Bücher von Yangquan Chen

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. On the other hand, while remote sensing is able to provide spatially distributed measurements, the spatial resolution of multispectral satellite images is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. This book examines the different UAV-based approaches of ET estimation. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are discussed. It also covers the challenges and opportunities for UAVs in ET estimation, with the final chapters devoted to new ET estimation methods and their potential applications for future research.

This SpringerBrief presents novel methods of approaching challenging problems in the reconstruction of accurate 3D models and serves as an introduction for further 3D reconstruction methods. It develops a 3D reconstruction system that produces accurate results by cascading multiple novel loop detection, sifting, and optimization methods.The authors offer a fast point cloud registration method that utilizes optimized randomness in random sample consensus for surface loop detection. The text also proposes two methods  for surface-loop sifting. One is supported by a sparse-feature-based optimization graph. This graph is more robust to different scan patterns than earlier methods and can cope with tracking failure and recovery. The other is an offline algorithm that can sift loop detections based on their impact on loop optimization results and which is enabled by a dense map posterior metric for 3D reconstruction and mapping performance evaluation works without any costly ground-truth data.The methods presented in Towards Optimal Point Cloud Processing for 3D Reconstruction will be of assistance to researchers developing 3D modelling methods and to workers in the wide variety of fields that exploit such technology including metrology, geological animation and mass customization in smart manufacturing.

Incorporating a number of illustrations, this book focuses on distributed-parameter systems, the dynamics of which can be modelled with partial differential equations. Application examples cover fields ranging from environmental science to national security.

This book describes complex random signals, characterized by the presence of a heavy-tailed distribution or non-negligible dependence between distant observations. Coverage includes simulation, fractional-order modeling and filtering fractional-order systems.

Shows the reader how to use robust iterative learning control in the face of model uncertaintyHelps to improve the performance of repetitive electromechanical tasks, widespread in industryProvides a rounded and self-contained approach to the subject of iterative learning control not available elsewhere

Fractional-order Systems and Controls provides a basic understanding of FOC concepts and methods. Readers will learn the broad range of uses of FOC in control systems through a variety of real-world problems in fields like mechatronics, civil engineering and biological systems.

A unified theoretical framework, based on the well-established theory of optimal experimental design and providing consistent solutions to problems hitherto requiring a variety of approaches, is put forward to solve a large class of optimal observation problems.

Distributed-order differential equations, a generalization of fractional calculus, are of increasing importance in many fields of science and engineering from the behaviour of complex dielectric media to the modelling of nonlinear systems.

From aerodynamic curve identification robotics to neuromuscular stimulation, Iterative Learning Control (ILC), has many applications. A system may have uncertainties in its dynamic model and its environment. Using system repetitiveness, ILC reduces uncertainties and improves control performance.