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A Deductive Approach for the Sensitivity Analysis of Software Defined Network Parameters
Get PDFWith the exponential growth in the number of internet-enabled devices, large scale security threats such as distributed denial of service (DDoS) attacks significantly affect software defined networks (SDNs). This necessitates efficient detection and mitigation solutions. Monitoring of SDN activities (typically identified using metrics such as throughput, jitter and response time) to ascertain deviations from profiles of normality (previously learned from benign traffic) is a key approach in detecting attacks on SDNs. In this paper, local sensitivity analysis (LSA) is implemented to identify the key network metrics that mainly influence the prediction of whether an SDN is under attack or secure. Using throughput, jitter and response time as the network impact metrics and a mathematical cost function based on min-max feature scaling to associate SDN scenarios with their respective SDN impact metrics, an artificial neural network (ANN)-based prediction model is built. The sensitivity of throughput, jitter and response time is then evaluated using the deviations of newly predicted target values of the ANN model from the actual target values when an additive white Gaussian noise (AWGN) is added to the respective impact metrics. The results of this study show that throughput, jitter and response time are all statistically sensitive to a DDoS flooding attack of the SDN. Also, Jitter was found to be the most sensitive network metric to a DDoS flooding attack of the SDN
Model predictive torque control of a switched reluctance drive with heat dissipation balancing in a power converter
Get PDFThe switched reluctance machines are known for their torque pulsations. The precise torque control is usually implemented by varying commutation angles and applying direct torque control. But still the problem of pulsation occurs during phase change. This paper proposes a model predictive direct torque control method, which cost function takes into account both torque and current control including heat dissipation balancing in a power converter. The proposed control strategy was examined using a simulation model and provides precise torque control. Heat dissipation balancing helps to equalize power modules temperature increasing the maximum output power of the power converter
Detection, Quantification and Classification of Ripened Tomatoes: A Comparative Analysis of Image Processing and Machine Learning
Get PDFIn this paper, specifically for detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared. One is a machine learning approach, known as ‘Cascaded Object Detector’ and the other is a composition of traditional customized methods, individually known as ‘Colour Transformation’, ‘Colour Segmentation’ and ‘Circular Hough Transformation’. The (Viola Jones) Cascaded Object Detector generates ‘histogram of oriented gradient’ (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, color thresholding is applied to detect tomatoes either from a natural or solid background and RGB colour is adjusted to identify ripened tomatoes. In this work, Colour Segmentation is applied in the detection of tomatoes with defects, which has not previously been applied under machine learning techniques. The function modules of this algorithm are fed formatted images, captured by a camera mounted on a mobile robot. This robot was designed, built and operated in a tomato field to identify and quantify both green and ripened tomatoes as well as to detect damaged/blemished ones. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of O(n3) for traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB. This result is potentially significant for farmers in crop fields to identify the condition of tomatoes quickly
Toward Kinecting cognition by behaviour recognition-based deep learning and big data
Get PDFThe majority of older people wish to live independently at home as long as possible despite having a range of age-related conditions including cognitive impairment. To facilitate this, there has been an extensive focus on exploring the capability of new technologies with limited success. This paper investigates whether MS Kinect (a motion-based sensing 3-D scanner device) within the MiiHome (My Intelligent Home) project in conjunction with other sensory data, machine learning and big data techniques can assist in the diagnosis and prognosis of cognitive impairment and hence prolong independent living. A pool of Kinect devices and various sensors powered by minicomputers providing internet connectivity are being installed in up to 200 homes. This enables continuous remote monitoring of elderly residents living alone. Passive and off-the-shelf sensor technologies were chosen to implement data acquisition specifically from sources that are part of the fabric of the homes, so that no extra effort is required from the participants. Various constraints including environmental, geometrical and big data were identified and appropriately dealt with. A visualization tool (MAGID) was developed