This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of li...This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.展开更多
Aiming at the problems of demagnetization effect of electromagnetic buffer(EMB)caused by high velocity under intensive impact load and the difficulty and error of machining composite thin-walled long tube,a segmented ...Aiming at the problems of demagnetization effect of electromagnetic buffer(EMB)caused by high velocity under intensive impact load and the difficulty and error of machining composite thin-walled long tube,a segmented EMB is proposed.The inner tube and air-gap are divided into initial segments and the traversing segments.Through theoretical analysis,impact test and simulation,it can be found that the RRF curve has two peaks.Firstly,in order to reduce the resultant resistance force(RRF)peaks,the sensitivity analysis based on optimal Latin hypercube design(OLHD)and polynomial regression was performed.The results show that the smallest contribution ratio to the dynamic response is the seventh and ninth segments of the inner tube,which are less than 1%.Then,fully considering the uncertain factors,important parameters are selected for uncertain optimization after sensitivity analysis.The interval order and interval probability degree methods are used to establish interval uncertain optimization model of the RRF considering robustness.The model was solved using an interval nested optimization method based on radial basis function(RBF)neural network.Finally,the Pareto front is obtained and numerical simulation is performed to verify the optimal value.It indicates that the two kinds of RRF peak is obviously reduced,and the optimization object and strategy are effective.展开更多
This paper discusses the basic principle and design m ethod for light distribution of car lamp, introduces an important development: h igh efficient and flexible car lamp with reflecting light distribution-segmente d ...This paper discusses the basic principle and design m ethod for light distribution of car lamp, introduces an important development: h igh efficient and flexible car lamp with reflecting light distribution-segmente d reflector (multi-patch) car lamp, and puts out a design method for segmented reflector based on error analysis. Unlike classical car lamp with refractive lig ht distribution, the method of reflecting light distribution gives car lamp desi gn more flexibility. In the case of guarantying the lightness of car lamp and sa tisfying the standard demand for the light distribution, the design of the exter ior of the car lamp has more freedom. The high gradient of car lamp is more suit able for the demand of streamline of the car exterior. The shape of segmented re flector obtained by theory calculating of car lamp reflecting light distribution is only an ideal shape, which usually has considerable differences with the fin al shape and will influences light distribution. There exist difference between the calculated reflector and manufactured reflector. Owing to light reflecting c haracter, the small diversification of the reflector will reduce the big diver sification of the light distribution shape on light distribution screen in 25 -meter place, so light distribution quality can’t be ensured. To ensure the re flector by light distribution calculation accordance to reflector by practical m anufacture, the error effect of surface shape must be reasonably considered name ly the error effect of manufacture tache. The paper establishes error-analyzing model for segmented reflector according to the analysis of error of making proc ess for segmented reflector. Based on this error-analyzing model and by use of the analyzing software developed for segmented reflector light distribution, it could reasonably consider reflector errors made by manufacturing, such as reflec tor surface spray-painting and plating aluminum, which could give out shapes of reflector patches for segmented reflector well and truly, and direct the plan o f making process efficiently. The method put by the paper has successfully appli ed to develop segmented reflector lamp for several type cars and obtained good e ffect for the factory.展开更多
For segmented detectors,surface flatness is critical as it directly influences both energy resolution and image clarity.Additionally,the limited adjustment range of the segmented detectors necessitates precise benchma...For segmented detectors,surface flatness is critical as it directly influences both energy resolution and image clarity.Additionally,the limited adjustment range of the segmented detectors necessitates precise benchmark construction.This paper proposes an architecture for detecting detector flatness based on channel spectral dispersion.By measuring the dispersion fringes for coplanar adjustment,the final adjustment residual is improved to better than 300 nm.This result validates the feasibility of the proposed technology and provides significant technical support for the development of next-generation large-aperture sky survey equipment.展开更多
