HVDC (High Voltage Direct Current) is used all over the world for transmission of electric power due to lower losses compared to traditional HVAC (High Voltage Alternating Current). However, the procedure of converting AC into DC puts great demand on the control system of the converter stations. These control systems need to be tuned properly to give the HVDC system the correct dynamics to handle variations in the network load and other disturbances. In this thesis, it was investigated if optimization algorithms can be used for tuning of the control parameters. Focus was on three parts of the control system, the Current Control Amplifier, Voltage Dependent Current Order Limiter and the Rectifier Alpha Minimum Limiter. The Nelder & Mead Simplex method was used and several different objective functions were tested, including combinations of integral square error, integral absolute error, rise time and overshoot. Several different fault cases and scenarios were tested and results of the optimization were compared to the manually tuned control system. It was found that the results of the optimization were comparable with the manually tuned parameters for many of the cases tested. The biggest issue encountered was that the optimization algorithm often finds a local minimum in the objective function, leading to a suboptimal solution. This issue could be solved by running the optimization several times, using different initial values. It is concluded that using optimization algorithms could be a useful tool for tuning of the HVDC control system.

The main goal of this project is to explore the use of stochastic simulation,genetic algorithms, fuzzy decision making and other tools for solvingcomplex maintenance planning optimization problems. We use two dierentmaintenance activities, corrective maintenance and preventive maintenance.Since the evaluation of specic candidate maintenance policies can take a longtime to execute and the problem of nding the optimal policy is both nonlinearand non-convex, we propose the use of genetic algorithms (GA) for theoptimization. The main task of the GA is to nd the optimal maintenancepolicy, which involves: (1) the probability of breakdown, (2) calculation ofthe cost of corrective maintenance, (3) calculation of the cost of preventivemaintenance and (4) calculation of ROI (Return On Investment).Another goal of this project is to create a decision making model for multicriteriasystems. To nd a near-optimal maintenance policy, we need to havean overview over the health status of the system components. To modelthe health of a component we should nd all the operational criteria thataect it. We also need to analyze alternative maintenance activities in orderto make the best maintenance decisions. In order to do that, the TOPSISmethod and fuzzy decision making has been used.To evaluate the proposed methodology, internal combustion engine coolingof a typical Scania truck was used as a case study.

Diss. Stockholm : Handelshögskolan, 2008.

Diss. (sammanfattning) Linköping : Univ., 2001. / Härtill 7 uppsatser.

Almost every module or project needs to be optimized to get the best results and reduce costs. Multi-sensor, multi-input trackers require a huge number of parameters to run, which have an undefined or unknown to the output of the tracker. It becomes very difficult to manually initialize these parameters to get a good output and there was a need to automate the process of selecting the parameters, validating them and initialing the tracker. The optimizer built to cater for these issues uses heuristic genetic algorithms – Particle Swarm Optimization and Gravitational Search Algorithm to find the best solutions for the problem. The optimizer works with the help of a Parameter Evaluator (developed earlier) to study the output of the tracker and incorporate the multi objective (Pareto) aspect of the problem. The Optimizer can find solutions to any optimization problem if hooked to a corresponding evaluator or fitness function calculator. This feature makes the Optimizer not just another module to the tracker but an independent application that could be used for general purpose optimization solutions. / Thesis / Master of Applied Science (MASc)

Xinbao Li. / Thesis (Ph.D.)--Chinese University of Hong kong, 1997. / Includes bibliographical references (p. 62-64). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

Coin-flipping is the cryptographic task of generating a random coin-flip between two mistrustful parties. Kitaev discovered that the security of quantum coin-flipping protocols can be analyzed using semidefinite programming. This lead to his result that one party can force a desired coin-flip outcome with probability at least 1/√2. We give sufficient background in quantum computing and semidefinite programming to understand Kitaev's semidefinite programming formulation for coin-flipping cheating strategies. These ideas are specialized to a specific class of protocols singled out by Nayak and Shor. We also use semidefinite programming to solve for the maximum cheating probability of a particular protocol which has the best known security. Furthermore, we present a family of protocols where one party has a greater probability of forcing an outcome of 0 than an outcome of 1. We also discuss a computer search to find specific protocols which minimize the maximum cheating probability.

