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41 
A Development of Design and Control Methodology for Next Generation Parallel Hybrid Electric VehicleLai, Lin 02 October 2013 (has links)
Commercially available Hybrid Electric Vehicles (HEVs) have been around for more than ten years. However, their market share remains small. Focusing only on the improvement of fuel economy, the design tends to reduce the size of the internal combustion engine in the HEV, and uses the electrical drive to compensate for the power gap between the load demand and the engine capacity. Unfortunately, the low power density and the high cost of the combined electric motor drive and battery packs dictate that the HEV has either worse performance or much higher price than the conventional vehicle. In this research, a new design philosophy for parallel HEV is proposed, which uses a full size engine to guarantee the vehicle performance at least as good as the conventional vehicle, and hybridizes with an electrical drive in parallel to improve the fuel economy and performance beyond the conventional cars. By analyzing the HEV fuel economy versus the increasing of the electrical drive power on typical driving conditions, the optimal hybridization electric power capacity is determined. Thus, the full size engine HEV shows significant improvement in fuel economy and performance, with relatively short cost recovery period.
A new control strategy, which optimizes the fuel economy of parallel configured charge sustained hybrid electric vehicles, is proposed in the second part of this dissertation. This new approach is a constrained engine onoff strategy, which has been developed from the two extreme control strategies of maximum SOC and engine onoff, by taking their advantages and overcoming their disadvantages. A system optimization program using dynamic programming algorithm has been developed to calibrate the control parameters used in the developed control strategy, so that the control performance can be as close to the optimal solution as possible. In order to determine the sensitivity of the new control strategy to different driving conditions, a passenger car is simulated on different driving cycles. The performances of the vehicle with the new control strategy are compared with the optimal solution obtained on each driving condition with the dynamic programming optimization. The simulation result shows that the new control strategy always keeps its performance close to the optimal one, as the driving condition changes.

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A framework for discretetime dynamic programming with multiple objectives.Rakshit, Ananda. January 1988 (has links)
The investigation reported in this dissertation attempts to determine the feasibility of using a distancebased approach like compromise programming for discretetime dynamic programming problems with multiple objectives. In compromise programming, a function measuring the distance from a generally infeasible ideal solution to the feasible set of the problem is the single objective acting as a surrogate for the set of multiple objectives. Since, in general, there is no single best solution to a multiple objective problem, a framework to generate a family of compromise solutions interactively on a computer is proposed. Various quantities relevant to dynamic compromise programming are defined in precise terms. Dynamic compromise programming problems are computationally difficult to solve because in order to make the distance function decomposable over stages, dimensionality of the statespace must be increased by the number of objectives. To generate compromise solutions, quasiNewton differential dynamic programming (QDDP), a recently developed variablemetric method for discretetime optimal control, was employed. QDDP is attractive because no second order or Hessian information is required as input. Instead, Hessian matrices are approximated by first order or gradient information. Since very little is known about its numerical properties, computational experiments were conducted on QDDP. A new strategy for updating Hessian matrix approximations was computationally tested. A constrained QDDP algorithm is proposed, computationally tested, and applied to solve a multiobjective dynamic programming problem with inequality constraints at each stage. The algorithm has the potential for application to the more general discretetime optimal control problem with stage constraints. The framework for generating compromise solutions interactively was implemented for prototype problems. Because decision maker interaction is crucial in a multiple objective situation, special attention was paid towards developing a manmachine interface using onscreen windows. All implementation and computational testing were done on a UNIX based personal computer.

