Mathematical Modelling and Analysis of Vehicle Frontal Crash using Lumped Parameters Models

A full-scale crash test is conventionally used for vehicle crashworthiness analysis. However, this approach is expensive and time-consuming. Vehicle crash reconstructions using different numerical modelling approaches can predict vehicle behavior and reduce the need for multiple full-scale crash tests, thus research on the crash reconstruction has received a great attention in the last few decades. Among modelling approaches, lumped parameters models (LPM) and finite element models (FEM) are commonly used in the vehicle crash reconstruction. This thesis focuses on developing and improving the LPM for vehicle frontal crash analysis. The study aims at reconstructing crash scenarios for vehicle-to-barrier (VTB), vehicleoccupant (V-Occ), and vehicle-to-vehicle (VTV), respectively. In this study, a single mass-spring-damper (MSD) is used to simulate a vehicle to-barrier or a wall. A double MSD is used to model the response of the chassis and passenger compartment in a frontal crash, a vehicle-occupant, and a vehicle-tovehicle, respectively. A curve fitting, state-space, and genetic algorithm are used to estimate parameters of the model for reconstructing the vehicle crash kinematics. Further, the piecewise LPM is developed to mimic the crash characteristics for VTB, VO, and VTV crash scenarios, and its predictive capability is compared with the explicit FEM. Within the framework, the advantages of the proposed methods are explained in detail, and suggested solutions are presented to address the limitations in the study.publishedVersio

Query-driven learning for automating exploratory analytics in large-scale data management systems

As organizations collect petabytes of data, analysts spend most of their time trying to extract insights. Although data analytic systems have become extremely efficient and sophisticated, the data exploration phase is still a laborious task with high productivity, monetary and mental costs. This dissertation presents the Query-Driven learning methodology in which multiple systems/frameworks are introduced to address the need of more efficient methods to analyze large data sets. Countless queries are executed daily, in large deployments, and are often left unexploited but we believe they are of immense value. This work describes how Machine Learning can be used to expedite the data exploration process by (a) estimating the results of aggregate queries (b) explaining data spaces through interpretable Machine Learning models (c) identifying data space regions that could be of interest to the data analyst. Compared to related work in all the associated domains, the proposed solutions do not utilize any of the underlying data. Because of that, they are extremely efficient, decoupled from underlying infrastructure and can easily be adapted. This dissertation is a first account of how the Query-Driven methodology can be effectively used to expedite the data exploration process focusing solely on extracting knowledge from queries and not from data

Characterizing 51 Eri b from 1-5 μ\mum: a partly-cloudy exoplanet

We present spectro-photometry spanning 1-5 $\mu$m of 51 Eridani b, a 2-10 M$_\text{Jup}$ planet discovered by the Gemini Planet Imager Exoplanet Survey. In this study, we present new $K1$ (1.90-2.19 $\mu$m) and $K2$ (2.10-2.40 $\mu$m) spectra taken with the Gemini Planet Imager as well as an updated $L_P$ (3.76 $\mu$m) and new $M_S$ (4.67 $\mu$m) photometry from the NIRC2 Narrow camera. The new data were combined with $J$ (1.13-1.35 $\mu$m) and $H$ (1.50-1.80 $\mu$m) spectra from the discovery epoch with the goal of better characterizing the planet properties. 51 Eri b photometry is redder than field brown dwarfs as well as known young T-dwarfs with similar spectral type (between T4-T8) and we propose that 51 Eri b might be in the process of undergoing the transition from L-type to T-type. We used two complementary atmosphere model grids including either deep iron/silicate clouds or sulfide/salt clouds in the photosphere, spanning a range of cloud properties, including fully cloudy, cloud free and patchy/intermediate opacity clouds. Model fits suggest that 51 Eri b has an effective temperature ranging between 605-737 K, a solar metallicity, a surface gravity of $\log$(g) = 3.5-4.0 dex, and the atmosphere requires a patchy cloud atmosphere to model the SED. From the model atmospheres, we infer a luminosity for the planet of -5.83 to -5.93 ($\log L/L_{\odot}$), leaving 51 Eri b in the unique position as being one of the only directly imaged planet consistent with having formed via cold-start scenario. Comparisons of the planet SED against warm-start models indicates that the planet luminosity is best reproduced by a planet formed via core accretion with a core mass between 15 and 127 M$_{\oplus}$.Comment: 27 pages, 19 figures, Accepted for publication in The Astronomical Journa

