Energy efficiency is critical for sustainable industrial growth and the reduction of environmental impacts. Energy consumption by the industrial sector accounts for more than half of the total global energy usage and, therefore, greater attention is focused on enhancing this sector's energy efficiency. It is predicted that by 2020, more than 20% of today's energy consumption can be avoided in countries that have effectively implemented an action plan towards efficient energy utilization. Breakthroughs in material synthesis of high selective membranes have enabled the technology to be more energy efficient. Hence, high selective membranes are increasingly replacing conventional energy intensive separation processes, such as distillation and adsorption units. Moreover, the technology offers more special features (which are essential for special applications) and its small footprint makes membrane technology suitable for platform operations (e.g., nitrogen enrichment for oil and gas offshore sites). In addition, its low maintenance characteristics allow the technology to be applied to remote operations. For these reasons, amongst other, the membrane technology market is forecast to reach $\$$16 billion by 2017. This thesis is concerned with the engineering aspects of membrane technology and covers modeling, simulation and optimization of membranes as a stand-alone process or as a unit operation within a hybrid system. Incorporating the membrane model into a process modeling software simplifies the simulation and optimization of the different membrane processes and hybrid configurations, since all other unit operations are pre-configured. Various parametric analyses demonstrated that only the membrane selectivity and transmembrane pressure ratio parameters define a membrane's ability to accomplish a certain separation task. Moreover, it was found that both membrane selectivity and pressure ratio exhibit a minimum value that is only defined by the feed composition, product purity and the recovery ratio. These findings were utilized to develop simple and accurate empirical correlations to predict the attainability behavior in real membranes, which showed good agreement with experimental and simulation results for various applications. Furthermore, the attainability of the most promising two and three-stage membrane systems are discussed by considering the complete well mixed assumption. The same behaviors that describe single-stage attainability are also recognized for multiple-stages. This discussion leads to a major discovery regarding the nature of the relationship between the attainability parameters in a multiple-stage membrane system with that of a single-stage system. Study of the economics of the multiple-stage membrane process for propylene/propane separation identifies the technology as a potential alternative to the conventional distillation process, even at the existing membrane performance, but conditionally at low to moderate membrane cost and sufficient durability. To study the energy efficiency of membrane retrofitting to an existing distillation process, a shortcut method was developed to calculate the minimum practical separation energy (MPSE) of the membrane and distillation processes. It was discovered that the MPSE of the hybrid system is only determined by the membrane selectivity and the applied transmembrane pressure ratio in three stages. At the first stage, when selectivity is low, the membrane process is not competitive to the distillation process. At the second medium selectivity stage, the membrane/distillation hybrid system can help to reduce the energy consumption; the higher the membrane selectivity the lower the energy requirement. The energy conservation is further improved as the pressure ratio increases. At the third stage, when both the selectivity and pressure ratio are high, the hybrid system will change to a single-stage membrane unit, resulting in a significant reduction in energy consumption. The energy at this stage continues to slowly decrease with selectivity but increases slightly with pressure ratio. Overall, the higher the membrane selectivity, the more energy that is saved. These results should be very useful in guiding membrane research and their applications. Finally, an economic study is conducted concerning hypothetical membranes and the necessity for low cost and more durable membranes rises as the key for a viable hybrid process.

