With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the ...With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the transition toward an intent-driven task-oriented coordination paradigm across the space,ground,and user segments.This study presents a novel intent-driven task-oriented network(IDTN)framework to address task scheduling and resource allocation challenges in SINs.The scheduling problem is formulated as a three-sided matching game that incorporates the preference attributes of entities across all network segments.To manage the variability of random task arrivals and dynamic resources,a context-aware linear upper-confidence-bound online learning mechanism is integrated to reduce decision-making uncertainty.Simulation results demonstrate the effectiveness of the proposed IDTN framework.Compared with conventional baseline methods,the framework achieves significant performance improvements,including a 4.4%-28.9%increase in average system reward,a 6.2%-34.5%improvement in resource utilization,and a 5.6%-35.7%enhancement in user satisfaction.The proposed framework is expected to facilitate the integration and orchestration of space-based platforms.展开更多
Session-based recommendation systems(SBR)are pivotal in suggesting items by analyzing anonymized sequences of user interactions.Traditional methods,while competent,often fall short in two critical areas:they fail to a...Session-based recommendation systems(SBR)are pivotal in suggesting items by analyzing anonymized sequences of user interactions.Traditional methods,while competent,often fall short in two critical areas:they fail to address potential inter-session item transitions,which are behavioral dependencies that extend beyond individual session boundaries,and they rely on monolithic item aggregation to construct session representations.This approach does not capture the multi-scale and heterogeneous nature of user intent,leading to a decrease in modeling accuracy.To overcome these limitations,a novel approach called HMGS has been introduced.This system incorporates dual graph architectures to enhance the recommendation process.A global transition graph captures latent cross-session item dependencies,while a heterogeneous intra-session graph encodesmulti-scale item embeddings through localized feature propagation.Additionally,amulti-tier graphmatchingmechanism aligns user preference signals across different granularities,significantly improving interest localization accuracy.Empirical validation on benchmark datasets(Tmall and Diginetica)confirms HMGS’s efficacy against state-of-the-art baselines.Quantitative analysis reveals performance gains of 20.54%and 12.63%in Precision@10 on Tmall and Diginetica,respectively.Consistent improvements are observed across auxiliary metrics,with MRR@10,Precision@20,and MRR@20 exhibiting enhancements between 4.00%and 21.36%,underscoring the framework’s robustness in multi-faceted recommendation scenarios.展开更多
The impedance characteristics of distributed amplifiers are analyzed based on T-type matching networks, and a distributed power amplifier consisting of three gain cells is proposed. Non-uniform T-type matching network...The impedance characteristics of distributed amplifiers are analyzed based on T-type matching networks, and a distributed power amplifier consisting of three gain cells is proposed. Non-uniform T-type matching networks are adopted to make the impedance of artificial transmission lines connected to the gate and drain change stage by stage gradually, which provides good impedance matching and improves the output power and efficiency. The measurement results show that the amplifier gives an average forward gain of 6 dB from 3 to 16. 5 GHz. In the desired band, the input return loss is typically less than - 9. 5 dB, and the output return loss is better than -8.5 dB. The output power at 1-dB gain compression point is from 3.6 to 10. 6 dBm in the band of 2 to 16 GHz while the power added efficiency （PAE） is from 2% to 12. 5% . The power consumption of the amplifier is 81 mW with a supply of 1.8 V, and the chip area is 0.91 mm × 0.45 mm.展开更多
This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history matchi...This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history matching.The proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering schemes.DNN functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs geomodels.The iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration step.The proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population uncertainty.Iterative learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata matching.This confirms the proposed workflow constructs more plausible geo-models.The workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.展开更多
Domain adaptation and adversarial networks are two main approaches for transfer learning.Domain adaptation methods match the mean values of source and target domains,which requires a very large batch size during train...Domain adaptation and adversarial networks are two main approaches for transfer learning.Domain adaptation methods match the mean values of source and target domains,which requires a very large batch size during training.However,adversarial networks are usually unstable when training.In this paper,we propose a joint method of feature matching and adversarial networks to reduce domain discrepancy and mine domaininvariant features from the local and global aspects.At the same time,our method improves the stability of training.Moreover,the method is embedded into a unified convolutional neural network that can be easily optimized by gradient descent.Experimental results show that our joint method can yield the state-of-the-art results on three common public datasets.展开更多
