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检索条件"机构=Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing"

共 167 条记录，以下是61-70 订阅

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A Load Balancing Strategy Of "Container Virtual Machine" Cloud Microservice Based On Deadline Limit 14

A Load Balancing Strategy Of "Container Virtual Machine" Clo...

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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Xia, Hong Liu, MengDi Chen, YanPing Jin, XiaoMin Wang, ZhongMin Wang, FengWei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-tech Road South Shenzhen518057 China

ISBN: (纸本)9781665499781

Aiming at the problem of workflow scheduling under the "container virtual machine" two-tier structure in cloud microservice workflow application, a "container virtual machine" load balancing strategy based on deadline limit (container virtual machine load) is proposed. Balance strategy), CV LBMs strategy first obtains the optimal economic configuration of the corresponding instance of the micro service according to historical data, and calculated the list of remaining resources in the leased virtual machine;Determined whether the virtual machine is on the virtual machine according to the container image list in the virtual machine. Whether a corresponding container image exists, and create a new container on the virtual machine that already has a container image to reduce the time to extract the container image. The workflow simulation results showed that compared with the existing algorithms, the C-V LBMs strategy had the ability to meet the deadline success rate and reduced the cost. © 2022 IEEE

关键词： Containers

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An Object Detection Algorithm with Multi-scale Context Information Based on YOLOv4 4

An Object Detection Algorithm with Multi-scale Context Infor...

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4th International Conference on Natural Language processing, ICNLP 2022

作者： Ma, Sugang An, Wen Yang, Xiaobao Hou, Zhiqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Xi'an University of Posts and Telecommunications Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

Most current object detection algorithms have the issue of missing objects due to occlusion. As the great difference of scale between occlusion objects and their integrity is affected, how to reduce the missing rate of occlusion objects has become a challenging problem in object detection algorithms. Aiming at the problem, this paper proposes an object detection algorithm based on multi-scale context information on the basis of the YOLOv4 algorithm framework. Firstly, the object features are extracted through the CSPDarkNet53 feature extraction network. Then, the proposed attention-guided multi-scale context information module (AMCM) enhances the ability to perceive object context information and endows features with different influence factors through attention weighting to improve feature discrimination. The experimental results show that the detection accuracy and recall of the proposed algorithm reach 83.2% and 96.4% respectively on PASCAL VOC dataset, and 83.2% and 91% on KITTI dataset, which improves the detection performance of YOLOv4 algorithm. © 2022 IEEE.

关键词： Object detection

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A Hybrid Prediction Method Based on MODWT-EMD for Time Series in Wireless Sensor networks 16

A Hybrid Prediction Method Based on MODWT-EMD for Time Serie...

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16th IEEE International Conference on intelligent Systems and Knowledge Engineering, ISKE 2021

作者： Wang, Zhongmin Jia, Jingwen Gao, Cong Chen, Yanping Xia, Hong Xi'an University of Posts and Telecommunications Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an China Xi'an University of Posts and Telecommunications School of Computer Science Technology Xi'an China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China

ISBN: (纸本)9781665405539

Time series is the main form of sensor data. Time series prediction is conducive to reduce the energy consumption of sensor nodes and increase the service life. As time cost and computation cost of the existing hybrid prediction methods for time series are not good, this paper proposes a hybrid prediction method based on MODWT-EMD for time series. Firstly, the original time series is decomposed into multiple frequency components by Maximal Overlapping Discrete Wavelet Transform (MODWT). Secondly, the multiple frequency components are decomposed successively by Empirical Mode Decomposition (EMD) to obtain IMF components and residuals. IMFs and residuals contain the feature information of time series. Then, the importance of the extracted feature information is scored and sorted by random forest. We select feature information with a higher score. Finally, the selected feature information is input into Bi-GRU for training. We predicted the original time series by the trained Bi-GRU and obtained the prediction results. The experimental results show that the method proposed in this paper can predict the time series effectively. © 2021 IEEE.

关键词： Empirical mode decomposition

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Distance variety preserving hashing for large-scale retrieval

Distance variety preserving hashing for large-scale retrieva...

