Existing literature indicates that prolonged insertion time is associated with procedural complexity and may influence adenoma detection.Xu et al recently reported that longer insertion time correlates with lower aden...Existing literature indicates that prolonged insertion time is associated with procedural complexity and may influence adenoma detection.Xu et al recently reported that longer insertion time correlates with lower adenoma detection,but this effect can be mitigated by sufficient withdrawal duration.Insertion time should not be regarded merely as a numeric variable but rather as a multidimensional marker of technical difficulty.Integrating the insertion-to-withdrawal ratio with composite indicators such as looping or bowel preparation quality may enhance predictive models of colonoscopy performance.Conceptualizing insertion time in this way provides a more nuanced understanding of its role in adenoma detection and highlights the need for improved frameworks that link procedural complexity with quality outcomes.展开更多
Zernike polynomials have been used in different fields such as optics, astronomy, and digital image analysis for many years. To form these polynomials, Zernike moments are essential to be determined. One of the main i...Zernike polynomials have been used in different fields such as optics, astronomy, and digital image analysis for many years. To form these polynomials, Zernike moments are essential to be determined. One of the main issues in realizing the moments is using factorial terms in their equation which cause</span><span style="font-size:10.0pt;font-family:"">s</span><span style="font-size:10.0pt;font-family:""> higher time complexity. As a solution, several methods have been presented to reduce the time complexity of these polynomials in recent years. The purpose of this research is to study several methods among the most popular recursive methods for fast Zernike computation and compare them <span>together by a global theoretical evaluation system called worst-case time co</span><span>mplexity. In this study, we have analyzed the selected algorithms and calculate</span>d the worst-case time complexity for each one. After that, the results are represented and explained and finally, a conclusion has been made by comparing th</span><span style="font-size:10.0pt;font-family:"">ese</span><span style="font-size:10.0pt;font-family:""> criteria among the studied algorithms. According to time complexity, we have observed that although some algorithms </span><span style="font-size:10.0pt;font-family:"">such </span><span style="font-size:10.0pt;font-family:"">as Wee method and Modified Prata method were successful in having the smaller time complexit<span>ies, some other approaches did not make any significant difference compa</span>r</span><span style="font-size:10.0pt;font-family:"">ed</span><span style="font-size:10.0pt;font-family:""> to the classical algorithm.展开更多
Computational time complexity analyzes of evolutionary algorithms （EAs） have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the （1＋1）-EA, on toy problems....Computational time complexity analyzes of evolutionary algorithms （EAs） have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the （1＋1）-EA, on toy problems. These efforts produced a deeper understanding of how EAs perform on different kinds of fitness landscapes and general mathematical tools that may be extended to the analysis of more complicated EAs on more realistic problems. In fact, in recent years, it has been possible to analyze the （1＋1）-EA on combinatorial optimization problems with practical applications and more realistic population-based EAs on structured toy problems. This paper presents a survey of the results obtained in the last decade along these two research lines. The most common mathematical techniques are introduced, the basic ideas behind them are discussed and their elective applications are highlighted. Solved problems that were still open are enumerated as are those still awaiting for a solution. New questions and problems arisen in the meantime are also considered.展开更多
In this work,the computational complexity of a spin-glass three-dimensional(3D)Ising model(for the lattice sizeN=lmn,wherel,m,n are thenumbersof lattice points along three crystallographic directions)is studied.We pro...In this work,the computational complexity of a spin-glass three-dimensional(3D)Ising model(for the lattice sizeN=lmn,wherel,m,n are thenumbersof lattice points along three crystallographic directions)is studied.We prove that an absolute minimum core(AMC)model consisting of a spin-glass 2D Ising model interacting with its nearest neighboring plane,has its computational complexity O(2mn).Any algorithms to make the model smaller(or simpler)than the AMC model will cut the basic element of the spin-glass 3D Ising model and lost many important information of the original model.Therefore,the computational complexity of the spin-glass 3D Ising model cannot be reduced to be less than O(2mn)by any algorithms,which is in subexponential time,superpolynomial.展开更多
We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffin...We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.展开更多
