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
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
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
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
The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the posi...The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.展开更多
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
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
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
Based on sedimentary characteristics of the fine-grained rocks of the lower submember of second member of the Lower Cretaceous Shahezi Formation(K_(1)sh_(2)^(L))in the Lishu rift depression,combined with methods of or...Based on sedimentary characteristics of the fine-grained rocks of the lower submember of second member of the Lower Cretaceous Shahezi Formation(K_(1)sh_(2)^(L))in the Lishu rift depression,combined with methods of organic petrology,analysis of major and trace elements as well as biological marker compound,the enrichment conditions and enrichment model of organic matter in the fine-grained sedimentary rocks in volcanic rift lacustrine basin are investigated.The change of sedimentary paleoenvironment controls the vertical distribution of different lithofacies types in the K_(1)sh_(2)^(L)and divides it into the upper and lower parts.The lower part contains massive siliceous mudstone with bioclast-bearing siliceous mudstone,whereas the upper part is mostly composed of laminated siliceous shale and laminated fine-grained mixed shale.The kerogen types of organic matter in the lower and upper parts are typesⅡ_(2)–Ⅲand typesⅠ–Ⅱ_(1),respectively.The organic carbon content in the upper part is higher than that in the lower part generally.The enrichment of organic matter in volcanic rift lacustrine basin is subjected to three favorable conditions.First,continuous enhancement of rifting is the direct factor increasing the paleo-water depth,and the rise of base level leads to the expansion of deep-water mudstone/shale deposition range.Second,relatively strong underwater volcanic eruption and rifting are simultaneous,and such event can provide a lot of nutrients for the lake basin,which is conducive to the bloom of algae,resulting in higher productivity of typesⅠ–Ⅱ_(1)kerogen.Third,the relatively dry paleoclimate leads to a decrease in input of fresh water and terrestrial materials,including TypeⅢkerogen from terrestrial higher plants,resulting in a water body with higher salinity and anoxic stratification,which is more favorable for preservation of organic matter.The organic matter enrichment model of fine-grained sedimentary rocks of volcanic rift lacustrine basin is established,which is of reference significance to the understanding of the organic matter enrichment mechanism of fine-grained sedimentary rocks of Shahezi Formation in Songliao Basin and even in the northeast China.展开更多
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
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
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
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
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
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
The fine-grained sedimentary rocks have numerous bedding-parallel fractures that are essential for the migration,enrichment,and efficient development of oil and gas.However,because of their variety and the complexity ...The fine-grained sedimentary rocks have numerous bedding-parallel fractures that are essential for the migration,enrichment,and efficient development of oil and gas.However,because of their variety and the complexity of the factors that affect them,their spatial prediction by the industrial community becomes challenging.Based on sample cores,thin sections,and well-logging and seismic data,this study employed a multi-scale data matching approach to quantitatively predict the development of bedding-parallel fractures and investigate their spatial distribution.Bedding-parallel fractures in the Lucaogou Formation in Jimusar Sag frequently occur along preexisting bedding planes and lithological interfaces.Unfilled bedding-parallel fractures inside or near source-rocks exhibit enhanced oil-bearing capacity.They were identified on micro-resistivity scanning images by the presence of regularly continuous black or nearly black sinusoidal curves.Overall,the developmental degree of bedding-parallel fractures was positively related to the brittle mineral and total organic carbon contents and negatively related to single reservoir interval thickness.The maintained porosity of the reservoir matrix contributed to a thorough response to factors affecting the development of bedding-parallel fractures.Here,an effective and objective method was proposed for predicting the development and distribution of bedding-parallel fractures in the fine-grained sedimentary rocks.The method was based on the matched reservoir interval density,reservoir interval density,and matched sweet spot density of bedding-parallel fractures.The prediction method integrated the significant advantages of high vertical resolution from logging curves and strong lateral continuity from seismic data.The average relative prediction error was 8%in the upper sweet spot in the Lucaogou Formation,indicating that the evaluation parameters for bedding-parallel fractures in fine-grained sedimentary rocks were reasonable and reliable and that the proposed prediction method has a stronger adaptability than the previously reported methods.The workflow based on multi-scale matching and stepwise progression can be applied in similar fine-grained sedimentary rocks,providing reliable technological support for the exploration and development of hydrocarbons.展开更多
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
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
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
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
基金supported by the National Key R&D Program of China(2024YFB4609801)the National Natural Science Foundation of China(52075289)the Tsinghua-Jiangyin Innovation Special Fund(TJISF,2023JYTH0104).
