Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older adults.Since direct measurement of VO_(2max)is infeas...Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older adults.Since direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction algorithms.Methods:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)values.We developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging cohorts.Finally,we quantified the associations between measured and predicted VO_(2max)and mortality.Results:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/min.Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,respectively.Incremental quartiles of measured VO_(2max)showed an inverse gradient in mortality risk.Predicted VO_(2max)variables yielded similar effect estimates but were not robust to adjustment.Conclusion:Measured VO_(2max)is a strong predictor of mortality.Using ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to adjustment.Future studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.展开更多
Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice,which is of immense importance in mobilizing the entire society to reduce carbon emissions.The method of calcul...Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice,which is of immense importance in mobilizing the entire society to reduce carbon emissions.The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid.Therefore,it cannot provide carbon factor information beforehand.To address this issue,a prediction model based on the graph attention network is proposed.The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised network using the loads of the grid nodes and the corresponding carbon factor data.The network extracts features and transmits information more suitable for the power system and can flexibly adjust the equivalent topology,thereby increasing the diversity of the structure.Its input and output data are simple,without the power grid parameters.We demonstrated its effect by testing IEEE-39 bus and IEEE-118 bus systems with average error rates of 2.46%and 2.51%.展开更多
In recent years,the in silico epitopes prediction tools have facilitated the progress of vaccines development significantly and many have been applied to predict epitopes in viruses successfully. Herein,a general over...In recent years,the in silico epitopes prediction tools have facilitated the progress of vaccines development significantly and many have been applied to predict epitopes in viruses successfully. Herein,a general overview of different tools currently available,including T cell and B cell epitopes prediction tools,is presented. And the principles of different prediction algorithms are reviewed briefly. Finally,several examples are present to illustrate the application of the prediction tools.展开更多
Remote tracking for mobile targets is one of the most important applications in wireless sensor networks （WSNs）. A target tracking protoco–exponential distributed predictive tracking （EDPT） is proposed. To reduce...Remote tracking for mobile targets is one of the most important applications in wireless sensor networks （WSNs）. A target tracking protoco–exponential distributed predictive tracking （EDPT） is proposed. To reduce energy waste and response time, an improved predictive algorithm–exponential smoothing predictive algorithm （ESPA） is presented. With the aid of an additive proportion and differential （PD） controller, ESPA decreases the system predictive delay effectively. As a recovery mechanism, an optimal searching radius （OSR） algorithm is applied to calculate the optimal radius of the recovery zone. The simulation results validate that the proposed EDPT protocol performes better in terms of track failed ratio, energy waste ratio and enlarged sensing nodes ratio, respectively.展开更多
A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The prin...A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The principle of RPE learning algorithm is to adjust weights along the direction of Gauss-Newton. Meanwhile, it is unnecessary to calculate the second local derivative and the inverse matrixes, whose unbiasedness is proved. With application to the extremely short time prediction of large ship pitch, satisfactory results are obtained. Prediction effect of this algorithm is compared with that of auto-regression and periodical diagram method, and comparison results show that the proposed algorithm is feasible.展开更多
To coordinate the various access technologies in the 4G communication system,intelligent vertical handoff algorithms are required.This paper mainly deals with a novel vertical handoff decision algorithm based on fuzzy...To coordinate the various access technologies in the 4G communication system,intelligent vertical handoff algorithms are required.This paper mainly deals with a novel vertical handoff decision algorithm based on fuzzy logic with the aid of grey theory and dynamic weights adaptation.The grey prediction theory(GPT) takes 4 sampled received signal strengths as input parameters,and calculates the predicted received signal strength in order to reduce the call dropping probability.The fuzzy logic theory based quantitative decision algorithm takes 3 quality of service(QoS)metric,received signal strength(RSS),available bandwidth(BW),and monetary cost (MC)of candidate networks as input parameters.The weight of each QoS metrics is adjusted along with the networks changing to trace the network condition.The final optimized vertical handoff decision is made by comparing the quantitative decision values of the candidate networks.Simulation results demonstrate that the proposed algorithm provides high performance in heterogeneous as well as homogeneous network environments.展开更多
In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation ...In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.展开更多
Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral proce...Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.展开更多
