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Wang, Chongyu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 74-78). / 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

<p>The power to mass ratio of a heavy truck causes even moderate slopes to have a significant influence on the motion. The velocity will inevitable vary within an interval that is primarily determined by the ratio and the road topography. If further variations are actuated by a controller, there is a potential to lower the fuel consumption by taking the upcoming topography into account. This possibility is explored through theoretical and simulation studies as well as experiments in this work.</p><p>Look-ahead control is a predictive strategy that repeatedly solves an optimization problem online by means of a tailored dynamic programming algorithm. The scenario in this work is a drive mission for a heavy diesel truck where the route is known. It is assumed that there is road data on-board and that the current heading is known. A look-ahead controller is then developed to minimize fuel consumption and trip time.</p><p>The look-ahead control is realized and evaluated in a demonstrator vehicle and further studied in simulations. In the prototype demonstration, information about the road slope ahead is extracted from an on-board database in combination with a GPS unit. The algorithm calculates the optimal velocity trajectory online and feeds the conventional cruise controller with new set points. The results from the experiments and simulations confirm that look-ahead control reduces the fuel consumption without increasing the travel time. Also, the number of gear shifts is reduced. Drivers and passengers that have participated in tests and demonstrations have perceived the vehicle behavior as comfortable and natural.</p> / Report code: LIU-TEK-LIC-2007:28.

Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(lambda) algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD(lambda) and Q-learning belong.

The goals of gene prediction using computational approaches are to determine gene location and the corresponding functionality of the coding region. A subset of gene prediction is the gene structure prediction problem, which is to define the exon-intron boundaries of a gene. Gene prediction follows two general approaches: statistical patterns identification and sequence similarity comparison. Similarity based approaches have gained increasing popularity with the recent vast increase in genomic data in GenBank. The proposed gene prediction algorithm is a similarity based algorithm which capitalizes on the fact that similar sequences bear similar functions. The proposed algorithm, like most other similarity based algorithms, is based on dynamic programming. Given a genomic DNA, X = x1 xn and a closely related cDNA, Y = y1 yn, these sequences are aligned with matching pairs stored in a data set. These indexes of matching sets contain a large jumble of all matching pairs, with a lot of cross over indexes. Dynamic programming alignment is again used to retrieve the longest common non-crossing subsequence from the collection of matching fragments in the data set. This algorithm was implemented in Java on the Unix platform. Statistical comparisons were made against other software programs in the field. Statistical evaluation at both the DNA and exonic level were made against Est2genome, Sim4, Spidey, and Fgenesh-C. The proposed gene structure prediction algorithm, by far, has the best performance in the specificity category. The resulting specificity was greater than 98%. The proposed algorithm also has on par results in terms of sensitivity and correlation coeffcient. The goal of developing an algorithm to predict exonic regions with a very high level of correctness was achieved.

This thesis considers a variant of the classical problem for finding the longest common subsequence (LCS) called longest common subsequence problem with a gapped constraint (LCSGC). Given two sequences A, B, and a constrained sequence C, which is accomplished with a corresponding gapped constraint for each symbol, whose lengths are m, n, and r, respectively, the LCSGC problem is to find an LCS of A and B, such that C is also a subsequence of this LCS and the gapped constraints corresponding to C are satisfied. In this thesis, two algorithms with time complexities O(m2n2r) and O(mnr ¡Ñ min(m, n)) are proposed based on the dynamic programming technique for solving the LCSGC problem.

Unmanned Aerial Vehicles (UAVs) are being used more frequently in surveillance scenarios for both civilian and military applications. One such application addresses a UAV patrolling a perimeter, where certain stations can receive alerts at random intervals. Once the UAV arrives at an alert site it can take two actions: 1. Loiter and gain information about the site. 2. Move on around the perimeter. The information that is gained is transmitted to an operator to allow him to classify the alert. The information is a function of the amount of time the UAV is at the alert site, also called the dwell time, and the maximum delay. The goal of the optimization is to classify the alert so as to maximize the expected discounted information gained by the UAV's actions at a station about an alert. This optimization problem can be readily solved using Dynamic Programming. Even though this approach generates feasible solutions, there are reasons to experiment with different approaches. A complication for Dynamic Programming arises when the perimeter patrol problem is expanded. This is that the number of states increases rapidly when one adds additional stations, nodes, or UAVs to the perimeter. This in effect greatly increases the computation time making the determination of the solution intractable. The following attempts to alleviate this problem by implementing a Reinforcement Learning technique to obtain the optimal solution, more specifically Q-Learning. Reinforcement Learning is a simulation-based version of Dynamic Programming and requires lesser information to compute sub-optimal solutions. The effectiveness of the policies generated using Reinforcement Learning for the perimeter patrol problem have been corroborated numerically in this thesis.

Abstract On the whole, there are two ways to dispose of multiclass classification problem. One is deal it with directly. And the other is dividing it into several binary-class problems. For this reason, it will be simpler as regards individual binary-class problems. And it can improve the accuracy of the multiclass classification problem by reorganize the effect. So how to decompose several binary-class problems is the most important point. Here, based on our study, we use Dynamic Programming as foundation to get the optimal solution of multiclass¡¦s decomposition. Not only get it simplify but also can achieved the best classified result.

The goals of gene prediction using computational approaches are to determine gene location and the corresponding functionality of the coding region. A subset of gene prediction is the gene structure prediction problem, which is to define the exon-intron boundaries of a gene. Gene prediction follows two general approaches: statistical patterns identification and sequence similarity comparison. Similarity based approaches have gained increasing popularity with the recent vast increase in genomic data in GenBank. The proposed gene prediction algorithm is a similarity based algorithm which capitalizes on the fact that similar sequences bear similar functions. The proposed algorithm, like most other similarity based algorithms, is based on dynamic programming. Given a genomic DNA, X = x1 xn and a closely related cDNA, Y = y1 yn, these sequences are aligned with matching pairs stored in a data set. These indexes of matching sets contain a large jumble of all matching pairs, with a lot of cross over indexes. Dynamic programming alignment is again used to retrieve the longest common non-crossing subsequence from the collection of matching fragments in the data set. This algorithm was implemented in Java on the Unix platform. Statistical comparisons were made against other software programs in the field. Statistical evaluation at both the DNA and exonic level were made against Est2genome, Sim4, Spidey, and Fgenesh-C. The proposed gene structure prediction algorithm, by far, has the best performance in the specificity category. The resulting specificity was greater than 98%. The proposed algorithm also has on par results in terms of sensitivity and correlation coeffcient. The goal of developing an algorithm to predict exonic regions with a very high level of correctness was achieved.

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