Parallel Splitting and Decomposition Method for Computations of Heat Distribution in Permafrost

A mathematical model, numerical algorithm and program code for simulation and long-term forecasting of changes in permafrost as a result of operation of a multiple well pad of northern oil and gas field are presented. In the model the most significant climatic and physical factors are taken into account such as solar radiation, determined by specific geographical location, heterogeneous structure of frozen soil, thermal stabilization of soil, possible insulation of the objects, seasonal fluctuations in air temperature, and freezing and thawing of the upper soil layer. A parallel algorithm of decomposition with splitting by spatial variables is presented

Modeling Individual Cyclic Variation in Human Behavior

Cycles are fundamental to human health and behavior. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present CyHMMs, a cyclic hidden Markov model method for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to model variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets -- of menstrual cycle symptoms and physical activity tracking data -- yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.Comment: Accepted at WWW 201

Development of an ontology supporting failure analysis of surface safety valves used in Oil & Gas applications

Treball desenvolupat dins el marc del programa 'European Project Semester'.The project describes how to apply Root Cause Analysis (RCA) in the form of a Failure Mode Effect and Criticality Analysis (FMECA) on hydraulically actuated Surface Safety Valves (SSVs) of Xmas trees in oil and gas applications, in order to be able to predict the occurrence of failures and implement preventive measures such as Condition and Performance Monitoring (CPM) to improve the life-span of a valve and decrease maintenance downtime. In the oil and gas industry, valves account for 52% of failures in the system. If these failures happen unexpectedly it can cause a lot of problems. Downtime of the oil well quickly becomes an expensive problem, unscheduled maintenance takes a lot of extra time and the lead-time for replacement parts can be up to 6 months. This is why being able to predict these failures beforehand is something that can bring a lot of benefits to a company. To determine the best course of action to take in order to be able to predict failures, a FMECA report is created. This is an analysis where all possible failures of all components are catalogued and given a Risk Priority Number (RPN), which has three variables: severity, detectability and occurrence. Each of these is given a rating between 0 and 10 and then the variables are multiplied with each other, resulting in the RPN. The components with an RPN above an acceptable risk level are then further investigated to see how to be able to detect them beforehand and how to mitigate the risk that they pose. Applying FMECA to the SSV mean breaking the system down into its components and determining the function, dependency and possible failures. To this end, the SSV is broken up into three sub-systems: the valve, the actuator and the hydraulic system. The hydraulic system is the sub-system of the SSV responsible for containing, transporting and pressurizing of the hydraulic fluid and in turn, the actuator. It also contains all the safety features, such as pressure pilots, and a trip system in case a problem is detected in the oil line. The actuator is, as the name implies, the sub-system which opens and closes the valve. It is made up of a number of parts such as a cylinder, a piston and a spring. These parts are interconnected in a number of ways to allow the actuator to successfully perform its function. The valve is the actual part of the system which interacts with the oil line by opening and closing. Like the actuator, this sub-system is broken down into a number of parts which work together to perform its function. After breaking down and defining each subsystem on a functional level, a model was created using a functional block diagram. Each component also allows for the defining of dependencies and interactions between the different components and a failure diagram for each component. This model integrates the three sub-systems back into one, creating a complete picture of the entire system which can then be used to determine the effects of different failures in components to the rest of the system. With this model completed we created a comprehensive FMECA report and test the different possible CPM solutions to mitigate the largest risks

Automatic view schema generation in object-oriented databases

An object-oriented data schema is a complex structure of classes interrelated via generalization and property decomposition relationships. We define an object-oriented view to be a virtual schema graph with possibly restructured generalization and decomposition hierarchies - rather than just one individual virtual class as proposed in the literature. In this paper, we propose a methodology, called MultiView, for supporting multiple such view schemata. MultiView is anchored on the following complementary ideas: (a) the view definer derives virtual classes and then integrates them into one consistent global schema graph and (b) the view definer specifies arbitrarily complex view schemata on this augmented global schema. The focus of this paper is, however, on the second, less explored, issue. This part of the view definition is performed using the following two steps: (1) view class selection and (2) view schema graph generation. For the first, we have developed a view definition language that can be used by the view definer to specify the selection of the desired view classes from the global schema. For the second, we have developed two algorithms that automatically augment the set of selected view classes to generate a complete, minimal and consistent view class generalization hierarchy. The first algorithm has linear complexity but it assumes that the global schema graph is a tree. The second algorithm overcomes this restricting assumption and thus allows for multiple inheritance, but it does so at the cost of a higher complexity