Identification of acoustic emission wave modes for accurate source location in plate-like structures

Acoustic emission (AE) technique is a popular tool used for structural health monitoring of civil, mechanical and aerospace structures. It is a non-destructive method based on rapid release of energy within a material by crack initiation or growth in the form of stress waves. Recording of these waves by means of sensors and subsequent analysis of the recorded signals convey information about the nature of the source. Ability to locate the source of stress waves is an important advantage of AE technique; but as AE waves travel in various modes and may undergo mode conversions, understanding of the modes (‘modal analysis’) is often necessary in order to determine source location accurately. This paper presents results of experiments aimed at finding locations of artificial AE sources on a thin plate and identifying wave modes in the recorded signal waveforms. Different source locating techniques will be investigated and importance of wave mode identification will be explored.

Identification and control of induction machines using artificial neural networks

This paper proposes the use of artificial neural networks (ANNs) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics; and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme

Stability and convergence of an effective numerical method for the time-space fractional Fokker-Planck equation with a nonlinear source term

Fractional Fokker-Planck equations (FFPEs) have gained much interest recently for describing transport dynamics in complex systems that are governed by anomalous diffusion and nonexponential relaxation patterns. However, effective numerical methods and analytic techniques for the FFPE are still in their embryonic state. In this paper, we consider a class of time-space fractional Fokker-Planck equations with a nonlinear source term (TSFFPE-NST), which involve the Caputo time fractional derivative (CTFD) of order α ∈ (0, 1) and the symmetric Riesz space fractional derivative (RSFD) of order μ ∈ (1, 2). Approximating the CTFD and RSFD using the L1-algorithm and shifted Grunwald method, respectively, a computationally effective numerical method is presented to solve the TSFFPE-NST. The stability and convergence of the proposed numerical method are investigated. Finally, numerical experiments are carried out to support the theoretical claims.

Experimental evaluation of hotspot identification methods

Identifying crash “hotspots”, “blackspots”, “sites with promise”, or “high risk” locations is standard practice in departments of transportation throughout the US. The literature is replete with the development and discussion of statistical methods for hotspot identification (HSID). Theoretical derivations and empirical studies have been used to weigh the benefits of various HSID methods; however, a small number of studies have used controlled experiments to systematically assess various methods. Using experimentally derived simulated data—which are argued to be superior to empirical data, three hot spot identification methods observed in practice are evaluated: simple ranking, confidence interval, and Empirical Bayes. Using simulated data, sites with promise are known a priori, in contrast to empirical data where high risk sites are not known for certain. To conduct the evaluation, properties of observed crash data are used to generate simulated crash frequency distributions at hypothetical sites. A variety of factors is manipulated to simulate a host of ‘real world’ conditions. Various levels of confidence are explored, and false positives (identifying a safe site as high risk) and false negatives (identifying a high risk site as safe) are compared across methods. Finally, the effects of crash history duration in the three HSID approaches are assessed. The results illustrate that the Empirical Bayes technique significantly outperforms ranking and confidence interval techniques (with certain caveats). As found by others, false positives and negatives are inversely related. Three years of crash history appears, in general, to provide an appropriate crash history duration.

Nonprofit organizational and work unit identification and the occupational stress process

Research investigating the transactional approach to the work stressor-employee adjustment relationship has described many negative main effects between perceived stressors in the workplace and employee outcomes. A considerable amount of literature, theoretical and empirical, also describes potential moderators of this relationship. Organizational identification has been established as a significant predictor of employee job-related attitudes. To date, research has neglected investigation of the potential moderating effect of organizational identification in the work stressor-employee adjustment relationship. On the basis of identity, subjective fit and sense of belonging literature it was predicted that higher perceptions of identification at multiple levels of the organization would mitigate the negative effect of work stressors on employee adjustment. It was expected, further, that more proximal, lower order identifications would be more prevalent and potent as buffers of stressors on strain. Predictions were tested with an employee sample from five organizations (N = 267). Hierarchical moderated multiple regression analyses revealed some support for the stress-buffering effects of identification in the prediction of job satisfaction and organizational commitment, particularly for more proximal (i.e., work unit) identification. These positive stress-buffering effects, however, were present for low identifiers in some situations. The present study represents an extension of the application of organizational identity theory by identifying the effects of organizational and workgroup identification on employee outcomes in the nonprofit context. Our findings will contribute to a better understanding of the dynamics in nonprofit organizations and therefore contribute to the development of strategy and interventions to deal with identity-based issues in nonprofits.

