A clock bias data processing method based on interval correlation coefficient wavelet threshold denoising is suggested for minor mistakes in clock bias data in order to increase the efficacy of satellite clock bias pr...A clock bias data processing method based on interval correlation coefficient wavelet threshold denoising is suggested for minor mistakes in clock bias data in order to increase the efficacy of satellite clock bias prediction.Wavelet analysis was first used to break down the satellite clock frequency data into several levels,producing high and low frequency coefficients for each layer.The correlation coefficients of the high and low frequency coefficients in each of the three sub-intervals created by splitting these coefficients were then determined.The major noise region—the sub-interval with the lowest correlation coefficient—was chosen for thresholding treatment and noise threshold computation.The clock frequency data was then processed using wavelet reconstruction and reconverted to clock data.Lastly,three different kinds of satellite clock data—RTS,whu-o,and IGS-F—were used to confirm the produced data.Our method enhanced the stability of the Quadratic Polynomial(QP)model’s predictions for the C16 satellite by about 40%,according to the results.The accuracy and stability of the Auto Regression Integrated Moving Average(ARIMA)model improved up to 41.8%and 14.2%,respectively,whilst the Wavelet Neural Network(WNN)model improved by roughly 27.8%and 63.6%,respectively.Although our method has little effect on forecasting IGS-F series satellites,the experimental findings show that it can improve the accuracy and stability of QP,ARIMA,and WNN model forecasts for RTS and whu-o satellite clock bias.展开更多
To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achiev...To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achieves high-quality AFM imaging via line-by-line corrections for each distorted profile along the fast axis.The key to this line-by-line correction is to accurately simulate the profile distortion of each scanning row.Therefore,a data preprocessing approach is first developed to roughly filter out most of the height data that impairs the accuracy of distortion modeling.This process is implemented through an internal double-screening mechanism.A line-fitting method is adopted to preliminarily screen out the obvious specimens.Lifting wavelet analysis is then carried out to identify the base parts that are mistakenly filtered out as specimens so as to preserve most of the base profiles and provide a good basis for further distortion modeling.Next,an iterative thresholding algorithm is developed to precisely simulate the profile distortion.By utilizing the roughly screened base profile,the optimal threshold,which is used to screen out the pure bases suitable for distortion modeling,is determined through iteration with a specified error rule.On this basis,the profile distortion is accurately modeled through line fitting on the finely screened base data,and the correction is implemented by subtracting the modeling result from the distorted profile.Finally,the effectiveness of the proposed method is verified through experiments and applications.展开更多
Data reconstruction is a crucial step in seismic data preprocessing.To improve reconstruction speed and save memory,the commonly used three-dimensional(3D)seismic data reconstruction method divides the missing data in...Data reconstruction is a crucial step in seismic data preprocessing.To improve reconstruction speed and save memory,the commonly used three-dimensional(3D)seismic data reconstruction method divides the missing data into a series of time slices and independently reconstructs each time slice.However,when this strategy is employed,the potential correlations between two adjacent time slices are ignored,which degrades reconstruction performance.Therefore,this study proposes the use of a two-dimensional curvelet transform and the fast iterative shrinkage thresholding algorithm for data reconstruction.Based on the significant overlapping characteristics between the curvelet coefficient support sets of two adjacent time slices,a weighted operator is constructed in the curvelet domain using the prior support set provided by the previous reconstructed time slice to delineate the main energy distribution range,eff ectively providing prior information for reconstructing adjacent slices.Consequently,the resulting weighted fast iterative shrinkage thresholding algorithm can be used to reconstruct 3D seismic data.The processing of synthetic and field data shows that the proposed method has higher reconstruction accuracy and faster computational speed than the conventional fast iterative shrinkage thresholding algorithm for handling missing 3D seismic data.展开更多
