This thesis is focused on the area of automated data mining. The aim of this thesis is a description of the area of automated data mining, creation of a design of an automated data mining tasks creation process for verification of set domain knowledge and new knowledge search, and also an implementation of verification of set domain knowledge of attribute dependency type influence with search space adjustments. The implementation language is the LMCL language that enables usage of the LISp-Miner system's functionality in an automated way. These data analyses were performed on data from air pollution monitoring. The design and implementation were successful and the created scripts could be used (with some manual changes in initial parameters) for analyses of another dataset as well.

Då artificiella intelligenta agenter försöker navigera i en virtuell värld med statiska hinder så behöver dess väg beräknas. För denna beräkning används ett lager som beskriver hur och vart agenten kan röra sig i världen kallat för sökrymd. Detta kan göras på flera olika sätt vilket ger skillnad i prestanda och minnesåtgång samt indirekta fördelar genom sättet sökrymden är uppbyggd på. Genom att jämföra dessa skillnader i olika miljöer så kan resultatet påvisa vilka egenskaper sökrymdstyperna ger i de olika miljöerna. Rapporten visar att n-sidat polygonbaserat navigationsnät ger mycket bra egenskaper i de olika mätningarna och ger även flera fördelar på grund av dess utformning som gör att det kan anpassa sig bra till olika miljöer.

<p>Då artificiella intelligenta agenter försöker navigera i en virtuell värld med statiska hinder så behöver dess väg beräknas. För denna beräkning används ett lager som beskriver hur och vart agenten kan röra sig i världen kallat för sökrymd. Detta kan göras på flera olika sätt vilket ger skillnad i prestanda och minnesåtgång samt indirekta fördelar genom sättet sökrymden är uppbyggd på. Genom att jämföra dessa skillnader i olika miljöer så kan resultatet påvisa vilka egenskaper sökrymdstyperna ger i de olika miljöerna.</p><p>Rapporten visar att n-sidat polygonbaserat navigationsnät ger mycket bra egenskaper i de olika mätningarna och ger även flera fördelar på grund av dess utformning som gör att det kan anpassa sig bra till olika miljöer.</p>

A spacecraft tour trajectory is a trajectory in which a spacecraft visits a number of objects in sequence. The target objects may consist of satellites, moons, planets or any other body in orbit, and the spacecraft may visit these in a variety of ways, for example flying by or rendezvousing with them. The key characteristic is the target object sequence which can be represented as a discrete set of decisions that must be made along the trajectory. When this sequence is free to be chosen, the result is a hybrid discrete-continuous optimization problem that combines the challenges of discrete and combinatorial optimization with continuous optimization. The problem can be viewed as a generalization of the traveling salesman problem; such problems are NP-hard and their computational complexity grows exponentially with the problem size. The focus of this dissertation is the development of a novel methodology for the solution of spacecraft tour trajectory optimization problems. A general model for spacecraft tour trajectories is first developed which defines the parameterization and decision variables for use in the rest of the work. A global search methodology based on the tabu search metaheuristic is then developed. The tabu search approach is extended to operate on a tree-based solution representation and neighborhood structure, which is shown to be especially efficient for problems with expensive solution evaluations. Concepts of tabu search including recency-based tabu memory and strategic intensification and diversification are then applied to ensure a diverse exploration of the search space. The result is an automated, adaptive and efficient search algorithm for spacecraft tour trajectory optimization problems. The algorithm is deterministic, and results in a diverse population of feasible solutions upon termination. A novel numerical search space pruning approach is then developed, based on computing upper bounds to the reachable domain of the spacecraft, to accelerate the search. Finally, the overall methodology is applied to the fourth annual Global Trajectory Optimization Competition (GTOC4), resulting in previously unknown solutions to the problem, including one exceeding the best known in the literature. / text

Tracking of tree logs from a harvesting site to its processing site is a legal requirement for timber-based industries for social and economic reasons. Biometric tree log detection systems use images of the tree logs to track the logs by checking whether a given log image matches any of the logs registered in the system. However, as the number of registered tree logs in the database increases, the number of pairwise comparisons, and consequently the search time increase proportionally. Growing search space degrades the accuracy and the response time of matching queries and slows down the tracking process, costing time and resources. This work introduces database filtering and partitioning approaches based on discriminative log-end features to reduce the search space of the biometric log identification algorithms. In this study, 252 unique log images are used to train and test models for extracting features from the log images and to filter and cluster a database of logs. Experiments are carried out to show the end-to-end accuracy and speed-up impact of the individual approaches as well as the combinations thereof. The findings of this study indicate that the proposed approaches are suited for speeding-up tree log identification systems and highlight further opportunities in this field

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Rail track asset sighting distance must be checked regularly to ensure the continued and safe operation of rolling stock. Methods currently used to check asset line-of-sight involve manual labour or laser systems. Video cameras and computer vision techniques provide one possible route for cheaper, automated systems. Three categories of computer vision method are identified for possible application: two-dimensional object recognition, two-dimensional object tracking and three-dimensional scene recovery. However, presented experimentation shows recognition and tracking methods produce less accurate asset line-of-sight results for increasing asset-camera distance. Regarding three-dimensional scene recovery, evidence is presented suggesting a relationship between image feature and recovered scene information. A novel framework which learns these relationships is proposed. Learnt relationships from recovered image features probabilistically limit the search space of future features, improving efficiency. This framework is applied to several scene recovery methods and is shown (on average) to decrease computation by two-thirds for a possible, small decrease in accuracy of recovered scenes. Asset line-of-sight results computed from recovered three-dimensional terrain data are shown to be more accurate than two-dimensional methods, not effected by increasing asset-camera distance. Finally, the analysis of terrain in terms of effect on asset line-of-sight is considered. Terrain elements, segmented using semantic information, are ranked with a metric combining a minimum line-of-sight blocking distance and the growth required to achieve this minimum distance. Since this ranking measure is relative, it is shown how an approximation of the terrain data can be applied, decreasing computation time. Further efficiency increases are found by decomposing the problem into a set of two-dimensional problems and applying binary search techniques. The combination of the research elements presented in this thesis provide efficient methods for automatically analysing asset line-of-sight and the impact of the surrounding terrain, from captured monocular video.

