Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to t...In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.展开更多
Model-free,data-driven prediction of chaotic motions is a long-standing challenge in nonlinear science.Stimulated by the recent progress in machine learning,considerable attention has been given to the inference of ch...Model-free,data-driven prediction of chaotic motions is a long-standing challenge in nonlinear science.Stimulated by the recent progress in machine learning,considerable attention has been given to the inference of chaos by the technique of reservoir computing(RC).In particular,by incorporating a parameter-control channel into the standard RC,it is demonstrated that the machine is able to not only replicate the dynamics of the training states,but also infer new dynamics not included in the training set.The new machine-learning scheme,termed parameter-aware RC,opens up new avenues for data-based analysis of chaotic systems,and holds promise for predicting and controlling many real-world complex systems.Here,using typical chaotic systems as examples,we give a comprehensive introduction to this powerful machine-learning technique,including the algorithm,the implementation,the performance,and the open questions calling for further studies.展开更多
Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditiona...Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.展开更多
BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To ...BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.展开更多
Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the cor...BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocar...Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocardiographic data,traditional Chinese medicine(TCM)tongue manifestations,and facial features were collected from patients who underwent coro-nary computed tomography angiography(CTA)in the Cardiac Care Unit(CCU)of Shanghai Tenth People's Hospital between May 1,2023 and May 1,2024.An adaptive weighted multi-modal data fusion(AWMDF)model based on deep learning was constructed to predict the severity of coronary artery stenosis.The model was evaluated using metrics including accura-cy,precision,recall,F1 score,and the area under the receiver operating characteristic(ROC)curve(AUC).Further performance assessment was conducted through comparisons with six ensemble machine learning methods,data ablation,model component ablation,and various decision-level fusion strategies.Results A total of 158 patients were included in the study.The AWMDF model achieved ex-cellent predictive performance(AUC=0.973,accuracy=0.937,precision=0.937,recall=0.929,and F1 score=0.933).Compared with model ablation,data ablation experiments,and various traditional machine learning models,the AWMDF model demonstrated superior per-formance.Moreover,the adaptive weighting strategy outperformed alternative approaches,including simple weighting,averaging,voting,and fixed-weight schemes.Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.展开更多
This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect ...While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect leads and other trails behind, they coexist in life, as is often the case. The trailing aspect cannot remain far behind because, without application and use, there would be a dead end. Everything, whether an object, software, or tool, must have a practical use for humans. Without this, it will become obsolete. We can see this in many instances, such as blockchain technology, which is superior yet faces challenges in practical implementation, leading to a decline in adoption. This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application. There are multiple building blocks that make predictive maintenance a practical application. Each block performs a function leading to an output. This output forms an input to the receiving block. There are also foundational parts for all these building blocks to perform a function. Eventually, once the building blocks are connected, they form a loop and start to lead the path to predictive maintenance. Predictive maintenance is indeed practically achievable, but one must comprehend all the building blocks necessary for its implementation. Although detailed explanations will be provided in the upcoming sections, it is important to understand that simply purchasing software and plugging it in might be a far-fetched approach.展开更多
The predictive model and design of heavy-duty metal rubber shock absorber for the powertrains of heavy-load mining vehicles were investigated.The microstructural characteristics of the wire mesh were elucidated using ...The predictive model and design of heavy-duty metal rubber shock absorber for the powertrains of heavy-load mining vehicles were investigated.The microstructural characteristics of the wire mesh were elucidated using fractal graphs.A numerical model based on virtual fabrication technique was established to propose a design scheme for the wire mesh component.Four sets of wire mesh shock absorbers with various relative densities were prepared and a predictive model based on these relative densities was established through mechanical testing.To further enhance the predictive accuracy,a variable transposition fitting method was proposed to refine the model.Residual analysis was employed to quantitatively validate the results against those obtained from an experimental control group.The results show that the improved model exhibits higher predictive accuracy than the original model,with the determination coefficient(R^(2))of 0.9624.This study provides theoretical support for designing wire mesh shock absorbers with reduced testing requirements and enhanced design efficiency.