Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The ...Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The intercepted signal is difficult to separate with conventional parameters because of severe overlapping in both time and frequency domains. On the contrary, time-frequency analysis maps the 1D signal into a 2D time-frequency plane, which provides a better insight into the signal than traditional methods. Particularly, the parameterized time-frequency analysis (PTFA) shows great potential in processing such non stationary signals. Five procedures for the PTFA are proposed to separate the overlapped multi-radar signal, including initiation, instantaneous frequency estimation with PTFA, signal demodulation, signal separation with adaptive filter and signal recovery. The proposed method is verified with both simulated and real signals, which shows good performance in the application on multi-radar signal separation.展开更多
The exponential stabilization problem for finite dimensional switched systems is extended to the infinite dimensional distributed parameter systems in the Hilbert space. Based on the semigroup theory, by applying the ...The exponential stabilization problem for finite dimensional switched systems is extended to the infinite dimensional distributed parameter systems in the Hilbert space. Based on the semigroup theory, by applying the multiple Lyapunov function method, the exponential stabilization conditions are derived. These conditions are given in the form of linear operator inequalities where the decision variables are operators in the Hilbert space; while the stabilization properties depend on the switching rule. Being applied to the two-dimensional heat switched propagation equations with the Dirichlet boundary conditions, these linear operator inequalities are transformed into standard linear matrix inequalities. Finally, two examples are given to illustrate the effectiveness of the proposed results.展开更多
Through the differentiating and integrating process, a mathematical model for tempering time effect on quenched steel was derived based on the attribute of state function and the general equation of Hollomon parameter...Through the differentiating and integrating process, a mathematical model for tempering time effect on quenched steel was derived based on the attribute of state function and the general equation of Hollomon parameter, which correlates the tempering hardness with the tempering time at different tempering temperature. Using the established model, the linear relationship between the tempering hardness and the tempering time in logarithm was proved theoretically, and the tempering hardness for various tempering time was reduced to the measurement and calculation of a hardness experiment tempered for 1 h at different tempering temperatures. Moreover, the hardness of steel 42CrMo and T8Mn tempered for various times at 200-600℃ was calculated using this method. The predicted results are in good agreement with those of the available experiments.展开更多
Active disturbance rejection controller (ADRC) has good performance in induction motor (IM) control system, but controller parameter is difficult to tune. A method of tuning ADRC parameter by time scale is analyzed. T...Active disturbance rejection controller (ADRC) has good performance in induction motor (IM) control system, but controller parameter is difficult to tune. A method of tuning ADRC parameter by time scale is analyzed. The IM time scale is obtained by theoretical analysis. Combining the relations between scale time and ADRC parameters, ADRC parameter tuning in IM vector control based stator flux oriented is obtained. This parameter tuning method is validated by simulations and it provides a new technique for tuning of ADRC parameters of IM.展开更多
In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propa...In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR)threshold,a parameter estimation method for LFM signals based on time reversal is proposed.The proposed method avoids SNR loss in the process of estimating the frequency,thus reducing the SNR threshold.The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform(DPT)method,and the root-mean-square error(RMSE)of the proposed estimator is close to the Cramer-Rao lower bound(CRLB).展开更多
Using the order parameter of seismicity defined in natural time, we suggest a simple model for the expla- nation of Bath law, according to which a mainshock differs in magnitude from its largest aftershock by approxim...Using the order parameter of seismicity defined in natural time, we suggest a simple model for the expla- nation of Bath law, according to which a mainshock differs in magnitude from its largest aftershock by approximately 1.2 regardless of the mainshock magnitude. In addition, the validity of Bath law is studied in the Global Centroid Moment Tensor catalogue by using two different aftershock definitions. It is found that the mean of this difference, when considering all the pairs mainshock-largest aftershock, does not markedly differ from 1.2 and the corresponding distributions do not depend on the mainshock＇s magnitude threshold in a statistically significant manner. Finally, the analysis of the cumulative distribution functions provides evidence in favour of the proposed model.展开更多
The time domain parameter laenuncauon memoa oi me iounuauon-structure interaction system is presented. On the basis of building the computation mode and the motion equation of the foundation-structure interaction syst...The time domain parameter laenuncauon memoa oi me iounuauon-structure interaction system is presented. On the basis of building the computation mode and the motion equation of the foundation-structure interaction system, the system parameter identification method was established by using the extended Kalman filter （EKF） technique and taking the unknown parameters in the system as the augment state variables. And the time parameter identification process of the foundation-structure interaction system was implemented by using the data of the layer foundation-storehouse interaction system model test on the large vibration platform. The computation result shows that the established parameter identification method can induce good parameter estimation.展开更多
