Adaptive dynamic surface control of a hypersonic flight vehicle with improved tracking

This work presents a nonlinear adaptive dynamic surface air speed and a flight path angle control design procedure for the longitudinal dynamics of a generic hypersonic flight vehicle. The proposed design scheme takes into account the magnitude, rate, and bandwidth constraints on the actuator signals. A new approach is used to enhance tracking performance and avoid a large initial control signal. The uncertain nonlinear functions in the flight vehicle model are approximated by using radial basis function neural networks. A detailed stability analysis of the designed controllers shows that all the signals of the closed‐loop system are uniformly ultimately bounded. The robust performance of the design scheme is verified through numerical simulations of the flight vehicle model for various parameter variation test cases. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Estimation of Defects Based on Defect Decay Model: ED^{3}M

An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product's required maintenance. Here, a new approach called ED³M is presented that computes an estimate of the total number of defects in an ongoing testing process. ED³M is based on estimation theory. Unlike many existing approaches the technique presented here does not depend on historical data from previous projects or any assumptions about the requirements and/or testers' productivity. It is a completely automated approach that relies only on the data collected during an ongoing testing process. This is a key advantage of the ED³M approach, as it makes it widely applicable in different testing environments. Here, the ED³M approach has been evaluated using five data sets from large industrial projects and two data sets from the literature. In addition, a performance analysis has been conducted using simulated data sets to explore its behavior using different models for the input data. The results are very promising; they indicate the ED³M approach provides accurate estimates with as fast or better convergence time in comparison to well-known alternative techniques, while only using defect data as the input.     

Structural study of titanium oxide films synthesized by ion beam-assisted deposition

The application of titanium dioxide (TiO(2)) films as surgical implant coatings for antibiotic attachment depends crucially on their available surface area and thus their surface morphology and crystallinity. Here, we report our fabrication of high Wenzel ratio TiO(2) films targeted to increase the film surface area using the ion beam-assisted deposition (IBAD) technique at high-deposition temperatures (approximately 610 degrees C). The modulation of the films' surface morphology was accomplished by varying the chemical identity of the concurrent ion beams bombarded on the films during the e-beam evaporation process. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) were employed to investigate the surface morphology of the as-deposited films. X-ray diffractometry (XRD) revealed that these nanocrystalline films primarily consist of anatase phase TiO(2). Wenzel ratio, the ratio of the actual surface area to the projected area, of IBAD films prepared with argon, oxygen, and nitrogen ion beams was measured to be 1.52, 1.31 and 1.49, respectively. The effect of the differences in chemical reactivity and ion size of these three type ion beams are discussed to explain the present results.     

Towards a standard in assessment of bone cutting for total knee replacement

Arthroplasty outcome is influenced by the 'quality' of bone preparation for implant insertion. Surgeons face increasing choices of technique and instrumentation, yet clinical scoring methods assess the overall outcome and patient satisfaction but not the bone cuts directly. 'Quality management' of bone reshaping is needed to evaluate different bone cutting methods and computer assisted orthopaedic surgery (CAOS) systems. Analyses and experiments in this study were formulated for measurement and computation of four quantitative characteristics of bone preparation 'quality' and produced a highly condensed index for each. These represented (a) surface finish of cuts, (b) implant fit/looseness possible with the cut shape, (c) implant location/misalignment, and (d) accuracy of individual planar cuts. Assessment of synthetic bone cuts verified the robustness of the method for wide application in arthroplasty intraoperatively, in vitro and for comparing navigation systems.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

Robust Multimodal Biometric System Based on Feature Level Fusion of Optimiseddeepnet Features

Wireless Personal Communications - Multimodal biometric systems combine feature knowledge from multiple traits to overcome shortcomings of unimodal systems. However, most of the traditional...     

Local calibration of rigid pavement performance models using resampling methods

The performance prediction models in the Pavement-ME design software are nationally calibrated using in-service pavement material properties, pavement structure, climate and truck loadings, and performance data obtained from the Long-Term Pavement Performance programme. The nationally calibrated models may not perform well if the inputs and performance data used to calibrate those do not represent the local design and construction practices. Therefore, before implementing the new M-E design procedure, each state highway agency (SHA) should evaluate how well the nationally calibrated performance models predict the measured field performance. The local calibrations of the Pavement-ME performance models are recommended to improve the performance prediction capabilities to reflect the unique conditions and design practices. During the local calibration process, the traditional calibration techniques (split sampling) may not necessarily provide adequate results when limited number of pavement sections are available. Consequently, there is a need to employ statistical and resampling methodologies that are more efficient and robust for model calibrations given the data related challenges encountered by SHAs. The main objectives of the paper are to demonstrate the local calibration of rigid pavement performance models and compare the calibration results based on different resampling techniques. The bootstrap is a non-parametric and robust resampling technique for estimating standard errors and confidence intervals of a statistic. The main advantage of bootstrapping is that model parameters estimation is possible without making distribution assumptions. This paper presents the use of bootstrapping and jackknifing to locally calibrate the transverse cracking and IRI performance models for newly constructed and rehabilitated rigid pavements. The results of the calibration show that the standard error of estimate and bias are lower compared to the traditional sampling methods. In addition, the validation statistics are similar to that of the locally calibrated model, especially for the IRI model, which indicates robustness of the local model coefficients.     