Systems modelling and ethical decision algorithms for autonomous vehicle collisions

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.There has been an increasing interest in autonomous vehicles (AVs) in recent years. Through the use of advanced safety systems (ASS), it is expected that driverless AVs will result in a reduced number of road traffic accidents (RTAs) and fatalities on the roads. However, until the technology matures, collisions involving AVs will inevitably take place. Herein lies the hub of the problem: if AVs are to be programmed to deal with a collision scenario, which set of ethically acceptable rules should be applied? The two main philosophical doctrines are the utilitarian and deontological approaches of Bentham and Kant, with the two competing societal actions being altruistic and selfish as defined by Hamilton. It is shown in simulation, that the utilitarian approach is likely to be the most favourable candidate to succeed as a serious contender for developments in the programming and decision making for control of AV technologies in the future. At the heart of the proposed approach is the development of an ethical decision-maker (EDM), with this forming part of a model-to-decision (M2D) approach. Lumped parameter models (LPMs) are developed that capture the key features of AV collisions into an immovable rigid wall (IRW) or another AV, i.e. peak deformation and peak acceleration. The peak acceleration of the AV is then related to the accelerations experienced by the occupant(s) on-board the AV, e.g. peak head acceleration. Such information allows the M2D approach to decide on the collision target depending on the selected algorithm, e.g. utilitarian or altruistic. Alongside the EDM is an active collision system (ACS) which is able to change the AV structural stiffness properties. The ACS is able to compensate for situations when AVs are predicted to experience potentially severe and fatal injury severity levels

Methods and Systems for Fault Diagnosis in Nuclear Power Plants

This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed. A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model. A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system. A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF. For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system. To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP. The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research. In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified

Characterizing 51 Eri b from 1 to 5 μm: A Partly Cloudy Exoplanet

We present spectrophotometry spanning 1–5 μm of 51 Eridani b, a 2–10 M_(Jup) planet discovered by the Gemini Planet Imager Exoplanet Survey. In this study, we present new K1 (1.90–2.19 μm) and K2 (2.10–2.40 μm) spectra taken with the Gemini Planet Imager as well as an updated L_P (3.76 μm) and new M_S (4.67 μm) photometry from the NIRC2 Narrow camera. The new data were combined with J (1.13–1.35 μm) and H (1.50–1.80 μm) spectra from the discovery epoch with the goal of better characterizing the planet properties. The 51 Eri b photometry is redder than field brown dwarfs as well as known young T-dwarfs with similar spectral type (between T4 and T8), and we propose that 51 Eri b might be in the process of undergoing the transition from L-type to T-type. We used two complementary atmosphere model grids including either deep iron/silicate clouds or sulfide/salt clouds in the photosphere, spanning a range of cloud properties, including fully cloudy, cloud-free, and patchy/intermediate-opacity clouds. The model fits suggest that 51 Eri b has an effective temperature ranging between 605 and 737 K, a solar metallicity, and a surface gravity of log(g) = 3.5–4.0 dex, and the atmosphere requires a patchy cloud atmosphere to model the spectral energy distribution (SED). From the model atmospheres, we infer a luminosity for the planet of −5.83 to −5.93 (log L/L_⊙), leaving 51 Eri b in the unique position of being one of the only directly imaged planets consistent with having formed via a cold-start scenario. Comparisons of the planet SED against warm-start models indicate that the planet luminosity is best reproduced by a planet formed via core accretion with a core mass between 15 and 127 M_⊕

