89 resultados para Classifier Generalization Ability
Resumo:
Owing to the successful use of non-invasive vibration analysis to monitor the progression of dental implant healing and stabilization, it is now being considered as a method to monitor femoral implants in transfemoral amputees. This study uses composite femur-implant physical models to investigate the ability of modal analysis to detect changes at the interface between the implant and bone simulating those that occur during osseointegration. Using electromagnetic shaker excitation, differences were detected in the resonant frequencies and mode shapes of the model when the implant fit in the bone was altered to simulate the two interface cases considered: firm and loose fixation. The study showed that it is beneficial to examine higher resonant frequencies and their mode shapes (rather than the fundamental frequency only) when assessing fixation. The influence of the model boundary conditions on the modal parameters was also demonstrated. Further work is required to more accurately model the mechanical changes occurring at the bone-implant interface in vivo, as well as further refinement of the model boundary conditions to appropriately represent the in vivo conditions. Nevertheless, the ability to detect changes in the model dynamic properties demonstrates the potential of modal analysis in this application and warrants further investigation.
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PURPOSE: To examine the basis of previous findings of an association between indices of driving safety and visual motion sensitivity and to examine whether this association could be explained by low-level changes in visual function. METHODS: 36 visually normal participants (aged 19 – 80 years), completed a battery of standard vision tests including visual acuity, contrast sensitivity and automated visual fields. and two tests of motion perception including sensitivity for movement of a drifting Gabor stimulus, and sensitivity for displacement in a random-dot kinematogram (Dmin). Participants also completed a hazard perception test (HPT) which measured participants’ response times to hazards embedded in video recordings of real world driving which has been shown to be linked to crash risk. RESULTS: Dmin for the random-dot stimulus ranged from -0.88 to -0.12 log minutes of arc, and the minimum drift rate for the Gabor stimulus ranged from 0.01 to 0.35 cycles per second. Both measures of motion sensitivity significantly predicted response times on the HPT. In addition, while the relationship involving the HPT and motion sensitivity for the random-dot kinematogram was partially explained by the other visual function measures, the relationship with sensitivity for detection of the drifting Gabor stimulus remained significant even after controlling for these variables. CONCLUSION: These findings suggest that motion perception plays an important role in the visual perception of driving-relevant hazards independent of other areas of visual function and should be further explored as a predictive test of driving safety. Future research should explore the causes of reduced motion perception in order to develop better interventions to improve road safety.
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The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.
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Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.
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Many state of the art vision-based Simultaneous Localisation And Mapping (SLAM) and place recognition systems compute the salience of visual features in their environment. As computing salience can be problematic in radically changing environments new low resolution feature-less systems have been introduced, such as SeqSLAM, all of which consider the whole image. In this paper, we implement a supervised classifier system (UCS) to learn the salience of image regions for place recognition by feature-less systems. SeqSLAM only slightly benefits from the results of training, on the challenging real world Eynsham dataset, as it already appears to filter less useful regions of a panoramic image. However, when recognition is limited to specific image regions performance improves by more than an order of magnitude by utilising the learnt image region saliency. We then investigate whether the region salience generated from the Eynsham dataset generalizes to another car-based dataset using a perspective camera. The results suggest the general applicability of an image region salience mask for optimizing route-based navigation applications.
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Plastic deformation behavior of Cu/Ni/Wmetallicmultilayers with individual layer thickness ranging from 5 nm to 300 nm is investigated by nanoindentation testing. The experimental results reveal that the composite still exhibits indentation-induced plastic deformation instability and the loss of strain hardening ability at the nanometer scale even if the composite contains two kinds of layer interfaces (face centered cubic(FCC)/FCC and FCC/ body centered cubic) simultaneously. Plastic deformation behavior of the nanolayered material was evaluated and analyzed.
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A big challenge for classification on text is the noisy of text data. It makes classification quality low. Many classification process can be divided into two sequential steps scoring and threshold setting (thresholding). Therefore to deal with noisy data problem, it is important to describe positive feature effectively scoring and to set a suitable threshold. Most existing text classifiers do not concentrate on these two jobs. In this paper, we propose a novel text classifier with pattern-based scoring that describe positive feature effectively, followed by threshold setting. The thresholding is based on score of training set, make it is simple to implement in other scoring methods. Experiment shows that our pattern-based classifier is promising.
