Component Maintenance Strategies and Risk Analysis for Random Shock Effects Considering Maintenance Costs

Maintenance can improve a system’s reliability in a long operation period or when a component has failed. The reliability modeling method that uses the stochastic process degradation model to describe the system degradation process has been widely used. However, the existing reliability models established using stochastic processes only consider the internal degradation process, and do not fully consider the impact of external random shocks on their reliability modeling. Furthermore, the existing theory of importance does not consider the actual factors of maintenance cost. In this paper, based on the reliability modeling of random processes, the degradation rate under the influence of random shocks is introduced into the time scale function to solve the impact of random shocks on product reliability, and two cost importance measures are proposed to guide the maintenance selection of the components under limited resources in the system.Finally, a subsystem of an aircraft hydraulic system is analyzed to verify the proposed method’s performance

RESEARCH TOOLS AND THEIR USES FOR DETERMINING THE THERMAL INACTIVATION KINETICS OF SALMONELLA IN LOW-MOISTURE FOODS

The reputation of low-moisture foods as safe foods has been crumbling over the past decade due to repeated involvement in foodborne illness outbreaks. Although various pasteurization technologies exist, a majority are thermal processes and have not been well-characterized for pasteurizing low-moisture foods. In addition, the nature of a low-moisture food matrix introduces various experimental complications that are not encountered in high-moisture foods. In this dissertation, the development, building instructions, and characterization of various open source tools for studying the inactivation kinetics of microorganisms in low-moisture foods are described. The first tool is the TDT Sandwich, a dry heating device for measuring the thermal inactivation kinetics of microorganisms. The second tool is the HumidOSH, a self-contained environmental chamber for adjusting the water activity of food samples. Accompanying these tools are two studies that characterized the thermal inactivation kinetics of Salmonella and Enterococcus faecium NRRL-B2354 in whole milk powder and chia seeds. The TDT Sandwich was shown to produce thermal inactivation kinetics that are comparable with commonly used methods while also demonstrating less variation in microbial data collected with this tool. The comparison of model parameters using statistical tests of significance is discussed with the use of Monte Carlo simulations. E. faecium was shown to be a conservative surrogate to Salmonella in chia seeds. The variability between production lots of chia seeds was found to have a large impact on the inactivation kinetics of both Salmonella and E. faecium. The open source tools presented in this dissertation and the accompanying conclusions of the thermal inactivation studies can be used to accelerate scientific progress in understanding and improving the microbiological safety of low-moisture foods. Advisers: Dr. Curtis L. Weller and Dr. David D. Jone

An ANN based combined classifier approach for facial emotion recognition

Facial expressions are the simplest reflections of human emotions, which are at the same time an integral part of any communication. Over the last decade, facial emotion recognition has attracted a great deal of research interest due to its various applications in the fields such as human computer interaction, robotics and data analytics. In this thesis, we present a facial emotion recognition approach that is based on facial expressions to classify seven emotional states: neutral, joy, sadness, surprise, anger, fear and disgust. To perform classification, two different facial features called Action Units (AUs) and Feature Point Positions (FPPs) are extracted from image sequences. A depth camera is used to capture image sequences collected from 13 volunteers to classify seven emotional states. Having extracted two sets of features, separate artificial neural network classifiers are trained. Logarithmic Opinion Pool (LOP) is then employed to combine the decision probabilities coming from each classifier. Experimental results are quite promising and establish a basis for future work on the topic

Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors

Enhanced Algorithms for Analysis and Design of Nucleic Acid Reaction Pathways

Nucleic acids provide a powerful platform for programming at the molecular level. This is possible because the free energy of nucleic acid structures is dominated by the local interactions of base pairing and base pair stacking. The nearest neighbor secondary structure model implied by these energetics has enabled development of a set of algorithms for calculating thermodynamic quantities of nucleic acid sequences. Molecular programmers and synthetic biologists continue to extend their reach to larger, more complicated nucleic acid complexes, reaction pathways, and systems. This necessitates a focus on new algorithm development and efficient implementations to enable analysis and design of such systems. Concerning analysis of nucleic acids, we collect seemingly diverse algorithms under a unified three-component dynamic programming framework consisting of: 1) recursions that specify the dependencies between subproblems and incorporate the details of the structural ensemble and the free energy model, 2) evaluation algebras that define the mathematical form of each subproblem, 3) operation orders that specify the computational trajectory through the dependency graph of subproblems. Changes to the set of recursions allows operation over the complex ensemble including coaxial and dangle stacking states, affecting all thermodynamic quantities. An updated operation order for structure sampling allows simultaneous generation of a set of structures sampled from the Boltzmann distribution in time that scales empirically sublinearly in the number of samples and leads to an order of magnitude or more speedup over repeated single-structure sampling. For the problem of sequence design for reaction pathway engineering, we introduce an optimization algorithm to minimize the multitstate test tube ensemble defect, which simultaneously designs for reactant, intermediate, and product states along the reaction pathway (positive design) and against crosstalk interactions (negative design). Each of these on-pathway or crosstalk states is represented as a target test tube ensemble containing arbitrary numbers of on-target complexes, each with a target secondary structure and target concentration, and arbitrary numbers of off-target complexes, each with vanishing target concentration. Our test tube specification formalism enables conversion of a reaction pathway specification into a set of target test tubes. Sequences are designed subject to a set of hard constraints allowing specification of properties such as sequence composition, sequence complementarity, prevention of unwanted sequence patterns, and inclusion of biological sequences. We then extend this algorithm with soft constraints, enhancing flexibility through new constraint types and reducing design cost by up to two orders of magnitude in the most highly constrained cases. These soft constraints enable multiobjective design of the multitstate test tube ensemble defect simultaneously with heuristics for avoiding kinetic traps and equalizing reaction rates to further aid reaction pathway engineering.</p

