8,173 research outputs found
Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage
We propose and experimentally validate a control strategy to dispatch the
operation of a distribution feeder interfacing heterogeneous prosumers by using
a grid-connected battery energy storage system (BESS) as a controllable element
coupled with a minimally invasive monitoring infrastructure. It consists in a
two-stage procedure: day-ahead dispatch planning, where the feeder 5-minute
average power consumption trajectory for the next day of operation (called
\emph{dispatch plan}) is determined, and intra-day/real-time operation, where
the mismatch with respect to the \emph{dispatch plan} is corrected by applying
receding horizon model predictive control (MPC) to decide the BESS
charging/discharging profile while accounting for operational constraints. The
consumption forecast necessary to compute the \emph{dispatch plan} and the
battery model for the MPC algorithm are built by applying adaptive data driven
methodologies. The discussed control framework currently operates on a daily
basis to dispatch the operation of a 20~kV feeder of the EPFL university campus
using a 750~kW/500~kWh lithium titanate BESS.Comment: Submitted for publication, 201
A Model-based transformation process to validate and implement high-integrity systems
Despite numerous advances, building High-Integrity Embedded systems remains a complex task. They come with strong requirements to ensure safety, schedulability or security properties; one needs to combine multiple analysis to validate each of them. Model-Based Engineering is an accepted solution to address such complexity: analytical models are derived from an abstraction of the system to be built. Yet, ensuring that all abstractions are semantically consistent, remains an issue, e.g. when performing model checking for assessing safety, and then for schedulability using timed automata, and then when generating code. Complexity stems from the high-level view of the model compared to the low-level mechanisms used. In this paper, we present our approach based on AADL and its behavioral annex to refine iteratively an architecture description. Both application and runtime components are transformed into basic AADL constructs which have a strict counterpart in classical programming languages or patterns for verification. We detail the benefits of this process to enhance analysis and code generation. This work has been integrated to the AADL-tool support OSATE2
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
An Entry Point for Formal Methods: Specification and Analysis of Event Logs
Formal specification languages have long languished, due to the grave
scalability problems faced by complete verification methods. Runtime
verification promises to use formal specifications to automate part of the more
scalable art of testing, but has not been widely applied to real systems, and
often falters due to the cost and complexity of instrumentation for online
monitoring. In this paper we discuss work in progress to apply an event-based
specification system to the logging mechanism of the Mars Science Laboratory
mission at JPL. By focusing on log analysis, we exploit the "instrumentation"
already implemented and required for communicating with the spacecraft. We
argue that this work both shows a practical method for using formal
specifications in testing and opens interesting research avenues, including a
challenging specification learning problem
EXODUS: Integrating intelligent systems for launch operations support
Kennedy Space Center (KSC) is developing knowledge-based systems to automate critical operations functions for the space shuttle fleet. Intelligent systems will monitor vehicle and ground support subsystems for anomalies, assist in isolating and managing faults, and plan and schedule shuttle operations activities. These applications are being developed independently of one another, using different representation schemes, reasoning and control models, and hardware platforms. KSC has recently initiated the EXODUS project to integrate these stand alone applications into a unified, coordinated intelligent operations support system. EXODUS will be constructed using SOCIAL, a tool for developing distributed intelligent systems. EXODUS, SOCIAL, and initial prototyping efforts using SOCIAL to integrate and coordinate selected EXODUS applications are described
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
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