In high intensity focused ultrasound(HIFU)treatment,it is crucial to accurately identify denatured and normal biological tissues.In this paper,a novel method based on compressed sensing(CS)and refined composite multi-...In high intensity focused ultrasound(HIFU)treatment,it is crucial to accurately identify denatured and normal biological tissues.In this paper,a novel method based on compressed sensing(CS)and refined composite multi-scale fuzzy entropy(RCMFE)is proposed.First,CS is used to denoise the HIFU echo signals.Then the multi-scale fuzzy entropy(MFE)and RCMFE of the denoised HIFU echo signals are calculated.This study analyzed 90 cases of HIFU echo signals,including 45 cases in normal status and 45 cases in denatured status,and the results show that although both MFE and RCMFE can be used to identify denatured tissues,the intra-class distance of RCMFE on each scale factor is smaller than MFE,and the inter-class distance is larger than MFE.Compared with MFE,RCMFE can calculate the complexity of the signal more accurately and improve the stability,compactness,and separability.When RCMFE is selected as the characteristic parameter,the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE,which helps doctors evaluate the treatment effect more accurately.When the scale factor is selected as 16,the best distinguishing effect can be obtained.展开更多
In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on m...In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio(SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than-4 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.展开更多
This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by hig...This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by high- and low-frequency.In the high-frequency part the wavelet multiscale was used for the edge detection,and the low-frequency part conducted on segmentation using the entropy iterative threshold selection method.Through the consideration of the image edge and region,a CT image of the thorax was chosen to test the proposed method for the segmentation of the lungs.Experimental results show that the method is efficient to segment the interesting region of an image compared with conventional methods.展开更多
We extend the complexity entropy causality plane（CECP） to propose a multi-scale complexity entropy causality plane（MS-CECP） and further use the proposed method to discriminate the deterministic characteristics of ...We extend the complexity entropy causality plane（CECP） to propose a multi-scale complexity entropy causality plane（MS-CECP） and further use the proposed method to discriminate the deterministic characteristics of different oil-in-water flows. We first take several typical time series for example to investigate the characteristic of the MS-CECP and find that the MS-CECP not only describes the continuous loss of dynamical structure with the increase of scale, but also reflects the determinacy of the system. Then we calculate the MS-CECP for the conductance fluctuating signals measured from oil–water two-phase flow loop test facility. The results indicate that the MS-CECP could be an intrinsic measure for indicating oil-in-water two-phase flow structures.展开更多
The use of time-frequency entropy to quantitatively assess the stability of submerged arc welding process considering the distribution features of arc energy is reported in this paper. Time-frequency entropy is employ...The use of time-frequency entropy to quantitatively assess the stability of submerged arc welding process considering the distribution features of arc energy is reported in this paper. Time-frequency entropy is employed to calculate and analyze the stationary current signals, non-stationary current and voltage signals in the submerged arc welding process. It is obtained that time-frequency entropy of arc signal can be used as arc stability judgment criteria of submerged arc welding. Experimental results are provided to confirm the effectiveness of this approach.展开更多
To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature （MSVC） methodology is presented in this paper. W...To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature （MSVC） methodology is presented in this paper. We also propose a fast algorithm for computing 3D volumetric curvature. In comparison to conventional volumetric curvature attributes, its main improvements and key algorithms introduce multi-frequency components expansion in time-frequency domain and the corresponding multi-scale adaptive differential operator in the wavenumber domain, into the volumetric curvature calculation. This methodology can simultaneously depict seismic multi-scale features in both time and space. Additionally, we use data fusion of volumetric curvatures at various scales to take full advantage of the geologic features and anomalies extracted by curvature measurements at different scales. The 3D MSVC can highlight geologic anomalies and reduce noise at the same time. Thus, it improves the interpretation efficiency of curvature attributes analysis. The 3D MSVC is applied to both land and marine 3D seismic data. The results demonstrate that it can indicate the spatial distribution of reservoirs, detect faults and fracture zones, and identify their multi-scale properties.展开更多
The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the targe...The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.展开更多
