Periods of sub-optimal production rates, or complete shut-downs, add negative numbers to the revenuegraph for oil companies. Oil and gas are produced from several reservoirs and through many wells withvarying gas/oil proportion, making it a complex process that is difficult to control. As a part of a threestepprocess for utilizing data in the oil production domain, this thesis derive methods for combiningevent patterns, called restricted association rules, in time series in order to warn about future anomalies inoil production processes. Two problems have been considered: Network learning and network reasoning.The suggested solution consists of building an Association Rules Network (ARN) from the rule set givenas input. After transforming the hypergraph-based ARN to a directed acyclic graph, correlations betweennodes are found by applying the shortest-path principle. Motivated by the shortcomings of this simplesolution, it is shown how a method for learning Bayesian networks with support for representation oftemporal dependencies can be derived from the initial ARN. The concept, named Temporal BayesianNetwork of Events (TBNE), is a powerful, but yet complex solution that enjoys the properties of Bayesiannetwork reasoning while at the same time representing temporal information. This thesis has shown thatit is theoretically feasible to combine restricted association rules in order to create a network structurefor reasoning. It is concluded that the final choice of solution must be based on a carefully considerationof the trade-off between complexity and expressiveness, and that a natural continuation is testing thesuggested concepts with real data.

Periods of sub-optimal production rates, or complete shut-downs, add negative numbers to the revenue graph for oil companies. Oil and gas are produced from several reservoirs and through many wells with varying gas/oil proportion, making it a complex process that is difficult to control. As a part of a three step process for utilizing data in the oil production domain, this thesis derive methods for discovering event patterns, called restricted association rules, from time series in order to pre-warn about future problems in oil production processes. A restricted rule syntax and semantics is derived to explicitly target rules suited for prediction. Based on the defined rule syntax, a two step process is derived where restricted rule mining based on the concept of minimal occurrences is used to discover restricted association rules from a sequence of events. Next, redundant rules are removed based on the concept of minimum improvement and chaining of rules, during a rule selection phase. Information theory is applied in order to identify the most interesting rules, which can be submitted to an expert for validation. Both a simple solution for easy implementation in ConocoPhillips and a more advanced solution appropriate for general prediction cases are derived. This thesis concludes that it is feasible to discover dependencies between events from actual process data. It is also concluded that a large number of rules can be pruned, in order to get a manageable set of rules which is believed to have good predictive performance.

This project is a study of the development of the Rule Engine, which is a validation system for quality assurance of product data used in the grocery business. The authors was asked by Cogitare AS to develop the Rule Engine. A system where users without programming skills can build rules and validate product data. The main quality attribute focus is robustness and user friendliness. A survey has been used by the authors to be able to explore if our objectives have been achieved and to identify further work. The questionnaire has been conducted on students and software developers.

The theory of Connectology sets forth three psychologically founded synaptic learning mechanisms that may describe all aspects of animal learning. Of particular interest to this thesis is the learning of animal motion behavior, or, more specifically, the development of synchronized and repetitive movement patterns - gaits.Computer simulations are performed according to the methodology of computational neuroethology: Artificial neural networks are simulated operating in a tight feedback loop with a structurally simple but mechanically realistic body and a physically realistic environment. Neural network learning is purely synaptical and is performed solely within the lifetime of one such ANN-controlled system. Additionally, the configuration parameter space is searched by means of genetic algorithms.Simulation results show examples of synchronized and repetitive movement patterns developing when neuronal and mechanical model parameters are appropriately specified. These simulations thereby provide the first examples known to us of a fully unsupervised and self-organized artificial neural system that synaptically learns synchronized and repetitive motor control. In spite of limited mechanical model complexity, the most efficient movement patterns to some degree resemble the gaits seen in nature.

