Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Making YAWL and SmartPM interoperate : managing highly dynamic processes by exploiting automatic adaptation features

In the last years, the trade-o between exibility and sup- port has become a leading issue in work ow technology. In this paper we show how an imperative modeling approach used to de ne stable and well-understood processes can be complemented by a modeling ap- proach that enables automatic process adaptation and exploits planning techniques to deal with environmental changes and exceptions that may occur during process execution. To this end, we designed and imple- mented a Custom Service that allows the Yawl execution environment to delegate the execution of subprocesses and activities to the SmartPM execution environment, which is able to automatically adapt a process to deal with emerging changes and exceptions. We demonstrate the fea- sibility and validity of the approach by showing the design and execution of an emergency management process de ned for train derailments.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Построение автоматической навигационной системы малогабаритного беспилотного летательного аппарата многократного применения = Development of automatic navigation management system for small unmanned aerial vehicle

Современный этап развития комплексов автоматического управления и навигации малогабаритными БЛА многократного применения предъявляет высокие требования к автономности, точности и миниатюрности данных систем. Противоречивость требований диктует использование функционального и алгоритмического объединения нескольких разнотипных источников навигационной информации в едином вычислительном процессе на основе методов оптимальной фильтрации. Получили широкое развитие бесплатформенные инерциальные навигационные системы (БИНС) на основе комплексирования данных микромеханических датчиков инерциальной информации и датчиков параметров движения в воздушном потоке с данными спутниковых навигационных систем (СНС). Однако в современных условиях такой подход не в полной мере реализует требования к помехозащищённости, автономности и точности получаемой навигационной информации. Одновременно с этим достигли значительного прогресса навигационные системы, использующие принципы корреляционно экстремальной навигации по оптическим ориентирам и цифровым картам местности. Предлагается схема построения автономной автоматической навигационной системы (АНС) для БЛА многоразового применения на основе объединения алгоритмов БИНС, спутниковой навигационной системы и оптической навигационной системы. The modern stage of automatic control and guidance systems development for small unmanned aerial vehicles (UAV) is determined by advanced requirements for autonomy, accuracy and size of the systems. The contradictory of the requirements dictates novel functional and algorithmic tight coupling of several different onboard sensors into one computational process, which is based on methods of optimal filtering. Nowadays, data fusion of micro-electro mechanical sensors of inertial measurement units, barometric pressure sensors, and signals of global navigation satellite systems (GNSS) receivers is widely used in numerous strap down inertial navigation systems (INS). However, the systems do not fully comply with such requirements as jamming immunity, fault tolerance, autonomy, and accuracy of navigation. At the same time, the significant progress has been recently demonstrated by the navigation systems, which use the correlation extremal principle applied for optical data flow and digital maps. This article proposes a new architecture of automatic navigation management system (ANMS) for small UAV, which combines algorithms of strap down INS, satellite navigation and optical navigation system.

Исследования и испытания алгоритмов автоматической пилотажно-навигационной системы малогабаритного беспилотного летательного аппарата = Research and testing of automatic navigation management system’s algorithms for small unmanned aerial vehicle

В статье представлено развитие принципа построения автоматической пилотажно-навигационной системы (АПНС) для беспилотного летательного аппарата (БЛА). Принцип заключается в синтезе комплексных систем управления БПЛА не только на основе использования алгоритмов БИНС, но и алгоритмов, объединяющих в себе решение задач формирования и отработки сформированной траектории резервированной системой управления и навигации. Приведены результаты аналитического исследования и данные летных экспериментов разработанных алгоритмов АПНС БЛА, обеспечивающих дополнительное резервирование алгоритмов навигации и наделяющих БЛА новым функциональной способностью по выходу в заданную точку пространства с заданной скоростью в заданный момент времени с учетом атмосферных ветровых возмущений. Предложена и испытана методика идентификации параметров воздушной атмосферы: направления и скорости W ветра. Данные летных испытаний полученного решения задачи терминальной навигации демонстрируют устойчивую работу синтезированных алгоритмов управления в различных метеоусловиях. The article presents a progress in principle of development of automatic navigation management system (ANMS) for small unmanned aerial vehicle (UAV). The principle defines a development of integrated control systems for UAV based on tight coupling of strap down inertial navigation system algorithms and algorithms of redundant flight management system to form and control flight trajectory. The results of the research and flight testing of the developed ANMS UAV algorithms are presented. The system demonstrates advanced functional redundancy of UAV guidance. The system enables new UAV capability to perform autonomous multidimensional navigation along waypoints with controlled speed and time of arrival taking into account wind. The paper describes the technique for real-time identification of atmosphere parameters such as wind direction and wind speed. The flight test results demonstrate robustness of the algorithms in diverse meteorological conditions.