For the problem of track correlation failure under the influence of sensor system deviation in wireless sensor networks,a new track correlation method which is based on relative positional relation chart matching is p...For the problem of track correlation failure under the influence of sensor system deviation in wireless sensor networks,a new track correlation method which is based on relative positional relation chart matching is proposed.This method approximately simulates the track correlation determination process using artificial data,and integrally matches the relative position relation between multiple targets in the common measuring space of various sensors in order to fulfill the purpose of multi-target track correlation.The simulation results show that this method has high correlation accuracy and robustness.展开更多
Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph structured data. A graph ma...Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph structured data. A graph may contain many subgraphs isomorphic to a given target graph. In this paper GNN is modeled to identify a subgraph that matches the target graph along with its characteristics. The simulation results show that GNN is capable of identifying a target sub-graph in a graph.展开更多
The stereo matching method based on a space-aware network is proposed, which divides the network into threesections: Basic layer, scale layer, and decision layer. This division is beneficial to integrate residue netwo...The stereo matching method based on a space-aware network is proposed, which divides the network into threesections: Basic layer, scale layer, and decision layer. This division is beneficial to integrate residue network and densenetwork into the space-aware network model. The vertical splitting method for computing matching cost by usingthe space-aware network is proposed for solving the limitation of GPU RAM. Moreover, a hybrid loss is broughtforward to boost the performance of the proposed deep network. In the proposed stereo matching method, thespace-aware network is used to calculate the matching cost and then cross-based cost aggregation and semi-globalmatching are employed to compute a disparity map. Finally, a disparity-post processing method is utilized suchas subpixel interpolation, median filter, and bilateral filter. The experimental results show this method has a goodperformance on running time and accuracy, with a percentage of erroneous pixels of 1.23% on KITTI 2012 and1.94% on KITTI 2015.展开更多
Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important dire...Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important direction and has achieved fruitful results.In this paper,amethodof soft tissue surface feature tracking basedonadepthmatching network is proposed.This method is described based on the triangular matching algorithm.First,we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm.The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set.After the training,the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame.From this matrix,the inter-frame feature matching results can be obtained.In this way,the inter-frame speckle tracking is completed.On this basis,the results of this method are compared with the matching results based on the convolutional neural network.The experimental results show that the proposed method has higher matching accuracy.In particular,the accuracy of the MNIST handwritten data set has reached more than 90%.展开更多
Among mobile users, ad-hoc social network (ASN) is becoming a popular platform to connect and share their interests anytime anywhere. Many researchers and computer scientists investigated ASN architecture, implementat...Among mobile users, ad-hoc social network (ASN) is becoming a popular platform to connect and share their interests anytime anywhere. Many researchers and computer scientists investigated ASN architecture, implementation, user experience, and different profile matching algorithms to provide better user experience in ad-hoc social network. We emphasize that strength of an ad-hoc social network depends on a good profile-matching algorithm that provides meaningful friend suggestions in proximity. Keeping browsing history is a good way to determine user’s interest, however, interests change with location. This paper presents a novel profile-matching algorithm for automatically building a user profile based on dynamic GPS (Global Positing System) location and browsing history of users. Building user profile based on GPS location of a user provides benefits to ASN users as this profile represents user’s dynamic interests that keep changing with location e.g. office, home, or some other location. Proposed profile-matching algorithm maintains multiple local profiles based on location of mobile device.展开更多
