1,802 research outputs found

    Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging

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    Virtual Synchronous Generator Control Using Twin Delayed Deep Deterministic Policy Gradient Method

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    This paper presents a data-driven approach that adaptively tunes the parameters of a virtual synchronous generator to achieve optimal frequency response against disturbances. In the proposed approach, the control variables, namely, the virtual moment of inertia and damping factor, are transformed into actions of a reinforcement learning agent. Different from the state-of-the-art methods, the proposed study introduces the settling time parameter as one of the observations in addition to the frequency and rate of change of frequency (RoCoF). In the reward function, preset indices are considered to simultaneously ensure bounded frequency deviation, low RoCoF, fast response, and quick settling time. To maximize the reward, this study employs the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm. TD3 has an exceptional capacity for learning optimal policies and is free of overestimation bias, which may lead to suboptimal policies. Finally, numerical validation in MATLAB/Simulink and real-time simulation using RTDS confirm the superiority of the proposed method over other adaptive tuning methods

    Magnetic Domains and Domain Wall Oscillations in Planar and 3D Curved Membranes

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    This dissertation presents a substantial contribution to a new field of material science, the investigation of the magnetic properties of 3D curved surfaces, achieved by using a self-assembled geometrical transformation of an initially planar membrane. Essential magnetic properties of thin films can be modified by the process of transforming them from a 2D planar film to a 3D curved surface. By investigating and controlling the reasons that influence the properties, it is possible to improve the functionality of existing devices in addition to laying the foundation for the future development of microelectronic devices based on curved magnetic structures. To accomplish this, it is necessary both to fabricate high-quality 3D curved objects and to establish reliable characterization methods based on commonly available technology. The primary objective of this dissertation is to develop techniques for characterizing the static and dynamic magnetic properties of self-assembled rolled 3D geometries. The second objective is to examine the origin of shape-, size- and strain/curvature-induced effects. The developed approach based on anisotropic magnetoresistance (AMR) measurement can quantitatively define the rolling-induced static magnetic changes, namely the induced magnetoelastic anisotropy, thus eliminating the need for microscopic imaging to characterize the structures. The interpretation of the AMR signal obtained on curved stripes is enabled by simultaneous visualization of the domain patterns and micromagnetic simulations. The developed approach is used to examine the effect of sign and magnitude of curvature on the induced anisotropies by altering the rolling direction and diameter of the 'Swiss-roll'. Furthermore, a time-averaged imaging technique based on conventional microscopies (magnetic force microscopy and Kerr microscopy) offers a novel strategy for investigating nanoscale periodic domain wall oscillations and hence dynamic magnetic characteristics of flat and curved structures. This method exploits the benefit of a position-dependent dwell time of periodically oscillating DWs and can determine the trajectory and amplitude of DW oscillation with sub-100 nm resolution. The uniqueness of this technique resides in the ease of the imaging procedure, unlike other DW dynamics imaging methods. The combined understanding of rolling-induced anisotropy and imaging DW oscillation is utilized to examine the dependence of DW dynamics on external stimuli and the structure's physical properties, such as lateral size, film thickness, and curvature-induced anisotropy. The presented methods and fundamental studies help to comprehend the rapidly expanding field of 3-dimensional nanomagnetism and advance high-performance magneto-electronic devices based on self-assembly rolling

    Responsive Building Envelope for Grid-Interactive Efficient Buildings – Thermal Performance and Control

