The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
Objective:There is currently no consensus on whether extra-gastrointestinal stromal tumors(EGISTs)and gastrointestinal stromal tumors(GISTs)are the same type of tumor,and whether the diagnosis and treatment of EGISTs ...Objective:There is currently no consensus on whether extra-gastrointestinal stromal tumors(EGISTs)and gastrointestinal stromal tumors(GISTs)are the same type of tumor,and whether the diagnosis and treatment of EGISTs can directly replicate the current diagnostic and treatment standards for GISTs.This study aims to further elucidate the clinical and pathological characteristics,diagnosis,treatment,and prognosis of EGISTs by analyzing the research results of domestic scholars in the field of EGISTs in the past decade.Methods:A review was conducted on original Chinese and English research articles published from 2013 to 2022 focusing on EGISTs.A descriptive approach was used to extract key information from the literature,including patient demographics,tumor location,tumor diameter,mitotic figures,risk stratification,immunohistochemical markers,cell type,and prognostic factors.The data were subjected to statistical analysis.Results:A total of 12 articles containing 780 EGIST patients were included.The male-to female incidence of EGISTs was 0.92꞉1.The most common sites of EGISTs were mesentery(30.96%),peritoneum or retroperitoneum(28.53%),omentum(20.32%),and pelvic cavity(12.52%).52.77%of EGISTs had tumor diameters greater than 10 cm,and the proportions of EGISTs with nuclear fission patterns greater than 5/50 high power field(HPF)and greater than 10/50 HPF were 51.24%and 26.11%,respectively.The proportion of high-risk EGISTs was 79.05%.The positive rates of immune markers CD117,CD34,and DOG-1 in EGISTs were 82.3%,69.0%,and 79.5%,respectively.The proportion of Ki-67>5%was 49.2%,and the proportion of Ki-67>10%was 24.8%.The proportions of EGISTs in spindle cells,epithelial cells,and mixed cells were 74.4%,14.8%,and 13.1%,respectively.The diameter of the tumor,resection method,risk level,Ki-67 index,mitotic counts,presence of rupture/bleeding/necrosis/peripheral tissue invasion/recurrence and metastasis,as well as the use of imatinib treatment after surgery were important factors affecting the prognosis of EGISTs.Conclusion:Current medical research is relatively well cognizant of GISTs with primary sites in the gastrointestinal tract.Compared with GISTs,EGISTs have large tumor diameters,high mitotic counts,a high percentage of high-risk grades,relatively unique molecular expression,and high aggressiveness.EGISTs differ from GISTs in clinicopathological characteristics.Whether EGISTs and GISTs share a common origin remains unclear.If they are distinct tumor entities,separate diagnostic and treatment guidelines for EGISTs should be established.If EGISTs are ultimately confirmed to be a special subtype of GISTs,then directly applying existing GIST-based standards to EGISTs may be inappropriate.A more scientific approach would involve subclassifying EGISTs based on anatomical location and then tailoring treatment strategies accordingly with reference to GIST guidelines.展开更多
Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevita...Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevitably,influencing the quality of enhanced images.To alleviate this problem,a low-light image enhancement model called RetinexNet model based on Retinex theory was proposed in this study.The model was composed of an image decomposition module and a brightness enhancement module.In the decomposition module,a convolutional block attention module(CBAM)was incorporated to enhance feature representation capacity of the network,focusing on crucial features and suppressing irrelevant ones.A multifeature fusion denoising module was designed within the brightness enhancement module,circumventing the issue of feature loss during downsampling.The proposed model outperforms the existing algorithms in terms of PSNR and SSIM metrics on the publicly available datasets LOL and MIT-Adobe FiveK,as well as gives superior results in terms of NIQE metrics on the publicly available dataset LIME.展开更多
Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o...Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.展开更多
The spin-exchange relaxation-free atomic gyroscope,with its exceptionally high theoretical precision,demonstrates immense potential to become the next-generation strategic-grade gyroscope.However,due to technological ...The spin-exchange relaxation-free atomic gyroscope,with its exceptionally high theoretical precision,demonstrates immense potential to become the next-generation strategic-grade gyroscope.However,due to technological noise,there is still a significant gap between its actual precision and theoretical precision.This study identifies the key factor limiting the precision of the SERF gyroscope as coupling noise.By optimizing the detection loop structure,a distinction between the dual-axis signals'response to optical and magnetic fields was achieved-where the optical errors responded similarly,while the response to magnetic noise was opposite.Based on the differences in the optical-magnetic response of the dual-axis signals,empirical mode decomposition was used to decompose the dual-axis gyroscope signals into multiple intrinsic mode functions,and Allan deviation analysis was applied to analyze the noise characteristics of the intrinsic mode functions over various periods.This study successfully reveals that optical errors caused by thermal-optical coupling and long-period magnetic noise induced by thermal-magnetic coupling are the dominant factors limiting the long-term stability of the SERF gyroscope.Based on these analyses,the study concludes that to achieve strategic-grade precision for the SERF gyroscope,it is essential to effectively address the noise issues caused by multi-physical field couplings.展开更多
Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint featur...Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and fur-ther reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power ampli-fiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and ampli-tude of the signal kurtosis (FA-SK) is further proposed. Simula-tion and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.展开更多
In this study,a uniaxial cyclic compression test is conducted on coal-rock composite structures under two cyclic loads using MTSE45.104 testing apparatus to investigate the macro-mesoscopic deformation,damage behavior...In this study,a uniaxial cyclic compression test is conducted on coal-rock composite structures under two cyclic loads using MTSE45.104 testing apparatus to investigate the macro-mesoscopic deformation,damage behavior,and energy evolution characteristics of these structures under different cyclic stress disturbances.Three loading and unloading rates(LURs)are tested to examine the damage behaviors and energy-driven characteristics of the composites.The findings reveal that the energy-driven behavior,mechanical properties,and macro-micro degradation characteristics of the composites are significantly influenced by the loading rate.Under the gradual cyclic loading and unloading(CLU)path with a constant lower limit(path I)and the CLU path with variable upper and lower boundaries(path II),an increase in LURs from 0.05 to 0.15 mm/min reduces the average loading time by 32.39%and 48.60%,respectively.Consequently,the total number of cracks in the samples increases by 1.66-fold for path I and 1.41-fold for path II.As LURs further increase,the energy storage limit of samples expands,leading to a higher proportion of transmatrix and shear cracks.Under both cyclic loading conditions,a broader cyclic stress range promotes energy dissipation and the formation of internal fractures.Notably,at higher loading rates,cracks tend to propagate along primary weak surfaces,leading to an increased incidence of intermatrix fractures.This behavior indicates a microscopic feature of the failure mechanisms in composite structures.These results provide a theoretical basis for elucidating the damage and failure characteristics of coal-rock composite structures under cyclic stress disturbances.展开更多
In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection cr...In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)response.Maximal information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear correlations.The EM response range is utilized to evaluate the sensitivity.The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive.Only the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly reduced.The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method.The number of input parameters decreases from8 to 4.The testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.展开更多
[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
Understanding the physical,mechanical behavior,and seepage characteristics of coal under hydro-mechanical coupling holds significant importance for ensuring the stability of surrounding rock formations and preventing ...Understanding the physical,mechanical behavior,and seepage characteristics of coal under hydro-mechanical coupling holds significant importance for ensuring the stability of surrounding rock formations and preventing gas outbursts.Scanning electron microscopy,uniaxial tests,and triaxial tests were conducted to comprehensively analyze the macroscopic and microscopic physical and mechanical characteristics of coal under different soaking times.Moreover,by restoring the stress path and water injection conditions of the protective layer indoors,we explored the coal mining dynamic behavior and the evolution of permeability.The results show that water causes the micro-surface of coal to peel off and cracks to expand and develop.With the increase of soaking time,the uniaxial and triaxial strengths were gradually decreased with nonlinear trend,and decreased by 63.31%and 30.95%after soaking for 240 h,respectively.Under different water injection pressure conditions,coal permeability undergoes three stages during the mining loading process and ultimately increases to higher values.The peak stress of coal,the deviatoric stress and strain at the permeability surge point all decrease with increasing water injection pressure.The results of this research can help improve the understanding of the coal mechanical properties and seepage evolution law under hydro-mechanical coupling.展开更多
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.展开更多
Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emi...Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.展开更多
As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decompos...As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.展开更多
In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise p...In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.展开更多