The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to pr...The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to predict the date of failures for a fleet of vehicles in order to allow the maintenance department to efficiently deploy the proper resources;we further provide specific details regarding the origins of failures, and finally, give recommendations. This study used the Société de transport de Montréal (STM)’s historical bus failure data as well as weather data from Environment Canada. We thank Facebook’s Prophet, Simple Feed-forward, and Beats algorithms (Uber), we proposed a set of computer codes that allow us to identify the 20% of buses that are responsible for the 80% of failures by mean of the failure history. Then, we deepened our study on the unreliable equipments identified during the diffusion of our computer code This allowed us to propose probable predictions of the dates of future failures. To ensure the validity of the proposed algorithm, we carried out simulations with more than 250,000 data. The results obtained are similar to the predicted theoretical values.展开更多
With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing predictio...With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing prediction,a significant component of the SCS,has also garnered widespread attention.According to the Chinese criminal law,actual sentencing data exhibits a saturated property due to statutory penalty ranges,but this mechanism has been ignored by most existing studies.Given this,the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural networks.Building on the saturated structure of our model,a more effective adaptive prediction algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the L1 loss together with the corresponding gradient update strategy,a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge,the choice of appropriate initial conditions for the learning algorithm and the construction of a double-hidden-layer network structure.An empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods.展开更多
The worldwide research status of head tracking is introduced and the works made in the research of the predictive algorithm and in the exploration of the rule of the head tracking are set forth. A time delay model f...The worldwide research status of head tracking is introduced and the works made in the research of the predictive algorithm and in the exploration of the rule of the head tracking are set forth. A time delay model for the telerobotic scout system is built. In respect of eliminating error caused by time delay and making reasonable prediction to the data stream, many methods are experimented in order to realize the aim of real time tracking. The application of extrapolation algorithm and auto recursive algorithm in the orientation tracking is described in detail. These two algorithms are realized in Matlab environment. Through analysis of the curves generated by using these two predictive algorithms, an appropriate method was applied in the telerobotic scout system. The effect is satisfying.展开更多
A new technique for predicting species' geographic distribution is described.The approach involves 3 steps:①setting up geographic base data;②collecting and georeferencing distributional points;③modeling ecologi...A new technique for predicting species' geographic distribution is described.The approach involves 3 steps:①setting up geographic base data;②collecting and georeferencing distributional points;③modeling ecological niches using the biodiversity species workshop implementation of the genetic algorithm for rule set prediction (GARP).To illustrate these procedures,an example based on the Brown Eared Pheasant (Crossoptilon mantchuricum) is developed.This technique constitutes a useful tool for assessing geographic distribution for questions of ecology,biogeography,systematics,and conservation biology.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
The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is...The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme.展开更多
A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference ...A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference image scheme was used to update the reference image and to decrease the computation time when the displacement was larger than a certain number.In this way,the search range and computational complexity were cut down,and less EMS memory was occupied.The capability of proposed search algorithm was then verified by the results of both computer simulation and experiments.The results showed that the algorithm could improve the efficiency of correlation method and satisfy the accuracy requirement for practical displacement measuring.展开更多
The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear p...The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear prediction residual of speech is modeled as Student-t distribution, and the additive noise is incorporated explicitly to increase the robustness, thus a probabilistic model for sparse linear prediction of speech is built, Furthermore, variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters, and then the optimal linear prediction parameters are estimated robustly. The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l1 norm minimization based sparse linear prediction algorithm proposed in recent years. Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.展开更多
