10,977 research outputs found
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
A two way process – Social capacity as a driver and outcome of equitable marine spatial planning
Although stakeholder engagement is one of the founding principles of marine spatial planning (MSP), meaningful representation of people and their connections to marine resources within marine governance is still lacking. A broad understanding of how concepts surrounding social capital and capacity is translated into MSP practice is missing. With this article, we describe detailed case studies in the United Kingdom, Brazil and South Africa to build a better understanding of the ways in which MSP and other ocean governance initiatives operationalise the concepts of social capital and capacity. Drawing on insights from the cases, we call for a rethinking of capacitation as a two-way process. In particular, trust-building, social learning and efforts to build social capacity should be elaborated without imposing a hierarchy between people ‘who know’ and people ‘who don’t’. Innovative approaches to relationship building, knowledge development, and collaboration highlighted in the case studies highlight ways to build social capacity both among stakeholders and planners, as is necessary for more equitable and sustainable MSP development and implementation
Reinforcement Learning-based User-centric Handover Decision-making in 5G Vehicular Networks
The advancement of 5G technologies and Vehicular Networks open a new paradigm for Intelligent Transportation Systems (ITS) in safety and infotainment services in urban and highway scenarios. Connected vehicles are vital for enabling massive data sharing and supporting such services. Consequently, a stable connection is compulsory to transmit data across the network successfully. The new 5G technology introduces more bandwidth, stability, and reliability, but it faces a low communication range, suffering from more frequent handovers and connection drops. The shift from the base station-centric view to the user-centric view helps to cope with the smaller communication range and ultra-density of 5G networks. In this thesis, we propose a series of strategies to improve connection stability through efficient handover decision-making. First, a modified probabilistic approach, M-FiVH, aimed at reducing 5G handovers and enhancing network stability. Later, an adaptive learning approach employed Connectivity-oriented SARSA Reinforcement Learning (CO-SRL) for user-centric Virtual Cell (VC) management to enable efficient handover (HO) decisions. Following that, a user-centric Factor-distinct SARSA Reinforcement Learning (FD-SRL) approach combines time series data-oriented LSTM and adaptive SRL for VC and HO management by considering both historical and real-time data. The random direction of vehicular movement, high mobility, network load, uncertain road traffic situation, and signal strength from cellular transmission towers vary from time to time and cannot always be predicted. Our proposed approaches maintain stable connections by reducing the number of HOs by selecting the appropriate size of VCs and HO management. A series of improvements demonstrated through realistic simulations showed that M-FiVH, CO-SRL, and FD-SRL were successful in reducing the number of HOs and the average cumulative HO time. We provide an analysis and comparison of several approaches and demonstrate our proposed approaches perform better in terms of network connectivity
Qluster: An easy-to-implement generic workflow for robust clustering of health data
The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors
Intelligent Control Schemes for Maximum Power Extraction from Photovoltaic Arrays under Faults
Investigation of power output from PV arrays under different fault conditions is an essential task to enhance performance of a photovoltaic system under all operating conditions. Significant reduction in power output can occur during various PV faults such as module disconnection, bypass diode failure, bridge fault, and short circuit fault under non-uniform shading conditions. These PV faults may cause several peaks in the characteristics curve of PV arrays, which can lead to failure of the MPPT control strategy. In fact, impact of a fault can differ depending on the type of PV array, and it can make the control of the system more complex. Therefore, consideration of suitable PV arrays with an effective control design is necessary for maximum power output from a PV system. For this purpose, the proposed study presents a comparative study of two intelligent control schemes, i.e., fuzzy logic (FL) and particle swarm optimization (PSO), with a conventional control scheme known as perturb and observe (P&O) for power extraction from a PV system. The comparative analysis is based on the performance of the control strategies under several faults and the types of PV modules, i.e., monocrystalline and thin-film PV arrays. In this study, numerical analysis for complex fault scenarios like multiple faults under partial shading have also been performed. Different from the previous literature, this study will reveal the performance of FL-, PSO-, and P&O-based MPPT strategies to track maximum peak power during multiple severe fault conditions while considering the accuracy and fast-tracking efficiencies of the control techniques. A thorough analysis along with in-depth quantitative data are presented, confirming the superiority of intelligent control techniques under multiple faults and different PV types