In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,ex...In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time monitoring.The proposed system is based on Commodity WiFi and is easy to deploy.Leveraging WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel entropy.The feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.展开更多
The mold friction(MDF)is an important parameter reflecting the lubrication between the mold and slab quantitatively.The mold/slab friction was detected using an online monitoring system on a slab continuous caster equ...The mold friction(MDF)is an important parameter reflecting the lubrication between the mold and slab quantitatively.The mold/slab friction was detected using an online monitoring system on a slab continuous caster equipped with hydraulic oscillators.Wavelet entropy theory was introduced to describe the fluctuation characteristics of the MDF signal in order to quantitatively estimate the mold/slab lubrication.Furthermore,MDF signal and its wavelet entropy were analyzed under typical casting conditions,such as normal casting,different models(to control the relationship among the amplitude,the frequency and the casting speed),changing casting speeds and breakout.The results showed that the wavelet entropy could accurately reflect the overall changing trend of the mold friction as well as the local variation features.Besides,the wavelet entropy of the hydraulic cylinder force and the MDF was compared and analyzed,and the relationship between them was further discussed.展开更多
We analyze correlations and patterns of oxidative activity of 3D DNA at DNA fluorescence in complete sets of chromosomes in neutrophils of peripheral blood. Fluorescence of DNA is registered by method of flow cytometr...We analyze correlations and patterns of oxidative activity of 3D DNA at DNA fluorescence in complete sets of chromosomes in neutrophils of peripheral blood. Fluorescence of DNA is registered by method of flow cytometry with nanometer spatial resolution. Experimental data present fluorescence of many ten thousands of cells, from different parts of body in each population, in various blood samples. Data is presented in histograms as frequency distributions of flashes in the dependence on their intensity. Normalized frequency distribution of information in these histograms is used as probabilistic measure for definition of Shannon entropy. Data analysis shows that for this measure of Shannon entropy common sum of entropy, i.e. total entropy E, for any histogram is invariant and has identical trends of changes all values of E (r) = lnr at reduction of rank r of histogram. This invariance reflects informational homeostasis of chromosomes activity inside cells in multi-scale networks of entropy, for varied ranks r. Shannon entropy in multi-scale DNA networks has much more dense packing of correlations than in “small world” networks. As the rule, networks of entropy differ by the mix of normal D 2 and abnormal D > 2 fractal dimensions for varied ranks r, the new types of fractal patterns and hinges for various topology (fractal dimension) at different states of health. We show that all distributions of information entropy are divided on three classes, which associated in diagnostics with a good health or dominants of autoimmune or inflammatory diseases. This classification based on switching of stability at transcritical bifurcation in homeostasis regulation. We defined many ways for homeostasis regulation, coincidences and switching patterns in branching sequences, the averages of Hölder for deviations of entropy from homeostasis at different states of health, with various saturation levels the noises of entropy at activity of all chromosomes in support regulation of homeostasis.展开更多
Short-term offshore wind power forecasting is crucial for stable power system operations.However,accurate forecasting is hindered by multivariate interactions that generate multi-scale repetitive patterns and obscure ...Short-term offshore wind power forecasting is crucial for stable power system operations.However,accurate forecasting is hindered by multivariate interactions that generate multi-scale repetitive patterns and obscure cross-variate correlations.In this paper,we propose a hierarchical framework,the Time-Frequency Clustering Inverted Transformer,for multivariate offshore wind power forecasting.First,a Time-Frequency Clustering component applies Wavelet Packet Decomposition to each series and quantifies sub-series similarity by overall activity and evolutionary trend,grouping repetitive patterns into structured clusters.Second,an inverted Transformer captures multivariate correlations within clusters by treating time points of individual sub-series as variate tokens,enabling self-attention to focus on multivariate correlations rather than temporal dependencies.On two real-world offshore wind datasets(horizons 8-48 h),our proposed framework reduces MSE/MAE by 14.11%and outperforms 12 recognised baselines(e.g.,PatchTST,TimesNet),with the advantage persisting even when the TFC component is applied to the baselines.Moreover,our method demonstrates remarkable generalisability on three public datasets(Solar-Energy,Traffic,and ECL),reducing MSE/MAE by 7.36%.These results indicate that associating repetitive patterns with attention to cross-variate structure materially improves multivariate offshore wind power forecasting.展开更多
