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Multi-band spectral-temporal deep learning network for structural dynamic data forecasting aided by signal decomposition and bandpass filtering

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ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Shen, Wei Fu, Yuguang He, Xinhao Zhang, Fan Nanyang Technol Univ Sch Civil & Environm Engn Singapore Singapore Tohoku Univ Grad Sch Engn Dept Civil & Environm Engn Sendai 9808579 Japan

This study proposes a novel online data forecasting strategy for dynamic measurements in structural health monitoring applications, which excels in both time and frequency domain, based on a spectral-temporal deep learning network integrated with signal decomposition and filtering techniques in moving windows. For complex structural dynamic measurements from bridges or buildings, the varying time-frequency features within moving windows are difficult to capture for conventional deep learning networks, e.g., CNN or GNN, resulting in spectral bias or other related issues. To address this challenge, in this study, signal decomposition is adopted to simplify the frequency domain features, i.e., variational mode decomposition (VMD), into multiple intrinsic mode functions (IMFs), each of which has limited bandwidth frequency and regular time domain pattern. The decomposed signals are then fed into a spectral-temporal deep learning network for data forecasting step by step, and the predicted datasets are further finetuned by associated bandpass filters, respectively, to selectively remove the unwanted frequency components to enhance the accuracy. The performance of proposed method is validated on both numerical simulation data and field test data, and the results demonstrate that the predicted datasets match well with ground truth values in both time and frequency domains. Compared to the state-of-the-art solution, the proposed method can achieve much higher forecasting accuracy. In addition, the effects of hyperparameters and VMD are investigated, and the result shows that the introduction of signal decomposition plays a critical role to improve the state-of-the-art solution for data forecasting. The proposed solution can efficiently help to reconstruct dynamic measurements from data anomalies (e.g., missing, bias, and drifts) or to detect structural anomalies (e.g., rare events and damages) in long-term structural health monitoring systems.

关键词： Data forecasting Variational mode decomposition deep learning network Time-frequency analysis Bandpass filter

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deep learning network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

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Energy Engineering 2022年第5期119卷 1829-1841页

作者： Yunlei Zhang RuifengCao Danhuang Dong Sha Peng RuoyunDu Xiaomin Xu State Grid Zhejiang Electric Power Co. Ltd.Hangzhou310007China Strategy and Development Research Center Economic and Technical Research InstituteState Grid Zhejiang Electric Power Co.Hangzhou310000China Beijing Key Laboratory of New Energy Power and Low Carbon Development Research North China University of Electric PowerBeijing102206China

In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many *** studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction *** the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction *** experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.

关键词： Energy storage scheduling short-term load forecasting deep learning network convolutional neural network CNN long and short term memory network LTSM

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deep learning network on Waveform Components Recognition for Distributed Optic-Fiber Sensing

Deep Learning Network on Waveform Components Recognition for...

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Annual Conference of the IEEE-Photonics-Society (IPC)

作者： Wu, Zhengting Liang, Zihan Shi, Yonguan Liu, Huanhuan Dang, Hong Liao, Luoyuan Chen, Jinna Shum, Perry Ping Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen Guangdong Peoples R China

We have proposed a deep learning network of SqueezeNet in the application of distributed optic-fiber sensing based on phase-sensitive optical time-domain reflectometry technique. The obtained results show the improvem... 详细信息

ISBN: (纸本)9781665416016

关键词： Distributed Optic-fiber Sensing Phi-OTDR Waveform Components Recognition deep learning network SqueezeNet

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deep learning network for Face Detection 16

Deep Learning Network for Face Detection

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IEEE 16th International Conference on Communication Technology (ICCT)

作者： Ye, Xueyi Chen, Xueting Chen, Huahua Gu, Yafeng Lv, Qiuyun Hangzhou Dianzi Univ Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9781467370059

By the multi-layer nonlinear mapping and the semantic feature extraction of the deep learning, a deep learning network is proposed for face detection to overcome the challenge of detecting faces accurately and rapidly in the non-ideal case. Key to this deep network is that, to better simulate the response for information in the human brain, the status probability of the neuron is used to model the status of the human brain neuron which is a continuous distribution from the most active to the least active. Moreover, the number of the hidden layer's neuron decreases layer-by-layer to eliminate the redundant information of the input data and accelerate the detection speed combining with the skin color detection. Experimental results show that, besides the fast detection speed and strong robustness to face rotation, the proposed method possesses lower false detection rate and lower missing detection rate.

