to apply optimization approaches in precision engineering to obtain the control system parameters. In this book, we present a result of several years of work in the area of advanced optimization for motion control systems.

The book is organized into two parts: Part I focuses on the model-based approaches, and Part II presents the data-based approaches. To illustrate the practical appeal of the proposed optimization techniques, theoretical results are verified with practical examples in each chapter. Industrial problems explored in the book are formulated systematically with necessary analysis of the control system synthesis.

By virtue of the design and implementation nature, this book can be used as a reference for engineers, researchers, and students who want to utilize control theories to solve the practical control problems. As the methodologies have extensive applicability in many control engineering problems, the research results in the field of optimization can be applied to full-fledged industrial processes, filling in the gap between research and application to achieve a technology frontier increment.

Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects.

Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes.

Features

The book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.

Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects.

Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes.

Features

The book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.

This first of three volumes from the inaugural NODYCON, held at the University of Rome, in February of 2019, presents papers devoted to Nonlinear Dynamics of Structures, Systems and Devices. The collection features both well-established streams of research as well as novel areas and emerging fields of investigation. Topics in Volume I include multi-scale dynamics: coexistence of multiple time/space scales, large system dynamics; dynamics of structures/industrial machines/equipment/facilities (e.g., cable transportation systems, suspension bridges, cranes, vehicles);  nonlinear interactions: parametric vibrations with single/multi-frequency excitations, multiple external and autoparametric resonances in multi-dof systems;  nonlinear system identification: parametric/nonparametric identification, data-driven identification; experimental dynamics: benchmark experiments, experimental methods, instrumentation techniques, measurements in harsh environments, experimental validation of nonlinear models; wave propagation, solitons, kinks, breathers; solution methods for pdes: Lie groups, Hirota’s method, perturbation methods, etc; nonlinear waves in media (granular materials, porous materials, materials with memory); composite structures: multi-layer, functionally graded, thermal loading; fluid/structure interaction; nonsmooth and retarded dynamics: systems with impacts, free play, stick-slip, friction hysteresis; nonlinear systems with time and/or space delays; stability of delay differential equations, differential-algebraic equations; space/time reduced-order modeling: enhanced discretization methods, center manifold reduction, nonlinear normal modes, normal forms; fractional-order systems; computational techniques: efficient algorithms, use of symbolic manipulators, integration of symbolic manipulation and numerical methods, use of parallel processors; and multibody dynamics: rigid and flexible multibody system dynamics, impact and contact mechanics, tire modeling, railroad vehicle dynamics, computational multibody dynamics.

This third of three volumes from the inaugural NODYCON, held at the University of Rome, in February of 2019, presents papers devoted to New Trends in Nonlinear Dynamics. The collection features both well-established streams of research as well as novel areas and emerging fields of investigation. Topics in Volume III include NEMS/MEMS and nanomaterials: multi-sensors, actuators exploiting nonlinear working principles; adaptive, multifunctional, and meta material structures; nanocomposite structures (e.g., carbon nanotube/polymer composites, composites with functionalized nanoparticles); 0D,1D,2D,3D nanostructures; biomechanics applications, DNA modeling, walking dynamics, heart dynamics, neurodynamics, capsule robots, jellyfish-like robots, nanorobots; cryptography based on chaotic maps; ecosystem dynamics, social media dynamics (user behavior dynamics in multi-messages social hotspots, prediction models), financial engineering, complexity in engineering; and network dynamics(multi-agent systems, leader-follower dynamics, swarm dynamics, biological networks dynamics).

This second of three volumes from the inaugural NODYCON, held at the University of Rome, in February of 2019, presents papers devoted to Nonlinear Dynamics and Control. The collection features both well-established streams of research as well as novel areas and emerging fields of investigation. Topics in Volume II include influence of nonlinearities on vibration control systems; passive, semi-active, active control of structures and systems; synchronization; robotics and human-machine interaction; network dynamics control (multi-agent systems, leader-follower dynamics, swarm dynamics, biological networks dynamics); and fractional-order control.

This book provides up-to-date guidance that will assist radiation oncologists during the day-to-day management of nasopharyngeal cancer. After discussion of diagnosis and staging, target delineation techniques and treatment planning are described for both intensity-modulated and particle radiation therapy. Detailed information is then presented on the application of radiation therapy in different disease settings, from early stage disease to metastatic disease. Due attention is paid to the role of multimodality treatment and new and advanced technologies in particular circumstances, such as local recurrence. In addition, follow-up and the management of late toxicities are explained and management strategies are documented for special situations and groups, including pediatric patients. The book is published within the Springer series Practical Guides in Radiation Oncology. Like other volumes in the series, it is designed for hands-on use by both radiation oncology residents and practicing radiation oncologists. It will also be of value for head and neck physicians.

This first of three volumes includes papers from the second series of NODYCON, which was held virtually in February of 2021. The conference papers reflect a broad coverage of topics in nonlinear dynamics, ranging from traditional topics from established streams of research to  those from  relatively unexplored and emerging venues of research. These include

This second of three volumes includes  papers from the second series of NODYCON which was held virtually in February of 2021. The conference papers reflect a broad coverage of topics in nonlinear dynamics, ranging from  traditional  topics from  established streams of research to those from  relatively unexplored and emerging venues of research. These include

·         Nonlinear vibration control 

·         Control of nonlinear systems and synchronization

·         Experimental dynamics       

·         System identification and SHM       

·         Multibody dynamics

This third of three volumes includes papers from the second series of NODYCON, which was held virtually in February of 2021. The conference papers reflect a broad coverage of topics in nonlinear dynamics, ranging from  traditional  topics from  established streams of research to those from  relatively unexplored and emerging venues of research. These include

·         Complex dynamics of COVID-19: modeling, prediction and control

·         Nonlinear phenomena  in bio-systems and eco-systems  

·         Energy harvesting  

·         MEMS/NEMS          

·         Multifunctional structures, materials and metamaterials

·         Nonlinear waves    

·         Chaotic systems, stochasticity, and uncertainty