Lau Yung Hang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 78-82). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / List of Figures --- p.viii / List of Tables --- p.x / List of Algorithms --- p.xi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Justifying the Need for US and DTW --- p.1 / Chapter 1.2 --- Motivating Examples --- p.3 / Chapter 1.3 --- Contributions --- p.9 / Chapter 1.4 --- Thesis Organization --- p.10 / Chapter 2 --- Problem Definition --- p.11 / Chapter 3 --- Preliminaries --- p.13 / Chapter 3.1 --- Time Warping Distance --- p.13 / Chapter 3.2 --- Constraints and Lower Bounding --- p.16 / Chapter 3.3 --- Uniform Scaling --- p.20 / Chapter 3.3.1 --- Lower bounding uniform scaling --- p.21 / Chapter 4 --- Scaling and Time Warping --- p.23 / Chapter 4.1 --- Tightness of the lower bounds --- p.27 / Chapter 4.2 --- Experimental Evaluation --- p.32 / Chapter 5 --- A Faster and more Flexible Approach --- p.41 / Chapter 5.1 --- The Enveloping Sequences Revisited --- p.41 / Chapter 5.2 --- Speeding up LB Distance Computation --- p.43 / Chapter 5.3 --- Experimental Evaluation --- p.44 / Chapter 5.3.1 --- Query Time Comparison --- p.44 / Chapter 5.3.2 --- Effect on Pruning Power --- p.46 / Chapter 6 --- Indexing for SWM --- p.49 / Chapter 6.1 --- Related Work --- p.49 / Chapter 6.1.1 --- Fast subsequence matching --- p.49 / Chapter 6.1.2 --- Duality-based subsequence matching --- p.50 / Chapter 6.1.3 --- Nearest Neighbor Search --- p.53 / Chapter 6.1.4 --- Dimension Reduction --- p.57 / Chapter 6.2 --- Proposed Indexing for SWM --- p.60 / Chapter 6.2.1 --- Index construction algorithm --- p.60 / Chapter 6.2.2 --- Utilizing the index --- p.61 / Chapter 6.2.3 --- Nearest Neighbor Search --- p.63 / Chapter 6.3 --- Experimental Evaluation --- p.64 / Chapter 6.3.1 --- Range Queries --- p.64 / Chapter 6.3.2 --- One nearest neighbor search --- p.68 / Chapter 6.3.3 --- k-nearest neighbor search --- p.72 / Chapter 7 --- Conclusion --- p.76 / Bibliography --- p.78

In the management of agricultural insect pests, short-term costs must be balanced against long-term benefits. Controls should be selected to account for both their immediate and downstream effects upon the demography and genetics of the pest, enabling suppression today without threatening suppression tomorrow. The iterative, algorithmic method of dynamic programming can provide optimal solutions to problems of this type, in which actions are taken sequentially and each action may influence those which follow it. However, this approach is fundamentally constrained with regards to the magnitude of the problems it may solve. As questions of insect pest management can be subject to ecological and evolutionary complexities, this may place them beyond the scope of dynamic programming. When it is the intricacies of a problem that are of interest, it may be more productive to utilise approximate dynamic programming (ADP) methods which can attempt problems of arbitrary complexity, although at the expense of no longer guaranteeing optimality. In this thesis I first challenge a dynamic programming algorithm with the management of a hypothetical insect pest feeding upon a transgenic insecticidal crop. The model explores how different realisations of fitness costs to resistance influence the algorithms suggested actions. I then apply a brute-force variant of ADP, a lookahead policy, to the management of a stage-structured, continuously reproducing pest population. This was to explore the extent to which an algorithm with a limited temporal perspective is able to balance the timetable of pest demography against the timescale over which insecticidal sprays and bisex-lethal sterile insect releases unfold. This same decision framework is then applied to a modified problem in which resistance to insecticidal toxins may evolve and releases are now male-selecting. This was used to assess the efficacy with which simple lookahead policies utilise a control with delayed benefits (the male-selecting releases) and possible constraints on their capacity to respond to resistance evolution. Dynamic programming and ADP methods offer a versatile toolbox for accounting for the potential impacts of the evolutionary and ecological peculiarities of particular pests upon control decisions.

The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.

<p>New and exciting possibilities in vehicle control are revealed by the consideration of topography through the combination GPS and three dimensional road maps. This thesis explores how information about future road slopes can be utilized in a heavy truck with the aim at reducing the fuel consumption over a route without increasing the total travel time. </p><p>A model predictive control (MPC) scheme is used to control the longitudinal behavior of the vehicle, which entails determining accelerator and brake levels and also which gear to engage. The optimization is accomplished through discrete dynamic programming. A cost function is used to define the optimization criterion. Through the function parameters the user is enabled to decide how fuel use, negative deviations from the reference velocity, velocity changes, gear shifts and brake use are weighed. </p><p>Computer simulations with a load of 40 metric tons shows that the fuel consumption can be reduced with 2.5% with a negligible change in travel time, going from Link¨oping to J¨onk¨oping and back. The road slopes are calculated by differentiation of authentic altitude measurements along this route. The complexity of the algorithm when achieving these results allows the simulations to run two to four times faster than real time on a standard PC, depending on the desired update frequency of the control signals.</p>

Weber and Stidham (1987) used submodularity to establish transition monotonicity (a service completion at one station cannot reduce the service rate at another station) for Markovian queueing networks that meet certain regularity conditions and are controlled to minimize service and queueing costs. We give an extension of monotonicity to other directions in the state space, such as arrival transitions, and to arrival routing problems. The conditions used to establish monotonicity, which deal with the boundary of the state space, are easily verified for many queueing systems. We also show that, without service costs, transition-monotone controls can be described by simple control regions and switching functions, extending earlier results. The theory is applied to production/inventory systems with holding costs at each stage and finished goods backorder costs.

Weber and Stidham (1987) used submodularity to establish transition monotonicity (a service completion at one station cannot reduce the service rate at another station) for Markovian queueing networks that meet certain regularity conditions and are controlled to minimize service and queueing costs. We give an extension of monotonicity to other directions in the state space, such as arrival transitions, and to arrival routing problems. The conditions used to establish monotonicity, which deal with the boundary of the state space, are easily verified for many queueing systems. We also show that, without service costs, transition-monotone controls can be described by simple control regions and switching functions, extending earlier results. The theory is applied to production/inventory systems with holding costs at each stage and finished goods backorder costs.

Dimitri P. Bertsekas. / Bibliography: leaf 7. / Caption title. "August, 1981." / NSF Grant No. NSF/ECS 79-19880