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adaptin...Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions.This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations,such as transparency,fairness,and explainability,in artificial intelligence driven decision-making.The framework employs an Autoencoder for feature reduction,a Convolutional Neural Network for pattern recognition,and a Long Short-Term Memory network for temporal analysis.To enhance transparency,the decision-making process of the framework is made interpretable,allowing stakeholders to understand and trust the model’s predictions.Additionally,Particle Swarm Optimization is used to refine hyperparameters for optimal performance and mitigate potential biases in the model.Experiments are conducted on multiple datasets from different industrial scenarios,with performance validated using accuracy,precision,recall,F1-score,and training time metrics.The results demonstrate an impressive accuracy of up to 99.92%and 99.45%across different datasets,highlighting the framework’s effectiveness in enhancing predictive maintenance strategies.Furthermore,the model’s explainability ensures that the decisions can be audited for fairness and accountability,aligning with ethical standards for critical systems.By addressing transparency and reducing potential biases,this framework contributes to the responsible and trustworthy deployment of artificial intelligence in industrial environments,particularly in safety-critical applications.The results underscore its potential for wide application across various industrial contexts,enhancing both performance and ethical decision-making.展开更多
The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain deg...The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.展开更多
Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorit...Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade.A comprehensive preprocessing pipeline,including feature selection,data transformation,and class balancing,was implemented to ensure data quality and enhance model performance.Exploratory analysis revealed key patterns,such as the influence of age and the number of diagnoses on readmission rates,guiding the development of predictive models.Rigorous validation strategies,including 5-fold cross-validation and hyperparameter tuning,were employed to ensure model reliability and generalizability.Among the models tested,the RandomForest algorithmdemonstrated superior performance,achieving 96% accuracy for predicting readmissions and 87% for LOS prediction.These results underscore the potential of predictive analytics in diabetic patient care,enabling proactive interventions,better resource allocation,and improved clinical outcomes.展开更多
The introduction of PD-1 blockades to chemotherapy and radiotherapy has shown promising outcomes in patients with nasopharyngeal carcinoma, but anti-PD-1 therapies are only effective in a small proportion of patients,...The introduction of PD-1 blockades to chemotherapy and radiotherapy has shown promising outcomes in patients with nasopharyngeal carcinoma, but anti-PD-1 therapies are only effective in a small proportion of patients, indicating the need for reliable predictive biomarkers of benefit from immunotherapy. Here, we summarized recent advances in immunotherapy for nasopharyngeal carcinoma and studies on potential predictors that correlated with treatment response or long-term survival after immunotherapy, including biomarkers in both the tumor microenvironment and the tumor macroenvironment. Some of these biomarkers have been validated as truly predictive of immunotherapy benefit using cohorts from randomized controlled trials, while others still require further validation of their predictive value. We also summarized the challenges and future directions of biomarker studies, hopefully facilitating the development of predictive biomarkers for immunotherapy that can eventually enter clinical practice.展开更多
As a new research direction in contemporary cognitive science,predictive processing surpasses traditional computational representation and embodied cognition and has emerged as a new paradigm in cognitive science rese...As a new research direction in contemporary cognitive science,predictive processing surpasses traditional computational representation and embodied cognition and has emerged as a new paradigm in cognitive science research.The predictive processing theory advocates that the brain is a hierarchical predictive model based on Bayesian inference,and its purpose is to minimize the difference between the predicted world and the actual world,so as to minimize the prediction error.Predictive processing is therefore essentially a context-dependent model representation,an adaptive representational system designed to achieve its cognitive goals through the minimization of prediction error.展开更多
Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands...Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
基金supported in part by the National Natural Science Foundation of China(62403396,62433018,62373113)the Guangdong Basic and Applied Basic Research Foundation(2023A1515011527,2023B1515120010)the Postdoctoral Fellowship Program of CPSF(GZB20240621)
文摘In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.