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    The building sector accounts for 30% of total energy consumption worldwide. Responsive building envelopes (or RBEs) are one of the approaches to achieving net-zero energy and grid-interactive efficient buildings. However, research and development of RBEs are still in the early stages of technologies, simulation, control, and design. The control strategies in prior studies did not fully explore the potential of RBEs or they obtained good performance with high design and deployment costs. A low-cost strategy that does not require knowledge of complex systems is needed, while no studies have investigated online implementations of model-free control approaches for RBEs. To address these challenges, this dissertation describes a multidisciplinary study of the modeling, control, and design of RBEs, to understand mechanisms governing their dynamic properties and synthesis rules of multiple technologies through simulation analyses. Widely applicable mathematical models are developed that can be easily extended for multiple RBE types with validation. Computational frameworks (or co-simulation testbeds) that flexibly integrate multiple control methods and building simulation models are established with higher computation efficiency than that using commercial software during offline training. To overcome the limitations of the control strategies (e.g., rule-based control and MPC) in prior research, a novel easy-to-implement yet flexible ‘demand-based’ control strategy, and model-free online control strategies using deep reinforced learning are proposed for RBEs composed of active insulation systems (AISs). Both the physics-derived and model-free control strategies fully leverage the advantages of AISs and provide higher energy savings and thermal comfort improvement over traditional temperature-based control methods in prior research and demand-based control. The case studies of RBEs that integrate AISs and high thermal mass or self-adaptive/active modules (e.g., evaporative cooling techniques and dynamic glazing/shading) demonstrate the superior performance of AISs in regulating thermal energy transfer to offset AC demands during the synergy. Moreover, the controller design and training implications are elaborated. The applicability assessment of promising RBE configurations is presented along with design implications based on building energy analyses in multiple scenarios. The design and control implications represent an interactive and holistic way to operate RBEs allowing energy and thermal comfort performances to be tuned for maximum efficiency

    Collaborative Multi-Agent Video Fast-Forwarding

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    Multi-agent applications have recently gained significant popularity. In many computer vision tasks, a network of agents, such as a team of robots with cameras, could work collaboratively to perceive the environment for efficient and accurate situation awareness. However, these agents often have limited computation, communication, and storage resources. Thus, reducing resource consumption while still providing an accurate perception of the environment becomes an important goal when deploying multi-agent systems. To achieve this goal, we identify and leverage the overlap among different camera views in multi-agent systems for reducing the processing, transmission and storage of redundant/unimportant video frames. Specifically, we have developed two collaborative multi-agent video fast-forwarding frameworks in distributed and centralized settings, respectively. In these frameworks, each individual agent can selectively process or skip video frames at adjustable paces based on multiple strategies via reinforcement learning. Multiple agents then collaboratively sense the environment via either 1) a consensus-based distributed framework called DMVF that periodically updates the fast-forwarding strategies of agents by establishing communication and consensus among connected neighbors, or 2) a centralized framework called MFFNet that utilizes a central controller to decide the fast-forwarding strategies for agents based on collected data. We demonstrate the efficacy and efficiency of our proposed frameworks on a real-world surveillance video dataset VideoWeb and a new simulated driving dataset CarlaSim, through extensive simulations and deployment on an embedded platform with TCP communication. We show that compared with other approaches in the literature, our frameworks achieve better coverage of important frames, while significantly reducing the number of frames processed at each agent.Comment: IEEE Transactions on Multimedia, 2023. arXiv admin note: text overlap with arXiv:2008.0443

    Children with Williams Syndrome: Experiences of mainstream primary schools

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    Four children with Williams Syndrome shared their daily experiences of attending a mainstream primary school in the South West of England, UK. Using an adaptation of the Mosaic approach (Clark, 2011), children shared their perceptions and experiences of belonging to a class, developing responsibility and learning to follow the rules. Children guided the researcher during a visit lasting one week in each school. Methods included videos, a child-led tour, photographs and interviews with staff. Informed consent was obtained by gatekeepers including children’s parents, head teacher and school staff. Children were continually monitored for assent using a reflective, ethically conscious total communication approach. Findings show close relationships with practitioners were essential for supporting child centred inclusion for children with disabilities. Outside the classroom the space was more open and supportive for practitioners to recognize, respect and respond to children's own paces. Whilst children are included inside the classroom, practitioners provide a safe space that celebrates children’s own priorities and paces outside of the classroom. This study highlights the need for settings to facilitate space based on Elkind’s (2006) unhurried approach. Teaching assistants play a significant role in supporting children and staff, by developing knowledge of both the child and the disability through close, responsive working with children. Implications for practice indicate staff would benefit from WS specific knowledge and training as well as strategic school inclusion practices to enable staff to share their knowledge-from-experience with class teachers

