The power sector is an important factor in ensuring the development of the national *** simulation and prediction of power consumption help achieve the balance between power generation and power *** this paper,a Multi...
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The power sector is an important factor in ensuring the development of the national *** simulation and prediction of power consumption help achieve the balance between power generation and power *** this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity ***,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning *** improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the ***,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test ***,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting *** experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.
Telemedicine is of great importance as it increases the availability of health care for people living in remote or undeveloped areas. It reduces the cost of health care, allows for early diagnosis and treatment of chr...
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Black-box optimization (BBO) can be used to optimize functions whose analytic form is unknown. A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which ca...
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The Cranfield paradigm has served as a foundational approach for developing test collections, with relevance judgments typically conducted by human assessors. However, the emergence of large language models (LLMs) has...
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Fast technical advancements have been implemented in multiple domains of life, including agriculture. Technology can help the agriculture industry cut down on the energy and time lost on using traditional methods. The...
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The well-known replicator equation in evolutionary game theory describes how population-level behaviors change over time when individuals make decisions using simple imitation learning rules. In this paper, we study e...
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A novel technique,named auxiliary equation method,is applied in this research work for obtaining new traveling wave solutions for two interesting proposed systems:the Kaup-Boussinesq system and generalized Hirota-Sats...
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A novel technique,named auxiliary equation method,is applied in this research work for obtaining new traveling wave solutions for two interesting proposed systems:the Kaup-Boussinesq system and generalized Hirota-Satsuma coupled KdV system with beta time fractional *** solutions were obtained using MAPLE *** technique shows a great potential to be applied in solving various nonlinear fractional differential equations arising from mathematical physics and ocean *** a standard equation has not been used as an auxiliary equation for this technique,different and novel solutions are obtained via this technique.
Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,com...
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Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain *** research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP *** this paper,we propose a blockchain-based trust model for inter-domain *** model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the *** BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective *** incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing *** forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing *** use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust *** with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.
SMEs play a vital role in driving economic growth, creating jobs, and fostering innovation. However, unlike larger businesses, SMEs often struggle to adopt Industry 4.0 technologies due to limited resources. Addressin...
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Diffusion Probabilistic Models (DPMs) show significant potential in image generation, yet their performance hinges on having access to large datasets. Previous works, like Generative Adversarial Networks (GANs), have ...
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Diffusion Probabilistic Models (DPMs) show significant potential in image generation, yet their performance hinges on having access to large datasets. Previous works, like Generative Adversarial Networks (GANs), have tackled the limited data problem by transferring pretrained models learned with sufficient data. However, those methods are hard to utilize in DPMs because of the distinct differences between DPM-based and GAN-based methods, which show the integral of the unique iterative denoising process and the need for many time steps with no target noise in DPMs. In this paper, we propose a novel DPM-based transfer learning method, called DPMs-ANT, to address the limited data problem. It includes two strategies: similarity-guided training, which boosts transfer with a classifier, and adversarial noise selection, which adaptively chooses targeted noise based on the input image. Extensive experiments in the context of few-shot image generation tasks demonstrate that our method is efficient and excels in terms of image quality and diversity compared to existing GAN-based and DPM-based methods. Copyright 2024 by the author(s)
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