Fish farming is a million dollar business world wide, and fish is in fact the third mostimportant export product after oil/gas and metal in Norway. There are a lot of different aquaculture sites which produce fish along our long coast line and they all have somedifferences in the production rates and procedures. The fish farmer at these sites holdvaluable information about the production, which is almost impossible to derive onlyfrom empirical data.In this thesis we introduce Glaucus, a Case-Based Reasoning system which aims tohelp the fish farmers with their decision making when conduction sorting operations attheir aquaculture sites. The system is built in Java and uses the jColibri developmentframework for Case-Based Reasoning. It retrieves cases based on similarity function frommyCBR and jColibri in addition to custom made ones. The case base is generated fromreal world data and the case queries are populated by a combination of user input anddata from a database with continuous data flow.Our approach is just the beginning of what we hope will be a even greater journeytowards a complete decision support system that will meet the expectations of the fishfarmers.Keywords: Case-Based Reasoning, Machine learning, Fish farming, jColibri, myCBR

OpenVG is a new open standard for 2 dimensions vector graphics for handheld devices. This project, which is a master thesis and an internship, aims to study the standard itself deeply before to study the role it can play in the future of artistic content creation.We will see that under some few conditions, OpenVG has everything to fulfil its role in the market and to attract digital artists who would like to enlarge their creation field. But the major aim of the project is to develop a benchmark for both hardware and software implementations. And to achieve this goal, a study of the theory of performance evaluation is necessary. And after developing the benchmark, it is interesting to run some few tests to illustrate some principles enounced while studying performance evaluation.

The purpose of this thesis was to examine ways to adapt common 2D segmentation techniques to work with 3D grids. The focus of the thesis became how to automate and improve the performance of region growing in 3D grids. After examining relevant literature and developing a tool to run experiments, a simple automatic region grower for 3D grids was developed. Quantitative performance measures and qualitative analysis of the segmentation results were performed. This algorithm was then used as a baseline for comparison when developing a more advanced region grower for 3D grids based on the seeded region grower (SRG) for 2D grids. This new algorithm was then modified to improve its speed and later extended to allow fully automatic operation by automating the placement of starting seeds. It was found that for the algorithms that were extended to a 3D grid, the main challenge was the resources needed by these algorithms when operating on high resolution grids. It was found that even though there have been steady and rapid improvements in consumer hardware since the original region growing algorithms were used on 2D grids, the very large amounts of data resulting from an extension from surface grids to volume grids requires that special attention is paid to handling resources effectively. It was further revealed that what was considered the best data structures and algorithms for the SRG algorithm when it was first introduced, is not necessarily the best choice on todays computing hardware. Also, the conclusion is drawn that with regards to performance, it is now possible to segment volumes approximately as fast as surfaces were segmented in the early 1990s.

Evolutionary artificial neural networks can adapt to new circumstances, and handle slight changes without catastrophic failure. However, under constantly changing circumstances, resulting in unpredictable grounds for evaluating success, the lack of memory of previous adaptations are a limiting factor. While further evolution can allow adaptations to new changes, the same is required for a return to a previous environment. To reduce the need for further evolution to deal with previously seen problems, this thesis looks at an approach to encourage previous knowledge to be retained across generations. It does this using back propagation in conjunction with an implementation of the HyperNEAT neuroevolutionary algorithm.

Many problems in the field of automatic video surveillance exists today. Some have yet to be overcome. One of these problems is how a computer system automatically can determine if a situation should cause an alarm or not. To resolve this problem, the use of Case-based reasoning (CBR) is proposed. CBR is a technique that allows a system to reason about different situations and to learn from them. The aim is to produce a system that utilizes these abilities. The system should learn to recognize the situations that causes different alarms. When a situation is recognized and categorized, these false alarms can be completely avoided. This master thesis explains and shows the advantages of using such a system together with advanced image processing techniques.

Task-based programming involves creating tasks, which can be run independently of each other, and letting the run-time system schedule the tasks on the underlying architecture. Wool is a new library for task-based programming created at SICS in Sweden. To assist a developer who is using Wool to parallelize a program, as well as the scientists who are actually developing Wool, a profiler which shows what happened in a computation can be very helpful.In this project we modify the Wool library to print more data about its computations. When the output is given to a Java application also developed in this project, the Java application produces a graphical representation of the execution. Each worker thread is visualized separately, with spawns, steals, leaps, critical path and CPU usage information included at a position corresponding to when the events actually occurred.The profiler, which we have named WoolPlot, is put to the test using a few real-world benchmarks, as well as some created especially for this project. The benchmarks show that WoolPlot works well when describing the distinct events such as steals and spawns. The reporting on the CPU load is too inaccurate to be sufficient for all practical uses. The overhead of the profiler is estimated to be between 3% and 6%.