Various mobile devices and applications are now used in daily life.These devices require high-speed data processing,low energy consumption,low communication latency,and secure data transmission,especially in 5G and 6G...Various mobile devices and applications are now used in daily life.These devices require high-speed data processing,low energy consumption,low communication latency,and secure data transmission,especially in 5G and 6G mobile networks.High-security cryptography guarantees that essential data can be transmitted securely;however,it increases energy consumption and reduces data processing speed.Therefore,this study proposes a low-energy data encryption(LEDE)algorithm based on the Advanced Encryption Standard(AES)for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things(IoT)devices.In the proposed LEDE algorithm,the system time parameter is employed to create a dynamic S-Box to replace the static S-Box of AES.Tests indicated that six-round LEDE encryption achieves the same security level as 10-round conventional AES encryption.This reduction in encryption time results in the LEDE algorithm having a 67.4%lower energy consumption and 43.9%shorter encryption time than conventional AES;thus,the proposed LEDE algorithm can improve the performance and the energy consumption of IoT edge devices.展开更多
That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter es- timation using filtering theory and methodology. Depending on the nature of associated physics and...That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter es- timation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being esti- mated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency deter- mined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.展开更多
Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
In this paper, the parameter estimation problem is investigated for the continuous time stochastic logistic diffusion system. A new continuous process is built based on the likelihood ratio scheme, the Radon-Nikodym d...In this paper, the parameter estimation problem is investigated for the continuous time stochastic logistic diffusion system. A new continuous process is built based on the likelihood ratio scheme, the Radon-Nikodym derivative and the explicit expressions of the error of estimation are given under this new continuous process. By using the random time transformations, law of large numbers for martingales, law of iterated logarithm and stationary distribution of solution, the consistency property are proved for the estimation error. Finally, a numerical simulation is presented to demonstrate the effectiveness of the proposed method in this paper.展开更多
The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of paralle...The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.展开更多
Bianchi type-IX cosmological models with variable equation of state (EoS) parameter have been investigated in general relativity when universe is filled with dark energy. The field equations have been solved by consid...Bianchi type-IX cosmological models with variable equation of state (EoS) parameter have been investigated in general relativity when universe is filled with dark energy. The field equations have been solved by considering (i) q=B (variable);(ii) , where k and m are constants;(iii) , where k is constant and R is average scale factor;(iv) which gives . This renders early decelerating and late time accelerating cosmological models. The physical and geometrical properties of the models are also discussed.展开更多
AIM:To analyze the characteristics and correlation of phacoemulsification parameters and anterior segment parameters in cataract patients with different blood glucose levels.METHODS:A total of 45 type 2 diabetic catar...AIM:To analyze the characteristics and correlation of phacoemulsification parameters and anterior segment parameters in cataract patients with different blood glucose levels.METHODS:A total of 45 type 2 diabetic cataract patients(45 eyes)treated in our hospital from March 2023 to April 2024 were stratified into two groups based on glycosylated hemoglobin(HbA1c)levels:group A:HbA1c<7%(n=18)and group B:7%≤HbA1c<8.5%(n=27);a total of 94 age-matched age-related cataract patients(94 eyes)were enrolled as the control group(group C).All underwent phacoemulsification with intraocular lens implantation.Anterior segment parameters,including corneal,lens and anterior chamber measurements,were recorded.Correlations between phacoemulsification parameters and anterior segment parameters were analyzed,and differences among groups were compared.RESULTS:In groups A and B,effective phacoemulsification time(EPT)negatively correlated with corneal endothelial cell density(CECD)(r=-0.315,P=0.035).Average phacoemulsification time(APT)positively correlated with the anterior corneal surface radius of curvature(Rm;r=0.402,P=0.006)and negatively correlated with the flat axis meridian curvature(K 1),steep axis meridian curvature(K 2),mean curvature(Km)of the anterior corneal surface,and lens density at 6 mm zones(PDZ3;all P<0.05).Average phacoemulsification energy(AVE)positively correlated with mean lens density(LD-mean),lens density at 2 mm zones(PDZ1),lens density at 4 mm zones(PDZ2),and PDZ3(all P<0.05),and negatively with pupil diameter(r=-0.385,P=0.009).In the group C,EPT showed a positive correlation with Pentacam nucleus staging(PNS)density grade,PDZ1,PDZ2,and PDZ3(all P<0.05).A positive correlation was observed between AVE and PNS classification(r=0.246,P=0.018).Conversely,AVE exhibited a negative correlation with CECD(r=-0.245,P=0.018).EPT in groups A and B was higher than that in the group C(P<0.05).Both EPT and APT in the group B were higher than those in the group A(P<0.05).In diabetic cataract patients,CECD,corneal density(CD),and posterior corneal surface height positively correlated with diabetes duration(P<0.05).Posterior corneal surface K 1 and Rm positively correlated with 7%≤HbA1c<8.5%(P<0.05).Total corneal astigmatism negatively correlated with HbA1c,2-hour post-breakfast blood glucose(2hPBG),and fasting insulin(FINS;P<0.05).CD and lens thickness(LT)positively correlated with FINS(P<0.05).CONCLUSION:Phacoemulsification parameters and blood glucose-related indices exhibited varying degrees of correlation with anterior segment parameters in cataract patients with different blood glucose levels.EPT in diabetic cataract patients was higher than that in age-related cataract patients,while EPT and APT in diabetic cataract patients with poor glycemic control were higher than those with good glycemic control.展开更多
