An improved modal strain energy method for structural damage detection, 2D simulation

Structural damage detection using modal strain energy (MSE) is one of the most efficient and reliable structural health monitoring techniques. However, some of the existing MSE methods have been validated for special types of structures such as beams or steel truss bridges which demands improving the available methods. The purpose of this study is to improve an efficient modal strain energy method to detect and quantify the damage in complex structures at early stage of formation. In this paper, a modal strain energy method was mathematically developed and then numerically applied to a fixed-end beam and a three-story frame including single and multiple damage scenarios in absence and presence of up to five per cent noise. For each damage scenario, all mode shapes and natural frequencies of intact structures and the first five mode shapes of assumed damaged structures were obtained using STRAND7. The derived mode shapes of each intact and damaged structure at any damage scenario were then separately used in the improved formulation using MATLAB to detect the location and quantify the severity of damage as compared to those obtained from previous method. It was found that the improved method is more accurate, efficient and convergent than its predecessors. The outcomes of this study can be safely and inexpensively used for structural health monitoring to minimize the loss of lives and property by identifying the unforeseen structural damages.

Detection of rain in acoustic recordings of the environment using machine learning techniques

This thesis is concerned with the detection and prediction of rain in environmental recordings using different machine learning algorithms. The results obtained in this research will help ecologists to efficiently analyse environmental data and monitor biodiversity.

Fast and accurate business process drift detection

Business processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs – also known as business process drift detection – enables analysts to identify and act upon changes that may otherwise affect process performance. Previous methods for business process drift detection are based on an exploration of a potentially large feature space and in some cases they require users to manually identify the specific features that characterize the drift. Depending on the explored feature set, these methods may miss certain types of changes. This paper proposes a fully automated and statistically grounded method for detecting process drift. The core idea is to perform statistical tests over the distributions of runs observed in two consecutive time windows. By adaptively sizing the window, the method strikes a trade-off between classification accuracy and drift detection delay. A validation on synthetic and real-life logs shows that the method accurately detects typical change patterns and scales up to the extent it is applicable for online drift detection.

Deterrence of drug driving: The impact of the ACT drug driving legislation and detection techniques

The study sought to explore the initial impact of the ACT's implementation of roadside oral fluid drug screening program. The results suggest that a number of individuals reported intentions to drug drive in the future. The classical deterrence theory variables of certainty of apprehension, severity and swiftness of sanctions were not predictive of intentions to drug drive in the future. In contrast, having avoided apprehension and having known of others that have avoided apprehension were predictive of intentions to drug drive in the future. Increasing perceptions of the certainty of apprehension, increased testing frequency, and increased awareness of the oral fluid drug screening program could potentially lead to reductions of drug driving and result in safer road environment for all ACT community members.

Direct measurement as a tool for assessing the accuracy of inverse dynamics

The understanding of the loads generated within the prosthetic leg can aid engineers in the design of components and clinicians in the process of rehabilitation. Traditional methods to assess these loads have relied on inverse dynamics. This indirect method estimates the applied load using video recordings and force-plates located at a distance from the region of interest, such as the base of the residuum. The well-known limitations of this method are related to the accuracy of this recursive model and the experimental conditions required (Frossard et al., 2003). Recent developments in sensors (Frossard et al., 2003) and prosthetic fixation (Brånemark et al., 2000) permit the direct measurement of the loads applied on the residuum of transfemoral amputees. In principle, direct measurement should be an appropriate tool for assessing the accuracy of inverse dynamics. The purpose of this paper is to determine the validity of this assumption. The comparative variable used in this study is the velocity of the relative body center of mass (VCOM(t)). The relativity is used to align the static (w.r.t. position) force plate measurement with the dynamic load cell measurement.

Review of direct measurement of inner prosthesis loading in transfemoral amputation: Potential benefits for evidence-based practice

The understanding of the load applied on the residuum through the prosthesis of individuals with transfemoral amputation (TFA) is essential to address a number of concerns that could strongly reduce their quality of life (e.g., residuum skin lesion, prosthesis fitting, alignment). This inner prosthesis loading could be estimated using a typical gait laboratory relying on inverse dynamics equations. Alternative, technological advances proposed over the last decade enabled direct measurement of this kinetic information in a broad variety of situations that could potentially be more relevant in clinical settings. The purposes of this presentation are (A) to review the literature about recent developments in measure and analyses of inner prosthesis loading of TFA, and (B) to extract information that could potentially contribute to a better evidence-based practice.