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Applications in optimization and investment lag problemAlForaih, Mishari Najeeb January 2015 (has links)
This thesis studies two optimization problems: the optimization of a staffing policy assuming non stationary Poisson demand, and exponential travel and job times, and the optimization of investment decisions with an investment lag. In the staffing policy optimization, we solve a novel timedynamic HamiltonJacobiBellman equation that models jobs as a Poisson jump process. The model gives the employer the flexibility to control the number of staff hired by two factors: the cost of hiring and the effect of delay. We have solved the optimal staffing policy problem using different approaches, which are compared. We produce accurate numerical results for different parameters, and discuss the advantages and disadvantages of each approach. Moreover, we have solved a staffing problem for a national utility company, using a standard linear programming approach, which is compared with our methods. In addition to the Poisson jump process, we extend the model to treat a continuous job model, and two locations model that is extendible to a larger network problem. In the investment lag problem, we use a mixture of numerical methods including finite difference and body fitted coordinates to form a robust and stable numerical scheme which is applied to solve the investment lag problem for a geometric Brownian motion presented in the paper by BarIlan and Strange (1996). The problem is to calculate the optimal price to invest in a project that have a time lag period between the decision to invest and production, and the optimal price to mothball the project. The method presented in this thesis is more flexible as we compare it with the previous results, and solves the problem for different stochastic processes, such as CoxIngersollRoss model, which does not have analytic solution.

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Globally convergent and efficient methods for unconstrained discretetime optimal controlNg, Chi Kong 01 January 1998 (has links)
No description available.

45 
Linking music metadataMacrae, Robert January 2012 (has links)
The internet has facilitated music metadata production and distribution on an unprecedented scale. A contributing factor of this data deluge is a change in the authorship of this data from the expert few to the untrained crowd. The resulting unordered flood of imperfect annotations provides challenges and opportunities in identifying accurate metadata and linking it to the music audio in order to provide a richer listening experience. We advocate novel adaptations of Dynamic Programming for music metadata synchronisation, ranking and comparison. This thesis introduces Windowed Time Warping, Greedy, Constrained OnLine Time Warping for synchronisation and the Concurrence Factor for automatically ranking metadata. We begin by examining the availability of various music metadata on the web. We then review Dynamic Programming methods for aligning and comparing two source sequences whilst presenting novel, specialised adaptations for efficient, realtime synchronisation of music and metadata that make improvements in speed and accuracy over existing algorithms. The Concurrence Factor, which measures the degree in which an annotation of a song agrees with its peers, is proposed in order to utilise the wisdom of the crowds to establish a ranking system. This attribute uses a combination of the standard Dynamic Programming methods Levenshtein Edit Distance, Dynamic Time Warping, and Longest Common Subsequence to compare annotations. We present a synchronisation application for applying the aforementioned methods as well as a tablatureparsing application for mining and analysing guitar tablatures from the web. We evaluate the Concurrence Factor as a ranking system on a largescale collection of guitar tablatures and lyrics to show a correlation with accuracy that is superior to existing methods currently used in internet search engines, which are based on popularity and human ratings.

46 
Surrogate dual search in nonlinear integer programming.January 2009 (has links)
Wang, Chongyu. / Thesis (M.Phil.)Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 7478). / Abstract also in Chinese. / Abstract  p.1 / Abstract in Chinese  p.3 / Acknowledgement  p.4 / Contents  p.5 / List of Tables  p.7 / List of Figures  p.8 / Chapter 1.  Introduction  p.9 / Chapter 2.  Conventional Dynamic Programming  p.15 / Chapter 2.1.  Principle of optimality and decomposition  p.15 / Chapter 2.2.  Backward dynamic programming  p.17 / Chapter 2.3.  Forward dynamic programming  p.20 / Chapter 2.4.  Curse of dimensionality  p.23 / Chapter 3.  Surrogate Constraint Formulation  p.26 / Chapter 3.1.  Surrogate constraint formulation  p.26 / Chapter 3.2.  Singly constrained dynamic programming  p.28 / Chapter 3.3.  Surrogate dual search  p.29 / Chapter 4.  Distance Confined Path Algorithm  p.34 / Chapter 4.1.  Yen´ةs algorithm for the kth shortest path problem  p.35 / Chapter 4.2.  Application of Yen´ةs method to integer programming  p.36 / Chapter 4.3.  Distance confined path problem  p.42 / Chapter 4.4.  Application of distance confined path formulation to integer programming  p.50 / Chapter 5.  Convergent Surrogate Dual Search  p.59 / Chapter 5.1.  Algorithm for convergent surrogate dual search  p.62 / Chapter 5.2.  "Solution schemes for (Pμ{αk,αβ)) and f(x) = αk"  p.63 / Chapter 5.3.  Computational Results and Analysis  p.68 / Chapter 6.  Conclusions  p.72 / Bibliography  p.74

