Under the complex condition of nuclear power plant, all kinds of influence factors may cause distortion of on-line monitoring data. It is essential that on-line monitoring data should be de-noised in order to ensure t...Under the complex condition of nuclear power plant, all kinds of influence factors may cause distortion of on-line monitoring data. It is essential that on-line monitoring data should be de-noised in order to ensure the accuracy of diagnosis. Based on the research of wavelet analysis and threshold de-noising, a new threshold denoising method based on Mallat transform is proposed. This method adopts factor weighing method for threshold quantization. Through the specific case of nuclear power plant, it is verified that the algorithm is of validity and superiority.展开更多
In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on...In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on the multi-resolution analysis wavelet threshold de-noising method which was put forward by Donoho and Johnstone, and applied this method in the de-noising of the defective signals. This threshold function overcomes the discontinuous shortcoming of the hard-threshold function and the disadvantage of soft threshold function which causes an invariable deviation between the estimated wavelet coeffwients and the decomposed wavelet coefficients. The improved threshold function is of simple expression and convenient for calculation. The actual test results of defect noise signal show that this improved method can get less mean square error （ MSE ） and higher signal-to-noise ratio of reconstructed signals than those calculated from hard threshold and soft threshold methods. The improved threshold function has excellent de-noising effect.展开更多
Gyro's drift is not only the main drift error which influences gyro's precision but also the primary factor that affects gyro's reliability.Reducing zero drift and random drift is a key problem to the outp...Gyro's drift is not only the main drift error which influences gyro's precision but also the primary factor that affects gyro's reliability.Reducing zero drift and random drift is a key problem to the output of a gyro signal.A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro(FOG).The coefficients are obtained from the three-layer wavelet packet decomposition.By setting the high frequency part which is greater than wavelet packet threshold as zero,then reconstructing the nodes which have been filtered out noise and interruption,the soft threshold function is constructed by the coefficients of the third nodes.Compared wavelet packet de-noise with forced de-noising method,the proposed method is more effective.Simulation results show that the random drift compensation is enhanced by 13.1%,and reduces zero drift by 0.0526°/h.展开更多
As a relatively new method of processing non-stationary signal with high time-frequency resolution, S transform can be used to analyze the time-frequency characteristics of seismic signals. It has the following charac...As a relatively new method of processing non-stationary signal with high time-frequency resolution, S transform can be used to analyze the time-frequency characteristics of seismic signals. It has the following characteristics: its time-frequency resolution corresponding to the signal frequency, reversible inverse transform, basic wavelet that does not have to meet the permit conditions. We combined the threshold method, proposed the S-transform threshold filtering on the basis of S transform timefrequency filtering, and processed airgun seismic records from temporary stations in "Yangtze Program"(the Anhui experiment). Compared with the results of the bandpass filtering, the S transform threshold filtering can improve the signal to noise ratio(SNR) of seismic waves and provide effective help for first arrival pickup and accurate travel time. The first arrival wave seismic phase can be traced farther continuously, and the Pm seismic phase in the subsequent zone is also highlighted.展开更多
An effective de-noising method for fiber optic gyroscopes(FOGs)is proposed.This method is based on second-generation Daubechies D4(DB4)wavelet transform(WT)and level-dependent threshold estimator called Stein's un...An effective de-noising method for fiber optic gyroscopes(FOGs)is proposed.This method is based on second-generation Daubechies D4(DB4)wavelet transform(WT)and level-dependent threshold estimator called Stein's unbiased risk estimator(SURE).The whole approach consists of three critical parts:wavelet decomposition module,parameters estimation module and SURE de-noising module.First,DB4 wavelet is selected as lifting base of the second-generation wavelet in the decomposition module.Second,in the parameters estimation module,maximum likelihood estimation(MLE)is used for stochastic noise parameters estimation.Third,combined with soft threshold de-noising technique,the SURE de-noising module is designed.For comparison,both the traditional universal threshold wavelet and the second-generation Harr wavelet method are also investigated.The experiment results show that the computation cost is 40%less than that of the traditional wavelet method.The standard deviation of de-noised FOG signal is 0.012 and the three noise terms such as angle random walk,bias instability and quantization noise are reduced to 0.0072°/√h,0.0041°/h,and 0.0081°,respectively.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