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Visual abnormalities, both at the sensory input and the higher interpretive levels, have been associated with many of the symptoms of schizophrenia. Individuals with schizophrenia typically experience distortions of sensory perception, resulting in perceptual hallucinations and delusions that are related to the observed visual deficits. Disorganised speech, thinking and behaviour are commonly experienced by sufferers of the disorder, and have also been attributed to perceptual disturbances associated with anomalies in visual processing. Compounding these issues are marked deficits in cognitive functioning that are observed in approximately 80% of those with schizophrenia. Cognitive impairments associated with schizophrenia include: difficulty with concentration and memory (i.e. working, visual and verbal), an impaired ability to process complex information, response inhibition and deficits in speed of processing, visual and verbal learning. Deficits in sustained attention or vigilance, poor executive functioning such as poor reasoning, problem solving, and social cognition, are all influenced by impaired visual processing. These symptoms impact on the internal perceptual world of those with schizophrenia, and hamper their ability to navigate their external environment. Visual processing abnormalities in schizophrenia are likely to worsen personal, social and occupational functioning. Binocular rivalry provides a unique opportunity to investigate the processes involved in visual awareness and visual perception. Binocular rivalry is the alternation of perceptual images that occurs when conflicting visual stimuli are presented to each eye in the same retinal location. The observer perceives the opposing images in an alternating fashion, despite the sensory input to each eye remaining constant. Binocular rivalry tasks have been developed to investigate specific parts of the visual system. The research presented in this Thesis provides an explorative investigation into binocular rivalry in schizophrenia, using the method of Pettigrew and Miller (1998) and comparing individuals with schizophrenia to healthy controls. This method allows manipulations to the spatial and temporal frequency, luminance contrast and chromaticity of the visual stimuli. Manipulations to the rival stimuli affect the rate of binocular rivalry alternations and the time spent perceiving each image (dominance duration). Binocular rivalry rate and dominance durations provide useful measures to investigate aspects of visual neural processing that lead to the perceptual disturbances and cognitive dysfunction attributed to schizophrenia. However, despite this promise the binocular rivalry phenomenon has not been extensively explored in schizophrenia to date. Following a review of the literature, the research in this Thesis examined individual variation in binocular rivalry. The initial study (Chapter 2) explored the effect of systematically altering the properties of the stimuli (i.e. spatial and temporal frequency, luminance contrast and chromaticity) on binocular rivalry rate and dominance durations in healthy individuals (n=20). The findings showed that altering the stimuli with respect to temporal frequency and luminance contrast significantly affected rate. This is significant as processing of temporal frequency and luminance contrast have consistently been demonstrated to be abnormal in schizophrenia. The current research then explored binocular rivalry in schizophrenia. The primary research question was, "Are binocular rivalry rates and dominance durations recorded in participants with schizophrenia different to those of the controls?" In this second study binocular rivalry data that were collected using low- and highstrength binocular rivalry were compared to alternations recorded during a monocular rivalry task, the Necker Cube task to replicate and advance the work of Miller et al., (2003). Participants with schizophrenia (n=20) recorded fewer alternations (i.e. slower alternation rates) than control participants (n=20) on both binocular rivalry tasks, however no difference was observed between the groups on the Necker cube task. Magnocellular and parvocellular visual pathways, thought to be abnormal in schizophrenia, were also investigated in binocular rivalry. The binocular rivalry stimuli used in this third study (Chapter 4) were altered to bias the task for one of these two pathways. Participants with schizophrenia recorded slower binocular rivalry rates than controls in both binocular rivalry tasks. Using a ‘within subject design’, binocular rivalry data were compared to data collected from a backwardmasking task widely accepted to bias both these pathways. Based on these data, a model of binocular rivalry, based on the magnocellular and parvocellular pathways that contribute to the dorsal and ventral visual streams, was developed. Binocular rivalry rates were compared with performance on the Benton’s Judgment of Line Orientation task, in individuals with schizophrenia compared to healthy controls (Chapter 5). The Benton’s Judgment of Line Orientation task is widely accepted to be processed within the right cerebral hemisphere, making it an appropriate task to investigate the role of the cerebral hemispheres in binocular rivalry, and to investigate the inter-hemispheric switching hypothesis of binocular rivalry proposed by Pettigrew and Miller (1998, 2003). The data were suggestive of intra-hemispheric rather than an inter-hemispheric visual processing in binocular rivalry. Neurotransmitter involvement in binocular rivalry, backward masking and Judgment of Line Orientation in schizophrenia were investigated using a genetic indicator of dopamine receptor distribution and functioning; the presence of the Taq1 allele of the dopamine D2 receptor (DRD2) receptor gene. This final study (Chapter 6) explored whether the presence of the Taq1 allele of the DRD2 receptor gene, and thus, by inference the distribution of dopamine receptors and dopamine function, accounted for the large individual variation in binocular rivalry. The presence of the Taq1 allele was associated with slower binocular rivalry rates or poorer performance in the backward masking and Judgment of Line Orientation tasks seen in the group with schizophrenia. This Thesis has contributed to what is known about binocular rivalry in schizophrenia. Consistently slower binocular rivalry rates were observed in participants with schizophrenia, indicating abnormally-slow visual processing in this group. These data support previous studies reporting visual processing abnormalities in schizophrenia and suggest that a slow binocular rivalry rate is not a feature specific to bipolar disorder, but may be a feature of disorders with psychotic features generally. The contributions of the magnocellular or dorsal pathways and parvocellular or ventral pathways to binocular rivalry, and therefore to perceptual awareness, were investigated. The data presented supported the view that the magnocellular system initiates perceptual awareness of an image and the parvocellular system maintains the perception of the image, making it available to higher level processing occurring within the cortical hemispheres. Abnormal magnocellular and parvocellular processing may both contribute to perceptual disturbances that ultimately contribute to the cognitive dysfunction associated with schizophrenia. An alternative model of binocular rivalry based on these observations was proposed.
