Ant algorithms have been used for a variety of combinatorial optimization problems. One of these problems, where ant algorithms haven't been used, is the design of transition rules for cellular automata (CA). Which is a problem that this master's thesis is focused on. This work begins with an introduction into ant algorithms and a overview of its applications, followed by an introduction into CA. In the next part the author proposes a way how to encode rules of CA into a graph which is used in ant algorithms. The last part of this thesis contains an application of encoded graph on elitist ant system and MAX-MIN ant system. This is followed by experimental results of creating transition rules for CA problems by these algorithms.

Focus of this master's thesis is on evolutionary design of cellular automata and it's optimalization. There are described Evolutionary algorithms and Cellular automata in first part. Thereafter, one of the new ways of transition function representation and its possible evolutionary design is presented. Name of this method is Conditionally Matching Rules. This is followed by description of realized experiments with detailed results. Finally, success with optimalization for some tasks is presented in last chapter along with discussion.

This thesis deals with the pattern matching methods based on finite automata and describes their optimizations. It presents a methodology for the measurement of reliability of pattern matching methods, by comparing their results to the results of the PCRE library. Experiments were conducted for a finite automaton with perfect hashing and faulty transition table. Finally, the resulting reliability evaluation of the algorithm is shown and possible solutions of the identified problems are proposed.

Disertační práce se zabývá rychlým vyhledáváním regulárních výrazů v síťovém provozu s použitím technologie FPGA. Vyhledávání regulárních výrazů v síťovém provozu je výpočetně náročnou operací využívanou převážně v oblasti síťové bezpečnosti a v oblasti monitorování provozu vysokorychlostních počítačových sítí. Současná řešení neumožňují dosáhnout požadovaných multigigabitových propustností při dodržení všech požadavků, které jsou na vyhledávací jednotky kladeny. Nejvyšších propustností dosahují implementace založené na využití inovativních hardwarových architektur implementovaných v FPGA případně v ASIC. Tato disertační práce popisuje nové architektury vyhledávací jednotky, které jsou vhodné pro implementaci jak v FPGA tak v ASIC. Základní myšlenkou navržených architektur je využití perfektní hashovací funkce pro implementaci přechodové tabulky konečného automatu. Dále byla navržena architektura, která umožňuje uživateli zanést malou pravděpodobnost chyby při vyhledávání a tím snížit paměťové nároky vyhledávací jednotky. Disertační práce analyzuje vliv pravděpodobnosti této chyby na celkovou spolehlivost systému a srovnává ji s řešením používaným v současnosti. V rámci disertační práce byla provedena měření vlastností regulárních výrazů používaných při analýze provozu moderních počítačových sítí. Z provedené analýzy vyplývá, že velká část regulárních výrazů je vhodná pro implementaci pomocí navržených architektur. Pro dosažení vysoké propustnosti vyhledávací jednotky práce navrhuje nový algoritmus transformace abecedy, který umožňuje, aby vyhledávací jednotka zpracovala více znaků v jednom kroku. Na rozdíl od současných metod, navržený algoritmus umožňuje konstrukci automatu zpracovávajícího libovolný počet symbolů v jednom taktu. Implementované architektury dosahují v porovnání se současnými metodami úspory paměti zlepšení až 200MB.

The thesis explains several algorithms for pattern matching. Algorithms work in both software and hardware. A part of the thesis is dedicated to extensions of finite automatons. The second part explains hashing and introduces concept of perfect hashing and CRC. The thesis also includes a suggestion of possible structure of a pattern matching unit based on deterministic finite automatons in FPGA. Experiments for determining the structure and size of resulting automatons were done in this thesis.