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
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
A noise reduction method for infrared detector output signal is studied during dynamic calibration of thermocou- pie. Firstly, the deficiency of the classical filter method is analyzed and the application of the wavel...A noise reduction method for infrared detector output signal is studied during dynamic calibration of thermocou- pie. Firstly, the deficiency of the classical filter method is analyzed and the application of the wavelet analysis is introduced for signal de-noising during the dynamic testing. Secondly, the theoretical basis of wavelet analysis, the choice of wavelet base and the determination of decomposed series and threshold are analyzed. Finally, the de-noising experiment for infrared detector signal is carried out on the Matlab platform. The results indicate the proposed wavelet de-noising method is effective to remove fixed frequency and high-frequency noise; furthermore, good synchronization is achieved between the de-noised signal and the useful signal components in the original signal, which is of great significance to thermocouple modeling analys- is.展开更多
To reduce the drift error existing in the output signal of fiber optic gyroscopes （FOG）, a mathematical model of the FOG output signal is set up; the error characteristics of the FOG output signal are analyzed, and ...To reduce the drift error existing in the output signal of fiber optic gyroscopes （FOG）, a mathematical model of the FOG output signal is set up; the error characteristics of the FOG output signal are analyzed, and semi-soft threshold filtering is chosen based on the comparison of hard threshold and soft threshold filtering. The semi-soft threshold wavelet package filtering method is applied in the filtering of the FOG output signal. Experiments of the stationary and dynamic FOG output signals filtered with the wavelet package analysis are carried out in a lab environment, respectively. Experiments done with the real-time measured FOG signal show that the method of semi-soft threshold wavelet package filtering reduces the mean square error from 5 （°）/h to 1 （°）/h, so it is effective in eliminating the white noises and the fractal noises existing in the FOG. The novel method proposed here is proved valid in reducing the FOG drift error, satisfying the technical demands of high precision and realtime processing.展开更多
Neurochip based on light-addressable potentiometric sensor(LAPS),whose sensing elements are excitable cells,can monitor electrophysiological properties of cultured neuron networks with cellular signals well analyzed.H...Neurochip based on light-addressable potentiometric sensor(LAPS),whose sensing elements are excitable cells,can monitor electrophysiological properties of cultured neuron networks with cellular signals well analyzed.Here we report a kind of neurochip with rat pheochromocytoma(PC12) cells hybrid with LAPS and a method of de-noising signals based on wavelet transform.Cells were cultured on LAPS for several days to form networks,and we then used LAPS system to detect the extracellular potentials with signals de-noised according to decomposition in the time-frequency space.The signal was decomposed into various scales,and coefficients were processed based on the properties of each layer.At last,signal was reconstructed based on the new coefficients.The results show that after de-noising,baseline drift is removed and signal-to-noise ratio is increased.It suggests that the neurochip of PC12 cells coupled to LAPS is stable and suitable for long-term and non-invasive measurement of cell electrophysiological properties with wavelet transform,taking advantage of its time-frequency localization analysis to reduce noise.展开更多
