Environmental DNA(e DNA)methods have emerged as a promising tool for studying a broad spectrum of biological taxa.However,metabarcoding studies of avian biodiversity using e DNA have received little attention.In this ...Environmental DNA(e DNA)methods have emerged as a promising tool for studying a broad spectrum of biological taxa.However,metabarcoding studies of avian biodiversity using e DNA have received little attention.In this study,we compared waterbird biodiversity derived from e DNA metabarcoding with that obtained from traditional point counting surveys at 23 sites in Tai Lake of eastern China and evaluated the accuracy of e DNA metabarcoding for waterbird community studies.The point counting method recorded a higher total number of waterbird species(22)compared to the e DNA technique(16).While e DNA achieved a 74.5%detection rate for waterbird species and was able to identify a significantly greater number of species(12.48±1.97)at each sampling site than point counting method(6.13±2.69),particularly highlighting several rare and elusive species,it failed to detect some species commonly observed by the point counting method.The alpha diversity analysis revealed no significant differences in waterbird diversity between the e DNA method and the point counting method,except that the e DNA method exhibited lower Pielou evenness.Waterbird e DNA sequencing abundance correlated significantly with species occurrence,whereas Spearman's analysis indicated no significant difference between e DNA sequence abundance and species abundance from the point counting method.e DNA method detected no significant difference in waterbird composition between sampling sites,while the point counting method revealed significant differences.Consequently,e DNA is an effective complementary tool for assessing the diversity of wintering waterbirds in lakes,though it is unable to capture the full diversity of waterbird communities.It is crucial to develop sampling strategies that comprehensively monitor species composition and integrate e DNA with traditional survey methods for accurate evaluation of community structure.展开更多
According to the World Health Organization(WHO)manual,sperm concentration should be measured using an improved Neubauer hemocytometer,while sperm motility should be measured by manual assessment.However,in China,thous...According to the World Health Organization(WHO)manual,sperm concentration should be measured using an improved Neubauer hemocytometer,while sperm motility should be measured by manual assessment.However,in China,thousands of laboratories do not use the improved Neubauer hemocytometer or method;instead,the Makler counting chamber is one of the most widely used chambers.To study sources of error that could impact the measurement of the apparent concentration and motility of sperm using the Makler counting chamber and to verify its accuracy for clinical application,67 semen samples from patients attending the Department of Andrology,West China Second University Hospital,Sichuan University(Chengdu,China)between 13 September 2023 and 27 September 2023,were included.Compared with applying the cover glass immediately,delaying the application of the cover glass for 5 s,10 s,and 30 s resulted in average increases in the sperm concentration of 30.3%,74.1%,and 107.5%,respectively(all P<0.0001)and in the progressive motility(PR)of 17.7%,30.8%,and 39.6%,respectively(all P<0.0001).However,when the semen specimens were fixed with formaldehyde,a delay in the application of the cover glass for 5 s,10 s,and 30 s resulted in an average increase in the sperm concentration of 6.7%,10.8%,and 14.6%,respectively,compared with immediate application of the cover glass.The accumulation of motile sperm due to delays in the application of the cover glass is a significant source of error with the Makler counting chamber and should be avoided.展开更多
The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm ...The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm fertilizing capacity.Among these,sperm concentration and motility are the first parameters to be evaluated through an estimation carried out by expert examiners.展开更多
Aiming at problems such as large errors and low efficiency in manual counting of drill pipes during drilling depth measurement,an intelligent detection and counting method for the small targets at the end of drill pip...Aiming at problems such as large errors and low efficiency in manual counting of drill pipes during drilling depth measurement,an intelligent detection and counting method for the small targets at the end of drill pipes based on the improved YOLO11n is proposed.This method realizes the high-precision detection of targets at drill pipe ends in the image by optimizing the target detection model,and combines a post-processing correction mechanism to improve the drill pipe counting accuracy.In order to alleviate the low-precision problem of YOLO11n algorithm for small target recognition in the complex underground background,the YOLO11n algorithm is improved.First,the key module C3k2 in the backbone network was improved,and Poly Kernel Inception(PKI)Block was introduced to replace Bottleneck in it to fully integrate the target context information and the model’s capability of feature extraction;Second,within the model’s neck network,a new feature fusion pyramid ISOP(Improved Small Object Pyramid)is proposed,SPDConv is introduced to strengthen the P2 feature,and CSP and OmniKernel are combined to integrate multi-scale features;Finally,the default loss function is substituted with Powerful-IoU(PIoU)to solve the anchor box expansion problem.On the self-built dataset,experimental verification was conducted.The findings showed that the Recall rose by 6.4%,mAP@0.5 increased by 4.5%,and mAP@0.5:0.95 improved by 6%compared with the baseline model,effectively solving the issues of false detection and missed detection problems in small target detection task.Meanwhile,we conducted counting tests on drilling videos from 5 different scenarios,achieving an average accuracy of 97.3%,which meets the accuracy needs for drill pipe recognition and counting in coal mine drilling sites.The research findings offer theoretical basis and technical backing for promoting the intelligent development of coal mine gas extraction drilling sites.展开更多
