Performance of the generalized delta rule in structural damage detection

The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage stares generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and strains) at suitable selected locations in the structure. Issues related to the performance of the network with reference to hidden layers and hidden. neurons are examined. Some heuristics are proposed for the design of neural networks for damage identification in structures. These are further supported by an investigation conducted on five other bridge truss configurations.

Improving web server performance by network aware data buffering and caching

In this paper we propose a new method of data handling for web servers. We call this method Network Aware Buffering and Caching (NABC for short). NABC facilitates reduction of data copies in web server's data sending path, by doing three things: (1) Layout the data in main memory in a way that protocol processing can be done without data copies (2) Keep a unified cache of data in kernel and ensure safe access to it by various processes and kernel and (3) Pass only the necessary meta data between processes so that bulk data handling time spent during IPC can be reduced. We realize NABC by implementing a set of system calls and an user library. The end product of the implementation is a set of APIs specifically designed for use by the web servers. We port an in house web server called SWEET, to NABC APIs and evaluate performance using a range of workloads both simulated and real. The results show a very impressive gain of 12% to 21% in throughput for static file serving and 1.6 to 4 times gain in throughput for lightweight dynamic content serving for a server using NABC APIs over the one using UNIX APIs.

A novel approach for Tuning of minimum performance robust PSS using Genetic algorithm

This paper presents a novel approach for designing a fixed gain robust power system stabilizer (PSS) with particu lar emphasis on achieving a minimum closed loop perfor mance, over a wide range of operating and system condi tion. The minimum performance requirements of the con troller has been decided apriori and obtained by using a genetic algorithm (GA) based power system stabilizer. The proposed PSS is robust to changes in the plant parameters brought about due to changes in system and operating con dition, guaranteeing a minimum performance. The efficacy of the proposed method has been tested on a multimachine system. The proposed method of tuning the PSS is an at tractive alternative to conventional fixed gain stabilizer de sign, as it retains the simplicity of the conventional PSS and still guarantees a robust acceptable performance over a wider range of operating and system condition.

Performance evaluation of neural network algorithms for multisensor data fusion in an airborne track while scan radar

This paper deals with the solution to the problem of multisensor data fusion for a single target scenario as detected by an airborne track-while-scan radar. The details of a neural network implementation, various training algorithms based on standard backpropagation, and the results of training and testing the neural network are presented. The promising capabilities of RPROP algorithm for multisensor data fusion for various parameters are shown in comparison to other adaptive techniques

Performance of cellular CDMA with voice/data traffic with an SIR based admission control

We analyze the performance of an SIR based admission control strategy in cellular CDMA systems with both voice and data traffic. Most studies In the current literature to estimate CDMA system capacity with both voice and data traf-Bc do not take signal-tlFlnterference ratio (SIR) based admission control into account In this paper, we present an analytical approach to evaluate the outage probability for voice trafllc, the average system throughput and the mean delay for data traffic for a volce/data CDMA system which employs an SIR based admission controL We show that for a dataaniy system, an improvement of about 25% In both the Erlang capacity as well as the mean delay performance is achieved with an SIR based admission control as compared to code availability based admission control. For a mixed voice/data srtem with 10 Erlangs of voice traffic, the Lmprovement in the mean delay performance for data Is about 40%.Ah, for a mean delay of 50 ms with 10 Erlangs voice traffic, the data Erlang capacity improves by about 9%.

Performance analysis of data and voice connections in a cognitive radio network

We study the performance of cognitive (secondary) users in a cognitive radio network which uses a channel whenever the primary users are not using the channel. The usage of the channel by the primary users is modelled by an ON-OFF renewal process. The cognitive users may be transmitting data using TCP connections and voice traffic. The voice traffic is given priority over the data traffic. We theoretically compute the mean delay of TCP and voice packets and also the mean throughput of the different TCP connections. We compare the theoretical results with simulations.

Fuzzy data envelopment analysis for performance evaluation of an irrigation system

Sixteen irrigation subsystems of the Mahi Bajaj Sagar Project, Rajasthan, India, are evaluated and selection of the most suitable/best is made using data envelopment analysis (DEA) in both deterministic and fuzzy environments. Seven performance-related indicators, namely, land development works (LDW), timely supply of inputs (TSI), conjunctive use of water resources (CUW), participation of farmers (PF), environmental conservation (EC), economic impact (EI) and crop productivity (CPR) are considered. Of the seven, LDW, TSI, CUW, PF and EC are considered inputs, whereas CPR and EI are considered outputs for DEA modelling purposes. Spearman rank correlation coefficient values are also computed for various scenarios. It is concluded that DEA in both deterministic and fuzzy environments is useful for the present problem. However, the outcome of fuzzy DEA may be explored for further analysis due to its simple, effective data and discrimination handling procedure. It is inferred that the present study can be explored for similar situations with suitable modifications.

A performance-based framework for assessing liquefaction potential based on CPT data

This article describes a new performance-based approach for evaluating the return period of seismic soil liquefaction based on standard penetration test (SPT) and cone penetration test (CPT) data. The conventional liquefaction evaluation methods consider a single acceleration level and magnitude and these approaches fail to take into account the uncertainty in earthquake loading. The seismic hazard analysis based on the probabilistic method clearly shows that a particular acceleration value is being contributed by different magnitudes with varying probability. In the new method presented in this article, the entire range of ground shaking and the entire range of earthquake magnitude are considered and the liquefaction return period is evaluated based on the SPT and CPT data. This article explains the performance-based methodology for the liquefaction analysis – starting from probabilistic seismic hazard analysis (PSHA) for the evaluation of seismic hazard and the performance-based method to evaluate the liquefaction return period. A case study has been done for Bangalore, India, based on SPT data and converted CPT values. The comparison of results obtained from both the methods have been presented. In an area of 220 km2 in Bangalore city, the site class was assessed based on large number of borehole data and 58 Multi-channel analysis of surface wave survey. Using the site class and peak acceleration at rock depth from PSHA, the peak ground acceleration at the ground surface was estimated using probabilistic approach. The liquefaction analysis was done based on 450 borehole data obtained in the study area. The results of CPT match well with the results obtained from similar analysis with SPT data.

Large Hadron Collider (LHC) large scale high volume data transport – end-to-end performance from the scientific application viewpoint

Presentation slides as part of the Janet network end-to-end performance initiative

Performance tuning - lessons from the coal face

Presentation slides as part of the Janet network end to end performance initiative workshop

Combined useage statistics as a basis for Research intelligence

Knowledge Exchange hosted a workshop in March 2010 with the aim to bring together technical experts working in partner projects collecting usage statistics including PIRUS2, OAstatistik and SURFsure projects. Experts from other related projects (RePec and NeeO) were also involved. The workshop produced a briefing paper on combined usage statistics as a basis for research intelligence. In this paper, the experts make a cause for collecting and exchanging usage statistics as this can provide valuable insight in how research is being used, not only by the research community, but also by business and society in general. This would provide a basis for 'Research Intelligence', an intelligent use of reliable numerical data which can be used as a basis for decision making in higher education and research. Usage statistics are a clear example of data which can offer a valuable contribution to the information required to make informed decisions. To allow for the meaningful collection, exchange and analysis of usage statistics, a number of challenges and opportunities need to be addressed and further steps need to be taken.