BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intr...BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intraoperative strategies.AIM To evaluate the predictive performance of machine learning(ML)algorithms for DCI by comparing three modeling approaches,identify factors influencing DCI,and develop a preoperative prediction model using ML algorithms to enhance colonoscopy quality and efficiency.METHODS This cross-sectional study enrolled 712 patients who underwent colonoscopy at a tertiary hospital between June 2020 and May 2021.Demographic data,past medical history,medication use,and psychological status were collected.The endoscopist assessed DCI using the visual analogue scale.After univariate screening,predictive models were developed using multivariable logistic regression,least absolute shrinkage and selection operator(LASSO)regression,and random forest(RF)algorithms.Model performance was evaluated based on discrimination,calibration,and decision curve analysis(DCA),and results were visualized using nomograms.RESULTS A total of 712 patients(53.8%male;mean age 54.5 years±12.9 years)were included.Logistic regression analysis identified constipation[odds ratio(OR)=2.254,95%confidence interval(CI):1.289-3.931],abdominal circumference(AC)(77.5–91.9 cm,OR=1.895,95%CI:1.065-3.350;AC≥92 cm,OR=1.271,95%CI:0.730-2.188),and anxiety(OR=1.071,95%CI:1.044-1.100)as predictive factors for DCI,validated by LASSO and RF methods.Model performance revealed training/validation sensitivities of 0.826/0.925,0.924/0.868,and 1.000/0.981;specificities of 0.602/0.511,0.510/0.562,and 0.977/0.526;and corresponding area under the receiver operating characteristic curves(AUCs)of 0.780(0.737-0.823)/0.726(0.654-0.799),0.754(0.710-0.798)/0.723(0.656-0.791),and 1.000(1.000-1.000)/0.754(0.688-0.820),respectively.DCA indicated optimal net benefit within probability thresholds of 0-0.9 and 0.05-0.37.The RF model demonstrated superior diagnostic accuracy,reflected by perfect training sensitivity(1.000)and highest validation AUC(0.754),outperforming other methods in clinical applicability.CONCLUSION The RF-based model exhibited superior predictive accuracy for DCI compared to multivariable logistic and LASSO regression models.This approach supports individualized preoperative optimization,enhancing colonoscopy quality through targeted risk stratification.展开更多
A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence da...A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.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
In view of DC speed control system, this paper presents a predictive control algorithm to replace traditional PID control. System predictive model requires little information of the controlled object, and because it...In view of DC speed control system, this paper presents a predictive control algorithm to replace traditional PID control. System predictive model requires little information of the controlled object, and because it adopts rolling optimum method, system展开更多
In this paper, an adaptive line spectral pair filter is derived from an adaptive lattice filter. A least-mean-square(LMS) type adaptive algorithm used to calculate directly the line spectral pair(LSP) coefficients on ...In this paper, an adaptive line spectral pair filter is derived from an adaptive lattice filter. A least-mean-square(LMS) type adaptive algorithm used to calculate directly the line spectral pair(LSP) coefficients on a stage-by-stage basis is presented. Experimental results show that the algorithm has higher convergence rate and lower misadjustment as compared with the other algorithms. The LSP coefficients calculated by the algorithm have been used to carry out speech linear predictive synthesis, resulting in better results than PARCOR coefficients.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
A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle,but also effectively save fuel and reduce emissions.In this paper,the construction of model predictive control in hybrid el...A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle,but also effectively save fuel and reduce emissions.In this paper,the construction of model predictive control in hybrid electric vehicle is proposed.The solving process and the use of reference trajectory are discussed for the application of MPC based on dynamic programming algorithm.The simulation of hybrid electric vehicle is carried out under a specific working condition.The simulation results show that the control strategy can effectively reduce fuel consumption when the torque of engine and motor is reasonably distributed,and the effectiveness of the control strategy is verified.展开更多
Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing h...Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance.展开更多
Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV pred...Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV prediction by combining conventional empirical equations with physics-informed neural networks(PINN)and optimizing the model parameters via the Particle Swarm Optimization(PSO)algorithm.The proposed PSO-PINN framework was rigorously benchmarked against seven established machine learning approaches:Multilayer Perceptron(MLP),Extreme Gradient Boosting(XGBoost),Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting Decision Tree(GBDT),Adaptive Boosting(Adaboost),and Gene Expression Programming(GEP).Comparative analysis showed that PSO-PINN outperformed these models,achieving RMSE reductions of 17.82-37.63%,MSE reductions of 32.47-61.10%,AR improvements of 2.97-21.19%,and R^(2)enhancements of 7.43-29.21%,demonstrating superior accuracy and generalization.Furthermore,the study determines the impact of incorporating empirical formulas as physical constraints in neural networks and examines the effects of different empirical equations,particle swarm size,iteration count in PSO,regularization coefficient,and learning rate in PINN on model performance.Lastly,a predictive system for blast vibration PPV is designed and implemented.The research outcomes offer theoretical references and practical recommendations for blast vibration forecasting in similar engineering applications.展开更多
The integration of eco-driving and cooperative adaptive cruise control(CACC)with platoon cooperative control(eco-CACC)has emerged as a pivotal approach for improving vehicle energy efficiency.Nonetheless,the prevailin...The integration of eco-driving and cooperative adaptive cruise control(CACC)with platoon cooperative control(eco-CACC)has emerged as a pivotal approach for improving vehicle energy efficiency.Nonetheless,the prevailing eco-CACC implementations still exhibit limitations in fully harnessing the potential energy savings.This can be attributed to the intricate nature of the problem,characterized by its high nonlinearity and non-convexity,making it challenging for conventional solving methods to find solutions.In this paper,a novel strategy based on a decentralized model predictive control(MPC)framework,called predictive ecological cooperative control(PECC),is proposed for vehicle platoon control on hilly roads,aiming to maximize the overall energy efficiency of the platoon.Unlike most existing literature that focuses on suboptimal coordination under predefined leading vehicle trajectories,this strategy employs an approach based on the combination of a long short-term memory network(LSTM)and genetic algorithm(GA)optimization(GA-LSTM)to predict the future speed of the leading vehicle.Notably,a function named the NotchFilter function(NF(?))is introduced to transform the hard state constraints in the eco-CACC problem,thereby alleviating the burden of problem-solving.Finally,through simulation comparisons between PECC and a strategy based on the common eco-CACC modifications,the effectiveness of PECC in improving platoon energy efficiency is demonstrated.展开更多
Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software ...Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models.展开更多
Accurate prediction of solubility data in the Sodium Chloride-Sodium Sulfate-Water system is essential.It provides theoretical support for salt lake resource development and wastewater treatment technologies.This stud...Accurate prediction of solubility data in the Sodium Chloride-Sodium Sulfate-Water system is essential.It provides theoretical support for salt lake resource development and wastewater treatment technologies.This study proposes an innovative solubility prediction approach.It addresses the limitations of traditional thermodynamic models.This is particularly important when experimental data from various sources contain inconsistencies.Our approach combines the Weighted Local Outlier Factor technique for anomaly detection with a Deep Ensemble Neural Network architecture.This methodology effectively removes local outliers while preserving data distribution integrity,and integrates multiple neural network sub-models to comprehensively capture system features while minimizing individual model biases.Experimental validation demonstrates exceptional prediction performance across temperatures from−20℃to 150℃,achieving a coefficient of determination of 0.989 after Bayesian hyperparameter optimization.This data-driven approach provides more accurate and universally applicable solubility predictions than conventional thermodynamic models,offering theoretical guidance for industrial applications in salt lake resource utilization,separation process optimization,and environmental salt management systems.展开更多
According to these characteristics of the movement of the special platform servo,a new improved grey predictive PID control algorithm was proposed based on the grey predictive PID,and then the algorithm was simulated ...According to these characteristics of the movement of the special platform servo,a new improved grey predictive PID control algorithm was proposed based on the grey predictive PID,and then the algorithm was simulated by MATLAB.As a result that it can improve the response speed and stability of the system,and meet the demand of the system.展开更多
The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent ...The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.展开更多