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Dynamic Programming MultiObjective Combinatorial OptimizationMankowski, Michal 18 October 2020 (has links)
In this dissertation, we consider extensions of dynamic programming for combinatorial optimization. We introduce two exact multiobjective optimization algorithms: the multistage optimization algorithm that optimizes the problem relative to the ordered sequence of objectives (lexicographic optimization) and the bicriteria optimization algorithm that simultaneously optimizes the problem relative to two objectives (Pareto optimization). We also introduce a counting algorithm to count optimal solution before and after every optimization stage of multistage optimization. We propose a fairly universal approach based on socalled circuits without repetitions in which each element is generated exactly one time. Such circuits represent the sets of elements under consideration (the sets of feasible solutions) and are used by counting, multistage, and bicriteria optimization algorithms. For a given optimization problem, we should describe an appropriate circuit and cost functions. Then, we can use the designed algorithms for which we already have proofs of their correctness and ways to evaluate the required number of operations and the time. We construct conventional (which work directly with elements) circuits without repetitions for matrix chain multiplication, global sequence alignment, optimal paths in directed graphs, binary search trees, convex polygon triangulation, line breaking (text justification), onedimensional clustering, optimal bitonic tour, and segmented least squares. For these problems, we evaluate the number of operations and the time required by the optimization and counting algorithms, and consider the results of computational experiments. If we cannot find a conventional circuit without repetitions for a problem, we can either create custom algorithms for optimization and counting from scratch or can transform a circuit with repetitions into a socalled syntactical circuit, which is a circuit without repetitions that works not with elements but with formulas representing these elements. We apply both approaches to the optimization of matchings in trees and apply the second approach to the 0/1 knapsack problem. We also briefly introduce our work in operation research with applications to health care. This work extends our interest in the optimization field from developing new methods included in this dissertation towards the practical application.

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Modern Dynamic Programming Approaches to Sequential Decision MakingMin, Seungki January 2021 (has links)
Dynamic programming (DP) has long been an essential framework for solving sequential decisionmaking problems. However, when the state space is intractably large or the objective contains a risk term, the conventional DP framework often fails to work. In this dissertation, we investigate such issues, particularly those arising in the context of multiarmed bandit problems and risksensitive optimal execution problems, and discuss the use of modern DP techniques to overcome these challenges such as information relaxation, policy gradient, and state augmentation. We develop frameworks formalize and improve existing heuristic algorithms (e.g., Thompson sampling, aggressiveinthemoney trading), while shedding new light on the adopted DP techniques.

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A new Hilbert time warping principle for pattern matching / by Arulnesan MaheswaranMaheswaran, Arulnesan January 1985 (has links)
Includes bibliographical references / 1 v. (various pagings) : ill ; 31 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)University of Adelaide, Dept. of Electrical and Electronic Engineering, 1985

50 
Fast Headandshoulder SegmentationDeng, Xiaowei January 2016 (has links)
Many tasks of visual computing and communications such as object recognition, matting, compression, etc., need to extract and encode the outer boundary of the object in a digital image or video. In this thesis, we focus on a particular video segmentation task and propose an efficient method for headandshoulder of humans through video frames. The key innovations for our work are as follows: (1) a novel head descriptor in polar coordinate is proposed, which can characterize intrinsic head object well and make it easy for computer to process, classify
and recognize. (2) a learningbased method is proposed to provide highly precise and robust headandshoulder segmentation results in applications where the headandshoulder object in the question is a known prior and the background is too complex. The efficacy of our method is
demonstrated on a number of challenging experiments. / Thesis / Master of Applied Science (MASc)

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