Reverse Engineering of Biological Systems

Gene regulatory network (GRN) consists of a set of genes and regulatory relationships between the genes. As outputs of the GRN, gene expression data contain important information that can be used to reconstruct the GRN to a certain degree. However, the reverse engineer of GRNs from gene expression data is a challenging problem in systems biology. Conventional methods fail in inferring GRNs from gene expression data because of the relative less number of observations compared with the large number of the genes. The inherent noises in the data make the inference accuracy relatively low and the combinatorial explosion nature of the problem makes the inference task extremely difficult. This study aims at reconstructing the GRNs from time-course gene expression data based on GRN models using system identification and parameter estimation methods. The main content consists of three parts: (1) a review of the methods for reverse engineering of GRNs, (2) reverse engineering of GRNs based on linear models and (3) reverse engineering of GRNs based on a nonlinear model, specifically S-systems. In the first part, after the necessary background and challenges of the problem are introduced, various methods for the inference of GRNs are comprehensively reviewed from two aspects: models and inference algorithms. The advantages and disadvantages of each method are discussed. The second part focus on inferring GRNs from time-course gene expression data based on linear models. First, the statistical properties of two sparse penalties, adaptive LASSO and SCAD, with an autoregressive model are studied. It shows that the proposed methods using these two penalties can asymptotically reconstruct the underlying networks. This provides a solid foundation for these methods and their extensions. Second, the integration of multiple datasets should be able to improve the accuracy of the GRN inference. A novel method, Huber group LASSO, is developed to infer GRNs from multiple time-course data, which is also robust to large noises and outliers that the data may contain. An efficient algorithm is also developed and its convergence analysis is provided. The third part can be further divided into two phases: estimating the parameters of S-systems with system structure known and inferring the S-systems without knowing the system structure. Two methods, alternating weighted least squares (AWLS) and auxiliary function guided coordinate descent (AFGCD), have been developed to estimate the parameters of S-systems from time-course data. AWLS takes advantage of the special structure of S-systems and significantly outperforms one existing method, alternating regression (AR). AFGCD uses the auxiliary function and coordinate descent techniques to get the smart and efficient iteration formula and its convergence is theoretically guaranteed. Without knowing the system structure, taking advantage of the special structure of the S-system model, a novel method, pruning separable parameter estimation algorithm (PSPEA) is developed to locally infer the S-systems. PSPEA is then combined with continuous genetic algorithm (CGA) to form a hybrid algorithm which can globally reconstruct the S-systems

