The block classical Gram–Schmidt (BCGS) algorithm and its reorthogonalized variant are widely-used methods for computing the economic QR factorization of block columns X due to their lower communication cost compared...
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This paper presents a first-order distributed algorithm for solving a convex semi-infinite program (SIP) over a time-varying network. In this setting, the objective function associated with the optimization problem is...
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Zhu and Melnykov (2018) develop a model to fit mixture models when the components are derived from the Manly transformation. Their EM algorithm utilizes Nelder-Mead optimization in the M-step to update the skew parame...
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The electrification of the transportation sector, via the integration of battery electric-powered vehicles (BEV), is one of the solutions, which could help in reducing greenhouse gas (GHG) emission. Smart charging tec...
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ISBN:
(数字)9798331519131
ISBN:
(纸本)9798331519148
The electrification of the transportation sector, via the integration of battery electric-powered vehicles (BEV), is one of the solutions, which could help in reducing greenhouse gas (GHG) emission. Smart charging techniques with bidirectional flow, in which the electric power can flow back and forth (i.e., V2G and G2V) from/into the grid according the peak hours have been recently proposed for improving power quality and regulating frequency/voltage of the utility grid. In this paper, we aim to develop a blockchain-based IoT platform for peer-to-peer energy trading between BEV and smart buildings management systems in the context of home healthcare by using Hyperledger Besu; a private network based on Ethereum with the use of its consensus algorithm Clique. The developed platform's prototype made a success transactions of energy trading which makes renewable energy available to everyone. Moreover, the concrete implementation of consensus, transaction record layout, and self-implemented smart contracts make Hyperledger Besu a green framework for implementing the proposed blockchain-IoT system.
Efficient algorithms for solving the Smallest Enclosing Sphere (SES) problem, such as Welzl's algorithm, often fail to handle degenerate subsets of points in 3D space. De- generacies and ill-posed configurations p...
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In this paper, we provide algorithmic methods for conducting exhaustive searches for periodic Golay pairs. Our methods enumerate several lengths beyond the currently known state-of-the-art available searches: we condu...
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Based on the multi-agent consensus algorithm, a distributed economic dispatch strategy for multi-microgrid considering environmental costs is proposed. Different from the traditional centralized optimization method, t...
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ISBN:
(数字)9798331504755
ISBN:
(纸本)9798331504762
Based on the multi-agent consensus algorithm, a distributed economic dispatch strategy for multi-microgrid considering environmental costs is proposed. Different from the traditional centralized optimization method, this strategy takes the lowest operating cost of the generator set as the objective function and combines power balance of the multi-microgrid and output constraint of power generation unit. It can cope with technical requirements of multi-microgrid system including topological changes and “plug and play”, and is closer to the actual power generation cost as considering the environmental cost. Through distributed optimization, the communication burden can be reduced and economic dispatch of multi-microgrid achieved. The effectiveness and flexibility of this strategy are proved by simulation and analysis.
We propose a O(log k log n)-competitive randomized algorithm for online node-weighted Steiner forest. This is essentially optimal and significantly improves over the previous bound of O(log2 k log n) by Hajiaghayi et ...
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Evaluating the candidate answers during online examination by a human examiner is wearisome task. Along with that, security concerns also associated in the E-Learning environment in terms of false data injection and m...
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ISBN:
(数字)9798350369083
ISBN:
(纸本)9798350369090
Evaluating the candidate answers during online examination by a human examiner is wearisome task. Along with that, security concerns also associated in the E-Learning environment in terms of false data injection and modification attacks respectively. The state of the art work, either focus on designing intelligent and robust question answering model using Machine Learning (ML)/Deep Learning (DL) algorithm, or designing a secure framework for E-Learning environment using blockchain. To neutralize that issue, we design a secure and intelligent model for E-Learning environment using Deep Reinforcement Learning (DRL) and blockchain methodology respectively named EvauleBlock. The EvauleBlock composed of four major processes such as data assortment, data pre-processing, intelligent answer evaluation, and blockchain storage. In data assortment stage, the training of the proposed DRL algorithm named Multi Agent Deep Deterministic Policy Gradient (MADDPG) is taken place by crawling the related question answering data from the various websites. Followed by, we have performed pre-processing for the candidate written answers using adequate pre-processing steps. Once the answers are pre-processed, it is then provided to the MADDPG algorithm which employs four agents for validating the candidate answers by checking the semantic, syntactic, keyword, and context similarity scoring respectively. At last, the overall scores are securely stored in the blockchain using Proof of Work (PoW) consensus algorithm. The implementation of this work is carried out using python 3.11.0 tool and performance of the proposed model are validated and compared with adequate performance metrics. The results are promising when compared to the conventional models.
In this paper, we introduce an inexact approach to the Boosted Difference of Convex Functions algorithm (BDCA) for solving nonconvex and nondifferentiable problems involving the difference of two convex functions (DC ...
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