The prediction of intrinsically disordered proteins is a hot research area in bio-information.Due to the high cost of experimental methods to evaluate disordered regions of protein sequences,it is becoming increasingl...The prediction of intrinsically disordered proteins is a hot research area in bio-information.Due to the high cost of experimental methods to evaluate disordered regions of protein sequences,it is becoming increasingly important to predict those regions through computational methods.In this paper,we developed a novel scheme by employing sequence complexity to calculate six features for each residue of a protein sequence,which includes the Shannon entropy,the topological entropy,the sample entropy and three amino acid preferences including Remark 465,Deleage/Roux,and Bfactor(2STD).Particularly,we introduced the sample entropy for calculating time series complexity by mapping the amino acid sequence to a time series of 0-9.To our knowledge,the sample entropy has not been previously used for predicting IDPs and hence is being used for the first time in our study.In addition,the scheme used a properly sized sliding window in every protein sequence which greatly improved the prediction performance.Finally,we used seven machine learning algorithms and tested with 10-fold cross-validation to get the results on the dataset R80 collected by Yang et al.and of the dataset DIS1556 from the Database of Protein Disorder(DisProt)(https://www.disprot.org)containing experimentally determined intrinsically disordered proteins(IDPs).The results showed that k-Nearest Neighbor was more appropriate and an overall prediction accuracy of 92%.Furthermore,our method just used six features and hence required lower computational complexity.展开更多
In this paper, a real-time computation method for the control problems in differential-algebraic systems is presented. The errors of the method are estimated, and the relation between the sampling stepsize and the con...In this paper, a real-time computation method for the control problems in differential-algebraic systems is presented. The errors of the method are estimated, and the relation between the sampling stepsize and the controlled errors is analyzed. The stability analysis is done for a model problem, and the stability region is ploted which gives the range of the sampling stepsizes with which the stability of control process is guaranteed.展开更多
The property of NP_completeness of topologic spatial reasoning problem has been proved.According to the similarity of uncertainty with topologic spatial reasoning,the problem of directional spatial reasoning should be...The property of NP_completeness of topologic spatial reasoning problem has been proved.According to the similarity of uncertainty with topologic spatial reasoning,the problem of directional spatial reasoning should be also an NP_complete problem.The proof for the property of NP_completeness in directional spatial reasoning problem is based on two important transformations.After these transformations,a spatial configuration has been constructed based on directional constraints,and the property of NP_completeness in directional spatial reasoning has been proved with the help of the consistency of the constraints in the configuration.展开更多
Based on Neumman series and epsilon-algorithm, an efficient computation for dynamic responses of systems with arbitrary time-varying characteristics is investigated. Avoiding the calculation for the inverses of the eq...Based on Neumman series and epsilon-algorithm, an efficient computation for dynamic responses of systems with arbitrary time-varying characteristics is investigated. Avoiding the calculation for the inverses of the equivalent stiffness matrices in each time step, the computation effort of the proposed method is reduced compared with the full analysis of Newmark method. The validity and applications of the proposed method are illustrated by a 4-DOF spring-mass system with periodical time-varying stiffness properties and a truss structure with arbitrary time-varying lumped mass. It shows that good approximate results can be obtained by the proposed method compared with the responses obtained by the full analysis of Newmark method.展开更多
A user-programmable computational/control platform was developed at the University of Toronto that offers real-time hybrid simulation （RTHS） capabilities. The platform was verified previously using several linear ph...A user-programmable computational/control platform was developed at the University of Toronto that offers real-time hybrid simulation （RTHS） capabilities. The platform was verified previously using several linear physical substructures. The study presented in this paper is focused on further validating the RTHS platform using a nonlinear viscoelastic-plastic damper that has displacement, frequency and temperature-dependent properties. The validation study includes damper component characterization tests, as well as RTHS of a series of single-degree-of-freedom （SDOF） systems equipped with viscoelastic-plastic dampers that represent different structural designs. From the component characterization tests, it was found that for a wide range of excitation frequencies and friction slip loads, the tracking errors are comparable to the errors in RTHS of linear spring systems. The hybrid SDOF results are compared to an independently validated thermal- mechanical viscoelastic model to further validate the ability for the platform to test nonlinear systems. After the validation, as an application study, nonlinear SDOF hybrid tests were used to develop performance spectra to predict the response of structures equipped with damping systems that are more challenging to model analytically. The use of the experimental performance spectra is illustrated by comparing the predicted response to the hybrid test response of 2DOF systems equipped with viscoelastic-plastic dampers.展开更多
