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Natural Language processing (ICNLP), International Conference on

作者： Xiaoying Pan Hao Wang Jia Sun Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Multivariate time series forecasting is particularly critical in a number of areas involving air forecasting, electricity consumption and exchange rate transformations. The time series data in these fields often contain present multivariate nature, fusing long and short-term patterns, and traditional forecasting methods are often difficult to work. To address this challenge, this paper proposes a new deep learning framework, TSARNet, which combines gated recurrent units (GRUs) and an adaptive scale processing layer to effectively capture short-term localized dependency patterns among multivariate variables and reveal long-term trends through a spatio-temporal attention mechanism. In addition, an autoregressive model is incorporated to enhance the network's sensitivity to time series scale changes. Comprehensive experiments on four publicly available datasets show that the proposed TSARNet algorithm outperforms other comparative methods in most cases in terms of mean absolute error (MAE) and root mean square error (RMSE) in the long term prediction of different datasets. The TSARNet method also achieves optimal results for doing long-term prediction for each dimension of the power transformer temperature dataset.

关键词： Deep learning Attention mechanisms Accuracy Sensitivity Time series analysis Predictive models Market research Prediction algorithms Forecasting Root mean square

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Trust-based distributed kalman filter estimation fusion under malicious cyber attacks 21

Trust-based distributed kalman filter estimation fusion unde...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Chen, Yanping Ma, Long Xia, Hong Gao, Cong Wang, Zhongmin Yu, Zhong School of Computer Science AND Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Shananxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an710121 China School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781728120584

We consider distributed Kalman filter for dynamic state estimation over wireless sensor networks. It is promising but challenging when network is under cyber attacks. Since the information exchange between nodes, the malicious attacks quickly spread across the entire network, which causing large measurement errors and even to the collapse of sensor networks. Aiming at the malicious network attack, a trust-based distributed processing frame is proposed. Which allows neighbor nodes to exchange information, and a series of trusted nodes are found using truth discovery. As a demonstration, distributed Cooperative Localization is considered, and numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks. © 2019 IEEE.

关键词： Kalman filters

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Msfe-Panet: Improved Yolov4-Based Small Object Detection Method in Complex Scenes

SSRN

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SSRN 2022年

作者： Pan, Xiaoying Jia, Ningxin Mu, Yuanzhen Bai, Weidong School of Computer Science and Technology Xi’an University of Posts and Telecommunications GuoDu Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China

Purpose With the rapid development of computer vision and artificial intelligence technology, visual object detection has made unprecedented progress, and small object detection in complex scenes has attracted more and more increasing *** of the limited feature information of small objects, the resolution is low. Therefore, in the actual complex scene, the effect of detecting small objects is not ideal. Methods To solve the problems of ambiguity, overlap and occlusion in small object detection in complex scenes. In this paper, a multiscale fusion feature enhanced path aggregation network MSFEPANet is proposed. By adding an attention mechanism and feature fusion, the fusion of strong positioning information of the deep feature map and strong semantic information of the shallow feature map is enhanced, which helps the network find interesting areas in complex scenes and improves its sensitivity to small objects. The rejection loss function and network prediction scale are designed © 2022, The Authors. All rights reserved.

关键词： Object detection

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Emotional analysis Based on Dynamic Functional Brain network

Emotional Analysis Based on Dynamic Functional Brain Network

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International Conference on networking and network Applications (NaNA)

作者： Zhongmin Wang Yue Tong Xia Heng Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

The human brain dynamically changes in the second and millisecond levels, so constructing a functional brain network using static connections can result in the absence of some time-related effective features. In this paper, a dynamic network model construction method is proposed. The basic idea of the proposed dynamic emotion analysis method is to construct different network modes based on the channel correlation mode and band power distribution method, and use the sliding window method to estimate the dynamic correlation mode and dynamic power distribution changes, Then the multivariate features of network-level brain dynamics are extracted and classified. We conducted experiments on the SEED database to study network-level dynamic transformations in the process of positive, neutral, and negative emotions. The results verify the feasibility of the emotional assessment method based on dynamic functional connection, and provide a new way to establishing brain dynamic model under different emotional states. The results show that emotion analysis based on dynamic networks is more accurate than static networks.