for validation and verification of numerous behavioural activities. Then, a subset of data, from twelve pensioners aged over 65 with age-related cognitive decline and frailty, were collected over a period of 6 months. These data were subjected to several machine learning algorithms (multilayer perceptron neural network, neuro-fuzzy and deep learning) for classification and to extract routine behavioural patterns. These patterns were then analysed further to ascertain any health-related information and their attributes. For the first time, important routine behaviour related to Activities of Daily Living (ADL) of elderly people with cognitive and physical decline has been learnt by machine learning techniques from selected sample data obtained by MS Kinect. Medically important behaviour, e.g. eating, walking, sitting, was best learnt by deep learning with accuracy of 99.30% during training stage and average error rate of 1.83% with maximum of 12.98% during the implementation phase. Observations obtained from the application of the above learnt behaviours are presented as trends over a period of time. These trends, supplemented by other sensory signals, have provided a clearer picture of physical (in)activities (including falls) of the pensioners. The calculated behavioural attributes related to key indicators of health events can be used to model the trajectory of health status related to cognitive decline in a home setting. These results, based on a small number of elderly residents over a short period of time, imply that within the results obtained from the MiiHome project, it is possible to find indicators of cognitive decline. However, further studies are needed for full clinical validation of these indications in conjunction with assessment of cognitive decline of the participants
Robot companion cats for people at home with dementia: a qualitative case study on companotics
Get PDFThe use of robot companion pets for people in care homes has been extensively studied. The results are largely positive, and suggest that they are valuable in enhancing wellbeing, communication and behavioural aspects. However, there has been little research in people’s own homes, possibly due to the cost and complexity of some of the robot pets currently available. As dementia affects people in different ways, this study explores the effects of a robot cat for people in their own homes, without specifically investigating the effects on a particular symptom. We utilised a case study design to investigate the proposition that various factors influence the impact of a robot cat on the person living with dementia and their carer, including acceptability of the robot pet and acceptance of dementia and its symptoms. The qualitative analysis explores the similarities and differences within the data which were gathered during interviews with people with dementia and their families. This analysis revealed four themes: Distraction, Communication, Acceptance and rejection, and Connecting with the cat and connecting with others. These themes were synthesized into two overarching themes: The effect of the cat on mood and behaviour, and The interaction with the cat. We present the acceptability and impact of the robot cat on symptoms of dementia, with data presented across and within the group of participants. Our analysis suggests that benefits of the robot pet were evident, and although this was a small-scale study, where they were accepted, robot pets provided positive outcomes for the participants and their families
Combined effect of both surface finish and sub‐surface porosity on component strength under repeated load conditions
Get PDFHigh duty engineering component life is usually demonstrated through extensive testing and statistical analysis applied to empirical curve-fit equations. Because of this, the extent of the testing required is huge and costly: it must consider the load cycle range and test to high numbers of cycles. Additive Manufacturing (AM) for high duty components has brought to the fore the question of the effect of porosity and surface roughness on fatigue life, and how the true life of a critical component can be assessed conservatively. The authors propose the first step towards the development of a fatigue model based on well-established engineering physics principles, by creating computational specimens with modelled surface roughness and porosity, and subjected to cyclic loading using Finite Element Analysis. They show that the combination of roughness features and sub-surface pores leads to an equivalent plastic strain (PEEQ) distribution pattern that suggests an emergent physical process that has not been reported before, and which indicates that the component strength and life reduction arising from surface roughness can be made significantly worse by the presence of porosity. The development of such phenomenological understanding should lead to improved life prediction techniques, more cost effective test procedures, and the development of better AM methods
Adventure or amusement? Image and identity challenges for the aerial adventure industry and implications for positioning and policy