The computational uncertainty principle states that the numerical computation of nonlinear ordinary differential equations（ODEs） should use appropriately sized time steps to obtain reliable solutions.However,the int...The computational uncertainty principle states that the numerical computation of nonlinear ordinary differential equations（ODEs） should use appropriately sized time steps to obtain reliable solutions.However,the interval of effective step size（IES） has not been thoroughly explored theoretically.In this paper,by using a general estimation for the total error of the numerical solutions of ODEs,a method is proposed for determining an approximate IES by translating the functions for truncation and rounding errors.It also illustrates this process with an example.Moreover,the relationship between the IES and its approximation is found,and the relative error of the approximation with respect to the IES is given.In addition,variation in the IES with increasing integration time is studied,which can provide an explanation for the observed numerical results.The findings contribute to computational step-size choice for reliable numerical solutions.展开更多
The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are or...The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood（SL） is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit（GPU）-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes,delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals（like EEG and f MRI） using similar recording techniques.展开更多
In this paper, we define two versions of Untrapped set (weak and strong Untrapped sets) over a finite set of alternatives. These versions, considered as choice procedures, extend the notion of Untrapped set in a more ...In this paper, we define two versions of Untrapped set (weak and strong Untrapped sets) over a finite set of alternatives. These versions, considered as choice procedures, extend the notion of Untrapped set in a more general case (i.e. when alternatives are not necessarily comparable). We show that they all coincide with Top cycle choice procedure for tournaments. In case of weak tournaments, the strong Untrapped set is equivalent to Getcha choice procedure and the Weak Untrapped set is exactly the Untrapped set studied in the litterature. We also present a polynomial-time algorithm for computing each set.展开更多
This paper investigates modified fixed-time synchronization(FxTS)of complex networks(CNs)with time-varying delays based on continuous and discontinuous controllers.First,for the sake of making the settling time(ST)of ...This paper investigates modified fixed-time synchronization(FxTS)of complex networks(CNs)with time-varying delays based on continuous and discontinuous controllers.First,for the sake of making the settling time(ST)of FxTS is independent of the initial values and parameters of the CNs,a modified fixed-time(FxT)stability theorem is proposed,where the ST is determined by an arbitrary positive number given in advance.Then,continuous controller and discontinuous controller are designed to realize the modified FxTS target of CNs.In addition,based on the designed controllers,CNs can achieve synchronization at any given time,or even earlier.And control strategies effectively solve the problem of ST related to the parameters of CNs.Finally,an appropriate simulation example is conducted to examine the effectiveness of the designed control strategies.展开更多
Flammable gas leakage in a semi-enclosed scenario can lead to catastrophic consequences,such as vapor cloud explosions.To reduce casualties and environmental damage,predicting the consequences based on the initial con...Flammable gas leakage in a semi-enclosed scenario can lead to catastrophic consequences,such as vapor cloud explosions.To reduce casualties and environmental damage,predicting the consequences based on the initial concentration time series monitored by sensors is of paramount importance.This paper proposes a consequence prediction model based on deep learning using variable-length concentration time series.Incomplete concentration values are padded and then passed through a masking layer,enabling the network to focus exclusively on valid data.The temporal correlations are extracted using a long short-term memory(LSTM)network,and the final prediction results are obtained by passing these features into a feedforward neural network(FNN).Computational fluid dynamics(CFD)software was used to simulate the leakage of hydrogen-mixed natural gas.Experiments were carried out for nine distinct prediction targets,derived from combinations of the mass and centroid coordinates of vapor clouds formed by various gases.These prediction targets were modeled using both fixed-length and variable-length input sequences.The high accuracy of the experimental results validates the effectiveness of the proposed method.展开更多