The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fi...The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fitting methods often overestimate the charge transfer overpotential,leading to substantial errors in reaction rate constant measurements.These inaccuracies hinder the accurate prediction of voltage profiles and overall cell performance.In this study,we propose the characteristic time-decomposed overpotential(CTDO)method,which employs a single-layer particle electrode(SLPE)structure to eliminate interference overpotentials.By leveraging the distribution of relaxation times(DRT),our method effectively isolates the characteristic time of the charge transfer process,enabling a more precise determination of the reaction rate constant.Simulation results indicate that our approach reduces measurement errors to below 2%,closely aligning with theoretical values.Furthermore,experimental validation demonstrates an 80% reduction in error compared to the conventional galvanostatic intermittent titration technique(GITT)method.Overall,this study provides a novel voltage-based approach for determining the reaction rate constant,enhancing the applicability of theoretical analysis in electrode structural design and facilitating rapid battery optimization.展开更多
The accuracy of conventional time delay estimation （TDE） algorithms is limited by the sampling interval. A novel algorithm of subsample TDE suitable for widehand signals is presented to improve the accuracy. This al...The accuracy of conventional time delay estimation （TDE） algorithms is limited by the sampling interval. A novel algorithm of subsample TDE suitable for widehand signals is presented to improve the accuracy. This algorithm applies periodogram and parabolic interpolation to the cross correlation spectrum of band limited stochastic signals, and can obtain a continuous time delay estimator. Simulations are carried out to compare the performance of the proposed algorithm with that of other subsample TDE algorithms. Results show that the proposed algorithm outperforms other algorithms and reachs the Cramer-Rao lower bound （CRLB） at a high signal- to-noise ratio. For the wideband characteristic and the randomness of the transmitting signal, the proposed algo- rithm is suitable for the low probability of intercept radars.展开更多
In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. ...In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. The tasks of noise reduction and parameter estimation which were fulfilled separately before are combined iteratively. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior work can be viewed as special cases of this general framework. The simulations for noise reduction and parameter estimation of contaminated chaotic time series show improved performance of our method compared with previous work.展开更多
The vibration caused blade High Cycle Fatigue(HCF)is seriously affects the safety operation of turbomachinery especially for aero-engine.Thus,it is crucial important to identify the blade vibration parameters and then...The vibration caused blade High Cycle Fatigue(HCF)is seriously affects the safety operation of turbomachinery especially for aero-engine.Thus,it is crucial important to identify the blade vibration parameters and then evaluate the dynamic stress amplitude.Blade Tip Timing(BTT)method is one of the promising method to solve these problems.While,it need a high resolution Once Per Revolution(OPR)signal which is difficult to get for the aero-engine.Here,a Coupled Vibration Analysis(CVA)method for identifying blade vibration parameters by a none OPR BTT is proposed.The method assumes that every real blade has its own vibration performance at a given speed.Whereby,it can take any blade as the reference blade,and the other blades using the reference blade as the OPR for vibration displacement calculating and further parameter identifying.The proposed method is validated by numerical model.Also,experimental studies are carried out on a straight blade and a twisted three dimensional blade test rig as well as a large industrial axial compressor respectively.The results show that the proposed method can accurately identify the blade synchronous vibration parameters and quantitatively evaluate the mistuning in bladed disks,which lays a foundation for the reliability improvement of aero-engine.展开更多
文摘Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The intercepted signal is difficult to separate with conventional parameters because of severe overlapping in both time and frequency domains. On the contrary, time-frequency analysis maps the 1D signal into a 2D time-frequency plane, which provides a better insight into the signal than traditional methods. Particularly, the parameterized time-frequency analysis (PTFA) shows great potential in processing such non stationary signals. Five procedures for the PTFA are proposed to separate the overlapped multi-radar signal, including initiation, instantaneous frequency estimation with PTFA, signal demodulation, signal separation with adaptive filter and signal recovery. The proposed method is verified with both simulated and real signals, which shows good performance in the application on multi-radar signal separation.