Waterproof performance of gaskets between segments is the focus of shield tunnels.This paper proposed an analytical method for determining seepage characteristics at tunnel-gasketed joints based on the hydraulic fract...Waterproof performance of gaskets between segments is the focus of shield tunnels.This paper proposed an analytical method for determining seepage characteristics at tunnel-gasketed joints based on the hydraulic fracturing theories.First,the mathematical model was established,and the seepage governing equation and boundary conditions were obtained.Second,three dimensionless parameters were introduced for simplifying the expressions,and the seepage governing equations were normalized.Third,analytical expressions were derived for the interface opening and liquid pressure.Moreover,the influencing factors of seepage process at the gasketed interface were analyzed.Parametric analyses revealed that,in the normalized criterion of liquid viscosity,the liquid tip coordinate was influenced by the degree of negative pressure in the liquid lag region,which was related to the initial contact stress.The coordinate of the liquid tip affected the liquid pressure distribution and the interface opening,which were analyzed under different liquid tip coordinate conditions.Finally,under two limit states,comparative analysis showed that the results of the variation trend of the proposed method agree well with those of previous research.Overall,the proposed analytical method provides a novel solution for the design of the waterproof in shield tunnels.展开更多
近日,郑州大学网络空间安全学院在医学图像处理方向取得进展,相关研究成果以题为“PointFormer:Keypoint-Guided Transformer for Simultaneous Nuclei Segmentation and Classification in Multi-Tissue Histology Images”的论文在线...近日,郑州大学网络空间安全学院在医学图像处理方向取得进展,相关研究成果以题为“PointFormer:Keypoint-Guided Transformer for Simultaneous Nuclei Segmentation and Classification in Multi-Tissue Histology Images”的论文在线发表在国际权威期刊《IEEE Transactions on Image Processing》(中科院一区TOP,CCF-A类期刊,IF=10.8)和以题为“SimCMC:A Simple Compact Multiview Contrastive Framework for Self-supervised Early Alzheimer’s Disease Diagnosis”的论文在线发表在国际权威期刊《IEEE Transactions on Instrumentation and Measurement》(中科院二区TOP,IF=5.6)。展开更多
In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-p...In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.展开更多
This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low ...This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low resolution thermal infrared imaging,various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction.Firstly,inspired by Boltzmann's law of thermal radiation,distance is incorporated into the NeRF model for the first time,resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance.Secondly,in terms of improving inference speed,based on the phenomenon of high and low frequency distribution of foreground and background in infrared images,a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene,reduce the distribution of light in the background,and significantly reduce training time without reducing accuracy.In addition,compared to visible light scenes,infrared images only have a single channel,so fewer network parameters are required.Experiments using the same training data and data filtering method showed that,compared to the original NeRF,the improved network achieved an average improvement of 13.8%and 4.62%in PSNR and SSIM,respectively,while an average decreases of 46%in LPIPS.And thanks to the optimization of network layers and data filtering methods,training only takes about 25%of the original method's time to achieve convergence.Finally,for scenes with weak backgrounds,this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.展开更多
Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging du...Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.展开更多
“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素...“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。展开更多
With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection abil...With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.展开更多
Testing rocket and space technology objects in ground conditions for resistance to the impact of meteoroids and fragments of space debris can be carried out using shaped charges. To substantiate the design parameters ...Testing rocket and space technology objects in ground conditions for resistance to the impact of meteoroids and fragments of space debris can be carried out using shaped charges. To substantiate the design parameters of shaped charges that ensure the formation of aluminum particles in a wide velocity range(from 2.5 to 16 km/s), numerical modeling of the formation process was carried out within the framework of a two-dimensional axisymmetric problem of continuum mechanics using three different computing codes to increase the reliability of the results. The calculations consider shaped charges with a diameter of 20-100 mm with aluminum liners of various shapes. It is shown that the formation of particles with velocities close to the lower limit of the considered range is ensured by gently sloping segmental liners of degressive thickness. To form higher-velocity particles with velocities over 5 km/s, it is proposed to use combined liners, the jet-forming part of which has the shape of a hemisphere of constant thickness or the shape of a semi-ellipsoid or semi-superellipsoid of rotation of degressive thickness.展开更多
Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition me...Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.展开更多
Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling cap...Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling capability of the model through the self-attention mechanism,better segmentation performance can be achieve.Moreover,the high computational cost of Transformer has motivated researchers to explore more efficient models,such as the Mamba model based on state-space modeling(SSM),and for the field of medical segmentation,reducing the number of model parameters is also necessary.In this study,a novel asymmetric model called LA-UMamba was proposed,which integrates visual Mamba module to efficiently capture complex visual features and remote dependencies.The classical design of U-Net was adopted in the upsampling phase to help reduce the number of references and recover more details.To mitigate the information loss problem,an auxiliary U-Net downsampling layer was designed to focus on sizing without extracting features,thus enhancing the protection of input information while maintaining the efficiency of the model.The experiments were conducted on the ACDC MRI cardiac segmentation dataset,and the results showed that the proposed LA-UMamba achieves proved performance compared to the baseline model in several evaluation metrics,such as IoU,Accuracy,Precision,HD and ASD,which improved that the model is successful in optimizing the detail processing and reducing the complexity of the model,providing a new perspective for further optimization of medical image segmentation techniques.展开更多
X-ray computed tomography(CT)has been an important technology in paleontology for several decades.It helps researchers to acquire detailed anatomical structures of fossils non-destructively.Despite its widespread appl...X-ray computed tomography(CT)has been an important technology in paleontology for several decades.It helps researchers to acquire detailed anatomical structures of fossils non-destructively.Despite its widespread application,developing an efficient and user-friendly method for segmenting CT data continues to be a formidable challenge in the field.Most CT data segmentation software operates on 2D interfaces,which limits flexibility for real-time adjustments in 3D segmentation.Here,we introduce Curves Mode in Drishti Paint 3.2,an open-source tool for CT data segmentation.Drishti Paint 3.2 allows users to manually or semi-automatically segment the CT data in both 2D and 3D environments,providing a novel solution for revisualizing CT data in paleontological studies.展开更多
The orchards usually have rough terrain,dense tree canopy and weeds.It is hard to use GNSS for autonomous navigation in orchard due to signal occlusion,multipath effect,and radio frequency interference.To achieve auto...The orchards usually have rough terrain,dense tree canopy and weeds.It is hard to use GNSS for autonomous navigation in orchard due to signal occlusion,multipath effect,and radio frequency interference.To achieve autonomous navigation in orchard,a visual navigation method based on multiple images at different shooting angles is proposed in this paper.A dynamic image capturing device is designed for camera installation and multiple images can be shot at different angles.Firstly,the obtained orchard images are classified into sky and soil detection stage.Each image is transformed to HSV space and initially segmented into sky,canopy and soil regions by median filtering and morphological processing.Secondly,the sky and soil regions are extracted by the maximum connected region algorithm,and the region edges are detected and filtered by the Canny operator.Thirdly,the navigation line in the current frame is extracted by fitting the region coordinate points.Then the dynamic weighted filtering algorithm is used to extract the navigation line for the soil and sky detection stage,respectively,and the navigation line for the sky detection stage is mirrored to the soil region.Finally,the Kalman filter algorithm is used to fuse and extract the final navigation path.The test results on 200 images show that the accuracy of visual navigation path fitting is 95.5%,and single frame image processing costs 60 ms,which meets the real-time and robustness requirements of navigation.The visual navigation experiments in Camellia oleifera orchard show that when the driving speed is 0.6 m/s,the maximum tracking offset of visual navigation in weed-free and weedy environments is 0.14 m and 0.24 m,respectively,and the RMSE is 30 mm and 55 mm,respectively.展开更多
基金supported by the National Natural Science Foundation of China(61571462)Weapons and Equipment Exploration Research Project(7131464)
文摘This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.