After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon ...After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon intensity index prediction is studied in this paper by using nonlinear genetic neural network ensemble prediction（GNNEP）modeling.It differs from traditional prediction modeling in the following aspects： （1）Input factors of the GNNEP model of monsoon index were selected from a large quantity of preceding period high correlation factors,such as monthly sea temperature fields,monthly 500-hPa air temperature fields,monthly 200-hPa geopotential height fields,etc.,and they were also highly information-condensed and system dimensionality-reduced by using the empirical orthogonal function（EOF）method,which effectively condensed the useful information of predictors and therefore controlled the size of network structure of the GNNEP model.（2）In the input design of the GNNEP model,a mean generating function（MGF）series of predictand（monsoon index）was added as an input factor;the contrast analysis of results of predic- tion experiments by a physical variable predictor-predictand MGF GNNEP model and a physical variable predictor GNNEP model shows that the incorporation of the periodical variation of predictand（monsoon index）is very effective in improving the prediction of monsoon index.（3）Different from the traditional neural network modeling,the GNNEP modeling is able to objectively determine the network structure of the GNNNEP model,and the model constructed has a better generalization capability.In the case of identical predictors,prediction modeling samples,and independent prediction samples,the prediction accuracy of our GNNEP model combined with the system dimensionality reduction technique of predictors is clearly higher than that of the traditional stepwise regression model using the traditional treatment technique of predictors,suggesting that the GNNEP model opens up a vast range of possibilities for operational weather prediction.展开更多
In this paper,we undertake further investigation to alleviate the issue of limit cycling behavior in training generative adversarial networks(GANs)through the proposed predictive centripetal acceleration algorithm(PCA...In this paper,we undertake further investigation to alleviate the issue of limit cycling behavior in training generative adversarial networks(GANs)through the proposed predictive centripetal acceleration algorithm(PCAA).Specifically,we first derive the upper and lower complexity bounds of PCAA for a general bilinear game,with the last-iterate convergence rate notably improving upon previous results.Then,we combine PCAA with the adaptive moment estimation algorithm(Adam)to propose PCAA-Adam,for practical training of GANs to enhance their generalization capability.Finally,we validate the effectiveness of the proposed algorithm through experiments conducted on bilinear games,multivariate Gaussian distributions,and the CelebA dataset,respectively.展开更多
The system time of the four major navigation satellite systems is mainly maintained by multiple high-performance atomic clocks at ground stations.This operational mode relies heavily on the support of ground stations....The system time of the four major navigation satellite systems is mainly maintained by multiple high-performance atomic clocks at ground stations.This operational mode relies heavily on the support of ground stations.To enhance the high-precision autonomous timing capability of next-generation navigation satellites,it is necessary to autonomously generate a comprehensive space-based time scale on orbit and make long-term,high-precision predictions for the clock error of this time scale.In order to solve these two problems,this paper proposed a two-level satellite timing system,and used multiple time-keeping node satellites to generate a more stable space-based time scale.Then this paper used the sparse sampling Long Short-Term Memory(LSTM)algorithm to improve the accuracy of clock error long-term prediction on space-based time scale.After simulation,at sampling times of 300 s,8.64×10^(4) s,and 1×10^(6) s,the frequency stabilities of the spaceborne timescale reach 1.35×10^(-15),3.37×10^(-16),and 2.81×10^(-16),respectively.When applying the improved clock error prediction algorithm,the ten-day prediction error is 3.16×10^(-10) s.Compared with those of the continuous sampling LSTM,Kalman filter,polynomial and quadratic polynomial models,the corresponding prediction accuracies are 1.72,1.56,1.83 and 1.36 times greater,respectively.展开更多
This article describes in detail a new method via the extension predictable algorithm of the matter-element model of parallel structure tuning the parameters of the extension PID controller. In comparison with fuzzy a...This article describes in detail a new method via the extension predictable algorithm of the matter-element model of parallel structure tuning the parameters of the extension PID controller. In comparison with fuzzy and extension PID controllers, the proposed extension PID predictable controller shows higher control gains when system states are away from equilibrium, and retains a lower profile of control signals at the same time. Consequently, better control performance is achieved. Through the proposed tuning formula, the weighting factors of an extension-logic predictable controller can be systematically selected according to the control plant. An experimental example through industrial field data and site engineers＇ experience demonstrates the superior performance of the proposed controller over the fuzzy controller.展开更多
基金supported in part by the Intramural Research Program of the National Institute on Agingsupported by the National Cancer Institute(K01 CA234317)+1 种基金the San Diego State University/UC San Diego Comprehensive Cancer Center Partnership(U54 CA132384 and U54 CA132379)the Alzheimer's Disease Resource Center for Minority Aging Research at the University of California San Diego(P30 AG059299)。
文摘Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older adults.Since direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction algorithms.Methods:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)values.We developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging cohorts.Finally,we quantified the associations between measured and predicted VO_(2max)and mortality.Results:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/min.Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,respectively.Incremental quartiles of measured VO_(2max)showed an inverse gradient in mortality risk.Predicted VO_(2max)variables yielded similar effect estimates but were not robust to adjustment.Conclusion:Measured VO_(2max)is a strong predictor of mortality.Using ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to adjustment.Future studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.