Classification of EMG signals to control a prosthetic hand using time-frequesncy representations and Support Vector Machines

Myoelectric signals (MES) are viable control signals for externally-powered prosthetic devices. They may improve both the functionality and the cosmetic appearance of these devices. Conventional controllers, based on the signal\u27s amplitude features in the control strategy, lack a large number of controllable states because signals from independent muscles are required for each degree of freedom (DoF) of the device. Myoelectric pattern recognition systems can overcome this problem by discriminating different residual muscle movements instead of contraction levels of individual muscles. However, the lack of long-term robustness in these systems and the design of counter-intuitive control/command interfaces have resulted in low clinical acceptance levels. As a result, the development of robust, easy to use myoelectric pattern recognition-based control systems is the main challenge in the field of prosthetic control. This dissertation addresses the need to improve the controller\u27s robustness by designing a pattern recognition-based control system that classifies the user\u27s intention to actuate the prosthesis. This system is part of a cost-effective prosthetic hand prototype developed to achieve an acceptable level of functional dexterity using a simple to use interface. A Support Vector Machine (SVM) classifier implemented as a directed acyclic graph (DAG) was created. It used wavelet features from multiple surface EMG channels strategically placed over five forearm muscles. The classifiers were evaluated across seven subjects. They were able to discriminate five wrist motions with an accuracy of 91.5%. Variations of electrode locations were artificially introduced at each recording session as part of the procedure, to obtain data that accounted for the changes in the user\u27s muscle patterns over time. The generalization ability of the SVM was able to capture most of the variability in the data and to maintain an average classification accuracy of 90%. Two principal component analysis (PCA) frameworks were also evaluated to study the relationship between EMG recording sites and the need for feature space reduction. The dimension of the new feature set was reduced with the goal of improving the classification accuracy and reducing the computation time. The analysis indicated that the projection of the wavelet features into a reduced feature space did not significantly improve the accuracy and the computation time. However, decreasing the number of wavelet decomposition levels did lower the computational load without compromising the average signal classification accuracy. Based on the results of this work, a myoelectric pattern recognition-based control system that uses an SVM classifier applied to time-frequency features may be used to discriminate muscle contraction patterns for prosthetic applications

Reassessment of the evolutionary history of the late Triassic and early Jurassic sauropodomorph dinosaurs through comparative cladistics and the supermatrix approach

Non-sauropod sauropodomorphs, also known as 'basal sauropodomorphs' or 'prosauropods', have been thoroughly studied in recent years. Several hypotheses on the interrelationships within this group have been proposed, ranging from a complete paraphyly, where the group represents a grade from basal saurischians to Sauropoda, to a group on its own. The grade-like hypothesis is the most accepted; however, the relationships between the different taxa are not consistent amongst the proposed scenarios. These inconsistencies have been attributed to missing data and unstable (i.e., poorly preserved) taxa, nevertheless, an extensive comparative cladistic analysis has found that these inconsistencies instead come from the character coding and character selection, plus the strategies on merging data sets. Furthermore, a detailed character analysis using information theory and mathematical topology as an approach for character delineation is explored here to operationalise characters and reduce the potential impact of missing data. This analysis also produced the largest and most comprehensive matrix after the reassessment and operationalisation of every character applied to this group far. Additionally, partition analyses performed on this data set have found consistencies in the interrelationships within non-sauropod Sauropodomorpha and has found strong support for smaller clades such as Plateosauridae, Riojasauridae, Anchisauridae, Massospondylinae and Lufengosarinae. The results of these analyses also highlight a different scenario on how quadrupedality evolved, independently originating twice within the group, and provide a better framework to understand the palaeo-biogeography and diversification rate of the first herbivore radiation of dinosaurs