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作者： Zhai, Sheping School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an710121 China

Hashing techniques have been seen huge adoption in large-scale retrieval owing to the computational and storage efficiencies of binary codes. However, the binary codes might not preserve the semantic similarity powerfully and lead to poor accuracy. To evade binary limitation but remain hashing superiority, this paper introduces a novel asymmetric hashing framework for distance variety preserving, which yields preferable performance via real-valued query and multi-valued database with shorter code length. Concretely, a binary sparse representation is proposed for multivalued database encoding to make computation and storage complexity tolerable. By exploiting the supervised information, we learn a projective matrix that project data onto a sub-space in which the similarity between realvalued query and multi-valued database can be measured. Due to the discrete constraints, for different discrete variables, we present different meticulous discrete solutions for their sub-problems. Exhaustive experiments on three multilabel datasets demonstrate our approach outperforms most state-of-the-art hashing methods. © 2018 John Wiley & Sons, Ltd.

关键词： database systems

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Landmark-based multi-objective route planning for large-scale road net 21

Landmark-based multi-objective route planning for large-scal...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Sun, Jiaze Han, Nan Huang, Jianbin Deng, Jiahui Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an China Xidian University Xi'an China Northwest University Xi'an China

ISBN: (纸本)9781450383516

In this paper, for two objectives of the minimum driving distance and the maximum number of passengers compatible, a landmark-based multi-objective route planning algorithm (LB-MORPA) is proposed. LB-MORP incorporates the idea of a bi-directional random walk into the generation of the initial population, designs a new crossover and mutation operator for the evolutionary process, and predicts a set of the nondominated solution set to provide drivers with multiple route options. The comparison experiments with other classical multi-objective evolutionary algorithms prove that the LB-MORP has good performance. © 2021 Owner/Author.

关键词： Multiobjective optimization

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Dual attention mechanism object tracking algorithm based on Fully-convolutional Siamese network

Dual attention mechanism object tracking algorithm based on ...

引用

2021 International Conference on networking and network Applications, NaNA 2021

作者： Ma, Sugang Zhang, Zixian Zhang, Lei Chen, Yanping Yang, Xiaobao Pu, Lei Hou, Zhiqiang Xi'an University of Posts and Telecommunications School of Computer Science and Technology Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Information and Navigation Air Force Engineering University Shaanxi Xi'an710077 China School of Information Engineering Chang'an University Shaanxi Xi'an710064 China

ISBN: (纸本)9781665441582

In an effort to the problem of insufficient tracking performance of the Fully-convolutional Siamese network (SiamFC) in complex scenarios, a dual attention mechanism object tracking algorithm based on the Fully-convolutional Siamese network is proposed to improve the generalization capability of the tracker by ameliorating the robustness of the template characteristics. Firstly, a global context attention module is appended after the backbone network of SiamFC to ameliorate the power of original feature extraction from two dimensions of spatial and channel. Then, a coordinate attention module is introduced to augment the capability of feature extraction in the channel dimension. Finally, the model of the proposed algorithm is trained on the Got-10k dataset. Five related algorithms are tested on the OTB2015 dataset, the results of experiments manifest that our algorithm outperforms the baseline trackers, the success and precision rate of the proposed algorithm are improved by 3.3% and 6.3%. The average tracking speed is 145FPS, which can demand the requirement of real-time tracking. © 2021 IEEE.

关键词： Feature extraction

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Remaining Useful Life Prediction of Lithium Battery via Neural network Ensemble 16

Remaining Useful Life Prediction of Lithium Battery via Neur...

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16th IEEE International Conference on intelligent Systems and Knowledge Engineering, ISKE 2021

作者： Zhang, Hengshan Wu, Di Wang, Zhongmin Chen, Yanping Xu, Jiaxuan Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an710000 China Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710000 China

ISBN: (纸本)9781665405539

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for accelerating the technology development. The neural network via data driven can avoid manual feature extraction and release the difficulty of model construction. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. The single neural network cannot tackle these challenges efficiently. In this paper, the RUL prediction of lithium battery via neural network ensemble is developed to improve the accuracy and generalization of the prediction model. The predicting results of multiple neural networks are considered as the base vectors, and the liner combination on the base vectors are conducted to obtain the ensemble RUL prediction of lithium-ion battery. The experiment is performed on the MIT-Stanford University lithium battery accelerated degradation dataset, and the experimental results show that the developed method has achieved better generalization and higher accuracy comparing with the other methods. © 2021 IEEE.