In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationshi...In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationship between samples,resulting in poor accuracy in recognizing anomalous samples.To address this problem,a knowledge distillation anomaly detection method based on feature reconstruction was proposed in this study.Knowledge distillation was performed after inverting the structure of the teacher-student network to avoid the teacher-student network sharing the same inputs and similar structure.Representability was improved by using feature splicing to unify features at different levels,and the merged features were processed and reconstructed using an improved Transformer.The experimental results show that the proposed method achieves better performance on the MVTec dataset,verifying its effectiveness and feasibility in anomaly detection tasks.This study provides a new idea to improve the accuracy and efficiency of anomaly detection.展开更多
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
Synthetic aperture radar(SAR)is a high-resolution two-dimensional imaging radar.However,during the imaging process,SAR is susceptible to intentional and unintentional interference,with radio frequency inter⁃ference(RF...Synthetic aperture radar(SAR)is a high-resolution two-dimensional imaging radar.However,during the imaging process,SAR is susceptible to intentional and unintentional interference,with radio frequency inter⁃ference(RFI)being the most common type,leading to a severe degradation in image quality.To address the above problem,numerous algorithms have been proposed.Although inpainting networks have achieved excellent results,their generalization is unclear.Whether they still work effectively in cross-sensor experiments needs fur⁃ther verification.Through the time-frequency analysis to interference signals,this work finds that interference holds domain invariant features between different sensors.Therefore,this work reconstructs the loss function and extracts the domain invariant features to improve its generalization.Ultimately,this work proposes a SAR RFI suppression method based on domain invariant features,and embeds the RFI suppression into SAR imaging pro⁃cess.Compared to traditional notch filtering methods,the proposed approach not only removes interference but also effectively preserves strong scattering targets.Compared to PISNet,our method can extract domain invariant features and hold better generalization ability,and even in the cross-sensor experiments,our method can still achieve excellent results.In cross-sensor experiments,training data and testing data come from different radar platforms with different parameters,so cross-sensor experiments can provide evidence for the generalization.展开更多
In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of ea...In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.展开更多
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
基金supported by the National Natural Science Foundation(81960508)。
文摘Objective:There is currently no consensus on whether extra-gastrointestinal stromal tumors(EGISTs)and gastrointestinal stromal tumors(GISTs)are the same type of tumor,and whether the diagnosis and treatment of EGISTs can directly replicate the current diagnostic and treatment standards for GISTs.This study aims to further elucidate the clinical and pathological characteristics,diagnosis,treatment,and prognosis of EGISTs by analyzing the research results of domestic scholars in the field of EGISTs in the past decade.Methods:A review was conducted on original Chinese and English research articles published from 2013 to 2022 focusing on EGISTs.A descriptive approach was used to extract key information from the literature,including patient demographics,tumor location,tumor diameter,mitotic figures,risk stratification,immunohistochemical markers,cell type,and prognostic factors.The data were subjected to statistical analysis.Results:A total of 12 articles containing 780 EGIST patients were included.The male-to female incidence of EGISTs was 0.92꞉1.The most common sites of EGISTs were mesentery(30.96%),peritoneum or retroperitoneum(28.53%),omentum(20.32%),and pelvic cavity(12.52%).52.77%of EGISTs had tumor diameters greater than 10 cm,and the proportions of EGISTs with nuclear fission patterns greater than 5/50 high power field(HPF)and greater than 10/50 HPF were 51.24%and 26.11%,respectively.The proportion of high-risk EGISTs was 79.05%.The positive rates of immune markers CD117,CD34,and DOG-1 in EGISTs were 82.3%,69.0%,and 79.5%,respectively.The proportion of Ki-67>5%was 49.2%,and the proportion of Ki-67>10%was 24.8%.The proportions of EGISTs in spindle cells,epithelial cells,and mixed cells were 74.4%,14.8%,and 13.1%,respectively.The diameter of the tumor,resection method,risk level,Ki-67 index,mitotic counts,presence of rupture/bleeding/necrosis/peripheral tissue invasion/recurrence and metastasis,as well as the use of imatinib treatment after surgery were important factors affecting the prognosis of EGISTs.Conclusion:Current medical research is relatively well cognizant of GISTs with primary sites in the gastrointestinal tract.Compared with GISTs,EGISTs have large tumor diameters,high mitotic counts,a high percentage of high-risk grades,relatively unique molecular expression,and high aggressiveness.EGISTs differ from GISTs in clinicopathological characteristics.Whether EGISTs and GISTs share a common origin remains unclear.If they are distinct tumor entities,separate diagnostic and treatment guidelines for EGISTs should be established.If EGISTs are ultimately confirmed to be a special subtype of GISTs,then directly applying existing GIST-based standards to EGISTs may be inappropriate.A more scientific approach would involve subclassifying EGISTs based on anatomical location and then tailoring treatment strategies accordingly with reference to GIST guidelines.
