This paper introduces a novel local fine-grained visual tracking task, aiming to precisely locate arbitrary local parts of objects. This task is motivated by our observation that in many realistic scenarios, the user ...
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With the advancement of electronic information technology and the growth of the intelligent industry,the industrial sector has undergone a shift from simplex,linear,and vertical chains to complex,multi-level,and multi...
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With the advancement of electronic information technology and the growth of the intelligent industry,the industrial sector has undergone a shift from simplex,linear,and vertical chains to complex,multi-level,and multi-dimensional networked industrial *** order to enhance energy efficiency in multiplex networked industrial chains under time-of-use price,a coarse time granularity task scheduling approach has been *** approach adjusts the distribution of electricity supply based on task deadlines,dividing it into longer periods to facilitate batch access to task ***,traditional simplex-network task assignment optimization methods are unable to achieve a globally optimal solution for cross-layer links in multiplex networked industrial *** solutions struggle to balance execution costs and completion efficiency in time-of-use price ***,this paper presents a mixed-integer linear programming model for solving the problem scenario and two algorithms:an exact algorithm based on the branch-and-bound method and a multi-objective heuristic algorithm based on cross-layer policy *** algorithms are designed to adapt to small-scale and large-scale problem scenarios under coarse time *** extensive simulation experiments and theoretical analysis,the proposed methods effectively optimize the energy and time costs associated with the task execution.
As a promising edge computing paradigm, task offloading involves transferring data from resource-limited devices to high-performance servers to expedite processing. However, devices in isolated networks without direct...
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As a promising edge computing paradigm, task offloading involves transferring data from resource-limited devices to high-performance servers to expedite processing. However, devices in isolated networks without direct Internet connections face challenges in task offloading. To address this issue, we propose a novel Low-cost Unmanned Aerial Vehicle (UAV) Task Offloading Scheme based on Trustable and Trackable Data Routing (LTOTT) for deadline-aware tasks in non-connected networks. The main contributions of LTOTT are as follows: (1) A novel dissemination method that devices route different numbers of Copied Tasks (CTs) and Task Computing Notices (TCNs) in different directions based on task deadlines is proposed to enable the UAV to get tasks earlier and complete them in time. (2) In order to reduce the risk of malicious attacks during the spreading of CTs and TCNs, a trust evaluation based on a trackable data routing method is proposed to ensure secure transmission. (3) In addition, based on the evaluated trust and the received information, a dynamic UAV flight trajectory optimization is proposed to enable tasks completed before their deadlines. A large number of experimental results show that LTOTT increases the task completion rate by 41.41% - 134.15%;reduces average delay and UAV's flight distance respectively by 26.88% - 51.52%, 16.37% -73.40% compared with the existing schemes. IEEE
While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the ...
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While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.
Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and ...
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Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w
Effective resource allocation can exploit the advantage of intelligent reflective surface(IRS)assisted mobile edge computing(MEC)***,it is challenging to balance the limited energy of MTs and the strict delay requirem...
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Effective resource allocation can exploit the advantage of intelligent reflective surface(IRS)assisted mobile edge computing(MEC)***,it is challenging to balance the limited energy of MTs and the strict delay requirement of their *** this paper,in order to tackle the challenge,we jointly optimize the offloading delay and energy consumption of mobile terminals(MTs)to realize the delay-energy tradeoff in an IRS-assisted MEC network,in which non-orthogonal multiple access(NOMA)and multiantenna are applied to improve spectral *** achieve the optimal delay-energy tradeoff,an offloading cost minimization model is proposed,in which the edge computing resource allocation,signal detecting vector,uplink transmission power,and IRS phase shift coefficient are needed to be jointly *** optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension *** solve the intractable problem,we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable *** computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource ***,the wireless transmission subproblem is solved iteratively through decoupling the residual *** each iteration,the closed-form solution of residual variables is obtained through different successive convex approximation(SCA)*** verify the proposed algorithm can converge to an optimum with polynomial *** results indicate the proposed method achieves average saved costs of 65.64%,11.24%,and 9.49%over three benchmark methods respectively.
This paper aimed to propose two algorithms,DA-M and RF-M,of reducing the impact of multipath interference(MPI)on intensity modulation direct detection(IM-DD)systems,particularly for four-level pulse amplitude modulati...
