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A new method for the prediction of network security situations based on recurrent neural network with gated recurrent unit

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International Journal of Intelligent Computing and Cybernetics 2020年第1期13卷 25-39页

作者： Wei Feng Yuqin Wu Yexian Fan Department of Computer Science College of Information and Electrical EngineeringNingde Normal UniversityNingdeChina College of Information and Electrical Engineering Ningde Normal UniversityNingdeChina

Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent ***/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first ***,the extracted features are applied to the deep RNN model for training and *** iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network ***-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional *** the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for ***/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.

关键词： gated recurrent unit Internal and external information features Network security situation recurrent neural network Time-series data processing

Micro-seismic Event Detection of Hot Dry Rock based on the gated recurrent unit Model and a Support Vector Machine

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Acta Geologica Sinica(English Edition) 2021年第6期95卷 1940-1947页

作者： SUN Feng HU Haotian ZHAO Fa YANG Xinran CHEN Zubin WU Haidong ZHANG Linyou College of Instrumentation&Electrical Engineering Jilin UniversityChangchun 130026China Key Lab of Geo-Exploration Instrumentation of Ministry of Education Jilin UniversityChangchun 130026China Center for Hydrogeological and Environment Geological Survey China Geological SurveyBaodingHebei 071000China

Micro-seismic monitoring is one of the most critical technologies that guide hydraulic fracturing in hot dry rock resource development. Micro-seismic monitoring requires high precision detection of micro-seismic events with a low signal-to-noise ratio. Because of this requirement, we propose a recurrent neural network model named gated recurrent unit and support vector machine(GRU;VM). The proposed model ensures high accuracy while reducing the parameter number and hardware requirement in the training process. Since micro-seismic events in hot dry rock produce large wave amplitudes and strong vibrations, it is difficult to reverse the onset of each individual event. In this study, we utilize a support vector machine(SVM) as a classifier to improve the micro-seismic event detection accuracy. To validate the methodology, we compare the simulation results of the short-term-average to the long-term-average(STA/LTA) method with GRU;VM method by using hot dry rock micro-seismic event data in Qinghai Province, China. Our proposed method has an accuracy of about 95% for identifying micro-seismic events with low signal-to-noise ratios. By ignoring smaller micro-seismic events, the detection procedure can be processed more efficiently, which is able to provide a real-time observation on the types of hydraulic fracturing in the reservoirs.

关键词： hot dry rock micro-seismic detection gated recurrent unit support vector machine

The Motor Imagination EEG Recognition Combined with Convolution Neural Network and gated recurrent unit

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The Motor Imagination EEG Recognition Combined with Convolut...

第37届中国控制会议

作者： Jun CAI Chang WEI Xian-lun TANG Can XUE Quan CHANG Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications

For the common electroencephalogram（EEG） feature extraction methods, the temporal sequence of EEG signals is often neglected. A new model called Convolutional gated recurrent Neural Network that combines Convolution Neural Network（CNN） and gated recurrent unit（GRU） is proposed. The model extracts the combinatorial features of the preprocessed Motor Imagination EEG by CNN, it enriches the GRU input, and then it uses GRU to extract some sequence information hidden in the EEG signals to improve the recognition accuracy of MI EEG signals. In addition, batch normalization（BN） is added between the CNN and the GRU to speed up the operation of the network, and a dropout is introduced to sparse networks’ connection to prevent over-fitting. Experiments results show that the average recognition accuracy of the EEG data collected by ourselves is 93.57%. In the open data set BCI competition IV data set 2 b, compared with the traditional CSP algorithm, it can improve the recognition rate of EEG by 2.56% on average.

关键词： Motor Imagination EEG feature extraction Convolution Neural Network gated recurrent unit

B2GRU: Boosted Bidirectional gated recurrent unit for Remaining Useful Life Prediction of Mechanical Bearings

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B2GRU: Boosted Bidirectional Gated Recurrent Unit for Remain...