Amid accelerating global land degradation,establishing high-efficiency ecological restoration principles and frameworks is crucial.Here,we explore the application of threshold effects in the ecological restoration pro...Amid accelerating global land degradation,establishing high-efficiency ecological restoration principles and frameworks is crucial.Here,we explore the application of threshold effects in the ecological restoration process based on field experiments and globally available experimental data from 173 sites.Combining data integration analysis and meta-analysis,we collectively verified the universality of threshold effects in grasslands.The global grasslands’average nitrogen application threshold is 3.78 g·m^(-2)·yr^(−1),while the threshold value of degraded grassland(3.65 g·m^(-2)·yr^(−1))is lower than that of nondegraded grassland(5.90 g·m^(-2)·yr^(−1)).The low nitrogen-driven thresholds are affected by degradation status,climate(precipitation and temperature),and other site conditions,but not fertilization forms.Independent experiments further demonstrated that an increase in soil moisture content can lead to the disappearance of nitrogen threshold effects,revealing that ecological threshold effects are influenced by ecosystem stress factors.Following the significant increase in plant biomass triggered by the nitrogen threshold,the ecosystem undergoes systemic improvement.Soil organic carbon,urease activity,soil microbial diversity,and other soil properties are significantly enhanced.Soil nitrogen cycle-related microbial communities and soil physicochemical attributes are significantly activated.The results indicate that a threshold response pattern may develop before nitrogen saturation is reached,and low nitrogen input can boost productivity and improve the plant-soil-microbe system.Our findings reveal a nonprogressive path of restoration in degraded ecosystems,and thus,restoration based on threshold effects can offer an efficient and safe solution to combat ecological degradation.展开更多
An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performanc...An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performance and control load while ensuring continuous optimization.First,evaluation indicators are introduced to comprehensively analyze the impact of power fluctuations on the objective function and system voltage at both the system-wide and local levels.Based on these indicators,a multi-stage centralized optimization(MCO)is selectively applied,addressing system state deviations to achieve optimal operating states while maintaining a voltage security margin to ensure system safety.Then,distributed optimization(DO)is carried out at each bus with a renewable energy source or random load integration to accommodate short-term uncertainties using a self-adaptive reactive power algorithm.The optimal threshold value for event-triggered DO is calculated to balance control burden and optimization effectiveness.Utilizing the local state deviation evaluation indicator,unnecessary DOs are skipped when minor power fluctuations occur at the local level.Finally,following the linear superposition principle,event-triggered DOs executed at all distributed controllers collectively constitute the self-adaptive optimization strategy for the entire system.A case study on the IEEE New England 39-bus power system illustrates the effectiveness of the proposed strategy.展开更多
Understanding Cd contamination in the soil-rice ecosystem and the underlying its threshold and interaction effects is crucial for controlling Cd pollution and ensuring food safety.Although the quantitative relationshi...Understanding Cd contamination in the soil-rice ecosystem and the underlying its threshold and interaction effects is crucial for controlling Cd pollution and ensuring food safety.Although the quantitative relationships between Cd and environmental variables have been extensively studied,the threshold and interaction effects of multi-source environmental variables remain largely unexplored.This study employs a combination of random forest analysis and a human health risk model to investigate the effects of variables on Cd levels in rice grains,with the goal of quantifying their contributions and elucidating their relationships.The results indicated that the 15 selected variables collectively explained 47.36%of the variation in Cd content,with the top three variables being soil pH,distance from industrial park,and soil Zn.The majority of variables exhibited threshold effects on Cd levels in rice grains.By visualizing the interaction between Soil pH,distance from industrial park,and soil Zn with Cd levels in rice,we demonstrate the threshold effects of them on Cd level in rice grains,thereby providing further insight into the variation observed.Furthermore,oral intake of rice has been identified as the primary route of human exposure,significantly contributing to overall exposure pathways.Understanding these interactions is crucial for gaining insights into the underlying processes driving Cd pollution and fostering sustainable development within the industry.Our findings underscore the crucial need to consider multiple environmental variables and their interactions when managing heavy metals(HMs)contamination and mitigating health risks.展开更多