With the increasing adoption of intelligent operation and maintenance technologies in urban rail transit,most maintenance systems have been equipped with fault diagnosis modules targeting key components of metro vehic...With the increasing adoption of intelligent operation and maintenance technologies in urban rail transit,most maintenance systems have been equipped with fault diagnosis modules targeting key components of metro vehicles.However,the integration between engineering-level diagnostic algorithms and advanced academic research remains limited.Two major challenges hinder vibration-based fault diagnosis under real-world operating conditions:the complex noise and interference caused by wheel-rail coupling and the typically weak expression of fault features.Considering the widespread application of wavelet transform in noise reduction and the maturity of ensemble empirical mode decomposition(EEMD)in handling nonlinear and non-stationary signals without parameter tuning,this study proposes a diagnostic method that combines wavelet threshold denoising with EEMD.The method was applied to bearing vibration signals collected from an operational subway line.The diagnostic results were consistent with actual disassembly findings,demonstrating the effectiveness and practical value of the proposed approach.展开更多
Using numerical simulation data of the forward differential propagation shift （ΦDP） of polarimetric radar,the principle and performing steps of noise reduction by wavelet analysis are introduced in detail.Profiting...Using numerical simulation data of the forward differential propagation shift （ΦDP） of polarimetric radar,the principle and performing steps of noise reduction by wavelet analysis are introduced in detail.Profiting from the multiscale analysis,various types of noises can be identified according to their characteristics in different scales,and suppressed in different resolutions by a penalty threshold strategy through which a fixed threshold value is applied,a default threshold strategy through which the threshold value is determined by the noise intensity,or a ΦDP penalty threshold strategy through which a special value is designed for ΦDP de-noising.Then,a hard-or soft-threshold function,depending on the de-noising purpose,is selected to reconstruct the signal.Combining the three noise suppression strategies and the two signal reconstruction functions,and without loss of generality,two schemes are presented to verify the de-noising effect by dbN wavelets：（1） the penalty threshold strategy with the soft threshold function scheme （PSS）; （2） the ΦDP penalty threshold strategy with the soft threshold function scheme （PPSS）.Furthermore,the wavelet de-noising is compared with the mean,median,Kalman,and finite impulse response （FIR） methods with simulation data and two actual cases.The results suggest that both of the two schemes perform well,especially when ΦDP data are simultaneously polluted by various scales and types of noises.A slight difference is that the PSS method can retain more detail,and the PPSS can smooth the signal more successfully.展开更多
Aiming at the existing problems of discrete cosine transform(DCT) de-noising method, we introduce the idea of wavelet neighboring coefficients(WNC) de-noising method, and propose the cosine neighboring coefficients(CN...Aiming at the existing problems of discrete cosine transform(DCT) de-noising method, we introduce the idea of wavelet neighboring coefficients(WNC) de-noising method, and propose the cosine neighboring coefficients(CNC) de-noising method. Based on DCT, a novel method for the fault feature extraction of hydraulic pump is analyzed. The vibration signal of pump is de-noised with CNC de-noising method, and the fault feature is extracted by performing Hilbert-Huang transform(HHT) to the output signal. The analysis results of the simulation signal and the actual one demonstrate that the proposed CNC de-noising method and the fault feature extraction method have more superior ability than the traditional ones.展开更多
In the present study of peak particle velocity(PPV)and frequency,an improved algorithm(principal empirical mode decomposition,PEMD)based on principal component analysis(PCA)and empirical mode decomposition(EMD)is prop...In the present study of peak particle velocity(PPV)and frequency,an improved algorithm(principal empirical mode decomposition,PEMD)based on principal component analysis(PCA)and empirical mode decomposition(EMD)is proposed,with the goal of addressing poor filtering de-noising effects caused by the occurrences of modal aliasing phenomena in EMD blasting vibration signal decomposition processes.Test results showed that frequency of intrinsic mode function(IMF)components decomposed by PEMD gradually decreases and that the main frequency is unique,which eliminates the phenomenon of modal aliasing.In the simulation experiment,the signal-to-noise(SNR)and root mean square errors(RMSE)ratio of the signal de-noised by PEMD are the largest when compared to EMD and ensemble empirical mode decomposition(EEMD).The main frequency of the de-noising signal through PEMD is 75 Hz,which is closest to the frequency of the noiseless simulation signal.In geotechnical engineering blasting experiments,compared to EMD and EEMD,the signal de-noised by PEMD has the lowest level of distortion,and the frequency band is distributed in a range of 0-64 Hz,which is closest to the frequency band of the blasting vibration signal.In addition,the proportion of noise energy was the lowest,at 1.8%.展开更多