This thesis concerns the analysis of the unconditional security of quantum cryptographic protocols using convex optimization techniques. It is divided into the study of coin-flipping and oblivious transfer. We first examine a family of coin-flipping protocols. Almost all of the handful of explicitly described coin-flipping protocols are based on bit-commitment. To explore the possibility of finding explicit optimal or near-optimal protocols, we focus on a class which generalizes such protocols. We call these $\BCCF$-protocols, for bit-commitment based coin-flipping. We use the semidefinite programming (SDP) formulation of cheating strategies along the lines of Kitaev to analyze the structure of the protocols. In the first part of the thesis, we show how these semidefinite programs can be used to simplify the analysis of the protocol. In particular, we show that a particular set of cheating strategies contains an optimal strategy. This reduces the problem to optimizing a linear combination of fidelity functions over a polytope which has several benefits. First, it allows one to model cheating probabilities using a simpler class of optimization problems known as second-order cone programs (SOCPs). Second, it helps with the construction of point games due to Kitaev as described in Mochon's work. Point games were developed to give a new perspective for studying quantum protocols. In some sense, the notion of point games is dual to the notion of protocols. There has been increased research activity in optimization concerning generalizing theory and algorithms for linear programming to much wider classes of optimization problems such as semidefinite programming. For example, semidefinite programming provides a tool for potentially improving results based on linear programming or investigating old problems that have eluded analysis by linear programming. In this sense, the history of semidefinite programming is very similar to the history of quantum computation. Quantum computing gives a generalized model of computation to tackle new and old problems, improving on and generalizing older classical techniques. Indeed, there are striking differences between linear programming and semidefinite programming as there are between classical and quantum computation. In this thesis, we strengthen this analogy by studying a family of classical coin-flipping protocols based on classical bit-commitment. Cheating strategies for these ``classical $\BCCF$-protocols'' can be formulated as linear programs (LPs) which are closely related to the semidefinite programs for the quantum version. In fact, we can construct point games for the classical protocols as well using the analysis for the quantum case. Using point games, we prove that every classical $\BCCF$-protocol allows exactly one of the parties to entirely determine the outcome. Also, we rederive Kitaev's lower bound to show that only ``classical'' protocols can saturate Kitaev's analysis. Moreover, if the product of Alice and Bob's optimal cheating probabilities is $1/2$, then at least one party can cheat with probability $1$. The second part concerns the design of an algorithm to search for $\BCCF$-protocols with small bias. Most coin-flipping protocols with more than three rounds have eluded direct analysis. To better understand the properties of optimal $\BCCF$-protocols with four or more rounds, we turn to computational experiments. We design a computational optimization approach to search for the best protocol based on the semidefinite programming formulations of cheating strategies. We create a protocol filter using cheating strategies, some of which build upon known strategies and others are based on convex optimization and linear algebra. The protocol filter efficiently eliminates candidate protocols with too high a bias. Using this protocol filter and symmetry arguments, we perform searches in a matter of days that would have otherwise taken millions of years. Our experiments checked $10^{16}$ four and six-round $\BCCF$-protocols and suggest that the optimal bias is $1/4$. The third part examines the relationship between oblivious transfer, bit-commitment, and coin-flipping. We consider oblivious transfer which succeeds with probability $1$ when the two parties are honest and construct a simple protocol with security provably better than any classical protocol. We also derive a lower bound by constructing a bit-commitment protocol from an oblivious transfer protocol. Known lower bounds for bit-commitment then lead to a constant lower bound on the bias of oblivious transfer. Finally, we show that it is possible to use Kitaev's semidefinite programming formulation of cheating strategies to obtain optimal lower bounds on a ``forcing'' variant of oblivious transfer related to coin-flipping.