This work investigates the modal properties of general cyclically symmetric systems and the multi-stage systems with cyclically symmetric stages. The work generalizes the modal properties of engineering applications, such as planetary gears, centrifugal pendulum vibration absorber (CPVA) systems, multi-stage planetary gears, etc., and provides methods to improve the computational efficiency to numerically solve the system modes when cyclically symmetric structures exist. Modal properties of cyclically symmetric systems with vibrating central components as three-dimensional rigid bodies are studied without any assumptions on the system matrix symmetries: asymmetric inertia matrix, damping, gyroscopic, and circulatory terms can be present. In the equation of motion of such a cyclically symmetric system, the matrix operators are proved to have properties related to the cyclic symmetry. These symmetry-related properties are used to prove the modal properties of general cyclically symmetric systems. Only three types of modes can exist: substructure modes, translational-tilting modes, and rotational-axial modes. Each mode type is characterized by specific central component modal deflections and substructure phase relations. Instead of solving the full eigenvalue problem,all vibration modes and natural frequencies can be obtained by solving smaller eigenvalue problems associated with each mode type. This computational advantage is dramatic for systems with many substructures or many degrees of freedom per substructure. Group theory is applied to further generalize the modal properties of cyclically symmetric systems when both rigid-body and compliant central components exist, such as planetary gears with an elastic continuum ring gear. The group theory for symmetry groups is introduced, and the group-theory-based modal analysis does not rely on any knowledge of the properties of system matrices in system equations of motion. The three types of modes (substructure modes, translational-tilting modes, and rotational-axial modes) are characterized by specific rigid-body central component modal defections, substructure phase relations, and nodal diameter components of compliant central components. The general formulation of reduced eigenvalue problems for each mode type is obtained through group-theory-based method, and it applies to discrete, continuous, or hybrid discrete-continuous cyclically symmetric systems. The group-theory-based modal analysis also applies to systems with other symmetry types. The group-theory-based modal analysis is generalized to analyze the multi-stage systems that are composed of symmetric stages coupled through the motions of rigid-body central components. The proposed group-theory-based modal analysis applies to multi-stage systems with cyclically symmetric stages, such as multi-stage planetary gears and CPVA systems with multiple groups of absorbers. The method also applies to multi-stage systems with component stages that have different types of symmetry. For a multi-stage system with symmetric stages, a unitary transformation matrix can be built through an algorithmic and computationally inexpensive procedure. The obtained unitary transformation matrix provides the foundation to analyze the modal properties based on the principles of group-theory-based modal analysis. For general multi-stage systems with symmetric component stages, the vibration modes are classified into two general types, single-stage substructure modes and overall modes, according to the non-zero modal deflections in each component stage. Reduced eigenvalue problems for each mode type are formulated to reduce the computational cost for eigensolutions. Finite element models of multi-stage bladed disk assemblies consist of multiple cyclically symmetric bladed disks that are coupled through the boundary nodes at the inter-stage interface. To improve the computational efficiency of calculating the full system modes, a numerical method is proposed by combination of the multi-stage cyclic symmetry reduction method and the subspace iteration method. Compared to the multi-stage cyclic symmetry reduction method, the proposed method improves the accuracy of obtained eigensolutions through an iterative process that is derived from the subspace iteration method. Based on the cyclic symmetry in each component stage of bladed disk, the proposed iterative method that can be performed using single stage sector models only, instead of using matrix operators for the full multi-stage bladed disks. Parallel computations can be performed in the proposed iterative method, and the computational speed for eigensolutions can be increased significantly. / Doctor of Philosophy / Cyclically symmetric structures exist in many engineering applications such as bladed disks, circular plates, planetary gears, centrifugal pendulum vibration absorbers (CPVA), etc. During steady operation, these cyclically symmetric systems are subjected to traveling wave dynamic loading. Component vibrations result in undesirable effects, including high cycle fatigue (HCF) failure, noise, performance reduction, etc. Knowledge of the modal properties of cyclically symmetric systems is helpful to analyze the system forced response and understand experimental modal testing. In this work, single stage cyclically symmetric systems are proved to have highly structured modes. The single stage systems considered in this work can have both rigid bodies and elastic continua as components. Group theory is used to study the modal properties, including the system mode types and the characteristics of different mode types. All the vibration modes of single stage cyclically symmetric systems can be solved from reduced eigenvalue problems. The methodology also applies to systems with other types of symmetry. For multi-stage systems with cyclically symmetric substructures, such as multi-stage planetary gears, a group-theory-based method is developed to analyze the modal properties. For industrial applications, such as multi-stage bladed disk assemblies, this work develops an iterative method to facilitate the calculations of system modes. The modal properties and methods for solving system modes apply to mechanical systems, including CPVA systems, the single/multi-stage planetary gears in power transmission systems, bladed disk assemblies in turbines, circular plates, elastic rings, etc.