文摘Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevitably,influencing the quality of enhanced images.To alleviate this problem,a low-light image enhancement model called RetinexNet model based on Retinex theory was proposed in this study.The model was composed of an image decomposition module and a brightness enhancement module.In the decomposition module,a convolutional block attention module(CBAM)was incorporated to enhance feature representation capacity of the network,focusing on crucial features and suppressing irrelevant ones.A multifeature fusion denoising module was designed within the brightness enhancement module,circumventing the issue of feature loss during downsampling.The proposed model outperforms the existing algorithms in terms of PSNR and SSIM metrics on the publicly available datasets LOL and MIT-Adobe FiveK,as well as gives superior results in terms of NIQE metrics on the publicly available dataset LIME.
文摘Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.
基金supported by Hefei National Laboratory,Innovation Program for Quantum Science and Technology(2021ZD0300400/2021ZD0300402)the Beijing Natural Science Foundation(3252013)the China Postdoctoral Science Foundation(2024T171116).
文摘The spin-exchange relaxation-free atomic gyroscope,with its exceptionally high theoretical precision,demonstrates immense potential to become the next-generation strategic-grade gyroscope.However,due to technological noise,there is still a significant gap between its actual precision and theoretical precision.This study identifies the key factor limiting the precision of the SERF gyroscope as coupling noise.By optimizing the detection loop structure,a distinction between the dual-axis signals'response to optical and magnetic fields was achieved-where the optical errors responded similarly,while the response to magnetic noise was opposite.Based on the differences in the optical-magnetic response of the dual-axis signals,empirical mode decomposition was used to decompose the dual-axis gyroscope signals into multiple intrinsic mode functions,and Allan deviation analysis was applied to analyze the noise characteristics of the intrinsic mode functions over various periods.This study successfully reveals that optical errors caused by thermal-optical coupling and long-period magnetic noise induced by thermal-magnetic coupling are the dominant factors limiting the long-term stability of the SERF gyroscope.Based on these analyses,the study concludes that to achieve strategic-grade precision for the SERF gyroscope,it is essential to effectively address the noise issues caused by multi-physical field couplings.
基金supported by the Youth Science and Technology Innovation Award of National University of Defense Technology (18/19-QNCXJ)the National Science Foundation of China (62271494)
文摘Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and fur-ther reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power ampli-fiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and ampli-tude of the signal kurtosis (FA-SK) is further proposed. Simula-tion and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.
基金Project(2023YFC3009003) supported by the National Key R&D Program of ChinaProjects(52130409, 52121003, 52374249, 52204220) supported by the National Natural Science Foundation of ChinaProject(2024JCCXAQ01) supported by the Fundamental Research Funds for the Central Universities,China。
文摘In this study,a uniaxial cyclic compression test is conducted on coal-rock composite structures under two cyclic loads using MTSE45.104 testing apparatus to investigate the macro-mesoscopic deformation,damage behavior,and energy evolution characteristics of these structures under different cyclic stress disturbances.Three loading and unloading rates(LURs)are tested to examine the damage behaviors and energy-driven characteristics of the composites.The findings reveal that the energy-driven behavior,mechanical properties,and macro-micro degradation characteristics of the composites are significantly influenced by the loading rate.Under the gradual cyclic loading and unloading(CLU)path with a constant lower limit(path I)and the CLU path with variable upper and lower boundaries(path II),an increase in LURs from 0.05 to 0.15 mm/min reduces the average loading time by 32.39%and 48.60%,respectively.Consequently,the total number of cracks in the samples increases by 1.66-fold for path I and 1.41-fold for path II.As LURs further increase,the energy storage limit of samples expands,leading to a higher proportion of transmatrix and shear cracks.Under both cyclic loading conditions,a broader cyclic stress range promotes energy dissipation and the formation of internal fractures.Notably,at higher loading rates,cracks tend to propagate along primary weak surfaces,leading to an increased incidence of intermatrix fractures.This behavior indicates a microscopic feature of the failure mechanisms in composite structures.These results provide a theoretical basis for elucidating the damage and failure characteristics of coal-rock composite structures under cyclic stress disturbances.