Railway point machine(RPM)condition monitoring has attracted engineers’attention for safe train operation and accident prevention.To realize the fast and accurate fault diagnosis of RPMs,this paper proposes a method ...Railway point machine(RPM)condition monitoring has attracted engineers’attention for safe train operation and accident prevention.To realize the fast and accurate fault diagnosis of RPMs,this paper proposes a method based on entropy measurement and broad learning system(BLS).Firstly,the modified multi-scale symbolic dynamic entropy(MMSDE)module extracts dynamic characteristics from the collected acoustic signals as entropy features.Then,the fuzzy BLS takes the above entropy features as input to complete model training.Fuzzy BLS introduces the Takagi-Sug eno fuzzy system into BLS,which improves the model’s classification performance while considering computational speed.Experimental results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.展开更多
Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault diagnosis.In this paper,we first introduce RCMDE int...Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault diagnosis.In this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed RCMDE-KNN.The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and MDE-KNN.It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.展开更多
Nonstationary time series are ubiquitous in almost all natural and engineering systems.Capturing the time-varying signatures from nonstationary time series is still a challenging problem for data mining.Quadratic Time...Nonstationary time series are ubiquitous in almost all natural and engineering systems.Capturing the time-varying signatures from nonstationary time series is still a challenging problem for data mining.Quadratic Time-Frequency Distribution(TFD)provides a powerful tool to analyze these data.However,they suffer from Cross-Term(CT)issues that impair the readability of TFDs.Therefore,to achieve high-resolution and CT-free TFDs,an end-to-end architecture termed Quadratic TF-Net(QTFN)is proposed in this paper.Guided by classic TFD theory,the design of this deep learning architecture is heuristic,which firstly generates various basis functions through data-driven.Thus,more comprehensive TF features can be extracted by these basis functions.Then,to balance the results of various basis functions adaptively,the Efficient Channel Attention(ECA)block is also embedded into QTFN.Moreover,a new structure called Muti-scale Residual Encoder-Decoder(MRED)is also proposed to improve the learning ability of the model by highly integrating the multi-scale learning and encoder-decoder architecture.Finally,although the model is only trained by synthetic signals,both synthetic and real-world signals are tested to validate the generalization capability and superiority of the proposed QTFN.展开更多
Driven by increasing global population and by growing demand for individual wealth, the consumption of energy and raw materials as well as the steadily growing CO2 concentration in atmosphere pose great challenges to ...Driven by increasing global population and by growing demand for individual wealth, the consumption of energy and raw materials as well as the steadily growing CO2 concentration in atmosphere pose great challenges to process engineering. This complex multi-scale discipline deals with the transformation of mass by energy to manifold products in different industrial fields under economical and ecological sus- tainable conditions. In growing circular economy, process engineering increasingly plays an important role in recovering valuable components from very diffuse material flows leaving the user stocks following widely variable time periods of use. As well it is engaged in thermal recovery of energy therefrom and in environmentally safe disposal of residual solid wastes whose recovery economically is not feasible. An efficient recovery of materials and energy following the laws of entropy is a must. A complex network of mass, energy, transportation and information flows has to be regarded with growing traded quantities of used goods even on global level. Important constraints in time, however, exist for a necessary realization of innovative new processes and communal mobility and industrial infrastructure on medium and large scale. Based on reasonable long term and highly reliable statistics from industrial organizations repre- senting steel and paper industry, some limits and trends of possible developments in processing of those industries with long recycling experience will be discussed.展开更多
Most information used to evaluate diabetic statuses is collected at a special time-point,such as taking fasting plasma glucose test and providing a limited view of individual’s health and disease risk.As a new parame...Most information used to evaluate diabetic statuses is collected at a special time-point,such as taking fasting plasma glucose test and providing a limited view of individual’s health and disease risk.As a new parameter for continuously evaluating personal clinical statuses,the newly developed technique“continuous glucose monitoring”(CGM)can characterize glucose dynamics.By calculating the complexity of glucose time series index(CGI)with refined composite multi-scale entropy analysis of the CGM data,the study showed for the first time that the complexity of glucose time series in subjects decreased gradually from normal glucose tolerance to impaired glucose regulation and then to type 2 diabetes(P for trend<0.01).Furthermore,CGI was significantly associated with various parameters such as insulin sensitivity/secretion(all P<0.01),and multiple linear stepwise regression showed that the disposition index,which reflectsβ-cell function after adjusting for insulin sensitivity,was the only independent factor correlated with CGI(P<0.01).Our findings indicate that the CGI derived from the CGM data may serve as a novel marker to evaluate glucose homeostasis.展开更多