The aim of this research paper is to improve the performance of Fast Transversal Filter (FTF) adaptive algorithm used for mobile channel estimation. A multi-ray Jakes mobile channel model with a Doppler frequency shif...The aim of this research paper is to improve the performance of Fast Transversal Filter (FTF) adaptive algorithm used for mobile channel estimation. A multi-ray Jakes mobile channel model with a Doppler frequency shift is used in the simulation. The channel estimator obtains the sampled channel impulse response (SIR) from the predetermined training sequence. The FTF is a computationally efficient implementation of the recursive least squares (RLS) algorithm of the conventional Kalman filter. A stabilization FTF is used to overcome the problem caused by the accumulation of roundoff errors, and, in addition, degree-one prediction is incorporated into the algorithm (Predictive FTF) to improve the estimation performance and to track changes of the mobile channel. The efficiency of the algorithm is confirmed by simulation results for slow and fast varying mobile channel. The results show about 5 to 15 dB improvement in the Mean Square Error (Deviation) between the estimated taps and the actual ones depending on the speed of channel time variations. Slow and fast vehicular channels with Doppler frequencies 100 Hz and 222 Hz respectively are used in these tests. The predictive FTF (PFTF) algorithm give a better channel SIR estimation performance than the conventional FTF algorithm, and it involves only a small increase in complexity.展开更多
Urban rail transit has the advantages of large traffic capacity,high punctuality and zero congestion,and it plays an increasingly important role in modern urban life.Braking system is an important system of urban rail...Urban rail transit has the advantages of large traffic capacity,high punctuality and zero congestion,and it plays an increasingly important role in modern urban life.Braking system is an important system of urban rail train,which directly affects the performance and safety of train operation and impacts passenger comfort.The braking performance of urban rail trains is directly related to the improvement of train speed and transportation capacity.Also,urban rail transit has the characteristics of high speed,short station distance,frequent starting,and frequent braking.This makes the braking control system constitute a time-varying,time-delaying and nonlinear control system,especially the braking force changes directly disturb the parking accuracy and comfort.To solve these issues,a predictive control algorithm based on T-S fuzzy model was proposed and applied to the train braking control system.Compared with the traditional PID control algorithm and self-adaptive fuzzy PID control algorithm,the braking capacity of urban rail train was improved by 8%.The algorithm can achieve fast and accurate synchronous braking,thereby overcoming the dynamic influence of the uncertainty,hysteresis and time-varying factors of the controlled object.Finally,the desired control objectives can be achieved,the system will have superior robustness,stability and comfort.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
In predictive direct power control(PDPC)system of three-phase pulse width modulation(PWM)rectifier,grid voltage sensor makes the whole system more complex and costly.Therefore,third-order generalized integrator(TOGI)i...In predictive direct power control(PDPC)system of three-phase pulse width modulation(PWM)rectifier,grid voltage sensor makes the whole system more complex and costly.Therefore,third-order generalized integrator(TOGI)is used to generate orthogonal signals with the same frequency to estimate the grid voltage.In addition,in view of the deviation between actual and reference power in the three-phase PWM rectifier traditional PDPC strategy,a power correction link is designed to correct the power reference value.The grid voltage sensor free algorithm based on TOGI and the corrected PDPC strategy are applied to three-phase PWM rectifier and simulated on the simulation platform.Simulation results show that the proposed method can effectively eliminate the power tracking deviation and the grid voltage.The effectiveness of the proposed method is verified by comparing the simulation results.展开更多
This paper studied a fast recursive predictive algorithm used for medical X-ray image compression. This algorithm consists of mathematics model building, fast recursive algorithm deducing, initial value determining, s...This paper studied a fast recursive predictive algorithm used for medical X-ray image compression. This algorithm consists of mathematics model building, fast recursive algorithm deducing, initial value determining, step-size selecting, image compression encoding and original image recovering. The experiment result indicates that this algorithm has not only a higher compression ratio to medical X-ray images compression, but also promotes image compression speed greatly.展开更多
基金the Chinese Clinical Trial Registry(No.ChiCTR2000040109)approved by the Hospital Ethics Committee(No.20210130017).
