The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously ...The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously developed tools,including the norm-bounding technique for relaxing the disturbance-related constraint handling,the dynamic output feedback law,the notion of quadratic boundedness for specifying the closed-loop stability,and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility,are merged in the control design.Some previous approaches are shown to be the special cases.An example of continuous stirred tank reactor(CSTR) is given to show the effectiveness of the proposed approaches.展开更多
A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physic...A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.展开更多
The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(...The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(Kuala Lumpur)as the study area,the slope characteristics of geometrical parameters are obtained from a multidisciplinary approach(consisting of geological,geotechnical,and remote sensing analyses).18 factors,including rock strength,rock quality designation(RQD),joint spacing,continuity,openness,roughness,filling,weathering,water seepage,temperature,vegetation index,water index,and orientation,are selected to construct model input variables while the factor of safety(FOS)functions as an output.The area under the curve(AUC)value of the receiver operating characteristic(ROC)curve is obtained with precision and accuracy and used to analyse the predictive model ability.With a large training set and predicted parameters,an area under the ROC curve(the AUC)of 0.95 is achieved.A precision score of 0.88 is obtained,indicating that the model has a low false positive rate and correctly identifies a substantial number of true positives.The findings emphasise the importance of using a variety of terrain characteristics and different approaches to characterise the rock slope.展开更多
Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77...Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.展开更多
The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning mode...The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.展开更多
Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection ...Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.展开更多
Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control s...Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication technologies.In SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption.Since the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s stability.Recent advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in SGs.In this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)model.The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner.To attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level.Then,WWO algorithm is applied to choose an optimal subset of features from the pre-processed data.Next,Deep Belief Network(DBN)model is followed to predict the stability level of SGs.Finally,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN model.In order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was performed.The simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.展开更多
Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body...Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.展开更多
This paper introduces the mathematical model of ammonia and urea reactors and suggested three methods for designing a special purpose controller. The first proposed method is Adaptive model predictive controller, the ...This paper introduces the mathematical model of ammonia and urea reactors and suggested three methods for designing a special purpose controller. The first proposed method is Adaptive model predictive controller, the second is Adaptive Neural Network Model Predictive Control, and the third is Adaptive neuro-fuzzy sliding mode controller. These methods are applied to a multivariable nonlinear system as an ammonia–urea reactor system. The main target of these controllers is to achieve stabilization of the outlet concentration of ammonia and urea, a stable reaction rate, an increase in the conversion of carbon monoxide(CO) into carbon dioxide(CO_2) to reduce the pollution effect, and an increase in the ammonia and urea productions, keeping the NH_3/CO_2 ratio equal to 3 to reduce the unreacted CO_2 and NH_3, and the two reactors' temperature in the suitable operating ranges due to the change in reactor parameters or external disturbance. Simulation results of the three controllers are compared. Comparative analysis proves the effectiveness of the suggested Adaptive neurofuzzy sliding mode controller than the two other controllers according to external disturbance and the change of parameters. Moreover, the suggested methods when compared with other controllers in the literature show great success in overcoming the external disturbance and the change of parameters.展开更多