Bias in the nonlinear filter generator output sequence

Nonlinear filter generators are common components used in the keystream generators for stream ciphers and more recently for authentication mechanisms. They consist of a Linear Feedback Shift Register (LFSR) and a nonlinear Boolean function to mask the linearity of the LFSR output. Properties of the output of a nonlinear filter are not well studied. Anderson noted that the m-tuple output of a nonlinear filter with consecutive taps to the filter function is unevenly distributed. Current designs use taps which are not consecutive. We examine m-tuple outputs from nonlinear filter generators constructed using various LFSRs and Boolean functions for both consecutive and uneven (full positive difference sets where possible) tap positions. The investigation reveals that in both cases, the m-tuple output is not uniform. However, consecutive tap positions result in a more biased distribution than uneven tap positions, with some m-tuples not occurring at all. These biased distributions indicate a potential flaw that could be exploited for cryptanalysis

Nonlinear diffusion and exclusion processes with contact interactions

Exclusion processes on a regular lattice are used to model many biological and physical systems at a discrete level. The average properties of an exclusion process may be described by a continuum model given by a partial differential equation. We combine a general class of contact interactions with an exclusion process. We determine that many different types of contact interactions at the agent-level always give rise to a nonlinear diffusion equation, with a vast variety of diffusion functions D(C). We find that these functions may be dependent on the chosen lattice and the defined neighborhood of the contact interactions. Mild to moderate contact interaction strength generally results in good agreement between discrete and continuum models, while strong interactions often show discrepancies between the two, particularly when D(C) takes on negative values. We present a measure to predict the goodness of fit between the discrete and continuous model, and thus the validity of the continuum description of a motile, contact-interacting population of agents. This work has implications for modeling cell motility and interpreting cell motility assays, giving the ability to incorporate biologically realistic cell-cell interactions and develop global measures of discrete microscopic data.

Asset identification : addressing the climate change in public asset management process

Climate change is becoming increasingly apparent that is largely caused by human activities such as asset management processes, from planning to disposal, of property and infrastructure. One essential component of asset management process is asset identification. The aims of the study are to identify the information needed in asset identification and inventory as one of public asset management process in addressing the climate change issue; and to examine its deliverability in developing countries’ local governments. In order to achieve its aims, this study employs a case study in Indonesia. This study only discusses one medium size provincial government in Indonesia. The information is gathered through interviews of the local government representatives in South Sulawesi Province, Indonesia and document analysis provided by interview participants. The study found that for local government, improving the system in managing their assets is one of emerging biggest challenge. Having the right information in the right place and at the right time are critical factors in response to this challenge. Therefore, asset identification as the frontline step in public asset management system is holding an important and critical role. Furthermore, an asset identification system should be developed to support the mainstream of adaptation to climate change vulnerability and to help local government officers to be environmentally sensitive. Finally, findings from this study provide useful input for the policy makers, scholars and asset management practitioners to develop an asset inventory system as a part of public asset management process in addressing the climate change.

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.

Identification of factors contributing to recurrence of venous leg ulcers

Background and Significance Venous leg ulcers are a significant cause of chronic ill-health for 1–3% of those aged over 60 years, increasing in incidence with age. The condition is difficult and costly to heal, consuming 1–2.5% of total health budgets in developed countries and up to 50% of community nursing time. Unfortunately after healing, there is a recurrence rate of 60 to 70%, frequently within the first 12 months after heaing. Although some risk factors associated with higher recurrence rates have been identified (e.g. prolonged ulcer duration, deep vein thrombosis), in general there is limited evidence on treatments to effectively prevent recurrence. Patients are generally advised to undertake activities which aim to improve the impaired venous return (e.g. compression therapy, leg elevation, exercise). However, only compression therapy has some evidence to support its effectiveness in prevention and problems with adherence to this strategy are well documented. Aim The aim of this research was to identify factors associated with recurrence by determining relationships between recurrence and demographic factors, health, physical activity, psychosocial factors and self-care activities to prevent recurrence. Methods Two studies were undertaken: a retrospective study of participants diagnosed with a venous leg ulcer which healed 12 to 36 months prior to the study (n=122); and a prospective longitudinal study of participants recruited as their ulcer healed and data collected for 12 months following healing (n=80). Data were collected from medical records on demographics, medical history and ulcer history and treatments; and from self-report questionnaires on physical activity, nutrition, psychosocial measures, ulcer history, compression and other self-care activities. Follow-up data for the prospective study were collected every three months for 12 months after healing. For the retrospective study, a logistic regression model determined the independent influences of variables on recurrence. For the prospective study, median time to recurrence was calculated using the Kaplan-Meier method and a Cox proportional-hazards regression model was used to adjust for potential confounders and determine effects of preventive strategies and psychosocial factors on recurrence. Results In total, 68% of participants in the retrospective study and 44% of participants in the prospective study suffered a recurrence. After mutual adjustment for all variables in multivariable regression models, leg elevation, compression therapy, self efficacy and physical activity were found to be consistently related to recurrence in both studies. In the retrospective study, leg elevation, wearing Class 2 or 3 compression hosiery, the level of physical activity, cardiac disease and self efficacy scores remained significantly associated (p<0.05) with recurrence. The model was significant (p <0.001); with a R2 equivalent of 0.62. Examination of relationships between psychosocial factors and adherence to wearing compression hosiery found wearing compression hosiery was significantly positively associated with participants’ knowledge of the cause of their condition (p=0.002), higher self-efficacy scores (p=0.026) and lower depression scores (p=0.009). Analysis of data from the prospective study found there were 35 recurrences (44%) in the 12 months following healing and median time to recurrence was 27 weeks. After adjustment for potential confounders, a Cox proportional hazards regression model found that at least an hour/day of leg elevation, six or more days/week in Class 2 (20–25mmHg) or 3 (30–40mmHg) compression hosiery, higher social support scale scores and higher General Self-Efficacy scores remained significantly associated (p<0.05) with a lower risk of recurrence, while male gender and a history of DVT remained significant risk factors for recurrence. Overall the model was significant (p <0.001); with an R2 equivalent 0.72. Conclusions The high rates of recurrence found in the studies highlight the urgent need for further information in this area to support development of effective strategies for prevention. Overall, results indicate leg elevation, physical activity, compression hosiery and strategies to improve self-efficacy are likely to prevent recurrence. In addition, optimal management of depression and strategies to improve patient knowledge and self-efficacy may positively influence adherence to compression therapy. This research provides important information for development of strategies to prevent recurrence of venous leg ulcers, with the potential to improve health and decrease health care costs in this population.