For non-stationary complex dynamic systems,a standardized algorithm is developed to compute time correlation functions,addressing the limitations of traditional methods reliant on the stationary assumption.The propose...For non-stationary complex dynamic systems,a standardized algorithm is developed to compute time correlation functions,addressing the limitations of traditional methods reliant on the stationary assumption.The proposed algorithm integrates two-point and multi-point time correlation functions into a unified framework.Further,it is verified by a practical application in complex financial systems,demonstrating its potential in various complex dynamic systems.展开更多
1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers ...1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers are now able to incorporate intricate features such as delays,stochastic effects,fractional dynamics,variable-order systems,and uncertainty into epidemic models.These advancements not only improve predictive accuracy but also enable deeper insights into disease transmission,control,and policy-making.Tashfeen et al.展开更多
Federated learning combined with edge computing has greatly facilitated transportation in real-time applications such as intelligent traffic sys-tems.However,synchronous federated learning is in-efficient in terms of ...Federated learning combined with edge computing has greatly facilitated transportation in real-time applications such as intelligent traffic sys-tems.However,synchronous federated learning is in-efficient in terms of time and convergence speed,mak-ing it unsuitable for high real-time requirements.To address these issues,this paper proposes an Adap-tive Waiting time Asynchronous Federated Learn-ing(AWTAFL)based on Dueling Double Deep Q-Network(D3QN).The server dynamically adjusts the waiting time using the D3QN algorithm based on the current task progress and energy consumption,aim-ing to accelerate convergence and save energy.Addi-tionally,this paper presents a new federated learning global aggregation scheme,where the central server performs weighted aggregation based on the freshness and contribution of client parameters.Experimen-tal simulations demonstrate that the proposed algo-rithm significantly reduces the convergence time while ensuring model quality and effectively reducing en-ergy consumption in asynchronous federated learning.Furthermore,the improved global aggregation update method enhances training stability and reduces oscil-lations in the global model convergence.展开更多
This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid s...This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid synchronization of heterogeneous duplex complex networks.Therefore,we study the finite time hybrid synchronization of heterogeneous duplex networks,which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time.To be specific,the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function,the internal synchronization and external synchronization are achieved simultaneously in finite time.Finally,numerical examples are presented to illustrate the validness of theoretical results.展开更多
文摘Existing literature indicates that prolonged insertion time is associated with procedural complexity and may influence adenoma detection.Xu et al recently reported that longer insertion time correlates with lower adenoma detection,but this effect can be mitigated by sufficient withdrawal duration.Insertion time should not be regarded merely as a numeric variable but rather as a multidimensional marker of technical difficulty.Integrating the insertion-to-withdrawal ratio with composite indicators such as looping or bowel preparation quality may enhance predictive models of colonoscopy performance.Conceptualizing insertion time in this way provides a more nuanced understanding of its role in adenoma detection and highlights the need for improved frameworks that link procedural complexity with quality outcomes.
文摘Zernike polynomials have been used in different fields such as optics, astronomy, and digital image analysis for many years. To form these polynomials, Zernike moments are essential to be determined. One of the main issues in realizing the moments is using factorial terms in their equation which cause</span><span style="font-size:10.0pt;font-family:"">s</span><span style="font-size:10.0pt;font-family:""> higher time complexity. As a solution, several methods have been presented to reduce the time complexity of these polynomials in recent years. The purpose of this research is to study several methods among the most popular recursive methods for fast Zernike computation and compare them <span>together by a global theoretical evaluation system called worst-case time co</span><span>mplexity. In this study, we have analyzed the selected algorithms and calculate</span>d the worst-case time complexity for each one. After that, the results are represented and explained and finally, a conclusion has been made by comparing th</span><span style="font-size:10.0pt;font-family:"">ese</span><span style="font-size:10.0pt;font-family:""> criteria among the studied algorithms. According to time complexity, we have observed that although some algorithms </span><span style="font-size:10.0pt;font-family:"">such </span><span style="font-size:10.0pt;font-family:"">as Wee method and Modified Prata method were successful in having the smaller time complexit<span>ies, some other approaches did not make any significant difference compa</span>r</span><span style="font-size:10.0pt;font-family:"">ed</span><span style="font-size:10.0pt;font-family:""> to the classical algorithm.