The challenges in automating the design of planar mechanisms are tremendous especially in areas related to computational representation, kinematic analysis and synthesis of planar mechanisms. The challenge in computational representation relates to the development of a comprehensive methodology to completely define and manipulate the topologies of planar mechanisms while in kinematic analysis, the challenge is primarily in the development of generalized analysis routines to analyze different mechanism topologies. Combining the aforementioned challenges along with appropriate optimization algorithms to synthesize planar mechanisms for different user-defined applications presents the final challenge in the automated design of planar mechanisms. The methods presented in the literature demonstrate synthesis of standard four-bar and six-bar mechanisms with revolute and prismatic joints. But a detailed review of these methods point to the fact that they are not scalable when the topologies and the parameters of n-bar mechanisms are required to be simultaneously synthesized. Through this research, a comprehensive and scalable methodology for synthesizing different mechanism topologies and their parameters simultaneously is presented that overcomes the limitations in different challenge areas in the following ways. In representation, a graph-grammar based scheme for planar mechanisms is developed to completely describe the topology of a mechanism. Grammar rules are developed in conjunction with this representation scheme to generate different mechanism topologies in a tree-search process. In analysis, a generic kinematic analysis routine is developed to automatically analyze one-degree of freedom mechanisms consisting of revolute and prismatic joints. Two implementations of kinematic analysis have been included. The first implementation involves the use of graphical methods for position and velocity analyses and the equation method for acceleration analysis for mechanisms with a four-bar loop. The second implementation involves the use of an optimization-based method that has been developed to handle position kinematics of indeterminate mechanisms while the velocity and acceleration analyses of such mechanisms are carried out by formulating appropriate linear equations. The representation and analysis schemes are integrated to parametrically synthesize different mechanism topologies using a hybrid implementation of Particle Swarm Optimization and Nelder-Mead simplex algorithm. The hybrid implementation is able to produce better results for the problems found in the literature using a four-bar mechanism with revolute joints as well as through other higher order mechanisms from the design space. The implementation has also been tested on three new challenge problems with satisfactory results subject to computational constraints. The difficulties in the search have been studied that indicates the reasons for the lack of solution repeatability. This dissertation concludes with a discussion of the results and future directions. / text

Battery powered wireless sensors are widely used in industrial and regulatory monitoring applications. This is primarily due to the ease of installation and the ability to monitor areas that are difficult to access. Additionally, they can be left unattended for long periods of time. However, there are many challenges to successful deployments of wireless sensor networks (WSNs). In this thesis we draw attention to two major challenges. Firstly, with a view to extending network range, modern WSNs use mesh network topologies, where data is sent either directly or by relaying data from node-to-node en route to the central base station. The additional load of relaying other nodes’ data is expensive in terms of energy consumption, and depending on the routes taken some nodes may be heavily loaded. Hence, it is crucial to locate routes that achieve energy efficiency in the network and extend the time before the first node exhausts its battery, thus improving the network lifetime. Secondly, WSNs operate in a dynamic radio environment. With changing conditions, such as modified buildings or the passage of people, links may fail and data will be lost as a consequence. Therefore in addition to finding energy efficient routes, it is important to locate combinations of routes that are robust to the failure of radio links. Dealing with these challenges presents a routing optimisation problem with multiple objectives: find good routes to ensure energy efficiency, extend network lifetime and improve robustness. This is however an NP-hard problem, and thus polynomial time algorithms to solve this problem are unavailable. Therefore we propose hybrid evolutionary approaches to approximate the optimal trade-offs between these objectives. In our approach, we use novel search space pruning methods for network graphs, based on k-shortest paths, partially and edge disjoint paths, and graph reduction to combat the combinatorial explosion in search space size and consequently conduct rapid optimisation. The proposed methods can successfully approximate optimal Pareto fronts. The estimated fronts contain a wide range of robust and energy efficient routes. The fronts typically also include solutions with a network lifetime close to the optimal lifetime if the number of routes per nodes were unconstrained. These methods are demonstrated in a real network deployed at the Victoria & Albert Museum, London, UK.

Kvantová výpočetní technika je rychle rostoucí obor informatiky, který přenáší principy kvantových jevu do našeho každodenního života. Díky své kvantové podstatě získávají kvantové počítače převahu nad klasickými počítači. V této práci jsme se zaměřili na vysvětlení základů kvantového počítání a jeho implementaci na kvantovém počítači. Zejména se zaměřujeme na popis fungování, konstrukci a implementaci Groverova algoritmu jako jednoho ze základních kvantových algoritmů. Demonstrovali jsme sílu tohoto kvantového algoritmu při prohledávání databáze a porovnávali ho s klasickými nekvantovými algoritmy pomocí implementace prostřednictvím simulačního prostředí QISKit. Pro simulaci jsme použili QASM Simulator a State vector Simulator Aer backends a ukázali, že získané výsledky korelují s dříve diskutovanými teoretickými poznatky. Toto ukazuje, že Groverův algoritmus umožňuje kvadratické zrychlení oproti klasickému nekvantovému vyhledávacímu algoritmu, Použitelnost algoritmu stejně jako ostatních kvantových algoritmů je ale stále omezena několika faktory, mezi které patří vysoké úrovně dekoherence a chyby hradla.