基金The National Natural Science Foundation of China(No.61273119,61104068,61374038)the Natural Science Foundation of Jiangsu Province(No.BK2011253)
文摘The exponential stabilization problem for finite dimensional switched systems is extended to the infinite dimensional distributed parameter systems in the Hilbert space. Based on the semigroup theory, by applying the multiple Lyapunov function method, the exponential stabilization conditions are derived. These conditions are given in the form of linear operator inequalities where the decision variables are operators in the Hilbert space; while the stabilization properties depend on the switching rule. Being applied to the two-dimensional heat switched propagation equations with the Dirichlet boundary conditions, these linear operator inequalities are transformed into standard linear matrix inequalities. Finally, two examples are given to illustrate the effectiveness of the proposed results.
文摘Through the differentiating and integrating process, a mathematical model for tempering time effect on quenched steel was derived based on the attribute of state function and the general equation of Hollomon parameter, which correlates the tempering hardness with the tempering time at different tempering temperature. Using the established model, the linear relationship between the tempering hardness and the tempering time in logarithm was proved theoretically, and the tempering hardness for various tempering time was reduced to the measurement and calculation of a hardness experiment tempered for 1 h at different tempering temperatures. Moreover, the hardness of steel 42CrMo and T8Mn tempered for various times at 200-600℃ was calculated using this method. The predicted results are in good agreement with those of the available experiments.
文摘Active disturbance rejection controller (ADRC) has good performance in induction motor (IM) control system, but controller parameter is difficult to tune. A method of tuning ADRC parameter by time scale is analyzed. The IM time scale is obtained by theoretical analysis. Combining the relations between scale time and ADRC parameters, ADRC parameter tuning in IM vector control based stator flux oriented is obtained. This parameter tuning method is validated by simulations and it provides a new technique for tuning of ADRC parameters of IM.
基金supported by the Regional Joint Fund for Basic and Applied Basic Research of Guangdong Province(2019B1515120009)the Defense Basic Scientific Research Program(61424132005).
文摘In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR)threshold,a parameter estimation method for LFM signals based on time reversal is proposed.The proposed method avoids SNR loss in the process of estimating the frequency,thus reducing the SNR threshold.The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform(DPT)method,and the root-mean-square error(RMSE)of the proposed estimator is close to the Cramer-Rao lower bound(CRLB).
文摘Using the order parameter of seismicity defined in natural time, we suggest a simple model for the expla- nation of Bath law, according to which a mainshock differs in magnitude from its largest aftershock by approximately 1.2 regardless of the mainshock magnitude. In addition, the validity of Bath law is studied in the Global Centroid Moment Tensor catalogue by using two different aftershock definitions. It is found that the mean of this difference, when considering all the pairs mainshock-largest aftershock, does not markedly differ from 1.2 and the corresponding distributions do not depend on the mainshock＇s magnitude threshold in a statistically significant manner. Finally, the analysis of the cumulative distribution functions provides evidence in favour of the proposed model.
文摘The time domain parameter laenuncauon memoa oi me iounuauon-structure interaction system is presented. On the basis of building the computation mode and the motion equation of the foundation-structure interaction system, the system parameter identification method was established by using the extended Kalman filter （EKF） technique and taking the unknown parameters in the system as the augment state variables. And the time parameter identification process of the foundation-structure interaction system was implemented by using the data of the layer foundation-storehouse interaction system model test on the large vibration platform. The computation result shows that the established parameter identification method can induce good parameter estimation.