Concepts of the lattice Boltzmann method are discussed in detail for the one-dimensional kinetic model.Various techniques of constructing lattice Boltzmann models are discussed,and novel collision integrals are derive...Concepts of the lattice Boltzmann method are discussed in detail for the one-dimensional kinetic model.Various techniques of constructing lattice Boltzmann models are discussed,and novel collision integrals are derived.Geometry of the ki-netic space and the role of the thermodynamic projector is elucidated.展开更多
Red blood cells(RBC)' flickering present the dynamic properties of the cytomembrane. Its complexity could be used for aging analysis or the evaluation for the storage quality. The flickering activity is a kind of ...Red blood cells(RBC)' flickering present the dynamic properties of the cytomembrane. Its complexity could be used for aging analysis or the evaluation for the storage quality. The flickering activity is a kind of reversible perpendicular motion of the specified pixel. Therefore, the complexity analysis depends on the reliable detection of temporal variation for the gray-scale values from each pixel of the cells. In this paper, we improved our previous work on the screening of the horizontal drifted cells with a surface based on cell registration method and the effect of GSM exposure to the dynamic properties of the RBCs in terms of multi-scale sample entropy was presented in the paper.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11774088 and 11474090)。
文摘In high intensity focused ultrasound(HIFU)treatment,it is crucial to accurately identify denatured and normal biological tissues.In this paper,a novel method based on compressed sensing(CS)and refined composite multi-scale fuzzy entropy(RCMFE)is proposed.First,CS is used to denoise the HIFU echo signals.Then the multi-scale fuzzy entropy(MFE)and RCMFE of the denoised HIFU echo signals are calculated.This study analyzed 90 cases of HIFU echo signals,including 45 cases in normal status and 45 cases in denatured status,and the results show that although both MFE and RCMFE can be used to identify denatured tissues,the intra-class distance of RCMFE on each scale factor is smaller than MFE,and the inter-class distance is larger than MFE.Compared with MFE,RCMFE can calculate the complexity of the signal more accurately and improve the stability,compactness,and separability.When RCMFE is selected as the characteristic parameter,the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE,which helps doctors evaluate the treatment effect more accurately.When the scale factor is selected as 16,the best distinguishing effect can be obtained.
基金Project(61301095)supported by the National Natural Science Foundation of ChinaProject(QC2012C070)supported by Heilongjiang Provincial Natural Science Foundation for the Youth,ChinaProjects(HEUCF130807,HEUCFZ1129)supported by the Fundamental Research Funds for the Central Universities of China
文摘In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio(SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than-4 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.
基金Science Research Foundation of Yunnan Fundamental Research Foundation of Applicationgrant number:2009ZC049M+1 种基金Science Research Foundation for the Overseas Chinese Scholars,State Education Ministrygrant number:2010-1561
文摘This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by high- and low-frequency.In the high-frequency part the wavelet multiscale was used for the edge detection,and the low-frequency part conducted on segmentation using the entropy iterative threshold selection method.Through the consideration of the image edge and region,a CT image of the thorax was chosen to test the proposed method for the segmentation of the lungs.Experimental results show that the method is efficient to segment the interesting region of an image compared with conventional methods.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.41174109 and 61104148)the National Science and Technology Major Project of China(Grant No.2011ZX05020-006)the Zhejiang Key Discipline of Instrument Science and Technology,China(Grant No.JL130106)
文摘We extend the complexity entropy causality plane（CECP） to propose a multi-scale complexity entropy causality plane（MS-CECP） and further use the proposed method to discriminate the deterministic characteristics of different oil-in-water flows. We first take several typical time series for example to investigate the characteristic of the MS-CECP and find that the MS-CECP not only describes the continuous loss of dynamical structure with the increase of scale, but also reflects the determinacy of the system. Then we calculate the MS-CECP for the conductance fluctuating signals measured from oil–water two-phase flow loop test facility. The results indicate that the MS-CECP could be an intrinsic measure for indicating oil-in-water two-phase flow structures.