第35届中国过程控制会议

作者： Licheng Pan Rixin Su Hua Wu Zhenfeng Gu Changwen Cui Zuobing Wang Xinli Huang Jiageng Chen Jian Xu Xinggao Liu Jiangnan Institute of Mechanical and Electrical Design and Research Jiuquan Satellite Launch Center School of Mathematical Sciences Zhejiang University

Modern industrial and production realms heavily rely on rotating machinery, in which mechanical bearings play a crucial role in ensuring equipment reliability and efficiency. Accurate prognostics of these bearings' Remaining Useful Life(RUL)can significantly impact the operational effectiveness of the entire machinery. Utilizing comprehensive vibration data, we developed a novel predictive framework by integrating Stacked AutoEncoders(SAE) for advanced feature extraction with a Boosted Bidirectional gated recurrent unit(BiGRU) including Skip Connections for multi-scale joint prediction. This innovative approach, B2GRU, provides precise RUL predictions, significantly enhancing machinery reliability and efficiency while reducing operational downtime and costs. Our experimental results on the PRONOSTIA dataset validate the superior performance of BGRU over contemporary methods, achieving a substantial 42.9% decline in Root Mean Square Error(RMSE) compared to basic Artificial Neural Networks(ANN). This study highlights the robust capabilities of our model in feature extraction and model boosting mechanisms, demonstrating its effectiveness and suitability for RUL prediction of mechanical bearings.

关键词： Remaining Useful Life Prediction Mechanical Bearings Stacked AutoEncoders gated recurrent unit Skip Connection Multiple Scale

The gated recurrent unit recurrent neural network based nesterov accelerated fractional order gradient algorithm for SOC estimation

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The gated recurrent unit recurrent neural network based nest...

第43届中国控制会议

作者： Fei Xu Jing Chen Huitong Lu School of Science Jiangnan University

A gated recurrent unit recurrent neural network based nesterov accelerated fractional order gradient(GRU-RNN-NAFG) algorithm is introduced to estimate the state of charge(SOC). This algorithm uses the gated recurrent unit recurrent(GRU) with concise internal structures to reduce the computational efforts. Besides, fractional order derivatives instead of integer order derivatives are utilized to compute the gradient in the nesterov accelerated gradient(NAG) algorithm, which significantly improve the accuracy and stability of the algorithm. The simulation examples are provided to demonstrate the efficacy of the algorithm.

关键词： state of charge gated recurrent unit nesterov accelerated gradient neural network fractional order derivative

Modelling of Magnetorheological Sealing Device Based on iTransformer with gated recurrent unit

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Modelling of Magnetorheological Sealing Device Based on iTra...

第43届中国控制会议

作者： Yurui Shen Qiyu Wang Lai Peng Dezheng Hua Xinhua Liu School of Mechanical and Electrical Engineering China University of Mining and Technology

Magnetorheological fluids(MRFs) exhibit strong nonlinear properties, which pose a significant challenge in accurately quantifying the performance of magnetorheological sealing(MRS). To tackle this intricate problem, a novel modeling method utilizing iTransformer with gated recurrent unit(GRU-iTransformer) was proposed for modelling the rotating torque performance of MRS device. Initially, a novel sealing structure, tailored to fulfill the rigorous sealing requirements of large-diameter, high-speed optical beam deflectors, was introduced. Subsequently, an advanced modeling approach based on gated recurrent unit(GRU) and iTransformer is proposed for accurately characterizing the friction torque performance of MRS. Finally, an experimental setup is established, utilizing input parameters such as sealing clearance, sealing pressure, and rotational speed, with friction torque measured as the output variable, facilitating the creation of both training and testing datasets. It is demonstrated by the results that the predictive model developed in this research can achieve an impressive accuracy level.

关键词： Modeling Method Magnetorheological Sealing iTransformer gated recurrent unit

Semi-supervised Dynamic Soft Sensor Based on Complementary Ensemble Empirical Mode Decomposition and gated recurrent units

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Semi-supervised Dynamic Soft Sensor Based on Complementary E...