Sleep is a biological phenomenon with highly conserved evolutionary characteristics.The American Academy of Sleep Medicine and the Sleep Research Society recommend that adults get at least 7 hours of sleep per night[1...Sleep is a biological phenomenon with highly conserved evolutionary characteristics.The American Academy of Sleep Medicine and the Sleep Research Society recommend that adults get at least 7 hours of sleep per night[1].However,the stress caused by fast-paced life often leads to sleep deprivation(SD).SD is strongly associated with damage to the auditory system[2,3].Obstructive sleep apnea-hypopnea syndrome(OSAHS)is a common sleep disorder.Clinical observations indicate that some patients with OSAHS experience persistent hearing loss accompanied by tinnitus and other symptoms[4].More than 61.8%of patients with sudden deafness experienced SD[5].展开更多
Many machine learning-based Android malware detection often suffers from concept drift,where models trained on historical data fail to generalize to evolving threats.This paper proposes SCAN(Structural Clustering with...Many machine learning-based Android malware detection often suffers from concept drift,where models trained on historical data fail to generalize to evolving threats.This paper proposes SCAN(Structural Clustering with Adaptive thresholds for iNtelligent Android malware detection),a hybrid intelligent framework designed to mitigate concept drift without retraining.SCAN integrates Gaussian Mixture Models(GMMs)-based clustering with cluster-wise adaptive thresholding and supervised classifiers tailored to each cluster.A key challenge in clusteringbased malware detection is cluster-wise class imbalance,where clusters contain disproportionate distributions of benign and malicious samples.SCAN addresses this issue through adaptive thresholding,which dynamically adjusts the decision boundary of each cluster according to its malicious-to-benign ratio.In the final training stage,four supervised learning algorithms—Random Forest(RF),Support Vector Machine(SVM),k-NN,and XGBoost—are applied within the GMM-defined clusters.We train SCAN on Android applications collected from 2014-2017 and test it with applications from 2018-2023.Experimental results demonstrate that SCAN combined with RF consistently achieves superior performance,with both average accuracy and average F1-score exceeding 91%.These findings confirm SCAN’s robustness to concept drift and highlight its potential as a sustainable and intelligent solution for long-term Android malware detection in the real world.展开更多
To investigate the eco-economic thresholds of weeds and the critical period for their control,combining economic and ecological perspectives to achieve scientific weed management,four dominant weeds,Echinochloa crus-g...To investigate the eco-economic thresholds of weeds and the critical period for their control,combining economic and ecological perspectives to achieve scientific weed management,four dominant weeds,Echinochloa crus-galli(L.)P.Beauv,Chenopodium album L.,Digitaria sanguinalis(L.)Scop,and Commelina communis L.,were selected as experimental subjects,based on their common occurrence in spring maize planting areas in Northern China.A predictive model for maize yield loss caused by mixed weed populations was established.The study analyzed the eco-economic thresholds of weeds under different control measures and determined the optimal period for weed control by combining the critical control period.A logarithmic function model was developed to describe the relationship between mixed weed density and maize yield loss:y=5.9875ln(x)-6.5407(R^(2)=0.949,F=131.244,P=0.000).The optimal model for the critical period of competition between weeds and maize in maize fields was:y=-0.0027x^(2)+0.5624x-10.064(R2=0.968,F=30.513,P=0.032).When the weed density in maize fields reached 5.57 plants·m^(-2),manual weeding should be conducted promptly.When the weed density was 3.41 plants·m^(-2) or 3.48 plants·m^(-2),soil or foliar treatments should be applied,respectively.If the weed density reached 3.93 plants·m^(-2),a combination of soil and foliar treatment should be implemented.The critical period for manual weeding was 28.4 days after sowing,for soil treatment it was 19.9 days,for foliar treatment it was 21.8 days,and for the combined treatment of soil and foliar methods,it was 23.5 days after sowing.Retaining weeds for up to 15 days after maize sowing did not result in a yield loss and could even have a positive effect on maize yield.展开更多
