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Photonic Chip Based on Ultrafast Laser-Induced Reversible Phase Change for Convolutional Neural Network
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作者 Jiawang Xie Jianfeng Yan +5 位作者 Haoze Han Yuzhi Zhao Ma Luo Jiaqun Li Heng Guo Ming Qiao 《Nano-Micro Letters》 2025年第8期53-66,共14页
Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence.Due to the advantages in computing speed,integrated photonic chips... Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence.Due to the advantages in computing speed,integrated photonic chips have attracted wide research attention on performing convolutional neural network algorithm.Programmable photonic chips are vital for achieving practical applications of photonic computing.Herein,a programmable photonic chip based on ultrafast laser-induced phase change is fabricated for photonic computing.Through designing the ultrafast laser pulses,the Sb film integrated into photonic waveguides can be reversibly switched between crystalline and amorphous phase,resulting in a large contrast in refractive index and extinction coefficient.As a consequence,the light transmission of waveguides can be switched between write and erase states.To determine the phase change time,the transient laser-induced phase change dynamics of Sb film are revealed at atomic scale,and the time-resolved transient reflectivity is measured.Based on the integrated photonic chip,photonic convolutional neural networks are built to implement machine learning algorithm,and images recognition task is achieved.This work paves a route for fabricating programmable photonic chips by designed ultrafast laser,which will facilitate the application of photonic computing in artificial intelligence. 展开更多
关键词 Photonic chip Ultrafast laser Phase change convolutional neural network
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Atmospheric neutron single event effects for multiple convolutional neural networks based on 28-nm and 16-nm SoC
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作者 Xu Zhao Xuecheng Du +3 位作者 Chao Ma Zhiliang Hu Weitao Yang Bo Zheng 《Chinese Physics B》 2025年第1期477-484,共8页
The single event effects(SEEs)evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network(CNN)models(Yolov3,MNIST,and ResNet50)in the atmospheric neutron irradiation spect... The single event effects(SEEs)evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network(CNN)models(Yolov3,MNIST,and ResNet50)in the atmospheric neutron irradiation spectrometer(ANIS)at the China Spallation Neutron Source(CSNS).The Yolov3 and MNIST models were implemented on the XILINX28-nm system-on-chip(So C).Meanwhile,the Yolov3 and ResNet50 models were deployed on the XILINX 16-nm Fin FET Ultra Scale+MPSoC.The atmospheric neutron SEEs on the tested CNN systems were comprehensively evaluated from six aspects,including chip type,network architecture,deployment methods,inference time,datasets,and the position of the anchor boxes.The various types of SEE soft errors,SEE cross-sections,and their distribution were analyzed to explore the radiation sensitivities and rules of 28-nm and 16-nm SoC.The current research can provide the technology support of radiation-resistant design of CNN system for developing and applying high-reliability,long-lifespan domestic artificial intelligence chips. 展开更多
关键词 single event effects atmospheric neutron system on chip convolutional neural network
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Reconstruction of pile-up events using a one-dimensional convolutional autoencoder for the NEDA detector array
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作者 J.M.Deltoro G.Jaworski +15 位作者 A.Goasduff V.González A.Gadea M.Palacz J.J.Valiente-Dobón J.Nyberg S.Casans A.E.Navarro-Antón E.Sanchis G.de Angelis A.Boujrad S.Coudert T.Dupasquier S.Ertürk O.Stezowski R.Wadsworth 《Nuclear Science and Techniques》 2025年第2期62-70,共9页
Pulse pile-up is a problem in nuclear spectroscopy and nuclear reaction studies that occurs when two pulses overlap and distort each other,degrading the quality of energy and timing information.Different methods have ... Pulse pile-up is a problem in nuclear spectroscopy and nuclear reaction studies that occurs when two pulses overlap and distort each other,degrading the quality of energy and timing information.Different methods have been used for pile-up rejection,both digital and analogue,but some pile-up events may contain pulses of interest and need to be reconstructed.The paper proposes a new method for reconstructing pile-up events acquired with a neutron detector array(NEDA)using an one-dimensional convolutional autoencoder(1D-CAE).The datasets for training and testing the 1D-CAE are created from data acquired from the NEDA.The new pile-up signal reconstruction method is evaluated from the point of view of how similar the reconstructed signals are to the original ones.Furthermore,it is analysed considering the result of the neutron-gamma discrimination based on charge comparison,comparing the result obtained from original and reconstructed signals. 展开更多
关键词 1D-CAE Autoencoder CAE convolutional neural network(CNN) Neutron detector Neutron-gamma discrimination(NGD) Machine learning Pulse shape discrimination Pile-up pulse
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An improved deep dilated convolutional neural network for seismic facies interpretation 被引量:1