第31届中国过程控制会议（CPCC2020）

作者： GUO Runyuan LIU Han XIE Guo ZHANG Youmin School of Automation and Information Engineering Xi’an University of Technology

[Objectives] Noise, redundancy and dynamic characteristics in complex industrial process data have been regarded as the key factors that affect the prediction accuracy of data-driven soft sensor models. In this paper, a semi-supervised dynamic soft sensor method is proposed to capture the dynamic characteristics of variable data while removing the noise and redundancy of the data to establish an accurate dynamic soft sensor prediction model. [Methods] First, auxiliary variable signals are decomposed effectively by complementary ensemble empirical mode decomposition(CEEMD), and the noise is eliminated based on the intrinsic mode functions(IMFs) obtained by the decomposition. Then, using the isometric feature mapping(Isomap) method to perform feature transformation of the remaining intrinsic mode functions, the variable data are reconstructed to remove the internal redundancy and noise. Finally, the preprocessed and serialized data are used to build a deep learning model based on a semi-supervised deep gated recurrent unit network aided multilayer perceptron(SSDGRU-MLP). To ensure effective training of the proposed SSDGRU-MLP model, callback functions and dropout technology are adopted. After the above offline procedures, a semi-supervised dynamic soft sensor prediction model can be obtained. Then, by importing the new serialized dataset into the established soft sensor model, the predicted values of the primary variable can be obtained through propagation over time and network depth. Since power plant air preheater is a dynamic system with complex and variable operating conditions, the semi-supervised dynamic soft sensor method proposed in this paper is used to predict rotor thermal deformation in the air preheater. So, the effectiveness and superiority of the new dynamic soft sensor are verified by this real industrial case. In our experiments, four auxiliary variables(gas temperature of the flue inlet, gas temperature of the flue outlet, air temperature of the air

关键词： dynamic soft sensor complementary ensemble empirical mode decomposition deep learning gated recurrent unit semi-supervised and dynamic modeling isometric feature mapping

Forecasting traffic flows in irregular regions with multi-graph

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Frontiers of Information Technology & Electronic Engineering 2021年第9期22卷 1179-1193页

作者： Dewen SENG Fanshun LV Ziyi LIANG Xiaoying SHI Qiming FANG School of Computer Science and Technology Hangzhou DianziUniversityHangzhou 310018China

The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic *** the help of deep neural networks,the convolutional neural network or residual neural network,which can be applied only to regular grids,is adopted to capture the spatial dependence for flow ***,the obtained regions are always irregular considering the road network and administrative boundaries;thus,dividing the city into grids is inaccurate for *** this paper,we propose a new model based on multi-graph convolutional network and gated recurrent unit(MGCN-GRU)to predict traffic flows for irregular ***,we first construct heterogeneous inter-region graphs for a city to reflect the rela-tionships among *** each graph,nodes represent the irregular regions and edges represent the relationship types between ***,we propose a multi-graph convolutional network to fuse different inter-region graphs and additional *** GRU is further used to capture the temporal dependence and to predict future traffic *** results based on three real-world large-scale datasets(public bicycle system dataset,taxi dataset,and dockless bike-sharing dataset)show that our MGCN-GRU model outperforms a variety of existing methods.

关键词： Traffic flow prediction Multi-graph convolutional network gated recurrent unit Irregular regions

Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN

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Digital Communications and Networks 2022年第4期8卷 531-539页

作者： Ke Yan Xiaokang Zhou Department of the Built Environment National University of Singapore4 Architecture Drive117566Singapore Faculty of Data Science Shiga UniversityHikone5228522Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo1030027Japan

Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of Things(IoT).The cyber-physical system greatly enhances the safety and security of the working facilities,reducing time,saving energy and protecting humans’*** the current trends of smart building design and energy management optimization,Automated Fault Detection and Diagnosis(AFDD)of chillers integrated with IoT is highly *** studies show that standard machine learning techniques,such as Principal Component Analysis(PCA),Support Vector Machine(SVM)and tree-structure-based algorithms,are useful in capturing various chiller faults with high accuracy *** the fast development of deep learning technology,Convolutional Neural Networks(CNNs)have been widely and successfully applied to various ***,for chiller AFDD,few existing works are adopting CNN and its extensions in the feature extraction and classification *** this study,we propose to perform chiller FDD using a CNN-based *** proposed approach has two distinct advantages over existing machine learning-based chiller AFDD ***,the CNN-based approach does not require the feature selection/extraction *** CNN is reputable with its feature extraction capability,the feature extraction and classification processes are merged,leading to a more neat AFDD framework compared to traditional ***,the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.

关键词： Chiller Fault detection and diagnosis Deep learning neural network Long short term memory recurrent neural network gated recurrent unit