Analysis of the epigenetic landscape in murine macrophages

Macrophages are cells of the innate immune system and play essential roles in the regulation of inflammatory responses in all parts of the body. Furthermore, macrophages are also involved in different tissue–specific functions and maintenance of the tissue homeostasis. These functions are controlled by the epigenetic landscape, consisting of promoters and enhancers that together regulate gene expression. Enhancers are stretches of regulatory genomic sequences in the non–coding regions of the genome that can be bound by lineage– determining transcription factors. These enhancers can loop in three–dimensional space to be in close proximity to promoters and contribute to the regulation of gene expression. Previous studies suggest that there are about 1 million enhancers in the mammalian genome, of which only about 30,000 – 40,000 are selected in each specific cell type. This dissertation studies the regulation of the epigenetic landscape of murine macrophages by utilizing different tissue macrophages, different complex and simple stimuli, as well as natural genetic variation as a mutagenesis screen. The overarching research question of this dissertation is to understand how the enhancer landscape in macrophages gets selected and regulated in order to control gene expression. In more detail, the main questions answered in this dissertation are: What are the epigenetic mechanisms that are responsible for tissue–specific functions? How do complex stimuli change the epigenetic landscape of macrophages in comparison to simple stimuli? How does natural genetic variation influence the epigenetic landscape and gene expression in murine macrophages? In Chapter 1 (Gosselin, D., Link, V. M., Romanoski, C. E. et al. (2014) appeared in Cell) we investigate the influence of the tissue environment on the epigenetic landscape in mouse macrophages. We compare macrophages residing in the brain (microglia) with macrophages from the peritoneal cavity by measuring mRNA expression, as well as enhancer activation (H3K4me2, H3K27ac, and PU.1). We find highly expressed genes unique to one population of macrophages, which correlates well with the activity signature at enhancers in the corresponding cells. By analyzing the enhancer landscape, we find that the macrophage lineage–determining transcription factor PU.1 plays a key role in establishing the enhancer repertoire, creating a common, macrophage–specific enhancer landscape. Furthermore, expression of tissue–specific transcription factors in collaboration with PU.1 drives a subset of tissue–specific enhancers regulating the differences in gene expression between different tissue–specific macrophage populations. In Chapter 2 (Eichenfield, D. Z., Troutman, D. T., Link, V. M. et al. (2016) appeared in eLife) we investigate the effect of complex stimuli onto the epigenetic landscape in macrophages on the example of wounds. Stimulation of macrophages with homogenated tissue to mimic a wound environment shows a unique pattern of gene expression, which is different from gene expression patterns found after single stimuli (e.g. LPS, IL–4 etc.). To gain insight into the regulation of the enhancer landscape after complex stimuli, we compare the epigenome after single stimuli and tissue homogenate and find substantial differences in enhancer selection and activation. We find that the complex damage signal promotes co–localization of several signal–dependent transcription factors to enhancers not observed under the single stimuli. Therefore, more complex polarizations of cells lead to new combinations of signal–dependent transcription factors and an epigenetic landscape different than observed with single stimuli. In Chapter 3 (Link et al. (2018b) appeared in bioRxiv) MARGE (Mutation Analysis for Regulatory Genomic Elements) is presented, a new method to analyze the effect of natural genetic variation on transcription factor binding and open chromatin. MARGE provides a suite of software tools that integrates genome–wide genetic variation data (including insertions and deletions) with epigenetic data. It provides software to create custom genomes based on a reference genome and variation data, to shift coordinates between different custom genomes, as well as do downstream ChIP–seq analysis. The main algorithm in MARGE analyzes if mutations in transcription factor binding motifs are significantly affecting transcription factor binding or open chromatin. MARGE provides a pairwise comparison, in which the significance of each motif is calculated with a student’s t–test. It compares the transcription factor binding distribution of each mutated motif in individual one with the distribution in individual two. For a more general approach that allows comparisons of many individuals MARGE implements a linear mixed model, modeling transcription factor binding with fixed effects motif existence and random effects locus and genotype. The development of this software allows in depth analysis of genetic variation data in combination with epigenetic data. In Chapter 4 (Link et al. (2018a) under review in Cell) we analyze the effect of natural genetic variation in five diverse strains of mice on the epigenetic landscape. We choose three well–known laboratory inbred mouse strains, as well as two very diverse wild–derived inbred mouse strains. We investigate the enhancer landscape, open chromatin and binding of the most important macrophage lineage–determining transcription factors. We observe substantial strain–specific differences in gene expression of which the majority can be explained by cis–regulatory elements. Application of MARGE onto the transcription factor binding data reveals roles of about 100 transcription factors in establishing the enhancer repertoire in macrophages. Unexpectedly, we find that a substantial fraction of strain– specific DNA binding of transcription factors cannot be explained by local mutations. Investigation of this phenomenon in more detail shows highly interconnected clusters of transcription factors that reside within topologically associating domains. These interconnected clusters are highly correlated with activation of enhancers and gene expression of the nearest gene, uncovering a new layer of transcriptional regulation. In Chapter 5, I briefly discuss additional contributions to the field of macrophage biology I made during my Ph.D. Namely, I was involved in two additional projects. In the first project (Pirzgalska et al. (2017) appeared in Nature Medicine) we identify sympathetic neuron–associated macrophages (SAM) that import and degrade norepinephrine via expression of solute carrier family 6 member 2 (Slc6a2) and monoamine oxidase A (MAOa). We demonstrate that SAM–mediated clearance of extracellular norepinephrine contributes to obesity and we show the relevance of this finding in humans, as we found that SAMs are also present in human tissues. The second project (Oishi et al. (2017) appeared in Cell Metabolism) studies the role of nuclear receptors (LXR and SREBP) in induction of anti–inflammatory fatty acids. We find that right after stimulation of TLR4 (during the induction phase) NF–kB dependent genes are upregulated, whereas LXR dependent genes are repressed. This leads to activation of SREBP1, which drives the expression of enzymes involved in mono–unsaturated and omega–3 polyunsaturated fatty acid biosynthesis. The fatty acids produced by these enzymes repress inflammatory genes under the control of NF–kB and the inflammatory signal gets resolved. In summary, my studies used a combination of experimental and computational approaches to investigate the effect of tissue–environment and factors, complex stimuli and natural genetic variation on the epigenetic landscape in macrophages. These studies broadened our understanding of the regulation of gene expression by the epigenetic landscape substantially. We showed that there is a core set of lineage–determining transcription factors in macrophages, which require diverse signal–dependent transcription factors to establish the enhancer landscape. Not only did we show that transcription factors regulated by the local environment play essential roles in establishing and maintaining tissue–specific functions of macrophages, but also that more complex stimuli can re–direct and combine signal–dependent transcription factors to establish new enhancers, not observed under the single stimuli. Using natural genetic variation as a mutagenesis screen allowed us to estimate the involvement of about 100 transcription factors in shaping the enhancer landscape, as well as to uncover a new layer of transcription regulation due to highly interconnected clusters of concordantly bound transcription factors