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作者 Na-Xia Yang Guo-Fa Li +2 位作者 Ting-Hui Li Dong-Feng Zhao Wei-Wei Gu 《Petroleum Science》 SCIE EI CAS CSCD 2024年第3期1569-1583,共15页
With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural network... With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information. 展开更多
关键词 Seismic facies interpretation Dilated convolution Spatial pyramid pooling Internal feature maps Compound loss function
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A novel complex-high-order graph convolutional network paradigm:ChyGCN
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作者 郑和翔 苗书宇 顾长贵 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第5期665-672,共8页
In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability t... In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability to handle complex data correlations in practical applications.These limitations stem from the difficulty in establishing multiple hierarchies and acquiring adaptive weights for each of them.To address this issue,this paper introduces the latest concept of complex hypergraphs and constructs a versatile high-order multi-level data correlation model.This model is realized by establishing a three-tier structure of complexes-hypergraphs-vertices.Specifically,we start by establishing hyperedge clusters on a foundational network,utilizing a second-order hypergraph structure to depict potential correlations.For this second-order structure,truncation methods are used to assess and generate a three-layer composite structure.During the construction of the composite structure,an adaptive learning strategy is implemented to merge correlations across different levels.We evaluate this model on several popular datasets and compare it with recent state-of-the-art methods.The comprehensive assessment results demonstrate that the proposed model surpasses the existing methods,particularly in modeling implicit data correlations(the classification accuracy of nodes on five public datasets Cora,Citeseer,Pubmed,Github Web ML,and Facebook are 86.1±0.33,79.2±0.35,83.1±0.46,83.8±0.23,and 80.1±0.37,respectively).This indicates that our approach possesses advantages in handling datasets with implicit multi-level structures. 展开更多
关键词 raph convolutional network complex modeling complex hypergraph
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An efficient data-driven global sensitivity analysis method of shale gas production through convolutional neural network
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作者 Liang Xue Shuai Xu +4 位作者 Jie Nie Ji Qin Jiang-Xia Han Yue-Tian Liu Qin-Zhuo Liao 《Petroleum Science》 SCIE EI CAS CSCD 2024年第4期2475-2484,共10页
The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively... The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages. 展开更多
关键词 Shale gas Global sensitivity convolutional neural network DATA-DRIVEN
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High-resolution reconstruction of the ablative RT instability flowfield via convolutional neural networks
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作者 Xia Zhiyang Kuang Yuanyuan +1 位作者 Lu Yan Yang Ming 《强激光与粒子束》 CAS CSCD 北大核心 2024年第12期42-49,共8页
High-resolution flow field data has important applications in meteorology,aerospace engineering,high-energy physics and other fields.Experiments and numerical simulations are two main ways to obtain high-resolution fl... High-resolution flow field data has important applications in meteorology,aerospace engineering,high-energy physics and other fields.Experiments and numerical simulations are two main ways to obtain high-resolution flow field data,while the high experiment cost and computing resources for simulation hinder the specificanalysis of flow field evolution.With the development of deep learning technology,convolutional neural networks areused to achieve high-resolution reconstruction of the flow field.In this paper,an ordinary convolutional neuralnetwork and a multi-time-path convolutional neural network are established for the ablative Rayleigh-Taylorinstability.These two methods can reconstruct the high-resolution flow field in just a few seconds,and further greatlyenrich the application of high-resolution reconstruction technology in fluid instability.Compared with the ordinaryconvolutional neural network,the multi-time-path convolutional neural network model has smaller error and canrestore more details of the flow field.The influence of low-resolution flow field data obtained by the two poolingmethods on the convolutional neural networks model is also discussed. 展开更多
关键词 convolutional neural networks ablative Rayleigh-Taylor instability high-resolutionreconstruction multi-time-path pooling
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Intelligent geochemical interpretation of mass chromatograms:Based on convolution neural network
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作者 Kai-Ming Su Jun-Gang Lu +2 位作者 Jian Yu Zi-Xing Lu Shi-Jia Chen 《Petroleum Science》 SCIE EI CAS CSCD 2024年第2期752-764,共13页
Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic geochemistry.It is the only approach to capture biomarker features of organic matter and provide... Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic geochemistry.It is the only approach to capture biomarker features of organic matter and provides the key evidence for oil-source correlation and thermal maturity determination.However,the conventional way of processing and interpreting the mass chromatogram is both timeconsuming and labor-intensive,which increases the research cost and restrains extensive applications of this method.To overcome this limitation,a correlation model is developed based on the convolution neural network(CNN)to link the mass chromatogram and biomarker features of samples from the Triassic Yanchang Formation,Ordos Basin,China.In this way,the mass chromatogram can be automatically interpreted.This research first performs dimensionality reduction for 15 biomarker parameters via the factor analysis and then quantifies the biomarker features using two indexes(i.e.MI and PMI)that represent the organic matter thermal maturity and parent material type,respectively.Subsequently,training,interpretation,and validation are performed multiple times using different CNN models to optimize the model structure and hyper-parameter setting,with the mass chromatogram used as the input and the obtained MI and PMI values for supervision(label).The optimized model presents high accuracy in automatically interpreting the mass chromatogram,with R2values typically above 0.85 and0.80 for the thermal maturity and parent material interpretation results,respectively.The significance of this research is twofold:(i)developing an efficient technique for geochemical research;(ii)more importantly,demonstrating the potential of artificial intelligence in organic geochemistry and providing vital references for future related studies. 展开更多
关键词 Organic geochemistry BIOMARKER Mass chromatographic analysis Automated interpretation convolution neural network Machine learning
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Automatic Pavement Crack Detection Based on Octave Convolution Neural Network with Hierarchical Feature Learning
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作者 Minggang Xu Chong Li +1 位作者 Ying Chen Wu Wei 《Journal of Beijing Institute of Technology》 EI CAS 2024年第5期422-435,共14页
Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine ... Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance. 展开更多
关键词 automated pavement crack detection octave convolutional network hierarchical feature multiscale MULTIFREQUENCY
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User Churn Prediction Hierarchical Model Based on Graph Attention Convolutional Neural Networks
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作者 Mei Miao Tang Miao Zhou Long 《China Communications》 SCIE CSCD 2024年第7期169-185,共17页
The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications ... The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses. 展开更多
关键词 cloud-edge cooperative framework GAT-CNN self-attention and graph convolution models subscriber churn prediction
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Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip
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作者 赵旭 杜雪成 +4 位作者 熊旭 马超 杨卫涛 郑波 周超 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第7期638-644,共7页
Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic partic... Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects(SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm systemon-chip(SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips. 展开更多
关键词 single event effects convolutional neural networks alpha particle system on chip fault injection
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A multiscale adaptive framework based on convolutional neural network:Application to fluid catalytic cracking product yield prediction
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作者 Nan Liu Chun-Meng Zhu +1 位作者 Meng-Xuan Zhang Xing-Ying Lan 《Petroleum Science》 SCIE EI CAS CSCD 2024年第4期2849-2869,共21页
Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial pro... Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators.While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables,it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes.In light of this,a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net)is proposed for mining spatiotemporal multiscale information.First,the industrial data from the Fluid Catalytic Cracking(FCC)process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)extract the multi-energy scale information of the feature subset.Then,convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data.Finally,a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output.Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP).Subsequently,the performance of Msrt Net is evaluated in predicting product yield for a 2.80×10^(6) t/a FCC unit,taking diesel and gasoline yield as examples.In conclusion,Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30%in prediction error compared to other time-series models.Furthermore,its robustness and transferability underscore its promising potential for broader applications. 展开更多
关键词 Fluid catalytic cracking Product yield Data-driven modeling Multiscale prediction Data decomposition convolution neural network
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Analysis of learnability of a novel hybrid quantum-classical convolutional neural network in image classification
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作者 程涛 赵润盛 +2 位作者 王爽 王睿 马鸿洋 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第4期275-283,共9页