文摘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.
基金Project supported by the National Natural Science Foundation of China(Grant No.12305303)the Natural Science Foundation of Hunan Province of China(Grant Nos.2023JJ40520,2024JJ2044,and 2021JJ40444)+3 种基金the Science and Technology Innovation Program of Hunan Province,China(Grant No.2020RC3054)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(Grant No.CX20240831)the Natural Science Basic Research Plan in the Shaanxi Province of China(Grant No.2023-JC-QN0015)the Doctoral Research Fund of University of South China(Grant No.200XQD033)。
文摘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.
基金partially supported by MICIU MCIN/AEI/10.13039/501100011033Spain with grant PID2020-118265GB-C42,-C44,PRTR-C17.I01+1 种基金Generalitat Valenciana,Spain with grant CIPROM/2022/54,ASFAE/2022/031,CIAPOS/2021/114the EU NextGenerationEU,ESF funds,and the National Science Centre (NCN),Poland (grant No.2020/39/D/ST2/00466)
文摘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.
基金funded by the Fundamental Research Project of CNPC Geophysical Key Lab(2022DQ0604-4)the Strategic Cooperation Technology Projects of China National Petroleum Corporation and China University of Petroleum-Beijing(ZLZX 202003)。
文摘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.
基金the Open Project of Sichuan Provincial Key Laboratory of Philosophy and Social Science for Language Intelligence in Special Education under Grant No.YYZN-2023-4the Ph.D.Fund of Chengdu Technological University under Grant No.2020RC002.
文摘The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12275179 and 11875042)the Natural Science Foundation of Shanghai Municipality,China(Grant No.21ZR1443900)。
文摘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.
基金supported by the National Natural Science Foundation of China (Nos.52274048 and 52374017)Beijing Natural Science Foundation (No.3222037)the CNPC 14th five-year perspective fundamental research project (No.2021DJ2104)。
文摘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.
基金National Natural Science Foundation of China(1180500311947102+4 种基金12004005)Natural Science Foundation of Anhui Province(2008085MA162008085QA26)University Synergy Innovation Program of Anhui Province(GXXT-2022-039)State Key Laboratory of Advanced Electromagnetic Technology(Grant No.AET 2024KF006)。
文摘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.
基金Supported by the National Science and Technology Major Project of China(2017ZX05009-002)National Natural Science Foundation of China(41772090)。
文摘Based on sedimentary characteristics of the fine-grained rocks of the lower submember of second member of the Lower Cretaceous Shahezi Formation(K_(1)sh_(2)^(L))in the Lishu rift depression,combined with methods of organic petrology,analysis of major and trace elements as well as biological marker compound,the enrichment conditions and enrichment model of organic matter in the fine-grained sedimentary rocks in volcanic rift lacustrine basin are investigated.The change of sedimentary paleoenvironment controls the vertical distribution of different lithofacies types in the K_(1)sh_(2)^(L)and divides it into the upper and lower parts.The lower part contains massive siliceous mudstone with bioclast-bearing siliceous mudstone,whereas the upper part is mostly composed of laminated siliceous shale and laminated fine-grained mixed shale.The kerogen types of organic matter in the lower and upper parts are typesⅡ_(2)–Ⅲand typesⅠ–Ⅱ_(1),respectively.The organic carbon content in the upper part is higher than that in the lower part generally.The enrichment of organic matter in volcanic rift lacustrine basin is subjected to three favorable conditions.First,continuous enhancement of rifting is the direct factor increasing the paleo-water depth,and the rise of base level leads to the expansion of deep-water mudstone/shale deposition range.Second,relatively strong underwater volcanic eruption and rifting are simultaneous,and such event can provide a lot of nutrients for the lake basin,which is conducive to the bloom of algae,resulting in higher productivity of typesⅠ–Ⅱ_(1)kerogen.Third,the relatively dry paleoclimate leads to a decrease in input of fresh water and terrestrial materials,including TypeⅢkerogen from terrestrial higher plants,resulting in a water body with higher salinity and anoxic stratification,which is more favorable for preservation of organic matter.The organic matter enrichment model of fine-grained sedimentary rocks of volcanic rift lacustrine basin is established,which is of reference significance to the understanding of the organic matter enrichment mechanism of fine-grained sedimentary rocks of Shahezi Formation in Songliao Basin and even in the northeast China.