文摘The use of time-frequency entropy to quantitatively assess the stability of submerged arc welding process considering the distribution features of arc energy is reported in this paper. Time-frequency entropy is employed to calculate and analyze the stationary current signals, non-stationary current and voltage signals in the submerged arc welding process. It is obtained that time-frequency entropy of arc signal can be used as arc stability judgment criteria of submerged arc welding. Experimental results are provided to confirm the effectiveness of this approach.
基金supported by the National Natural Science Foundation of China (No. 41004054) Research Fund for the Doctoral Program of Higher Education of China (No. 20105122120002)Natural Science Key Project, Sichuan Provincial Department of Education (No. 092A011)
文摘To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature （MSVC） methodology is presented in this paper. We also propose a fast algorithm for computing 3D volumetric curvature. In comparison to conventional volumetric curvature attributes, its main improvements and key algorithms introduce multi-frequency components expansion in time-frequency domain and the corresponding multi-scale adaptive differential operator in the wavenumber domain, into the volumetric curvature calculation. This methodology can simultaneously depict seismic multi-scale features in both time and space. Additionally, we use data fusion of volumetric curvatures at various scales to take full advantage of the geologic features and anomalies extracted by curvature measurements at different scales. The 3D MSVC can highlight geologic anomalies and reduce noise at the same time. Thus, it improves the interpretation efficiency of curvature attributes analysis. The 3D MSVC is applied to both land and marine 3D seismic data. The results demonstrate that it can indicate the spatial distribution of reservoirs, detect faults and fracture zones, and identify their multi-scale properties.
基金This work was supported by the National Natural Science Foundation of China(62071475,61890541,62171447).
文摘The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.
基金supported in part by the Program for Science&Technology Innovation Talents in Universities of Henan Province(19HASTIT027)National Natural Science Foundation of China(62172141)+4 种基金Zhengzhou Major Scientific and Technological Innovation Project(2019CXZX0086)Youth Innovative Talents Cultivation Fund Project of Kaifeng University in 2020(KDQN-2020-GK002)the National Key Research and Development Program of China(2017YFD0401001)the NSFC(61741107),the NSF(CNS-2105416)by the Wireless Engineering Research and Education Center at Auburn University.
文摘In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time monitoring.The proposed system is based on Commodity WiFi and is easy to deploy.Leveraging WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel entropy.The feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.
基金The project was supported by the National Natural Science Foundation of China(No.51204063)the Anhui Provincial Natural Science Foundation(No.1308085QE72).
文摘The mold friction(MDF)is an important parameter reflecting the lubrication between the mold and slab quantitatively.The mold/slab friction was detected using an online monitoring system on a slab continuous caster equipped with hydraulic oscillators.Wavelet entropy theory was introduced to describe the fluctuation characteristics of the MDF signal in order to quantitatively estimate the mold/slab lubrication.Furthermore,MDF signal and its wavelet entropy were analyzed under typical casting conditions,such as normal casting,different models(to control the relationship among the amplitude,the frequency and the casting speed),changing casting speeds and breakout.The results showed that the wavelet entropy could accurately reflect the overall changing trend of the mold friction as well as the local variation features.Besides,the wavelet entropy of the hydraulic cylinder force and the MDF was compared and analyzed,and the relationship between them was further discussed.