In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demons...In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demonstrated and the issue is described as a linear inverse optimal problem using the L1 norm.Random noise suppression in seismic data is transformed into an L1 norm optimization problem based on the curvelet sparsity transform. Compared to the conventional methods such as median filter algorithm,FX deconvolution, and wavelet thresholding,the results of synthetic and field data processing show that the iterative curvelet thresholding proposed in this paper can sufficiently improve signal to noise radio（SNR） and give higher signal fidelity at the same time.Furthermore,to make better use of the curvelet transform such as multiple scales and multiple directions,we control the curvelet direction of the result after iterative curvelet thresholding to further improve the SNR.展开更多
To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is ex...To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.展开更多
In the multilevel thresholding segmentation of the image, the classification number is always given by the supervisor. To solve this problem, a fast multilevel thresholding algorithm considering both the threshold val...In the multilevel thresholding segmentation of the image, the classification number is always given by the supervisor. To solve this problem, a fast multilevel thresholding algorithm considering both the threshold value and the classification number is proposed based on the maximum entropy, and the self-adaptive criterion of the classification number is given. The algorithm can obtain thresholds and automatically decide the classification number. Experimental results show that the algorithm is effective.展开更多
In ghost imaging, an illumination light is split into test and reference beams which pass through two different optical systems respectively and an image is constructed with the second-order correlation between the tw...In ghost imaging, an illumination light is split into test and reference beams which pass through two different optical systems respectively and an image is constructed with the second-order correlation between the two light beams. Since both light beams are diffracted when passing through the optical systems, the spatial resolution of ghost imaging is in general lower than that of a corresponding conventional imaging system. When Gaussian-shaped light spots are used to illuminate an object, randomly scanning across the object plane, in the ghost imaging scheme, we show th√at by localizing central positions of the spots of the reference light beam, the resolution can be increased by a factor of 2^(1/2) same as that of the corresponding conventional imaging system. We also find that the resolution can be further enhanced by setting an appropriate threshold to the bucket measurement of ghost imaging.展开更多
In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding....In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.展开更多
Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obst...Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.展开更多
VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identic...VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.展开更多
Among all segmentation techniques, Otsu thresholding method is widely used. Line intercept histogram based Otsu thresholding method(LIH Otsu method) can be more resistant to Gaussian noise, highly efficient in computi...Among all segmentation techniques, Otsu thresholding method is widely used. Line intercept histogram based Otsu thresholding method(LIH Otsu method) can be more resistant to Gaussian noise, highly efficient in computing time, and can be easily extended to multilevel thresholding. But when images contain salt-and-pepper noise, LIH Otsu method performs poorly. An improved LIH Otsu method(ILIH Otsu method) is presented, which can be more resistant to Gaussian noise and salt-and-pepper noise. Moreover, it can be easily extended to multilevel thresholding. In order to improve the efficiency, the optimization algorithm based on the kinetic-molecular theory(KMTOA) is used to determine the optimal thresholds. The experimental results show that ILIH Otsu method has stronger anti-noise ability than two-dimensional Otsu thresholding method(2-D Otsu method), LIH Otsu method, K-means clustering algorithm and fuzzy clustering algorithm.展开更多
A method of segmenting vessels by morphological filters and dynamic thresholding for digital subtraction angiography （DSA） images is presented. The first step is to reduce the noise and enhance the details of image ...A method of segmenting vessels by morphological filters and dynamic thresholding for digital subtraction angiography （DSA） images is presented. The first step is to reduce the noise and enhance the details of image by using morpholngical operators. The second is to segment vessels by dynamic thresholding combined with global thresholding based on the properties of DSA images. Artificial images and actual images have been tested. Experiment results show that the proposed method is efficient and is of great potential for the segmentation of vessels in medical images.展开更多
基金2023 Liaoning Institute of Science and Technology Doctoral Program Launch fund(No.2307B29).