This article focuses on the first aspect of the album of deep learning: the deep convolutional method. The traditional matching point extraction algorithm typically uses manually designed feature descriptors and the s...This article focuses on the first aspect of the album of deep learning: the deep convolutional method. The traditional matching point extraction algorithm typically uses manually designed feature descriptors and the shortest distance between them to match as the matching criterion. The matching result can easily fall into a local extreme value, which causes missing of the partial matching point. Targeting this problem, we introduce a two-channel deep convolutional neural network based on spatial scale convolution, which performs matching pattern learning between images to realize satellite image matching based on a deep convolutional neural network. The experimental results show that the method can extract the richer matching points in the case of heterogeneous, multi-temporal and multi-resolution satellite images, compared with the traditional matching method. In addition, the accuracy of the final matching results can be maintained at above 90%.展开更多
Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck ...Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.展开更多
In this paper,we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data.The matching process is:Firstly,meshes are extracted from two different scales road ...In this paper,we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data.The matching process is:Firstly,meshes are extracted from two different scales road data.Secondly,several basic meshes in the larger scale road network will be merged into a composite one which is matched with one mesh in the smaller scale road network,to complete the N∶1(N>1)and 1∶1 matching.Thirdly,meshes of the two different scale road data with M∶N(M>1,N>1)matching relationships will be matched.Finally,roads will be classified into two categories under the constraints of meshes:mesh boundary roads and mesh internal roads,and then matchings between the two scales meshes will be carried out within their own categories according to the matching relationships.The results show that roads of different scales will be more precisely matched using the proposed method.展开更多
In view of the “Node-Arc” data model of road network in the aspect of structured expressing the deficiencies, the hierarchical area partitioning of road network based on the principle of stroke, which made road netw...In view of the “Node-Arc” data model of road network in the aspect of structured expressing the deficiencies, the hierarchical area partitioning of road network based on the principle of stroke, which made road network space structure characteristics of the expression with the hierarchical feature was designed. Based on road hierarchy and connected relationship with the area domain boundaries, the road in the area was hierarchically divided. A hierarchical model was established based on “whole-part-object” data model. Finally, the model of urban road network matching is proposed, which used consistency evaluation model selected matching objects from high-grade road to the low-level road. The experiment results indicated that the method was suitable to solve the road matching problem with typical urban features.展开更多
With the rapid development of intelligent transportation, carpooling with the help of Vehicular Networks plays an important role in improving transportati<span>on efficiency and solving environmental problems. H...With the rapid development of intelligent transportation, carpooling with the help of Vehicular Networks plays an important role in improving transportati<span>on efficiency and solving environmental problems. However, attackers us</span>ually launch attacks and cause privacy leakage of carpooling users. In addition, the trust issue between unfamiliar vehicles and passengers reduces the efficiency of carpooling. To address these issues, this paper introduced a trusted and pr<span>ivacy-preserving carpooling matching scheme in Vehicular Networks (T</span>PCM). TPC<span>M scheme introduced travel preferences during carpooling matching, according to the passengers’ individual travel preferences needs, which adopt</span>ed th<span>e privacy set intersection technology based on the Bloom filter to match t</span>he passengers with the vehicles to achieve the purpose of protecting privacy an<span>d meeting the individual needs of passengers simultaneously. TPCM sch</span>eme adopted a multi-faceted trust management model, which calculated the trust val<span>ue of different travel preferences of vehicle based on passengers’ carp</span>ooling feedback to evaluate the vehicle’s trustworthiness from multi-faceted when carpooling matching. Moreover, a series of experiments were conducted to verify the effectiveness and robustness of the proposed scheme. The results show that the proposed scheme has high accuracy, lower computational and communication costs when compared with the existing carpooling schemes.展开更多
The problem of profile matching in electronic social networks asks to find those offering profiles of actors in the network fitting best to a given search profile. In this article this problem is mathematically formul...The problem of profile matching in electronic social networks asks to find those offering profiles of actors in the network fitting best to a given search profile. In this article this problem is mathematically formulated as an optimization problem. For this purpose the underlying search space and the objective function are defined precisely. In particular, data structures of search and offering profiles are proposed, as well as a function measuring the matching of the attributes of a search profile with the corresponding attributes of an offering profile. This objective function, given in Equation (29), is composed of the partial matching degrees for numerical attributes, discrete non-numerical attributes, and fields of interests, respectively. For the matching degree of numerical profile attributes a fuzzy value approach is presented, see Equation (22), whereas for the matching degree of fields of interest a new measure function is introduced in Equation (26). The resulting algorithm is illustrated by a concrete example. It not only is applicable to electronic social networks but also could be adapted for resource discovery in grid computation or in matchmaking energy demand and supply in electrical power systems and smart grids, especially to efficiently integrate renewable energy resources.展开更多
Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune de...Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.展开更多
In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities.However,in clini...In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities.However,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and extraction.This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for patients.To address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of EfficientViT.Additionally,a novel lightweight feature extraction and matching network based on attention mechanism is proposed.This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network.It extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur scenarios.Global and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level features.Extensive ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic datasets.Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage efficiency.When compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense models.Furthermore,this method increases the F1 score by 33.2%and accelerates runtime by 70.2%.It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models,with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness.展开更多
In this paper,the recurrent neural network structure of a bidirectional long shortterm memory network(Bi-LSTM)with special memory cells that store information is used to characterize the deep features of the variation...In this paper,the recurrent neural network structure of a bidirectional long shortterm memory network(Bi-LSTM)with special memory cells that store information is used to characterize the deep features of the variation pattern between logging and seismic data.A mapping relationship model between high-frequency logging data and low-frequency seismic data is established via nonlinear mapping.The seismic waveform is infinitely approximated using the logging curve in the low-frequency band to obtain a nonlinear mapping model of this scale,which then stepwise approach the logging curve in the high-frequency band.Finally,a seismic-inversion method of nonlinear mapping multilevel well–seismic matching based on the Bi-LSTM network is developed.The characteristic of this method is that by applying the multilevel well–seismic matching process,the seismic data are stepwise matched to the scale range that is consistent with the logging curve.Further,the matching operator at each level can be stably obtained to effectively overcome the problems that occur in the well–seismic matching process,such as the inconsistency in the scale of two types of data,accuracy in extracting the seismic wavelet of the well-side seismic traces,and multiplicity of solutions.Model test and practical application demonstrate that this method improves the vertical resolution of inversion results,and at the same time,the boundary and the lateral characteristics of the sand body are well maintained to improve the accuracy of thin-layer sand body prediction and achieve an improved practical application effect.展开更多
Tuberculosis(TB)is a severe infection that mostly affects the lungs and kills millions of people’s lives every year.Tuberculosis can be diagnosed using chest X-rays(CXR)and data-driven deep learning(DL)approaches.Bec...Tuberculosis(TB)is a severe infection that mostly affects the lungs and kills millions of people’s lives every year.Tuberculosis can be diagnosed using chest X-rays(CXR)and data-driven deep learning(DL)approaches.Because of its better automated feature extraction capability,convolutional neural net-works(CNNs)trained on natural images are particularly effective in image cate-gorization.A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets.Ten different deep CNNs(Resnet50,Resnet101,Resnet152,InceptionV3,VGG16,VGG19,DenseNet121,DenseNet169,DenseNet201,MobileNet)are trained and tested for identifying TB and normal cases.This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmenta-tion of interest,and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection.Furthermore,this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs.It was able to accurately detect TB from CXR images using pre-processing,data augmentation,and deep CNN models.Without histogram matching the best accuracy,sensitivity,specificity,precision and F1-score in the detection of TB using CXR images among ten models are 99.25%,99.48%,99.52%,99.48%and 99.22%respectively.With histogram matching the best accuracy,sensitivity,specificity,precision and F1-score are 99.58%,99.82%,99.67%,99.65%and 99.56%respectively.The proposed meth-odology,which has cutting-edge performance,will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.展开更多
基金supported by the National Key Research and Development Program of China(2020YFB1807700)Innovation Capability Support Program of Shaanxi(2024RS-CXTD-01).