基金This work was supported by the National Science and Technology Council,Taiwan,under Project NSTC 112-2221-E-029-015.
文摘Various mobile devices and applications are now used in daily life.These devices require high-speed data processing,low energy consumption,low communication latency,and secure data transmission,especially in 5G and 6G mobile networks.High-security cryptography guarantees that essential data can be transmitted securely;however,it increases energy consumption and reduces data processing speed.Therefore,this study proposes a low-energy data encryption(LEDE)algorithm based on the Advanced Encryption Standard(AES)for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things(IoT)devices.In the proposed LEDE algorithm,the system time parameter is employed to create a dynamic S-Box to replace the static S-Box of AES.Tests indicated that six-round LEDE encryption achieves the same security level as 10-round conventional AES encryption.This reduction in encryption time results in the LEDE algorithm having a 67.4%lower energy consumption and 43.9%shorter encryption time than conventional AES;thus,the proposed LEDE algorithm can improve the performance and the energy consumption of IoT edge devices.
基金funded by the National Natural Science Foundation of China (Grant No.41676088)the National Key Research and Development Project of China (2016YFC1401800,2017YFC1404100,2017YFC1404102)+1 种基金the Fundamental Research Funds for the Central Universities (HEUCF 041705)the Foundation of the Key Laboratory of Marine Environmental Information Technology
文摘That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter es- timation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being esti- mated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency deter- mined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
文摘In this paper, the parameter estimation problem is investigated for the continuous time stochastic logistic diffusion system. A new continuous process is built based on the likelihood ratio scheme, the Radon-Nikodym derivative and the explicit expressions of the error of estimation are given under this new continuous process. By using the random time transformations, law of large numbers for martingales, law of iterated logarithm and stationary distribution of solution, the consistency property are proved for the estimation error. Finally, a numerical simulation is presented to demonstrate the effectiveness of the proposed method in this paper.
基金Supported by the National Natural Science Foundation of China (No. 60873235&60473099)the Science-Technology Development Key Project of Jilin Province of China (No. 20080318)the Program of New Century Excellent Talents in University of China (No. NCET-06-0300).
文摘The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.
文摘Bianchi type-IX cosmological models with variable equation of state (EoS) parameter have been investigated in general relativity when universe is filled with dark energy. The field equations have been solved by considering (i) q=B (variable);(ii) , where k and m are constants;(iii) , where k is constant and R is average scale factor;(iv) which gives . This renders early decelerating and late time accelerating cosmological models. The physical and geometrical properties of the models are also discussed.
文摘AIM:To analyze the characteristics and correlation of phacoemulsification parameters and anterior segment parameters in cataract patients with different blood glucose levels.METHODS:A total of 45 type 2 diabetic cataract patients(45 eyes)treated in our hospital from March 2023 to April 2024 were stratified into two groups based on glycosylated hemoglobin(HbA1c)levels:group A:HbA1c<7%(n=18)and group B:7%≤HbA1c<8.5%(n=27);a total of 94 age-matched age-related cataract patients(94 eyes)were enrolled as the control group(group C).All underwent phacoemulsification with intraocular lens implantation.Anterior segment parameters,including corneal,lens and anterior chamber measurements,were recorded.Correlations between phacoemulsification parameters and anterior segment parameters were analyzed,and differences among groups were compared.RESULTS:In groups A and B,effective phacoemulsification time(EPT)negatively correlated with corneal endothelial cell density(CECD)(r=-0.315,P=0.035).Average phacoemulsification time(APT)positively correlated with the anterior corneal surface radius of curvature(Rm;r=0.402,P=0.006)and negatively correlated with the flat axis meridian curvature(K 1),steep axis meridian curvature(K 2),mean curvature(Km)of the anterior corneal surface,and lens density at 6 mm zones(PDZ3;all P<0.05).Average phacoemulsification energy(AVE)positively correlated with mean lens density(LD-mean),lens density at 2 mm zones(PDZ1),lens density at 4 mm zones(PDZ2),and PDZ3(all P<0.05),and negatively with pupil diameter(r=-0.385,P=0.009).In the group C,EPT showed a positive correlation with Pentacam nucleus staging(PNS)density grade,PDZ1,PDZ2,and PDZ3(all P<0.05).A positive correlation was observed between AVE and PNS classification(r=0.246,P=0.018).Conversely,AVE exhibited a negative correlation with CECD(r=-0.245,P=0.018).EPT in groups A and B was higher than that in the group C(P<0.05).Both EPT and APT in the group B were higher than those in the group A(P<0.05).In diabetic cataract patients,CECD,corneal density(CD),and posterior corneal surface height positively correlated with diabetes duration(P<0.05).Posterior corneal surface K 1 and Rm positively correlated with 7%≤HbA1c<8.5%(P<0.05).Total corneal astigmatism negatively correlated with HbA1c,2-hour post-breakfast blood glucose(2hPBG),and fasting insulin(FINS;P<0.05).CD and lens thickness(LT)positively correlated with FINS(P<0.05).CONCLUSION:Phacoemulsification parameters and blood glucose-related indices exhibited varying degrees of correlation with anterior segment parameters in cataract patients with different blood glucose levels.EPT in diabetic cataract patients was higher than that in age-related cataract patients,while EPT and APT in diabetic cataract patients with poor glycemic control were higher than those with good glycemic control.