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The tremendous increase in usage and complexity of modern communication and network systems connected to the Internet, places demands upon security management to protect organisations' sensitive data and resources from malicious intrusion. Malicious attacks by intruders and hackers exploit flaws and weakness points in deployed systems through several sophisticated techniques that cannot be prevented by traditional measures, such as user authentication, access controls and firewalls. Consequently, automated detection and timely response systems are urgently needed to detect abnormal activities by monitoring network traffic and system events. Network Intrusion Detection Systems (NIDS) and Network Intrusion Prevention Systems (NIPS) are technologies that inspect traffic and diagnose system behaviour to provide improved attack protection. The current implementation of intrusion detection systems (commercial and open-source) lacks the scalability to support the massive increase in network speed, the emergence of new protocols and services. Multi-giga networks have become a standard installation posing the NIDS to be susceptible to resource exhaustion attacks. The research focuses on two distinct problems for the NIDS: missing alerts due to packet loss as a result of NIDS performance limitations; and the huge volumes of generated alerts by the NIDS overwhelming the security analyst which makes event observation tedious. A methodology for analysing alerts using a proposed framework for alert correlation has been presented to provide the security operator with a global view of the security perspective. Missed alerts are recovered implicitly using a contextual technique to detect multi-stage attack scenarios. This is based on the assumption that the most serious intrusions consist of relevant steps that temporally ordered. The pre- and post- condition approach is used to identify the logical relations among low level alerts. The alerts are aggregated, verified using vulnerability modelling, and correlated to construct multi-stage attacks. A number of algorithms have been proposed in this research to support the functionality of our framework including: alert correlation, alert aggregation and graph reduction. These algorithms have been implemented in a tool called Multi-stage Attack Recognition System (MARS) consisting of a collection of integrated components. The system has been evaluated using a series of experiments and using different data sets i.e. publicly available datasets and data sets collected using real-life experiments. The results show that our approach can effectively detect multi-stage attacks. The false positive rates are reduced due to implementation of the vulnerability and target host information.

The tremendous increase in usage and complexity of modern communication and network systems connected to the Internet, places demands upon security management to protect organisations¿ sensitive data and resources from malicious intrusion. Malicious attacks by intruders and hackers exploit flaws and weakness points in deployed systems through several sophisticated techniques that cannot be prevented by traditional measures, such as user authentication, access controls and firewalls. Consequently, automated detection and timely response systems are urgently needed to detect abnormal activities by monitoring network traffic and system events. Network Intrusion Detection Systems (NIDS) and Network Intrusion Prevention Systems (NIPS) are technologies that inspect traffic and diagnose system behaviour to provide improved attack protection. The current implementation of intrusion detection systems (commercial and open-source) lacks the scalability to support the massive increase in network speed, the emergence of new protocols and services. Multi-giga networks have become a standard installation posing the NIDS to be susceptible to resource exhaustion attacks. The research focuses on two distinct problems for the NIDS: missing alerts due to packet loss as a result of NIDS performance limitations; and the huge volumes of generated alerts by the NIDS overwhelming the security analyst which makes event observation tedious. A methodology for analysing alerts using a proposed framework for alert correlation has been presented to provide the security operator with a global view of the security perspective. Missed alerts are recovered implicitly using a contextual technique to detect multi-stage attack scenarios. This is based on the assumption that the most serious intrusions consist of relevant steps that temporally ordered. The pre- and post- condition approach is used to identify the logical relations among low level alerts. The alerts are aggregated, verified using vulnerability modelling, and correlated to construct multi-stage attacks. A number of algorithms have been proposed in this research to support the functionality of our framework including: alert correlation, alert aggregation and graph reduction. These algorithms have been implemented in a tool called Multi-stage Attack Recognition System (MARS) consisting of a collection of integrated components. The system has been evaluated using a series of experiments and using different data sets i.e. publicly available datasets and data sets collected using real-life experiments. The results show that our approach can effectively detect multi-stage attacks. The false positive rates are reduced due to implementation of the vulnerability and target host information.

Some oil and gas companies are drilling and developing fields in the Arctic Ocean, which has an environment with sea ice called ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs) and docking stations. The AUVs measure the under-water topography of the ice floes, while the docking stations launch the AUVs and recharge their batteries. Given resource constraints, we optimize quantities and locations for the docking stations and the AUVs, as well as the AUV scheduling policies, in order to provide the maximum protection level for the platform. We first use an queueing approach to model the problem as a queueing system with abandonments, with the objective to minimize the abandonment probability. Both M/M/k+M and M/G/k+G queueing approximations are applied and we also develop a detailed simulation model based on the queueing approximation. In a complementary approach, we model the system using a multi-stage stochastic facility location problem in order to optimize the docking station locations, the AUV allocations, and the scheduling policies of the AUVs. A two-stage stochastic facility location problem and several efficient online scheduling heuristics are developed to provide lower bounds and upper bounds for the multi-stage model, and also to solve large-scale instances of the optimization model. Even though the model is motivated by an oil industry project, most of the modeling and optimization methods apply more broadly to any radial detection problems with queueing dynamics. / text