文摘With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the transition toward an intent-driven task-oriented coordination paradigm across the space,ground,and user segments.This study presents a novel intent-driven task-oriented network(IDTN)framework to address task scheduling and resource allocation challenges in SINs.The scheduling problem is formulated as a three-sided matching game that incorporates the preference attributes of entities across all network segments.To manage the variability of random task arrivals and dynamic resources,a context-aware linear upper-confidence-bound online learning mechanism is integrated to reduce decision-making uncertainty.Simulation results demonstrate the effectiveness of the proposed IDTN framework.Compared with conventional baseline methods,the framework achieves significant performance improvements,including a 4.4%-28.9%increase in average system reward,a 6.2%-34.5%improvement in resource utilization,and a 5.6%-35.7%enhancement in user satisfaction.The proposed framework is expected to facilitate the integration and orchestration of space-based platforms.
基金funded by the State Grid Hebei Electric Power Company(Project Number:KJ2023-093).
文摘Session-based recommendation systems(SBR)are pivotal in suggesting items by analyzing anonymized sequences of user interactions.Traditional methods,while competent,often fall short in two critical areas:they fail to address potential inter-session item transitions,which are behavioral dependencies that extend beyond individual session boundaries,and they rely on monolithic item aggregation to construct session representations.This approach does not capture the multi-scale and heterogeneous nature of user intent,leading to a decrease in modeling accuracy.To overcome these limitations,a novel approach called HMGS has been introduced.This system incorporates dual graph architectures to enhance the recommendation process.A global transition graph captures latent cross-session item dependencies,while a heterogeneous intra-session graph encodesmulti-scale item embeddings through localized feature propagation.Additionally,amulti-tier graphmatchingmechanism aligns user preference signals across different granularities,significantly improving interest localization accuracy.Empirical validation on benchmark datasets(Tmall and Diginetica)confirms HMGS’s efficacy against state-of-the-art baselines.Quantitative analysis reveals performance gains of 20.54%and 12.63%in Precision@10 on Tmall and Diginetica,respectively.Consistent improvements are observed across auxiliary metrics,with MRR@10,Precision@20,and MRR@20 exhibiting enhancements between 4.00%and 21.36%,underscoring the framework’s robustness in multi-faceted recommendation scenarios.
基金The National Natural Science Foundation of China(No.61106021)the Postdoctoral Science Foundation of China(No.2015M582541)+1 种基金the Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.15KJB510020)the Research Fund of Nanjing University of Posts and Telecommunications(No.NY215140,No.NY215167)
文摘The impedance characteristics of distributed amplifiers are analyzed based on T-type matching networks, and a distributed power amplifier consisting of three gain cells is proposed. Non-uniform T-type matching networks are adopted to make the impedance of artificial transmission lines connected to the gate and drain change stage by stage gradually, which provides good impedance matching and improves the output power and efficiency. The measurement results show that the amplifier gives an average forward gain of 6 dB from 3 to 16. 5 GHz. In the desired band, the input return loss is typically less than - 9. 5 dB, and the output return loss is better than -8.5 dB. The output power at 1-dB gain compression point is from 3.6 to 10. 6 dBm in the band of 2 to 16 GHz while the power added efficiency （PAE） is from 2% to 12. 5% . The power consumption of the amplifier is 81 mW with a supply of 1.8 V, and the chip area is 0.91 mm × 0.45 mm.
基金supported by the basic science research program through the National Research Foundation of Korea(NRF)(2020R1F1A1073395)the basic research project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)(GP2021-011,GP2020-031,21-3117)funded by the Ministry of Science and ICT,Korea。
文摘This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history matching.The proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering schemes.DNN functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs geomodels.The iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration step.The proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population uncertainty.Iterative learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata matching.This confirms the proposed workflow constructs more plausible geo-models.The workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.