文摘We analyze correlations and patterns of oxidative activity of 3D DNA at DNA fluorescence in complete sets of chromosomes in neutrophils of peripheral blood. Fluorescence of DNA is registered by method of flow cytometry with nanometer spatial resolution. Experimental data present fluorescence of many ten thousands of cells, from different parts of body in each population, in various blood samples. Data is presented in histograms as frequency distributions of flashes in the dependence on their intensity. Normalized frequency distribution of information in these histograms is used as probabilistic measure for definition of Shannon entropy. Data analysis shows that for this measure of Shannon entropy common sum of entropy, i.e. total entropy E, for any histogram is invariant and has identical trends of changes all values of E (r) = lnr at reduction of rank r of histogram. This invariance reflects informational homeostasis of chromosomes activity inside cells in multi-scale networks of entropy, for varied ranks r. Shannon entropy in multi-scale DNA networks has much more dense packing of correlations than in “small world” networks. As the rule, networks of entropy differ by the mix of normal D 2 and abnormal D > 2 fractal dimensions for varied ranks r, the new types of fractal patterns and hinges for various topology (fractal dimension) at different states of health. We show that all distributions of information entropy are divided on three classes, which associated in diagnostics with a good health or dominants of autoimmune or inflammatory diseases. This classification based on switching of stability at transcritical bifurcation in homeostasis regulation. We defined many ways for homeostasis regulation, coincidences and switching patterns in branching sequences, the averages of Hölder for deviations of entropy from homeostasis at different states of health, with various saturation levels the noises of entropy at activity of all chromosomes in support regulation of homeostasis.
基金supported in part by the Department of Human Resources and Social Security of Guangxi Zhuang Autonomous Region(grant number 202401950)in part by the Department of Science and Technology of Guangxi Zhuang Autonomous Region(grant number 2024JJB170087)+2 种基金in part by the National Natural Science Foundation of China(grant number 62371144)in part by the Guangxi Science and Technology Major Program(grant number AA23073019)in part by the The Project of the Philosophy and Social Sciences Fund of Jiangsu Province(grant number 24GLC023).
文摘Short-term offshore wind power forecasting is crucial for stable power system operations.However,accurate forecasting is hindered by multivariate interactions that generate multi-scale repetitive patterns and obscure cross-variate correlations.In this paper,we propose a hierarchical framework,the Time-Frequency Clustering Inverted Transformer,for multivariate offshore wind power forecasting.First,a Time-Frequency Clustering component applies Wavelet Packet Decomposition to each series and quantifies sub-series similarity by overall activity and evolutionary trend,grouping repetitive patterns into structured clusters.Second,an inverted Transformer captures multivariate correlations within clusters by treating time points of individual sub-series as variate tokens,enabling self-attention to focus on multivariate correlations rather than temporal dependencies.On two real-world offshore wind datasets(horizons 8-48 h),our proposed framework reduces MSE/MAE by 14.11%and outperforms 12 recognised baselines(e.g.,PatchTST,TimesNet),with the advantage persisting even when the TFC component is applied to the baselines.Moreover,our method demonstrates remarkable generalisability on three public datasets(Solar-Energy,Traffic,and ECL),reducing MSE/MAE by 7.36%.These results indicate that associating repetitive patterns with attention to cross-variate structure materially improves multivariate offshore wind power forecasting.
基金supported in part by the Fundamental Research Funds for the Central Universities(Grant No.2021RC271)NSFC(Grants No.62120106011,52172323 and U22A2046).
文摘Railway point machine(RPM)condition monitoring has attracted engineers’attention for safe train operation and accident prevention.To realize the fast and accurate fault diagnosis of RPMs,this paper proposes a method based on entropy measurement and broad learning system(BLS).Firstly,the modified multi-scale symbolic dynamic entropy(MMSDE)module extracts dynamic characteristics from the collected acoustic signals as entropy features.Then,the fuzzy BLS takes the above entropy features as input to complete model training.Fuzzy BLS introduces the Takagi-Sug eno fuzzy system into BLS,which improves the model’s classification performance while considering computational speed.Experimental results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.
基金supported by National Natural Science Foundation of China(No.61871318 and 61833013)Shaanxi Provincial Key Research and Development Project(No.2019GY-099).
文摘Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault diagnosis.In this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed RCMDE-KNN.The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and MDE-KNN.It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.
基金supported by the National Key R&D Program of China(No.2022YFB3305900)the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)and the National Natural Science Foundation of China(No.62073286).
