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Aeroengine thrust estimation and embedded verification based on improved temporal convolutional network

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Chinese Journal of Aeronautics 2024年第1期37卷 106-117页

作者： Wanzhi MENG Zhuorui PAN Sixin WEN Pan QIN Ximing SUN Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of TechnologyDalian 116024China School of Control Science and Engineering Dalian University of TechnologyDalian 116024China

Thrust estimation is a significant part of aeroengine thrust control *** traditional estimation methods are either low in accuracy or large in *** further improve the estimation effect,a thrust estimator based on Multi-layer Residual temporal convolutional network(M-RTCN)is *** solve the problem of dead Rectified Linear Unit(ReLU),the proposed method uses the Gaussian Error Linear Unit(GELU)activation function instead of ReLU in residual *** the overall architecture of the multi-layer convolutional network is adjusted by using residual connections,so that the network thrust estimation effect and memory consumption are further ***,the comparison with seven other methods shows that the proposed method has the advantages of higher estimation accuracy and faster convergence ***,six neural network models are deployed in the embedded controller of the micro-turbojet *** Hardware-in-the-Loop(HIL)testing results demonstrate the superiority of M-RTCN in terms of estimation accuracy,memory occupation and running ***,an ignition verification is conducted to confirm the expected thrust estimation and real-time performance.

关键词： Thrust estimation temporal convolutional network Embedded deployment Hardware-in-the-loop testing Ignition verification

A production prediction method of single well in water flooding oilfield based on integrated temporal convolutional network model

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Petroleum Exploration and Development 2022年第5期49卷 1150-1160页

作者： ZHANG Lei DOU Hongen WANG Tianzhi WANG Hongliang PENG Yi ZHANG Jifeng LIU Zongshang MI Lan JIANG Liwei PetroChina Research Institute of Petroleum Exploration & Development Beijing 100083China Research Institute of Petroleum Exploration and Development Daqing Oilfield CompanyDaqing 163000China

Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.

关键词： single well production prediction temporal convolutional network time series prediction water flooding reservoir

Spectrum Sensing via temporal convolutional network

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China Communications 2021年第9期18卷 37-47页

作者： Tao Ni Xiaojin Ding Yunfeng Wang Jun Shen Lifeng Jiang Gengxin Zhang Telecommunication and networks National Engineering Research Center(Nanjing University of Posts and Telecommunications) Nanjing 210003China Xi’an Institute of Space Radio Techonology Xi’an 710000China

In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing *** the conventional energy detection method is sensitive to the noise uncertainty,thus,a temporal convolutional network(TCN)based spectrum-sensing method is designed to eliminate the effect of the noise uncertainty and improve the performance of spectrum sensing,relying on the offline training and the online detection ***,in the offline training stage,spectrum data captured by the satellite is sent to the TCN deployed on the gateway for training ***,in the online detection stage,the well trained TCN is utilized to perform real-time spectrum sensing,which can upgrade spectrum-sensing performance by exploiting the temporal ***,simulation results demonstrate that the proposed method achieves a higher probability of detection than that of the conventional energy detection(ED),the convolutional neural network(CNN),and deep neural network(DNN).Furthermore,the proposed method outperforms the CNN and the DNN in terms of a lower computational complexity.

关键词： cognitive radio spectrum sensing deep learning temporal convolutional network satellite communication

An insider user authentication method based on improved temporal convolutional network

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High-Confidence Computing 2023年第4期3卷 87-95页

作者： Xiaoling Tao Yuelin Yu Lianyou Fu Jianxiang Liu Yunhao Zhang Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China

With the rapid development of information technology,information system security and insider threat detection have become important topics for organizational *** the current network environment,user behavioral bio-data presents the characteristics of nonlinearity and temporal *** of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature *** do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data,and also do not adequately reflect the personalized usage characteristics of users,leading to bottlenecks in the performance of the authentication *** order to solve the above problems,this paper proposes a temporal convolutional network method based on an Efficient Channel Attention mechanism(ECA-TCN)to extract user mouse dynamics features and constructs an one-class Support Vector Machine(OCSVM)for each user for *** results show that compared with four existing deep learning algorithms,the method retains more adequate key information and improves the classification performance of the neural *** the final authentication,the Area Under the Curve(AUC)can reach 96%.

