In recent years,live streaming has become a popular application,which uses TCP as its primary transport *** UDP Internet Connections(QUIC)protocol opens up new opportunities for live ***,how to leverage QUIC to transm...
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In recent years,live streaming has become a popular application,which uses TCP as its primary transport *** UDP Internet Connections(QUIC)protocol opens up new opportunities for live ***,how to leverage QUIC to transmit live videos has not been studied *** paper first investigates the achievable quality of experience(QoE)of streaming live videos over TCP,QUIC,and their multipath extensions Multipath TCP(MPTCP)and Multipath QUIC(MPQUIC).We observe that MPQUIC achieves the best performance with bandwidth aggregation and transmission ***,network fluctuations may cause heterogeneous paths,high path loss,and band-width degradation,resulting in significant QoE *** by the above observations,we investigate the multipath packet scheduling problem in live streaming and design 4D-MAP,a multipath adaptive packet scheduling scheme over ***,a linear upper confidence bound(LinUCB)-based online learning algorithm,along with four novel scheduling mechanisms,i.e.,Dispatch,Duplicate,Discard,and Decompensate,is proposed to conquer the above problems.4D-MAP has been evaluated in both controlled emulation and real-world networks to make comparison with the state-of-the-art multipath transmission *** results reveal that 4D-MAP outperforms others in terms of improving the QoE of live streaming.
The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of *** identifies better locally optimal solutions than the original K-means *** is,it achieves solutions that yield smaller objective function v...
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The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of *** identifies better locally optimal solutions than the original K-means *** is,it achieves solutions that yield smaller objective function values than the K-means ***,CDKM is sensitive to initialization,which makes the K-means objective function values not small *** selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of *** proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value *** split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains *** algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition *** on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional *** proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.
In the development of linear quadratic regulator(LQR) algorithms, the Riccati equation approach offers two important characteristics——it is recursive and readily meets the existence condition. However, these attribu...
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In the development of linear quadratic regulator(LQR) algorithms, the Riccati equation approach offers two important characteristics——it is recursive and readily meets the existence condition. However, these attributes are applicable only to transformed singular systems, and the efficiency of the regulator may be undermined if constraints are violated in nonsingular versions. To address this gap, we introduce a direct approach to the LQR problem for linear singular systems, avoiding the need for any transformations and eliminating the need for regularity assumptions. To achieve this goal, we begin by formulating a quadratic cost function to derive the LQR algorithm through a penalized and weighted regression framework and then connect it to a constrained minimization problem using the Bellman's criterion. Then, we employ a dynamic programming strategy in a backward approach within a finite horizon to develop an LQR algorithm for the original system. To accomplish this, we address the stability and convergence analysis under the reachability and observability assumptions of a hypothetical system constructed by the pencil of augmented matrices and connected using the Hamiltonian diagonalization technique.
In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a ***,this kind ofmethod is dependent on a...
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In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a ***,this kind ofmethod is dependent on a single video input source and few visual labels,and there is a problem with semantic alignment between video contents and generated natural sentences,which are not suitable for accurately comprehending and describing the video *** address this issue,this paper proposes a video captioning method by semantic topic-guided ***,a 3D convolutional neural network is utilized to extract the spatiotemporal features of videos during the ***,the semantic topics of video data are extracted using the visual labels retrieved from similar video *** the decoding,a decoder is constructed by combining a novel Enhance-TopK sampling algorithm with a Generative Pre-trained Transformer-2 deep neural network,which decreases the influence of“deviation”in the semantic mapping process between videos and texts by jointly decoding a baseline and semantic topics of video *** this process,the designed Enhance-TopK sampling algorithm can alleviate a long-tail problem by dynamically adjusting the probability distribution of the predicted ***,the experiments are conducted on two publicly used Microsoft Research Video Description andMicrosoft Research-Video to Text *** experimental results demonstrate that the proposed method outperforms several state-of-art ***,the performance indicators Bilingual Evaluation Understudy,Metric for Evaluation of Translation with Explicit Ordering,Recall Oriented Understudy for Gisting Evaluation-longest common subsequence,and Consensus-based Image Description Evaluation of the proposed method are improved by 1.2%,0.1%,0.3%,and 2.4% on the Microsoft Research Video Description dataset,and 0.1%,1.0%,0.1%,and 2.8% on the Microsoft Research-Video to Text dataset
Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target ***,a challenging issue is how to deploy these mob...