关键词： Face detection deep learning network Pre-training Greedy layer-wise learning The status probability of the neuron

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Satellite selection with an end-to-end deep learning network

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GPS SOLUTIONS 2018年第4期22卷 1-12页

作者： Huang, Panpan Rizos, Chris Roberts, Craig Univ New South Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia

Benefiting from multi-constellation Global Navigation Satellite Systems (GNSS), more and more visible satellites can be used to improve user positioning performance. However, due to limited tracking receiver channels and power consumption, and other issues, it may be not possible, or desirable, to use all satellites in view for positioning. The optimal subset is generally selected from all possible satellite combinations to minimize either Geometric Dilution of Precision (GDOP) or weighted GDOP (WGDOP). However, this brute force approach is difficult to implement in real-time applications due to the time- and power-consuming calculation of the DOP values. As an alternative to a brute force satellite selection procedure, the authors propose an end-to-end deep learning network for satellite selection based on the PointNet and VoxelNet networks. The satellite selection is converted to a satellite segmentation problem, with specified input channel for each satellite and two class labels, one for selected satellites and the other for those not selected. The aim of the satellite segmentation is that a fixed number of satellites with the minimum GDOP/WGDOP value can be segmented from any feeding order of input satellites. To validate the proposed satellite segmentation network, training and test data from 220 IGS stations tracking GPS and GLONASS satellites were used. The segmentation performance using different architectures and representations of input channels, including receiver-to-satellite unit vector and elevation and azimuth, were compared. It was found that the input channel with elevation and azimuth can achieve better performance than using the receiver-to-satellite unit vector, and an architecture with stacked feature encoding (FE) layers has better satellite segmentation performance than one without stacked FE layers. In addition, the models with GDOP and WGDOP criteria for selecting 9 and 12 satellites were trained. It was demonstrated that the satellite segm

关键词： Satellite selection GDOP/WGDOP GNSS deep learning network

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End to end system for hazy image classification and reconstruction based on mean channel prior using deep learning network

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IET IMAGE PROCESSING 2020年第17期14卷 4736-4743页

作者： Satrasupalli, Sivaji Daniel, Ebenezer Guntur, Sitaramanjaneya Reddy Shehanaz, Shaik Vignans Fdn Sci Technol & Res Dept Elect & Commun Engn Guntur 522213 Andhra Pradesh India

Outdoor images are having several applications including autonomous vehicles, geo-mapping, and surveillance. It is a common phenomenon that the images captured outdoor are prone to noise, which arises due to natural and manmade extreme atmospheric conditions such as haze, fog, and smog. Importantly in autonomous vehicle navigation, it is very important to recover the ground truth image to get the better decision by the system. Estimation of the transmission map and air-light is very crucial in recovering the ground truth image. In this study, the authors proposed a new method to estimate the transmission map based on a mean channel prior (MCP), which represents the depth map to estimate the transmission map. The authors proposed a deep neural network to identify the hazy image for the further dehazing process. In this study, the authors presented, two novel contributions, first an MCP-based image dehazing and second, a deep neural network-based identification of hazy images as a pre-processing block in the proposed end to end system. The proposed deep learning network using the TensorFlow platform provided validation accuracy of 93.4% for hazy image classification. Finally, the proposed MCP-based dehazing network showed better performance in terms of peak-signal-to-noise ratio, structural similarity index, and computational time than that of existing methods.

关键词： image classification image enhancement learning (artificial intelligence) neural nets image reconstruction estimation theory end to end system hazy image classification deep learning network outdoor images autonomous vehicles geo-mapping manmade extreme atmospheric conditions autonomous vehicle navigation ground truth image transmission map estimation depth map MCP-based image dehazing deep neural network-based identification MCP-based dehazing network image reconstruction mean channel prior air-light TensorFlow platform peak-signal-to-noise ratio structural similarity index

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Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020年 234卷 118237-118237页

作者： Weng, Shizhuang Tang, Peipei Yuan, Hecai Guo, Bingqing Yu, Shuan Huang, Linsheng Xu, Chao Anhui Univ Natl Engn Res Ctr Agroecol Big Data Anal & Applic 111 Jiulong Rd Hefei Peoples R China

The phenomena of rice adulteration and shoddy rice arise continuously in high-quality rice and reduce the interests of producers, consumers and traders. Hyperspectral imaging (HSI) was conducted to determine rice variety using a deep learning network with multiple features, namely, spectroscopy, texture and morphology. HSI images of 10 representative high-quality rice varieties in China were measured. Spectroscopy and morphology were extracted from HSI images and binary images in region of interest, respectively. And texture was obtained from the monochromatic images of characteristic wavelengths which were highly correlated with rice varieties. A deep learning network, namely principal component analysis network (PCANet), was adopted with these features to develop classification models for determining rice variety, and machine learning methods as K-nearest neighbour and random forest were used to compare with PCANet. Meanwhile, multivariate scatter correction, standard normal variate, Savitzky-Golay smoothing and Savitzky-Golay's first-order were applied to eliminate spectral interference, and principal component analysis (PCA) was performed to obtain the main information of high-dimensional features. Multi-feature fusion improved recognition accuracy, and PCANet demonstrated considerable advantage in classification performance. The best result was achieved by PCANet with PCA-processed spectroscopic and texture features with correct classification rates of 98.66% and 98.57% for the training and prediction sets, respectively. In summary, the proposed method provides an accurate identification of rice variety and can be easily extended to the classification, attribution and grading of other agricultural products. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Hyperspectral imaging High-quality rice Multi-feature fusion deep learning network