<p>The design of vehicle components for crashworthiness is one of the most challenging problems in the automotive industry. The safety of the occupants during a crash event relies on the energy absorption capability of vehicle structures. Therefore, the body components of a vehicle are required to be lightweight and highly integrated structures. Moreover, reducing vehicle weight is another crucial design requirement since fuel economy is directly related to the mass of a vehicle. In order to address these requirements, various design concepts for vehicle bodies have been proposed using high-strength steel and different aluminum alloys. However, the price factor has always been an obstacle to completely replace regular body steels with more advanced alloys. To this end, the integration of numerical simulation and structural optimization techniques has been widely practiced addressing these requirements. Advancements in nonlinear structural design have shown the promising potential to generate innovative, safe, and lightweight vehicle structures. In addition, the implementation of structural optimization techniques has the capability to shorten the design cycle time for new models. A reduced design cycle time can provide the automakers with an opportunity to stay ahead of their competitors. During the last few decades, enormous structural optimization methods were proposed. A vast majority of these methods use mathematical programming for optimization, a method that relies on availability sensitivity analysis of objective functions. Thus, due to the necessity of sensitivity analyses, these methods remain limited to linear (or partially nonlinear) material models under static loading conditions. In other words, these methods are no able to capture all non-linearities involved in multi-body crash simulation. As an alternative solution, heuristic approaches, which do need sensitivity analyses, have been developed to address structural optimization problems for crashworthiness. The Hybrid Cellular Automaton (HCA), as a bio-inspired algorithm, is a well-practiced heuristic method that has shown promising capabilities in the structural design for vehicle components. The HCA has been continuously developed during the last two decades and designated to solve specific structural design applications. Despite all advancements, some fundamental aspects of the algorithm are still not adequately addressed in the literature. For instance, the HCA numerically implemented as a closed-loop control system. The local controllers, which dictate the design variable updates, need parameter tuning to efficiently solve different sets of problems. Previous studies suggest that one can identify some default values for the controllers. However, still, there is no well-organized strategy to tune these parameters, and proper tuning still relies on the designer’s experience.</p> <p> </p> <p> Moreover, structures with multiple materials have now become one of the perceived necessities for the automotive industry to address vehicle design requirements such as weight, safety, and cost. However, structural design methods for crashworthiness, including the HCA, are mainly applied to binary structural design problems. Furthermore, the conventional methods for the design of multi-material structures do not fully utilize the capabilities of premium materials. In other words, the development of a well-established method for the design of multi-material structures and capable of considering the cost of the materials, bonding between different materials (especially categorical materials), and manufacturing considering is still an open problem. Lastly, the HCA algorithm relies only on one hyper-parameter, the mass fraction, to synthesize structures. For a given problem, the HCA only provides one design option directed by the mass constraint. In other words, the HCA cannot tailor the dynamic response of the structure, namely, intrusion and deceleration profiles.</p> <p> </p> <p>The main objective of this dissertation is to develop new methodologies to design structures for crashworthiness applications. These methods are built upon the HCA algorithm. The first contribution is about introducing s self-tuning scheme for the controller of the algorithm. The proposed strategy eliminates the need to manually tune the controller for different problems and improve the computational performance and numerical stability. The second contribution of this dissertation is to develop a systematic approach to design multi-material crashworthy structures. To this end, the HCA algorithm is integrated with an ordered multi-material SIMP (Solid Isotropic Material with Penalization) interpolation. The proposed multi-material HCA (MMHCA) framework is a computationally efficient method since no additional design variables are introduced. The MMHCA can synthesize multi-material structures subjected to volume fraction constraints. In addition, an elemental bonding method is introduced to simulate the laser welding applied to multi-material structures. The effect of the bonding strength on the final topology designs is studied using numerical simulations. In the last step, after obtaining the multi-material designs, the HCA is implemented to remove the desired number of bonding elements and reduce the weld length.</p> <p> </p> <p>The third contribution of this dissertation is to introduce a new Cluster-based Structural Optimization method (CBSO) for the design of multi-material structures. This contribution introduces a new Cluster Validity Index with manufacturing considerations referred to as CVI_m. The proposed index can characterize the quality of the cluster in structural design considering volume fraction, size, interface as a measure of manufacturability. This multi-material structural design approach comprises three main steps: generating the conceptual design using adaptive HCA algorithm, clustering of the design domain using Multi-objective Genetic Algorithm (MOGA) optimization. In the third step, MOGA optimization is used to choose categorical materials in order to optimize the crash indicators (e.g., peak intrusion, peak contact force, load uniformity) or the cost of the raw materials. The effectiveness of the algorithm is investigated using numerical examples.</p>

Tableaux-based decision procedures for satisfiability of modal and description logics behave quite well in practice, but it is sometimes hard to obtain exact worst-case complexity results using these approaches, especially for EXPTIME-complete logics. In contrast, automata-based approaches often yield algorithms for which optimal worst-case complexity can easily be proved. However, the algorithms obtained this way are usually not only worst-case, but also best-case exponential: they first construct an automaton that is always exponential in the size of the input, and then apply the (polynomial) emptiness test to this large automaton. To overcome this problem, one must try to construct the automaton 'on-the-fly' while performing the emptiness test. In this paper we will show that Voronkov´s inverse method for the modal logic K can be seen as an on-the-fly realization of the emptiness test done by the automata approach for K. The benefits of this result are two-fold. First, it shows that Voronkov´s implementation of the inverse method, which behaves quite well in practice, is an optimized on-the-fly implementation of the automata-based satisfiability procedure for K. Second, it can be used to give a simpler proof of the fact that Voronkov´s optimizations do not destroy completeness of the procedure. We will also show that the inverse method can easily be extended to handle global axioms, and that the correspondence to the automata approach still holds in this setting. In particular, the inverse method yields an EXPTIME-algorithm for satisfiabilty in K w.r.t. global axioms.