The particle identification(PID)of hadrons plays a crucial role in particle physics experiments,especially in flavor physics and jet tagging.The cluster counting method,which measures the number of primary ionizations...The particle identification(PID)of hadrons plays a crucial role in particle physics experiments,especially in flavor physics and jet tagging.The cluster counting method,which measures the number of primary ionizations in gaseous detectors,is a promising breakthrough in PID.However,developing an effective reconstruction algorithm for cluster counting remains challenging.To address this challenge,we propose a cluster counting algorithm based on long short-term memory and dynamic graph convolutional neural networks for the CEPC drift chamber.Experiments on Monte Carlo simulated samples demonstrate that our machine learning-based algorithm surpasses traditional methods.It improves the K/πseparation of PID by 10%,meeting the PID requirements of CEPC.展开更多
In this paper,we study composition operators on weighted Bergman spaces of Dirichlet series.We first establish some Littlewood-type inequalities for generalized mean counting functions.Then we give sufficient conditio...In this paper,we study composition operators on weighted Bergman spaces of Dirichlet series.We first establish some Littlewood-type inequalities for generalized mean counting functions.Then we give sufficient conditions for a composition operator with zero characteristic to be bounded or compact on weighted Bergman spaces of Dirichlet series.The corresponding sufficient condition for compactness in the case of positive characteristics is also obtained.展开更多
When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ...When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.展开更多
Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an ima...Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.展开更多
Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges i...Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.展开更多
Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety.Crowd counting ha...Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety.Crowd counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and methodologies.These approaches vary in terms of supervision techniques,network architectures,and model complexity.Currently,most crowd counting methods rely on fully supervised learning,which has proven to be effective.However,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often scarce.As a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical applications.This paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision paradigms.We summarize and critically analyze the key methods in these two categories,highlighting their strengths and limitations.Furthermore,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark datasets.We believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.展开更多
A signal chain model of single-bit and multi-bit quanta image sensors(QISs)is established.Based on the proposed model,the photoresponse characteristics and signal error rates of QISs are investigated,and the effects o...A signal chain model of single-bit and multi-bit quanta image sensors(QISs)is established.Based on the proposed model,the photoresponse characteristics and signal error rates of QISs are investigated,and the effects of bit depth,quantum efficiency,dark current,and read noise on them are analyzed.When the signal error rates towards photons and photoelectrons counting are lower than 0.01,the high accuracy photon and photoelectron counting exposure ranges are determined.Furthermore,an optimization method of integration time to ensure that the QIS works in these high accuracy exposure ranges is presented.The trade-offs between pixel area,the mean value of incident photons,and integration time under different illuminance level are analyzed.For the 3-bit QIS with 0.16 e-/s dark current and 0.21 e-r.m.s.read noise,when the illuminance level and pixel area are 1 lux and 1.21μm^(2),or 10000 lux and 0.21μm^(2),the recommended integration time is 8.8 to 30 ms,or 10 to21.3μs,respectively.The proposed method can guide the design and operation of single-bit and multi-bit QISs.展开更多
A quantitative survey of rice planthoppers in paddy fields is important to assess the population density and make forecasting decisions. Manual rice planthopper survey methods in paddy fields are time-consuming, fatig...A quantitative survey of rice planthoppers in paddy fields is important to assess the population density and make forecasting decisions. Manual rice planthopper survey methods in paddy fields are time-consuming, fatiguing and tedious. This paper describes a handheld device for easily capturing planthopper images on rice stems and an automatic method for counting rice planthoppers based on image processing. The handheld device consists of a digital camera with WiFi, a smartphone and an extrendable pole. The surveyor can use the smartphone to control the camera, which is fixed on the front of the pole by WiFi, and to photograph planthoppers on rice stems. For the counting of planthoppers on rice stems, we adopt three layers of detection that involve the following：（a） the first layer of detection is an AdaBoost classifier based on Haar features;（b） the second layer of detection is a support vector machine（SVM） classifier based on histogram of oriented gradient（HOG） features;（c） the third layer of detection is the threshold judgment of the three features. We use this method to detect and count whiteback planthoppers（Sogatella furcifera） on rice plant images and achieve an 85.2% detection rate and a 9.6% false detection rate. The method is easy, rapid and accurate for the assessment of the population density of rice planthoppers in paddy fields.展开更多