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An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based deep learning network

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2018年第4期22卷 1227-1237页

作者： Jiang, Hongyang Ma, He Qian, Wei Gao, Mengdi Li, Yan Northeastern Univ Sinodutch Biomed & Informat Engn Sch Shenyang 110819 Liaoning Peoples R China Northeastern Univ Sinodutch Biomed & Informat Engn Sch Minist Educ Shenyang 110819 Liaoning Peoples R China Northeastern Univ Key Lab Med Image Comp Minist Educ Shenyang 110819 Liaoning Peoples R China Univ Texas El Paso Coll Engn El Paso TX 79968 USA Troops 31411 Peoples Liberat Army Shenyang 119402 Liaoning Peoples R China

High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer-aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography image transformation, the lung nodule segmentation, and the feature extraction, to construct a whole CADe system. It is difficult for these schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multigroup patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80.06% with 4.7 false positives per scan and the sensitivity of 94% with 15.1 false positives per scan. The results demonstrate that the multigroup patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.

关键词： Computer aided detection (CADe) computed tomography (CT) images deep learning network Frangi filter lung nodule detection

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A Fast and Robust Range Alignment Method for ISAR Imaging Based on a deep learning network and Regional Multi-Scale Minimum Entropy Method

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REMOTE SENSING 2024年第19期16卷 3677页

作者： Ning, Qianhao Wang, Hongyuan Yan, Zhiqiang Wang, Zijian Lu, Yinxi Harbin Inst Technol Space Opt Engn Res Ctr Harbin 150001 Peoples R China

In inverse synthetic aperture radar (ISAR) imaging, range alignment (RA) is crucial for translational compensation. To address the need for rapidity and accuracy in the RA process, a fast and robust range alignment method is proposed based on a deep learning network and minimum entropy (ME) method. The proposed method consists primarily of two components: the CNN-RNN attention mechanism network (CRAN) architecture and the regional multi-scale minimum entropy (RMSME) method. The main distinction of this method from existing approaches lies in its utilization of a deep learning network for rapid coarse alignment, followed by the search for minimum entropy within local regions at multiple scales. The integration strategy effectively addresses the current challenges of poor generalization in deep learning networks and low efficiency in the traditional ME method. The experimental results of simulation data indicate that the proposed method achieves the best range alignment performance compared to RNN, CRAN, and the traditional ME method. The experimental results of the measured data further validate the practicality of the proposed method. This research provides reference significance for the joint application of deep learning and traditional methods in the RA process.

关键词： inverse synthetic aperture radar (ISAR) range alignment (RA) deep learning network minimum entropy (ME) method

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Semantic Pneumonia Segmentation and Classification for Covid-19 Using deep learning network

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Computers, Materials & Continua 2022年第10期73卷 1141-1158页

作者： M.M.Lotfy Hazem M.El-Bakry M.M.Elgayar Shaker El-Sappagh G.Abdallah M.I A.A.Soliman Kyung Sup Kwak Department of Mathematics Faculty of ScienceArish UniversityArish45511Egypt Department of Information Systems Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Centro Singular de Investigación en Tecnoloxías Intelixentes(CiTIUS) Universidade de Santiago de CompostelaSantiago de Compostela15705Spain Information Systems Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13512Egypt Department of Information and Communication Engineering Inha UniversityIncheon22212Korea

Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people,and its high number of deaths also by 7%.For that purpose,a proposed model of several stages was *** first stage is optimizing the images using dynamic adaptive histogram equalization,performing a semantic segmentation using deepLabv3Plus,then augmenting the data by flipping it horizontally,rotating it,then flipping it *** second stage builds a custom convolutional neural network model using several pre-trained ***,the model compares the pre-trained data to the new output,while repeatedly trimming the best-performing models to reduce complexity and improve memory *** experiments were done using different techniques and ***,the proposed model achieved an average accuracy of 99.6%and an area under the curve of 0.996 in the Covid-19 *** paper will discuss how to train a customized intelligent convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

关键词： SARS-COV2 COVID-19 pneumonia deep learning network semantic segmentation smart classification

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