Software-defined vehicles (SDVs) are an emerging paradigm in the automotive industry where vehicles' functionality, performance, and safety can be enhanced and updated through software, even after production. Unli...
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The importance of predictive modelling for green selection-making in a variety of fields has expanded because of the fast development of information-driven technologies. The basic and primary goal of this research is ...
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In today's digital landscape, the demand for highly realistic 3D representations of human faces has surged, driven by the rapid expansion of virtual reality, augmented reality, and face recognition technologies. D...
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The rapidly developing e-commerce environment offers consumers a multitude of products and services, making it difficult to find products that suit their interests and budgets. This research work presents a solution c...
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In response to the increasing penetration of volatile and uncertain renewable energy,the regional transmission organizations(RTOs)have been recently focusing on enhancing the models of pump storage hydropower(PSH)plan...
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In response to the increasing penetration of volatile and uncertain renewable energy,the regional transmission organizations(RTOs)have been recently focusing on enhancing the models of pump storage hydropower(PSH)plants,which are one of the key flexibility assets in the day-ahead(DA)and real-time(RT)markets,to further boost their flexibility provision *** by the recent research works that explored the potential benefits of excluding PSHs’cost-related terms from the objective functions of the DA market clearing model,this paper completes a rolling RT market scheme that is compatible with the DA ***,with the vision that PSHs could be permitted to submit state-of-charge(SOC)headrooms in the DA market and to release them in the RT market,this paper uncovers that PSHs could increase the total revenues from the two markets by optimizing their SOC headrooms,assisted by the proposed tri-level optimal SOC headroom ***,in the proposed tri-level model,the middle and lower levels respectively mimic the DA and RT scheduling processes of PSHs,and the upper level determines the optimal headrooms to be submitted to the RTO for maximizing the total revenue from the two *** case studies quantify the profitability of the optimal SOC headroom submissions as well as the associated financial risks.
This research endeavors to address the critical issue of Road Accident Detection, presenting novel solutions to the identified challenges. The paper introduces an advanced framework specifically tailored for the effic...
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The automotive industry is increasing exponentially day by day providing a safer and efficient experience to the consumers. Intelligent Transportation Systems (ITS) has become more crucial with the increasing number o...
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The expansion of decentralized cryptocurrencies poses notable complexities for law enforcement in terms of detecting unlawful behaviors, as well as in the identification of individuals and the retrieval of transaction...
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Waste management has become critical in the twenty-first century and demands immediate attention, particularly with regard to food waste management. With rising waste in landfills and billions of dollars in government...
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The primary objective of fog computing is to minimize the reliance of IoT devices on the cloud by leveraging the resources of fog network. Typically, IoT devices offload computation tasks to fog to meet different task...
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The primary objective of fog computing is to minimize the reliance of IoT devices on the cloud by leveraging the resources of fog network. Typically, IoT devices offload computation tasks to fog to meet different task requirements such as latency in task execution, computation costs, etc. So, selecting such a fog node that meets task requirements is a crucial challenge. To choose an optimal fog node, access to each node's resource availability information is essential. Existing approaches often assume state availability or depend on a subset of state information to design mechanisms tailored to different task requirements. In this paper, OptiFog: a cluster-based fog computing architecture for acquiring the state information followed by optimal fog node selection and task offloading mechanism is proposed. Additionally, a continuous time Markov chain based stochastic model for predicting the resource availability on fog nodes is proposed. This model prevents the need to frequently synchronize the resource availability status of fog nodes, and allows to maintain an updated state information. Extensive simulation results show that OptiFog lowers task execution latency considerably, and schedules almost all the tasks at the fog layer compared to the existing state-of-the-art. IEEE
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