KFHE-HOMER: A multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter

Multi-label classification allows a datapoint to be labelled with more than one class at the same time. In spite of their success in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman Filter-based Heuristic Ensemble (KFHE) is a recent ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective. This article proposes KFHE-HOMER, an extension of the KFHE ensemble approach to the multi-label domain. KFHE-HOMER sequentially trains multiple HOMER multi-label classifiers and aggregates their outputs using the sensor fusion properties of the Kalman filter. Experiments described in this article show that KFHE-HOMER performs consistently better than existing multi-label methods including existing approaches based on ensembles.Comment: The paper is under consideration at Pattern Recognition Letters, Elsevie

Real-Time Scheduling Algorithm Design on Stochastic Processors

Recent studies have shown that significant power savings are possible with the use of in- exact processors, which may contain a small percentage of errors in computation. However, use of such processors in time-sensitive systems is challenging as these processors significantly hamper the system performance. In this thesis, a design framework is developed for real-time applications running on stochastic processors. To identify hardware error pat- terns, two methods are proposed to predict the occurrence of hardware errors. In addition, an algorithm is designed that uses knowledge of the hardware error patterns to judiciously schedule real-time jobs in order to maximize real-time performance. Both analytical and simulation results show that the proposed approach provides significant performance improvements when compared to an existing real-time scheduling algorithm and is efficient enough for online use

Employers have a role to play in encouraging increased participation in physical activities

Research by Grace Lordan and Debayan Pakrashi quantified the benefits of exercise for mental and physical health. Given that the average person recognizes the benefits but does not meet the recommended amount, how can we motivate people to exercise? Simply disseminating information on the benefits is not enough to motivate people to exercise. Because many of us lack the time or work in sedentary jobs, one policy option is for employers to encourage exercise during work hours. The benefits of a healthier work-force would pay off in terms of reduced absenteeism and productivity gains

Do all activities “weigh” equally?: how different physical activities differ as predictors of weight

In Britain, it is recommended that, to stay healthy, adults should do 150 minutes of moderate-intensity physical activity every week. The recommendations provided by the U.K. government, however, remain silent in regard to the type of activity that should be done. Using the annual Health Survey for England we compare how different types of physical activities predict a person's weight. In particular, we consider clinically measured body mass index and waist circumference. We document mean slopes emanating from ordinary least squares regressions with these measures as the dependent variables. We show that individuals who walk at a brisk or fast pace are more likely to have a lower weight when compared to individuals doing other activities. Additionally, we highlight that the association between physical activity and weight is stronger for females and individuals over the age of 50. Our overall conclusions are robust to a number of specifications

Damage detection in beams with an open crack using S transform

The successful detection of change in a data or in any of its derivatives in the presence of noise is a critical component of structural health monitoring and damage detection. This sudden change can be brought about by a sudden change in the strain or the stress field of the structural system under consideration. Two very typical examples of such sudden changes are the sudden change in stiffness of a vibrating single degree of freedom system in time and the local perturbation of stress and strain fields of a beamlike structure in space due to the presence of an open crack. New methods and analysis techniques have become popular in the field of structural health monitoring to detect and characterise such changes. Time – frequency techniques, like wavelet analysis are being more widely used in this regard in the recent times for the detection of presence, location and the calibration of the extent of these changes. This paper presents the application of S transform for the successful detection and calibration of damage in time and in space in the presence of additive Gaussian white noise. The performance of S transform based detection is compared with wavelet based and statistics based methodologies. The application and use of S transform in the field of structural health monitoring is observed to be extremely promising

Detection of pitting corrosion in steel using image processing

This paper presents an image processing based detection method for detecting pitting corrosion in steel structures. High Dynamic Range (HDR) imaging has been carried out in this regard to demonstrate the effectiveness of such relatively inexpensive techniques that are of immense benefit to Non – Destructive – Tesing (NDT) community. The pitting corrosion of a steel sample in marine environment is successfully detected in this paper using the proposed methodology. It is observed, that the proposed method has a definite potential to be applied to a wider range of applications

Damage calibration of a beam using wavelet analysis and image processing

Efficient damage detection and calibration of structures have gained great importance in recent times in terms of health monitoring and maintenance programmes. Wavelet analysis based damage detection and calibration from the deflected shape of beams are theoretically known to be a simple and efficient way of assessing damage. However, the measurement of the static or dynamic deflected shape of a vibrating beam is often difficult. The use of sophisticated devices to measure such spatial characteristics suffer from the disadvantage of high cost of the instrument and its unavailability. This paper considers a simply supported aluminium beam with an open crack and presents a video camera based inexpensive laboratory study to assess the damage using wavelet analysis. The vibrating deflected shape recorded by the camera has been processed using image processing methods and an intelligent pattern recognition procedure for the quantification of such the dynamic deflected shape at a particular instant of time. Wavelet analysis was subsequently performed on the damaged deflected shape to successfully identify the location of the damage and estimate the degree of damage for different crack depth ratios. The image analysis based detection is found to be a novel, easy and an inexpensive technique and the method is seen to have a potential for unmanned online structural health monitoring process

Effect of road quality in structural health monitoring under operational conditions

The effect of unevenness in a bridge deck for the purpose of Structural Health Monitoring (SHM) under operational conditions is studied in this paper. The moving vehicle is modelled as a single degree of freedom system traversing the damaged beam at a constant speed. The bridge is modelled as an Euler-Bernoulli beam with a breathing crack, simply supported at both ends. The breathing crack is treated as a nonlinear system with bilinear stiffness characteristics related to the opening and closing of crack. The unevenness in the bridge deck considered is modelled using road classification according to ISO 8606:1995(E). Numerical simulations are conducted considering the effects of changing road surface classes from class A - very good to class E - very poor. Cumulant based statistical parameters, based on a new algorithm are computed on stochastic responses of the damaged beam due to passages of the load in order to calibrate the damage. Possibilities of damage detection and calibration under benchmarked and non-benchmarked cases are considered. The findings of this paper are important for establishing the expectations from different types of road roughness on a bridge for damage detection purposes using bridge-vehicle interaction where the bridge does not need to be closed for monitoring