Taxifolin has a plethora of therapeutic activities and is currently isolated from the stem bark of the tree Larix gmelinni(Dahurian larch). It is a flavonoid of high commercial interest for its use in supplements or i...Taxifolin has a plethora of therapeutic activities and is currently isolated from the stem bark of the tree Larix gmelinni(Dahurian larch). It is a flavonoid of high commercial interest for its use in supplements or in antioxidant-rich functional foods. However, its poor stability and low bioavailability hinder the use of flavonoid in nutritional or pharmaceutical formulations. In this work, taxifolin isolated from the seeds of Mimusops balata, was evaluated by in silico stability prediction studies and in vitro forced degradation studies(acid and alkaline hydrolysis, oxidation, visible/UV radiation, dry/humid heating) monitored by high performance liquid chromatography with ultraviolet detection(HPLC-UV) and ultrahigh performance liquid chromatography-electrospray ionization-mass spectrometry(UPLC-ESI-MS). The in silico stability prediction studies indicated the most susceptible regions in the molecule to nucleophilic and electrophilic attacks, as well as the sites susceptible to oxidation. The in vitro forced degradation tests were in agreement with the in silico stability prediction, indicating that taxifolin is extremely unstable(class 1) under alkaline hydrolysis. In addition, taxifolin thermal degradation was increased by humidity.On the other hand, with respect to photosensitivity, taxifolin can be classified as class 4(stable).Moreover, the alkaline degradation products were characterized by UPLC-ESI-MS/MS as dimers of taxifolin. These results enabled an understanding of the intrinsic lability of taxifolin, contributing to the development of stability-indicating methods, and of appropriate drug release systems, with the aims of preserving its stability and improving its bioavailability.展开更多
Due to the drastic increase in global population as well as economy,electricity demand becomes considerably high.The recently developed smart grid(SG)technology has the ability to minimize power loss at the time of po...Due to the drastic increase in global population as well as economy,electricity demand becomes considerably high.The recently developed smart grid(SG)technology has the ability to minimize power loss at the time of power distribution.Machine learning(ML)and deep learning(DL)models can be effectually developed for the design of SG stability techniques.This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability(SSODLSA-SGS)pre-diction model.Primarily,class imbalance data handling process is performed using Synthetic minority oversampling technique(SMOTE)technique.The SSODLSA-SGS model involves two stages of pre-processing namely data nor-malization and transformation.Besides,the SSODLSA-SGS model derives a deep belief-back propagation neural network(DBN-BN)model for the pre-diction of SG stability.Finally,social spider optimization(SSO)algorithm can be applied for determining the optimal hyperparameter values of the DBN-BN model.The design of SSO algorithm helps to appropriately modify the hyperparameter values of the DBN-BN model.A series of simulation analyses are carried out to highlight the enhanced outcomes of the SSODLSA-SGS model.The extensive comparative study reported the enhanced performance of the SSODLSA-SGS algorithm over the other recent techniques interms of several measures.展开更多
Stability of a networked predictive control system subject to network-induced delay and data dropout is investigated in this study. By modeling the closed-loop system as a switched system with an upper-triangular stru...Stability of a networked predictive control system subject to network-induced delay and data dropout is investigated in this study. By modeling the closed-loop system as a switched system with an upper-triangular structure, a necessary and sufficient stability criterion is developed. From the criterion, it also can be seen that separation principle holds for networked predictive control systems. A numerical example is provided to confirm the validity and effectiveness of the obtained results.展开更多
Predicting free energy changes(DDG)is essential for enhancing our understanding of protein evolution and plays a pivotal role in protein engineering and pharmaceutical development.While traditional methods offer valua...Predicting free energy changes(DDG)is essential for enhancing our understanding of protein evolution and plays a pivotal role in protein engineering and pharmaceutical development.While traditional methods offer valuable insights,they are often constrained by computational speed and reliance on biased training datasets.These constraints become particularly evident when aiming for accurate DDG predictions across a diverse array of protein sequences.Herein,we introduce Pythia,a self-supervised graph neural network specifically designed for zero-shot DDG predictions.Our comparative benchmarks demonstrate that Pythia outperforms other self-supervised pretraining models and force field-based approaches while also exhibiting competitive performance with fully supervised models.Notably,Pythia shows strong correlations and achieves a remarkable increase in computational speed of up to 105-fold.We further validated Pythia’s performance in predicting the thermostabilizing mutations of limonene epoxide hydrolase,leading to higher experimental success rates.This exceptional efficiency has enabled us to explore 26 million high-quality protein structures,marking a significant advancement in our ability to navigate the protein sequence space and enhance our understanding of the relationships between protein genotype and phenotype.In addition,we established a web server at https://pythia.wulab.xyz to allow users to easily perform such predictions.展开更多