关键词： Lithium-ion batteries

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Moving Forward Together: A Multi-User Pattern Extraction Neural Model for Sequential Recommendations 23

Moving Forward Together: A Multi-User Pattern Extraction Neu...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Xiangpeng Du Xiaoge Li Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xian University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

The sequential recommendation is a very important task in recommendation systems. The aim of it is to dynamic predict user's interests based on their historical behaviors. Despite recent progress, most of deep learning approaches focus on extracting the behavioral patterns of a single user. Pulling multiple users can increase the diversity of historical information. In this paper, we propose multi-user behavioral pattern extraction model (TMGN) via a combination of traditional behavioral pattern extraction and graph convolutional aggregation. Concretely, TMGN first learns the graph structure of users and items. Then it performs graph convolution aggregation to derive a representation of users to extract user interest features. We recall the users with the highest similarity by calculating the cosine similarity of each candidate user. Finally, we inject multiple users' sequence patterns and time information into the muti-head attention mechanism. Experiments on actual data (Movielens1m, Movielen20m and Amazon beauty) show that the proposed method outperforms current state-of-the-art sequential recommendation methods. TMGN achieves an average improvement of 5.67%, 5.75%, 7.32%, and 7.19% over the most robust baseline in Hit@5, Hit@10, NDCG@5, and NDCG@10, respectively. We provide extensive ablation studies to analyze the impact of each model component on TMGN.

关键词： Graph Convolutional network Self-attention Sequential Recommendation Sequential prediction Temporal Embedding

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A trajectory association strategy for multi-object tracking 6

A trajectory association strategy for multi-object tracking

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Ma, Sugang Duan, Shuaipeng Pu, Lei Hou, Zhiqiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China School of Operational Support Rocket Force Engineering University Shaanxi Xi'an710025 China

ISBN: (纸本)9798400707674

When the multi-object tracking (MOT) algorithm confronts complex scenarios such as target occlusion and blurring, the trajectory missing and identity switching problems frequently occur. To address this issue, a trajectory association strategy for MOT is presented. First, using the similarity relationship between two frames, the similarity matrix is introduced to predict a more trustworthy offset vector and lower the number of Identity Switch. Then, a trajectory reconnection strategy is provided in order to retain a portion of the low-score detection frames due to occlusion and to perform secondary matching on the low-score detection frames in order to reduce missing trajectories. On a typical multi-object tracking dataset MOT17, experimental comparison study is performed with several known advanced algorithms, and MOTA and IDF1 achieve 67.9% and 67.8%, respectively. The experimental results show that the proposed algorithm better solves the trajectory missing and identity switching problems, and achieves excellent tracking performance in complex scenarios. © 2023 ACM.

关键词： Trajectories

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A Multi-direction Prediction Multi-objective Hybrid Chemical Reaction Optimization Algorithm for Dynamic Multi-objective Optimization 16th

A Multi-direction Prediction Multi-objective Hybrid Chemical...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Li, Hongye Pan, Xiaoying Gan, Wei Wang, Lei School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China Xi’an Shiyou University Shaanxi Xi’an710065 China School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi University of Technology Shaanxi Han Zhong723001 China

ISBN: (纸本)9789811912559

The challenge of solving dynamic multi-objective optimization problems is to trace the varying Pareto optimal front and/or Pareto optimal set quickly and efficiently. This paper proposes a multi-direction prediction strategy using a hybrid chemical reaction optimization, aimed at finding the dynamic Pareto optimal front and/or Pareto optimal set as quickly and accurately as possible before the next environmental change occurs. The proposed method, multi-direction prediction multi-objective hybrid chemical reaction optimization algorithm which mainly includes a hybrid chemical reaction optimization algorithm is proposed for solving dynamic multi-objective problems, which can guide population trace the optimum. When the environment has changed, the population is divided into several subpopulations. In subpopulation, a center point was found to construct multi-direction prediction model. As a result, this approach enhances the diversity of algorithm. While the environment has not changed, the hybrid chemical reaction algorithm with particle swarm optimization algorithm can efficiency find the optimal solution, it can achieve good diversity as well as guarantee the avoidance of local optimal solutions. The proposed algorithm is measured on several benchmark test suites with various dynamic characteristics and different difficulties. Experimental results show that this algorithm is very competitive in dealing with dynamic multi-objective optimization problems when compared with four state-of-the-art approaches. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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