关键词： Correlation Electroencephalography Power distribution Windows Feature extraction Time series analysis databases

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An Efficient Image Captioning Method Based on Generative Adversarial networks 4

An Efficient Image Captioning Method Based on Generative Adv...

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4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021

作者： Lin, Zhenxian Feng, Feirong Yang, Xiaobao Ding, Chen School of Science Xi'an University of Posts and Telecommunications China School of Telecommunication and Information Engineering Xi'an University of Posts and Telecommunications China School of Computer Science and Technology Northwestern Polytechnical University China School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

Image captioning is usually based on the mainstream encoder-decoder framework, the accuracy of the encoders and the generation capability of the decoders directly affect the quality of image captions. But no matter how the encoders and the decoders are combined, the generated captions will only imitate the ground-truth captions, and its diversity is suppressed. To generate more accurate and diverse captions while exploring the impact of different encoder-decoder frameworks for the quality of the generated captions, we conducted an exploration of an efficient image captioning method based on GAN. Specifically, we use ResNet152, EfficientNet-B0, and EfficientNet-B2 as the encoders to extract image features, GRU or Transformer as the decoders to generate captions, respectively. Based on MS COCO2014 dataset, a large number of experiment results show that the EfficientNet-B2+GRU framework has best performance, training time is reduced, BLUE-4 score increases by 0.6, parameters reduce by 0.66 million, which can be used as an efficient encoder-decoder framework for generating diverse captions. © 2021 ACM.

关键词： Generative adversarial networks

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

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

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International Conference on intelligent System and Knowledge Engineering, ISKE

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

ISBN: (纸本)9781665405546

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.

关键词： Training Wireless communication Wireless sensor networks Time-frequency analysis Costs Time series analysis Predictive models

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Probabilistic Adaptive Genetic Algorithm for Pre-treatment Radiomics Features Predict Patient Survival

Probabilistic Adaptive Genetic Algorithm for Pre-treatment R...

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2018 International Conference on Computer, Communications and Mechatronics Engineering（CCME 2018）

作者： Xiao-ying PAN Ting ZHANG Qing-ping YANG Liu CHEN School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications

In order to provide personalized treatment for patients with tongue carcinoma, a probabilistic adaptive genetic algorithm neural network(PAGA-BP) model is proposed in this paper. The PAGA-BP model ameliorates selection sorting operator, adaptive crossover operator and u-adaptive mutation operator to optimize the initial weight of BP neural network. By comparing with traditional GA-BP neural network and BP neural network survival prediction model, the results show that PAGA-BP prediction model has the highest approximation accuracy and better survival period prediction.

关键词： PGA-BP neural network Radiomics features Survival Prediction

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Remaining Useful Life Prediction of Lithium Battery via Neural network Ensemble

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

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International Conference on intelligent System and Knowledge Engineering, ISKE

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

ISBN: (纸本)9781665405546

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.

关键词： Lithium-ion batteries Knowledge engineering Training Neural networks Time series analysis Manuals Voltage

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

Dual attention mechanism object tracking algorithm based on ...

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International Conference on networking and network Applications (NaNA)

作者： Sugang Ma Zixian Zhang Lei Zhang Yanping Chen Xiaobao Yang Lei Pu Zhiqiang Hou School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi China School of Information Engineering Chang’an University Xi’an Shaanxi 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 Xi’an Shaanxi China School of Information and Navigation Air Force Engineering University Xi’an Shaanxi China

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.

关键词： Target tracking Feature extraction Real-time systems Robustness Object tracking Task analysis

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Visual Tracking Algorithm Based on Color Name Histogram

Visual Tracking Algorithm Based on Color Name Histogram

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作者： Chenchen Yue Wangsheng Yu Zhiqiang Hou Sugang Ma College of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Information and Navigation Institute of Air Force Engineering University

Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking,combined with the CN(Color Name) feature,we proposed a new feature(denotes CNH,Color Name Histogram) based on color ***,the method projected the original RGB image to CN space to obtain robust 11 feature ***,we counted the each pixel numbers of feature ***,normalizing the amount of pixels in each *** addition,we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient).In order to prove validity of the proposed algorithm,we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled *** contrast with the reference algorithms,the success of our algorithm increases by 1.5% and the precision increases by 1.7%.The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation,but also enhances the robustness to illumination change.

关键词： Visual tracking Color histogram Color name Feature fusion

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