In the context of accelerated global energy transition,the high proportion of renewable energy grid connections and the proliferation of power control devices have significantly increased the tangled and haziness of t...In the context of accelerated global energy transition,the high proportion of renewable energy grid connections and the proliferation of power control devices have significantly increased the tangled and haziness of the electromechanical transients in power grids,and the transient stability prediction has become an international forefront problem in the construction of smart grid security and defense system.However,existing methods face triple limitations:traditional physical models rely on ideal assumptions and are computationally inefficient;shallow data-driven models have insufficient feature extraction capabilities;and existing deep learning methods have poor generalization and lack interpretability.To manage the issues highlighted above,this study proposes a deep learning-based Denseception architecture and its accompanying data modeling method,which achieves a breakthrough in high-precision continuous numerical prediction of transient stability indicator(TSI)with engineering practicality.The heterogeneous multi-scale feature fusion network is constructed by integrating the DenseNet dense cross-layer connectivity,Xception deep separable convolution,and the dynamic weighting mechanism of the fully connected layers,which significantly improves the efficiency of the cross-scale dynamic feature extraction;and the three-channel two-dimensional spatial-temporal feature reconstruction method is innovatively designed,which reconstructs the temporal data of the whole fault process into an image-like structure,and combines with the adversarial training strategy to enhance the cross-topology generalization capability.The experiment reveals that the TSI prediction error of the Denseception model is prominently lower than that of the mainstream deep learning model in the IEEE 39-10 and 145-50 systems,which is the best performance.This study overcomes the contradiction between speed,accuracy,and generalizability of traditional methods,provides a full chain solution for the dynamic security defense of a high percentage new energy power grids,and provides a critical time window for emergency control.展开更多
This paper analyzes the stability of milling with variable pitch cutter and tool runout cases characterized by multiple delays,and proposes a new variable-step numerical integration method for efficient and accurate s...This paper analyzes the stability of milling with variable pitch cutter and tool runout cases characterized by multiple delays,and proposes a new variable-step numerical integration method for efficient and accurate stability prediction. The variable-step technique is emphasized here to expand the numerical integration method,especially for the low radial immersion cases with multiple delays. First,the calculation accuracy of the numerical integration method is discussed and the variable-step algorithm is developed for milling stability prediction for single-delay and multiple-delay cases,respectively. The milling stability with variable pitch cutter is analyzed and the result is compared with those predicted with the frequency domain method and the improved full-discretization method. The influence of the runout effect on the stability boundary is investigated by the presented method. The numerical simulation shows that the cutter runout effect increases the stability boundary,and the increasing stability limit is verified by the milling chatter experimental results in the previous research. The numerical and experiment results verify the validity of the proposed method.展开更多
Emergency control is an essential means to help system maintain synchronism after fault clearance.Traditional“offline calculation,online matching”scheme faces significant challenges on adaptiveness and robustness pr...Emergency control is an essential means to help system maintain synchronism after fault clearance.Traditional“offline calculation,online matching”scheme faces significant challenges on adaptiveness and robustness problems.To address these challenges,this paper proposes a novel closed-loop framework of transient stability prediction(TSP)and emergency control based on Deep Belief Network(DBN).First,a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted,which takes into account accuracy and rapidity at the same time.Next,a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity.When impending instability of the system is foreseen,optimal emergency control strategy can be determined in time.Lastly,responses after emergency control are fed back to the TSP model.If prediction result is still unstable,an additional control strategy will be implemented.Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council(NPCC)140-bus system.Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.展开更多