文摘A clock bias data processing method based on interval correlation coefficient wavelet threshold denoising is suggested for minor mistakes in clock bias data in order to increase the efficacy of satellite clock bias prediction.Wavelet analysis was first used to break down the satellite clock frequency data into several levels,producing high and low frequency coefficients for each layer.The correlation coefficients of the high and low frequency coefficients in each of the three sub-intervals created by splitting these coefficients were then determined.The major noise region—the sub-interval with the lowest correlation coefficient—was chosen for thresholding treatment and noise threshold computation.The clock frequency data was then processed using wavelet reconstruction and reconverted to clock data.Lastly,three different kinds of satellite clock data—RTS,whu-o,and IGS-F—were used to confirm the produced data.Our method enhanced the stability of the Quadratic Polynomial(QP)model’s predictions for the C16 satellite by about 40%,according to the results.The accuracy and stability of the Auto Regression Integrated Moving Average(ARIMA)model improved up to 41.8%and 14.2%,respectively,whilst the Wavelet Neural Network(WNN)model improved by roughly 27.8%and 63.6%,respectively.Although our method has little effect on forecasting IGS-F series satellites,the experimental findings show that it can improve the accuracy and stability of QP,ARIMA,and WNN model forecasts for RTS and whu-o satellite clock bias.
基金supported by the National Natural Science Foundation of China under Grant No.21933006.
文摘To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achieves high-quality AFM imaging via line-by-line corrections for each distorted profile along the fast axis.The key to this line-by-line correction is to accurately simulate the profile distortion of each scanning row.Therefore,a data preprocessing approach is first developed to roughly filter out most of the height data that impairs the accuracy of distortion modeling.This process is implemented through an internal double-screening mechanism.A line-fitting method is adopted to preliminarily screen out the obvious specimens.Lifting wavelet analysis is then carried out to identify the base parts that are mistakenly filtered out as specimens so as to preserve most of the base profiles and provide a good basis for further distortion modeling.Next,an iterative thresholding algorithm is developed to precisely simulate the profile distortion.By utilizing the roughly screened base profile,the optimal threshold,which is used to screen out the pure bases suitable for distortion modeling,is determined through iteration with a specified error rule.On this basis,the profile distortion is accurately modeled through line fitting on the finely screened base data,and the correction is implemented by subtracting the modeling result from the distorted profile.Finally,the effectiveness of the proposed method is verified through experiments and applications.
基金National Natural Science Foundation of China under Grant 42304145Jiangxi Provincial Natural Science Foundation under Grant 20242BAB26051,20242BAB25191 and 20232BAB213077+1 种基金Foundation of National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing under Grant 2024QZ-TD-13Open Fund(FW0399-0002)of SINOPEC Key Laboratory of Geophysics。
文摘Data reconstruction is a crucial step in seismic data preprocessing.To improve reconstruction speed and save memory,the commonly used three-dimensional(3D)seismic data reconstruction method divides the missing data into a series of time slices and independently reconstructs each time slice.However,when this strategy is employed,the potential correlations between two adjacent time slices are ignored,which degrades reconstruction performance.Therefore,this study proposes the use of a two-dimensional curvelet transform and the fast iterative shrinkage thresholding algorithm for data reconstruction.Based on the significant overlapping characteristics between the curvelet coefficient support sets of two adjacent time slices,a weighted operator is constructed in the curvelet domain using the prior support set provided by the previous reconstructed time slice to delineate the main energy distribution range,eff ectively providing prior information for reconstructing adjacent slices.Consequently,the resulting weighted fast iterative shrinkage thresholding algorithm can be used to reconstruct 3D seismic data.The processing of synthetic and field data shows that the proposed method has higher reconstruction accuracy and faster computational speed than the conventional fast iterative shrinkage thresholding algorithm for handling missing 3D seismic data.