The Gan-Hang Belt in Southeast China is characterized by several igneous and siliciclastic basins associated with crustal extension during Late Mesozoic. The sedimentary evolution of the red basins is still poorly und...The Gan-Hang Belt in Southeast China is characterized by several igneous and siliciclastic basins associated with crustal extension during Late Mesozoic. The sedimentary evolution of the red basins is still poorly understood. In this study, sedimentary fades analysis and pebble counting were performed on outcrop sections of the Late Cretaceous Guifeng Group in the Yongfeng-Chongren Basin in central Jiangxi Province. Thirty-five conglomerate outcrops were chosen to measure pebble lithology, size, roundness, weathering degree and preferred orientation. Results show that gravels are mostly fine to coarse pebbles and comprise dominantly quartzites, metamorphic rocks, granitoids and sandstones. Rose diagrams based on imbricated pebbles indicate variable paleocurrent directions. Combining with typical sedimentary structures and vertical successions, we suggest that the Guifeng Group were deposited in alluvial fan, river and playa lake depositional systems. The proposed depositional model indicates that the Hekou Formation represents the start-up stage of the faulted basin, accompanied by sedimentation in alluvial fan and braided river environments. Then this basin turned into a stable expansion stage during the deposition of the Tangbian Formation. Except for minor coarse sediments at the basin margin, the other area is covered with fine-grained sediments of lake and river environments. The Lianhe Formation, however, is once again featured by conglomerates, suggesting a probable tectonic event. Therefore, the study region possibly suffered two tectonic events represented by the conglomerates of the Hekou and Lianhe formations in the context of the crustal extension in Southeast China.展开更多
4H-SiC single photon counting avalanche photodiodes(SPADs)are prior devices for weak ultraviolet(UV)signal detection with the advantages of small size,low leakage current,high avalanche multiplication gain,and high qu...4H-SiC single photon counting avalanche photodiodes(SPADs)are prior devices for weak ultraviolet(UV)signal detection with the advantages of small size,low leakage current,high avalanche multiplication gain,and high quantum efficiency,which benefit from the large bandgap energy,high carrier drift velocity and excellent physical stability of 4 H-SiC semiconductor material.UV detectors are widely used in many key applications,such as missile plume detection,corona discharge,UV astronomy,and biological and chemical agent detection.In this paper,we will describe basic concepts and review recent results on device design,process development,and basic characterizations of 4 H-SiC avalanche photodiodes.Several promising device structures and uniformity of avalanche multiplication are discussed,which are important for achieving high performance of 4 HSiC UV SPADs.展开更多
A filtering method of aero-engine load spectrum based on the rain flow counting is proposed in this paper.Firstly,the original load spectrum is counted through the rain flow method to get the peak and valley values.Th...A filtering method of aero-engine load spectrum based on the rain flow counting is proposed in this paper.Firstly,the original load spectrum is counted through the rain flow method to get the peak and valley values.Then,some data points in the original load spectrum are added between the peak and valley values.Finally,the filtering spectrum is obtained.The proposed method can reflect the path information of the original load spectrum.In addition,it can also eliminate the noise in the signal and improve the efficiency of signal processing,which is of practical significance for the research of aero-engine.展开更多
The growing trend of network virtualization results in a widespread adoption of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet...The growing trend of network virtualization results in a widespread adoption of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet classification especially in Open Flow-based software defined networks. This paper first takes an insight into packet classification in virtual Open Flow switching, and points out that its performance bottleneck is dominated by flow table traversals of multiple failed mask probing for each arrived packet. Then we are motivated to propose an efficient packet classification algorithm based on counting bloom filters. In particular, counting bloom filters are applied to predict the failures of flow table lookups with great possibilities, and bypass flow table traversals for failed mask probing. Finally, our proposed packet classification algorithm is evaluated with real network traffic traces by experiments. The experimental results indicate that our proposed algorithm outperforms the classical one in Open v Switch in terms of average search length, and contributes to promote virtual Open Flow switching performance.展开更多