This paper analyzes Bernoulli’s binary sequences in the representation of empirical nonlinear events,analyzing the distribution of natural resources,population sizes and other variables that influence the possible ou...This paper analyzes Bernoulli’s binary sequences in the representation of empirical nonlinear events,analyzing the distribution of natural resources,population sizes and other variables that influence the possible outcomes of resource’s usage.Consider the event as a nonlinear system and the metrics of analysis consisting of two dependent random variables 0 and 1,with memory and probabilities in maximum finite or infinite lengths,constant and equal to 1/2 for both variables(stationary process).The expressions of the possible trajectories of metric space represented by each binary parameter remain constant in sequences that are repeated alternating the presence or absence of one of the binary variables at each iteration(symmetric or asymmetric).It was observed that the binary variables X_(1)and X_(2)assume on time T_(k)→∞specific behaviors(geometric variable)that can be used as management tools in discrete and continuous nonlinear systems aiming at the optimization of resource’s usage,nonlinearity analysis and probabilistic distribution of trajectories occurring about random events.In this way,the paper presents a model of detecting fixed-point attractions and its probabilistic distributions at a given population-resource dynamic.This means that coupling oscillations in the event occur when the binary variables X_(1)and X_(2)are limited as a function of time Y.展开更多
基金Supported by the National High Technology Research and Development Program of China(2014AA041802)the National Natural Science Foundation of China(61573269)
文摘The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously developed tools,including the norm-bounding technique for relaxing the disturbance-related constraint handling,the dynamic output feedback law,the notion of quadratic boundedness for specifying the closed-loop stability,and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility,are merged in the control design.Some previous approaches are shown to be the special cases.An example of continuous stirred tank reactor(CSTR) is given to show the effectiveness of the proposed approaches.
基金supported by the Researchers Supporting Program(TUMA-Project-2021-27)Almaarefa University,Riyadh,Saudi ArabiaTaif University Researchers Supporting Project number(TURSP-2020/161),Taif University,Taif,Saudi Arabia。
文摘A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.
基金support in providing the data and the Universiti Teknologi Malaysia supported this work under UTM Flagship CoE/RG-Coe/RG 5.2:Evaluating Surface PGA with Global Ground Motion Site Response Analyses for the highest seismic activity location in Peninsular Malaysia(Q.J130000.5022.10G47)Universiti Teknologi Malaysia-Earthquake Hazard Assessment in Peninsular Malaysia Using Probabilistic Seismic Hazard Analysis(PSHA)Method(Q.J130000.21A2.06E9).
文摘The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(Kuala Lumpur)as the study area,the slope characteristics of geometrical parameters are obtained from a multidisciplinary approach(consisting of geological,geotechnical,and remote sensing analyses).18 factors,including rock strength,rock quality designation(RQD),joint spacing,continuity,openness,roughness,filling,weathering,water seepage,temperature,vegetation index,water index,and orientation,are selected to construct model input variables while the factor of safety(FOS)functions as an output.The area under the curve(AUC)value of the receiver operating characteristic(ROC)curve is obtained with precision and accuracy and used to analyse the predictive model ability.With a large training set and predicted parameters,an area under the ROC curve(the AUC)of 0.95 is achieved.A precision score of 0.88 is obtained,indicating that the model has a low false positive rate and correctly identifies a substantial number of true positives.The findings emphasise the importance of using a variety of terrain characteristics and different approaches to characterise the rock slope.
基金by the National Natural Science Foundation of China(No.52174114)the State Key Laboratory of Hydroscience and Engineering of Tsinghua University(No.61010101218).
文摘Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.
基金funded by the National Natural Science Foundation of China (41807285)。
文摘The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.
基金This research work is supported by Sichuan Science and Technology Program(Grant No.2022YFS0586)the National Key R&D Program of China(Grant No.2019YFC1509301)the National Natural Science Foundation of China(Grant No.61976046).
文摘Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(180/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR23).
文摘Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication technologies.In SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption.Since the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s stability.Recent advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in SGs.In this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)model.The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner.To attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level.Then,WWO algorithm is applied to choose an optimal subset of features from the pre-processed data.Next,Deep Belief Network(DBN)model is followed to predict the stability level of SGs.Finally,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN model.In order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was performed.The simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.