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Regular physical activity (PA) in youth has numerous immediate and long-term health benefits. With several studies indicating low levels of youth PA globally, schools settings have become increasingly critical settings for youth health promotion strategies. The role of physical education (PE) teachers has long been considered central to the facilitation of such strategies. However, PE teachers have a selfreported lack of knowledge, skills, understanding, and competence to successfully implement these strategies. Tertiary education programs are fundamental to adequately preparing, and shaping the attitudes and philosophies of future PE teachers towards their involvement within these programs. The aim of this investigation was to explore the beliefs and perceptions of future secondary school PE teachers, regarding their potential roles in future school-based programs designed to promote student PA. Fifty-seven (21 males and 36 females) pre-service PE teachers completed a series of open-ended survey questions concerning their perceptions towards participating in school-based PA promotion programs both as preservice during practicum, and prospectively as practising teachers. Responses were analysed thematically. Participants responded both positively and enthusiastically to both questions. Concerns regarding time, and the intention or expectation to participate in such programs were also key themes for pre-service and practicing teacher participation respectively. Critically in this study, participants did not identify any limitations which may impact upon their ability to successfully promote youth PA in school settings. This may indicate that participants have misconceptions regarding their ability to fulfil this role, or conversely, the deficiency of current PE teachers regarding school-based PA promotion has been recognised by the tertiary institution, and addressed to adequately prepare its students. School-based PA promotion is an integral element of pre-service PE teacher education, and ongoing professional development of practicing PE teachers. This trend is expected to continue in the future, in order to address ongoing public health concerns.
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Background The Achenbach child behaviour checklist (CBCL/YSR) is a widely used screening tool for affective problems. Several studies report good association between the checklists and psychiatric diagnoses; although with varying degrees of agreement. Most are cross-sectional studies involving adolescents referred to mental health services. This paper aims to evaluate the performance of the youth self report (YSR) empirical and DSM-oriented internalising scales in predicting later depressive disorders in young adults. Methods Sample was 2431 young adults from an Australian birth cohort study. The strength of association between the empirical and DSM-oriented scales assessed at 14 and 21 years and structured-interview derived depression in young adulthood (18 to 22 years) were tested using odds ratios, ROC analyses and related diagnostic efficiency tests (sensitivity, specificity, positive and negative predictive values). Results Adolescents with internalising symptoms were twice (OR 2.3, 95%CI 1.7 to 3.1) as likely to be diagnosed with DSM-IV depression by age 21. Use of DSM-oriented depressive scales did not improve the concordance between the internalising behaviour and DSM-IV diagnosed depression at age 14 (ORs ranged from 1.9 to 2.5). Limitations Some loss to follow-up over the 7-year gap between the two waves of follow-up. Conclusion DSM-oriented scales perform no better than the standard internalising or anxious/depressed scales in identifying young adults with later DSM-IV depressive disorder.
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Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the `best combination performance' rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.
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In a classification problem typically we face two challenging issues, the diverse characteristic of negative documents and sometimes a lot of negative documents that are closed to positive documents. Therefore, it is hard for a single classifier to clearly classify incoming documents into classes. This paper proposes a novel gradual problem solving to create a two-stage classifier. The first stage identifies reliable negatives (negative documents with weak positive characteristics). It concentrates on minimizing the number of false negative documents (recall-oriented). We use Rocchio, an existing recall based classifier, for this stage. The second stage is a precision-oriented “fine tuning”, concentrates on minimizing the number of false positive documents by applying pattern (a statistical phrase) mining techniques. In this stage a pattern-based scoring is followed by threshold setting (thresholding). Experiment shows that our statistical phrase based two-stage classifier is promising.
Resumo:
Objective. To investigate the reliability and validity of five squat-based loading tests that are clinically appropriate for jumper's knee. The loading tests were step up, double leg squat, double leg squat on a 25-degree decline (decline squat), single leg decline squat, and decline hop. Design. Cross-sectional controlled cohort. Subjects without knee pain comprised controls, those with extensor tendon pain comprised the jumper's knee group. Setting. Institutional athlete study group in Australia Participants. Fifty-six elite adolescent basketball players participated in this study, thirteen comprised the jumper's knee group, fifteen athletes formed a control group. Intervention. Each subject performed each loading test for baseline and reliability data on the first testing day. Subjects then performed three days of intensive (6 h daily) basketball training, after which each loading test was reexamined. Main outcome measures. Eleven point interval scale for pain. Results. The tests that best detected a change in pain due to intensive workload were the single leg decline squat and single leg decline hop. This study found that decline tests have better discriminative ability than the standard squat to detect change in jumper's knee pain due to intensive training. The typical error for these tests ranged from 0.3 to 0.5, however, caution should be exercised in the interpretation of these reliability figures due to relatively low scores. Conclusions. The single leg decline squat is recommended in the physical assessment of adolescent jumper's knee. The decline squat was selected as the best clinical test over the decline hop because it was easier to standardise performance.