基金financially supported by China Postdoctoral Science Foundation(Grant No.2023M730365)Natural Science Foundation of Hubei Province of China(Grant No.2023AFB232)。
文摘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.
基金supported in part by the National Natural Foundation of China(No.62176147)。
文摘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.
基金supported by National Key R&D Program of China(No.2022YFB3104500)Natural Science Foundation of Jiangsu Province(No.BK20222013)Scientific Research Foundation of Nanjing Institute of Technology(No.3534113223036)。
文摘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.
基金Project supported by the National Natural Science Foundation of China(Grant No.12305303)the Natural Science Foundation of Hunan Province of China(Grant Nos.2023JJ40520,2021JJ40444,and 2019JJ30019)+3 种基金the Research Foundation of Education Bureau of Hunan Province of China(Grant No.20A430)the Science and Technology Innovation Program of Hunan Province(Grant No.2020RC3054)the Natural Science Basic Research Plan in the Shaanxi Province of China(Grant No.2023-JC-QN-0015)the Doctoral Research Fund of University of South China。
文摘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.
文摘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.
基金Project supported by the Natural Science Foundation of Shandong Province,China (Grant No.ZR2021MF049)the Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos.ZR2022LLZ012 and ZR2021LLZ001)。
文摘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.
基金financially supported by the National Natural Science Foundation of China(No.41872116)National Science and Technology Major Project(No.2017ZX05001-003)+1 种基金Initial Scientific Research Foundation of Xinjiang University for Doctor(No.620321016)Research Foundation of Tianchi Outstanding Doctor(No.51052300560)。
文摘The fine-grained sedimentary rocks have numerous bedding-parallel fractures that are essential for the migration,enrichment,and efficient development of oil and gas.However,because of their variety and the complexity of the factors that affect them,their spatial prediction by the industrial community becomes challenging.Based on sample cores,thin sections,and well-logging and seismic data,this study employed a multi-scale data matching approach to quantitatively predict the development of bedding-parallel fractures and investigate their spatial distribution.Bedding-parallel fractures in the Lucaogou Formation in Jimusar Sag frequently occur along preexisting bedding planes and lithological interfaces.Unfilled bedding-parallel fractures inside or near source-rocks exhibit enhanced oil-bearing capacity.They were identified on micro-resistivity scanning images by the presence of regularly continuous black or nearly black sinusoidal curves.Overall,the developmental degree of bedding-parallel fractures was positively related to the brittle mineral and total organic carbon contents and negatively related to single reservoir interval thickness.The maintained porosity of the reservoir matrix contributed to a thorough response to factors affecting the development of bedding-parallel fractures.Here,an effective and objective method was proposed for predicting the development and distribution of bedding-parallel fractures in the fine-grained sedimentary rocks.The method was based on the matched reservoir interval density,reservoir interval density,and matched sweet spot density of bedding-parallel fractures.The prediction method integrated the significant advantages of high vertical resolution from logging curves and strong lateral continuity from seismic data.The average relative prediction error was 8%in the upper sweet spot in the Lucaogou Formation,indicating that the evaluation parameters for bedding-parallel fractures in fine-grained sedimentary rocks were reasonable and reliable and that the proposed prediction method has a stronger adaptability than the previously reported methods.The workflow based on multi-scale matching and stepwise progression can be applied in similar fine-grained sedimentary rocks,providing reliable technological support for the exploration and development of hydrocarbons.
基金National Key Research and Development Program of China (Grant No. 2022YFE0102700)National Natural Science Foundation of China (Grant No. 52102420)+2 种基金research project “Safe Da Batt” (03EMF0409A) funded by the German Federal Ministry of Digital and Transport (BMDV)China Postdoctoral Science Foundation (Grant No. 2023T160085)Sichuan Science and Technology Program (Grant No. 2024NSFSC0938)。
文摘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.
文摘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.
基金Supported by the National Natural Science Foundation of China(61071165)the Aeronautical Science Foundation of China(20080152004)+1 种基金the Ph.D.Programs Foundation of Ministry of Education of China(20070280531)the Program for New Century Excellent Talents in University(NCET-09-0069)~~
文摘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%.
基金supported by the National Natural Science Foundation of China (No.61631013)National Key Basic Research Program of China (973 Program) (No. 2013CB329002)National Major Project (NO. 2018ZX03001006003)
文摘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.