A search for vector-like quarks (VLQs) decaying to a Z boson using multi-stage machine learning was compared to a search using a standard square cuts search strategy. VLQs are predicted by several new theories beyond the Standard Model. The searches used 20.3 inverse femtobarns of proton-proton collisions at a center-of-mass energy of 8 TeV collected with the ATLAS detector in 2012 at the CERN Large Hadron Collider. CLs upper limits on production cross sections of vector-like top and bottom quarks were computed for VLQs produced singly or in pairs, Tsingle, Bsingle, Tpair, and Bpair. The two stage machine learning classification search strategy did not provide any improvement over the standard square cuts strategy, but for Tpair, Bpair, and Tsingle, a third stage of machine learning regression was able to lower the upper limits of high signal masses by as much as 50%. Additionally, new test statistics were developed for use in the Neyman construction of confidence regions in order to address deficiencies in current frequentist methods, such as the generation of empty set confidence intervals. A new method for treating nuisance parameters was also developed that may provide better coverage properties than current methods used in particle searches. Finally, significance ratio functions were derived that allow a more nuanced interpretation of the evidence provided by measurements than is given by confidence intervals alone.

Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set of decision trees for the given decision table which was previously only attainable by brute-force algorithms. We study decision tables with many-valued decisions (each row may contain multiple decisions) because they are more reasonable models of data in many cases. To address this problem in a broad sense, we consider not only decision trees but also inhibitory trees where terminal nodes are labeled with “̸= decision”. Inhibitory trees can sometimes describe more knowledge from datasets than decision trees. As for cost functions, we consider depth or average depth to minimize time complexity of trees, and the number of nodes or the number of the terminal, or nonterminal nodes to minimize the space complexity of trees. We investigate the multi-stage optimization of trees relative to some cost functions, and also the possibility to describe the whole set of strictly optimal trees. Furthermore, we study the bi-criteria optimization cost vs. cost and cost vs. uncertainty for decision trees, and cost vs. cost and cost vs. completeness for inhibitory trees. The most interesting application of the developed technique is the creation of multi-pruning and restricted multi-pruning approaches which are useful for knowledge representation and prediction. The experimental results show that decision trees constructed by these approaches can often outperform the decision trees constructed by the CART algorithm. Another application includes the comparison of 12 greedy heuristics for single- and bi-criteria optimization (cost vs. cost) of trees. We also study the three approaches (decision tables with many-valued decisions, decision tables with most common decisions, and decision tables with generalized decisions) to handle inconsistency of decision tables. We also analyze the time complexity of decision and inhibitory trees over arbitrary sets of attributes represented by information systems in the frameworks of local (when we can use in trees only attributes from problem description) and global (when we can use in trees arbitrary attributes from the information system) approaches.

<div> <div> <div> <p>Nowadays, the pace of coordinated cyber security crimes has become drastically more rapid, and network attacks have become more advanced and diversified. The explosive growth of network security threats poses serious challenges for building secure Cyber-based Systems (CBS). Existing studies have addressed a breadth of challenges related to detecting network attacks. However, there is still a lack of studies on the detection of sophisticated Multi-stage Attacks (MSAs). </p> <p>The objective of this dissertation is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved MSAs. We present the interleaving concept and investigate how interleaving multiple MSAs can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop three architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a set of HMM templates of known attacks and exhibit varying performance and complexity. </p> <p>For performance evaluation, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are conducted to demonstrate the efficacy of the proposed architecture in the presence of multiple multi-stage attack scenarios, and in the presence of false alerts with various rates. </p> </div> </div> </div>

Drug addiction is a complex behavioral disorder that has been extensively studied in an attempt to uncover its underlying biological mechanisms. This paper contributes to the literature on addiction by demonstrating that addiction is a result of an improperly functioning decision making process. The areas of the brain that are most implicated in decision making demonstrate significant overlap with those areas most affected by addiction. Specifically, the limbic structures of the brain (amygdala, basal ganglia, and mesolimbic reward pathway) and the prefrontal cortices (orbitofrontal cortex, dorsolateral prefrontal cortex, and ventromedial prefrontal cortex) are discussed in relation to their involvement in prominent theories of decision making such as Prospect Theory and the Somatic Marker Hypothesis. This paper will then use the above knowledge regarding the specific brain mechanisms that control decision making and apply it to neurobiological theories of addiction. The view that addiction is a behavioral disorder that results primarily from a degradation of the brain mechanisms involved in decision making processes is important to consider because it can help provide a concrete approach to developing more individualized and effective treatment programs in the future.