Obstacle detection for a mining vehicle using a 2D laser

This paper discusses a number of key issues for the development of robust obstacle detection systems for autonomous mining vehicles. Strategies for obstacle detection are described and an overview of the state-of-the-art in obstacle detection for outdoor autonomous vehicles using lasers is presented, with their applicability to the mining environment noted. The development of an obstacle detection system for a mining vehicle is then detailed. This system uses a 2D laser scanner as the prime sensor and combines dead-reckoning data with laser data to create local terrain maps. The slope of the terrain maps is then used to detect potential obstacles.

Issues in obstacle detection for autonomous mining and construction vehicles

This paper discusses a Dumber of key issues for the development of robust, obstacle detection systems for autonomous mining and construction vehicles. A taxonomy of obstacle detection systems is described; An overview of the state-of- the-art in obstacle detection for outdoor autonomous vehicles is presented with their applicability to the mining and construction environments noted. The issue of so-called fail-safe obstacle detection is then discussed. Finally, we describe the development of an obstacle detection system for a mining vehicle.

A nationwide web-based automated system for early outbreak detection and rapid response in China

Timely reporting, effective analyses and rapid distribution of surveillance data can assist in detecting the aberration of disease occurrence and further facilitate a timely response. In China, a new nationwide web-based automated system for outbreak detection and rapid response was developed in 2008. The China Infectious Disease Automated-alert and Response System (CIDARS) was developed by the Chinese Center for Disease Control and Prevention based on the surveillance data from the existing electronic National Notifiable Infectious Diseases Reporting Information System (NIDRIS) started in 2004. NIDRIS greatly improved the timeliness and completeness of data reporting with real time reporting information via the Internet. CIDARS further facilitates the data analysis, aberration detection, signal dissemination, signal response and information communication needed by public health departments across the country. In CIDARS, three aberration detection methods are used to detect the unusual occurrence of 28 notifiable infectious diseases at the county level and to transmit that information either in real-time or on a daily basis. The Internet, computers and mobile phones are used to accomplish rapid signal generation and dissemination, timely reporting and reviewing of the signal response results. CIDARS has been used nationwide since 2008; all Centers for Disease Control and Prevention (CDC) in China at the county, prefecture, provincial and national levels are involved in the system. It assists with early outbreak detection at the local level and prompts reporting of unusual disease occurrences or potential outbreaks to CDCs throughout the country.

Role of big data in the early detection of Ebola and other emerging infectious diseases

The lack of adequate disease surveillance systems in Ebola-affected areas has both reduced the ability to respond locally and has increased global risk. There is a need to improve disease surveillance in vulnerable regions, and digital surveillance could present a viable approach.

The prevention, detection and management of cancer treatment-induced cardiotoxicity: a meta-review

Background The benefits associated with some cancer treatments do not come without risk. A serious side effect of some common cancer treatments is cardiotoxicity. Increased recognition of the public health implications of cancer treatment-induced cardiotoxicity has resulted in a proliferation of systematic reviews in this field to guide practice. Quality appraisal of these reviews is likely to limit the influence of biased conclusions from systematic reviews that have used poor methodology related to clinical decision-making. The aim of this meta-review is to appraise and synthesise evidence from only high quality systematic reviews focused on the prevention, detection or management of cancer treatment-induced cardiotoxicity. Methods Using Cochrane methodology, we searched databases, citations and hand-searched bibliographies. Two reviewers independently appraised reviews and extracted findings. A total of 18 high quality systematic reviews were subsequently analysed, 67 % (n = 12) of these comprised meta-analyses. Results One systematic review concluded that there is insufficient evidence regarding the utility of cardiac biomarkers for the detection of cardiotoxicity. The following strategies might reduce the risk of cardiotoxicity: 1) The concomitant administration of dexrazoxane with anthracylines; 2) The avoidance of anthracyclines where possible; 3) The continuous administration of anthracyclines (>6 h) rather than bolus dosing; and 4) The administration of anthracycline derivatives such as epirubicin or liposomal-encapsulated doxorubicin instead of doxorubicin. In terms of management, one review focused on medical interventions for treating anthracycline-induced cardiotoxicity during or after treatment of childhood cancer. Neither intervention (enalapril and phosphocreatine) was associated with statistically significant improvement in ejection fraction or mortality. Conclusion This review highlights the lack of high level evidence to guide clinical decision-making with respect to the detection and management of cancer treatment-associated cardiotoxicity. There is more evidence with respect to the prevention of this adverse effect of cancer treatment. This evidence, however, only applies to anthracycline-based chemotherapy in a predominantly adult population. There is no high-level evidence to guide clinical decision-making regarding the prevention, detection or management of radiation-induced cardiotoxicity.

