Study of acoustic emission data analysis tools for structural health monitoring applications

Acoustic emission (AE) technique is one of the popular diagnostic techniques used for structural health monitoring of mechanical, aerospace and civil structures. But several challenges still exist in successful application of AE technique. This paper explores various tools for analysis of recorded AE data to address two primary challenges: discriminating spurious signals from genuine signals and devising ways to quantify damage levels.

Structural health monitoring of bridges : source localisation in acoustic emission technique

Managing the sustainability of urban infrastructure requires regular health monitoring of key infrastructure such as bridges. The process of structural health monitoring involves monitoring a structure over a period of time using appropriate sensors, extracting damage sensitive features from the measurements made by the sensors, and analysing these features to determine the current state of the structure. Various techniques are available for structural health monitoring of structures, and acoustic emission is one technique that is finding an increasing use in the monitoring of civil infrastructures such as bridges. Acoustic emission technique is based on the recording of stress waves generated by rapid release of energy inside a material, followed by analysis of recorded signals to locate and identify the source of emission and assess its severity. This chapter first provides a brief background of the acoustic emission technique and the process of source localization. Results from laboratory experiments conducted to explore several aspects of the source localization process are also presented. The findings from the study can be expected to enhance knowledge of the acoustic emission process, and to aid the development of effective bridge structure diagnostics systems.

Enhancing acoustic emission signals from multi-cylinder diesel engine

This paper presents techniques which can be viewed as pre-processing step towards diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outlined, including time-frequency analysis, selection of optimum frequency band. Some results of applying mean field independent component analysis (MFICA) to separate the AE root mean square (RMS) signals are also outlined. The results on separation of RMS signals show this technique has the potential of increasing the probability to successfully identify the AE events associated with the various mechanical events.

Primary students' success on the structured number line

Number lines are part of our everyday life (e.g., thermometers, kitchen scales) and are frequently used in primary mathematics as instructional aids, in texts and for assessment purposes on mathematics tests. There are two major types of number lines; structured number lines, which are the focus of this paper, and empty number lines. Structured number lines represent mathematical information by the placement of marks on a horizontal or vertical line which has been marked into proportional segments (Figure 1). Empty number lines are blank lines which students can use for calculations (Figure 2) and are not discussed further here (see van den Heuvel-Panhuizen, 2008, on the role of empty number lines). In this article, we will focus on how students’ knowledge of the structured number line develops and how they become successful users of this mathematical tool.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

Analysis of acoustic emission data for structural health monitoring applications

Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. It is one of the several diagnostic techniques currently used for structural health monitoring (SHM) of civil infrastructure such as bridges. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. But several challenges still exist. Due to high sampling rate required for data capture, large amount of data is generated during AE testing. This is further complicated by the presence of a number of spurious sources that can produce AE signals which can then mask desired signals. Hence, an effective data analysis strategy is needed to achieve source discrimination. This also becomes important for long term monitoring applications in order to avoid massive date overload. Analysis of frequency contents of recorded AE signals together with the use of pattern recognition algorithms are some of the advanced and promising data analysis approaches for source discrimination. This paper explores the use of various signal processing tools for analysis of experimental data, with an overall aim of finding an improved method for source identification and discrimination, with particular focus on monitoring of steel bridges.

Application of acoustic emission technology in monitoring structure integrity of bridges

Bridges are an important part of a nation’s infrastructure and reliable monitoring methods are necessary to ensure their safety and efficiency. Most bridges in use today were built decades ago and are now subjected to changes in load patterns that can cause localized distress, which can result in bridge failure if not corrected. Early detection of damage helps in prolonging lives of bridges and preventing catastrophic failures. This paper briefly reviews the various technologies currently used in health monitoring of bridge structures and in particular discusses the application and challenges of acoustic emission (AE) technology. Some of the results from laboratory experiments on a bridge model are also presented. The main objectives of these experiments are source localisation and assessment. The findings of the study can be expected to enhance the knowledge of acoustic emission process and thereby aid in the development of an effective bridge structure diagnostics system.