This project is about security of computer networks using Intrusion Detection Systems. IDS contain rules for detection expressed with regular expressions, which are for detection represented by finite-state automata. The complexity of this detection with non-deterministic and deterministic finite-state automata is explained. This complexity can be reduced with help of regular expressions grouping. Grouping algorithm and approaches for speedup and improvement are introduced. One of the approches is Genetic algorithm, which can work real-time. Finally Random search algorithm for grouping of regular expressions is presented. Experiment results with these approches are shown and compared between each other.

The focus of this thesis is the numerical study of subcritical transition to turbulence in boundary-layer flows. For the most part, boundary layers with uniform suction are considered. Constant homogeneous suction counteracts the spatial growth of the boundary layer, rendering the flow parallel. This enables research approaches which are not feasible in the context of spatially developing flows. In the first part, the laminar–turbulent separatrix of the asymptotic suction boundary layer (ASBL) is investigated numerically by means of an edge-tracking algorithm. The obtained edge states experience recurrent dynamics, going through calm and bursting phases. The self-sustaining mechanism bears many similarities with the classical regeneration cycle of near-wall turbulence. The recurrent simple structure active during calm phases is compared to the nucleation of turbulence events in bypass transition originating from delocalised initial conditions. The implications on the understanding of the bypass-transition process and the edge state's role are discussed. Based on this understanding, a model is constructed which predicts the position of the nucleation of turbulent spots during free-stream turbulence induced transition in spatially developing boundary-layer flow. This model is used together with a probabilistic cellular automaton (PCA), which captures the spatial spreading of the spots, correctly reproducing the main statistical characteristics of the transition process. The last part of the thesis is concerned with the spatio-temporal aspects of turbulent ASBL in extended numerical domains near the onset of sustained turbulence. The different behaviour observed in ASBL, i.e. absence of sustained laminar–turbulent patterns, which have been reported in other wall-bounded flows, is associated with different character of the large-scale flow. In addition, an accurate quantitative estimate for the lowest Reynolds number with sustained turbulence is obtained / <p>QC 20160429</p>

This thesis deals with chemical plants constituted by parallel batch-continuous production lines with shared resources. For such plants, it is highly desirable to have optimal operation schedules which determine the starting times of the various batch processes and the flow rates of the continuous processes in order to maximize the average plant productivity and to have a continuous production without interruptions. This optimization problem is constrained by the limitation of the resources that are shared by the reactors and by the capacities of the various devices that constitute the plant. Such plants are "hybrid" by nature because they combine both continuous-time dynamics and discrete-event dynamics. The formalism of "Hybrid Automata" is there fore well suited for the design of plant models. The first contribution of this thesis is the development of a hybrid automaton model of the chemical plant in the Matlab-Simulink-Stateflow environment and its use for the design of an optimal periodic schedule that maximises the plant productivity. Using a sensitivity analysis and the concept of Poincaré; map, it is shown that the optimal schedule is a stable limit cycle of the hybrid system that attracts the system trajectories starting in a wide set of initial conditions. The optimal periodic schedule is valid under the assumption that the hybrid model is an exact description of the plant. Under perturbations on the plant parameters, it is shown that two types of problems may arise. The first problem is a drift of the hybrid system trajectory which can either lead to a convergence to a new stable sub-optimal schedule or to a resource conflict. The second problem is a risk of overflow or underflow of the output buffer tank. The second contribution of the thesis is the analysis of feedback control strategies to avoid these problems. For the first problem, a control policy based on a model predictive control (MPC) approach is proposed to avoid resource conflicts. The feedback control is run on - line with the hybrid Simulink-Stateflow simulator used as an internal model. For the solution of the second problem, a classical PI control is used. The goal is not only to avoid over- or under-filling of the tank but also to reduce the amplitude of outflow rate variations as much as possible. A methodological analysis for the PI controller tuning is presented in order to achieve an acceptable trade-off between these conflicting objectives.