文摘Nonstationary time series are ubiquitous in almost all natural and engineering systems.Capturing the time-varying signatures from nonstationary time series is still a challenging problem for data mining.Quadratic Time-Frequency Distribution(TFD)provides a powerful tool to analyze these data.However,they suffer from Cross-Term(CT)issues that impair the readability of TFDs.Therefore,to achieve high-resolution and CT-free TFDs,an end-to-end architecture termed Quadratic TF-Net(QTFN)is proposed in this paper.Guided by classic TFD theory,the design of this deep learning architecture is heuristic,which firstly generates various basis functions through data-driven.Thus,more comprehensive TF features can be extracted by these basis functions.Then,to balance the results of various basis functions adaptively,the Efficient Channel Attention(ECA)block is also embedded into QTFN.Moreover,a new structure called Muti-scale Residual Encoder-Decoder(MRED)is also proposed to improve the learning ability of the model by highly integrating the multi-scale learning and encoder-decoder architecture.Finally,although the model is only trained by synthetic signals,both synthetic and real-world signals are tested to validate the generalization capability and superiority of the proposed QTFN.
文摘Driven by increasing global population and by growing demand for individual wealth, the consumption of energy and raw materials as well as the steadily growing CO2 concentration in atmosphere pose great challenges to process engineering. This complex multi-scale discipline deals with the transformation of mass by energy to manifold products in different industrial fields under economical and ecological sus- tainable conditions. In growing circular economy, process engineering increasingly plays an important role in recovering valuable components from very diffuse material flows leaving the user stocks following widely variable time periods of use. As well it is engaged in thermal recovery of energy therefrom and in environmentally safe disposal of residual solid wastes whose recovery economically is not feasible. An efficient recovery of materials and energy following the laws of entropy is a must. A complex network of mass, energy, transportation and information flows has to be regarded with growing traded quantities of used goods even on global level. Important constraints in time, however, exist for a necessary realization of innovative new processes and communal mobility and industrial infrastructure on medium and large scale. Based on reasonable long term and highly reliable statistics from industrial organizations repre- senting steel and paper industry, some limits and trends of possible developments in processing of those industries with long recycling experience will be discussed.
基金the National Natural Science Foundation of China(Nos.81873646 and 61903071)the Shanghai United Developing Technology Project of Municipal Hospitals(Nos.SHDC12006101 and SHDC12010115)the Shanghai Municipal Education Commission Gaofeng Clinical Medicine grant support(Nos.20161430).
文摘Most information used to evaluate diabetic statuses is collected at a special time-point,such as taking fasting plasma glucose test and providing a limited view of individual’s health and disease risk.As a new parameter for continuously evaluating personal clinical statuses,the newly developed technique“continuous glucose monitoring”(CGM)can characterize glucose dynamics.By calculating the complexity of glucose time series index(CGI)with refined composite multi-scale entropy analysis of the CGM data,the study showed for the first time that the complexity of glucose time series in subjects decreased gradually from normal glucose tolerance to impaired glucose regulation and then to type 2 diabetes(P for trend<0.01).Furthermore,CGI was significantly associated with various parameters such as insulin sensitivity/secretion(all P<0.01),and multiple linear stepwise regression showed that the disposition index,which reflectsβ-cell function after adjusting for insulin sensitivity,was the only independent factor correlated with CGI(P<0.01).Our findings indicate that the CGI derived from the CGM data may serve as a novel marker to evaluate glucose homeostasis.
基金support by the BFE Project 100862.S.S.C.was supported by the ETH Project 0-20280-05.
文摘Concepts of the lattice Boltzmann method are discussed in detail for the one-dimensional kinetic model.Various techniques of constructing lattice Boltzmann models are discussed,and novel collision integrals are derived.Geometry of the ki-netic space and the role of the thermodynamic projector is elucidated.
文摘Red blood cells(RBC)' flickering present the dynamic properties of the cytomembrane. Its complexity could be used for aging analysis or the evaluation for the storage quality. The flickering activity is a kind of reversible perpendicular motion of the specified pixel. Therefore, the complexity analysis depends on the reliable detection of temporal variation for the gray-scale values from each pixel of the cells. In this paper, we improved our previous work on the screening of the horizontal drifted cells with a surface based on cell registration method and the effect of GSM exposure to the dynamic properties of the RBCs in terms of multi-scale sample entropy was presented in the paper.
