Material Identification of Loose Particles in Sealed Electronic Devices Using PCA and SVM

The existence of loose particles left inside the sealed electronic devices is one of the main factors affecting the reliability of the whole system. It is important to identify the particle material for analyzing their source. The conventional material identification algorithms mainly rely on time, frequency and wavelet domain features. However, these features are usually overlapped and redundant, resulting in unsatisfactory material identification accuracy. The main objective of this paper is to improve the accuracy of material identification. First, the principal component analysis (PCA) is employed to reselect the nine features extracted from time and frequency domains, leading to six less correlated principal components. And then the reselected principal components are used for material identification using a support vector machine (SVM). Finally, the experimental results show that this new method can effectively distinguish the type of materials including wire, aluminum and tin particles.

Kernel based approaches to local nonlinear non-parametric variable selection

In this paper, we consider the variable selection problem for a nonlinear non-parametric system. Two approaches are proposed, one top-down approach and one bottom-up approach. The top-down algorithm selects a variable by detecting if the corresponding partial derivative is zero or not at the point of interest. The algorithm is shown to have not only the parameter but also the set convergence. This is critical because the variable selection problem is binary, a variable is either selected or not selected. The bottom-up approach is based on the forward/backward stepwise selection which is designed to work if the data length is limited. Both approaches determine the most important variables locally and allow the unknown non-parametric nonlinear system to have different local dimensions at different points of interest. Further, two potential applications along with numerical simulations are provided to illustrate the usefulness of the proposed algorithms.

A Parametric Study Of A Turbogenerator On A Diesel-Electric Hybrid Bus

The fuel consumption of automotive vehicles has become a prime consideration to manufacturers and operators as fuel prices continue to rise steadily, and legislation governing toxic emissions becomes ever more strict. This is particularly true for bus operators as government fuel subsidies are cut or removed.

In an effort to reduce the fuel consumption of a diesel-electric hybrid bus, an exhaust recovery turbogenerator has been selected from a wide ranging literature review as the most appropriate method of recovering some of the wasted heat in the exhaust line. This paper examines the effect on fuel consumption of a turbogenerator applied to a 2.4-litre diesel engine.

A validated one-dimensional engine model created using Ricardo WAVE was used as a baseline, and was modified in subsequent models to include a turbogenerator downstream, and in series with, the turbocharger turbine. A fuel consumption map of the modified engine was produced, and an in-house simulation tool was then used to examine the fuel economy benefit delivered by the turbogenerator on a bus operating on various drive-cycles.

A parametric study is presented which examined the performance of turbogenerators of various size and power output. The operating strategy of the turbogenerator was also discussed with a view to maximising turbine efficiency at each operating point.

The performance of the existing turbocharger on the hybrid bus was also investigated; both the compressor and turbine were optimised and the subsequent benefits to the fuel consumption of the vehicle were shown.

The final configuration is then presented and the overall improvement in fuel economy of the hybrid bus was determined over various drive-cycles.

Long-Term Economic Model for Allocation of FACTS Devices in Restructured Power System Integrated Wind Generation

This study proposes an approach to optimally allocate multiple types of flexible AC transmission system (FACTS) devices in market-based power systems with wind generation. The main objective is to maximise profit by minimising device investment cost, and the system's operating cost considering both normal conditions and possible contingencies. The proposed method accurately evaluates the long-term costs and benefits gained by FACTS devices (FDs) installation to solve a large-scale optimisation problem. The objective implies maximising social welfare as well as minimising compensations paid for generation re-scheduling and load shedding. Many technical operation constraints and uncertainties are included in problem formulation. The overall problem is solved using both particle swarm optimisations for attaining optimal FDs allocation as main problem and optimal power flow as sub-optimisation problem. The effectiveness of the proposed approach is demonstrated on modified IEEE 14-bus test system and IEEE 118-bus test system.

On Variable Selection of a Nonlinear Non-parametric System with a Limited Data Set: A Stepwise Algorithm

his paper considers a problem of identification for a high dimensional nonlinear non-parametric system when only a limited data set is available. The algorithms are proposed for this purpose which exploit the relationship between the input variables and the output and further the inter-dependence of input variables so that the importance of the input variables can be established. A key to these algorithms is the non-parametric two stage input selection algorithm.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

Parametric study to extract bridge frequencies from the dynamic response of passing vehicles

A conventional way to identify bridge frequencies is utilizing vibration data measured directly from the bridge. A drawback with this approach is that the deployment and maintenance of the vibration sensors are generally costly and time-consuming. One way to cope with the drawback is an indirect approach utilizing vehicle vibrations while the vehicle passes over the bridge. In the indirect approach, however, the vehicle vibration includes the effect of road surface roughness, which makes it difficult to extract the bridge modal properties. One solution may be subtracting signals of two trailers towed by a vehicle to reduce the effect of road surface roughness. A simplified vehicle-bridge interaction model is used in the numerical simulation; the vehicle - trailer and bridge system are modeled as a coupled model. In addition, a laboratory experiment is carried out to verify results of the simulation and examine feasibility of the damage detection by the indirect method.

