Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

Trends in Microgrid Control

This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures; and (2) provides a link between structural control and other fields of control theory, pointing out both differences and similarities, and points out where future research and application efforts are likely to prove fruitful. The paper consists of the following sections: section 1 is an introduction; section 2 deals with passive energy dissipation; section 3 deals with active control; section 4 deals with hybrid and semiactive control systems; section 5 discusses sensors for structural control; section 6 deals with smart material systems; section 7 deals with health monitoring and damage detection; and section 8 deals with research needs. An extensive list of references is provided in the references section.

Structural control: past, present, and future

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

In control system analysis and design, finding a reduced-order model, optimal in the L/sup 2/ sense, to a given system model is a fundamental problem. The problem is very difficult without the global convergence of homotopy methods, and a homotopy based approach has been proposed. The issues are the number of degrees of freedom, the well posedness of the finite dimensional optimization problem, and the numerical robustness of the resulting homotopy algorithm. A homotopy algorithm based on the input normal form characterization of the reduced-order model is developed here and is compared with the homotopy algorithms based on Hyland and Bernstein's optimal projection equations. The main conclusions are that the input normal form algorithm can be very efficient, but can also be very ill conditioned or even fail. >

An input normal form homotopy for the L/sup 2/ optimal model order reduction problem

This paper presents a methodology for computationally ecient, direct trajectory generation for autonomous spacecraft rendezvous using sampling with a distance or time cost function to be minimized. The approach utilizes a randomized A* algorithm called Sampling-Based Model Predictive Optimization (SBMPO) that exclusively samples the input space and integrates the dynamic model of the system. A primary contribution of this paper is the development of appropriate optimistic A* heuristics that take into account a goal position and velocity and are based on the minimum distance and minimum time control problems for the vehicle. These heuristics enable fast computation of trajectories that end in zero relative velocity. Additionally, this paper introduces an alternative approach to collision avoidance for use in graph-based planning algorithms. By referencing the full state of the vehicle, a method of imminent collision detection is applied within the graph search process of the trajectory planner. Using a six degree-of-freedom relative motion spacecraft dynamic model, simulation results are illustrated for generating rendezvous-feasible trajectories in cluttered environments.

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Sampling-Based Trajectory Generation for Autonomous Spacecraft Rendezvous and Docking

For efficient and safe locomotion the gaits of legged robots should vary with the type of terrain. Hence, terrain surface classification is an important problem for this class of mobile robots. Prior research has developed approaches to proprioceptive terrain classification for both wheeled and limbed robots that use sensor measurements dependent upon the dynamics of the robot, which ultimately requires the classification system to be trained at a large number of operating conditions (e.g., vehicle speeds and loads). This research develops an approach to terrain identification based on pressure images generated through direct surface contact using a robot skin constructed around a high-resolution pressure sensing array. Terrain signatures for classification are formulated from the magnitude frequency responses of the pressure images. The methodology is used to train and test a classifier using dynamically measured pressure images from a one-legged hopping robot. Experimental tests yield high classification accuracies, which are independent with respect to changing robot dynamics (i.e., different leg gaits). The findings of this paper suggest the methodology can be extended to autonomous field robots, providing the robot with crucial information about the environment that can be used to aid stability over rough terrains and enhance motion planning over varying terrains.

Terrain identification on a one-legged hopping robot using high-resolution pressure images.

Aerial missions that require unmanned aerial vehicles (UAVs) to fly autonomously in unknown and hostile environments are inevitable. These UAVs must be equipped with a fully autonomous navigation system. Many methods that have been proposed for navigation of autonomous systems either lack the necessary intelligence or are not responsive enough to cope with the flying speeds of UAVs. This paper presents a new method for autonomous navigation of UAVs using reactive fuzzy behaviors. It extends a 2D fuzzy behavior navigation system used in unmanned ground vehicles to a 3D navigation system suitable for UAVs. The research is based on a range finding sensor system by judicious use of a 2D range finder. A novel defuzzification method for 3D navigation systems is developed to generate the most desired flying orientation. Simulation results carried out using MATLAB's Virtual Reality Toolbox show that the proposed system works very well to avoid static obstacles.

Fuzzy behavior navigation for an unmanned helicopter in unknown environments

The motors or engines of an autonomous ground vehicles (AGV) have torque and power limitations, which limit their abilities to climb steep hills, which are defined to be hills that have high grade sections in which the vehicle is forced to decelerate. Traversal of a steep hill requires the vehicle to have sufficient momentum before entering the hill. This problem is part of a larger class of momentum-based motion planning problems such as the problem of lifting heavy objects with manipulators. Hence, solutions to the steep hill climbing problem have much wider applicability. The motion planning here is accomplished using a dynamic model of the skid-steered AGV used in the experiments along with Sampling Based Model Predictive Control (SBMPC), a recently developed input sampling planning algorithm that may be viewed as a generalization of LPA* to the direct use of kinodynamic models. The motion planning is demonstrated experimentally using two scenarios, one in which the robot starts at rest at the bottom of a hill and one in which the robot starts at rest a distance from the hill. The first scenario requires the AGV to first reverse direction so that the vehicle can gather enough momentum before reaching the hill. This corresponds to having the vehicle begin at a local minimum, which results in a problem that many traditional model predictive control methods cannot solve. It is seen that, whereas open loop trajectories can lead to vehicle immobilization, SBMPC successfully uses the information provided by the dynamic model to ensure that the AGV has the requisite momentum.

Emmanuel G. Collins

Papers

An input normal form homotopy for the L/sup 2/ optimal model order reduction problem

Sampling-Based Trajectory Generation for Autonomous Spacecraft Rendezvous and Docking

Terrain identification on a one-legged hopping robot using high-resolution pressure images.

Fuzzy behavior navigation for an unmanned helicopter in unknown environments

Motion planning for steep hill climbing