关键词： Insider users Mouse dynamics Feature extraction temporal convolutional network Efficient channel attention mechanism Authentication

Training-based symbol detection with temporal convolutional neural network in single-polarized optical communication system

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数字通信与网络:英文版 2023年第4期9卷 920-930页

作者： Yingzhe Luo Jianhao Hu National Key Laboratory of Science and Technology on Communications University of Electronic and Science Technology of ChinaChengdu611731China

In order to reduce the physical impairment caused by signal distortion,in this paper,we investigate symbol detection with Deep Learning(DL)methods to improve bit-error performance in the optical communication *** DL-based methods have been applied to such systems to improve bit-error *** to the speech-to-text method of automatic speech recognition,this paper proposes a signal-to-symbol method based on DL and designs a receiver for symbol detection on single-polarized optical communications *** realize this detection method,we propose a non-causal temporal convolutional network-assisted receiver to detect symbols directly from the baseband signal,which specifically integrates most modules of the ***,we adopt three training approaches for different signal-to-noise *** also apply a parametric rectified linear unit to enhance the noise robustness of the proposed *** to the simulation experiments,the biterror-rate performance of the proposed method is close to or even superior to that of the conventional receiver and better than the recurrent neural network-based receiver.

关键词： Deep learning Optical communications Symbol detection temporal convolutional network

The Temperature Prediction of Hydro-generating Units based on temporal convolutional network and Recurrent Neural network

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The Temperature Prediction of Hydro-generating Units based o...

A Lightweight temporal convolutional network for Human Motion Prediction

第40届中国控制会议

作者： Junyan Li Yuechao Wu Yanjun Li Ji Xiang Bo Zheng Zhejiang University Power China Huadong Engineering Corporation Limited Zhejiang University City College

Temperature prediction is an important means to ensure the safety of hydro-generating unit（HGU） due to the fact that a lot of faults of the HGU are reflected in the form of temperature rise. Because of huge monitoring data of the HGU, it is difficult to accurately predict the trend of temperature by the traditional recurrent neural network（RNN）. In this paper, a time series prediction model based on temporal convolutional network（TCN） and RNN is proposed. First of all, a TCN including a temporal convolutional layer and a maximum pooling layer is built. By introducing a sliding window mechanism and a maximum pooling structure, the effective information is extracted from large-scale time series data, thus to reduce data size. Subsequently, a RNN model is established to predict the trend of the temperature. The TCN-RNN model avoids the problem of ineffective use of hidden layer information in traditional models, improves accuracy and speed of large-scale time series data prediction. The experimental results show that compared with RNN, CNN and CNN-RNN, the prediction accuracy of this model is significant improved, which can better meet the temperature prediction requirements of the HGU.

关键词： hydro-generating unit temporal convolutional network recurrent neural network temperature prediction

来源： cnki会议评论

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Transactions of Nanjing University of Aeronautics and Astronautics 2022年第S1期39卷 150-157页

作者： WANG You QIAO Bing College of Astronautics Nanjing University of Aeronautics and AstronauticsNanjing 210016P.R.China

A lightweight multi-layer residual temporal convolutional network model(RTCN)is proposed to target the highly complex kinematics and temporal correlation of human *** uses 1-D convolution to efficiently obtain the spatial structure information of human motion and extract the correlation in the time series of human *** residual structure is applied to the proposed network model to alleviate the problem of gradient disappearance in the deep *** on the Human 3.6M dataset demonstrate that the proposed method effectively reduces the errors of motion prediction compared with previous methods,especially of long-term prediction.

关键词： human motion prediction temporal convolutional network short-term prediction long-term prediction deep neural network

TSCND:temporal Subsequence-Based convolutional network with Difference for Time Series Forecasting

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Computers, Materials & Continua 2024年第3期78卷 3665-3681页

作者： Haoran Huang Weiting Chen Zheming Fan MOE Research Center of Software/Hardware Co-Design Engineering East China Normal UniversityShanghai200062China

Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.

关键词： Difference data prediction time series temporal convolutional network dilated convolution

A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

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Computers, Materials & Continua 2024年第1期78卷 845-871页

作者： Zhenxiang He Xunxi Wang Chunwei Li School of Cyberspace Security Gansu University of Political Science and LawLanzhou730000China

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated ***,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network *** leads to compromised detection accuracy and overlooked temporal *** this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection *** approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a temporal convolutional network(TCN)and Bidirectional Long Short-term Memory network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection *** the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 *** the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 *** findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is *** contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.

关键词： network intrusion detection bidirectional long short-term memory network time series stacked sparse autoencoder temporal convolutional network time steps