A Parametric Study of a Drive by Bridge Inspection System Based on the Morlet Wavelet

Many of the bridges currently in use worldwide are approaching the end of their design lives. However, rehabilitating and extending the lives of these structures raises important safety issues. There is also a need for increased monitoring which has considerable cost implications for bridge management systems. Existing structural health monitoring (SHM) techniques include vibration-based approaches which typically involve direct instrumentation of the bridge and are important as they can indicate the deterioration of the bridge condition. However, they can be labour intensive and expensive. In the past decade, alternative indirect vibration-based approaches which utilise the response of a vehicle passing over a bridge have been developed. This paper investigates such an approach; a low-cost approach for the monitoring of bridge structures which consists of the use of a vehicle fitted with accelerometers on its axles. The approach aims to detect damage in the bridge while obviating the need for direct instrumentation of the bridge. Here, the effectiveness of the approach in detecting damage in a bridge is investigated using a simplified vehicle-bridge interaction (VBI) model in theoretical simulations and a scaled VBI model in a laboratory experiment. In order to identify the existence and location of damage, the vehicle accelerations are recorded and processed using a continuous Morlet wavelet transform and a damage index is established. A parametric study is carried out to investigate the effect of parameters such as the bridge span length, vehicle speed, vehicle mass, damage level and road surface roughness on the accuracy of results.

Validity of model devices used to assess canine temperament in behavioral tests

Temperament tests are widely accepted as instruments for profiling behavioral variability in dogs, and they are applied in numerous areas of investigation (e.g. suitability for adoption or for breeding). During testing, to elicit a dog's reaction toward novel stimuli and predict its behavior in everyday life, model devices such as a child-like doll, or a fake dog, are often employed. However, the reliability of these devices to accurately stimulate dogs' reactions to children or dogs, is unknown and perhaps overestimated. This may be a particular concern in the case of aggressive behavior toward humans, a significant public health issue. The aim of this study was to: (1) evaluate the correlation between dogs' reactions to these devices, and owners' reports of their dog's aggression history (using the C-BARQ ??); (2) compare reactions toward the devices of dogs with and without histories of aggression. Subjects were selected among those visiting for behavioral consultation at the Veterinary Hospital of the University of Pennsylvania, and previously categorized as aggressive toward unfamiliar children, conspecifics, or as non-aggressive dogs (control). The test consisted of different components: an unfamiliar female tester approaching the dog; the presentation of a child-like doll, an ambiguous object, and a fake plastic dog. All tests were videotaped and durations of behaviors were later analyzed on the basis of a specified ethogram. Dogs' reactions were compared to C-BARQ scores, and interesting correlations emerged for 'dog-directed aggression/fear' (R = 0.48, P = 0.004), and 'stranger-directed aggression' (R = 0.58, P <0.001) factors. Dogs differed in their reactions toward the devices: the child-like doll and the fake dog elicited more social behaviors than the ambiguous object used as a control stimulus. Issues concerning the reliability of these tools to assess canine temperament are discussed. ?? 2012 Elsevier B.V. All rights reserved.

Comprehensive numerical modelling of the performance of a second harmonic generation stage coupled with a low-gain optical parametric amplifier

We present a comprehensive model for predicting the full performance of a second harmonic generation-optical parametric amplification system that aims at enhancing the temporal contrast of laser pulses. The model simultaneously takes into account all the main parameters at play in the system such as the group velocity mismatch, the beam divergence, the spectral content, the pump depletion, and the length of the nonlinear crystals. We monitor the influence of the initial parameters of the input pulse and the interdependence of the two related non-linear processes on the performance of the system and show its optimum configuration. The influence of the initial beam divergence on the spectral and the temporal characteristics of the generated pulse is discussed. In addition, we show that using a crystal slightly longer than the optimum length and introducing small delay between the seed and the pump ensures maximum efficiency and compensates for the spectral shift in the optical parametric amplification stage in case of chirped input pulse. As an example, calculations for bandwidth transform limited and chirped pulses of sub-picosecond duration in beta barium borate crystal are presented.

Parametric scans of HB and LS-RPA regimes employing Petawatt laser

By contrast to the Target Normal Sheath acceleration (TNSA) mechanism [1], Radiation Pressure Acceleration (RPA) is currently attracting a substantial amount of experimental [2,3] and theoretical [4-6] attention worldwide due to its superior scaling in terms of ion energy and laser-ion conversion efficiency. Employing Vulcan Petawatt lasers of the Rutherford Appleton Laboratory, UK, both the Hole-boring (HB) and the Light-Sail (LS) regimes of the RPA have been extensively explored. When the target thickness is of the order of hole-boring velocity times the laser pulse duration, highly collimated plasma jets of near solid density are ejected from the foil, lasting up to ns after the laser interaction. By changing the linear polarisation of the laser to circular, improved homogeneity in the jet's spatial density profile is achieved which suggests more uniform and sustained radiation pressure drive on target ions. By decreasing the target areal density or increasing irradiance on the target, the LS regime of the RPA is accessed where relatively high flux (~ 1012 particles/MeV/Sr) of ions are accelerated to ~ 10 MeV/nucleon energies in a narrow energy bandwidth. The ion energy scaling obtained from the parametric scans agrees well with theoretical estimation based on RPA mechanism and the narrow bandwidth feature in the ion spectra is studied by 2D particle-in-simulations.