基金the National Key R&D Program of China(No.2022YFC2904103)the Key Program of the National Natural Science Foundation of China(No.52034001)+1 种基金the 111 Project(No.B20041)the China National Postdoctoral Program for Innovative Talents(No.BX20230041)。
文摘Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.
文摘This paper introduces the mathematical model of ammonia and urea reactors and suggested three methods for designing a special purpose controller. The first proposed method is Adaptive model predictive controller, the second is Adaptive Neural Network Model Predictive Control, and the third is Adaptive neuro-fuzzy sliding mode controller. These methods are applied to a multivariable nonlinear system as an ammonia–urea reactor system. The main target of these controllers is to achieve stabilization of the outlet concentration of ammonia and urea, a stable reaction rate, an increase in the conversion of carbon monoxide(CO) into carbon dioxide(CO_2) to reduce the pollution effect, and an increase in the ammonia and urea productions, keeping the NH_3/CO_2 ratio equal to 3 to reduce the unreacted CO_2 and NH_3, and the two reactors' temperature in the suitable operating ranges due to the change in reactor parameters or external disturbance. Simulation results of the three controllers are compared. Comparative analysis proves the effectiveness of the suggested Adaptive neurofuzzy sliding mode controller than the two other controllers according to external disturbance and the change of parameters. Moreover, the suggested methods when compared with other controllers in the literature show great success in overcoming the external disturbance and the change of parameters.
基金supported by CAPES(PVE,Grant No.88887.116106/2016-00)(Coordenaao de Aperfei-oamento de Pessoal de Nível Superior)Brazil,which provided financial support in the form of a doctoral’s degree scholarship to Stenger,F.C.and financial support(Science Program Without Borders-Researcher Special Visitor-PVE)CNPq(Conselho Nacional de Desenvolvimento Científico e Tecnológico),Edital Universal(Grant No.88887.122964/2016-00)。
文摘Taxifolin has a plethora of therapeutic activities and is currently isolated from the stem bark of the tree Larix gmelinni(Dahurian larch). It is a flavonoid of high commercial interest for its use in supplements or in antioxidant-rich functional foods. However, its poor stability and low bioavailability hinder the use of flavonoid in nutritional or pharmaceutical formulations. In this work, taxifolin isolated from the seeds of Mimusops balata, was evaluated by in silico stability prediction studies and in vitro forced degradation studies(acid and alkaline hydrolysis, oxidation, visible/UV radiation, dry/humid heating) monitored by high performance liquid chromatography with ultraviolet detection(HPLC-UV) and ultrahigh performance liquid chromatography-electrospray ionization-mass spectrometry(UPLC-ESI-MS). The in silico stability prediction studies indicated the most susceptible regions in the molecule to nucleophilic and electrophilic attacks, as well as the sites susceptible to oxidation. The in vitro forced degradation tests were in agreement with the in silico stability prediction, indicating that taxifolin is extremely unstable(class 1) under alkaline hydrolysis. In addition, taxifolin thermal degradation was increased by humidity.On the other hand, with respect to photosensitivity, taxifolin can be classified as class 4(stable).Moreover, the alkaline degradation products were characterized by UPLC-ESI-MS/MS as dimers of taxifolin. These results enabled an understanding of the intrinsic lability of taxifolin, contributing to the development of stability-indicating methods, and of appropriate drug release systems, with the aims of preserving its stability and improving its bioavailability.