We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in cl... We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from images.At the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of classification.Finally,we use the MNIST dataset to test the potential of the HQCCNN.The results indicate that the HQCCNN has good performance in solving classification problems.In binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed. 展开更多
关键词 parameterized quantum circuits quantum machine learning hybrid quantum-classical convolutional neural network
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State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks
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作者 Yvxin He Zhongwei Deng +4 位作者 Jue Chen Weihan Li Jingjing Zhou Fei Xiang Xiaosong Hu 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2024年第11期1-11,共11页
A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan.... A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively. 展开更多
关键词 Lithium-ion battery State of health estimation Feature extraction Graph convolutional network Long short-term memory network
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Improved Design and SOVA Algorithm for Serial Concatenated Convolutional Code 被引量:2
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作者 万蕾 匡镜明 《Journal of Beijing Institute of Technology》 EI CAS 2001年第2期180-185,共6页
To improve the performance of the short interleaved serial concatenated convolutional code(SCCC) with low decoding iterative times, the structure of Log MAP algorithm is introduced into the conventional SOVA decoder... To improve the performance of the short interleaved serial concatenated convolutional code(SCCC) with low decoding iterative times, the structure of Log MAP algorithm is introduced into the conventional SOVA decoder to improve its performance at short interleaving delay. The combination of Log MAP and SOVA avoids updating the matrices of the maximum path, and also makes a contribution to the requirement of short delay. The simulation results of several SCCCs show that the improved decoder can obtain satisfied performance with short frame interleaver and it is suitable to the high bit rate low delay communication systems. 展开更多
关键词 serial concatenated convolutional code Turbo code iterative decoder WCDMA
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FAST IMPLEMENTATION OF CONVOLUTION BACKPROJECTION ALGORITHM IN SPOTLIGHT SAR 被引量:1
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作者 聂鑫 朱岱寅 朱兆达 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2010年第4期333-337,共5页
A fast implementation of the convolution backprojection(CBP)algorithm in spotlight synthetic aperture radar(SAR)is presented based on the fast Fourier transform(FFT).Traditionally,the computation of the 'backpr... A fast implementation of the convolution backprojection(CBP)algorithm in spotlight synthetic aperture radar(SAR)is presented based on the fast Fourier transform(FFT).Traditionally,the computation of the 'backprojection' process is expensive,since resampling in the process is implemented by using the interpolation operation.By analyzing the relative location relationship among different pixels,the algorithm realizes the 'backprojection' using a series of FFTs instead of the interpolation operation.The point target simulation validates that the new algorithm accelerates the CBP algorithm,and the computational rate increases about 85%. 展开更多
关键词 synthetic aperture radar(SAR) fast Fourier transforms(FFTs) fast implementation convolution backprojection(CBP)
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Convolutional Neural Networks Based Indoor Wi-Fi Localization with a Novel Kind of CSI Images 被引量:10
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作者 Haihan Li Xiangsheng Zeng +2 位作者 Yunzhou Li Shidong Zhou Jing Wang 《China Communications》 SCIE CSCD 2019年第9期250-260,共11页
Indoor Wi-Fi localization of mobile devices plays a more and more important role along with the rapid growth of location-based services and Wi-Fi mobile devices.In this paper,a new method of constructing the channel s... Indoor Wi-Fi localization of mobile devices plays a more and more important role along with the rapid growth of location-based services and Wi-Fi mobile devices.In this paper,a new method of constructing the channel state information(CSI)image is proposed to improve the localization accuracy.Compared with previous methods of constructing the CSI image,the new kind of CSI image proposed is able to contain more channel information such as the angle of arrival(AoA),the time of arrival(TOA)and the amplitude.We construct three gray images by using phase differences of different antennas and amplitudes of different subcarriers of one antenna,and then merge them to form one RGB image.The localization method has off-line stage and on-line stage.In the off-line stage,the composed three-channel RGB images at training locations are used to train a convolutional neural network(CNN)which has been proved to be efficient in image recognition.In the on-line stage,images at test locations are fed to the well-trained CNN model and the localization result is the weighted mean value with highest output values.The performance of the proposed method is verified with extensive experiments in the representative indoor environment. 展开更多
关键词 convolutional NEURAL network INDOOR WI-FI LOCALIZATION channel state information CSI image
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Face Image Recognition Based on Convolutional Neural Network 被引量:13
