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.

From ImageNet to mining: Adapting visual object detection with minimal supervision

This paper presents visual detection and classification of light vehicles and personnel on a mine site.We capitalise on the rapid advances of ConvNet based object recognition but highlight that a naive black box approach results in a significant number of false positives. In particular, the lack of domain specific training data and the unique landscape in a mine site causes a high rate of errors. We exploit the abundance of background-only images to train a k-means classifier to complement the ConvNet. Furthermore, localisation of objects of interest and a reduction in computation is enabled through region proposals. Our system is tested on over 10km of real mine site data and we were able to detect both light vehicles and personnel. We show that the introduction of our background model can reduce the false positive rate by an order of magnitude.

Redefining dermatologists’ role in skin cancer early detection and follow-up care

Early diagnosis of melanoma leads to the best prognosis for patients and may be more likely achieved when those who are at high risk for melanoma undergo regular and systematic monitoring. However, many people rarely or never see a dermatologist. Risk prediction models (recently reviewed by Usher-Smith et al ) could assist to triage people into preventive care appropriate for their risk profile. Most risk prediction models contain measures of phenotype including skin, eye and hair colour as well as genetic mutations. Almost all also contain the number and size of naevi, as well as the presence of naevi with atypical features which are independently associated with melanoma risk. In the absence of formal population-based screening programs for melanoma in most countries worldwide, people with high risk phenotypes may need to consider regular monitoring or self-monitoring of their naevi , especially since the vast majority of melanomas are found by people themselves or their friend and relatives. Another group of patients that will require regular monitoring are patients who have been successfully treated for their first melanoma, whose risk to develop a second melanoma is greatly increased . In a US study of 89,515 melanoma survivors those with a previous diagnosis of melanoma had a 9-fold increased risk of developing subsequent melanoma compared with the general population, equating to a rate of 3.76 per 1000 person-years, while in an Australian study, risk of subsequent melanoma was 6 per 1000 person-years. Regular follow-up is therefore essential for melanoma survivors, especially during the first few years after initial melanoma diagnosis.

The Library beyond (just) the book: Collaboration and co-working spaces for new technology and new technological practices

Libraries have often been first adopters of many new technological innovations, such as, punch cards, computers, barcodes, and e-book readers. It is thus not surprising that many libraries have embraced the advent of the internet as an opportunity to move away from just being repositories of books, towards becoming ideas stores and local network hubs for entrepreneurial thinking and new creative practices. This presentation will look at the case of “The Edge” – an initiative of the State Library of Queensland in Brisbane, Australia, to establish a digital culture centre and learning environment deliberately designed for the co-creation and co-construction of knowledge. This initiative illustrates the potential role of libraries as testing grounds for new technologies and technological practices, which is particularly relevant in the context of the NBN rollout across Australia. It also provides an example of new engagement strategies for innovative co-working spaces that are a vital element in a trend that sees professionals, creatives and designers leave their traditional places of work and embrace the city as their office.

Seasonal variations of outdoor air pollution and factors driving them in the school environment in rural Bhutan

A quantitative understanding of outdoor air quality in school environments is crucial given that air pollution levels inside classrooms are significantly influenced by outdoor pollution sources. To date, only a handful of studies have been conducted on this important topic in developing countries. The aim of this study was to quantify pollutant levels in the outdoor environment of a school in Bhutan and assess the factors driving them. Measurements were conducted for 16 weeks, spanning the wet and dry seasons, in a rural school in Bhutan. PM10, PM2.5, particle number (PN) and CO were measured daily using real-time instruments, while weekly samples for volatile organic compounds (VOCs), carbonyls and NO2 were collected using a passive sampling method. Overall mean PM10 and PM2.5 concentrations (µg/m3) were 27 and 13 for the wet, and 36 and 29 for the dry season, respectively. Only wet season data were available for PN concentrations, with a mean of 2.56 × 103 particles/cm3. Mean CO concentrations were below the detection limit of the instrumentation for the entire measurement period. Only low levels of eight VOCs were detected in both the wet and dry seasons, which presented different seasonal patterns in terms of the concentration of different compounds. The notable carbonyls were formaldehyde and hexaldehyde, with mean concentrations (µg/m3) of 2.37 and 2.41 for the wet, and 6.22 and 0.34 for the dry season, respectively. Mean NO2 cocentration for the dry season was 1.7 µg/m3, while it was below the detection limit of the instrumentation for the wet season. The pollutant concentrations were associated with a number of factors, such as cleaning and combustion activities in and around the school. A comparison with other school studies showed comparable results with a few of the studies, but in general, we found lower pollutant concentrations in the present study.

Evaluation of object detection proposals under condition variations

Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain. This process gives insight into the capabilities of methods in handling environmental changes. In this paper, a new method for object detection is introduced that combines the Selective Search and EdgeBoxes. We tested these three methods under environmental variations. Our experiments demonstrate the outperformance of the combination method under illumination and view point variations.