基金the National Natural Science Foundation of China(grant number 301070603)。
文摘Aiming at the problems of demagnetization effect of electromagnetic buffer(EMB)caused by high velocity under intensive impact load and the difficulty and error of machining composite thin-walled long tube,a segmented EMB is proposed.The inner tube and air-gap are divided into initial segments and the traversing segments.Through theoretical analysis,impact test and simulation,it can be found that the RRF curve has two peaks.Firstly,in order to reduce the resultant resistance force(RRF)peaks,the sensitivity analysis based on optimal Latin hypercube design(OLHD)and polynomial regression was performed.The results show that the smallest contribution ratio to the dynamic response is the seventh and ninth segments of the inner tube,which are less than 1%.Then,fully considering the uncertain factors,important parameters are selected for uncertain optimization after sensitivity analysis.The interval order and interval probability degree methods are used to establish interval uncertain optimization model of the RRF considering robustness.The model was solved using an interval nested optimization method based on radial basis function(RBF)neural network.Finally,the Pareto front is obtained and numerical simulation is performed to verify the optimal value.It indicates that the two kinds of RRF peak is obviously reduced,and the optimization object and strategy are effective.
文摘This paper discusses the basic principle and design m ethod for light distribution of car lamp, introduces an important development: h igh efficient and flexible car lamp with reflecting light distribution-segmente d reflector (multi-patch) car lamp, and puts out a design method for segmented reflector based on error analysis. Unlike classical car lamp with refractive lig ht distribution, the method of reflecting light distribution gives car lamp desi gn more flexibility. In the case of guarantying the lightness of car lamp and sa tisfying the standard demand for the light distribution, the design of the exter ior of the car lamp has more freedom. The high gradient of car lamp is more suit able for the demand of streamline of the car exterior. The shape of segmented re flector obtained by theory calculating of car lamp reflecting light distribution is only an ideal shape, which usually has considerable differences with the fin al shape and will influences light distribution. There exist difference between the calculated reflector and manufactured reflector. Owing to light reflecting c haracter, the small diversification of the reflector will reduce the big diver sification of the light distribution shape on light distribution screen in 25 -meter place, so light distribution quality can’t be ensured. To ensure the re flector by light distribution calculation accordance to reflector by practical m anufacture, the error effect of surface shape must be reasonably considered name ly the error effect of manufacture tache. The paper establishes error-analyzing model for segmented reflector according to the analysis of error of making proc ess for segmented reflector. Based on this error-analyzing model and by use of the analyzing software developed for segmented reflector light distribution, it could reasonably consider reflector errors made by manufacturing, such as reflec tor surface spray-painting and plating aluminum, which could give out shapes of reflector patches for segmented reflector well and truly, and direct the plan o f making process efficiently. The method put by the paper has successfully appli ed to develop segmented reflector lamp for several type cars and obtained good e ffect for the factory.
文摘For segmented detectors,surface flatness is critical as it directly influences both energy resolution and image clarity.Additionally,the limited adjustment range of the segmented detectors necessitates precise benchmark construction.This paper proposes an architecture for detecting detector flatness based on channel spectral dispersion.By measuring the dispersion fringes for coplanar adjustment,the final adjustment residual is improved to better than 300 nm.This result validates the feasibility of the proposed technology and provides significant technical support for the development of next-generation large-aperture sky survey equipment.