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作者 Guangxin Lou Hongzhen Shi 《China Communications》 SCIE CSCD 2020年第2期117-124,共8页
With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communicati... With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communication,image is widely used as a carrier of communication because of its rich content,intuitive and other advantages.Image recognition based on convolution neural network is the first application in the field of image recognition.A series of algorithm operations such as image eigenvalue extraction,recognition and convolution are used to identify and analyze different images.The rapid development of artificial intelligence makes machine learning more and more important in its research field.Use algorithms to learn each piece of data and predict the outcome.This has become an important key to open the door of artificial intelligence.In machine vision,image recognition is the foundation,but how to associate the low-level information in the image with the high-level image semantics becomes the key problem of image recognition.Predecessors have provided many model algorithms,which have laid a solid foundation for the development of artificial intelligence and image recognition.The multi-level information fusion model based on the VGG16 model is an improvement on the fully connected neural network.Different from full connection network,convolutional neural network does not use full connection method in each layer of neurons of neural network,but USES some nodes for connection.Although this method reduces the computation time,due to the fact that the convolutional neural network model will lose some useful feature information in the process of propagation and calculation,this paper improves the model to be a multi-level information fusion of the convolution calculation method,and further recovers the discarded feature information,so as to improve the recognition rate of the image.VGG divides the network into five groups(mimicking the five layers of AlexNet),yet it USES 3*3 filters and combines them as a convolution sequence.Network deeper DCNN,channel number is bigger.The recognition rate of the model was verified by 0RL Face Database,BioID Face Database and CASIA Face Image Database. 展开更多
关键词 convolutional neural network face image recognition machine learning artificial intelligence multilayer information fusion
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Discrete-time movement model of a group of trains on a rail line with stochastic disturbance 被引量:3
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作者 杨立兴 李峰 +1 位作者 高自友 李克平 《Chinese Physics B》 SCIE EI CAS CSCD 2010年第10期184-193,共10页
This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are... This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are defined. To show the dynamic characteristics of train traffic flow with stochastic disturbance, some numerical experiments on a railway line are simulated. The computational results show that the discrete-time movement model can well describe the movements of trains on a rail line with the moving-block signalling system. Comparing with the results of no disturbance, it finds that the traffic capacity of the rail line will decrease with the influence of stochastic disturbance. Additionally, the delays incurred by stochastic disturbance can be propagated to the subsequent trains, and then prolong their traversing time on the rail line. It can provide auxiliary information for rescheduling trains When the stochastic disturbance occurs on the railway. 展开更多
关键词 train movement discrete-time model simulation stochastic disturbance
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Convolutional Neural Network Based on Spatial Pyramid for Image Classification 被引量:2
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作者 Gaihua Wang Meng Lu +2 位作者 Tao Li Guoliang Yuan Wenzhou Liu 《Journal of Beijing Institute of Technology》 EI CAS 2018年第4期630-636,共7页
A novel convolutional neural network based on spatial pyramid for image classification is proposed.The network exploits image features with spatial pyramid representation.First,it extracts global features from an orig... A novel convolutional neural network based on spatial pyramid for image classification is proposed.The network exploits image features with spatial pyramid representation.First,it extracts global features from an original image,and then different layers of grids are utilized to extract feature maps from different convolutional layers.Inspired by the spatial pyramid,the new network contains two parts,one of which is just like a standard convolutional neural network,composing of alternating convolutions and subsampling layers.But those convolution layers would be averagely pooled by the grid way to obtain feature maps,and then concatenated into a feature vector individually.Finally,those vectors are sequentially concatenated into a total feature vector as the last feature to the fully connection layer.This generated feature vector derives benefits from the classic and previous convolution layer,while the size of the grid adjusting the weight of the feature maps improves the recognition efficiency of the network.Experimental results demonstrate that this model improves the accuracy and applicability compared with the traditional model. 展开更多
关键词 convolutional neural network multiscale feature extraction image classification
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