With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due ...With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.展开更多
Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal...Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal deforms in the exposed and magnetically contaminated environment.In order to preciously recognize the roll information and effectively separate the noise component from the original geomagnetic sequence,based on the error source analysis,we propose a moving horizon based wavelet de-noising method for the dual-observed geomagnetic signal filtering where the captured rough roll frequency value provides reasonable wavelet decomposition and reconstruction level selection basis for sampled sequence;a moving horizon window guarantees real-time performance and non-cumulative calculation amount.The complete geomagnetic data in full ballistic range and three intercepted paragraphs are used for performance assessment.The positioning performance of the moving horizon wavelet de-noising method is compared with the band-pass filter.The results show that both noise reduction techniques improve the positioning accuracy while the wavelet de-noising method is always better than the band-pass filter.These results suggest that the proposed moving horizon based wavelet de-noising method of the dual-observed geomagnetic signal is more applicable for various launch conditions with better positioning performance.展开更多
In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the ...In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the process flow of RLS algorithm are described.Through example simulation,simulation figures of the adaptive de-noising system are obtained.By analysis and comparison,it can be proved that RLS adaptive filtering is capable of eliminating the noises and obtaining useful signals in a relatively good manner.Therefore,the validity of this method and the rationality of this system are demonstrated.展开更多
On the： basis of wavelet theory, we propose an outlier-detection algorithm for satellite gravity ometry by applying a wavelet-shrinkage-de-noising method to some simulation data with white noise and ers. The result S...On the： basis of wavelet theory, we propose an outlier-detection algorithm for satellite gravity ometry by applying a wavelet-shrinkage-de-noising method to some simulation data with white noise and ers. The result Shows that this novel algorithm has a 97% success rate in outlier identification and that be efficiently used for pre-processing real satellite gravity gradiometry data.展开更多
文摘Under the complex condition of nuclear power plant, all kinds of influence factors may cause distortion of on-line monitoring data. It is essential that on-line monitoring data should be de-noised in order to ensure the accuracy of diagnosis. Based on the research of wavelet analysis and threshold de-noising, a new threshold denoising method based on Mallat transform is proposed. This method adopts factor weighing method for threshold quantization. Through the specific case of nuclear power plant, it is verified that the algorithm is of validity and superiority.
文摘In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on the multi-resolution analysis wavelet threshold de-noising method which was put forward by Donoho and Johnstone, and applied this method in the de-noising of the defective signals. This threshold function overcomes the discontinuous shortcoming of the hard-threshold function and the disadvantage of soft threshold function which causes an invariable deviation between the estimated wavelet coeffwients and the decomposed wavelet coefficients. The improved threshold function is of simple expression and convenient for calculation. The actual test results of defect noise signal show that this improved method can get less mean square error （ MSE ） and higher signal-to-noise ratio of reconstructed signals than those calculated from hard threshold and soft threshold methods. The improved threshold function has excellent de-noising effect.
文摘Gyro's drift is not only the main drift error which influences gyro's precision but also the primary factor that affects gyro's reliability.Reducing zero drift and random drift is a key problem to the output of a gyro signal.A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro(FOG).The coefficients are obtained from the three-layer wavelet packet decomposition.By setting the high frequency part which is greater than wavelet packet threshold as zero,then reconstructing the nodes which have been filtered out noise and interruption,the soft threshold function is constructed by the coefficients of the third nodes.Compared wavelet packet de-noise with forced de-noising method,the proposed method is more effective.Simulation results show that the random drift compensation is enhanced by 13.1%,and reduces zero drift by 0.0526°/h.