    Intelligent interface agents for biometric applications

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    This thesis investigates the benefits of applying the intelligent agent paradigm to biometric identity verification systems. Multimodal biometric systems, despite their additional complexity, hold the promise of providing a higher degree of accuracy and robustness. Multimodal biometric systems are examined in this work leading to the design and implementation of a novel distributed multi-modal identity verification system based on an intelligent agent framework. User interface design issues are also important in the domain of biometric systems and present an exceptional opportunity for employing adaptive interface agents. Through the use of such interface agents, system performance may be improved, leading to an increase in recognition rates over a non-adaptive system while producing a more robust and agreeable user experience. The investigation of such adaptive systems has been a focus of the work reported in this thesis. The research presented in this thesis is divided into two main parts. Firstly, the design, development and testing of a novel distributed multi-modal authentication system employing intelligent agents is presented. The second part details design and implementation of an adaptive interface layer based on interface agent technology and demonstrates its integration with a commercial fingerprint recognition system. The performance of these systems is then evaluated using databases of biometric samples gathered during the research. The results obtained from the experimental evaluation of the multi-modal system demonstrated a clear improvement in the accuracy of the system compared to a unimodal biometric approach. The adoption of the intelligent agent architecture at the interface level resulted in a system where false reject rates were reduced when compared to a system that did not employ an intelligent interface. The results obtained from both systems clearly express the benefits of combining an intelligent agent framework with a biometric system to provide a more robust and flexible application

    Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM

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    This paper explores the challenges of implementing Federated Learning (FL) in practical scenarios featuring isolated nodes with data heterogeneity, which can only be connected to the server through wireless links in an infrastructure-less environment. To overcome these challenges, we propose a novel mobilizing personalized FL approach, which aims to facilitate mobility and resilience. Specifically, we develop a novel optimization algorithm called Random Walk Stochastic Alternating Direction Method of Multipliers (RWSADMM). RWSADMM capitalizes on the server's random movement toward clients and formulates local proximity among their adjacent clients based on hard inequality constraints rather than requiring consensus updates or introducing bias via regularization methods. To mitigate the computational burden on the clients, an efficient stochastic solver of the approximated optimization problem is designed in RWSADMM, which provably converges to the stationary point almost surely in expectation. Our theoretical and empirical results demonstrate the provable fast convergence and substantial accuracy improvements achieved by RWSADMM compared to baseline methods, along with its benefits of reduced communication costs and enhanced scalability.Comment: 28 pages, 7 figures, 3 tables, 1 algorithm. Proof details are provided in the main body of the pape

    A Holistic Work System Approach to Creating Flow During Transactional Work

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    Psychological flow is a positive mental state where one is so fully concentrated in a challenging task that self-consciousness falls away, time seems to stand still, and the reward is the experience of meeting the challenge. Previous research on flow in the workplace has been performed on how to create conditions to promote its occurrence in workers, to describe its attendant individual and organizational benefits, and to measure it through self-reported means and physiologically. Such research has been focused on creative endeavors (such as the arts, sports, medicine, teaching), where individuals have high agency over the execution of activities needed to successfully complete the work. This research focuses on flow in back-office transactional work, which has been little studied to date. Transactional work are those tasks that are largely rote, repetitive, and prescribed by standardized procedures, leaving little room for agentic options. Examples of such work include data entry and bookkeeping A theory is next discussed that offers the notion of a holistic system of non-task variables working together with job tasks to create conditions conducive to increasing the likelihood of transactional workers experiencing flow. Flow will next be compared to similar constructs and their relatedness to flow will be discussed. Various flow measurement methods will be presented, along with their advantages and disadvantages. These discussions set the stage for the present set of qualitative and quantitative research efforts, whose objective is to offer support for the holistic work system approach to creating flow. First, a phenomenological study of flow in transactional workers is presented, where their lived experiences of flow are documented and the extent to which certain non-task work system variables support the occurrence of flow. Next, a proof-of-concept laboratory experiment is reviewed, where seat comfort (a non-task work system factor) is shown to be a first-order influencer of flow in the study\u27s participants. Finally, the results of a designed experiment incorporating multiple non-task work system factors are presented and the interaction of high seat comfort and low computer screen contrast are shown to directly impact the occurrence of flow in that study\u27s participants. Flow is also shown to predict productivity improvements in participants when combined with high seat comfort and low computer screen contrast. Additionally, certain physiological functions thought to correlate to flow are selected and measured in the participants. Lower heart rate variation partially correlates to flow. The results are applicable to the design of holistic work systems in organizations employing back-office transactional workers. Recommendations for future research are presented that will strengthen and build on the current results
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