基金This work was supported by an EPSRC grant (No.EP/C520696/1).
文摘Computational time complexity analyzes of evolutionary algorithms （EAs） have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the （1＋1）-EA, on toy problems. These efforts produced a deeper understanding of how EAs perform on different kinds of fitness landscapes and general mathematical tools that may be extended to the analysis of more complicated EAs on more realistic problems. In fact, in recent years, it has been possible to analyze the （1＋1）-EA on combinatorial optimization problems with practical applications and more realistic population-based EAs on structured toy problems. This paper presents a survey of the results obtained in the last decade along these two research lines. The most common mathematical techniques are introduced, the basic ideas behind them are discussed and their elective applications are highlighted. Solved problems that were still open are enumerated as are those still awaiting for a solution. New questions and problems arisen in the meantime are also considered.
基金This work has been supported by the National Natural Science Foundation of China under grant numbers 51590883 and 51331006by the State Key Project of Research and Development of China(No.2017YFA0206302).
文摘In this work,the computational complexity of a spin-glass three-dimensional(3D)Ising model(for the lattice sizeN=lmn,wherel,m,n are thenumbersof lattice points along three crystallographic directions)is studied.We prove that an absolute minimum core(AMC)model consisting of a spin-glass 2D Ising model interacting with its nearest neighboring plane,has its computational complexity O(2mn).Any algorithms to make the model smaller(or simpler)than the AMC model will cut the basic element of the spin-glass 3D Ising model and lost many important information of the original model.Therefore,the computational complexity of the spin-glass 3D Ising model cannot be reduced to be less than O(2mn)by any algorithms,which is in subexponential time,superpolynomial.
基金Project supported by the National Natural Science Foundation of China (Grant No. 51175316)the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20103108110006)
文摘We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.
文摘The prediction of intrinsically disordered proteins is a hot research area in bio-information.Due to the high cost of experimental methods to evaluate disordered regions of protein sequences,it is becoming increasingly important to predict those regions through computational methods.In this paper,we developed a novel scheme by employing sequence complexity to calculate six features for each residue of a protein sequence,which includes the Shannon entropy,the topological entropy,the sample entropy and three amino acid preferences including Remark 465,Deleage/Roux,and Bfactor(2STD).Particularly,we introduced the sample entropy for calculating time series complexity by mapping the amino acid sequence to a time series of 0-9.To our knowledge,the sample entropy has not been previously used for predicting IDPs and hence is being used for the first time in our study.In addition,the scheme used a properly sized sliding window in every protein sequence which greatly improved the prediction performance.Finally,we used seven machine learning algorithms and tested with 10-fold cross-validation to get the results on the dataset R80 collected by Yang et al.and of the dataset DIS1556 from the Database of Protein Disorder(DisProt)(https://www.disprot.org)containing experimentally determined intrinsically disordered proteins(IDPs).The results showed that k-Nearest Neighbor was more appropriate and an overall prediction accuracy of 92%.Furthermore,our method just used six features and hence required lower computational complexity.
文摘In this paper, a real-time computation method for the control problems in differential-algebraic systems is presented. The errors of the method are estimated, and the relation between the sampling stepsize and the controlled errors is analyzed. The stability analysis is done for a model problem, and the stability region is ploted which gives the range of the sampling stepsizes with which the stability of control process is guaranteed.
文摘The property of NP_completeness of topologic spatial reasoning problem has been proved.According to the similarity of uncertainty with topologic spatial reasoning,the problem of directional spatial reasoning should be also an NP_complete problem.The proof for the property of NP_completeness in directional spatial reasoning problem is based on two important transformations.After these transformations,a spatial configuration has been constructed based on directional constraints,and the property of NP_completeness in directional spatial reasoning has been proved with the help of the consistency of the constraints in the configuration.
基金supported by the Foundation of the Science and Technology of Jilin Province (20070541)985-Automotive Engineering of Jilin University and Innovation Fund for 985 Engineering of Jilin University (20080104).