文摘Due to the drastic increase in global population as well as economy,electricity demand becomes considerably high.The recently developed smart grid(SG)technology has the ability to minimize power loss at the time of power distribution.Machine learning(ML)and deep learning(DL)models can be effectually developed for the design of SG stability techniques.This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability(SSODLSA-SGS)pre-diction model.Primarily,class imbalance data handling process is performed using Synthetic minority oversampling technique(SMOTE)technique.The SSODLSA-SGS model involves two stages of pre-processing namely data nor-malization and transformation.Besides,the SSODLSA-SGS model derives a deep belief-back propagation neural network(DBN-BN)model for the pre-diction of SG stability.Finally,social spider optimization(SSO)algorithm can be applied for determining the optimal hyperparameter values of the DBN-BN model.The design of SSO algorithm helps to appropriately modify the hyperparameter values of the DBN-BN model.A series of simulation analyses are carried out to highlight the enhanced outcomes of the SSODLSA-SGS model.The extensive comparative study reported the enhanced performance of the SSODLSA-SGS algorithm over the other recent techniques interms of several measures.
基金supported by the National Natural Science Foundation of China(Grant Nos.6110409761321002+3 种基金61120106010&61522303)the Research Fund for the Doctoral Program of Higher Education of China(Grant No.20111101120027)the Program for New Century Excellent Talents in University(Grant No.NCET-13-0045)Beijing Higher Education Young Elite Teacher Project
文摘Stability of a networked predictive control system subject to network-induced delay and data dropout is investigated in this study. By modeling the closed-loop system as a switched system with an upper-triangular structure, a necessary and sufficient stability criterion is developed. From the criterion, it also can be seen that separation principle holds for networked predictive control systems. A numerical example is provided to confirm the validity and effectiveness of the obtained results.
基金supported by the National Key R&D Program of China(grant no.2023YFA0916000)the National Natural Science Foundation of China(32225002,32170033,and 32422001)+2 种基金the Key Research Program of Frontier Sciences(ZDBS-LYSM014)the Biological Resources Program(KFJ-BRP-009 and KFJ-BRP-017-58)from the Chinese Academy of Sciences,the Informatization Plan of Chinese Academy of Sciences(CAS-WX2021SF-0111)the Youth Innovation Promotion Association CAS(2022086).
文摘Predicting free energy changes(DDG)is essential for enhancing our understanding of protein evolution and plays a pivotal role in protein engineering and pharmaceutical development.While traditional methods offer valuable insights,they are often constrained by computational speed and reliance on biased training datasets.These constraints become particularly evident when aiming for accurate DDG predictions across a diverse array of protein sequences.Herein,we introduce Pythia,a self-supervised graph neural network specifically designed for zero-shot DDG predictions.Our comparative benchmarks demonstrate that Pythia outperforms other self-supervised pretraining models and force field-based approaches while also exhibiting competitive performance with fully supervised models.Notably,Pythia shows strong correlations and achieves a remarkable increase in computational speed of up to 105-fold.We further validated Pythia’s performance in predicting the thermostabilizing mutations of limonene epoxide hydrolase,leading to higher experimental success rates.This exceptional efficiency has enabled us to explore 26 million high-quality protein structures,marking a significant advancement in our ability to navigate the protein sequence space and enhance our understanding of the relationships between protein genotype and phenotype.In addition,we established a web server at https://pythia.wulab.xyz to allow users to easily perform such predictions.
基金supported by the National Key Research and Devel-opment Program of China(2023YFB2406600).