A novel nano crystalline Ag2O2-PbO2 film chemically modified electrode (CME) was prepared and the CME was characterized by X-ray diffractometer (XRD) and atomic force microscope (AFM). By chronoamperometry, the nano A...A novel nano crystalline Ag2O2-PbO2 film chemically modified electrode (CME) was prepared and the CME was characterized by X-ray diffractometer (XRD) and atomic force microscope (AFM). By chronoamperometry, the nano Ag2O2-PbO2 CME was used as bioelectro- chemical sensor to determine the population of Escherichia coli (E. coli) in water. Compared with conventional methods, it is found that the technique we used is fast and convenient in counting E. coli.展开更多
Imaging plates are widely used to detect alpha particles to track information,and the number of alpha particle tracks is affected by the overlapping and fading effects of the track information.In this study,an experim...Imaging plates are widely used to detect alpha particles to track information,and the number of alpha particle tracks is affected by the overlapping and fading effects of the track information.In this study,an experiment and a simulation were used to calibrate the efficiency parameter of an imaging plate,which was used to calculate the grayscale.Images were created by using grayscale,which trained the convolutional neural network to count the alpha tracks.The results demonstrated that the trained convolutional neural network can evaluate the alpha track counts based on the source and background images with a wider linear range,which was unaffected by the overlapping effect.The alpha track counts were unaffected by the fading effect within 60 min,where the calibrated formula for the fading effect was analyzed for 132.7 min.The detection efficiency of the trained convolutional neural network for inhomogeneous ^(241)Am sources(2π emission)was 0.6050±0.0399,whereas the efficiency curve of the photo-stimulated luminescence method was lower than that of the trained convolutional neural network.展开更多
基金funded by the National Key Research and Development Program of China(Award Number:2022YFC3202104)。
文摘Environmental DNA(e DNA)methods have emerged as a promising tool for studying a broad spectrum of biological taxa.However,metabarcoding studies of avian biodiversity using e DNA have received little attention.In this study,we compared waterbird biodiversity derived from e DNA metabarcoding with that obtained from traditional point counting surveys at 23 sites in Tai Lake of eastern China and evaluated the accuracy of e DNA metabarcoding for waterbird community studies.The point counting method recorded a higher total number of waterbird species(22)compared to the e DNA technique(16).While e DNA achieved a 74.5%detection rate for waterbird species and was able to identify a significantly greater number of species(12.48±1.97)at each sampling site than point counting method(6.13±2.69),particularly highlighting several rare and elusive species,it failed to detect some species commonly observed by the point counting method.The alpha diversity analysis revealed no significant differences in waterbird diversity between the e DNA method and the point counting method,except that the e DNA method exhibited lower Pielou evenness.Waterbird e DNA sequencing abundance correlated significantly with species occurrence,whereas Spearman's analysis indicated no significant difference between e DNA sequence abundance and species abundance from the point counting method.e DNA method detected no significant difference in waterbird composition between sampling sites,while the point counting method revealed significant differences.Consequently,e DNA is an effective complementary tool for assessing the diversity of wintering waterbirds in lakes,though it is unable to capture the full diversity of waterbird communities.It is crucial to develop sampling strategies that comprehensively monitor species composition and integrate e DNA with traditional survey methods for accurate evaluation of community structure.
基金supported by the Natural Science Foundation of China(No.32171264 and No.81974226)the Sichuan Science and Technology Program(2023NSFSC1609)。
文摘According to the World Health Organization(WHO)manual,sperm concentration should be measured using an improved Neubauer hemocytometer,while sperm motility should be measured by manual assessment.However,in China,thousands of laboratories do not use the improved Neubauer hemocytometer or method;instead,the Makler counting chamber is one of the most widely used chambers.To study sources of error that could impact the measurement of the apparent concentration and motility of sperm using the Makler counting chamber and to verify its accuracy for clinical application,67 semen samples from patients attending the Department of Andrology,West China Second University Hospital,Sichuan University(Chengdu,China)between 13 September 2023 and 27 September 2023,were included.Compared with applying the cover glass immediately,delaying the application of the cover glass for 5 s,10 s,and 30 s resulted in average increases in the sperm concentration of 30.3%,74.1%,and 107.5%,respectively(all P<0.0001)and in the progressive motility(PR)of 17.7%,30.8%,and 39.6%,respectively(all P<0.0001).However,when the semen specimens were fixed with formaldehyde,a delay in the application of the cover glass for 5 s,10 s,and 30 s resulted in an average increase in the sperm concentration of 6.7%,10.8%,and 14.6%,respectively,compared with immediate application of the cover glass.The accumulation of motile sperm due to delays in the application of the cover glass is a significant source of error with the Makler counting chamber and should be avoided.