The ghrelin receptor isoforms (GHS-R1a and GHS-R1b) and GPR39 : an investigation into receptor dimerisation

Prostate cancer is the second most common cause of cancer-related deaths in Western males. Current diagnostic, prognostic and treatment approaches are not ideal and advanced metastatic prostate cancer is incurable. There is an urgent need for improved adjunctive therapies and markers for this disease. GPCRs are likely to play a significant role in the initiation and progression of prostate cancer. Over the last decade, it has emerged that G protein coupled receptors (GPCRs) are likely to function as homodimers and heterodimers. Heterodimerisation between GPCRs can result in the formation of novel pharmacological receptors with altered functional outcomes, and a number of GPCR heterodimers have been implicated in the pathogenesis of human disease. Importantly, novel GPCR heterodimers represent potential new targets for the development of more specific therapeutic drugs. Ghrelin is a 28 amino acid peptide hormone which has a unique n-octanoic acid post-translational modification. Ghrelin has a number of important physiological roles, including roles in appetite regulation and the stimulation of growth hormone release. The ghrelin receptor is the growth hormone secretagogue receptor type 1a, GHS-R1a, a seven transmembrane domain GPCR, and GHS-R1b is a C-terminally truncated isoform of the ghrelin receptor, consisting of five transmembrane domains. Growing evidence suggests that ghrelin and the ghrelin receptor isoforms, GHS-R1a and GHS-R1b, may have a role in the progression of a number of cancers, including prostate cancer. Previous studies by our research group have shown that the truncated ghrelin receptor isoform, GHS-R1b, is not expressed in normal prostate, however, it is expressed in prostate cancer. The altered expression of this truncated isoform may reflect a difference between a normal and cancerous state. A number of mutant GPCRs have been shown to regulate the function of their corresponding wild-type receptors. Therefore, we investigated the potential role of interactions between GHS-R1a and GHS-R1b, which are co-expressed in prostate cancer and aimed to investigate the function of this potentially new pharmacological receptor. In 2005, obestatin, a 23 amino acid C-terminally amidated peptide derived from preproghrelin was identified and was described as opposing the stimulating effects of ghrelin on appetite and food intake. GPR39, an orphan GPCR which is closely related to the ghrelin receptor, was identified as the endogenous receptor for obestatin. Recently, however, the ability of obestatin to oppose the effects of ghrelin on appetite and food intake has been questioned, and furthermore, it appears that GPR39 may in fact not be the obestatin receptor. The role of GPR39 in the prostate is of interest, however, as it is a zinc receptor. Zinc has a unique role in the biology of the prostate, where it is normally accumulated at high levels, and zinc accumulation is altered in the development of prostate malignancy. Ghrelin and zinc have important roles in prostate cancer and dimerisation of their receptors may have novel roles in malignant prostate cells. The aim of the current study, therefore, was to demonstrate the formation of GHS-R1a/GHS-R1b and GHS-R1a/GPR39 heterodimers and to investigate potential functions of these heterodimers in prostate cancer cell lines. To demonstrate dimerisation we first employed a classical co-immunoprecipitation technique. Using cells co-overexpressing FLAG- and Myc- tagged GHS-R1a, GHS-R1b and GPR39, we were able to co-immunoprecipitate these receptors. Significantly, however, the receptors formed high molecular weight aggregates. A number of questions have been raised over the propensity of GPCRs to aggregate during co-immunoprecipitation as a result of their hydrophobic nature and this may be misinterpreted as receptor dimerisation. As we observed significant receptor aggregation in this study, we used additional methods to confirm the specificity of these putative GPCR interactions. We used two different resonance energy transfer (RET) methods; bioluminescence