基金the Aerospace Science and Technology Foundation(No.20115557007)the National Natural Science Foundation of China(No.61673262)the Military Science and Technology Foundation of China(No.18-H863-03-ZT-001-006-06)
文摘Domain adaptation and adversarial networks are two main approaches for transfer learning.Domain adaptation methods match the mean values of source and target domains,which requires a very large batch size during training.However,adversarial networks are usually unstable when training.In this paper,we propose a joint method of feature matching and adversarial networks to reduce domain discrepancy and mine domaininvariant features from the local and global aspects.At the same time,our method improves the stability of training.Moreover,the method is embedded into a unified convolutional neural network that can be easily optimized by gradient descent.Experimental results show that our joint method can yield the state-of-the-art results on three common public datasets.
基金Supported by the National Basic Research Program of China (973 Program) (2006CB303000)
文摘For the problem of track correlation failure under the influence of sensor system deviation in wireless sensor networks,a new track correlation method which is based on relative positional relation chart matching is proposed.This method approximately simulates the track correlation determination process using artificial data,and integrally matches the relative position relation between multiple targets in the common measuring space of various sensors in order to fulfill the purpose of multi-target track correlation.The simulation results show that this method has high correlation accuracy and robustness.
文摘Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph structured data. A graph may contain many subgraphs isomorphic to a given target graph. In this paper GNN is modeled to identify a subgraph that matches the target graph along with its characteristics. The simulation results show that GNN is capable of identifying a target sub-graph in a graph.
基金This work was supported in part by the Heilongjiang Provincial Natural Science Foundation of China under Grant F2018002the Research Funds for the Central Universities under Grants 2572016BB11 and 2572016BB12the Foundation of Heilongjiang Education Department under Grant 1354MSYYB003.
文摘The stereo matching method based on a space-aware network is proposed, which divides the network into threesections: Basic layer, scale layer, and decision layer. This division is beneficial to integrate residue network and densenetwork into the space-aware network model. The vertical splitting method for computing matching cost by usingthe space-aware network is proposed for solving the limitation of GPU RAM. Moreover, a hybrid loss is broughtforward to boost the performance of the proposed deep network. In the proposed stereo matching method, thespace-aware network is used to calculate the matching cost and then cross-based cost aggregation and semi-globalmatching are employed to compute a disparity map. Finally, a disparity-post processing method is utilized suchas subpixel interpolation, median filter, and bilateral filter. The experimental results show this method has a goodperformance on running time and accuracy, with a percentage of erroneous pixels of 1.23% on KITTI 2012 and1.94% on KITTI 2015.
基金supported by the Sichuan Science and Technology Program (Grant:2021YFQ0003,Acquired by Wenfeng Zheng).
文摘Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important direction and has achieved fruitful results.In this paper,amethodof soft tissue surface feature tracking basedonadepthmatching network is proposed.This method is described based on the triangular matching algorithm.First,we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm.The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set.After the training,the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame.From this matrix,the inter-frame feature matching results can be obtained.In this way,the inter-frame speckle tracking is completed.On this basis,the results of this method are compared with the matching results based on the convolutional neural network.The experimental results show that the proposed method has higher matching accuracy.In particular,the accuracy of the MNIST handwritten data set has reached more than 90%.
文摘Among mobile users, ad-hoc social network (ASN) is becoming a popular platform to connect and share their interests anytime anywhere. Many researchers and computer scientists investigated ASN architecture, implementation, user experience, and different profile matching algorithms to provide better user experience in ad-hoc social network. We emphasize that strength of an ad-hoc social network depends on a good profile-matching algorithm that provides meaningful friend suggestions in proximity. Keeping browsing history is a good way to determine user’s interest, however, interests change with location. This paper presents a novel profile-matching algorithm for automatically building a user profile based on dynamic GPS (Global Positing System) location and browsing history of users. Building user profile based on GPS location of a user provides benefits to ASN users as this profile represents user’s dynamic interests that keep changing with location e.g. office, home, or some other location. Proposed profile-matching algorithm maintains multiple local profiles based on location of mobile device.
