6,115 research outputs found

    Adaptive non-singular fast terminal sliding mode control and synchronization of a chaotic system via interval type-2 fuzzy inference system with proportionate controller

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    This paper introduces a novel adaptive nonsingular fast terminal sliding mode approach that benefits from an interval type-2 fuzzy logic estimator and a gain for control and synchronization of chaotic systems in the presence of uncertainty. The nonsingular fast terminal sliding mode controller is developed to increase the convergence rate and remove the singularity problem of the system. Using the proposed method, the finite-time convergence has been ensured. To eliminate the chattering phenomenon in the conventional sliding mode controller, the discontinuous sign function is estimated using an interval type-2 fuzzy inference system (FIS) based on the center of sets type reduction followed by defuzzification. By adding the proportionate gain to the interval type-2 FIS, the robustness and speed of the controller system is enhanced. An appropriate Lyapunov function is utilized to ensure the closed-loop stability of the control system. The performance of the controller is evaluated for a nonlinear time-varying second-order magnetic space-craft chaotic system with different initial conditions in the presence of uncertainty. The simulation results show the efficacy of the proposed approach for the tracking control problems. The time and frequency domain analysis of the control signal demonstrates that the chattering phenomenon is successfully diminished

    Is attention all you need in medical image analysis? A review

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    Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. The main disadvantage of typical CNN models is that they ignore global pixel relationships within images, which limits their generalisation ability to understand out-of-distribution data with different 'global' information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced a comprehensive analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated

    Fast Generation of Heterogeneous Mental Models from Longitudinal Data by Combining Genetic Algorithms and Fuzzy Cognitive Maps

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    Models that capture the heterogeneous perspectives of individuals are essential to test tailored interventions, such as behavior change interventions. Although Fuzzy Cognitive Maps (FCMs) have a rich history in depicting systems, they were either developed at an individual level through facilitated sessions, or created for an entire population through machine learning. The need to automatically create individual FCMs from data has started to be addressed, but the proposed solution was computationally prohibitive and thus could not be deployed over a large population. In this work, we use a state-of-the-art evolutionary algorithm (CMA-ES) to create individual FCMs by leveraging the growing availability of longitudinal data. We demonstrate on a real-world case study that our solution is both accurate and fast to compute. Our experiments on synthetic data also show that our approach can scale to a large number of measurements, but it cannot currently be applied to highly noisy datasets

    Fuzzy Natural Logic in IFSA-EUSFLAT 2021

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    The present book contains five papers accepted and published in the Special Issue, “Fuzzy Natural Logic in IFSA-EUSFLAT 2021”, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference “The 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferences”, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IF–THEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications

    Soundscape in Urban Forests

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    This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests

    Comparing the Performance of Initial Coin Offerings to Crowdfunded Equity Ventures

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    Uncertainty in markets increases the likelihood of market failure due to volatility and suboptimal functioning. While initial coin offerings (ICOs) and crowdfunded equity (CFE) offerings may improve functioning in growing markets, there is a lack of knowledge and understanding pertaining to the relative efficiency and behavior of ICO markets compared to CFE markets, potentially perpetuating and thwarting the various communities they are intended to serve. The purpose of this correlational study was to compare a group of ICOs with a group of CFE offerings to identify predictive factors of funding outcomes related to both capital offering types. Efficient market hypothesis was the study’s theoretical foundation, and analysis of variance was used to answer the research question, which examined whether capital offering type predicted the amount of funds raised while controlling for access to the offering companies’ secondary control factors: historical financial data, pro forma financial projections, detailed product descriptions, video of product demonstrations, company website, company history, company leadership, and company investors. Relying on a random sample of 115 campaigns (84 ICOs and 31 CFE) from websites ICOdrops.com, localstake.com, fundable.com, and mainvest.com, results showed differences in mean funds raised between CFEs and ICOs (346,075comparedto346,075 compared to 4,756,464, respectively). ANOVA results showed no single secondary control factors and only one two-factor interaction (company leadership and company investors) influenced mean funds raised. This study may contribute to positive social change by informing best practices among market participants including entrepreneurs, regulators, scholars, and investors

    Transforming electrical energy systems towards sustainability in a complex world: the cases of Ontario and Costa Rica