文摘The analysis of the ejaculate,better known as spermiogram,represents the first and main step to identify whether a series of sperm quality parameters are within the norm and therefore are consistent with normal sperm fertilizing capacity.Among these,sperm concentration and motility are the first parameters to be evaluated through an estimation carried out by expert examiners.
基金Henan Province University Science and Technology Innovation Team Support Program Project(22IRTSTHN005)..
文摘Aiming at problems such as large errors and low efficiency in manual counting of drill pipes during drilling depth measurement,an intelligent detection and counting method for the small targets at the end of drill pipes based on the improved YOLO11n is proposed.This method realizes the high-precision detection of targets at drill pipe ends in the image by optimizing the target detection model,and combines a post-processing correction mechanism to improve the drill pipe counting accuracy.In order to alleviate the low-precision problem of YOLO11n algorithm for small target recognition in the complex underground background,the YOLO11n algorithm is improved.First,the key module C3k2 in the backbone network was improved,and Poly Kernel Inception(PKI)Block was introduced to replace Bottleneck in it to fully integrate the target context information and the model’s capability of feature extraction;Second,within the model’s neck network,a new feature fusion pyramid ISOP(Improved Small Object Pyramid)is proposed,SPDConv is introduced to strengthen the P2 feature,and CSP and OmniKernel are combined to integrate multi-scale features;Finally,the default loss function is substituted with Powerful-IoU(PIoU)to solve the anchor box expansion problem.On the self-built dataset,experimental verification was conducted.The findings showed that the Recall rose by 6.4%,mAP@0.5 increased by 4.5%,and mAP@0.5:0.95 improved by 6%compared with the baseline model,effectively solving the issues of false detection and missed detection problems in small target detection task.Meanwhile,we conducted counting tests on drilling videos from 5 different scenarios,achieving an average accuracy of 97.3%,which meets the accuracy needs for drill pipe recognition and counting in coal mine drilling sites.The research findings offer theoretical basis and technical backing for promoting the intelligent development of coal mine gas extraction drilling sites.
基金supported by National Natural Science Foundation of China(NSFC)(Nos.12475200 and 12275296)Joint Fund of Research utilizing Large-Scale Scientific Facility of the NSFC and CAS(No.U2032114)Institute of High Energy Physics(Chinese Academy of Sciences)Innovative Project on Sciences and Technologies(Nos.E3545BU210 and E25456U210).
文摘The particle identification(PID)of hadrons plays a crucial role in particle physics experiments,especially in flavor physics and jet tagging.The cluster counting method,which measures the number of primary ionizations in gaseous detectors,is a promising breakthrough in PID.However,developing an effective reconstruction algorithm for cluster counting remains challenging.To address this challenge,we propose a cluster counting algorithm based on long short-term memory and dynamic graph convolutional neural networks for the CEPC drift chamber.Experiments on Monte Carlo simulated samples demonstrate that our machine learning-based algorithm surpasses traditional methods.It improves the K/πseparation of PID by 10%,meeting the PID requirements of CEPC.
基金supported by the National Natural Science Foundation of China(12171373)Chen's work also supported by the Fundamental Research Funds for the Central Universities of China(GK202207018).
文摘In this paper,we study composition operators on weighted Bergman spaces of Dirichlet series.We first establish some Littlewood-type inequalities for generalized mean counting functions.Then we give sufficient conditions for a composition operator with zero characteristic to be bounded or compact on weighted Bergman spaces of Dirichlet series.The corresponding sufficient condition for compactness in the case of positive characteristics is also obtained.
文摘When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.
基金funded by Naif Arab University for Security Sciences under grant No.NAUSS-23-R10.
文摘Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.
基金Double First-Class Innovation Research Project for People’s Public Security University of China(2023SYL08).
文摘Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.
基金supported by Research Project Support Program for Excellence Institute(2022,ESL)in Incheon National University.