文摘Based on Neumman series and epsilon-algorithm, an efficient computation for dynamic responses of systems with arbitrary time-varying characteristics is investigated. Avoiding the calculation for the inverses of the equivalent stiffness matrices in each time step, the computation effort of the proposed method is reduced compared with the full analysis of Newmark method. The validity and applications of the proposed method are illustrated by a 4-DOF spring-mass system with periodical time-varying stiffness properties and a truss structure with arbitrary time-varying lumped mass. It shows that good approximate results can be obtained by the proposed method compared with the responses obtained by the full analysis of Newmark method.
基金NSERC Discovery under Grant 371627-2009 and NSERC RTI under Grant 374707-2009 EQPEQ programs
文摘A user-programmable computational/control platform was developed at the University of Toronto that offers real-time hybrid simulation （RTHS） capabilities. The platform was verified previously using several linear physical substructures. The study presented in this paper is focused on further validating the RTHS platform using a nonlinear viscoelastic-plastic damper that has displacement, frequency and temperature-dependent properties. The validation study includes damper component characterization tests, as well as RTHS of a series of single-degree-of-freedom （SDOF） systems equipped with viscoelastic-plastic dampers that represent different structural designs. From the component characterization tests, it was found that for a wide range of excitation frequencies and friction slip loads, the tracking errors are comparable to the errors in RTHS of linear spring systems. The hybrid SDOF results are compared to an independently validated thermal- mechanical viscoelastic model to further validate the ability for the platform to test nonlinear systems. After the validation, as an application study, nonlinear SDOF hybrid tests were used to develop performance spectra to predict the response of structures equipped with damping systems that are more challenging to model analytically. The use of the experimental performance spectra is illustrated by comparing the predicted response to the hybrid test response of 2DOF systems equipped with viscoelastic-plastic dampers.
基金supported by the National Natural Science Foundation of China[grant numbers 41375110,11471244]
文摘The computational uncertainty principle states that the numerical computation of nonlinear ordinary differential equations（ODEs） should use appropriately sized time steps to obtain reliable solutions.However,the interval of effective step size（IES） has not been thoroughly explored theoretically.In this paper,by using a general estimation for the total error of the numerical solutions of ODEs,a method is proposed for determining an approximate IES by translating the functions for truncation and rounding errors.It also illustrates this process with an example.Moreover,the relationship between the IES and its approximation is found,and the relative error of the approximation with respect to the IES is given.In addition,variation in the IES with increasing integration time is studied,which can provide an explanation for the observed numerical results.The findings contribute to computational step-size choice for reliable numerical solutions.
基金supported by Grants from the National Natural Science Foundation of China(81230023,81571067,and 81521063)National Basic Research Development Program(973 Program)of China(2013CB531905)the‘‘111’’Project of China
文摘The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood（SL） is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit（GPU）-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes,delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals（like EEG and f MRI） using similar recording techniques.
文摘In this paper, we define two versions of Untrapped set (weak and strong Untrapped sets) over a finite set of alternatives. These versions, considered as choice procedures, extend the notion of Untrapped set in a more general case (i.e. when alternatives are not necessarily comparable). We show that they all coincide with Top cycle choice procedure for tournaments. In case of weak tournaments, the strong Untrapped set is equivalent to Getcha choice procedure and the Weak Untrapped set is exactly the Untrapped set studied in the litterature. We also present a polynomial-time algorithm for computing each set.
基金Supported by the National Natural Science Foundation of China(62476082)。
文摘This paper investigates modified fixed-time synchronization(FxTS)of complex networks(CNs)with time-varying delays based on continuous and discontinuous controllers.First,for the sake of making the settling time(ST)of FxTS is independent of the initial values and parameters of the CNs,a modified fixed-time(FxT)stability theorem is proposed,where the ST is determined by an arbitrary positive number given in advance.Then,continuous controller and discontinuous controller are designed to realize the modified FxTS target of CNs.In addition,based on the designed controllers,CNs can achieve synchronization at any given time,or even earlier.And control strategies effectively solve the problem of ST related to the parameters of CNs.Finally,an appropriate simulation example is conducted to examine the effectiveness of the designed control strategies.