基金Project supported by the National Natural Science Foundation of China(Grant No.12275165)XGW was also supported by the Fundamental Research Funds for the Central Universities(Grant No.GK202202003).
文摘Model-free,data-driven prediction of chaotic motions is a long-standing challenge in nonlinear science.Stimulated by the recent progress in machine learning,considerable attention has been given to the inference of chaos by the technique of reservoir computing(RC).In particular,by incorporating a parameter-control channel into the standard RC,it is demonstrated that the machine is able to not only replicate the dynamics of the training states,but also infer new dynamics not included in the training set.The new machine-learning scheme,termed parameter-aware RC,opens up new avenues for data-based analysis of chaotic systems,and holds promise for predicting and controlling many real-world complex systems.Here,using typical chaotic systems as examples,we give a comprehensive introduction to this powerful machine-learning technique,including the algorithm,the implementation,the performance,and the open questions calling for further studies.
基金supported in part by the Natural Science Foundation of China under Grant Nos.U2468201 and 62221001ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240420002。
文摘Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2503600。
文摘BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.
基金supported in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
文摘BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
基金Construction Program of the Key Discipline of State Administration of Traditional Chinese Medicine of China(ZYYZDXK-2023069)Research Project of Shanghai Municipal Health Commission (2024QN018)Shanghai University of Traditional Chinese Medicine Science and Technology Development Program (23KFL005)。
文摘Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocardiographic data,traditional Chinese medicine(TCM)tongue manifestations,and facial features were collected from patients who underwent coro-nary computed tomography angiography(CTA)in the Cardiac Care Unit(CCU)of Shanghai Tenth People's Hospital between May 1,2023 and May 1,2024.An adaptive weighted multi-modal data fusion(AWMDF)model based on deep learning was constructed to predict the severity of coronary artery stenosis.The model was evaluated using metrics including accura-cy,precision,recall,F1 score,and the area under the receiver operating characteristic(ROC)curve(AUC).Further performance assessment was conducted through comparisons with six ensemble machine learning methods,data ablation,model component ablation,and various decision-level fusion strategies.Results A total of 158 patients were included in the study.The AWMDF model achieved ex-cellent predictive performance(AUC=0.973,accuracy=0.937,precision=0.937,recall=0.929,and F1 score=0.933).Compared with model ablation,data ablation experiments,and various traditional machine learning models,the AWMDF model demonstrated superior per-formance.Moreover,the adaptive weighting strategy outperformed alternative approaches,including simple weighting,averaging,voting,and fixed-weight schemes.Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
文摘While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect leads and other trails behind, they coexist in life, as is often the case. The trailing aspect cannot remain far behind because, without application and use, there would be a dead end. Everything, whether an object, software, or tool, must have a practical use for humans. Without this, it will become obsolete. We can see this in many instances, such as blockchain technology, which is superior yet faces challenges in practical implementation, leading to a decline in adoption. This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application. There are multiple building blocks that make predictive maintenance a practical application. Each block performs a function leading to an output. This output forms an input to the receiving block. There are also foundational parts for all these building blocks to perform a function. Eventually, once the building blocks are connected, they form a loop and start to lead the path to predictive maintenance. Predictive maintenance is indeed practically achievable, but one must comprehend all the building blocks necessary for its implementation. Although detailed explanations will be provided in the upcoming sections, it is important to understand that simply purchasing software and plugging it in might be a far-fetched approach.
