Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat...
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Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of *** experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.
Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research *** fromnatural images,character images pay more attention to stroke ***,existingmodelsmainly cons...
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Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research *** fromnatural images,character images pay more attention to stroke ***,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character *** solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,*** existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription ***,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising *** proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure *** to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image *** experimental results show the superiority of our method both in the synthetic and real-inscription datasets.
With the great development of Multi-Target Tracking(MTT)technologies,many MTT algorithms have been proposed with their own advantages and *** to the fact that requirements to MTT algorithms vary from the application s...
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With the great development of Multi-Target Tracking(MTT)technologies,many MTT algorithms have been proposed with their own advantages and *** to the fact that requirements to MTT algorithms vary from the application scenarios,performance evaluation is significant to select an appropriate MTT algorithm for the specific application *** this paper,we propose a performance evaluation method on the sets of trajectories with temporal dimension specifics to compare the estimated trajectories with the true *** proposed method evaluates the estimate results of an MTT algorithm in terms of tracking accuracy,continuity and ***,its computation is based on a multi-dimensional assignment problem,which is formulated as a computable form using linear *** enhance the influence of recent estimated states of the trajectories in the evaluation,an attention function is used to reweight the trajectory errors at different time ***,simulation results show that the proposed performance evaluation method is able to evaluate many aspects of the MTT *** evaluations are worthy for selecting suitable MTT algorithms in different application scenarios.
This study aimed to investigate the effects of firefighters’protective gloves on physiological responses,psychological responses,and manual performance in a cold environment through human *** participants wearing fir...
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This study aimed to investigate the effects of firefighters’protective gloves on physiological responses,psychological responses,and manual performance in a cold environment through human *** participants wearing firefighter protective equipment were exposed to a 16℃ environment,while their hands were exposed to a small chamber of 0℃ with(FPG)and without(CON)firefighting protective *** the trials,physiological responses(core temperature(Tc),the mean skin temperature(Tsk),and heart rate(HR)),psychological responses(thermal sensation vote(TSV)and pain sensation vote(PSV)),and manual performance(handgrip strength,manual dexterity,maximum finger flexion,and tactile sensitivity)were *** results indicated a significant difference(p<0.05)between FPG and CON regarding ***,pain sensation occurred when the mean skin temperature of the hand was between 15℃ and 20℃.Gloves significantly(p<0.05)reduced handgrip strength,manual dexterity,and tactile sensitivity in the cold *** study provides fundamental knowledge for cold strain assessment and high-performance protective glove development with the potential to improve firefighters’safety and health.
In order to expand the advantages of strong durability and high compressive strength of calcium silicate hydrates(C-S-H),at the same time to make up for the poor early mechanical strength of magnesium silicate hydrate...
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In order to expand the advantages of strong durability and high compressive strength of calcium silicate hydrates(C-S-H),at the same time to make up for the poor early mechanical strength of magnesium silicate hydrates (M-S-H),we present the features and advantages of C-S-H and M-S-H and a comprehensive review of the progress on CaO-MgO-SiO_(2)-H_(2)***,we systematically describe natural calcium and magnesium silicate minerals and thermodynamic properties of CaO-MgO-SiO_(2)-H_(2)*** effect of magnesium on C-S-H and calcium on M-S-H is summarized deeply;the formation and structural feature of CaO-MgO-SiO_(2)-H_(2)O is also explained in ***,the development of calcium and magnesium silicate hydrates in the future is pointed out,and the further research is discussed and estimated.
Most Personalized Federated Learning (PFL) algorithms merge the model parameters of each client with other (similar or generic) model parameters to optimize the personalized model (PM). However, the merged model param...
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The use of generative adversarial network(GAN)-based models for the conditional generation of image semantic segmentation has shown promising results in recent ***,there are still some limitations,including limited di...
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The use of generative adversarial network(GAN)-based models for the conditional generation of image semantic segmentation has shown promising results in recent ***,there are still some limitations,including limited diversity of image style,distortion of detailed texture,unbalanced color tone,and lengthy training *** address these issues,we propose an asymmetric pre-training and fine-tuning(APF)-GAN model.
The data asset is emerging as a crucial component in both industrial and commercial *** valuable knowledge from the data benefits decision-making and ***,the usage of data assets raises tension between sensitive infor...
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The data asset is emerging as a crucial component in both industrial and commercial *** valuable knowledge from the data benefits decision-making and ***,the usage of data assets raises tension between sensitive information protection and value *** an emerging machine learning paradigm,Federated Learning(FL)allows multiple clients to jointly train a global model based on their data without revealing *** approach harnesses the power of multiple data assets while ensuring their *** the benefits,it relies on a central server to manage the training process and lacks quantification of the quality of data assets,which raises privacy and fairness *** this work,we present a novel framework that combines Federated Learning and Blockchain by Shapley value(FLBS)to achieve a good trade-off between privacy and ***,we introduce blockchain in each training round to elect aggregation and evaluation nodes for training,enabling decentralization and contribution-aware incentive distribution,with these nodes functionally separated and able to supervise each *** experimental results validate the effectiveness of FLBS in estimating contribution even in the presence of heterogeneity and noisy data.
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.
Energy conservation and low-carbon steelmaking processes require refractories with excellent service performances and low thermal conductivities. In this study, a type of multiphase materials of spinel-calcium alumina...
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