基金This work is supposed by the Science and Technology Projects of China Southern Power Grid(YNKJXM20222402).
文摘Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice,which is of immense importance in mobilizing the entire society to reduce carbon emissions.The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid.Therefore,it cannot provide carbon factor information beforehand.To address this issue,a prediction model based on the graph attention network is proposed.The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised network using the loads of the grid nodes and the corresponding carbon factor data.The network extracts features and transmits information more suitable for the power system and can flexibly adjust the equivalent topology,thereby increasing the diversity of the structure.Its input and output data are simple,without the power grid parameters.We demonstrated its effect by testing IEEE-39 bus and IEEE-118 bus systems with average error rates of 2.46%and 2.51%.
基金The National Natural Science Foundations of China (30870131)the National Key Projects in the Infectious Fields (2008ZX10002-011, 2008ZX10004-004)
文摘In recent years,the in silico epitopes prediction tools have facilitated the progress of vaccines development significantly and many have been applied to predict epitopes in viruses successfully. Herein,a general overview of different tools currently available,including T cell and B cell epitopes prediction tools,is presented. And the principles of different prediction algorithms are reviewed briefly. Finally,several examples are present to illustrate the application of the prediction tools.
基金supported by the National Basic Research Program of China (973 Program) (2010CB731800)the National Natural Science Foundation of China (60934003+2 种基金 60974123 60804010)the Hebei Provincial Educational Foundation of China (2008147)
文摘Remote tracking for mobile targets is one of the most important applications in wireless sensor networks （WSNs）. A target tracking protoco–exponential distributed predictive tracking （EDPT） is proposed. To reduce energy waste and response time, an improved predictive algorithm–exponential smoothing predictive algorithm （ESPA） is presented. With the aid of an additive proportion and differential （PD） controller, ESPA decreases the system predictive delay effectively. As a recovery mechanism, an optimal searching radius （OSR） algorithm is applied to calculate the optimal radius of the recovery zone. The simulation results validate that the proposed EDPT protocol performes better in terms of track failed ratio, energy waste ratio and enlarged sensing nodes ratio, respectively.
文摘A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The principle of RPE learning algorithm is to adjust weights along the direction of Gauss-Newton. Meanwhile, it is unnecessary to calculate the second local derivative and the inverse matrixes, whose unbiasedness is proved. With application to the extremely short time prediction of large ship pitch, satisfactory results are obtained. Prediction effect of this algorithm is compared with that of auto-regression and periodical diagram method, and comparison results show that the proposed algorithm is feasible.
基金the National Natural Science Foundation of China(Nos.60832009,60872017 and 60772100)
文摘To coordinate the various access technologies in the 4G communication system,intelligent vertical handoff algorithms are required.This paper mainly deals with a novel vertical handoff decision algorithm based on fuzzy logic with the aid of grey theory and dynamic weights adaptation.The grey prediction theory(GPT) takes 4 sampled received signal strengths as input parameters,and calculates the predicted received signal strength in order to reduce the call dropping probability.The fuzzy logic theory based quantitative decision algorithm takes 3 quality of service(QoS)metric,received signal strength(RSS),available bandwidth(BW),and monetary cost (MC)of candidate networks as input parameters.The weight of each QoS metrics is adjusted along with the networks changing to trace the network condition.The final optimized vertical handoff decision is made by comparing the quantitative decision values of the candidate networks.Simulation results demonstrate that the proposed algorithm provides high performance in heterogeneous as well as homogeneous network environments.
文摘In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.
基金the support of the Department of Research and Development of Sarcheshmeh copper plants for this research
文摘Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.
文摘The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to predict the date of failures for a fleet of vehicles in order to allow the maintenance department to efficiently deploy the proper resources;we further provide specific details regarding the origins of failures, and finally, give recommendations. This study used the Société de transport de Montréal (STM)’s historical bus failure data as well as weather data from Environment Canada. We thank Facebook’s Prophet, Simple Feed-forward, and Beats algorithms (Uber), we proposed a set of computer codes that allow us to identify the 20% of buses that are responsible for the 80% of failures by mean of the failure history. Then, we deepened our study on the unreliable equipments identified during the diffusion of our computer code This allowed us to propose probable predictions of the dates of future failures. To ensure the validity of the proposed algorithm, we carried out simulations with more than 250,000 data. The results obtained are similar to the predicted theoretical values.
基金supported by the National Natural Science Foundation of China under Grant Nos.T2293773,72371145,and 12288201the Special Funds for Taishan Scholars Project of Shandong Province,China under Grant No.tsqn202211004National Key Research and Development Program under Grant No.2022YFC3303000.
文摘With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing prediction,a significant component of the SCS,has also garnered widespread attention.According to the Chinese criminal law,actual sentencing data exhibits a saturated property due to statutory penalty ranges,but this mechanism has been ignored by most existing studies.Given this,the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural networks.Building on the saturated structure of our model,a more effective adaptive prediction algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the L1 loss together with the corresponding gradient update strategy,a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge,the choice of appropriate initial conditions for the learning algorithm and the construction of a double-hidden-layer network structure.An empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods.
文摘The worldwide research status of head tracking is introduced and the works made in the research of the predictive algorithm and in the exploration of the rule of the head tracking are set forth. A time delay model for the telerobotic scout system is built. In respect of eliminating error caused by time delay and making reasonable prediction to the data stream, many methods are experimented in order to realize the aim of real time tracking. The application of extrapolation algorithm and auto recursive algorithm in the orientation tracking is described in detail. These two algorithms are realized in Matlab environment. Through analysis of the curves generated by using these two predictive algorithms, an appropriate method was applied in the telerobotic scout system. The effect is satisfying.
文摘A new technique for predicting species' geographic distribution is described.The approach involves 3 steps:①setting up geographic base data;②collecting and georeferencing distributional points;③modeling ecological niches using the biodiversity species workshop implementation of the genetic algorithm for rule set prediction (GARP).To illustrate these procedures,an example based on the Brown Eared Pheasant (Crossoptilon mantchuricum) is developed.This technique constitutes a useful tool for assessing geographic distribution for questions of ecology,biogeography,systematics,and conservation biology.
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 project is supported by Foundation of Public Laboratory on Robotics of Chinese Academy of Sciences.
文摘The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme.
文摘A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference image scheme was used to update the reference image and to decrease the computation time when the displacement was larger than a certain number.In this way,the search range and computational complexity were cut down,and less EMS memory was occupied.The capability of proposed search algorithm was then verified by the results of both computer simulation and experiments.The results showed that the algorithm could improve the efficiency of correlation method and satisfy the accuracy requirement for practical displacement measuring.
基金supported by the Natural Science Foundation of Jiangsu Province(BK2012510,BK20140074)the National Postdoctoral Foundation of China(20090461424)
文摘The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear prediction residual of speech is modeled as Student-t distribution, and the additive noise is incorporated explicitly to increase the robustness, thus a probabilistic model for sparse linear prediction of speech is built, Furthermore, variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters, and then the optimal linear prediction parameters are estimated robustly. The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l1 norm minimization based sparse linear prediction algorithm proposed in recent years. Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.
基金the New Technology Extension Project of China Meteorological Administration under Grant No.GMATG2008M49the National Natural Science Foundation of China under Grant No.40675023
文摘After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon intensity index prediction is studied in this paper by using nonlinear genetic neural network ensemble prediction（GNNEP）modeling.It differs from traditional prediction modeling in the following aspects： （1）Input factors of the GNNEP model of monsoon index were selected from a large quantity of preceding period high correlation factors,such as monthly sea temperature fields,monthly 500-hPa air temperature fields,monthly 200-hPa geopotential height fields,etc.,and they were also highly information-condensed and system dimensionality-reduced by using the empirical orthogonal function（EOF）method,which effectively condensed the useful information of predictors and therefore controlled the size of network structure of the GNNEP model.（2）In the input design of the GNNEP model,a mean generating function（MGF）series of predictand（monsoon index）was added as an input factor;the contrast analysis of results of predic- tion experiments by a physical variable predictor-predictand MGF GNNEP model and a physical variable predictor GNNEP model shows that the incorporation of the periodical variation of predictand（monsoon index）is very effective in improving the prediction of monsoon index.（3）Different from the traditional neural network modeling,the GNNEP modeling is able to objectively determine the network structure of the GNNNEP model,and the model constructed has a better generalization capability.In the case of identical predictors,prediction modeling samples,and independent prediction samples,the prediction accuracy of our GNNEP model combined with the system dimensionality reduction technique of predictors is clearly higher than that of the traditional stepwise regression model using the traditional treatment technique of predictors,suggesting that the GNNEP model opens up a vast range of possibilities for operational weather prediction.
基金supported by the Major Program of National Natural Science Foundation of China(Grant Nos.11991020 and 11991024)the Team Project of Innovation Leading Talent in Chongqing(Grant No.CQYC20210309536)+1 种基金NSFC-RGC(Hong Kong)Joint Research Program(Grant No.12261160365)the Scientific and Technological Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202300528)。
文摘In this paper,we undertake further investigation to alleviate the issue of limit cycling behavior in training generative adversarial networks(GANs)through the proposed predictive centripetal acceleration algorithm(PCAA).Specifically,we first derive the upper and lower complexity bounds of PCAA for a general bilinear game,with the last-iterate convergence rate notably improving upon previous results.Then,we combine PCAA with the adaptive moment estimation algorithm(Adam)to propose PCAA-Adam,for practical training of GANs to enhance their generalization capability.Finally,we validate the effectiveness of the proposed algorithm through experiments conducted on bilinear games,multivariate Gaussian distributions,and the CelebA dataset,respectively.
基金funded by the National Natural Science Foundation of China Youth Project (Grant No.12104485)funding support from the basic research project of Microsatellite Innovation Institute,Chinese Academy of Sciences with grant number JCZX2022B-03.
文摘The system time of the four major navigation satellite systems is mainly maintained by multiple high-performance atomic clocks at ground stations.This operational mode relies heavily on the support of ground stations.To enhance the high-precision autonomous timing capability of next-generation navigation satellites,it is necessary to autonomously generate a comprehensive space-based time scale on orbit and make long-term,high-precision predictions for the clock error of this time scale.In order to solve these two problems,this paper proposed a two-level satellite timing system,and used multiple time-keeping node satellites to generate a more stable space-based time scale.Then this paper used the sparse sampling Long Short-Term Memory(LSTM)algorithm to improve the accuracy of clock error long-term prediction on space-based time scale.After simulation,at sampling times of 300 s,8.64×10^(4) s,and 1×10^(6) s,the frequency stabilities of the spaceborne timescale reach 1.35×10^(-15),3.37×10^(-16),and 2.81×10^(-16),respectively.When applying the improved clock error prediction algorithm,the ten-day prediction error is 3.16×10^(-10) s.Compared with those of the continuous sampling LSTM,Kalman filter,polynomial and quadratic polynomial models,the corresponding prediction accuracies are 1.72,1.56,1.83 and 1.36 times greater,respectively.
基金This work is supported by Youth Foundation of University of Science and Techonology of China(No.KA0001).
文摘This article describes in detail a new method via the extension predictable algorithm of the matter-element model of parallel structure tuning the parameters of the extension PID controller. In comparison with fuzzy and extension PID controllers, the proposed extension PID predictable controller shows higher control gains when system states are away from equilibrium, and retains a lower profile of control signals at the same time. Consequently, better control performance is achieved. Through the proposed tuning formula, the weighting factors of an extension-logic predictable controller can be systematically selected according to the control plant. An experimental example through industrial field data and site engineers＇ experience demonstrates the superior performance of the proposed controller over the fuzzy controller.