Automatic inference of driving task demand from visual cues of emotion and attention

Sensing the mental, physical and emotional demand of a driving task is of primary importance in road safety research and for effectively designing in-vehicle information systems (IVIS). Particularly, the need of cars capable of sensing and reacting to the emotional state of the driver has been repeatedly advocated in the literature. Algorithms and sensors to identify patterns of human behavior, such as gestures, speech, eye gaze and facial expression, are becoming available by using low cost hardware: This paper presents a new system which uses surrogate measures such as facial expression (emotion) and head pose and movements (intention) to infer task difficulty in a driving situation. 11 drivers were recruited and observed in a simulated driving task that involved several pre-programmed events aimed at eliciting emotive reactions, such as being stuck behind slower vehicles, intersections and roundabouts, and potentially dangerous situations. The resulting system, combining face expressions and head pose classification, is capable of recognizing dangerous events (such as crashes and near misses) and stressful situations (e.g. intersections and way giving) that occur during the simulated drive.

RPAS integration within an Australian ATM system : what equipment and which airspace

The Australian Civil Aviation Safety Authority (CASA) currently lists more than 100 separate entities or organisations which maintain a UAS Operator Certificate (UOC) [1]. Approved operations are overwhelmingly a permutation of aerial photography, surveillance, survey or spotting and predominantly, are restricted to Visual Line of Sight (VLOS) operations, below 400 feet, and not within 3 NM of an aerodrome. However, demand is increasing for a Remote Piloted Aerial System (RPAS) regulatory regime which facilitates more expansive operations, in particular unsegregated, Beyond Visual Line of Sight (BVLOS) operations. Despite this demand, there is national and international apprehension regarding the necessary levels of airworthiness and operational regulation required to maintain safety and minimise the risk associated with unsegregated operations. Fundamental to addressing these legitimate concerns will be the mechanisms that underpin safe separation and collision avoidance. Whilst a large body of research has been dedicated to investigating on-board, Sense and Avoid (SAA) technology necessary to meet this challenge, this paper focuses on the contribution of the NAS to separation assurance, and how it will support, as well as complicate RPAS integration. The paper collates and presents key, but historically disparate, threads of Australian RPAS and NAS related information, and distils it with a filter focused on minimising RPAS collision risk. Our ongoing effort is motivated by the need to better understand the separation assurance contribution provided by the NAS layers, in the first instance, and subsequently employ this information to identify scenarios where the coincident collision risk is demonstrably low, providing legitimate substantiation for concessions on equipage and airworthiness standards.

Clinically-based automatic implant fitting for shape optimization of fracture fixation plates

Complex bone contour and anatomical variations between individual bones complicate the process of deriving an implant shape that fits majority of the population. This thesis proposes an automatic fitting method for anatomically-precontoured plates based on clinical requirements, and investigated if 100% anatomical fit for a group of bone is achievable through manual bending of one plate shape. It was found that, for the plate used, 100% fit is impossible to achieve through manual bending alone. Rather, newly-developed shapes are also required to obtain anatomical fit in areas with more complex bone contour.