基金supported by the National Key Research and Development Program of China(2022YFB3305900)National Natural Science Foundation of China(62373153,62173147)+1 种基金the Programme of Introducing Talents of Discipline to Universities(the 111 Project)under Grant B17017Fundamental Research Funds for the Central Universities(222202517006)。
文摘Flammable gas leakage in a semi-enclosed scenario can lead to catastrophic consequences,such as vapor cloud explosions.To reduce casualties and environmental damage,predicting the consequences based on the initial concentration time series monitored by sensors is of paramount importance.This paper proposes a consequence prediction model based on deep learning using variable-length concentration time series.Incomplete concentration values are padded and then passed through a masking layer,enabling the network to focus exclusively on valid data.The temporal correlations are extracted using a long short-term memory(LSTM)network,and the final prediction results are obtained by passing these features into a feedforward neural network(FNN).Computational fluid dynamics(CFD)software was used to simulate the leakage of hydrogen-mixed natural gas.Experiments were carried out for nine distinct prediction targets,derived from combinations of the mass and centroid coordinates of vapor clouds formed by various gases.These prediction targets were modeled using both fixed-length and variable-length input sequences.The high accuracy of the experimental results validates the effectiveness of the proposed method.
基金Project supported by the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(Grant No.GZC20231050)the National Natural Science Foundation of China(Grant Nos.12175193 and 11905183)the 13th Five-year plan for Education Science Funding of Guangdong Province(Grant No.2021GXJK349)。
文摘For non-stationary complex dynamic systems,a standardized algorithm is developed to compute time correlation functions,addressing the limitations of traditional methods reliant on the stationary assumption.The proposed algorithm integrates two-point and multi-point time correlation functions into a unified framework.Further,it is verified by a practical application in complex financial systems,demonstrating its potential in various complex dynamic systems.
文摘1 Summary Mathematical modeling has become a cornerstone in understanding the complex dynamics of infectious diseases and chronic health conditions.With the advent of more refined computational techniques,researchers are now able to incorporate intricate features such as delays,stochastic effects,fractional dynamics,variable-order systems,and uncertainty into epidemic models.These advancements not only improve predictive accuracy but also enable deeper insights into disease transmission,control,and policy-making.Tashfeen et al.
基金supported by the National Natural Science Foundation of China(62371082)Guangxi Science and Technology Project(AB24010317)+1 种基金Science and Technology Project of Chongqing Education Commission(KJZD-K202400606)Natural Science Foundation of Chongqing(CSTB2023NSCQ-MSX0726,CSTB2023NSCQ-LZX0014).
文摘Federated learning combined with edge computing has greatly facilitated transportation in real-time applications such as intelligent traffic sys-tems.However,synchronous federated learning is in-efficient in terms of time and convergence speed,mak-ing it unsuitable for high real-time requirements.To address these issues,this paper proposes an Adap-tive Waiting time Asynchronous Federated Learn-ing(AWTAFL)based on Dueling Double Deep Q-Network(D3QN).The server dynamically adjusts the waiting time using the D3QN algorithm based on the current task progress and energy consumption,aim-ing to accelerate convergence and save energy.Addi-tionally,this paper presents a new federated learning global aggregation scheme,where the central server performs weighted aggregation based on the freshness and contribution of client parameters.Experimen-tal simulations demonstrate that the proposed algo-rithm significantly reduces the convergence time while ensuring model quality and effectively reducing en-ergy consumption in asynchronous federated learning.Furthermore,the improved global aggregation update method enhances training stability and reduces oscil-lations in the global model convergence.
基金Project supported by Jilin Provincial Science and Technology Development Plan(Grant No.20220101137JC).
文摘This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid synchronization of heterogeneous duplex complex networks.Therefore,we study the finite time hybrid synchronization of heterogeneous duplex networks,which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time.To be specific,the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function,the internal synchronization and external synchronization are achieved simultaneously in finite time.Finally,numerical examples are presented to illustrate the validness of theoretical results.
