206,123 research outputs found

    Advanced Data Analytics and Optimal Control of Building Energy Systems

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    This research addresses key issues for applying advanced building data analytics to energy efficient control opportunities. First the research identifies advancements and potential hurdles around the three primary means for acquiring data: energy management systems, dedicated measurement systems, and advanced computer software that accesses and archives data from energy management systems. These are described using case studies from commercial building control systems and web-based real time dedicated measurement technology. Next, the research describes effective rule-based data analytics and control strategies that are traditionally used. Rule-based data analytics utilize specific knowledge about HVAC systems to identify key data points and analytical methods to identify energy saving opportunities and develop improved control algorithms. The research describes both theory and application of these rule-based analytics for the control of systems like air-side economizer, ventilation fans, pumping and chilled water systems. Finally, the research proposes a framework to apply advanced machine learning and data mining techniques to the same problem. Machine-learning control differs from rule-based control in that this control type requires less specific knowledge about HVAC systems. The proposed framework uses existing data, where available, to pattern match and build robust models emulating the performance of the system under consideration. To these models, classical optimization algorithms (knapsack, greedy and shortest distance) and mathematical framework (Game theory and Design of Experiments) are adapted and applied to reach the best control strategy. For systems without past performance data, a stochastic framework using decision chains (Markov processes) and adaptive controls using the reinforcement learning method is proposed for the same. These techniques are demonstrated on select systems e.g. Pumping plants and HVAC systems.https://ecommons.udayton.edu/stander_posters/2611/thumbnail.jp

    Large-scale adaptive multiple testing for sequential data controlling false discovery and nondiscovery rates

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    In modern scientific experiments, we frequently encounter data that have large dimensions, and in some experiments, such high dimensional data arrive sequentially rather than full data being available all at a time. We develop multiple testing procedures with simultaneous control of false discovery and nondiscovery rates when mm-variate data vectors X1,X2,…\mathbf{X}_1, \mathbf{X}_2, \dots are observed sequentially or in groups and each coordinate of these vectors leads to a hypothesis testing. Existing multiple testing methods for sequential data uses fixed stopping boundaries that do not depend on sample size, and hence, are quite conservative when the number of hypotheses mm is large. We propose sequential tests based on adaptive stopping boundaries that ensure shrinkage of the continue sampling region as the sample size increases. Under minimal assumptions on the data sequence, we first develop a test based on an oracle test statistic such that both false discovery rate (FDR) and false nondiscovery rate (FNR) are nearly equal to some prefixed levels with strong control. Under a two-group mixture model assumption, we propose a data-driven stopping and decision rule based on local false discovery rate statistic that mimics the oracle rule and guarantees simultaneous control of FDR and FNR asymptotically as mm tends to infinity. Both the oracle and the data-driven stopping times are shown to be finite (i.e., proper) with probability 1 for all finite mm and converge to a finite constant as mm grows to infinity. Further, we compare the data-driven test with the existing gap rule proposed in He and Bartroff (2021) and show that the ratio of the expected sample sizes of our method and the gap rule tends to zero as mm goes to infinity. Extensive analysis of simulated datasets as well as some real datasets illustrate the superiority of the proposed tests over some existing methods.Comment: 44 pages, 4 figures, 2 table

    A study in the use of fuzzy logic in the management of an automotive heat engine / electric hybrid vehicle powertrain

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    This thesis addresses the problem of the instant-by-instant control of the powertrain of a hybrid heat engine/electric vehicle. In the absence of a prototype vehicle on which the work could be carried out the work has taken the form of computer simulation experiments. In order to develop the powertrain control strategies, a computer model of a conceptual hybrid vehicle is then developed, containing components from real, production and prototype vehicles. The use of this component based modelling approach allows the models to be validated by comparing their predictions with the performance of the real vehicles in which the components are used. The previous work conducted in the field of hybrid vehicle powertrain control is then reviewed. It is found that fuzzy logic could potentially provide a means of controlling the hybrid powertrain in a realistic manner, in which some of the disadvantages of previous hybrid powertrain control strategies could be overcome. The results of initial simulation experiments are then reported, finding that whilst the basic method appears to have the potential to successfully control the powertrain, there is a need for an adaptive fuzzy powertrain controller. A review is then presented of previous work conducted in the field of adaptive fuzzy control, finding that none of the reported adaptive fuzzy control methods are capable of being easily applied in the case of the hybrid powertrain. An adaptive fuzzy controller is then developed, whose rule modification strategy is specifically designed to work in the hybrid powertrain control problem. This initial adaptive powertrain controller is then modified to improve its ability to control the overall performance of a hybrid vehicle, whilst maintaining vehicle driveability. It is found that this controller is able to adapt to the different driving styles of individual vehicle users within the space of a few simulated urban journeys. Experiments are then performed in which improvements in the overall efficiency of the vehicle powertrain are investigated. It is found that significant improvements in the operation of the powertrain are impossible, due to some of the features of the vehicle model and constraints placed upon the control strategy. Conclusions are then drawn, for the work done in the field of hybrid vehicle powertrain control and, also, for the work done in adaptive methods of fuzzy control. The most significant contribution in the field of hybrid powertrain control is the development of a controller that can adapt to the habits of different users. The most significant contribution in the field of fuzzy control is the form of the basic hybrid powertrain controller and the use of small fuzzy controllers in the powertrain controller adaptation strategy

    Reinforcement Learning Approaches for Traffic Signal Control under Missing Data

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    The emergence of reinforcement learning (RL) methods in traffic signal control tasks has achieved better performance than conventional rule-based approaches. Most RL approaches require the observation of the environment for the agent to decide which action is optimal for a long-term reward. However, in real-world urban scenarios, missing observation of traffic states may frequently occur due to the lack of sensors, which makes existing RL methods inapplicable on road networks with missing observation. In this work, we aim to control the traffic signals in a real-world setting, where some of the intersections in the road network are not installed with sensors and thus with no direct observations around them. To the best of our knowledge, we are the first to use RL methods to tackle the traffic signal control problem in this real-world setting. Specifically, we propose two solutions: the first one imputes the traffic states to enable adaptive control, and the second one imputes both states and rewards to enable adaptive control and the training of RL agents. Through extensive experiments on both synthetic and real-world road network traffic, we reveal that our method outperforms conventional approaches and performs consistently with different missing rates. We also provide further investigations on how missing data influences the performance of our model.Comment: Published as a conference paper at IJCAI202

    Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

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    Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.</jats:p

    Self-Adaptive Role-Based Access Control for Business Processes

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    © 2017 IEEE. We present an approach for dynamically reconfiguring the role-based access control (RBAC) of information systems running business processes, to protect them against insider threats. The new approach uses business process execution traces and stochastic model checking to establish confidence intervals for key measurable attributes of user behaviour, and thus to identify and adaptively demote users who misuse their access permissions maliciously or accidentally. We implemented and evaluated the approach and its policy specification formalism for a real IT support business process, showing their ability to express and apply a broad range of self-adaptive RBAC policies
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