基金supported by the Major Special Projects of the National Natural Science Foundation of China(Grants No.52374170 and 42377465)the Third Comprehensive Scientific Exploration in Xinjiang(Grant No.2022xjkk1005)+1 种基金the Special Technology Innovation Fund of Carbon Peak and Carbon Neutrality in Jiangsu Province(Grant No.BK20231515)the Shaanxi Shenmu Natural Field Observation and Research Station of Erosion and Environment,which provided the site and data on experimental conditions for field trials.
文摘Amid accelerating global land degradation,establishing high-efficiency ecological restoration principles and frameworks is crucial.Here,we explore the application of threshold effects in the ecological restoration process based on field experiments and globally available experimental data from 173 sites.Combining data integration analysis and meta-analysis,we collectively verified the universality of threshold effects in grasslands.The global grasslands’average nitrogen application threshold is 3.78 g·m^(-2)·yr^(−1),while the threshold value of degraded grassland(3.65 g·m^(-2)·yr^(−1))is lower than that of nondegraded grassland(5.90 g·m^(-2)·yr^(−1)).The low nitrogen-driven thresholds are affected by degradation status,climate(precipitation and temperature),and other site conditions,but not fertilization forms.Independent experiments further demonstrated that an increase in soil moisture content can lead to the disappearance of nitrogen threshold effects,revealing that ecological threshold effects are influenced by ecosystem stress factors.Following the significant increase in plant biomass triggered by the nitrogen threshold,the ecosystem undergoes systemic improvement.Soil organic carbon,urease activity,soil microbial diversity,and other soil properties are significantly enhanced.Soil nitrogen cycle-related microbial communities and soil physicochemical attributes are significantly activated.The results indicate that a threshold response pattern may develop before nitrogen saturation is reached,and low nitrogen input can boost productivity and improve the plant-soil-microbe system.Our findings reveal a nonprogressive path of restoration in degraded ecosystems,and thus,restoration based on threshold effects can offer an efficient and safe solution to combat ecological degradation.
基金supported in part by National Key R&D Program of China(2022YFF0610600).
文摘An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performance and control load while ensuring continuous optimization.First,evaluation indicators are introduced to comprehensively analyze the impact of power fluctuations on the objective function and system voltage at both the system-wide and local levels.Based on these indicators,a multi-stage centralized optimization(MCO)is selectively applied,addressing system state deviations to achieve optimal operating states while maintaining a voltage security margin to ensure system safety.Then,distributed optimization(DO)is carried out at each bus with a renewable energy source or random load integration to accommodate short-term uncertainties using a self-adaptive reactive power algorithm.The optimal threshold value for event-triggered DO is calculated to balance control burden and optimization effectiveness.Utilizing the local state deviation evaluation indicator,unnecessary DOs are skipped when minor power fluctuations occur at the local level.Finally,following the linear superposition principle,event-triggered DOs executed at all distributed controllers collectively constitute the self-adaptive optimization strategy for the entire system.A case study on the IEEE New England 39-bus power system illustrates the effectiveness of the proposed strategy.
基金supported by the GDAS’Project of Science and Technology Development(No.2022GDASZH-2022010104-2)Guangdong Major Project of Basic and Applied Basic Research(No.2023B0303000006).
文摘Understanding Cd contamination in the soil-rice ecosystem and the underlying its threshold and interaction effects is crucial for controlling Cd pollution and ensuring food safety.Although the quantitative relationships between Cd and environmental variables have been extensively studied,the threshold and interaction effects of multi-source environmental variables remain largely unexplored.This study employs a combination of random forest analysis and a human health risk model to investigate the effects of variables on Cd levels in rice grains,with the goal of quantifying their contributions and elucidating their relationships.The results indicated that the 15 selected variables collectively explained 47.36%of the variation in Cd content,with the top three variables being soil pH,distance from industrial park,and soil Zn.The majority of variables exhibited threshold effects on Cd levels in rice grains.By visualizing the interaction between Soil pH,distance from industrial park,and soil Zn with Cd levels in rice,we demonstrate the threshold effects of them on Cd level in rice grains,thereby providing further insight into the variation observed.Furthermore,oral intake of rice has been identified as the primary route of human exposure,significantly contributing to overall exposure pathways.Understanding these interactions is crucial for gaining insights into the underlying processes driving Cd pollution and fostering sustainable development within the industry.Our findings underscore the crucial need to consider multiple environmental variables and their interactions when managing heavy metals(HMs)contamination and mitigating health risks.