基金The National Natural Science Foundation of China (41401534)The Open Fund of State Key Laboratory of Geographic Information Engineering (SKLGIE2013-M-3-1).
文摘This article focuses on the first aspect of the album of deep learning: the deep convolutional method. The traditional matching point extraction algorithm typically uses manually designed feature descriptors and the shortest distance between them to match as the matching criterion. The matching result can easily fall into a local extreme value, which causes missing of the partial matching point. Targeting this problem, we introduce a two-channel deep convolutional neural network based on spatial scale convolution, which performs matching pattern learning between images to realize satellite image matching based on a deep convolutional neural network. The experimental results show that the method can extract the richer matching points in the case of heterogeneous, multi-temporal and multi-resolution satellite images, compared with the traditional matching method. In addition, the accuracy of the final matching results can be maintained at above 90%.
基金supported by China MOST project (No.2012BAH46B04)
文摘Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.
基金The National Natural Science Foundation of China(Nos.4110136241471386)。
文摘In this paper,we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data.The matching process is:Firstly,meshes are extracted from two different scales road data.Secondly,several basic meshes in the larger scale road network will be merged into a composite one which is matched with one mesh in the smaller scale road network,to complete the N∶1(N>1)and 1∶1 matching.Thirdly,meshes of the two different scale road data with M∶N(M>1,N>1)matching relationships will be matched.Finally,roads will be classified into two categories under the constraints of meshes:mesh boundary roads and mesh internal roads,and then matchings between the two scales meshes will be carried out within their own categories according to the matching relationships.The results show that roads of different scales will be more precisely matched using the proposed method.
基金The National Natural Science Foundation of China (41471386).
文摘In view of the “Node-Arc” data model of road network in the aspect of structured expressing the deficiencies, the hierarchical area partitioning of road network based on the principle of stroke, which made road network space structure characteristics of the expression with the hierarchical feature was designed. Based on road hierarchy and connected relationship with the area domain boundaries, the road in the area was hierarchically divided. A hierarchical model was established based on “whole-part-object” data model. Finally, the model of urban road network matching is proposed, which used consistency evaluation model selected matching objects from high-grade road to the low-level road. The experiment results indicated that the method was suitable to solve the road matching problem with typical urban features.
文摘With the rapid development of intelligent transportation, carpooling with the help of Vehicular Networks plays an important role in improving transportati<span>on efficiency and solving environmental problems. However, attackers us</span>ually launch attacks and cause privacy leakage of carpooling users. In addition, the trust issue between unfamiliar vehicles and passengers reduces the efficiency of carpooling. To address these issues, this paper introduced a trusted and pr<span>ivacy-preserving carpooling matching scheme in Vehicular Networks (T</span>PCM). TPC<span>M scheme introduced travel preferences during carpooling matching, according to the passengers’ individual travel preferences needs, which adopt</span>ed th<span>e privacy set intersection technology based on the Bloom filter to match t</span>he passengers with the vehicles to achieve the purpose of protecting privacy an<span>d meeting the individual needs of passengers simultaneously. TPCM sch</span>eme adopted a multi-faceted trust management model, which calculated the trust val<span>ue of different travel preferences of vehicle based on passengers’ carp</span>ooling feedback to evaluate the vehicle’s trustworthiness from multi-faceted when carpooling matching. Moreover, a series of experiments were conducted to verify the effectiveness and robustness of the proposed scheme. The results show that the proposed scheme has high accuracy, lower computational and communication costs when compared with the existing carpooling schemes.