基金funded by the National Natural Science Foundation Item (41674068)Seismic Youth Funding of GEC (YFGEC2016001)
文摘As a relatively new method of processing non-stationary signal with high time-frequency resolution, S transform can be used to analyze the time-frequency characteristics of seismic signals. It has the following characteristics: its time-frequency resolution corresponding to the signal frequency, reversible inverse transform, basic wavelet that does not have to meet the permit conditions. We combined the threshold method, proposed the S-transform threshold filtering on the basis of S transform timefrequency filtering, and processed airgun seismic records from temporary stations in "Yangtze Program"(the Anhui experiment). Compared with the results of the bandpass filtering, the S transform threshold filtering can improve the signal to noise ratio(SNR) of seismic waves and provide effective help for first arrival pickup and accurate travel time. The first arrival wave seismic phase can be traced farther continuously, and the Pm seismic phase in the subsequent zone is also highlighted.
基金Supported by the Aerospace Science and Technology Innovation Foundation of China(2006)
文摘An effective de-noising method for fiber optic gyroscopes(FOGs)is proposed.This method is based on second-generation Daubechies D4(DB4)wavelet transform(WT)and level-dependent threshold estimator called Stein's unbiased risk estimator(SURE).The whole approach consists of three critical parts:wavelet decomposition module,parameters estimation module and SURE de-noising module.First,DB4 wavelet is selected as lifting base of the second-generation wavelet in the decomposition module.Second,in the parameters estimation module,maximum likelihood estimation(MLE)is used for stochastic noise parameters estimation.Third,combined with soft threshold de-noising technique,the SURE de-noising module is designed.For comparison,both the traditional universal threshold wavelet and the second-generation Harr wavelet method are also investigated.The experiment results show that the computation cost is 40%less than that of the traditional wavelet method.The standard deviation of de-noised FOG signal is 0.012 and the three noise terms such as angle random walk,bias instability and quantization noise are reduced to 0.0072°/√h,0.0041°/h,and 0.0081°,respectively.
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 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 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.
文摘A noise reduction method for infrared detector output signal is studied during dynamic calibration of thermocou- pie. Firstly, the deficiency of the classical filter method is analyzed and the application of the wavelet analysis is introduced for signal de-noising during the dynamic testing. Secondly, the theoretical basis of wavelet analysis, the choice of wavelet base and the determination of decomposed series and threshold are analyzed. Finally, the de-noising experiment for infrared detector signal is carried out on the Matlab platform. The results indicate the proposed wavelet de-noising method is effective to remove fixed frequency and high-frequency noise; furthermore, good synchronization is achieved between the de-noised signal and the useful signal components in the original signal, which is of great significance to thermocouple modeling analys- is.
基金Pre-Research Program of General Armament Departmentduring the11th Five-Year Plan Period(No.51309020503)the National De-fense Basic Research Program of China(973 Program)(No.973-61334)+1 种基金the National Natural Science Foundation of China(No.50575042)Specialized Research Fund for the Doctoral Program of Higher Education ( No.20050286026).
文摘To reduce the drift error existing in the output signal of fiber optic gyroscopes （FOG）, a mathematical model of the FOG output signal is set up; the error characteristics of the FOG output signal are analyzed, and semi-soft threshold filtering is chosen based on the comparison of hard threshold and soft threshold filtering. The semi-soft threshold wavelet package filtering method is applied in the filtering of the FOG output signal. Experiments of the stationary and dynamic FOG output signals filtered with the wavelet package analysis are carried out in a lab environment, respectively. Experiments done with the real-time measured FOG signal show that the method of semi-soft threshold wavelet package filtering reduces the mean square error from 5 （°）/h to 1 （°）/h, so it is effective in eliminating the white noises and the fractal noises existing in the FOG. The novel method proposed here is proved valid in reducing the FOG drift error, satisfying the technical demands of high precision and realtime processing.