文摘BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intraoperative strategies.AIM To evaluate the predictive performance of machine learning(ML)algorithms for DCI by comparing three modeling approaches,identify factors influencing DCI,and develop a preoperative prediction model using ML algorithms to enhance colonoscopy quality and efficiency.METHODS This cross-sectional study enrolled 712 patients who underwent colonoscopy at a tertiary hospital between June 2020 and May 2021.Demographic data,past medical history,medication use,and psychological status were collected.The endoscopist assessed DCI using the visual analogue scale.After univariate screening,predictive models were developed using multivariable logistic regression,least absolute shrinkage and selection operator(LASSO)regression,and random forest(RF)algorithms.Model performance was evaluated based on discrimination,calibration,and decision curve analysis(DCA),and results were visualized using nomograms.RESULTS A total of 712 patients(53.8%male;mean age 54.5 years±12.9 years)were included.Logistic regression analysis identified constipation[odds ratio(OR)=2.254,95%confidence interval(CI):1.289-3.931],abdominal circumference(AC)(77.5–91.9 cm,OR=1.895,95%CI:1.065-3.350;AC≥92 cm,OR=1.271,95%CI:0.730-2.188),and anxiety(OR=1.071,95%CI:1.044-1.100)as predictive factors for DCI,validated by LASSO and RF methods.Model performance revealed training/validation sensitivities of 0.826/0.925,0.924/0.868,and 1.000/0.981;specificities of 0.602/0.511,0.510/0.562,and 0.977/0.526;and corresponding area under the receiver operating characteristic curves(AUCs)of 0.780(0.737-0.823)/0.726(0.654-0.799),0.754(0.710-0.798)/0.723(0.656-0.791),and 1.000(1.000-1.000)/0.754(0.688-0.820),respectively.DCA indicated optimal net benefit within probability thresholds of 0-0.9 and 0.05-0.37.The RF model demonstrated superior diagnostic accuracy,reflected by perfect training sensitivity(1.000)and highest validation AUC(0.754),outperforming other methods in clinical applicability.CONCLUSION The RF-based model exhibited superior predictive accuracy for DCI compared to multivariable logistic and LASSO regression models.This approach supports individualized preoperative optimization,enhancing colonoscopy quality through targeted risk stratification.
文摘A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.
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In view of DC speed control system, this paper presents a predictive control algorithm to replace traditional PID control. System predictive model requires little information of the controlled object, and because it adopts rolling optimum method, system
文摘In this paper, an adaptive line spectral pair filter is derived from an adaptive lattice filter. A least-mean-square(LMS) type adaptive algorithm used to calculate directly the line spectral pair(LSP) coefficients on a stage-by-stage basis is presented. Experimental results show that the algorithm has higher convergence rate and lower misadjustment as compared with the other algorithms. The LSP coefficients calculated by the algorithm have been used to carry out speech linear predictive synthesis, resulting in better results than PARCOR coefficients.
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 work was supported by the youth backbone teachers training program of Henan colleges and universities under Grant No.2016ggjs-287the project of science and technology of Henan province under Grant Nos.172102210124,202102210269the Key Scientific Research projects in Colleges and Universities in Henan(Grant No.18B460003).
文摘A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle,but also effectively save fuel and reduce emissions.In this paper,the construction of model predictive control in hybrid electric vehicle is proposed.The solving process and the use of reference trajectory are discussed for the application of MPC based on dynamic programming algorithm.The simulation of hybrid electric vehicle is carried out under a specific working condition.The simulation results show that the control strategy can effectively reduce fuel consumption when the torque of engine and motor is reasonably distributed,and the effectiveness of the control strategy is verified.
基金supported by the Research Incentive Grant 23200 of Zayed University,United Arab Emirates.
文摘Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance.
基金supported by the National Natural Science Foundation of China(Grant No.52409143)the Basic Scientific Research Fund of Changjiang River Scientific Research Institute for Central-level Public Welfare Research Institutes(Grant No.CKSF2025184/YT)the Hubei Provincial Natural Science Foundation of China(Grant No.2022CFB673).