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Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target ***,a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users,while considering the mobility of on-ground *** this paper,to solve this issue,we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage ***,we propose a hybrid deployment algorithm based on the improved quick artificial bee *** algorithm is composed of a centralized deployment algorithm and a distributed *** proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically *** results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.
Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a se...
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Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a second-order optimizer has stronger ability in approaching a better solution when dealing with the non-convex optimization problems, thus obtaining better performance in extracting the latent factors(LFs) well representing the known information from high-dimensional and incomplete data.
Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great ***,due to complex device interactions,time series exhibit diverse abnormal ...
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Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great ***,due to complex device interactions,time series exhibit diverse abnormal signal shapes,subtle anomalies,and imbalanced abnormal instances,which make anomaly detection in time series still a *** and analysis of multivariate time series can help uncover their intrinsic spatio-temporal characteristics,and contribute to the discovery of complex and subtle *** this paper,we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder(MCFMAAE)for multivariate time series anomaly *** is an encoder-decoder-based framework with four main ***-scale convolution fusion module fuses multi-sensor signals and captures various scales of temporal ***-attention-based encoder adopts the multi-head attention mechanism for sequence modeling to capture global context *** module is introduced to explore the internal structure of normal samples,capturing it into the latent space,and thus remembering the typical ***,the decoder is used to reconstruct the signals,and then a process is coming to calculate the anomaly ***,an additional discriminator is added to the model,which enhances the representation ability of autoencoder and avoids *** on public datasets demonstrate that MCFMAAE improves the performance compared to other state-of-the-art methods,which provides an effective solution for multivariate time series anomaly detection.
Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of ***,existing studies neglect positional information when learni...
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Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of ***,existing studies neglect positional information when learning discrete snapshots,resulting in insufficient network topology *** the same time,due to the lack of appropriate data augmentation methods,it is difficult to capture the evolving patterns of the network *** address the above problems,a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic ***,the global snapshot is built based on the historical snapshots to express the stable pattern of the dynamic graph,and the random walk is used to obtain the position representation by learning the positional information of the ***,a new data augmentation method is carried out from the perspectives of short-term changes and long-term stable structures of dynamic ***,subgraph sampling based on snapshots and global snapshots is used to obtain two structural augmentation views,and node structures and evolving patterns are learned by combining graph neural network,gated recurrent unit,and attention ***,the quality of node representation is improved by combining the contrastive learning between different structural augmentation views and between the two representations of structure and *** results on four real datasets show that the performance of the proposed method is better than the existing unsupervised methods,and it is more competitive than the supervised learning method under a semi-supervised setting.
The application of the electronic control unit (ECU) motivates dynamic models with high precision to simulate mechatronic systems for various analysis and design tasks like hardware-in-the-loop (HiL) simulation. Unlik...
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This paper introduces the quantum control of Lyapunov functions based on the state distance, the mean of imaginary quantities and state *** this paper, the specific control laws under the three forms are *** is analyz...
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This paper introduces the quantum control of Lyapunov functions based on the state distance, the mean of imaginary quantities and state *** this paper, the specific control laws under the three forms are *** is analyzed by the La Salle invariance principle and the numerical simulation is carried out in a 2D test *** calculation process for the Lyapunov function is based on a combination of the average of virtual mechanical quantities, the particle swarm algorithm and a simulated annealing ***, a unified form of the control laws under the three forms is given.
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