文摘In the context of accelerated global energy transition,the high proportion of renewable energy grid connections and the proliferation of power control devices have significantly increased the tangled and haziness of the electromechanical transients in power grids,and the transient stability prediction has become an international forefront problem in the construction of smart grid security and defense system.However,existing methods face triple limitations:traditional physical models rely on ideal assumptions and are computationally inefficient;shallow data-driven models have insufficient feature extraction capabilities;and existing deep learning methods have poor generalization and lack interpretability.To manage the issues highlighted above,this study proposes a deep learning-based Denseception architecture and its accompanying data modeling method,which achieves a breakthrough in high-precision continuous numerical prediction of transient stability indicator(TSI)with engineering practicality.The heterogeneous multi-scale feature fusion network is constructed by integrating the DenseNet dense cross-layer connectivity,Xception deep separable convolution,and the dynamic weighting mechanism of the fully connected layers,which significantly improves the efficiency of the cross-scale dynamic feature extraction;and the three-channel two-dimensional spatial-temporal feature reconstruction method is innovatively designed,which reconstructs the temporal data of the whole fault process into an image-like structure,and combines with the adversarial training strategy to enhance the cross-topology generalization capability.The experiment reveals that the TSI prediction error of the Denseception model is prominently lower than that of the mainstream deep learning model in the IEEE 39-10 and 145-50 systems,which is the best performance.This study overcomes the contradiction between speed,accuracy,and generalizability of traditional methods,provides a full chain solution for the dynamic security defense of a high percentage new energy power grids,and provides a critical time window for emergency control.
基金supported by the National Key Basic Research Program (Grant No. 2011CB706804)the National Natural Science Foundation of China (Grant No. 50835004)the Ministry of Science and Technology of China (Grant No. 2010ZX04016-012)
文摘This paper analyzes the stability of milling with variable pitch cutter and tool runout cases characterized by multiple delays,and proposes a new variable-step numerical integration method for efficient and accurate stability prediction. The variable-step technique is emphasized here to expand the numerical integration method,especially for the low radial immersion cases with multiple delays. First,the calculation accuracy of the numerical integration method is discussed and the variable-step algorithm is developed for milling stability prediction for single-delay and multiple-delay cases,respectively. The milling stability with variable pitch cutter is analyzed and the result is compared with those predicted with the frequency domain method and the improved full-discretization method. The influence of the runout effect on the stability boundary is investigated by the presented method. The numerical simulation shows that the cutter runout effect increases the stability boundary,and the increasing stability limit is verified by the milling chatter experimental results in the previous research. The numerical and experiment results verify the validity of the proposed method.
基金supported in part by the Fundamental Research Funds for the Central Universities(No.2020YJS162)the National Key R&D Program of China(No.2018YFB0904500)Science and Technology Projects of State Grid Corporation of China(No.SGLNDK00KJJS1800236).
文摘Emergency control is an essential means to help system maintain synchronism after fault clearance.Traditional“offline calculation,online matching”scheme faces significant challenges on adaptiveness and robustness problems.To address these challenges,this paper proposes a novel closed-loop framework of transient stability prediction(TSP)and emergency control based on Deep Belief Network(DBN).First,a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted,which takes into account accuracy and rapidity at the same time.Next,a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity.When impending instability of the system is foreseen,optimal emergency control strategy can be determined in time.Lastly,responses after emergency control are fed back to the TSP model.If prediction result is still unstable,an additional control strategy will be implemented.Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council(NPCC)140-bus system.Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.
文摘This paper analyzes Bernoulli’s binary sequences in the representation of empirical nonlinear events,analyzing the distribution of natural resources,population sizes and other variables that influence the possible outcomes of resource’s usage.Consider the event as a nonlinear system and the metrics of analysis consisting of two dependent random variables 0 and 1,with memory and probabilities in maximum finite or infinite lengths,constant and equal to 1/2 for both variables(stationary process).The expressions of the possible trajectories of metric space represented by each binary parameter remain constant in sequences that are repeated alternating the presence or absence of one of the binary variables at each iteration(symmetric or asymmetric).It was observed that the binary variables X_(1)and X_(2)assume on time T_(k)→∞specific behaviors(geometric variable)that can be used as management tools in discrete and continuous nonlinear systems aiming at the optimization of resource’s usage,nonlinearity analysis and probabilistic distribution of trajectories occurring about random events.In this way,the paper presents a model of detecting fixed-point attractions and its probabilistic distributions at a given population-resource dynamic.This means that coupling oscillations in the event occur when the binary variables X_(1)and X_(2)are limited as a function of time Y.
