In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a sma...
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In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity *** consists of gas turbines and renewable energy sources such as photovoltaic systems and wind *** bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage *** solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy *** uncertainty related to solar and wind units is modeled using lognormal and Weibull probability ***-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.
The tremendous advancement in distributed computing and Internet of Things(IoT)applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud ***,suitable and effectiv...
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The tremendous advancement in distributed computing and Internet of Things(IoT)applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud ***,suitable and effective applications could be performed to satisfy the applications’latency *** allocation techniques are essential aspects of fog networks which prevent unbalanced load *** resource management techniques can improve the quality of service *** to the limited and heterogeneous resources available within the fog infrastructure,the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT *** has been limited research on resource management strategies in fog networks in recent years,and a limited systematic review has been done to compile these *** article focuses on current developments in resource allocation strategies for fog-IoT networks.A systematic review of resource allocation techniques with the key objective of enhancing QoS is *** involved in conducting this systematic literature review include developing research goals,accessing studies,categorizing and critically analysing the *** resource management approaches engaged in this article are load balancing and task offloading *** the load balancing approach,a brief survey of recent work done according to their sub-categories,including stochastic,probabilistic/statistic,graph theory and hybrid techniques is provided whereas for task offloading,the survey is performed according to the destination of task *** load balancing and task-offloading approaches contribute significantly to resource management,and tremendous effort has been put into this critical ***,this survey presents an overview of these extents and a comparative ***,the study discusses ongoing research issues and potential future directio
Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal *** current deepfake generator...
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Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal *** current deepfake generators strive for high realism in visual effects,they do not replicate biometric signals indicative of cardiac *** this gap,many researchers have developed detection methods focusing on biometric *** methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography(rPPG)signal,resulting in high detection ***,in the spectral analysis,existing approaches often only consider the power spectral density and neglect the amplitude spectrum—both crucial for assessing cardiac *** introduce a novel method that extracts rPPG signals from multiple regions of interest through remote photoplethysmography and processes them using Fast Fourier Transform(FFT).The resultant time-frequency domain signal samples are organized into matrices to create Matrix Visualization Heatmaps(MVHM),which are then utilized to train an image classification ***,we explored various combinations of time-frequency domain representations of rPPG signals and the impact of attention *** experimental results show that our algorithm achieves a remarkable detection accuracy of 99.22%in identifying fake videos,significantly outperforming mainstream algorithms and demonstrating the effectiveness of Fourier Transform and attention mechanisms in detecting fake faces.
Unsupervised domain adaptation (UDA) is a popular technique to reduce the manual annotation cost in semantic segmentation. However, due to the absence of strong supervision in the target domain, UDA is prone to biasin...
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Unsupervised domain adaptation (UDA) is a popular technique to reduce the manual annotation cost in semantic segmentation. However, due to the absence of strong supervision in the target domain, UDA is prone to biasing the decision boundary towards the source domain. To alleviate this issue, this paper proposes a more effective semi-supervised domain adaptation (SSDA) method for semantic segmentation via active learning with feature- and semantic-level alignments. Specifically, active learning is utilized to select those samples with high diversity and uncertainty from the target domain for labeling. These selected data could provide reliable clues for domain transfer since they reveal the intrinsic distribution of the target domain as well as including hard samples at boundaries. Moreover, to better adapt the segmentation model from the source data to the labeled target data selected above, we propose a scheme based on both feature- and semantic-level domain alignments. The feature-level domain alignment imposes the distribution consistency between the Transformer features of the two domains by adversarial learning, which is a global alignment. In contrast, the semantic-level domain alignment optimizes the affinity and divergence of the semantic representations across domains via contrastive learning, which is a local alignment. These two alignments jointly bridge the domain gap from both the global and the local views, respectively. In addition, the pseudo labels of the unlabeled data are generated to expand the labeled data and further strengthen the cross-domain segmentation in a self-training manner. Extensive experiments on segmentation benchmarks demonstrate the effectiveness of our proposed method. IEEE
This paper addresses an optimal, cooperative output regulation problem for multi-agent systems with distributed denial of service attacks and unknown system dynamics. Unlike existing studies, the proposed solution is ...
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This paper addresses an optimal, cooperative output regulation problem for multi-agent systems with distributed denial of service attacks and unknown system dynamics. Unlike existing studies, the proposed solution is essentially a learning-based control strategy such that one can obtain a distributed control policy with internal models through online data and analyze the resilience of closed-loop systems, both without the precise knowledge of system dynamics in the state-space model. The efficiency of the proposed methodology is validated using computer simulations.
Wind power plants(WPPs)are increasingly mandated to provide temporary frequency support to power systems during contingencies involving significant power ***,the frequency support capabilities of WPPs under derated op...
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Wind power plants(WPPs)are increasingly mandated to provide temporary frequency support to power systems during contingencies involving significant power ***,the frequency support capabilities of WPPs under derated operations remain insufficiently investigated,highlighting the potential for further improvement of the frequency *** paper proposes a bi-level optimized temporary frequency support(OTFS)strategy for a *** implementation of the OTFS strategy is collaboratively accomplished by individual wind turbine(WT)controllers and the central WPP ***,to exploit the frequency support capability of WTs,the stable operational region of WTs is expanded by developing a novel dynamic power control approach in WT *** approach synergizes the WTs'temporary frequency support with the secondary frequency control of synchronous generators,enabling WTs to release more kinetic energy without causing a secondary frequency ***,a model predictive control strategy is developed for the WPP *** strategy ensures that multiple WTs operating within the expanded stable region are coordinated to minimize the magnitude of the frequency drop through efficient kinetic energy ***,comprehensive case studies are conducted on a real-time simulation platform to validate the effectiveness of the proposed strategy.
Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacit...
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Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.
Mobility support to change the connection from one access point(AP)to the next(i.e.,handover)becomes one of the important issues in IEEE 802.11 wireless local area networks(WLANs).During handover,the channel scanning ...
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Mobility support to change the connection from one access point(AP)to the next(i.e.,handover)becomes one of the important issues in IEEE 802.11 wireless local area networks(WLANs).During handover,the channel scanning procedure,which aims to collect neighbor AP(NAP)information on all available channels,accounts for most of the delay *** reduce the channel scanning procedure,a neighbor beacon frame transmission scheme(N-BTS)was proposed for a seamless handover.N-BTS can provide a seamless handover by removing the channel scanning ***,N-BTS always requires operating overhead even if there are few mobile stations(MSs)for the ***,this paper proposes a reinforcement learning-based handover scheme with neighbor beacon frame transmission(MAN-BTS)to properly consider the use of *** optimization equation is defined to maximize the expected reward tofind the optimal policy and is solved using *** results show that the proposed scheme outperforms the comparison schemes in terms of the expected reward.
To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are sti...
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To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible ***,we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring *** successfully demonstrate programming and erasing the memory using both electrical and optical methods,assisted by optical-to-electrical-to-optical *** memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4×10^(4) ***,the multi-level storage capability is analyzed in detail,revealing stable performance with a raw bit-error-rate smaller than 5.9×10^(−2).This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems,targeting a wide range of applications such as photonic interconnect,high-speed data communication,and neuromorphic computing.
With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid ***,most studies have focused on measurement noise,while they seldom think about the...
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With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid ***,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid *** solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency ***,a threephase unbalanced network’s positive sequence fast estimation model is ***,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss ***,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).
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