文摘Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety.Crowd counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and methodologies.These approaches vary in terms of supervision techniques,network architectures,and model complexity.Currently,most crowd counting methods rely on fully supervised learning,which has proven to be effective.However,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often scarce.As a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical applications.This paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision paradigms.We summarize and critically analyze the key methods in these two categories,highlighting their strengths and limitations.Furthermore,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark datasets.We believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.
基金supported by the Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology。
文摘A signal chain model of single-bit and multi-bit quanta image sensors(QISs)is established.Based on the proposed model,the photoresponse characteristics and signal error rates of QISs are investigated,and the effects of bit depth,quantum efficiency,dark current,and read noise on them are analyzed.When the signal error rates towards photons and photoelectrons counting are lower than 0.01,the high accuracy photon and photoelectron counting exposure ranges are determined.Furthermore,an optimization method of integration time to ensure that the QIS works in these high accuracy exposure ranges is presented.The trade-offs between pixel area,the mean value of incident photons,and integration time under different illuminance level are analyzed.For the 3-bit QIS with 0.16 e-/s dark current and 0.21 e-r.m.s.read noise,when the illuminance level and pixel area are 1 lux and 1.21μm^(2),or 10000 lux and 0.21μm^(2),the recommended integration time is 8.8 to 30 ms,or 10 to21.3μs,respectively.The proposed method can guide the design and operation of single-bit and multi-bit QISs.
基金the National Natural Science Foundation of China (31071678)the National High Technology Research and Development Program of China (863 Program, 2013AA102402)Zhejiang Provincial Natural Science Foundation of China (LY13C140009)
文摘A quantitative survey of rice planthoppers in paddy fields is important to assess the population density and make forecasting decisions. Manual rice planthopper survey methods in paddy fields are time-consuming, fatiguing and tedious. This paper describes a handheld device for easily capturing planthopper images on rice stems and an automatic method for counting rice planthoppers based on image processing. The handheld device consists of a digital camera with WiFi, a smartphone and an extrendable pole. The surveyor can use the smartphone to control the camera, which is fixed on the front of the pole by WiFi, and to photograph planthoppers on rice stems. For the counting of planthoppers on rice stems, we adopt three layers of detection that involve the following：（a） the first layer of detection is an AdaBoost classifier based on Haar features;（b） the second layer of detection is a support vector machine（SVM） classifier based on histogram of oriented gradient（HOG） features;（c） the third layer of detection is the threshold judgment of the three features. We use this method to detect and count whiteback planthoppers（Sogatella furcifera） on rice plant images and achieve an 85.2% detection rate and a 9.6% false detection rate. The method is easy, rapid and accurate for the assessment of the population density of rice planthoppers in paddy fields.
基金supported by China Geological Survey projects (Grant Nos.1212011120836,1212011220248)China Scholarship Council (Grant No.201308360142)+2 种基金Gan-Po Excellent Talents 555 Project of Jiangxi Province (GCZ 2012-1)Research Foundation of Jiangxi Education Department (Grant No.GJJ13438)the open fund of Fundamental Science on Radioactive Geology and Exploration Technology Laboratory (Grant No.RGET1304)
文摘The Gan-Hang Belt in Southeast China is characterized by several igneous and siliciclastic basins associated with crustal extension during Late Mesozoic. The sedimentary evolution of the red basins is still poorly understood. In this study, sedimentary fades analysis and pebble counting were performed on outcrop sections of the Late Cretaceous Guifeng Group in the Yongfeng-Chongren Basin in central Jiangxi Province. Thirty-five conglomerate outcrops were chosen to measure pebble lithology, size, roundness, weathering degree and preferred orientation. Results show that gravels are mostly fine to coarse pebbles and comprise dominantly quartzites, metamorphic rocks, granitoids and sandstones. Rose diagrams based on imbricated pebbles indicate variable paleocurrent directions. Combining with typical sedimentary structures and vertical successions, we suggest that the Guifeng Group were deposited in alluvial fan, river and playa lake depositional systems. The proposed depositional model indicates that the Hekou Formation represents the start-up stage of the faulted basin, accompanied by sedimentation in alluvial fan and braided river environments. Then this basin turned into a stable expansion stage during the deposition of the Tangbian Formation. Except for minor coarse sediments at the basin margin, the other area is covered with fine-grained sediments of lake and river environments. The Lianhe Formation, however, is once again featured by conglomerates, suggesting a probable tectonic event. Therefore, the study region possibly suffered two tectonic events represented by the conglomerates of the Hekou and Lianhe formations in the context of the crustal extension in Southeast China.