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Design, manufacturing and maintenance of sugar milling equipment : observations from the 2008 ISSCT Engineering Workshop

An International Society of Sugar Cane Technologists (ISSCT) Engineering Workshop was held in Piracicaba, Brazil from 30 June to 4 July 2008. The theme of the workshop was Design, manufacturing and maintenance of sugar mill equipment. The workshop consisted of a series of technical sessions and site visits. The Brazilian sugar industry is growing rapidly. The growth has occurred as the result of the sugar industry’s position as a key provider of renewable energy in the form of ethanol and, more recently, electricity. The increased focus on electricity is seeing investment in high pressure (100 bar) boilers, cane cleaning plants that allow an increased biomass supply from trash and digesters that produce biogas from dunder. It is clear that the Brazilian sugar industry has a well defined place in the country’s future. The ISSCT workshop provided a good opportunity to gain information from equipment suppliers and discuss new technology that may have application in Australia. The new technologies of interest included IMCO sintered carbide shredder hammer tips, Fives Cail MillMax mills, planetary mill gearboxes, Bosch Projects chainless diffusers, Fives Cail Zuka centrifugals and Vaperma Siftek membrane systems.

Design, manufacturing and maintenance of sugar mill equipment

The ISSCT Engineering Workshop 2008 in Brazil was well attended with 62 participants including 39 overseas visitors from 15 countries. The workshop addressed the theme Design, manufacturing and maintenance of sugar mill equipment. From the technical sessions, the following conclusions were drawn: • Several speakers articulated a shared vision of the future of the Brazilian sugar industry. This shared vision gives considerable confidence that the vision can become a reality. • There is an increased focus on energy products. As a result, the reduction of factory energy consumption in order to maximise the energy available for products is also a focus. • New equipment and products are being developed with reduced power consumption, lower capital and maintenance costs, and better performance. • Methods presented for reducing maintenance costs included the use of a maintenance management system, condition monitoring and material selection. The workshop was held in conjunction with Piracicaba’s annual SIMTEC exhibition for the sugar and alcohol industries that provides a forum for technical presentations and discussion, and showcases products and services from manufacturers and service providers. In return for holding the workshop in conjunction with SIMTEC, SIMTEC provided sponsorship for the workshop, including paying travel and accommodation costs for two invited speakers, and organisation for the workshop. The ISSCT and SIMTEC technical programs were arranged so that their technical sessions did not clash, and the ISSCT program was extended a day to provide an opportunity for ISSCT participants to attend the SIMTEC exhibition. Informal feedback from workshop participants suggested that the arrangement between ISSCT and SIMTEC worked well. Site visits to two manufacturing facilities and two sugar mills were arranged as part of the workshop.

Modelling and performance of an active heave compensator for offshore operations

The development of offshore oil and gas fields require the placement of different equipment on the sea floor. This is done by deploying the equipment from vessels operating in dynamic positioning on the surface. The deployment operation has different phases, and in higher sea states, it may require wave-load synchronization, when the load is going through the splash zone, and heave compensation when the load is close to the sea floor. In this paper, we analyse the performance of a particular type of hardware operating in a heave compensation mode. We derive a comprehensive model, analyse limits of performance and evaluate a control strategy.

Douglas Shire Water Quality Monitoring Strategy: Final Report: a Report to Douglas Shire Council and the Department of the Environment and Heritage

This report describes results and conclusions from the monitoring component of the Douglas Shire Council (DSC) water quality project. The components of this project that this report addresses are: • Site selection and installation of in-stream and off-paddock automatic water quality monitoring equipment in the Douglas Shire. • Design of appropriate sampling strategies for automatic stations. • Estimation of loads of suspended sediment, total nitrogen and total phosphorus in rivers and also estimation of the changes in nutrient loads from sugar cane under different fertilizer application rates. • Development of a community-based water quality sampling program to complement the automatic sampling efforts. • Design of an optimised, long-term water quality monitoring strategy.

The effects of metabolic work rate and ambient environment on physiological tolerance times while wearing explosive and chemical personal protective equipment

This study evaluated the physiological tolerance times when wearing explosive and chemical (>35kg) personal protective equipment (PPE) in simulated environmental extremes across a range of differing work intensities. Twelve healthy males undertook nine trials which involved walking on a treadmill at 2.5, 4 and 5.5 km.h-1 in the following environmental conditions, 21, 30 and 37 °C wet bulb globe temperature (WBGT). Participants exercised for 60 min or until volitional fatigue, core temperature reached 39 °C, or heart rate exceeded 90% of maximum. Tolerance time, core temperature, skin temperature, mean body temperature, heart rate and body mass loss were measured. Exercise time was reduced in the higher WBGT environments (WBGT37

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.