基金supported by the National Key R&D Program of China 2022YFB2404300the National Natural Science Foundation of China U22B2069the China Postdoctoral Science Foundation 2024M761006。
文摘The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fitting methods often overestimate the charge transfer overpotential,leading to substantial errors in reaction rate constant measurements.These inaccuracies hinder the accurate prediction of voltage profiles and overall cell performance.In this study,we propose the characteristic time-decomposed overpotential(CTDO)method,which employs a single-layer particle electrode(SLPE)structure to eliminate interference overpotentials.By leveraging the distribution of relaxation times(DRT),our method effectively isolates the characteristic time of the charge transfer process,enabling a more precise determination of the reaction rate constant.Simulation results indicate that our approach reduces measurement errors to below 2%,closely aligning with theoretical values.Furthermore,experimental validation demonstrates an 80% reduction in error compared to the conventional galvanostatic intermittent titration technique(GITT)method.Overall,this study provides a novel voltage-based approach for determining the reaction rate constant,enhancing the applicability of theoretical analysis in electrode structural design and facilitating rapid battery optimization.
基金Supported by the National Mobile Communications Research Laboratory Foundation (N200902)~~
文摘The accuracy of conventional time delay estimation （TDE） algorithms is limited by the sampling interval. A novel algorithm of subsample TDE suitable for widehand signals is presented to improve the accuracy. This algorithm applies periodogram and parabolic interpolation to the cross correlation spectrum of band limited stochastic signals, and can obtain a continuous time delay estimator. Simulations are carried out to compare the performance of the proposed algorithm with that of other subsample TDE algorithms. Results show that the proposed algorithm outperforms other algorithms and reachs the Cramer-Rao lower bound （CRLB） at a high signal- to-noise ratio. For the wideband characteristic and the randomness of the transmitting signal, the proposed algo- rithm is suitable for the low probability of intercept radars.
文摘In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. The tasks of noise reduction and parameter estimation which were fulfilled separately before are combined iteratively. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior work can be viewed as special cases of this general framework. The simulations for noise reduction and parameter estimation of contaminated chaotic time series show improved performance of our method compared with previous work.
基金supported financially by Natural Science Foundation of China(Nos.51775030,91860126)the Fundamental Research Funds for the Central Universities(No.BHYC1703A)。
文摘The vibration caused blade High Cycle Fatigue(HCF)is seriously affects the safety operation of turbomachinery especially for aero-engine.Thus,it is crucial important to identify the blade vibration parameters and then evaluate the dynamic stress amplitude.Blade Tip Timing(BTT)method is one of the promising method to solve these problems.While,it need a high resolution Once Per Revolution(OPR)signal which is difficult to get for the aero-engine.Here,a Coupled Vibration Analysis(CVA)method for identifying blade vibration parameters by a none OPR BTT is proposed.The method assumes that every real blade has its own vibration performance at a given speed.Whereby,it can take any blade as the reference blade,and the other blades using the reference blade as the OPR for vibration displacement calculating and further parameter identifying.The proposed method is validated by numerical model.Also,experimental studies are carried out on a straight blade and a twisted three dimensional blade test rig as well as a large industrial axial compressor respectively.The results show that the proposed method can accurately identify the blade synchronous vibration parameters and quantitatively evaluate the mistuning in bladed disks,which lays a foundation for the reliability improvement of aero-engine.