resonance energy transfer (BRET) and fluorescence resonance energy transfer (FRET), to investigate interactions between the ghrelin receptor isoforms and GPR39. RET is the transfer of energy from a donor fluorophore to an acceptor fluorophore when they are in close proximity, and RET methods are, therefore, applicable to the observation of specific protein-protein interactions. Extensive studies using the second generation bioluminescence resonance energy transfer (BRET2) technology were performed, however, a number of technical limitations were observed. The substrate used during BRET2 studies, coelenterazine 400a, has a low quantum yield and rapid signal decay. This study highlighted the requirement for the expression of donor and acceptor tagged receptors at high levels so that a BRET ratio can be determined. After performing a number of BRET2 experimental controls, our BRET2 data did not fit the predicted results for a specific interaction between these receptors. The interactions that we observed may in fact represent ‘bystander BRET’ resulting from high levels of expression, forcing the donor and acceptor into close proximity. Our FRET studies employed two different FRET techniques, acceptor photobleaching FRET and sensitised emission FRET measured by flow cytometry. We were unable to observe any significant FRET, or FRET values that were likely to result from specific receptor dimerisation between GHS-R1a, GHS-R1b and GPR39. While we were unable to conclusively demonstrate direct dimerisation between GHS-R1a, GHS-R1b and GPR39 using several methods, our findings do not exclude the possibility that these receptors interact. We aimed to investigate if co-expression of combinations of these receptors had functional effects in prostate cancers cells. It has previously been demonstrated that ghrelin stimulates cell proliferation in prostate cancer cell lines, through ERK1/2 activation, and GPR39 can stimulate ERK1/2 signalling in response to zinc treatments. Additionally, both GHS-R1a and GPR39 display a high level of constitutive signalling and these constitutively active receptors can attenuate apoptosis when overexpressed individually in some cell types. We, therefore, investigated ERK1/2 and AKT signalling and cell survival in prostate cancer the potential modulation of these functions by dimerisation between GHS-R1a, GHS-R1b and GPR39. Expression of these receptors in the PC-3 prostate cancer cell line, either alone or in combination, did not alter constitutive ERK1/2 or AKT signalling, basal apoptosis or tunicamycin-stimulated apoptosis, compared to controls. In summary, the potential interactions between the ghrelin receptor isoforms, GHS-R1a and GHS-R1b, and the related zinc receptor, GPR39, and the potential for functional outcomes in prostate cancer were investigated using a number of independent methods. We did not definitively demonstrate the formation of these dimers using a number of state of the art methods to directly demonstrate receptor-receptor interactions. We investigated a number of potential functions of GPR39 and GHS-R1a in the prostate and did not observe altered function in response to co-expression of these receptors. The technical questions raised by this study highlight the requirement for the application of extensive controls when using current methods for the demonstration of GPCR dimerisation. Similar findings in this field reflect the current controversy surrounding the investigation of GPCR dimerisation. Although GHS-R1a/GHS-R1b or GHS-R1a/GPR39 heterodimerisation was not clearly demonstrated, this study provides a basis for future investigations of these receptors in prostate cancer. Additionally, the results presented in this study and growing evidence in the literature highlight the requirement for an extensive understanding of the experimental method and the performance of a range of controls to avoid the spurious interpretation of data gained from artificial expression systems. The future development of more robust techniques for investigating GPCR dimerisation is clearly required and will enable us to elucidate whether GHS-R1a, GHS-R1b and GPR39 form physiologically relevant dimers.