基金Project(52278421)supported by the National Natural Science Foundation of ChinaProject(2024ZZTS0754)supported by the Fundamental Research Funds for the Central Universities of Central South University,China+2 种基金Project(2023CXQD067)supported by the Central South University Innovation-Driven Research Programme,ChinaProject(2022QNRC001)supported by Young Elite Scientists Sponsorship Program by CASTProject(2023TJ-N24)supported by the Youth Talent Program by China Railway Society and the Hunan Provincial Science and Technology Promotion Talent Project。
文摘Waterproof performance of gaskets between segments is the focus of shield tunnels.This paper proposed an analytical method for determining seepage characteristics at tunnel-gasketed joints based on the hydraulic fracturing theories.First,the mathematical model was established,and the seepage governing equation and boundary conditions were obtained.Second,three dimensionless parameters were introduced for simplifying the expressions,and the seepage governing equations were normalized.Third,analytical expressions were derived for the interface opening and liquid pressure.Moreover,the influencing factors of seepage process at the gasketed interface were analyzed.Parametric analyses revealed that,in the normalized criterion of liquid viscosity,the liquid tip coordinate was influenced by the degree of negative pressure in the liquid lag region,which was related to the initial contact stress.The coordinate of the liquid tip affected the liquid pressure distribution and the interface opening,which were analyzed under different liquid tip coordinate conditions.Finally,under two limit states,comparative analysis showed that the results of the variation trend of the proposed method agree well with those of previous research.Overall,the proposed analytical method provides a novel solution for the design of the waterproof in shield tunnels.
文摘近日,郑州大学网络空间安全学院在医学图像处理方向取得进展,相关研究成果以题为“PointFormer:Keypoint-Guided Transformer for Simultaneous Nuclei Segmentation and Classification in Multi-Tissue Histology Images”的论文在线发表在国际权威期刊《IEEE Transactions on Image Processing》(中科院一区TOP,CCF-A类期刊,IF=10.8)和以题为“SimCMC:A Simple Compact Multiview Contrastive Framework for Self-supervised Early Alzheimer’s Disease Diagnosis”的论文在线发表在国际权威期刊《IEEE Transactions on Instrumentation and Measurement》(中科院二区TOP,IF=5.6)。
文摘In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.
基金Support by the Fundamental Research Funds for the Central Universities(2024300443)the National Natural Science Foundation of China(NSFC)Young Scientists Fund(62405131)。
文摘This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low resolution thermal infrared imaging,various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction.Firstly,inspired by Boltzmann's law of thermal radiation,distance is incorporated into the NeRF model for the first time,resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance.Secondly,in terms of improving inference speed,based on the phenomenon of high and low frequency distribution of foreground and background in infrared images,a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene,reduce the distribution of light in the background,and significantly reduce training time without reducing accuracy.In addition,compared to visible light scenes,infrared images only have a single channel,so fewer network parameters are required.Experiments using the same training data and data filtering method showed that,compared to the original NeRF,the improved network achieved an average improvement of 13.8%and 4.62%in PSNR and SSIM,respectively,while an average decreases of 46%in LPIPS.And thanks to the optimization of network layers and data filtering methods,training only takes about 25%of the original method's time to achieve convergence.Finally,for scenes with weak backgrounds,this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.
基金National Natural Science Foundation of China(Grant Nos.62005049 and 62072110)Natural Science Foundation of Fujian Province(Grant No.2020J01451).
文摘Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.
文摘“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。
基金supported in part by the Tianjin Technology Innovation Guidance Special Fund Project under Grant No.21YDTPJC00850in part by the National Natural Science Foundation of China under Grant No.41906161in part by the Natural Science Foundation of Tianjin under Grant No.21JCQNJC00650。
文摘With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.
文摘Testing rocket and space technology objects in ground conditions for resistance to the impact of meteoroids and fragments of space debris can be carried out using shaped charges. To substantiate the design parameters of shaped charges that ensure the formation of aluminum particles in a wide velocity range(from 2.5 to 16 km/s), numerical modeling of the formation process was carried out within the framework of a two-dimensional axisymmetric problem of continuum mechanics using three different computing codes to increase the reliability of the results. The calculations consider shaped charges with a diameter of 20-100 mm with aluminum liners of various shapes. It is shown that the formation of particles with velocities close to the lower limit of the considered range is ensured by gently sloping segmental liners of degressive thickness. To form higher-velocity particles with velocities over 5 km/s, it is proposed to use combined liners, the jet-forming part of which has the shape of a hemisphere of constant thickness or the shape of a semi-ellipsoid or semi-superellipsoid of rotation of degressive thickness.
