35 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Applied Methuerstic computing

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    For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

    Applied Metaheuristic Computing

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    For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

    Robotics 2010

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    Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

    Range images

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    This article gives an overview of range‐imaging techniques with an aim to let the reader better understand how the difficult issue, such as the registration of overlapping range images, can be approached and solved. It firstly introduces the characteristics of range images and highlights examples of 3D image visualizations, associated technical issues, applications, and the differences of range imaging with respect to the traditional digital broadband imaging. Subsequently, one of the most popular feature extraction and matching methods, the signature of histograms of orientations (SHOT) method, is then outlined. However, the “matched” points generated by SHOT usually generate high proportion of false positives due to various factors such as imaging noise, lack of features, and cluttered backgrounds. Thus, the article discusses more about image‐matching issues, particularly to emphasize how the widely employed range image alignment technique, the random sample consensus (RANSAC) method, is compared with a simple, yet effective, technique based on normalized error penalization (NEP). This simple NEP method utilizes a strategy to penalize point matches whose errors are far away from the majority. The capability of the method for the evaluation of point matches between overlapping range images is illustrated by experiments using real range image data sets. Interestingly enough, these range images appear to be easier to register than expected. Finally, some conclusions have been drawn and further readings for other fundamental techniques and concepts have been suggested

    Advancing Embedded and Extrinsic Solutions for Optimal Control and Efficiency of Energy Systems in Buildings

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    Buildings account for approximately 40% of all U.S. energy usage and carbon emissions. Reducing energy usage and improving efficiency in buildings has the potential for significant environmental and economic impacts. To do so, reoccurring identification of hardware and operational opportunities is needed to maintain building efficiency. Additionally, the development of controls that continually operate building systems and equipment at energy optimal conditions is required. This dissertation provides contributions to both of the aforementioned areas, which can be divided into two distinct portions. The first presents the framework for the development of an automated energy audit process, termed Autonomous Robotic Assessments of Energy (AuRAE). The automation of energy audits would decrease the cost of audits to customers, reduce the time auditors need to invest in an audit, and provide repeatable audit processes with enhanced data collection. In this framework of AuRAE, novel, audit-centric navigational strategies are presented that enable the complete exploration of a previously unknown space in a building while identifying and navigating to objects of interest in real-time as well as navigation around external building perimeters. Simulations of the navigational strategies show success in a variety of building layouts and size of objects of interest. Additionally, prototypes of robotic audit capabilities are demonstrated in the form of a lighting identification and analysis package on a ground vehicle and an environmental baseline measurement package on an aerial vehicle. The second portion presents the development and simulation of two advanced economic building energy controllers: one utilizes steady-state relationships for optimizing control setpoints while the other is an economic MPC method using dynamic models to optimize the same control setpoints. Both control methods balance the minimization of utility cost from energy usage with the cost of lost productivity due to occupant discomfort, differing from standard building optimal control that generally addresses occupant comfort through setpoint limits or comfort measure constraints. This is accomplished through the development of component-level economic objective functions for each subsystem in the modeled building. The results show that utility cost and the cost of occupant productivity from optimal comfort can be successfully balanced, and even improved over current control methods. The relative magnitude of the cost of lost productivity is shown to be significantly higher than the cost of utilities, suggesting that building operators, technicians, and researchers should make maintaining occupant comfort a top priority to achieve the greatest economic savings. Furthermore, the results demonstrate that by using steady-state predictions, the majority of the performance gains produced with a fully dynamic MPC solution can be recovered

    Robot Mapping and Localisation in Water Pipes

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    The demand for inspection and repair technologies for the water industries on their water mains and distribution pipes is increasing. In urban water distribution systems, due to the fact that water pipes are ageing, pipe leakages and serious damage may occur. Compared with the cost of pipe replacement in the underground distribution system, regular pipe inspection and repair is more cost effective to water companies and local communities. However, small-diameter pipes are not accessible to humans because they are small in size and often buried underground. Therefore, inspection robotic systems are more suited to this task in terms of underground pipe networks mapping and damage localisation, in order to target repair from above ground. There are a number of challenges for robot mapping and localisation in water pipes, which are: 1) feature sparsity in water pipes – lack of navigation landmarks, 2) in-pipe robot can only detect nearby features, and 3) unlike indoor/outdoor SLAM problems, in-pipe robot has less movement flexibility. The main aim of this thesis is to solve these challenges and address the problem of robot mapping and localisation in small-diameter feature-sparse water pipes. This thesis presents a number of novel contributions. Firstly, for the front end, where raw sensor data is transformed into signals useful for mapping and localisation algorithms, new types of maps are developed here for water pipes: for plastic pipes, ultrasound data is used to map the ground profile through the plastic pipe wall, whilst for metal pipes a hydrophone is used to determine a map based on pipe vibration amplitude over space. Secondly, a new sequential approach to mapping and localisation is developed, based on alignment of multiple maps based on dynamic time warping averaging. Thirdly, a new simultaneous localisation and mapping algorithm is developed, which overcomes the limitation of the sequential approach in that the map is not updated. Finally, a new sensor fusion algorithm is developed that transforms the robot location in the local coordinate frame to the world coordinate frame, which would be essential for targeting repairs from above ground
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