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    Electrical energy systems have been major contributors to sustainability-associated effects, positive and negative, and therefore are considered as key components in pursuing overall sustainability objectives. Conventional electrical energy systems have delivered essential services for human well-being and can play a key role in tackling ongoing threats including growing poverty, climate change effects, and the long-term impacts of the COVID-19 pandemic. At the same time, some participants in electrical energy systems at national and local scales have stressed that the conventional design of electrical energy systems requires change to ensure the positive contributions and to reduce socioeconomic and environmental risks. Continuing negative trends including significant contributions to climate change, rising energy costs, deepening inequities, and long-term environmental degradation, have raised concerns and prompted calls for transforming conventional electrical energy systems rapidly and safely. However, due in part to the complexity of electrical energy systems, national and local authorities have struggled to steer their systems towards delivering more consistently positive sustainability-associated effects. Usual approaches to electrical energy system management have sought to improve efficiency, reliability and capacity to meet anticipated demand. They have seldom treated electrical energy systems as potentially important contributors to overall sustainability in principle and in practice. Doing so would entail recognizing electrical energy systems as complex systems with interlinked effects and aiming to maximize the systems’ positive and transformative effects to deliver multiple, mutually reinforcing and overall sustainability gains. The research reported here considered whether and how sustainability-based assessments can be useful tools to fill this gap and advance sustainability objectives in particular plans, projects, and initiatives carried out in electrical energy systems. To aid in responding the main research questions, this dissertation builds and proposes a sustainability-based assessment framework for electrical energy systems that is suitable for application with further specification to the context of different jurisdictions. Use of the framework is illustrated and tested through two case applications – to the electrical energy systems of Ontario and Costa Rica. Building the proposed framework involved a literature review and synthesis of three foundational bodies of knowledge: sustainability in complexity, electrical energy systems and sustainability, and transformations towards sustainability. Further specifying and applying the framework to the context of the two case studies involved carrying out document research and semi-structured interviews with key participants in the electrical energy systems of the two jurisdictions. The resulting sustainability-based assessment framework from this dissertation proposes six main criteria categories that are mutually reinforcing and emphasize minimizing trade-offs scenarios. These are divided into a set of criteria for specification and application to electrical energy system-related projects, plans, and initiatives in different regions. The proposed criteria categories are 1) Climate safety and social-ecological integrity; 2) Intra- and inter-generational equity, accessibility, reliability, and affordability; 3) Cost-effectiveness, resource efficiency and conservation; 4) Democratic and participatory governance; 5) Precaution, modularity and resiliency; and 6) Transformation, integration of multiple positive effects, and minimization of adverse effects. Ontario’s electrical energy system has significant sustainability-related challenges to overcome. The case study has shown that there is little provincial interest in following national net-zero commitments and authorities have removed official requirements for long-term energy planning to pursue climate goals and related sustainability objectives. Rising electricity prices have also raised concerns for many years and have been accompanied by limited willingness to engage in democratic and participatory processes for public review of electrical energy system undertakings. Additionally, recent commitments to highly expensive and risky options can further aggravate long-term socioeconomic and environmental negative impacts. In the Costa Rica case, adopting technocentric approaches to electrical energy system management led to a path dependency on large hydroelectricity development. This background of development of large hydroelectricity projects, without public consultation, has also created a sustained context of tension between governments, Indigenous groups and local communities, and private actors. Since the country is expected to experience changes in natural systems’ patterns including intensified periods of hurricane, storm, flood, and drought, the strong reliance on hydroelectricity has at the same time raised concerns regarding the reliability of the national electrical energy system. Both Ontario and Costa Rica have electrical energy systems that require rapid responses to contribute more positively to sustainability, and to help to reduce and reverse ongoing social and environmental crises. The two cases are also suitably contrasting venues for specification and application of the sustainability-based assessment framework developed in this work. The findings showed that while Ontario and Costa Rica have different contextual characteristics (e.g., geographical, socioeconomic, and political), overall lessons can be learned for best designing electrical energy systems in different jurisdictions. The findings also revealed that context-specific sustainability approaches do not necessarily undermine the viability for comparing multiple cases. In fact, specification to context can support comparisons by facilitating the identification of similarities and differences that are closely tied to contextual characteristics. Overall, the study of the two cases indicates significant potential for future works that focus on the specification to context and application of sustainability-based assessments specified to electrical energy systems that seek for barriers and opportunities for unlocking transformative effects. Three key learnings were revealed by building, specifying to context, and applying the sustainability-based assessment framework in a comparative analysis of the electrical energy systems of Ontario and Costa Rica. First, the two jurisdictions require implementation of more effective options to minimize costs in electrical energy system operations and avoid economic risks that undermine the capacity of the system to provide affordable electricity for all. Second, efforts to meet democratic and participatory governance requirements have been insufficient in Ontario and Costa Rica. Both jurisdictions need to demonstrate the capacity to respect official processes for public approval and to ensure adequate representation of different actors’ interests. Particularly, Indigenous people, local communities, and other groups with limited influence need more meaningful inclusion in official decision-making. Third, the two jurisdictions would benefit from implementing strategies to identify and assess possible combinations of policy and technical pathways that could help to unlock an existing dependency on options that support system rigidity. The core overall conclusion is that application of the proposed sustainability-based assessment framework can inform better design electrical energy systems to deliver broader sustainability-related effects and advance transformations towards sustainability. However, the framework could be further developed by including insights from more key participants in electrical energy systems. The criteria set can be honed with specification to context and application to different jurisdictions, and to more particular initiatives that reflect evolving energy scenarios. Inclusion of transformation, integration of multiple positive effects, and minimization of adverse effects as a criteria category has been helpful to recognize political contexts, promote just transitions, and emphasize the interlinked effects of applying the rest of the criteria. Since this is a new component in sustainability-based assessment frameworks, the transformation criteria category will require particular attention in future applications. Among other matters, further work in the field of electrical energy systems transformation towards sustainability should also address continuing and emerging phenomena, including adverse political trends such as right-wing populism and post-truth politics, that would maintain gaps between current practices and the steps needed for progress towards sustainability. Generally, however, while there are many needs and opportunities for more applications of the framework and additional research into the barriers to and openings for energy system transition and transformation, the sustainability-based assessment framework proposed and tested in this dissertation research should be a useful tool for directing change in complex electrical energy systems towards broader contributions to sustainability