基金supported by the National Natural Science Foundation of China (Project No.72301293)。
文摘Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.
文摘Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling capability of the model through the self-attention mechanism,better segmentation performance can be achieve.Moreover,the high computational cost of Transformer has motivated researchers to explore more efficient models,such as the Mamba model based on state-space modeling(SSM),and for the field of medical segmentation,reducing the number of model parameters is also necessary.In this study,a novel asymmetric model called LA-UMamba was proposed,which integrates visual Mamba module to efficiently capture complex visual features and remote dependencies.The classical design of U-Net was adopted in the upsampling phase to help reduce the number of references and recover more details.To mitigate the information loss problem,an auxiliary U-Net downsampling layer was designed to focus on sizing without extracting features,thus enhancing the protection of input information while maintaining the efficiency of the model.The experiments were conducted on the ACDC MRI cardiac segmentation dataset,and the results showed that the proposed LA-UMamba achieves proved performance compared to the baseline model in several evaluation metrics,such as IoU,Accuracy,Precision,HD and ASD,which improved that the model is successful in optimizing the detail processing and reducing the complexity of the model,providing a new perspective for further optimization of medical image segmentation techniques.
文摘X-ray computed tomography(CT)has been an important technology in paleontology for several decades.It helps researchers to acquire detailed anatomical structures of fossils non-destructively.Despite its widespread application,developing an efficient and user-friendly method for segmenting CT data continues to be a formidable challenge in the field.Most CT data segmentation software operates on 2D interfaces,which limits flexibility for real-time adjustments in 3D segmentation.Here,we introduce Curves Mode in Drishti Paint 3.2,an open-source tool for CT data segmentation.Drishti Paint 3.2 allows users to manually or semi-automatically segment the CT data in both 2D and 3D environments,providing a novel solution for revisualizing CT data in paleontological studies.
基金National Key Research and Development Program of China(2022YFD2202103)National Natural Science Foundation of China(31971798)+2 种基金Zhejiang Provincial Key Research&Development Plan(2023C02049、2023C02053)SNJF Science and Technology Collaborative Program of Zhejiang Province(2022SNJF017)Hangzhou Agricultural and Social Development Research Project(202203A03)。
文摘The orchards usually have rough terrain,dense tree canopy and weeds.It is hard to use GNSS for autonomous navigation in orchard due to signal occlusion,multipath effect,and radio frequency interference.To achieve autonomous navigation in orchard,a visual navigation method based on multiple images at different shooting angles is proposed in this paper.A dynamic image capturing device is designed for camera installation and multiple images can be shot at different angles.Firstly,the obtained orchard images are classified into sky and soil detection stage.Each image is transformed to HSV space and initially segmented into sky,canopy and soil regions by median filtering and morphological processing.Secondly,the sky and soil regions are extracted by the maximum connected region algorithm,and the region edges are detected and filtered by the Canny operator.Thirdly,the navigation line in the current frame is extracted by fitting the region coordinate points.Then the dynamic weighted filtering algorithm is used to extract the navigation line for the soil and sky detection stage,respectively,and the navigation line for the sky detection stage is mirrored to the soil region.Finally,the Kalman filter algorithm is used to fuse and extract the final navigation path.The test results on 200 images show that the accuracy of visual navigation path fitting is 95.5%,and single frame image processing costs 60 ms,which meets the real-time and robustness requirements of navigation.The visual navigation experiments in Camellia oleifera orchard show that when the driving speed is 0.6 m/s,the maximum tracking offset of visual navigation in weed-free and weedy environments is 0.14 m and 0.24 m,respectively,and the RMSE is 30 mm and 55 mm,respectively.