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This paper aimed to propose two algorithms,DA-M and RF-M,of reducing the impact of multipath interference(MPI)on intensity modulation direct detection(IM-DD)systems,particularly for four-level pulse amplitude modulation(PAM4)***-M reduced the fluctuation by averaging the signal in blocks,RF-M estimated MPI by subtracting the decision value of the corresponding block from the mean value of a signal block,and then generated interference-reduced samples by subtracting the interference signal from the product of the corresponding MPI estimate and then weighting *** paper firstly proposed to separate the signal before decision-making into multiple blocks,which significantly reduced the complexity of DA-M and *** results showed that the MPI noise of 28 GBaud IMDD system under the linewidths of 1e5 Hz,1e6 Hz and 10e6 Hz can be effectively alleviated.
This paper presents a method for the optimized reconfiguration of radial distribution systems that explicitly considers the protection systems constraints. A fully automated method based on graph analysis is proposed ...
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Background: Fetal health monitoring throughout pregnancy is challenging and complex. Complications in the fetal health not identified at the right time lead to mortality of the fetus as well the pregnant women. Hence,...
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Background: Fetal health monitoring throughout pregnancy is challenging and complex. Complications in the fetal health not identified at the right time lead to mortality of the fetus as well the pregnant women. Hence, obstetricians check the fetal health state by monitoring the fetal heart rate (FHR). Cardiotocography (CTG) is a technique used by obstetricians to access the physical well-being of fetal during pregnancy. It provides information on the fetal heart rate and uterine respiration, which can assist in determining whether the fetus is normal or suspect or pathology. CTG data has typically been evaluated using machine learning (ML) algorithms in predicting the wellness of the fetal and speeding up the detection process. Methods: In this work, we developed LightGBM with a Grid search-based hyperparameter tuning model to predict fetal health classification. The classification results are analysed quantitatively using the performance measures, namely, precision, Recall, F1-Score, and Accuracy Comparisons were made between different classification models like Logistic Regression, Decision Tree, Random Forest, k-nearest neighbors, Bagging, ADA boosting, XG boosting, and LightGBM, which were trained with the CTG Dataset obtained by the patented fetal monitoring system of 2,216 data points from pregnantwomen in their third trimester available in the Kaggle dataset. The dataset contains three classes: normal, suspect, and pathology. Our proposed model will give better results in predicting fetal health classification. Results: In this paper, the performance of the proposed algorithm LightGBM is compared and experimented with various Machine learning Techniques namely LR, DT, RF, KNN, Boosting, Ada boosting, and XG Boost and the classification accuracy of the respective algorithms are 84%, 94%, 93%, 88%, 94%, 89%, 96%. The LightGBM achieved a performance of 97% and outperforms the former models. Conclusion: The LightGBM-based fetal health classification has been pres
Hypernym detection and discovery are fundamental tasks in natural language *** former task aims to identify all possible hypernyms of a given hyponym term,whereas the latter attempts to determine whether the given two...
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Hypernym detection and discovery are fundamental tasks in natural language *** former task aims to identify all possible hypernyms of a given hyponym term,whereas the latter attempts to determine whether the given two terms hold a hypernymy relation or *** research on hypernym detection and discovery tasks projects a term into various semantic spaces with single mapping *** their success,these methods may not be adequate in capturing complex semantic relevance between hyponym/hypernymy pairs in two ***,they may fall short in modeling the hierarchical structure in the hypernymy relations,which may help them learn better term ***,the polysemy phenomenon that hypernyms may express distinct senses is *** this paper,we propose a Multi-Projection Recurrent model(MPR)to simultaneously capture the hierarchical relationships between terms and deal with diverse senses caused by the polysemy ***,we build a multi-projection mapping block to deal with the polysemy phenomenon,which learns various word senses by multiple ***,we adopt a hierarchy-aware recurrent block with the recurrent operation followed by a multi-hop aggregation module to capture the hierarchical structure of hypernym *** on 11 benchmark datasets in various task settings illustrate that our multi-projection recurrent model outperforms the *** experimental analysis and case study demonstrate that our multi-projection module and the recurrent structure are effective for hypernym detection and discovery tasks.
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