438,821 research outputs found
Fast Damage Recovery in Robotics with the T-Resilience Algorithm
Damage recovery is critical for autonomous robots that need to operate for a
long time without assistance. Most current methods are complex and costly
because they require anticipating each potential damage in order to have a
contingency plan ready. As an alternative, we introduce the T-resilience
algorithm, a new algorithm that allows robots to quickly and autonomously
discover compensatory behaviors in unanticipated situations. This algorithm
equips the robot with a self-model and discovers new behaviors by learning to
avoid those that perform differently in the self-model and in reality. Our
algorithm thus does not identify the damaged parts but it implicitly searches
for efficient behaviors that do not use them. We evaluate the T-Resilience
algorithm on a hexapod robot that needs to adapt to leg removal, broken legs
and motor failures; we compare it to stochastic local search, policy gradient
and the self-modeling algorithm proposed by Bongard et al. The behavior of the
robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using
only 25 tests on the robot and an overall running time of 20 minutes,
T-Resilience consistently leads to substantially better results than the other
approaches
Learning Higher-order Transition Models in Medium-scale Camera Networks
We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures. Our approach to estimating higher-order transition models relies on the accurate assignment of camera observations to the underlying trajectories of objects moving through the network. We addresses this data association problem by gathering the observations and evaluating alternative partitions of the observation set into individual object trajectories. Searching the complete partition space is intractable, so an incremental approach is taken, iteratively adding observations and pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model. When the algorithm has considered all observations, the most likely (MAP) partition is taken as the true object trajectories. From these recovered trajectories, the higher-order statistics we seek can be derived and employed for tracking. The partitioning algorithm we present is parallel in nature and can be readily extended to distributed computation in medium-scale smart camera networks. 1
Performance modelling of fuel cell systems through Petri nets
This paper introduces a model based on the Petri net method for the performance evaluation of fuel cell systems during operation. The model simulates the operation of the fuel cell stack and its supporting systems by taking into account the causal relationships between the operation of the balance of plant and the fuel cell stack performance. Failures of the supporting system affect the operating parameters such as the stack temperature and humidity, the reactants’ flow and pressure, and, in turn, the stack performance in terms of output voltage. Voltage degradation rates are needed in order to evaluate the system lifetime. The voltage degradation is related to the important operating parameters by means of empirical relationships. In order to demonstrate the capability of the model, numerical simulations are performed using data for voltage degradation rates collected from the literature. The voltage decay rate is modelled as a random variable within the aforementioned ranges. Time to failure and time to repair of components are generated from stochastic distributions. The use of a stochastic approach allows taking into account data uncertainty and variability. The modelling process produces distributions of the output parameters rather than point estimates delivered by alternative methods. This enables an appreciation of the best and worst possible output lifetime as well as the expected system performance. The model can be used to support the design, operation and maintenance of fuel cell systems
Design-time Models for Resiliency
Resiliency in process-aware information systems is based on the availability of recovery flows and alternative data for coping with missing data. In this paper, we discuss an approach to process and information modeling to support the specification of recovery flows and alternative data. In particular, we focus on processes using sensor data from different sources. The proposed model can be adopted to specify resiliency levels of information systems, based on event-based and temporal constraints
Development of a self-tuned drive-train damper for utility-scale variable-speed wind turbines
This thesis describes the development of a procedure that tunes a wind turbine drivetrain
damper (DTD) automatically. This procedure, when integrated into the controller
of any utility-scale variable-speed wind turbine, will allow the turbine to
autonomously and automatically tune its DTD on site. In practice this means that the
effectiveness of the damper becomes independent on the accuracy of the model or the
simulations used by the control engineers in order to tune the damper. This research is
motivated by the fact that drive-train failures are still one of the biggest problems that
stigmatises the wind turbine industry. The development of an automatically tuned
DTD that alleviates the drive-train fatigue loads and thus increases the reliability and
lifetime of the drive-train is thus considered very beneficial for the wind turbine
industry.
The procedure developed begins by running an experimental procedure to collect data
that is then used to automatically system identify a linear model describing the drivetrain.
Based on this model a single band-pass filter acting as a DTD is automatically
tuned. This procedure is run for a number of times, and the resulting DTDs are
compared in order to select the optimal one.
The thesis demonstrates the effectiveness of the developed procedure and presents
alternative procedures devised during research. Finally, insight into future work that
could be performed is indicated in the last chapter of the thesis
Techno-economic evaluation of cognitive radio in a factory scenario
Wireless applications gradually enter every aspect of our life. Unfortunately, these applications must reuse the same scarce spectrum, resulting in increased interference and limited usability. Cognitive Radio proposes to mitigate this problem by adapting the operational parameters of wireless devices to varying interference conditions. However, it involves an increase in cost. In this paper we examine the economic balance between the added cost and the increased usability in one particular real-life scenario. We focus on the production floor of an industrial installation where wireless sensors monitor production machinery, and a wireless LAN is used as the data backbone. We examine the effects of implementing dynamic spectrum access by means of ideal RE sensing, and model the benefit in terms of increased reliability and battery lifetime. We estimate the financial cost of interference and the potential gain, and conclude that cognitive radio can bring business gains in real-life applications
Analysis of offshore wind turbine operation & maintenance using a novel time domain meteo-ocean modeling approach
This paper presents a novel approach to repair modeling using a time domain Auto-Regressive model to represent meteo-ocean site conditions. The short term hourly correlations, medium term access windows of periods up to days and the annual distibution of site data are captured. In addition, seasonality is included. Correlation observed between wind and wave site can be incorporated if simultaneous data exists. Using this approach a time series for both significant wave height and mean wind speed is described. This allows MTTR to be implemented within the reliability simulation as a variable process, dependent on significant wave height. This approach automatically captures site characteristics including seasonality and allows for complex analysis using time dependent constaints such as working patterns to be implemented. A simple cost model for lost revenues determined by the concurrent simulated wind speed is also presented. A preliminary investigation of the influence of component reliability and access thresholds at various existing sites on availability is presented demonstrating the abiltiy of the modeling approach to offer new insights into offshore wind turbine operation and maintenance
Rapid Recovery for Systems with Scarce Faults
Our goal is to achieve a high degree of fault tolerance through the control
of a safety critical systems. This reduces to solving a game between a
malicious environment that injects failures and a controller who tries to
establish a correct behavior. We suggest a new control objective for such
systems that offers a better balance between complexity and precision: we seek
systems that are k-resilient. In order to be k-resilient, a system needs to be
able to rapidly recover from a small number, up to k, of local faults
infinitely many times, provided that blocks of up to k faults are separated by
short recovery periods in which no fault occurs. k-resilience is a simple but
powerful abstraction from the precise distribution of local faults, but much
more refined than the traditional objective to maximize the number of local
faults. We argue why we believe this to be the right level of abstraction for
safety critical systems when local faults are few and far between. We show that
the computational complexity of constructing optimal control with respect to
resilience is low and demonstrate the feasibility through an implementation and
experimental results.Comment: In Proceedings GandALF 2012, arXiv:1210.202
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