Effective management of electricity consumption (EC) in smart buildings (SBs) is crucial for optimizing operational efficiency, cost savings, and ensuring sustainable resource utilization. Accurate EC prediction enabl...
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We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the c...
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We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.
Accurate and reliable wind power forecasting is of great importance for stable grid operation and advanced dispatch planning. Due to the complex, non-stationary, and highly volatile nature of wind power data, Transfor...
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Human pose estimation is a critical research area in the field of computer vision,playing a significant role in applications such as human-computer interaction,behavior analysis,and action *** this paper,we propose a ...
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Human pose estimation is a critical research area in the field of computer vision,playing a significant role in applications such as human-computer interaction,behavior analysis,and action *** this paper,we propose a U-shaped keypoint detection network(DAUNet)based on an improved ResNet subsampling structure and spatial grouping *** network addresses key challenges in traditional methods,such as information loss,large network redundancy,and insufficient sensitivity to low-resolution *** is composed of three main ***,we introduce an improved BottleNeck block that employs partial convolution and strip pooling to reduce computational load and mitigate feature ***,after upsampling,the network eliminates redundant features,improving the overall ***,a lightweight spatial grouping attention mechanism is applied to enhance low-resolution semantic features within the feature map,allowing for better restoration of the original image size and higher *** results demonstrate that DAUNet achieves superior accuracy compared to most existing keypoint detection models,with a mean PCKh@0.5 score of 91.6%on the MPII dataset and an AP of 76.1%on the COCO ***,real-world experiments further validate the robustness and generalizability of DAUNet for detecting human bodies in unknown environments,highlighting its potential for broader applications.
Mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive *** task offloading of MEC has become an important research ho...
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Mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive *** task offloading of MEC has become an important research hotspot,as it solves the problems of insufficient computing capability and battery capacity of Internet of things(IoT)*** study investigates task offloading scheduling in a dynamic MEC *** integrating energy harvesting technology into IoT devices,we propose a hybrid energy supply *** jointly optimize local computing,offloading duration,and edge computing decisions to minimize system *** the basis of stochastic optimization theory,we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called *** can make task offloading decisions by weighing system cost and queue *** quote dynamic programming theory to obtain the optimal task offloading *** results verify the effectiveness of DTOME,and show that DTOME entails lower system cost than two baseline task offloading strategies.
Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency respo...
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The underlying vertical components represented by vehicleto-everything networks will largely accelerate the advance of the 6th generation wireless communications [1]. In this context, a plethora of Internet-of-Vehicle...
The underlying vertical components represented by vehicleto-everything networks will largely accelerate the advance of the 6th generation wireless communications [1]. In this context, a plethora of Internet-of-Vehicles(IoV) applications have increasingly permeated our daily lives with the development of advocated intelligent connected vehicles [2].
Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning a...
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Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using multi-view camera images. Specifically, a visual BEV(bird-eye-view) encoder extracts and flattens the multi-view images into BEV space. While the semantic map features are structurally embedded as map query sequences. Then a cross-model transformer associates the BEV features and semantic map queries. The localization information of ego-car is recursively queried out by cross-attention modules. Finally, the ego pose can be inferred by decoding the transformer outputs. This end-to-end model speaks to its broad applicability across different driving environments, including high-speed scenarios. We evaluate the proposed method in large-scale nuScenes and Qcraft datasets. The experimental results show that the BEV-Locator is capable of estimating the vehicle poses under versatile scenarios, which effectively associates the cross-model information from multi-view images and global semantic maps. The experiments report satisfactory accuracy with mean absolute errors of 0.052 m, 0.135 m and 0.251° in lateral, longitudinal translation and heading angle degree.
Graph neural networks have proven their effectiveness for user-item interaction graph collaborative filtering. However, most of the existing recommendation models highly depended on abundant and high-quality datasets ...
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With advancements in technology, the study of data hiding (DH) in images has become more and more important. In this paper, we introduce a novel data hiding scheme that employs a voting strategy to predict pixels base...
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