Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels

Passive localization model in wireless sensor networks based on adaptive hybrid heuristic algorithms

Предмет истраживања ове докторске дисертације је проблем пасивног лоцирања заснован на мерењу времена пропагације сигнала (Time of Arrival, ТОА), или временске разлике пропагације сигнала (Time Difference of Arrival, TDOA) ради одређивања непознате локације неког објекта. За постављене моделе лоцирања формирана је функција максималне веродостојности (Maximum Likelihood, ML) са Гаусовом случајном расподелом за грешку мерења. Разматрани естимациони модел описан је нелинеарном, неконвексном функцијом циља, односно мултимодалном функцијом. При томе, за формирану функцију циља, глобално оптимално решење не може се нумерички одредити класичним методама оптимизације...The research in this dissertation is focused on the problem of passive target localization based on the noisy time of arrival (TOA) or time Difference of Arrival (TDOA) measurements, with the aim to accurately estimate the unknown passive target location. The maximum likelihood (ML) estimation problem is formulated for the considered localization problem, with measurement errors modelled as Gaussian distributed random variables. However, the ML objective function of the considered estimation problem is nonlinear and multimodal function, and in this case, the global optimal solution cannot be determined numerically by classical optimization methods..

Optimal transportation meshfree method in geotechnical engineering problems under large deformation regime

Meshfree methods have been demonstrated as suitable and strong alternatives to the more standard numerical schemes such as finite elements or finite differences. Moreover, when formulated in a Lagrangian approach, they are appropriate for capturing soil behavior under high‐strain levels. In this paper, the optimal transportation meshfree method has been applied for the first time to geotechnical problems undergoing large deformations. All the features employed in the current methodology (ie, F‐bar, explicit viscoplastic integration, and master‐slave contact) are described and validated separately. Finally, the model is applied to the particular case of shallow foundations by using von Mises and Drucker‐Prager yield criteria to find the load at failure in the. The presented methodology is demonstrated to be robust and accurate when solving this type of problems

Mechanical design for the tactile exploration of constrained internal geometries

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.MIT Institute Archives copy: with CD-ROM; divisional library copy with no CD-ROM.Includes bibliographical references (p. 93-98).Rising world oil prices and advanced oil recovery techniques have made it economically attractive to rehabilitate abandoned oil wells. This requires guiding tools through well junctions where divergent branches leave the main wellbore. The unknown locations and shapes of these junctions must be determined. Harsh down-well conditions prevent the use of ranged sensors. However, robotic tactile exploration using a manipulator is well suited to this problem. This tactile characterization must be done quickly because of the high costs of working on oil wells. Consequently, intelligent tactile exploration algorithms that can characterize a shape using sparse data sets must be developed. This thesis explores the design and system architecture of robotic manipulators for down-well tactile exploration. A design approach minimizing sensing is adopted to produce a system that is mechanically robust and suited to the harsh down-well environment. A feasibility study on down-well tactile exploration manipulators is conducted. This study focuses on the mature robotic technology of link and joint manipulators with zero or low kinematic redundancy. This study produces a field system architecture that specifies a unified combination of control, sensing, kinematic solutions for down-well applications. An experimental system is built to demonstrate the proposed field system architecture and test control and intelligent tactile exploration algorithms. Experimental results to date have indicated acceptability of the proposed field system architecture and have demonstrated the ability to characterize geometry with sparse tactile data.(cont.) Serpentine manipulators implemented using digital mechatronic actuation are also considered. Digital mechatronic devices use actuators with discrete output states and the potential to be mechanically robust and inexpensive. The design of digital mechatronic devices is challenging. Design parameter optimization methods are developed and applied to a design case study of a manipulator in a constrained workspace. This research demonstrates that down-well tactile exploration with a manipulator is feasible. Experimental results show that the proposed field system architecture, a 4 degree-of-freedom anthropomorphic manipulator, can obtain accurate tactile data without using any sensor feedback besides manipulator joint angles.by Daniel Terrance Kettler.S.M

Quantum Chemistry in the Age of Quantum Computing

Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging complexity landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry such as the electronic structure of molecules. In the past two decades significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This article is an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing, and quantum computing researchers who would like to explore applications in quantum chemistry

Water pressure optimisation for leakage management using deep reinforcement learning

In this thesis, we introduce a novel approach to pressure management using deep reinforcement learning (DRL) algorithms. Exploiting DRL algorithms to optimise pressure management in water distribution networks (WDNs) provides a more computationally efficient and resilient method to reduce background and burst leakage. Using DRL to manage pressure has proven as a valuable method to reduce leakage and carbon emissions in two case studies based on a real and benchmark water network. A cohort of eight DRL algorithms of varying natures are implemented on a benchmark test network and real network model of varying sizes to prove their scalability. An investigation on their ability to reduce both background and burst leakage is conducted to highlight their abilities with regards to different leak sizes. The application of deep reinforcement learning algorithms to control leakage in WDNs builds on from two extensive reviews of leakage management and DRL applications in the urban water systems. Collating this literature pinpoints the novelty in applying deep reinforcement learning algorithms to control pressure in WDNs and provides context to the thesis. To develop DRL algorithms fit for WDN operations, a novel python-based environment is created that can communicate the hydraulic capabilities of EPANET to the DRL agent. This involved multiple design choices including action space and observation space selection as well as formulating a reward function suitable for the multiple objectives relating to leakage reduction. Regarding background leakage, the best performing DRL algorithm resulted in 65.2% reduction in leakage in the benchmark network. However, the investigation on the real water network provided by Northumbrian Water Living has proved the strong dependency between valve locations and pressure management hence resulting in a negligible background leakage reduction. The ability of the DRL algorithms to deal with uncertainty through randomised burst nodes was investigated in the second case study. DRL policies demonstrated resilience in comparison to the standard optimisation algorithms used (differential evolution, particle swarm optimisation, and nelder mead). The best performing DRL algorithm predicted a 58.46% leakage reduction and 5650kg of reduced CO2 emissions in the benchmark water network. On the other hand, the best DRL performance optimised the real water network by reducing the leakage by 5.79% and carbon emissions by 1999kg of CO2

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference