Vector-based continuous models for nematic liquid crystals such as the Oseen-Frank model and the Ericksen model are relatively simpler compared with tensor-based models such as the Landau-de Gennes model.However,these...Vector-based continuous models for nematic liquid crystals such as the Oseen-Frank model and the Ericksen model are relatively simpler compared with tensor-based models such as the Landau-de Gennes model.However,these vector models do not respect head-to-tail symmetry.As a result,they cannot predict configurations corresponding to non-orientable line fields,particularly the half-integer defects.This paper confirms a significant discrepancy between the transition dynamics predicted by the Oseen-Frank vector model and Landau-de Gennes tensor model for liquid crystals confined in a two-dimensional square well.The so-called inner product weighted Laplacian operator is introduced as an anisotropic diffusion operator to evolve the Euler-Lagrange equations corresponding to the modified Oseen-Frank model.Numerical results show that both the predicted equilibrium configurations and the transition dynamics from one equilibrium states to another satisfies head-to-tail symmetry and can accommodate half-integer defects.The connections of anisotropic diffusion operator to the graph Laplacian and the discrete Lebwohl-Lasher model are also discussed.The numerical trick proposed in this paper can be considered a simple remedy to restore head-to-tail symmetry in vector models of liquid crystals,making them more applicable in situations such as systems containing half-integer defects where the traditional numerical approach would fail.展开更多
A cardiac vector model is presented and verified, and then the forward problem for cardiac magnetic fields and electric potential are discussed based on this model and the realistic human torso volume conductor model,...A cardiac vector model is presented and verified, and then the forward problem for cardiac magnetic fields and electric potential are discussed based on this model and the realistic human torso volume conductor model, including lungs. A torso-cardiac vector model is used for a 12-lead electrocardiographic （ECG） and magneto-cardiogram （MCG） simulation study by using the boundary element method （BEM）. Also, we obtain the MCG wave picture using a compound four-channel HTc.SQUID system in a magnetically shielded room. By comparing the simulated results and experimental results, we verify the cardiac vector model and then do a preliminary study of the forward problem of MCG and ECG. Therefore, the results show that the vector model is reasonable in cardiac electrophysiology.展开更多
To avoid the machine problems of excessive axial force, complex process flow and frequent tool changing during robotic drilling holes, a new hole-making technology (i.e., helical milling hole) was introduced for desig...To avoid the machine problems of excessive axial force, complex process flow and frequent tool changing during robotic drilling holes, a new hole-making technology (i.e., helical milling hole) was introduced for designing a new robotic helical milling hole system, which could further improve robotic hole-making ability in airplane digital assembly. After analysis on the characteristics of helical milling hole, advantages and limitations of two typical robotic helical milling hole systems were summarized. Then, vector model of helical milling hole movement was built on vector analysis method. Finally, surface roughness calculation formula was deduced according to the movement principle of helical milling hole, then the influence of main technological parameters on surface roughness was analyzed. Analysis shows that theoretical surface roughness of hole becomes poor with the increase of tool speed ratio and revolution radius. Meanwhile, the roughness decreases according to the increase of tool teeth number. The research contributes greatly to the construction of roughness prediction model in helical milling hole.展开更多
Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling a...Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling and using equilateral triangles and square prism surfaces, the kinetic vector model is given any equilateral polygon prismic surface. Kinetic tracks are simulated through these kinetic models respectively, thus it can be seen that the axial turn-milling is a very effective method in manufacturing any equilateral, polygon, prismic surface.展开更多
This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing(LSI)and the vector space model(VSM).It integrates the theory of technological eco...This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing(LSI)and the vector space model(VSM).It integrates the theory of technological ecological niches and evaluates four key dimensions:patent quality,energy efficiency engineering,technological modules,and intelligent computing power.The findings reveal that USA has established strong technological barriers through standard-essential patents(SEPs)in wireless communication and integrated circuits.In contrast,Chinese mainland firms rely heavily on fundamental technologies.Qualcomm Inc.in USA and Taiwan Semiconductor Manufacturing Company(TSMC)in Chineses Taiwan have built a comprehensive patent porfolio in energy efficiency engineering.While Chinese mainland faces challenges in advancing dynamic frequency modulation algorithms and high-end manufacturing processes.Huawei Inc.in Chinese mainland leads in 5G module technology but struggles with ecosystem collaboration.Semiconductor manufacturing and radio frequency(RF)components still rely on external suppliers.This highlights the urgent need for innovation in new materials and open'source architectures.To enhance intelligent computing power,Chinese mainland firms must address coordination challenges.They should adopt scenario-driven technological strategies and build a comprehensive ecosystem that includes hardware,operating systems,and developer networks.展开更多
Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument...Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument faults,and human operational errors,thereby often hindering the accurate assessment of actual deformation patterns.This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data.It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring.The proposed method was validated in a concrete dam project,with the model error maintaining within 5%,demonstrating its effectiveness in processing missing deformation data.This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.展开更多
Correlation functions in the O(n)models below the critical temperature are considered.Based on Monte Carlo(MC)data,we confirm the fact stated earlier by Engels and Vogt,that the transverse two-plane correlation functi...Correlation functions in the O(n)models below the critical temperature are considered.Based on Monte Carlo(MC)data,we confirm the fact stated earlier by Engels and Vogt,that the transverse two-plane correlation function of the O(4)model for lattice sizes about L=120 and small external fields h is very well described by a Gaussian approximation.However,we show that fits of not lower quality are provided by certain non-Gaussian approximation.We have also tested larger lattice sizes,up to L=512.The Fourier-transformed transverse and longitudinal two-point correlation functions have Goldstone mode singularities in the thermodynamic limit at k→0 and h=+0,i.e.,G_(⊥)(k)≈ak−λ_(⊥)and G_(||)(k)≈bk−λk,respectively.Here a and b are the amplitudes,k=|k|is the magnitude of the wave vector k.The exponentsλ_(⊥),λk and the ratio bM^(2)/a^(2),where M is the spontaneous magnetization,are universal according to the GFD(grouping of Feynman diagrams)approach.Here we find that the universality follows also from the standard(Gaussian)theory,yielding bM^(2)/a^(2)=(n−1)/16.Our MC estimates of this ratio are 0.06±0.01 for n=2,0.17±0.01 for n=4 and 0.498±0.010 for n=10.According to these and our earlier MC results,the asymptotic behavior and Goldstone mode singularities are not exactly described by the standard theory.This is expected from the GFD theory.We have found appropriate analytic approximations for G_(⊥)(k)and G_(||)(k),well fitting the simulation data for small k.We have used them to test the Patashinski-Pokrovski relation and have found that it holds approximately。展开更多
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
We are associating the solutions of stochastic and deterministic vector borne plant disease model in this manuscript.The dynamics of plant model depends upon threshold number P^(∗).If P^(∗)<1 then condition helpful...We are associating the solutions of stochastic and deterministic vector borne plant disease model in this manuscript.The dynamics of plant model depends upon threshold number P^(∗).If P^(∗)<1 then condition helpful to eradicate the disease in plants while P^(∗)>1 explains the persistence of disease.Inappropriately,standard numerical systems do not behave well in certain scenarios.We have been proposed a structure preserving stochastic non-standard finite difference system to analyze the behavior of model.This system is dynamical consistent,positive and bounded as defined by Mickens.展开更多
Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measur...Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.展开更多
This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used ...This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.展开更多
Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector （tracking radar echo by correlation） rep...Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector （tracking radar echo by correlation） represents only the instantaneous trend of precipitation echo motion, the approach using derived echo motion vectors to extrapolate radar reflectivity as a rainfall forecast is not satisfactory if the lead time is beyond 30 minutes. For longer lead times, the effect of ambient winds on echo movement should be considered. In this paper, an extrapolation algorithm that extends forecast lead times up to 3 hours was developed to blend TREC vectors with model-predicted winds. The TREC vectors were derived from radar reflectivity patterns in 3 km height CAPPI （constant altitude plan position indicator） mosaics through a cross-correlation technique. The background steering winds were provided by predictions of the rapid update assimilation model CHAF （cycle of hourly assimilation and forecast）. A similarity index was designed to determine the vertical level at which model winds were applied in the extrapolation process, which occurs via a comparison between model winds and radar vectors. Based on a summer rainfall case study, it is found that the new algorithm provides a better forecast.展开更多
An epidemic model is a simplified means of describing the transmission of infectious diseases through individuals. The modeling of infectious diseases is a tool which has been used to study the mechanisms by which dis...An epidemic model is a simplified means of describing the transmission of infectious diseases through individuals. The modeling of infectious diseases is a tool which has been used to study the mechanisms by which diseases spread, to predict the future course of an outbreak and to evaluate strategies to control an epidemic. Epidemic models are of many types. Here, SEIR model is discussed. We first discuss the basics of SEIR model. Then it is applied for vector borne diseases. Steady state conditions are derived. A threshold parameter R0 is defined and is shown that the disease will spread only if its value exceeds 1. We have applied the basic model to one specific diseases-malaria and did the sensitivity analysis too using the data for India. We found sensitivity analysis very important as it told us the most sensitive parameter to be taken care of. This makes the work more of practical use. Numerical simulation is done for vector and host which shows the population dynamics in different compartments.展开更多
Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning fo...Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.展开更多
In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression ...In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control （SVMN-MPC） algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.展开更多
Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new s...Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.展开更多
As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a mult...As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a multi-step model predictive control based on online SVR(OSVR) optimized by multi-agent particle swarm optimization algorithm(MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.展开更多
Based on the quark-gluon contents of nucleon and strongly believing that the force mediators,Pomeronand its counterpart in the conventional approach of Regge theory,for high energy diffractive process would be the ten...Based on the quark-gluon contents of nucleon and strongly believing that the force mediators,Pomeronand its counterpart in the conventional approach of Regge theory,for high energy diffractive process would be the tensorglueball and Odderon respectively,we discuss photo-production of vector meson φ off the deuteron at energy less than 3GeV in the QCD inspired model in which the quark gluon degrees of freedom and glueball,Odderon exchange are takeninto account.A calculation is performed for γ + D →φ + D,and the theoretical predictions of the differential crosssection dσ^(γD)) /dt,are presented and compared with available experimental data.Our QCD inspired model reproducesdata quite well in the whole range of the experimental measurements up to |t| ■0.4 GeV.Our results can be used toextract γn→φn data,which cannot be measured in experiment.展开更多
Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector A...Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector Autoregression (VAR) model to forecast solar irradiance levels and weather characteristics in the San Francisco Bay Area. The results demonstrate a correlation between predicted and actual solar irradiance, indicating the effectiveness of the VAR model for this task. However, the model may not be sufficient for this region due to the requirement of additional weather features to reduce disparities between predictions and actual observations. Additionally, the current lag order in the model is relatively low, limiting its ability to capture all relevant information from past observations. As a result, the model’s forecasting capability is limited to short-term horizons, with a maximum horizon of four hours.展开更多
The goal of this research is to introduce the simulation studies of the vector-host disease nonlinear system(VHDNS)along with the numerical treatment of artificial neural networks(ANNs)techniques supported by Levenber...The goal of this research is to introduce the simulation studies of the vector-host disease nonlinear system(VHDNS)along with the numerical treatment of artificial neural networks(ANNs)techniques supported by Levenberg-Marquardt backpropagation(LMQBP),known as ANNs-LMQBP.This mechanism is physically appropriate,where the number of infected people is increasing along with the limited health services.Furthermore,the biological effects have fadingmemories and exhibit transition behavior.Initially,the model is developed by considering the two and three categories for the humans and the vector species.The VHDNS is constructed with five classes,susceptible humans Sh(t),infected humans Ih(t),recovered humans Rh(t),infected vectors Iv(t),and susceptible vector Sv(t)based system of the fractional-order nonlinear ordinary differential equations.To solve the number of variations of the VHDNS,the numerical simulations are performed using the stochastic ANNs-LMQBP.The achieved numerical solutions for solving the VHDNS using the stochastic ANNs-LMQBP have been described for training,verifying,and testing data to decrease the mean square error(MSE).An extensive analysis is provided using the correlation studies,MSE,error histograms(EHs),state transitions(STs),and regression to observe the accuracy,efficiency,expertise,and aptitude of the computing ANNs-LMQBP.展开更多
文摘Vector-based continuous models for nematic liquid crystals such as the Oseen-Frank model and the Ericksen model are relatively simpler compared with tensor-based models such as the Landau-de Gennes model.However,these vector models do not respect head-to-tail symmetry.As a result,they cannot predict configurations corresponding to non-orientable line fields,particularly the half-integer defects.This paper confirms a significant discrepancy between the transition dynamics predicted by the Oseen-Frank vector model and Landau-de Gennes tensor model for liquid crystals confined in a two-dimensional square well.The so-called inner product weighted Laplacian operator is introduced as an anisotropic diffusion operator to evolve the Euler-Lagrange equations corresponding to the modified Oseen-Frank model.Numerical results show that both the predicted equilibrium configurations and the transition dynamics from one equilibrium states to another satisfies head-to-tail symmetry and can accommodate half-integer defects.The connections of anisotropic diffusion operator to the graph Laplacian and the discrete Lebwohl-Lasher model are also discussed.The numerical trick proposed in this paper can be considered a simple remedy to restore head-to-tail symmetry in vector models of liquid crystals,making them more applicable in situations such as systems containing half-integer defects where the traditional numerical approach would fail.
基金supported by the State Key Development Program for Basic Research of China (Grant No. 2011CBA00106)the National Natural Science Foundation of China (Grant Nos. 10674006, 81171421, and 61101046)the National High Technology Research and Development Program of China (Grant No. 2007AA03Z238)
文摘A cardiac vector model is presented and verified, and then the forward problem for cardiac magnetic fields and electric potential are discussed based on this model and the realistic human torso volume conductor model, including lungs. A torso-cardiac vector model is used for a 12-lead electrocardiographic （ECG） and magneto-cardiogram （MCG） simulation study by using the boundary element method （BEM）. Also, we obtain the MCG wave picture using a compound four-channel HTc.SQUID system in a magnetically shielded room. By comparing the simulated results and experimental results, we verify the cardiac vector model and then do a preliminary study of the forward problem of MCG and ECG. Therefore, the results show that the vector model is reasonable in cardiac electrophysiology.
基金Foundation item： Projects（50975141, 51005118） supported by the National Natural Science Foundation of China Projects（20091652018, 2010352005） supported by Aviation Science Fund of China Project（YKJ11-001） supported by Key Program of Nanjing College of Information Technology Institute, China
文摘To avoid the machine problems of excessive axial force, complex process flow and frequent tool changing during robotic drilling holes, a new hole-making technology (i.e., helical milling hole) was introduced for designing a new robotic helical milling hole system, which could further improve robotic hole-making ability in airplane digital assembly. After analysis on the characteristics of helical milling hole, advantages and limitations of two typical robotic helical milling hole systems were summarized. Then, vector model of helical milling hole movement was built on vector analysis method. Finally, surface roughness calculation formula was deduced according to the movement principle of helical milling hole, then the influence of main technological parameters on surface roughness was analyzed. Analysis shows that theoretical surface roughness of hole becomes poor with the increase of tool speed ratio and revolution radius. Meanwhile, the roughness decreases according to the increase of tool teeth number. The research contributes greatly to the construction of roughness prediction model in helical milling hole.
基金Sponsored by the National High Technology Research and Development Program of China(Grant No.2003AA424020), Important Scientech ProblemTackling Subject Foundation Under the State 9th 5 -Year Plan(Grant No.96 -A22 -01 -01) and Provincial Doctoral Science Foundation of LiaoningProvince, China(Grant No.2001102034).
文摘Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling and using equilateral triangles and square prism surfaces, the kinetic vector model is given any equilateral polygon prismic surface. Kinetic tracks are simulated through these kinetic models respectively, thus it can be seen that the axial turn-milling is a very effective method in manufacturing any equilateral, polygon, prismic surface.
基金supported in part by the National Social Science Foundation of China(No.20BGL203).
文摘This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing(LSI)and the vector space model(VSM).It integrates the theory of technological ecological niches and evaluates four key dimensions:patent quality,energy efficiency engineering,technological modules,and intelligent computing power.The findings reveal that USA has established strong technological barriers through standard-essential patents(SEPs)in wireless communication and integrated circuits.In contrast,Chinese mainland firms rely heavily on fundamental technologies.Qualcomm Inc.in USA and Taiwan Semiconductor Manufacturing Company(TSMC)in Chineses Taiwan have built a comprehensive patent porfolio in energy efficiency engineering.While Chinese mainland faces challenges in advancing dynamic frequency modulation algorithms and high-end manufacturing processes.Huawei Inc.in Chinese mainland leads in 5G module technology but struggles with ecosystem collaboration.Semiconductor manufacturing and radio frequency(RF)components still rely on external suppliers.This highlights the urgent need for innovation in new materials and open'source architectures.To enhance intelligent computing power,Chinese mainland firms must address coordination challenges.They should adopt scenario-driven technological strategies and build a comprehensive ecosystem that includes hardware,operating systems,and developer networks.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3005401)the Fundamental Research Funds for the Central Universities(Grant No.B230201013)+2 种基金the National Natural Science Foundation of China(Grants No.52309152,U2243223,and U23B20150)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220978)the Open Fund of National Dam Safety Research Center(Grant No.CX2023B03).