Effectiveness of in-office blood pressure measurement by eye care practitioners in early detection and management of hypertension

AIM To investigate the number of hypertensive patients, the optometrist is able to identify by routinely taking blood pressure (BP) measurements for patients in "at -risk" groups, and to sample patients' opinions regarding in -office BP measurement. Many of the optometrists in Saudi Arabia practice in optical stores. These stores are wide spread, easily accessible and seldom need appointments. The expanding role of the optometrist as a primary health care provider (PHCP) and the increasing global prevalence of hypertension, highlight the need for an integrated approach towards detecting and monitoring hypertension. METHODS Automated BP measurements were made twice (during the same session) at five selected optometry practices using a validated BP monitor (Omron M6) to assess the number of patients with high BP (HBP) - in at -risk groups -visiting the eye clinic routinely. Prior to data collection, practitioners underwent a two-day training workshop by a cardiologist on hypertension and how to obtain accurate BP readings. A protocol for BP measurement was distributed and retained in all participating clinics. The general attitude towards cardiovascular health of 480 patients aged 37.2 (依12.4)y and their opinion towards in-office BP measurement was assessed using a self -administered questionnaire. RESULTS A response rate of 83.6% was obtained for the survey. Ninety -three of the 443 patients (21.0% ) tested for BP in this study had HBP. Of these, (62 subjects) 67.7% were unaware of their HBP status. Thirty of the 105 subjects (28.6%) who had previously been diagnosed with HBP, still had HBP at the time of this study, and only 22 (73.3%) of these patients were on medication. Also, only 25% of the diagnosed hypertensive patients owned a BP monitor. CONCLUSION Taking BP measurements in optometry practices, we were able to identify one previously undiagnosed patient with HBP for every 8 adults tested. We also identified 30 of 105 previously diagnosed patients whose BP was poorly controlled, twenty-two of whom were on medication. The patients who participated in this study were positively disposed toward the routine measurement of BP by optometrists.

Microscopy for the detection, identification and quantification of malaria parasites on stained thick and thin blood films in research settings

Summary This manual was developed to guide a move towards common standards for undertaking and reporting research microscopy for malaria parasite detection, identification and quantification. It contains procedures based on agreed quality assurance standards for research malaria microscopy defined at a consultation of: TDR, the Special Programme for Research and Training in Tropical Diseases; the Worldwide Antimalarial Resistance Network (WWARN), United Kingdom; the Foundation for Innovative New Diagnostics (FIND), Switzerland; the Centers for Disease Control and Prevention (CDC), USA; the Kenya Medical Research Institute (KEMRI) and later expanded to include Amref Health Africa (Kenya); the Eijkman-Oxford Clinical Research Unit (EOCRU), Indonesia; Institut Pasteur du Cambodge (IPC); Institut de recherche pour le Développement (IRD), Senegal; the Global Good and Intellectual Ventures Laboratory (GG-IVL), USA; the Mahidol-Oxford Tropical Medicine Research Unit (MORU), Thailand; Queensland University of Technology (QUT), Australia, and the Shoklo Malaria Research Unit (SMRU), Thailand. These collaborating institutions commit to adhering to these standards in published research studies. It is hoped that they will form a solid basis for the wider adoption of standardized reference microscopy protocols for malaria research.

Reproducible and label-free biosensor for the selective extraction and rapid detection of proteins in biological fluids

Erythropoietin (EPO), a glycoprotein hormone of ∼34 kDa, is an important hematopoietic growth factor, mainly produced in the kidney and controls the number of red blood cells circulating in the blood stream. Sensitive and rapid recombinant human EPO (rHuEPO) detection tools that improve on the current laborious EPO detection techniques are in high demand for both clinical and sports industry. A sensitive aptamer-functionalized biosensor (aptasensor) has been developed by controlled growth of gold nanostructures (AuNS) over a gold substrate (pAu/AuNS). The aptasensor selectively binds to rHuEPO and, therefore, was used to extract and detect the drug from horse plasma by surface enhanced Raman spectroscopy (SERS). Due to the nanogap separation between the nanostructures, the high population and distribution of hot spots on the pAu/AuNS substrate surface, strong signal enhancement was acquired. By using wide area illumination (WAI) setting for the Raman detection, a low RSD of 4.92% over 150 SERS measurements was achieved. The significant reproducibility of the new biosensor addresses the serious problem of SERS signal inconsistency that hampers the use of the technique in the field. The WAI setting is compatible with handheld Raman devices. Therefore, the new aptasensor can be used for the selective extraction of rHuEPO from biological fluids and subsequently screened with handheld Raman spectrometer for SERS based in-field protein detection.

Complete-linkage clustering for voice activity detection in audio and visual speech

We propose a novel technique for conducting robust voice activity detection (VAD) in high-noise recordings. We use Gaussian mixture modeling (GMM) to train two generic models; speech and non-speech. We then score smaller segments of a given (unseen) recording against each of these GMMs to obtain two respective likelihood scores for each segment. These scores are used to compute a dissimilarity measure between pairs of segments and to carry out complete-linkage clustering of the segments into speech and non-speech clusters. We compare the accuracy of our method against state-of-the-art and standardised VAD techniques to demonstrate an absolute improvement of 15% in half-total error rate (HTER) over the best performing baseline system and across the QUT-NOISE-TIMIT database. We then apply our approach to the Audio-Visual Database of American English (AVDBAE) to demonstrate the performance of our algorithm in using visual, audio-visual or a proposed fusion of these features.