Get PDFThis paper seeks to address the changing image and identity of the aerial adventure industry as it becomes increasingly commercialized, which has led to uncertainty over its positioning within either adventure tourism or amusement rides. Such a positioning is critical in order to mitigate the problems caused by an inappropriate identification and image that contributes to poor inspections, poor procedures and policies, and ultimately, poor perceived risks and safety. In an industry where one serious injury impacts all operators, it is essential for all stakeholders to have collective “buy in” to effective policies that are standardized across the entire industry. The current identity confusion has merely led to misconceptions from public stakeholders. Through a qualitative case-study, this paper finds that aerial adventure parks share characteristics with adventure tourism and amusement rides and so resembles a hybrid. This is largely due to the presence of inherent risk and the role of the participant, both of which are less present on amusement rides. The paper therefore calls for state agencies to identify the activity as a stand-alone activity and for the subsequent regulations and policies to reflect this hybrid status
Positioning the Arts in the Research Process: Perspectives from Higher Education
Get PDFResearch in the visual arts has contributed to the creation of environments that involve cross-disciplinary, multidisciplinary and interdisciplinary or transdisciplinary projects in departments within and across universities. An overview is provided of the historical context of doctoral awards in the arts with a definition of the terms practice-based, practice-led, and practice as research discussed. It articulates the challenges when acquiring explicit and exact knowledge alongside more subjective approaches that utilize tacit knowledge from artistic practice in research projects. Drawing on examples from art practice and doctoral students work, it analyzes objective, subjective, empirical, and hermeneutic paradigms, as described by Pierre Bourdieu, which can combine empirical approaches and individual understandings to re-enforce our understandings of the world
Octenyl-succinylated inulin for the encapsulation and release of hydrophobic compounds
Get PDFOctenyl-succinylated inulins (OSA-inulin) were synthesized in aqueous solutions using inulin with varying degrees of polymerization (DP). They were characterized using 1H NMR and FTIR and their degrees of substitution were determined. All the samples formed micellar aggregates in aqueous solution above a critical aggregation concentration (CAC) and solubilized beta-carotene. The amount of beta carotene solubilized within the micelles ranged from 12 -25mg/g of OSA-inulin and depended on the inulin molar mass. Dynamic light scattering showed that the aggregates, with and without dissolved beta-carotene, were ~10-15 nm in size and this was confirmed by Transmission Electron Microscopy which also indicated that the micelles had a globular shape. OSA-inulin particles containing encapsulated beta-carotene were produced by freeze-drying. The encapsulated beta-carotene was not released from the freeze-dried particles when introduced into simulated gastric fluid at pH 2.5 but was readily released in simulated small intestinal fluid at pH 7. The results demonstrate the potential application of OSA-inulin in the encapsulation, dissolution and targeted delivery of hydrophobic drug molecules for nutraceutical, pharmaceutical and medical applications
UK cardiac rehabilitation fit for purpose? A community-based observational cohort study
Get PDFObjectives This study aimed to characterise the exercise performed in UK cardiac rehabilitation (CR) and explore relationships between exercise dose and changes in physiological variables.
Design Observational cohort study.
Setting Outpatient community-based CR in Leeds, UK. Rehabilitation sessions were provided twice per week for 6 weeks.
Participants Sixty patients (45 male/15 female 33–86 years) were recruited following referral to local outpatient CR.
Outcome measures The primary outcome was heart rate achieved during exercise sessions. Secondary outcomes were measured before and after CR and included incremental shuttle walk test (ISWT) distance and speed, blood pressure, brachial artery flow-mediated dilatation, carotid arterial stiffness and accelerometer-derived habitual physical activity behaviours.
Results The mean % of heart rate reserve patients exercised at was low and variable at the start of CR (42%±16 %) and did not progress by the middle (48%±17 %) or end (48%±16 %) of the programme. ISWT performance increased following CR (440±150 m vs 633±217 m, p0.05).
Conclusion Patients in a UK CR cohort exercise at intensities that are variable but generally low. The exercise dose achieved using this CR format appears inadequate to impact markers of health. Attending CR had no effect on physical activity behaviours. Strategies to increase the dose of exercise patients achieve during CR and influence habitual physical activity behaviours may enhance the effectiveness of UK CR