IOTA is a typical blockchain designed for IoT applications. The Markov chain monte carlo algorithm(MCMC) used in IOTA may lead to a large number of unverified blocks, which increases transaction delay to a certain ext...
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IOTA is a typical blockchain designed for IoT applications. The Markov chain monte carlo algorithm(MCMC) used in IOTA may lead to a large number of unverified blocks, which increases transaction delay to a certain extent. We propose a stable matching algorithm(SMA) based on matching theory to stimulate nodes to verify blocks, thereby reducing the number of unverified blocks and the consensus delay. The structure of our Io T blockchain uses the Directed acyc1 ic graph(DAG)to improve the transaction processing capability. The nodes in the network are abstracted as transaction issuers and transaction verifiers. A verification service scheduling system is used to assign transactions to the verifiers and achieve the optimal matching. We designed a trust evaluation mechanism which offers verifiers references and awards to check transactions. The simulation results show that SMA can significantly reduce the number of orphan blocks and improve the transaction throughput, which helps to improve the reliability of the Io T blockchain.
A decentralized negotiation method for use with resource allocation problems has been developed that uses a protocol for resource allocation and an algorithm for resource demand adjustment. The protocol and algorithm ...
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ISBN:
(纸本)9781479982615
A decentralized negotiation method for use with resource allocation problems has been developed that uses a protocol for resource allocation and an algorithm for resource demand adjustment. The protocol and algorithm are based on and extend FIPA's standardized ContractNet protocol and the Gale-Shapley algorithm, respectively. The resource allocation results should satisfy operability and fairness. A multi-agent framework is used to achieve fair (envy-free) matching. Each agent makes multiple resource accommodation (exchange) plans and selects resource accommodation partners in accordance with the agent's ordinal utility function. The feasibility of this method was demonstrated in a simple toy model simulation.
An improved inverse analysis model for fuel loading pattern (LP) optimization is proposed to reproduce a desired in-core power distribution from available fuel assemblies based on the inverse analysis of diffusion equ...
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An improved inverse analysis model for fuel loading pattern (LP) optimization is proposed to reproduce a desired in-core power distribution from available fuel assemblies based on the inverse analysis of diffusion equations. In the present approach, the inverse analysis is simplified as a fixed-source problem rather than an eigenvalue problem in a previous work. By using the one-to-one correspondence between the power distribution and reactivity distribution, a target reactivity distribution is solved from a desired power distribution through the inverse analysis. The reactivity of available assemblies is evaluated using a modified effective fast-group model with a higher order correction of leakage effect. A candidate LP is determined by individually matching the reactivity of available assemblies to the target in-core reactivity distribution using a stable matching algorithm. The process of predicting the reactivity of available assemblies and matching them to the target reactivity distribution is repeated until the convergence condition is satisfied. Validation of the model has been confirmed with a reference PWR core. The model is expected to be applicable to a problem of multicycle loading pattern optimization.
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