Sustainability evaluation framework for building cooling systems: a comparative study of snow storage and conventional chiller systems

In Canada, the residential building sector consumes 17 % of the total energy and 15 % of the total greenhouse gas emissions. In particular, the energy demand for cooling in the residential sector is increasing due to the large occupancy floor area and high usage of air conditioning. Minimizing energy use and greenhouse gas emissions is one of the highest priority goals set for national energy management strategies in developed countries including Canada. In this study, a framework based on the life cycle assessment approach is developed to assess the environmental impacts of different building cooling systems, namely conventional snow storage system, watertight snow storage system, high-density snow storage system, and the conventional chiller cooling system. Moreover, all these systems have varying energy requirements and associated environmental impacts during different phases (extraction and construction, utilization, and end of life) of the life cycle of a building. A low-rise residential building in Kelowna (BC, Canada) has been selected for the pragmatic application of the proposed framework. The annual cooling energy demand for the building is estimated for different phases. Subsequently, the life cycle impact assessment has been carried out using SimaPro 8.1 software and TRACI 2.1 method. For sustainability evaluation of different cooling systems over their life cycle, multi-criteria decision analysis has been employed using the ‘Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II).’ The results showed that the snow storage systems tend to reduce greenhouse gas emissions and associated environmental impacts more than the conventional system.     

Characterization of Nicalon fibres with varying diameters: Part II Modified Weibull distribution

Diameters vary significantly in a tow of commercial NicalonTM fibres, which is one of the most attractive ceramic reinforcements for structural composites. It was found that the strength distribution of Nicalon fibres could not be adequately characterized using either single- or bi-modal Weibull distribution. A recently proposed modified Weibull distribution can account for the effect of varying diameter in the characterization of fibre strength. To verify the validity of the modified Weibull distribution, comprehensive mechanical testing and fractographic studies have been conducted on Nicalon SiC fibres with diameters varying from 8 to 22 m. The experimental results have been reported in Part I. Part II of this paper further modifies the derivation of the modified Weibull distribution to yield a relationship which is similar in form, but soundly based on experimental findings. Factors considered in the modified Weibull distribution include the dependence of fracture toughness and flaw density on fibre diameter, both of which may vary with fibre diameter, as reported in Part I. Comparison with experimental data shows that the current modified Weibull distribution works very well, while both single-modal and bi-modal Weibull distributions are inadequate for describing Nicalon fibres with varying diameters. © 1998 Chapman & Hall     

From dynamic influence nets to dynamic Bayesian networks: A transformation algorithm

This paper presents an algorithm to transform a dynamic influence net (DIN) into a dynamic Bayesian network (DBN). The transformation aims to bring the best of both probabilistic reasoning paradigms. The advantages of DINs lie in their ability to represent causal and time‐varying information in a compact and easy‐to‐understand manner. They facilitate a system modeler in connecting a set of desired effects and a set of actionable events through a series of dynamically changing cause and effect relationships. The resultant probabilistic model is then used to analyze different courses of action in terms of their effectiveness to achieve the desired effect(s). The major drawback of DINs is their inability to incorporate evidence that arrive during the execution of a course of action (COA). Several belief‐updating algorithms, on the other hand, have been developed for DBNs that enable a system modeler to insert evidence in dynamic probabilistic models. Dynamic Bayesian networks, however, suffer from the intractability of knowledge acquisition. The presented transformation algorithm combines the advantages of both DINs and DBNs. It enables a system analyst to capture a complex situation using a DIN and pick the best (or close‐to‐best) COA that maximizes the likelihood of achieving the desired effect. During the execution, if evidence becomes available, the DIN is converted into an equivalent DBN and beliefs of other nodes in the network are updated. If required, the selected COA can be revised on the basis of the recently received evidence. The presented methodology is applicable in domains requiring strategic level decision making in highly complex situations, such as war games, real‐time strategy video games, and business simulation games. © 2009 Wiley Periodicals, Inc.