A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching
An efficient team is essential for the company to successfully complete new
projects. To solve the team formation problem considering person-job matching
(TFP-PJM), a 0-1 integer programming model is constructed, which considers both
person-job matching and team members' willingness to communicate on team
efficiency, with the person-job matching score calculated using intuitionistic
fuzzy numbers. Then, a reinforcement learning-assisted genetic programming
algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP
adopts the ensemble population strategies. Before the population evolution at
each generation, the agent selects one from four population search modes
according to the information obtained, thus realizing a sound balance of
exploration and exploitation. In addition, surrogate models are used in the
algorithm to evaluate the formation plans generated by individuals, which
speeds up the algorithm learning process. Afterward, a series of comparison
experiments are conducted to verify the overall performance of RL-GP and the
effectiveness of the improved strategies within the algorithm. The
hyper-heuristic rules obtained through efficient learning can be utilized as
decision-making aids when forming project teams. This study reveals the
advantages of reinforcement learning methods, ensemble strategies, and the
surrogate model applied to the GP framework. The diversity and intelligent
selection of search patterns along with fast adaptation evaluation, are
distinct features that enable RL-GP to be deployed in real-world enterprise
environments.Comment: 16 page
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Co-design As Healing: Exploring The Experiences Of Participants Facing Mental Health Problems
This thesis is an exploration of the healing role of co-design in mental health. Although co-design projects conducted within mental health settings are rising, existing literature tends to focus on the object of design and its outcomes while the experiences of participants per se remain largely unexplored. The guiding research question of this study is not how we design things that improve mental health, but how co-designing, as an act, might do so.
The thesis presents two projects that were organized in collaboration with the mental health charity Islington Mind and the Psychosis Therapy Project (PTP) in London.
The project at Islington Mind used a structured design process inviting participants to design for wellbeing. A case study analysis provides insights on how participants were impacted, summarizing key challenges and opportunities.
The design at PTP worked towards creating a collective brief in an emergent fashion, finally culminating in a board game. The experiences of participants were explored through Interpretative Phenomenological Analysis (IPA), using semi-structured interview data. The analysis served to identify key themes characterising the experience of co-design such as contributing, connecting, thinking and intentioning. In addition, a mixed-methods analysis of questionnaires and interview data exploring participants' wellbeing, showed that all participants who engaged fairly consistently in the project improved after the project ended, although some participants' scores returned to baseline six months later.
Reflecting on both projects, an approach to facilitation within mental health is outlined, detailing how the dimensions of weaving and layered participation, nurturing mattering and facilitating attitudes interlace. This contribution raises awareness of tacit dimensions in the practice of facilitation, articulating the nuances of how to encourage and sustain meaningful and ethical engagement and offering insights into a range of tools. It highlights the importance of remaining reflexive in relation to attitudes and emotions and discusses practical methodological and ethical challenges and ways to resolve them which can be of benefit to researchers embarking on a similar journey.
The thesis also offers detailed insights on how methodologies from different fields were integrated into a whole, arguing for transparency and reflexivity about epistemological assumptions, and how underlying paradigms shift in an interdisciplinary context.
Based on the overall findings, the thesis makes a case for considering design as healing (or a designerly way of healing), highlighting implications at a systems, social and individual level. It makes an original contribution to our understanding of design, highlighting its healing character, and proposes a new way to support mental health. The participants in this study not only had increased their own wellbeing through co-designing, but were also empowered and contributed towards healing the world. Hence, the thesis argues for a unique, holistic perspective of design and mental health, recognizing the interconnectedness of the individual, social and systemic dimensions of the healing processes that are ignited
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process
Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine).
In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model.
AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development.
Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models.
In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
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