Inheritance of Resistance to Rice Tungro Spherical Virus in a Near-Isogenic Line Derived from Utri Merah and in Rice Cultivar TKM6

Resistance to rice virus diseases is an important requirement in many Southeast Asian rice breeding programs. Inheritance of resistance to rice tungro spherical virus (RTSV) in TW5, a near-isogenic line derived from Indonesian rice cultivar Utri Merah, was compared to that in TKM6, an Indian rice cultivar. Both TKM6 and Utri Merah are cultivars resistant to RTSV infections. Crosses were made between TKM6 and TN1, a susceptible cultivar, and between TW5 and TN1, and F3 lines were evaluated for their resistance to RTSV using two RTSV inoculum sources and a serological assay (ELISA). In TKM6, the resistance to the mixture of RTSV-V + RTBV inoculum source was controlled by a single recessive gene, whereas in TW5, the resistance was controlled by two recessive genes. A single recessive gene, however, controlled the resistance in TW5 when another RTSV variant, RTSV-VI, was used, suggesting that the resistance in TW5 depends on the nature of the RTSV inoculum used. RT-PCR, sequence, and phylogenetic analyses confirmed that RTSV-VI inoculum differs from RTSV-V inoculum and accurate phenotyping of the resistance to RTSV requires the use of a genetic marker.

Structure of selected basic zinc/copper (II) phosphate minerals based upon near-infrared spectroscopy : implications for hydrogen bonding

The NIR spectra of reichenbachite, scholzite and parascholzite have been studied at 298 K. The spectra of the minerals are different, in line with composition and crystal structural variations. Cation substitution effects are significant in their electronic spectra and three distinctly different electronic transition bands are observed in the near-infrared spectra at high wavenumbers in the 12000-7600 cm-1 spectral region. Reichenbachite electronic spectrum is characterised by Cu(II) transition bands at 9755 and 7520 cm-1. A broad spectral feature observed for ferrous ion in the 12000-9000 cm-1 region both in scholzite and parascholzite. Some what similarities in the vibrational spectra of the three phosphate minerals are observed particularly in the OH stretching region. The observation of strong band at 5090 cm-1 indicates strong hydrogen bonding in the structure of the dimorphs, scholzite and parascholzite. The three phosphates exhibit overlapping bands in the 4800-4000 cm-1 region resulting from the combinations of vibrational modes of (PO4)3- units.

Environmental aspects and challenges of oilseed produced biodiesel in Southeast Asia

Research on alternative fuel for the vehemently growing number of automotivesis intensified due to environmental reasons rather than turmoil in energy price and supply. From the policy and steps to emphasis the use of biofuel by governments all around the world, this can be comprehended that biofuel have placed itself as a number one substitute for fossil fuels. These phenomena made Southeast Asia a prominent exporter of biodiesel. But thrust in biodiesel production from oilseeds of palm and Jatropha curcas in Malaysia, Indonesia and Thailand is seriously threatening environmental harmony. This paper focuses on this critical issue of biodiesels environmental impacts, policy, standardization of this region as well as on the emission of biodiesel in automotive uses. To draw a bottom line on feasibilities of different feedstock of biodiesel, a critical analysis on oilseed yield rate, land use, engine emissions and oxidation stability is reviewed. Palm oil based biodiesel is clearly ahead in all these aspects of feasibility, except in the case of NOx where it lags from conventional petro diesel.

Infrared and infrared emission spectroscopy of nesquehonite Mg(OH)(HCO3)•2H2O : implications for the formula of nesquehonite

The mineral nesquehonite Mg(OH)(HCO3)•2H2O has been analysed by a combination of infrared (IR) and infrared emission spectroscopy (IES). Both techniques show OH vibrations, both stretching and deformation modes. IES proves the OH units are stable up to 450°C. The strong IR band at 934 cm-1 is evidence for MgOH deformation modes supporting the concept of HCO3- units in the molecular structure. Infrared bands at 1027, 1052 and 1098 cm-1 are attributed to the symmetric stretching modes of HCO3- and CO32- units. Infrared bands at 1419, 1439, 1511, and 1528 cm-1 are assigned to the antisymmetric stretching modes of CO32- and HCO3- units. IES supported by thermoanalytical results defines the thermal stability of nesquehonite IES defines the changes in the molecular structure of nesquehonite with temperature. The results of IR and IES supports the concept that the formula of nesquehonite is better defined as Mg(OH)(HCO3)•2H2O.

Application of higher order statistics/spectra in biomedical signals : a review

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.