A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin

Proving safety properties of software

The use of software is pervasive in areas as diverse as aerospace, automotive, chemical processes, civil infrastructure, energy, health-care, manufacturing, transportation, entertainment, and consumer appliances. Our safety, security, and economy are now closely linked to the reliability of software. This research is about a technique to prove event-based safety properties of program. A safety property is defined in terms of event traces. An event trace is associated with an execution path and it is the sequence of events that execute on the path. Each event is identified with a program statement or a block of statements. Particularly, this research has been focused on one type of problem that follows one type of safety property we call matching pair (MP) property. Memory leaks, asymmetric synchronization, and several other defects are examples of violation of the matching pair property. The property involves matching between two types of events on every execution path. We present a practical method to validate the MP property for large software. The method is designed to address the challenges resulting from the cross-cutting semantics and presence of invisible control flow. The method has two phases: the macro phrase and the micro phrase. The macro analysis phase incorporates important notions of signature and matching pair graph (MPG). Signatures enable a decomposition of the problem into small independent instances for validation, each identified by a unique signature. The MPG(X) defines for each signature X, a minimal set of functions to be analyzed for validating the instance. The micro analysis phase produces the event traces graph representing all the relevant execution paths through the functions belonging to a MPG(X). A fast and accurate analysis of large software is possible because the macro analysis can exactly identify the functions that need to be analyzed and the micro analysis further greatly reduces the amount of analysis required to cover all execution paths by creating event trace graph (ETG) from the control flow graph (CFG). We applied macro level analysis on eight versions of Linux kernels spanning for three years. We further calculated ETGs for all functions identified by macro analysis for three versions of Linux. With the combination of macro and micro analysis, we were able to prove the correctness of more than 90% of the synchronization instances in the Linux kernel. For each remaining case, we produced relevant ETGs for the further investigation by human experts

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Multilevel Contracts for Trusted Components

This article contributes to the design and the verification of trusted components and services. The contracts are declined at several levels to cover then different facets, such as component consistency, compatibility or correctness. The article introduces multilevel contracts and a design+verification process for handling and analysing these contracts in component models. The approach is implemented with the COSTO platform that supports the Kmelia component model. A case study illustrates the overall approach.Comment: In Proceedings WCSI 2010, arXiv:1010.233

Modelling and Analysis Using GROOVE

In this paper we present case studies that describe how the graph transformation tool GROOVE has been used to model problems from a wide variety of domains. These case studies highlight the wide applicability of GROOVE in particular, and of graph transformation in general. They also give concrete templates for using GROOVE in practice. Furthermore, we use the case studies to analyse the main strong and weak points of GROOVE

Third Conference on Artificial Intelligence for Space Applications, part 2

Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed

Catalog of selected heavy duty transport energy management models

A catalog of energy management models for heavy duty transport systems powered by diesel engines is presented. The catalog results from a literature survey, supplemented by telephone interviews and mailed questionnaires to discover the major computer models currently used in the transportation industry in the following categories: heavy duty transport systems, which consist of highway (vehicle simulation), marine (ship simulation), rail (locomotive simulation), and pipeline (pumping station simulation); and heavy duty diesel engines, which involve models that match the intake/exhaust system to the engine, fuel efficiency, emissions, combustion chamber shape, fuel injection system, heat transfer, intake/exhaust system, operating performance, and waste heat utilization devices, i.e., turbocharger, bottoming cycle