基金supported in part by National Key R&D Program of China under Grant No. 2016YFB0400902in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘4H-SiC single photon counting avalanche photodiodes(SPADs)are prior devices for weak ultraviolet(UV)signal detection with the advantages of small size,low leakage current,high avalanche multiplication gain,and high quantum efficiency,which benefit from the large bandgap energy,high carrier drift velocity and excellent physical stability of 4 H-SiC semiconductor material.UV detectors are widely used in many key applications,such as missile plume detection,corona discharge,UV astronomy,and biological and chemical agent detection.In this paper,we will describe basic concepts and review recent results on device design,process development,and basic characterizations of 4 H-SiC avalanche photodiodes.Several promising device structures and uniformity of avalanche multiplication are discussed,which are important for achieving high performance of 4 HSiC UV SPADs.
基金This work was supported by the National Basic Research Program of China,National Nature Science Foundation of China(No.51675266)the Foundation Research Funds for the Center in NUAA(Nos.NJ20160038,NS2017011)Foundation of Graduate Innovation Center in NUAA(No.kfjj20170220)。
文摘A filtering method of aero-engine load spectrum based on the rain flow counting is proposed in this paper.Firstly,the original load spectrum is counted through the rain flow method to get the peak and valley values.Then,some data points in the original load spectrum are added between the peak and valley values.Finally,the filtering spectrum is obtained.The proposed method can reflect the path information of the original load spectrum.In addition,it can also eliminate the noise in the signal and improve the efficiency of signal processing,which is of practical significance for the research of aero-engine.
基金supported in part by National Natural Science Foundation of China(61272148,61572525,61502056,and 61602525)Hunan Provincial Natural Science Foundation of China(2015JJ3010)Scientific Research Fund of Hunan Provincial Education Department(15B009,14C0285)
文摘The growing trend of network virtualization results in a widespread adoption of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet classification especially in Open Flow-based software defined networks. This paper first takes an insight into packet classification in virtual Open Flow switching, and points out that its performance bottleneck is dominated by flow table traversals of multiple failed mask probing for each arrived packet. Then we are motivated to propose an efficient packet classification algorithm based on counting bloom filters. In particular, counting bloom filters are applied to predict the failures of flow table lookups with great possibilities, and bypass flow table traversals for failed mask probing. Finally, our proposed packet classification algorithm is evaluated with real network traffic traces by experiments. The experimental results indicate that our proposed algorithm outperforms the classical one in Open v Switch in terms of average search length, and contributes to promote virtual Open Flow switching performance.
基金We are greateful to the National Narural Science Foundation of China(No.20455017)Science and Technology Committee of Shanghai Municipal(No.0452nm084).
文摘A novel nano crystalline Ag2O2-PbO2 film chemically modified electrode (CME) was prepared and the CME was characterized by X-ray diffractometer (XRD) and atomic force microscope (AFM). By chronoamperometry, the nano Ag2O2-PbO2 CME was used as bioelectro- chemical sensor to determine the population of Escherichia coli (E. coli) in water. Compared with conventional methods, it is found that the technique we used is fast and convenient in counting E. coli.
基金supported by the Hunan Provincial Innovation Foundation for Postgraduates (No.QL20210228)the National Natural Science Foundation of China (No.12075112)the National Natural Science Foundation of China (No.12175102).
文摘Imaging plates are widely used to detect alpha particles to track information,and the number of alpha particle tracks is affected by the overlapping and fading effects of the track information.In this study,an experiment and a simulation were used to calibrate the efficiency parameter of an imaging plate,which was used to calculate the grayscale.Images were created by using grayscale,which trained the convolutional neural network to count the alpha tracks.The results demonstrated that the trained convolutional neural network can evaluate the alpha track counts based on the source and background images with a wider linear range,which was unaffected by the overlapping effect.The alpha track counts were unaffected by the fading effect within 60 min,where the calibrated formula for the fading effect was analyzed for 132.7 min.The detection efficiency of the trained convolutional neural network for inhomogeneous ^(241)Am sources(2π emission)was 0.6050±0.0399,whereas the efficiency curve of the photo-stimulated luminescence method was lower than that of the trained convolutional neural network.