文摘Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV prediction by combining conventional empirical equations with physics-informed neural networks(PINN)and optimizing the model parameters via the Particle Swarm Optimization(PSO)algorithm.The proposed PSO-PINN framework was rigorously benchmarked against seven established machine learning approaches:Multilayer Perceptron(MLP),Extreme Gradient Boosting(XGBoost),Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting Decision Tree(GBDT),Adaptive Boosting(Adaboost),and Gene Expression Programming(GEP).Comparative analysis showed that PSO-PINN outperformed these models,achieving RMSE reductions of 17.82-37.63%,MSE reductions of 32.47-61.10%,AR improvements of 2.97-21.19%,and R^(2)enhancements of 7.43-29.21%,demonstrating superior accuracy and generalization.Furthermore,the study determines the impact of incorporating empirical formulas as physical constraints in neural networks and examines the effects of different empirical equations,particle swarm size,iteration count in PSO,regularization coefficient,and learning rate in PINN on model performance.Lastly,a predictive system for blast vibration PPV is designed and implemented.The research outcomes offer theoretical references and practical recommendations for blast vibration forecasting in similar engineering applications.
基金Supported by National Natural Science Foundation of China(Grant Nos.52172383,51805081)Jiangsu Provincial Postgraduate Research&Practice Innovation Program(Grant No.KYCX22_0196)。
文摘The integration of eco-driving and cooperative adaptive cruise control(CACC)with platoon cooperative control(eco-CACC)has emerged as a pivotal approach for improving vehicle energy efficiency.Nonetheless,the prevailing eco-CACC implementations still exhibit limitations in fully harnessing the potential energy savings.This can be attributed to the intricate nature of the problem,characterized by its high nonlinearity and non-convexity,making it challenging for conventional solving methods to find solutions.In this paper,a novel strategy based on a decentralized model predictive control(MPC)framework,called predictive ecological cooperative control(PECC),is proposed for vehicle platoon control on hilly roads,aiming to maximize the overall energy efficiency of the platoon.Unlike most existing literature that focuses on suboptimal coordination under predefined leading vehicle trajectories,this strategy employs an approach based on the combination of a long short-term memory network(LSTM)and genetic algorithm(GA)optimization(GA-LSTM)to predict the future speed of the leading vehicle.Notably,a function named the NotchFilter function(NF(?))is introduced to transform the hard state constraints in the eco-CACC problem,thereby alleviating the burden of problem-solving.Finally,through simulation comparisons between PECC and a strategy based on the common eco-CACC modifications,the effectiveness of PECC in improving platoon energy efficiency is demonstrated.
文摘Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models.
基金support of the Natural Science Foundation of Qinghai Province of China(2024-ZJ-940)Qinghai University Research Ability Enhancement Project(2025KTST02)are greatly appreciated.
文摘Accurate prediction of solubility data in the Sodium Chloride-Sodium Sulfate-Water system is essential.It provides theoretical support for salt lake resource development and wastewater treatment technologies.This study proposes an innovative solubility prediction approach.It addresses the limitations of traditional thermodynamic models.This is particularly important when experimental data from various sources contain inconsistencies.Our approach combines the Weighted Local Outlier Factor technique for anomaly detection with a Deep Ensemble Neural Network architecture.This methodology effectively removes local outliers while preserving data distribution integrity,and integrates multiple neural network sub-models to comprehensively capture system features while minimizing individual model biases.Experimental validation demonstrates exceptional prediction performance across temperatures from−20℃to 150℃,achieving a coefficient of determination of 0.989 after Bayesian hyperparameter optimization.This data-driven approach provides more accurate and universally applicable solubility predictions than conventional thermodynamic models,offering theoretical guidance for industrial applications in salt lake resource utilization,separation process optimization,and environmental salt management systems.
基金supported by the Chongqing Scientific and Technological Innovating Program under grant CSTC2008AC1014
文摘According to these characteristics of the movement of the special platform servo,a new improved grey predictive PID control algorithm was proposed based on the grey predictive PID,and then the algorithm was simulated by MATLAB.As a result that it can improve the response speed and stability of the system,and meet the demand of the system.