Broad Bean Stain Virus: Identification, Detectability with ELISA in Faba Bean Leaves and Seeds, Occurrence in West Asia and North Africa and Possible Wild Hosts

Abstract During a survey of faba bean viruses in West Asia and North Africa a virus was identified as broad bean stain virus (BBSV) based on host reactions, electron microscopy, physical properties and serology. An antiserum to a Syrian isolate was prepared. With this antiserum both the direct double antibody sandwich ELISA (DAS-ELISA) and dot-ELISA were very sensitive in detecting BBSV in leaf extracts, ground whole seeds and germi nated embryos. Sens it i vity was not reduced when the two-day procedure was replaced by a one-day procedure. us i ng ELISA the vi rus was detected in 73 out of 589 faba bean samples with virus-like symptoms collected from Egypt (4 out of 70 samples tested), Lebanon (6/44) , Morocco (017), Sudan (19/254), Syria (36/145) and Tunisia (8/69). This is the first report of BBSV infection of faba bean in Lebanon, Sudan, Syria and Tunisia. speci es i ndi genous to Syri a were Fourteen wild legume susceptible to BBSV infection, with only two producing obvious symptoms. The virus was found to be seed transmitted ~n Vicia palaestina.

Broad Bean Mottle Virus: Identification, Serology, Host Range, and Occurrence on Faba Bean (Vicia Faba) in West Asia and Norm Africa

One of the faba bean viruses found in West Asia and North Africa was identified as broad bean mottle virus (BBMV) by host reactions, particle morphology and size, serology, and granular, often vesiculated cytoplasmic inclusions. Detailed research on four isolates, one each from Morocco, Tunisia, Sudan and Syria, provided new information on the virus. The isolates, though indistinguishable in ELISA or gel-diffusion tests, differed slightly in host range and symptoms. Twenty-one species (12 legumes and 9 non-legumes) out of 27 tested were systemically infected, and 14 of these by all four isolates. Infection in several species was symptomless, but major legumes such as chickpea, lentil and especially pea, suffered severely from infection. All 23 genotypes of faba bean, 2 of chickpea, 4 of lentil, 11 out of 21 of Phaseolus bean, and 16 out of 17 of pea were systemically sensitive to the virus. Twelve plant species were found to be new potential hosts and cucumber a new local-lesion test plant of the virus. BBMV particles occurred in faba bean plants in very high concentrations and seed transmission in this species (1.37%) was confirmed. An isolate from Syria was purified and two antisera were produced, one of which was used in ELISA to detect BBMV in faba bean field samples. Two hundred and three out of the 789 samples with symptoms suggestive of virus infection collected in 1985, 1986 and 1987, were found infected with BBMV: 4 out of 70 (4/70) tested samples from Egypt, 0/44 from Lebanon, 1/15 from Morocco, 46/254 from Sudan, 72/269 from Syria and 80/137 from Tunisia. This is the first report on its occurrence in Egypt, Syria and Tunisia. The virus is a potential threat to crop improvement in the region.

Control of aircraft for inspection of linear infrastructure

Inspection aircraft equipped with cameras and other sensors are routinely used for asset location, inspection, monitoring and hazard identification of oil-gas pipelines, roads, bridges and power transmission grids. This paper is concerned with automated flight of fixed-wing inspection aircraft to track approximately linear infrastructure. We propose a guidance law approach that seeks to maintain aircraft trajectories with desirable position and orientation properties relative to the infrastructure under inspection. Furthermore, this paper also proposes the use of an adaptive maneuver selection approach, in which maneuver primitives are adaptively selected to improve the aircraft’s attitude behaviour. We employ an integrated design methodology particularly suited for an automated inspection aircraft. Simulation studies using full nonlinear semi-coupled six degree-of-freedom equations of motion are used to illustrate the effectiveness of the proposed guidance and adaptive maneuver selection approaches in realistic flight conditions. Experimental flight test results are given to demonstrate the performance of the design.