Effect of temperature on slumping behaviour of lead-free solder paste and its rheological simulation

Variation in temperature can have a significant impact on the rheological characterisation of solder pastes used in the electronic assembly of surface mount devices. This paper concerns the study of the effect of temperature on slumping characteristics of lead-free solder pastes. The identification of the slumping characteristics can help in the correlation of the pastes characteristics to its printing performance. Further issues, which aid in justifying the undertaking of such a study, include the temperature differences identified both at the squeegee during the print, and during reflow. Due to these temperature variations, it is imperative to understand how slump differs with a temperature gradient

Fault Diagnosis in Internal Combustion Engines Using Nonlinear Multivariate Statistics

This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

Nonlinear transport and localization in single-walled carbon nanotubes

We have measured the electrical transport properties of mats of single-walled carbon nanotubes (SWNT) as a function of applied electric and magnetic fields. We find that at low temperatures the resistance as a function of temperature R(T) follows the Mott variable range hopping (VRH) formula for hopping in three dimensions. Measurement of the electric field dependence of the resistance R(E) allows for the determination of the Bohr radius of a localized state a = 700nm. The magnetoresistance (MR) of SWNT mat samples is large and negative at all temperatures and fields studied. The low field negative MR is proportional to H2, in agreement with variable range hopping in two or three dimensions. 3D VRH indicates good intertube contacts, implying that the localization is due to the disorder experienced by the individual tubes. The 3D localization radius gives a measure of the ID localization length on the individual tubes, which we estimate to be >700 nm. Implications for the electron-phonon mean free path are discussed.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.

A taxonomy for wavelet neural networks applied to nonlinear modelling

This article presents a novel classification of wavelet neural networks based on the orthogonality/non-orthogonality of neurons and the type of nonlinearity employed. On the basis of this classification different network types are studied and their characteristics illustrated by means of simple one-dimensional nonlinear examples. For multidimensional problems, which are affected by the curse of dimensionality, the idea of spherical wavelet functions is considered. The behaviour of these networks is also studied for modelling of a low-dimension map.

Characterisation of microwave phase conjugate signal generation using nonlinearly loaded wire frequency selective surface (FSS)

Both experimental and theoretical information regarding the scattering and phase conjugate mixing properties of a 2D double-periodic array of wires loaded with nonlinear/linear lumped elements have been provided. An experimental means for assessing the phase conjugate energy production capability for the array is given. These investigations enable identification of the fundamental operational characteristics and underlying mechanisms associated with the production of phase conjugate energy by this type of artificial electromagnetic media. Means for enhancing the phase conjugate energy production capability of the structure by using additional linear lumped loads is examined theoretically and limits on the production of phase conjugate energy established. Theoretical far-field prediction of the behaviour of the structure indicates that retro-directive reflector action as well as negative refraction should be possible.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

Designed ultrafast optical nonlinearity in a plasmonic nanorod metamaterial enhanced by nonlocality

All-optical signal processing enables modulation and transmission speeds not achievable using electronics alone(1,2). However, its practical applications are limited by the inherently weak nonlinear effects that govern photon-photon interactions in conventional materials, particularly at high switching rates(3). Here, we show that the recently discovered nonlocal optical behaviour of plasmonic nanorod metamaterials(4) enables an enhanced, ultrafast, nonlinear optical response. We observe a large (80%) change of transmission through a subwavelength thick slab of metamaterial subjected to a low control light fluence of 7 mJ cm(-2), with switching frequencies in the terahertz range. We show that both the response time and the nonlinearity can be engineered by appropriate design of the metamaterial nanostructure. The use of nonlocality to enhance the nonlinear optical response of metamaterials, demonstrated here in plasmonic nanorod composites, could lead to ultrafast, low-power all-optical information processing in subwavelength-scale devices.

Correlation Between the Electrical and Optical Properties of an Atmospheric Pressure Plasma During Siloxane Coating Deposition

The effect of varying process parameters on atmospheric plasma characteristics and properties of nanometre thick siloxane coatings is investigated in a reel-to-reel deposition process. Varying plasma operation modes were observed with increasing applied power for helium and helium/oxygen plasmas. The electrical and optical behaviour of the dielectric barrier discharge were determined from current/voltage, emission spectroscopy and time resolved light emission measurements. As applied power increased, multiple discharge events occurred, producing a uniform multi-peak pseudoglow discharge, resulting in an increase in the discharge gas temperature. The effects of different operating modes on coating oxidation and growth rates were examined by injecting hexamethyldisiloxane liquid precursor into the chamber under varying operating conditions. A quenching effect on the plasma was observed, causing a decrease in plasma input power and emission intensity. Siloxane coatings deposited in helium plasmas had a higher organic component and higher growth rates than those deposited in helium/oxygen plasmas.

Detection of aberrant behaviour in home environments from video sequence

We introduce an application for the detection of aberrant behaviour within home based environments, with a focus on repetitive actions, which may be present in instance of persons suffering from dementia. Video based analysis has been used to detect the motion of a person within a given scene in addition to tracking them over the time. Detection of repetitive actions has been based on the analysis of a person's trajectory using the principles of signal correlation. Along with the ability to detect repetitive motion the developed approach also has the ability to measure the amount of activity/inactivity within the scene during a given period of time. Our results showed that the developed approach had the ability to detect all patterns in the data set examined with an average accuracy of 96.67%. This work has therefore validated the proposed concept of video based analysis for the detection of repetitive activities.

On visco-elastic modelling of polyethylene terephthalate behaviour during multiaxial elongations slightly over the glass transition temperature

The mechanical response of Polyethylene Terephthalate (PET) in elongation is strongly dependent on temperature, strain and strain rate. Near the glass transition temperature Tg, the stress-strain curve presents a strain softening effect vs strain rate but a strain hardening effect vs strain under conditions of large deformations. The main goal of this work is to propose a viscoelastic model to predict the PET behaviour when subjected to large deformations and to determine the material properties from the experimental data. To represent the non–linear effects, an elastic part depending on the elastic equivalent strain and a non-Newtonian viscous part depending on both viscous equivalent strain rate and cumulated viscous strain are tested. The model parameters can then be accurately obtained trough a comparison with the experimental uniaxial and biaxial tests. The in?uence of the temperature on the viscous part is also modelled and an evaluation of the adiabatic self heating of the specimen is compared to experimental results.