基金The research project,“Research on Power Safety Assisted Decision System Based on Large Language Models”(Project Number:JSDL24051414020001)acknowledges with gratitude the financial and logistical support it has received.
文摘The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.
文摘The aim of this research paper is to improve the performance of Fast Transversal Filter (FTF) adaptive algorithm used for mobile channel estimation. A multi-ray Jakes mobile channel model with a Doppler frequency shift is used in the simulation. The channel estimator obtains the sampled channel impulse response (SIR) from the predetermined training sequence. The FTF is a computationally efficient implementation of the recursive least squares (RLS) algorithm of the conventional Kalman filter. A stabilization FTF is used to overcome the problem caused by the accumulation of roundoff errors, and, in addition, degree-one prediction is incorporated into the algorithm (Predictive FTF) to improve the estimation performance and to track changes of the mobile channel. The efficiency of the algorithm is confirmed by simulation results for slow and fast varying mobile channel. The results show about 5 to 15 dB improvement in the Mean Square Error (Deviation) between the estimated taps and the actual ones depending on the speed of channel time variations. Slow and fast vehicular channels with Doppler frequencies 100 Hz and 222 Hz respectively are used in these tests. The predictive FTF (PFTF) algorithm give a better channel SIR estimation performance than the conventional FTF algorithm, and it involves only a small increase in complexity.
基金This work was supported by the Youth Backbone Teachers Training Program of Henan colleges and universities under Grant No.2016ggjs-287(W.X.K.,http://jyt.henan.gov.cn/)the Project of Science and Technology of Henan province under Grant Nos.172102210124 and 202102210269(W.X.K.,http://www.hnkjt.gov.cn/)the Key Scientific Research Projects in Colleges and Universities in Henan Grant No.18B460003(W.X.K.,http://jyt.henan.gov.cn/)
文摘Urban rail transit has the advantages of large traffic capacity,high punctuality and zero congestion,and it plays an increasingly important role in modern urban life.Braking system is an important system of urban rail train,which directly affects the performance and safety of train operation and impacts passenger comfort.The braking performance of urban rail trains is directly related to the improvement of train speed and transportation capacity.Also,urban rail transit has the characteristics of high speed,short station distance,frequent starting,and frequent braking.This makes the braking control system constitute a time-varying,time-delaying and nonlinear control system,especially the braking force changes directly disturb the parking accuracy and comfort.To solve these issues,a predictive control algorithm based on T-S fuzzy model was proposed and applied to the train braking control system.Compared with the traditional PID control algorithm and self-adaptive fuzzy PID control algorithm,the braking capacity of urban rail train was improved by 8%.The algorithm can achieve fast and accurate synchronous braking,thereby overcoming the dynamic influence of the uncertainty,hysteresis and time-varying factors of the controlled object.Finally,the desired control objectives can be achieved,the system will have superior robustness,stability and comfort.
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National Natural Science Foundation of China(Nos.51767013,52067013)。
文摘In predictive direct power control(PDPC)system of three-phase pulse width modulation(PWM)rectifier,grid voltage sensor makes the whole system more complex and costly.Therefore,third-order generalized integrator(TOGI)is used to generate orthogonal signals with the same frequency to estimate the grid voltage.In addition,in view of the deviation between actual and reference power in the three-phase PWM rectifier traditional PDPC strategy,a power correction link is designed to correct the power reference value.The grid voltage sensor free algorithm based on TOGI and the corrected PDPC strategy are applied to three-phase PWM rectifier and simulated on the simulation platform.Simulation results show that the proposed method can effectively eliminate the power tracking deviation and the grid voltage.The effectiveness of the proposed method is verified by comparing the simulation results.
文摘This paper studied a fast recursive predictive algorithm used for medical X-ray image compression. This algorithm consists of mathematics model building, fast recursive algorithm deducing, initial value determining, step-size selecting, image compression encoding and original image recovering. The experiment result indicates that this algorithm has not only a higher compression ratio to medical X-ray images compression, but also promotes image compression speed greatly.