基金National Natural Science Foundation of China(62161048)Sichuan Science and Technology Program(2022NSFSC0547,2022ZYD0109)。
文摘In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)response.Maximal information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear correlations.The EM response range is utilized to evaluate the sensitivity.The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive.Only the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly reduced.The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method.The number of input parameters decreases from8 to 4.The testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金Project(52225403)supported by the National Natural Science Foundation of ChinaProject(2023YFF0615401)supported by the National Key Research and Development Program of China+1 种基金Projects(2023NSFSC0004,2023NSFSC0790)supported by Science and Technology Program of Sichuan Province,ChinaProject(2021-CMCUKFZD001)supported by the Open Fund of State Key Laboratory of Coal Mining and Clean Utilization,China。
文摘Understanding the physical,mechanical behavior,and seepage characteristics of coal under hydro-mechanical coupling holds significant importance for ensuring the stability of surrounding rock formations and preventing gas outbursts.Scanning electron microscopy,uniaxial tests,and triaxial tests were conducted to comprehensively analyze the macroscopic and microscopic physical and mechanical characteristics of coal under different soaking times.Moreover,by restoring the stress path and water injection conditions of the protective layer indoors,we explored the coal mining dynamic behavior and the evolution of permeability.The results show that water causes the micro-surface of coal to peel off and cracks to expand and develop.With the increase of soaking time,the uniaxial and triaxial strengths were gradually decreased with nonlinear trend,and decreased by 63.31%and 30.95%after soaking for 240 h,respectively.Under different water injection pressure conditions,coal permeability undergoes three stages during the mining loading process and ultimately increases to higher values.The peak stress of coal,the deviatoric stress and strain at the permeability surge point all decrease with increasing water injection pressure.The results of this research can help improve the understanding of the coal mechanical properties and seepage evolution law under hydro-mechanical coupling.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006)the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
文摘This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.
基金supported by the National Natural Science Foundation of China(62061003)Sichuan Science and Technology Program(2021YFG0192)the Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04,J2020-033)。
文摘Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.
基金supported by the National Natural Science Foundation of China(62273354,61673387,61833016).
文摘As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.
基金supported by Shandong Provincial Natural Science Foundation(ZR2020MF015)Aerospace Technology Group Stability Support Project(ZY0110020009).
文摘In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.
文摘In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationship between samples,resulting in poor accuracy in recognizing anomalous samples.To address this problem,a knowledge distillation anomaly detection method based on feature reconstruction was proposed in this study.Knowledge distillation was performed after inverting the structure of the teacher-student network to avoid the teacher-student network sharing the same inputs and similar structure.Representability was improved by using feature splicing to unify features at different levels,and the merged features were processed and reconstructed using an improved Transformer.The experimental results show that the proposed method achieves better performance on the MVTec dataset,verifying its effectiveness and feasibility in anomaly detection tasks.This study provides a new idea to improve the accuracy and efficiency of anomaly detection.
基金supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
基金Supported by the National Natural Science Foundation of China(62001489)。
文摘Synthetic aperture radar(SAR)is a high-resolution two-dimensional imaging radar.However,during the imaging process,SAR is susceptible to intentional and unintentional interference,with radio frequency inter⁃ference(RFI)being the most common type,leading to a severe degradation in image quality.To address the above problem,numerous algorithms have been proposed.Although inpainting networks have achieved excellent results,their generalization is unclear.Whether they still work effectively in cross-sensor experiments needs fur⁃ther verification.Through the time-frequency analysis to interference signals,this work finds that interference holds domain invariant features between different sensors.Therefore,this work reconstructs the loss function and extracts the domain invariant features to improve its generalization.Ultimately,this work proposes a SAR RFI suppression method based on domain invariant features,and embeds the RFI suppression into SAR imaging pro⁃cess.Compared to traditional notch filtering methods,the proposed approach not only removes interference but also effectively preserves strong scattering targets.Compared to PISNet,our method can extract domain invariant features and hold better generalization ability,and even in the cross-sensor experiments,our method can still achieve excellent results.In cross-sensor experiments,training data and testing data come from different radar platforms with different parameters,so cross-sensor experiments can provide evidence for the generalization.
文摘In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.