基金Project supported by the National Natural Science Foundation of China (Nos 30700167 and 60725102)the Project of State Key Laboratory of Transducer Technology of China (No SKT0702)+1 种基金the Zhejiang Provincial Natural Science Foundation of China (No Y2080673)the Scientific Research Fund of the Education Department of Zhejiang Province, China (No Y200909323)
文摘Neurochip based on light-addressable potentiometric sensor(LAPS),whose sensing elements are excitable cells,can monitor electrophysiological properties of cultured neuron networks with cellular signals well analyzed.Here we report a kind of neurochip with rat pheochromocytoma(PC12) cells hybrid with LAPS and a method of de-noising signals based on wavelet transform.Cells were cultured on LAPS for several days to form networks,and we then used LAPS system to detect the extracellular potentials with signals de-noised according to decomposition in the time-frequency space.The signal was decomposed into various scales,and coefficients were processed based on the properties of each layer.At last,signal was reconstructed based on the new coefficients.The results show that after de-noising,baseline drift is removed and signal-to-noise ratio is increased.It suggests that the neurochip of PC12 cells coupled to LAPS is stable and suitable for long-term and non-invasive measurement of cell electrophysiological properties with wavelet transform,taking advantage of its time-frequency localization analysis to reduce noise.
文摘With the increasing adoption of intelligent operation and maintenance technologies in urban rail transit,most maintenance systems have been equipped with fault diagnosis modules targeting key components of metro vehicles.However,the integration between engineering-level diagnostic algorithms and advanced academic research remains limited.Two major challenges hinder vibration-based fault diagnosis under real-world operating conditions:the complex noise and interference caused by wheel-rail coupling and the typically weak expression of fault features.Considering the widespread application of wavelet transform in noise reduction and the maturity of ensemble empirical mode decomposition(EEMD)in handling nonlinear and non-stationary signals without parameter tuning,this study proposes a diagnostic method that combines wavelet threshold denoising with EEMD.The method was applied to bearing vibration signals collected from an operational subway line.The diagnostic results were consistent with actual disassembly findings,demonstrating the effectiveness and practical value of the proposed approach.
基金funded by National Natural Science Foundation of China (Grant No. 41375038)China Meteorological Administration Special Public Welfare Research Fund (Grant No. GYHY201306040,GYHY201306075)
文摘Using numerical simulation data of the forward differential propagation shift （ΦDP） of polarimetric radar,the principle and performing steps of noise reduction by wavelet analysis are introduced in detail.Profiting from the multiscale analysis,various types of noises can be identified according to their characteristics in different scales,and suppressed in different resolutions by a penalty threshold strategy through which a fixed threshold value is applied,a default threshold strategy through which the threshold value is determined by the noise intensity,or a ΦDP penalty threshold strategy through which a special value is designed for ΦDP de-noising.Then,a hard-or soft-threshold function,depending on the de-noising purpose,is selected to reconstruct the signal.Combining the three noise suppression strategies and the two signal reconstruction functions,and without loss of generality,two schemes are presented to verify the de-noising effect by dbN wavelets：（1） the penalty threshold strategy with the soft threshold function scheme （PSS）; （2） the ΦDP penalty threshold strategy with the soft threshold function scheme （PPSS）.Furthermore,the wavelet de-noising is compared with the mean,median,Kalman,and finite impulse response （FIR） methods with simulation data and two actual cases.The results suggest that both of the two schemes perform well,especially when ΦDP data are simultaneously polluted by various scales and types of noises.A slight difference is that the PSS method can retain more detail,and the PPSS can smooth the signal more successfully.
基金the National Natural Science Foundation of China(No.51275524)the General Armaments Department Equipment Support Research Project
文摘Aiming at the existing problems of discrete cosine transform(DCT) de-noising method, we introduce the idea of wavelet neighboring coefficients(WNC) de-noising method, and propose the cosine neighboring coefficients(CNC) de-noising method. Based on DCT, a novel method for the fault feature extraction of hydraulic pump is analyzed. The vibration signal of pump is de-noised with CNC de-noising method, and the fault feature is extracted by performing Hilbert-Huang transform(HHT) to the output signal. The analysis results of the simulation signal and the actual one demonstrate that the proposed CNC de-noising method and the fault feature extraction method have more superior ability than the traditional ones.