文摘Sleep is a biological phenomenon with highly conserved evolutionary characteristics.The American Academy of Sleep Medicine and the Sleep Research Society recommend that adults get at least 7 hours of sleep per night[1].However,the stress caused by fast-paced life often leads to sleep deprivation(SD).SD is strongly associated with damage to the auditory system[2,3].Obstructive sleep apnea-hypopnea syndrome(OSAHS)is a common sleep disorder.Clinical observations indicate that some patients with OSAHS experience persistent hearing loss accompanied by tinnitus and other symptoms[4].More than 61.8%of patients with sudden deafness experienced SD[5].
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(No.2021R1A2C2012574)in part by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259967).
文摘Many machine learning-based Android malware detection often suffers from concept drift,where models trained on historical data fail to generalize to evolving threats.This paper proposes SCAN(Structural Clustering with Adaptive thresholds for iNtelligent Android malware detection),a hybrid intelligent framework designed to mitigate concept drift without retraining.SCAN integrates Gaussian Mixture Models(GMMs)-based clustering with cluster-wise adaptive thresholding and supervised classifiers tailored to each cluster.A key challenge in clusteringbased malware detection is cluster-wise class imbalance,where clusters contain disproportionate distributions of benign and malicious samples.SCAN addresses this issue through adaptive thresholding,which dynamically adjusts the decision boundary of each cluster according to its malicious-to-benign ratio.In the final training stage,four supervised learning algorithms—Random Forest(RF),Support Vector Machine(SVM),k-NN,and XGBoost—are applied within the GMM-defined clusters.We train SCAN on Android applications collected from 2014-2017 and test it with applications from 2018-2023.Experimental results demonstrate that SCAN combined with RF consistently achieves superior performance,with both average accuracy and average F1-score exceeding 91%.These findings confirm SCAN’s robustness to concept drift and highlight its potential as a sustainable and intelligent solution for long-term Android malware detection in the real world.
基金Supported by the National Key Research and Development Program of China(2023YFD1400502)。
文摘To investigate the eco-economic thresholds of weeds and the critical period for their control,combining economic and ecological perspectives to achieve scientific weed management,four dominant weeds,Echinochloa crus-galli(L.)P.Beauv,Chenopodium album L.,Digitaria sanguinalis(L.)Scop,and Commelina communis L.,were selected as experimental subjects,based on their common occurrence in spring maize planting areas in Northern China.A predictive model for maize yield loss caused by mixed weed populations was established.The study analyzed the eco-economic thresholds of weeds under different control measures and determined the optimal period for weed control by combining the critical control period.A logarithmic function model was developed to describe the relationship between mixed weed density and maize yield loss:y=5.9875ln(x)-6.5407(R^(2)=0.949,F=131.244,P=0.000).The optimal model for the critical period of competition between weeds and maize in maize fields was:y=-0.0027x^(2)+0.5624x-10.064(R2=0.968,F=30.513,P=0.032).When the weed density in maize fields reached 5.57 plants·m^(-2),manual weeding should be conducted promptly.When the weed density was 3.41 plants·m^(-2) or 3.48 plants·m^(-2),soil or foliar treatments should be applied,respectively.If the weed density reached 3.93 plants·m^(-2),a combination of soil and foliar treatment should be implemented.The critical period for manual weeding was 28.4 days after sowing,for soil treatment it was 19.9 days,for foliar treatment it was 21.8 days,and for the combined treatment of soil and foliar methods,it was 23.5 days after sowing.Retaining weeds for up to 15 days after maize sowing did not result in a yield loss and could even have a positive effect on maize yield.
基金the National Science & Technology Major Projects(Grant No.2008ZX05023-005-013).