    Designing a combined Markov-bayesian model in order to predict stock prices in the stock exchange

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    Investing in shares offered on the stock exchange is one of the most profitable options in the capital market. The stock market has a non-linear and chaotic system that is influenced by political, economic, and psychological conditions. Forecasting time series, such as stock price forecasting, is one of the most important problems in the field of economics and finance because the data is unstable and has many variables that are influenced by many factors. There are many ways to predict stock prices. Non-linear intelligent systems such as artificial neural networks, fuzzy neural networks, and genetic algorithms can be used to predict stock prices. In this research, a hybrid system based on Bayesian networks and the Markov model is proposed to predict the daily trend of the stock market. Bayesian networks are used to specify relationships between variables in forecasting. Finally, the Markov model is used to predict the market trend in the sets extracted from the Bayesian network. The evaluation criteria in the proposed system show the high efficiency of this method

    The Active CryoCubeSat Technology: Active Thermal Control for Small Satellites

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    Modern CubeSats and Small Satellites have advanced in capability to tackle science and technology missions that would usually be reserved for more traditional, large satellites. However, this rapid growth in capability is only possible through the fast-to-production, low-cost, and advanced technology approach used by modern small satellite engineers. Advanced technologies in power generation, energy storage, and high-power density electronics have naturally led to a thermal bottleneck, where CubeSats and Small Satellites can generate more power than they can easily reject. The Active CryoCubeSat (ACCS) is an advanced active thermal control technology (ATC) for Small Satellites and CubeSats, which hopes to help solve this thermal problem. The ACCS technology is based on a two-stage design. An integrated miniature cryocooler forms the first stage, and a single-phase mechanically pumped fluid loop heat exchanger the second. The ACCS leverages advanced 3D manufacturing techniques to integrate the ATC directly into the satellite structure, which helps to improve the performance while simultaneously miniaturizing and simplifying the system. The ACCS system can easily be scaled to mission requirements and can control zonal temperature, bulk thermal rejection, and dynamic heat transfer within a satellite structure. The integrated cryocooler supports cryogenic science payloads such as advanced LWIR electro-optical detectors. The ACCS hopes to enable future advanced CubeSat and Small Satellite missions in earth science, heliophysics, and deep space operations. This dissertation will detail the design, development, and testing of the ACCS system technology
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