Community uses of co-creative media. Digital storytelling and Co-creative Media : The role of community arts and media in propagating and coordinating population-wide creative practice

This report describes a dynamic ‘Co-creative Media System’ that is emerging in the social space bounded by the following institutional pillars: • major cultural institutions (including screen culture agencies, libraries, museums, galleries and public service broadcasters) • the Community Arts and Cultural Development sector (historically supported through various programs of the Australia Council for the Arts) • the community broadcasting sector • the Indigenous media sector, and • the higher education sector. It illustrates how this system activates the immense creative potential of the Australian population through the ongoing development and application of participatory storytelling methods and media.

Use of modal flexibility method to detect damage in suspended cables and the effects of cable parameters

Modal flexibility is a widely accepted technique to detect structural damage using vibration characteristics. Its application to detect damage in long span large diameter cables such as those used in suspension bridge main cables has not received much attention. This paper uses the modal flexibility method incorporating two damage indices (DIs) based on lateral and vertical modes to localize damage in such cables. The competency of those DIs in damage detection is tested by the numerically obtained vibration characteristics of a suspended cable in both intact and damaged states. Three single damage cases and one multiple damage case are considered. The impact of random measurement noise in the modal data on the damage localization capability of these two DIs is next examined. Long span large diameter cables are characterized by the two critical cable parameters named bending stiffness and sag-extensibility. The influence of these parameters in the damage localization capability of the two DIs is evaluated by a parametric study with two single damage cases. Results confirm that the damage index based on lateral vibration modes has the ability to successfully detect and locate damage in suspended cables with 5% noise in modal data for a range of cable parameters. This simple approach therefore can be extended for timely damage detection in cables of suspension bridges and thereby enhance their service during their life spans.

Eye-safe UV stand-off Raman spectroscopy for the ranged detection of explosives in the field

Increasing worldwide terrorist attacks involving explosives presents a growing need for a rapid and ranged explosive detection method that can safely be deployed in the field. Stand-off Raman spectroscopy shows great promise; however, the radiant exposures of lasers required for adequate signal generation are often much greater than what is safe for the eye or the skin, restricting use of the technique to un-populated areas. Here, by determining the safe exposure levels for lasers typically used in Raman spectroscopy, optimal parameter values are identified, which produce the largest possible detection range using power densities that do not exceed the eye-safe limit. It is shown that safe ultraviolet pulse energies can be more than three orders of magnitude greater than equivalent safe visible pulse energies. Coupling this to the 16-fold increase in Raman signal obtained in the ultraviolet at 266 nm over that at 532 nm results in a 131 times larger detection range for the eye-safe 266-nm system over an equivalent eye-safe 532-nm laser system. For the Raman system described here, this translates to a maximum range of 42 m for detecting Teflon with a 266-nm laser emitting a 100-mm diameter beam of 23.5-mJ nanosecond pulses.

Robotic detection and tracking of Crown-Of-Thorns starfish

This paper presents a novel vision-based underwater robotic system for the identification and control of Crown-Of-Thorns starfish (COTS) in coral reef environments. COTS have been identified as one of the most significant threats to Australia's Great Barrier Reef. These starfish literally eat coral, impacting large areas of reef and the marine ecosystem that depends on it. Evidence has suggested that land-based nutrient runoff has accelerated recent outbreaks of COTS requiring extensive use of divers to manually inject biological agents into the starfish in an attempt to control population numbers. Facilitating this control program using robotics is the goal of our research. In this paper we introduce a vision-based COTS detection and tracking system based on a Random Forest Classifier (RFC) trained on images from underwater footage. To track COTS with a moving camera, we embed the RFC in a particle filter detector and tracker where the predicted class probability of the RFC is used as an observation probability to weight the particles, and we use a sparse optical flow estimation for the prediction step of the filter. The system is experimentally evaluated in a realistic laboratory setup using a robotic arm that moves a camera at different speeds and heights over a range of real-size images of COTS in a reef environment.

Acoustic adaptation in cross database audio visual SHMM training for phonetic spoken term detection

Visual information in the form of lip movements of the speaker has been shown to improve the performance of speech recognition and search applications. In our previous work, we proposed cross database training of synchronous hidden Markov models (SHMMs) to make use of external large and publicly available audio databases in addition to the relatively small given audio visual database. In this work, the cross database training approach is improved by performing an additional audio adaptation step, which enables audio visual SHMMs to benefit from audio observations of the external audio models before adding visual modality to them. The proposed approach outperforms the baseline cross database training approach in clean and noisy environments in terms of phone recognition accuracy as well as spoken term detection (STD) accuracy.