A homogeneous surface-enhanced Raman scattering platform for ultra-trace detection of trinitrotoluene in the environment

A facile and sensitive surface-enhanced Raman scattering substrate was prepared by controlled potentiostatic deposition of a closely packed single layer of gold nanostructures (AuNS) over a flat gold (pAu) platform. The nanometer scale inter-particle distance between the particles resulted in high population of ‘hot spots’ which enormously enhanced the scattered Raman photons. A renewed methodology was followed to precisely quantify the SERS substrate enhancement factor (SSEF) and it was estimated to be (2.2 ± 0.17) × 105. The reproducibility of the SERS signal acquired by the developed substrate was tested by establishing the relative standard deviation (RSD) of 150 repeated measurements from various locations on the substrate surface. A low RSD of 4.37 confirmed the homogeneity of the developed substrate. The sensitivity of pAu/AuNS was proven by determining 100 fM 2,4,6-trinitrotoluene (TNT) comfortably. As a proof of concept on the potential of the new pAu/AuNS substrate in field analysis, TNT in soil and water matrices was selectively detected after forming a Meisenheimer complex with cysteamine.

Rapid detection of TNT in aqueous media by selective label free surface enhanced Raman spectroscopy

We report rapid and ultra-sensitive detection system for 2,4,6-trinitrotoluene (TNT) using unmodified gold nanoparticles and surface-enhanced Raman spectroscopy (SERS). First, Meisenheimer complex has been formed in aqueous solution between TNT and cysteamine in less than 15 min of mixing. The complex formation is confirmed by the development of a pink colour and a new UV–vis absorption band around 520 nm. Second, the developed Meisenheimer complex is spontaneously self-assembled onto unmodified gold nanoparticles through a stable Au–S bond between the cysteamine moiety and the gold surface. The developed mono layer of cysteamine-TNT is then screened by SERS to detect and quantify TNT. Our experimental results demonstrate that the SERS-based assay provide an ultra-sensitive approach for the detection of TNT down to 22.7 ng/L. The unambiguous fingerprint identification of TNT by SERS represents a key advantage for our proposed method. The new method provides high selectivity towards TNT over 2,4 DNT and picric acid. Therefore it satisfies the practical requirements for the rapid screening of TNT in real life samples where the interim 24-h average allowable concentration of TNT in waste water is 0.04 mg/L.

Fault detection of wind turbine drivetrain utilizing power-speed characteristics

Wind energy, being the fastest growing renewable energy source in the present world, requires a large number of wind turbines to transform wind energy into electricity. One factor driving the cost of this energy is the reliable operation of these turbines. Therefore, it is a growing requirement within the wind farm community, to monitor the operation of the wind turbines on a continuous basis so that a possible fault can be detected ahead of time. As the wind turbine operates in an environment of constantly changing wind speed, it is a challenging task to design a fault detection technique which can accommodate the stochastic operational behavior of the turbines. Addressing this issue, this paper proposes a novel fault detection criterion which is robust against operational uncertainty, as well as having the ability to quantify severity level specifically of the drivetrain abnormality within an operating wind turbine. A benchmark model of wind turbine has been utilized to simulate drivetrain fault condition and effectiveness of the proposed technique has been tested accordingly. From the simulation result it can be concluded that the proposed criterion exhibits consistent performance for drivetrain faults for varying wind speed and has linear relationship with the fault severity level.