文摘In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demonstrated and the issue is described as a linear inverse optimal problem using the L1 norm.Random noise suppression in seismic data is transformed into an L1 norm optimization problem based on the curvelet sparsity transform. Compared to the conventional methods such as median filter algorithm,FX deconvolution, and wavelet thresholding,the results of synthetic and field data processing show that the iterative curvelet thresholding proposed in this paper can sufficiently improve signal to noise radio（SNR） and give higher signal fidelity at the same time.Furthermore,to make better use of the curvelet transform such as multiple scales and multiple directions,we control the curvelet direction of the result after iterative curvelet thresholding to further improve the SNR.
基金The National Natural Science Foundation of China(No.50805023)the Science and Technology Support Program of Jiangsu Province(No.BE2008081)+1 种基金the Transformation Program of Science and Technology Achievements of Jiangsu Province(No.BA2010093)the Program for Special Talent in Six Fields of Jiangsu Province(No.2008144)
文摘To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.
文摘In the multilevel thresholding segmentation of the image, the classification number is always given by the supervisor. To solve this problem, a fast multilevel thresholding algorithm considering both the threshold value and the classification number is proposed based on the maximum entropy, and the self-adaptive criterion of the classification number is given. The algorithm can obtain thresholds and automatically decide the classification number. Experimental results show that the algorithm is effective.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11534008,11605126,and 11804271)the Fund from the Ministry of Science and Technology of China(Grant No.2016YFA0301404)+2 种基金the Natural Science Basic Research Plan in Shaanxi Province,China(Grant No.2017JQ1025)the Doctoral Fund of the Ministry of Education of China(Grant Nos.2016M592772 and 2018M631137)the Fundamental Research Funds for the Central Universities
文摘In ghost imaging, an illumination light is split into test and reference beams which pass through two different optical systems respectively and an image is constructed with the second-order correlation between the two light beams. Since both light beams are diffracted when passing through the optical systems, the spatial resolution of ghost imaging is in general lower than that of a corresponding conventional imaging system. When Gaussian-shaped light spots are used to illuminate an object, randomly scanning across the object plane, in the ghost imaging scheme, we show th√at by localizing central positions of the spots of the reference light beam, the resolution can be increased by a factor of 2^(1/2) same as that of the corresponding conventional imaging system. We also find that the resolution can be further enhanced by setting an appropriate threshold to the bucket measurement of ghost imaging.
文摘In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(60525303)Doctoral Foundation of Yanshan University(B243).
文摘Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.
文摘VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.
基金Project(61440026)supported by the National Natural Science Foundation of ChinaProject(11KZ|KZ08062)supported by Doctoral Research Project of Xiangtan University,China
文摘Among all segmentation techniques, Otsu thresholding method is widely used. Line intercept histogram based Otsu thresholding method(LIH Otsu method) can be more resistant to Gaussian noise, highly efficient in computing time, and can be easily extended to multilevel thresholding. But when images contain salt-and-pepper noise, LIH Otsu method performs poorly. An improved LIH Otsu method(ILIH Otsu method) is presented, which can be more resistant to Gaussian noise and salt-and-pepper noise. Moreover, it can be easily extended to multilevel thresholding. In order to improve the efficiency, the optimization algorithm based on the kinetic-molecular theory(KMTOA) is used to determine the optimal thresholds. The experimental results show that ILIH Otsu method has stronger anti-noise ability than two-dimensional Otsu thresholding method(2-D Otsu method), LIH Otsu method, K-means clustering algorithm and fuzzy clustering algorithm.
文摘A method of segmenting vessels by morphological filters and dynamic thresholding for digital subtraction angiography （DSA） images is presented. The first step is to reduce the noise and enhance the details of image by using morpholngical operators. The second is to segment vessels by dynamic thresholding combined with global thresholding based on the properties of DSA images. Artificial images and actual images have been tested. Experiment results show that the proposed method is efficient and is of great potential for the segmentation of vessels in medical images.