This thesis concerns the analysis of the unconditional security of quantum cryptographic protocols using convex optimization techniques. It is divided into the study of coin-flipping and oblivious transfer. We first examine a family of coin-flipping protocols. Almost all of the handful of explicitly described coin-flipping protocols are based on bit-commitment. To explore the possibility of finding explicit optimal or near-optimal protocols, we focus on a class which generalizes such protocols. We call these $\BCCF$-protocols, for bit-commitment based coin-flipping. We use the semidefinite programming (SDP) formulation of cheating strategies along the lines of Kitaev to analyze the structure of the protocols. In the first part of the thesis, we show how these semidefinite programs can be used to simplify the analysis of the protocol. In particular, we show that a particular set of cheating strategies contains an optimal strategy. This reduces the problem to optimizing a linear combination of fidelity functions over a polytope which has several benefits. First, it allows one to model cheating probabilities using a simpler class of optimization problems known as second-order cone programs (SOCPs). Second, it helps with the construction of point games due to Kitaev as described in Mochon's work. Point games were developed to give a new perspective for studying quantum protocols. In some sense, the notion of point games is dual to the notion of protocols. There has been increased research activity in optimization concerning generalizing theory and algorithms for linear programming to much wider classes of optimization problems such as semidefinite programming. For example, semidefinite programming provides a tool for potentially improving results based on linear programming or investigating old problems that have eluded analysis by linear programming. In this sense, the history of semidefinite programming is very similar to the history of quantum computation. Quantum computing gives a generalized model of computation to tackle new and old problems, improving on and generalizing older classical techniques. Indeed, there are striking differences between linear programming and semidefinite programming as there are between classical and quantum computation. In this thesis, we strengthen this analogy by studying a family of classical coin-flipping protocols based on classical bit-commitment. Cheating strategies for these ``classical $\BCCF$-protocols'' can be formulated as linear programs (LPs) which are closely related to the semidefinite programs for the quantum version. In fact, we can construct point games for the classical protocols as well using the analysis for the quantum case. Using point games, we prove that every classical $\BCCF$-protocol allows exactly one of the parties to entirely determine the outcome. Also, we rederive Kitaev's lower bound to show that only ``classical'' protocols can saturate Kitaev's analysis. Moreover, if the product of Alice and Bob's optimal cheating probabilities is $1/2$, then at least one party can cheat with probability $1$. The second part concerns the design of an algorithm to search for $\BCCF$-protocols with small bias. Most coin-flipping protocols with more than three rounds have eluded direct analysis. To better understand the properties of optimal $\BCCF$-protocols with four or more rounds, we turn to computational experiments. We design a computational optimization approach to search for the best protocol based on the semidefinite programming formulations of cheating strategies. We create a protocol filter using cheating strategies, some of which build upon known strategies and others are based on convex optimization and linear algebra. The protocol filter efficiently eliminates candidate protocols with too high a bias. Using this protocol filter and symmetry arguments, we perform searches in a matter of days that would have otherwise taken millions of years. Our experiments checked $10^{16}$ four and six-round $\BCCF$-protocols and suggest that the optimal bias is $1/4$. The third part examines the relationship between oblivious transfer, bit-commitment, and coin-flipping. We consider oblivious transfer which succeeds with probability $1$ when the two parties are honest and construct a simple protocol with security provably better than any classical protocol. We also derive a lower bound by constructing a bit-commitment protocol from an oblivious transfer protocol. Known lower bounds for bit-commitment then lead to a constant lower bound on the bias of oblivious transfer. Finally, we show that it is possible to use Kitaev's semidefinite programming formulation of cheating strategies to obtain optimal lower bounds on a ``forcing'' variant of oblivious transfer related to coin-flipping.

Thesis (M.A. Sc.)--University of Toronto, 2005.