Power transmission network simulation for fault diagnosis

The authors discuss an implementation of an object oriented (OO) fault simulator and its use within an adaptive fault diagnostic system. The simulator models the flow of faults around a power network, reporting switchgear indications and protection messages that would be expected in a real fault scenario. The simulator has been used to train an adaptive fault diagnostic system; results and implications are discussed.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

Classification and fault detection methods for fuel cell monitoring and quality control

In this paper, various types of fault detection methods for fuel cells are compared. For example, those that use a model based approach or a data driven approach or a combination of the two. The potential advantages and drawbacks of each method are discussed and comparisons between methods are made. In particular, classification algorithms are investigated, which separate a data set into classes or clusters based on some prior knowledge or measure of similarity. In particular, the application of classification methods to vectors of reconstructed currents by magnetic tomography or to vectors of magnetic field measurements directly is explored. Bases are simulated using the finite integration technique (FIT) and regularization techniques are employed to overcome ill-posedness. Fisher's linear discriminant is used to illustrate these concepts. Numerical experiments show that the ill-posedness of the magnetic tomography problem is a part of the classification problem on magnetic field measurements as well. This is independent of the particular working mode of the cell but influenced by the type of faulty behavior that is studied. The numerical results demonstrate the ill-posedness by the exponential decay behavior of the singular values for three examples of fault classes.

‘It Was All My Fault’: negative interpretation bias in depressed adolescents

The extent to which cognitive models of development and maintenance of depression apply to adolescents is largely untested, despite the widespread application of Cognitive Behavior Therapy (CBT) for depressed adolescents. Cognitive models suggest that negative cognitions, including interpretation bias, play a role in etiology and maintenance of depression. Given that cognitive development is incomplete by the teenage years and that CBT is not superior to non-cognitive treatments in the treatment of adolescent depression, it is important to test the underlying model. The primary aim of this study was to test the hypothesis that interpretation biases are exhibited by depressed adolescents. Four groups of adolescents were recruited: clinically-referred depressed (n = 27), clinically-referred non-depressed (n = 24), community with elevated depression symptoms (n = 42) and healthy community (n = 150). Participants completed a 20 item ambiguous scenarios questionnaire. Clinically-referred depressed adolescents made significantly more negative interpretations and rated scenarios as less pleasant than all other groups. The results suggest that this element of the cognitive model of depression is applicable to adolescents. Other aspects of the model should be tested so that cognitive treatment can be modified or adapted if necessary.

Scalable and fault tolerant failure detection and consensus

Future extreme-scale high-performance computing systems will be required to work under frequent component failures. The MPI Forum's User Level Failure Mitigation proposal has introduced an operation, MPI_Comm_shrink, to synchronize the alive processes on the list of failed processes, so that applications can continue to execute even in the presence of failures by adopting algorithm-based fault tolerance techniques. This MPI_Comm_shrink operation requires a fault tolerant failure detection and consensus algorithm. This paper presents and compares two novel failure detection and consensus algorithms. The proposed algorithms are based on Gossip protocols and are inherently fault-tolerant and scalable. The proposed algorithms were implemented and tested using the Extreme-scale Simulator. The results show that in both algorithms the number of Gossip cycles to achieve global consensus scales logarithmically with system size. The second algorithm also shows better scalability in terms of memory and network bandwidth usage and a perfect synchronization in achieving global consensus.

Mobile sink based fault diagnosis scheme for wireless sensor networks

Network diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results.

Effects of intensity and positional predictability of a visual stimulus on simple reaction time

The influence of visual stimuli intensity on manual reaction time (RT) was investigated under two different attentional settings: high (Experiment 1) and low (Experiment 2) stimulus location predictability. These two experiments were also run under both binocular and monocular viewing conditions. We observed that RT decreased as stimulus intensity increased. It also decreased as the viewing condition was changed from monocular to binocular as well as the location predictability shifted from low to high. A significant interaction was found between stimulus intensity and viewing condition, but no interaction was observed between neither of these factors and location predictability. These findings support the idea that the stimulus intensity effect arises from purely sensory, pre-attentive mechanisms rather than deriving from more efficient attentional capture. (C) 2010 Elsevier Ireland Ltd. All rights reserved.