文摘The problem of profile matching in electronic social networks asks to find those offering profiles of actors in the network fitting best to a given search profile. In this article this problem is mathematically formulated as an optimization problem. For this purpose the underlying search space and the objective function are defined precisely. In particular, data structures of search and offering profiles are proposed, as well as a function measuring the matching of the attributes of a search profile with the corresponding attributes of an offering profile. This objective function, given in Equation (29), is composed of the partial matching degrees for numerical attributes, discrete non-numerical attributes, and fields of interests, respectively. For the matching degree of numerical profile attributes a fuzzy value approach is presented, see Equation (22), whereas for the matching degree of fields of interest a new measure function is introduced in Equation (26). The resulting algorithm is illustrated by a concrete example. It not only is applicable to electronic social networks but also could be adapted for resource discovery in grid computation or in matchmaking energy demand and supply in electrical power systems and smart grids, especially to efficiently integrate renewable energy resources.
基金funded by the Scientific Research Project of Leshan Normal University(No.2022SSDX002)the Scientific Plan Project of Leshan(No.22NZD012).
文摘Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.
基金This work was supported by Science and Technology Cooperation Special Project of Shijiazhuang(SJZZXA23005).
文摘In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities.However,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and extraction.This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for patients.To address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of EfficientViT.Additionally,a novel lightweight feature extraction and matching network based on attention mechanism is proposed.This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network.It extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur scenarios.Global and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level features.Extensive ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic datasets.Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage efficiency.When compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense models.Furthermore,this method increases the F1 score by 33.2%and accelerates runtime by 70.2%.It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models,with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness.
基金supported by the National Major Science and Technology Special Project(No.2016ZX05026-002).
文摘In this paper,the recurrent neural network structure of a bidirectional long shortterm memory network(Bi-LSTM)with special memory cells that store information is used to characterize the deep features of the variation pattern between logging and seismic data.A mapping relationship model between high-frequency logging data and low-frequency seismic data is established via nonlinear mapping.The seismic waveform is infinitely approximated using the logging curve in the low-frequency band to obtain a nonlinear mapping model of this scale,which then stepwise approach the logging curve in the high-frequency band.Finally,a seismic-inversion method of nonlinear mapping multilevel well–seismic matching based on the Bi-LSTM network is developed.The characteristic of this method is that by applying the multilevel well–seismic matching process,the seismic data are stepwise matched to the scale range that is consistent with the logging curve.Further,the matching operator at each level can be stably obtained to effectively overcome the problems that occur in the well–seismic matching process,such as the inconsistency in the scale of two types of data,accuracy in extracting the seismic wavelet of the well-side seismic traces,and multiplicity of solutions.Model test and practical application demonstrate that this method improves the vertical resolution of inversion results,and at the same time,the boundary and the lateral characteristics of the sand body are well maintained to improve the accuracy of thin-layer sand body prediction and achieve an improved practical application effect.
文摘Tuberculosis(TB)is a severe infection that mostly affects the lungs and kills millions of people’s lives every year.Tuberculosis can be diagnosed using chest X-rays(CXR)and data-driven deep learning(DL)approaches.Because of its better automated feature extraction capability,convolutional neural net-works(CNNs)trained on natural images are particularly effective in image cate-gorization.A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets.Ten different deep CNNs(Resnet50,Resnet101,Resnet152,InceptionV3,VGG16,VGG19,DenseNet121,DenseNet169,DenseNet201,MobileNet)are trained and tested for identifying TB and normal cases.This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmenta-tion of interest,and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection.Furthermore,this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs.It was able to accurately detect TB from CXR images using pre-processing,data augmentation,and deep CNN models.Without histogram matching the best accuracy,sensitivity,specificity,precision and F1-score in the detection of TB using CXR images among ten models are 99.25%,99.48%,99.52%,99.48%and 99.22%respectively.With histogram matching the best accuracy,sensitivity,specificity,precision and F1-score are 99.58%,99.82%,99.67%,99.65%and 99.56%respectively.The proposed meth-odology,which has cutting-edge performance,will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.