基金National Natural Science Foundation of China(12262028)Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(NJYT22085)Inner Mongolia Autonomous Region Science and Technology Plan Project(2021GG0437)。
文摘The predictive model and design of heavy-duty metal rubber shock absorber for the powertrains of heavy-load mining vehicles were investigated.The microstructural characteristics of the wire mesh were elucidated using fractal graphs.A numerical model based on virtual fabrication technique was established to propose a design scheme for the wire mesh component.Four sets of wire mesh shock absorbers with various relative densities were prepared and a predictive model based on these relative densities was established through mechanical testing.To further enhance the predictive accuracy,a variable transposition fitting method was proposed to refine the model.Residual analysis was employed to quantitatively validate the results against those obtained from an experimental control group.The results show that the improved model exhibits higher predictive accuracy than the original model,with the determination coefficient(R^(2))of 0.9624.This study provides theoretical support for designing wire mesh shock absorbers with reduced testing requirements and enhanced design efficiency.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
文摘Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions.This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations,such as transparency,fairness,and explainability,in artificial intelligence driven decision-making.The framework employs an Autoencoder for feature reduction,a Convolutional Neural Network for pattern recognition,and a Long Short-Term Memory network for temporal analysis.To enhance transparency,the decision-making process of the framework is made interpretable,allowing stakeholders to understand and trust the model’s predictions.Additionally,Particle Swarm Optimization is used to refine hyperparameters for optimal performance and mitigate potential biases in the model.Experiments are conducted on multiple datasets from different industrial scenarios,with performance validated using accuracy,precision,recall,F1-score,and training time metrics.The results demonstrate an impressive accuracy of up to 99.92%and 99.45%across different datasets,highlighting the framework’s effectiveness in enhancing predictive maintenance strategies.Furthermore,the model’s explainability ensures that the decisions can be audited for fairness and accountability,aligning with ethical standards for critical systems.By addressing transparency and reducing potential biases,this framework contributes to the responsible and trustworthy deployment of artificial intelligence in industrial environments,particularly in safety-critical applications.The results underscore its potential for wide application across various industrial contexts,enhancing both performance and ethical decision-making.
基金Financial support was provided by the State Grid Sichuan Electric Power Company Science and Technology Project“Key Research on Development Path Planning and Key Operation Technologies of New Rural Electrification Construction”under Grant No.52199623000G.
文摘The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.
文摘Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade.A comprehensive preprocessing pipeline,including feature selection,data transformation,and class balancing,was implemented to ensure data quality and enhance model performance.Exploratory analysis revealed key patterns,such as the influence of age and the number of diagnoses on readmission rates,guiding the development of predictive models.Rigorous validation strategies,including 5-fold cross-validation and hyperparameter tuning,were employed to ensure model reliability and generalizability.Among the models tested,the RandomForest algorithmdemonstrated superior performance,achieving 96% accuracy for predicting readmissions and 87% for LOS prediction.These results underscore the potential of predictive analytics in diabetic patient care,enabling proactive interventions,better resource allocation,and improved clinical outcomes.
基金supported by grants from the National Natural Science Foundation of China(Nos.82430085 and 82172870)。
文摘The introduction of PD-1 blockades to chemotherapy and radiotherapy has shown promising outcomes in patients with nasopharyngeal carcinoma, but anti-PD-1 therapies are only effective in a small proportion of patients, indicating the need for reliable predictive biomarkers of benefit from immunotherapy. Here, we summarized recent advances in immunotherapy for nasopharyngeal carcinoma and studies on potential predictors that correlated with treatment response or long-term survival after immunotherapy, including biomarkers in both the tumor microenvironment and the tumor macroenvironment. Some of these biomarkers have been validated as truly predictive of immunotherapy benefit using cohorts from randomized controlled trials, while others still require further validation of their predictive value. We also summarized the challenges and future directions of biomarker studies, hopefully facilitating the development of predictive biomarkers for immunotherapy that can eventually enter clinical practice.
基金supported by the National Social Science Fund of China’s project‘Philosophical Research on the Challenge of Artificial Cognition to Natural Cognition’(grant number 21&ZD061)
文摘As a new research direction in contemporary cognitive science,predictive processing surpasses traditional computational representation and embodied cognition and has emerged as a new paradigm in cognitive science research.The predictive processing theory advocates that the brain is a hierarchical predictive model based on Bayesian inference,and its purpose is to minimize the difference between the predicted world and the actual world,so as to minimize the prediction error.Predictive processing is therefore essentially a context-dependent model representation,an adaptive representational system designed to achieve its cognitive goals through the minimization of prediction error.
文摘Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.