基金National Natural Science Foundation of China under Grant Nos.52064015 and 51404111Jiangxi Provincial Natural Science Foundation under Grant No.20192BAB206017+1 种基金Scientific Research Project of Jiangxi Provincial Education Department under Grant No.GJJ160643the Program of Qingjiang Excellent Young Talents,Jiangxi University of Science and Technology under Grant No.JXUSTQJYX2016007。
文摘In the present study of peak particle velocity(PPV)and frequency,an improved algorithm(principal empirical mode decomposition,PEMD)based on principal component analysis(PCA)and empirical mode decomposition(EMD)is proposed,with the goal of addressing poor filtering de-noising effects caused by the occurrences of modal aliasing phenomena in EMD blasting vibration signal decomposition processes.Test results showed that frequency of intrinsic mode function(IMF)components decomposed by PEMD gradually decreases and that the main frequency is unique,which eliminates the phenomenon of modal aliasing.In the simulation experiment,the signal-to-noise(SNR)and root mean square errors(RMSE)ratio of the signal de-noised by PEMD are the largest when compared to EMD and ensemble empirical mode decomposition(EEMD).The main frequency of the de-noising signal through PEMD is 75 Hz,which is closest to the frequency of the noiseless simulation signal.In geotechnical engineering blasting experiments,compared to EMD and EEMD,the signal de-noised by PEMD has the lowest level of distortion,and the frequency band is distributed in a range of 0-64 Hz,which is closest to the frequency band of the blasting vibration signal.In addition,the proportion of noise energy was the lowest,at 1.8%.
基金This research is supported financially by Natural Science Foundation of China(Grant No.51505234,51405241,51575283).
文摘With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.
基金funded by National Natural Science Foundation of China(61201391)。
文摘Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal deforms in the exposed and magnetically contaminated environment.In order to preciously recognize the roll information and effectively separate the noise component from the original geomagnetic sequence,based on the error source analysis,we propose a moving horizon based wavelet de-noising method for the dual-observed geomagnetic signal filtering where the captured rough roll frequency value provides reasonable wavelet decomposition and reconstruction level selection basis for sampled sequence;a moving horizon window guarantees real-time performance and non-cumulative calculation amount.The complete geomagnetic data in full ballistic range and three intercepted paragraphs are used for performance assessment.The positioning performance of the moving horizon wavelet de-noising method is compared with the band-pass filter.The results show that both noise reduction techniques improve the positioning accuracy while the wavelet de-noising method is always better than the band-pass filter.These results suggest that the proposed moving horizon based wavelet de-noising method of the dual-observed geomagnetic signal is more applicable for various launch conditions with better positioning performance.
基金The Key Program of National Natural Science of China(No.U1261205)Shandong University of Science and Technology Research Fund(No.2010KYTD101)
文摘In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the process flow of RLS algorithm are described.Through example simulation,simulation figures of the adaptive de-noising system are obtained.By analysis and comparison,it can be proved that RLS adaptive filtering is capable of eliminating the noises and obtaining useful signals in a relatively good manner.Therefore,the validity of this method and the rationality of this system are demonstrated.
基金supported by the Director Foundation of the Institute of Seismology,China Earthquake Administration (IS201126025)The Basis Research Foundation of Key laboratory of Geospace Environment & Geodesy Ministry of Education,China (10-01-09)
文摘On the： basis of wavelet theory, we propose an outlier-detection algorithm for satellite gravity ometry by applying a wavelet-shrinkage-de-noising method to some simulation data with white noise and ers. The result Shows that this novel algorithm has a 97% success rate in outlier identification and that be efficiently used for pre-processing real satellite gravity gradiometry data.