文摘Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument faults,and human operational errors,thereby often hindering the accurate assessment of actual deformation patterns.This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data.It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring.The proposed method was validated in a concrete dam project,with the model error maintaining within 5%,demonstrating its effectiveness in processing missing deformation data.This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.
文摘Correlation functions in the O(n)models below the critical temperature are considered.Based on Monte Carlo(MC)data,we confirm the fact stated earlier by Engels and Vogt,that the transverse two-plane correlation function of the O(4)model for lattice sizes about L=120 and small external fields h is very well described by a Gaussian approximation.However,we show that fits of not lower quality are provided by certain non-Gaussian approximation.We have also tested larger lattice sizes,up to L=512.The Fourier-transformed transverse and longitudinal two-point correlation functions have Goldstone mode singularities in the thermodynamic limit at k→0 and h=+0,i.e.,G_(⊥)(k)≈ak−λ_(⊥)and G_(||)(k)≈bk−λk,respectively.Here a and b are the amplitudes,k=|k|is the magnitude of the wave vector k.The exponentsλ_(⊥),λk and the ratio bM^(2)/a^(2),where M is the spontaneous magnetization,are universal according to the GFD(grouping of Feynman diagrams)approach.Here we find that the universality follows also from the standard(Gaussian)theory,yielding bM^(2)/a^(2)=(n−1)/16.Our MC estimates of this ratio are 0.06±0.01 for n=2,0.17±0.01 for n=4 and 0.498±0.010 for n=10.According to these and our earlier MC results,the asymptotic behavior and Goldstone mode singularities are not exactly described by the standard theory.This is expected from the GFD theory.We have found appropriate analytic approximations for G_(⊥)(k)and G_(||)(k),well fitting the simulation data for small k.We have used them to test the Patashinski-Pokrovski relation and have found that it holds approximately。
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
基金The first author thanks Prince Sultan University for supporting this paper through the research group Nonlinear Analysis Methods in Applied Mathematics(NAMAM),group number RG-DES-2017-01-17.
文摘We are associating the solutions of stochastic and deterministic vector borne plant disease model in this manuscript.The dynamics of plant model depends upon threshold number P^(∗).If P^(∗)<1 then condition helpful to eradicate the disease in plants while P^(∗)>1 explains the persistence of disease.Inappropriately,standard numerical systems do not behave well in certain scenarios.We have been proposed a structure preserving stochastic non-standard finite difference system to analyze the behavior of model.This system is dynamical consistent,positive and bounded as defined by Mickens.
基金This project is supported by National Natural Science Foundation of China(No.50375153).
文摘Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.
基金Supported by the National Natural Science Foundation of China(21076179)the National Basic Research Program of China(2012CB720500)
文摘This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.
基金This study was provided by Natural Science Foundation of Guangdong Province under Grant No. 5001121the China Meteorological Administration under Grant Nos. CMATG2005Y05 and CMATG2008Z10the Guangdong Meteorological Bureau under Grant Nos. 2007A2 and GRMC2007Z03
文摘Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector （tracking radar echo by correlation） represents only the instantaneous trend of precipitation echo motion, the approach using derived echo motion vectors to extrapolate radar reflectivity as a rainfall forecast is not satisfactory if the lead time is beyond 30 minutes. For longer lead times, the effect of ambient winds on echo movement should be considered. In this paper, an extrapolation algorithm that extends forecast lead times up to 3 hours was developed to blend TREC vectors with model-predicted winds. The TREC vectors were derived from radar reflectivity patterns in 3 km height CAPPI （constant altitude plan position indicator） mosaics through a cross-correlation technique. The background steering winds were provided by predictions of the rapid update assimilation model CHAF （cycle of hourly assimilation and forecast）. A similarity index was designed to determine the vertical level at which model winds were applied in the extrapolation process, which occurs via a comparison between model winds and radar vectors. Based on a summer rainfall case study, it is found that the new algorithm provides a better forecast.
文摘An epidemic model is a simplified means of describing the transmission of infectious diseases through individuals. The modeling of infectious diseases is a tool which has been used to study the mechanisms by which diseases spread, to predict the future course of an outbreak and to evaluate strategies to control an epidemic. Epidemic models are of many types. Here, SEIR model is discussed. We first discuss the basics of SEIR model. Then it is applied for vector borne diseases. Steady state conditions are derived. A threshold parameter R0 is defined and is shown that the disease will spread only if its value exceeds 1. We have applied the basic model to one specific diseases-malaria and did the sensitivity analysis too using the data for India. We found sensitivity analysis very important as it told us the most sensitive parameter to be taken care of. This makes the work more of practical use. Numerical simulation is done for vector and host which shows the population dynamics in different compartments.
基金Supported by the National Natural Science Foundation of China(11072035)
文摘Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.
基金the 973 Program of China (No.2002CB312200)the National Science Foundation of China (No.60574019)
文摘In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control （SVMN-MPC） algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.
基金This project is supported by Special Foundation for Major State Basic Research of China (No.G1998030415).
文摘Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.
基金the National Natural Science Foundation of China(No.60905066)the Natural Science Foundation of Chongqing(No.cstc2018jcyjA0667)
文摘As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a multi-step model predictive control based on online SVR(OSVR) optimized by multi-agent particle swarm optimization algorithm(MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.
基金Supported by National Natural Science Foundation of China under Grant Nos.10647002 and 10565001
文摘Based on the quark-gluon contents of nucleon and strongly believing that the force mediators,Pomeronand its counterpart in the conventional approach of Regge theory,for high energy diffractive process would be the tensorglueball and Odderon respectively,we discuss photo-production of vector meson φ off the deuteron at energy less than 3GeV in the QCD inspired model in which the quark gluon degrees of freedom and glueball,Odderon exchange are takeninto account.A calculation is performed for γ + D →φ + D,and the theoretical predictions of the differential crosssection dσ^(γD)) /dt,are presented and compared with available experimental data.Our QCD inspired model reproducesdata quite well in the whole range of the experimental measurements up to |t| ■0.4 GeV.Our results can be used toextract γn→φn data,which cannot be measured in experiment.
文摘Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector Autoregression (VAR) model to forecast solar irradiance levels and weather characteristics in the San Francisco Bay Area. The results demonstrate a correlation between predicted and actual solar irradiance, indicating the effectiveness of the VAR model for this task. However, the model may not be sufficient for this region due to the requirement of additional weather features to reduce disparities between predictions and actual observations. Additionally, the current lag order in the model is relatively low, limiting its ability to capture all relevant information from past observations. As a result, the model’s forecasting capability is limited to short-term horizons, with a maximum horizon of four hours.
基金funded by National Research Council of Thailand(NRCT)and Khon Kaen University:N42A650291。
文摘The goal of this research is to introduce the simulation studies of the vector-host disease nonlinear system(VHDNS)along with the numerical treatment of artificial neural networks(ANNs)techniques supported by Levenberg-Marquardt backpropagation(LMQBP),known as ANNs-LMQBP.This mechanism is physically appropriate,where the number of infected people is increasing along with the limited health services.Furthermore,the biological effects have fadingmemories and exhibit transition behavior.Initially,the model is developed by considering the two and three categories for the humans and the vector species.The VHDNS is constructed with five classes,susceptible humans Sh(t),infected humans Ih(t),recovered humans Rh(t),infected vectors Iv(t),and susceptible vector Sv(t)based system of the fractional-order nonlinear ordinary differential equations.To solve the number of variations of the VHDNS,the numerical simulations are performed using the stochastic ANNs-LMQBP.The achieved numerical solutions for solving the VHDNS using the stochastic ANNs-LMQBP have been described for training,verifying,and testing data to decrease the mean square error(MSE).An extensive analysis is provided using the correlation studies,MSE,error histograms(EHs),state transitions(STs),and regression to observe the accuracy,efficiency,expertise,and aptitude of the computing ANNs-LMQBP.
