This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key de...This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.展开更多
Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are ...The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are computationally expensive and scarce.We propose a novel hyper-gradient type method with a warm-start strategy to address this challenge.Particularly,we first use a Taylor expansion-based approach to obtain a good initial point.Then we apply a hyper-gradient descent method with an ex-plicit approximate hyper-gradient.We establish the convergence results of our algorithm theoretically.Furthermore,when the follower employs the least squares loss function,our method is shown to reach an e-stationary point by solving quadratic subproblems.Numerical experiments show our algorithms are empirically orders of magnitude faster than the state-of-the-art.展开更多
Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to asse...Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to assess this complex toxicity endpoint and will be valuable for screening emerging pollutants as well as for m anaging new chemicals in China.Currently,there are few published DART prediction models in China,but many related research and development projects are in progress.In 2013,WU et al.published an expert rule-based DART decision tree(DT).This DT relies on known chemical structures linked to DART to forecast DART potential of a given chemical.Within this procedure,an accurate DART data interpretation is the foundation of building and expanding the DT.This paper excerpted case studies demonstrating DART data curation and interpretation of four chemicals(including 8-hydroxyquinoline,3,5,6-trichloro-2-pyridinol,thiacloprid,and imidacloprid)to expand the existing DART DT.Chemicals were first selected from the database of Solid Waste and Chemicals Management Center,Ministry of Ecology and Environment(MEESCC)in China.The structures of these 4 chemicals were analyzed and preliminarily grouped by chemists based on core structural features,functional groups,receptor binding property,metabolism,and possible mode of actions.Then,the DART conclusion was derived by collecting chemical information,searching,integrating,and interpreting DART data by the toxicologists.Finally,these chemicals were classified into either an existing category or a new category via integrating their chemical features,DART conclusions,and biological properties.The results showed that 8-hydroxyquinoline impacted estrous cyclicity,s exual organ weights,and embryonal development,and 3,5,6-trichloro-2-pyridinol caused central nervous system(CNS)malformations,which were added to an existing subcategory 8e(aromatic compounds with multi-halogen and nitro groups)of the DT.Thiacloprid caused dystocia and fetal skeletal malformation,and imidacloprid disrupted the endocrine system and male fertility.They both contain 2-chloro-5-methylpyridine substituted imidazolidine c yclic ring,which were expected to create a new category of neonicotinoids.The current work delineates a t ransparent process of curating toxicological data for the purpose of DART data interpretation.In the presence of sufficient related structures and DART data,the DT can be expanded by iteratively adding chemicals within the a pplicable domain of each category or subcategory.This DT can potentially serve as a tool for screening emerging pollutants and assessing new chemicals in China.展开更多
The acquisition,analysis,and prediction of the radar cross section(RCS)of a target have extremely important strategic significance in the military.However,the RCS values at all azimuths are hardly accessible for non-c...The acquisition,analysis,and prediction of the radar cross section(RCS)of a target have extremely important strategic significance in the military.However,the RCS values at all azimuths are hardly accessible for non-cooperative targets,due to the limitations of radar observation azimuth and detection resources.Despite their efforts to predict the azimuth-dimensional RCS value,traditional methods based on statistical theory fails to achieve the desired results because of the azimuth sensitivity of the target RCS.To address this problem,an improved neural basis expansion analysis for interpretable time series forecasting(N-BEATS)network considering the physical model prior is proposed to predict the azimuth-dimensional RCS value accurately.Concretely,physical model-based constraints are imposed on the network by constructing a scattering-center module based on the target scattering-center model.Besides,a superimposed seasonality module is involved to better capture high-frequency information,and augmenting the training set provides complementary information for learning predictions.Extensive simulations and experimental results are provided to validate the effectiveness of the proposed method.展开更多
With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid elec...With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.展开更多
[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the cha...[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.展开更多
In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel...In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel cells(PEMFC).Firstly,the Shapley additive explanations(SHAP)value method is used to select external characteristic parameters with high contributions as inputs for the data-driven approach.Next,a novel swarm optimization algorithm,the enhanced search ant colony optimization,is proposed.This algorithm improves the ant colony optimization(ACO)algorithm based on a reinforcement factor to avoid premature convergence and accelerate the convergence speed.Comparative experiments are set up to compare the performance differences between particle swarm optimization(PSO),ACO,and ENSACO.Finally,a data-driven method based on ENSACO-LSTM is proposed to predict the power degradation trend of PEMFCs.And actual aging data is used to validate the method.The results show that,within a limited number of iterations,the optimization capability of ENSACO is significantly stronger than that of PSO and ACO.Additionally,the prediction accuracy of the ENSACO-LSTM method is greatly improved,with an average increase of approximately 50.58%compared to LSTM,PSO-LSTM,and ACO-LSTM.展开更多
High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelations...High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.展开更多
Hypersonic Glide Vehicles(HGVs)are advanced aircraft that can achieve extremely high speeds(generally over 5 Mach)and maneuverability within the Earth's atmosphere.HGV trajectory prediction is crucial for effectiv...Hypersonic Glide Vehicles(HGVs)are advanced aircraft that can achieve extremely high speeds(generally over 5 Mach)and maneuverability within the Earth's atmosphere.HGV trajectory prediction is crucial for effective defense planning and interception strategies.In recent years,HGV trajectory prediction methods based on deep learning have the great potential to significantly enhance prediction accuracy and efficiency.However,it's still challenging to strike a balance between improving prediction performance and reducing computation costs of the deep learning trajectory prediction models.To solve this problem,we propose a new deep learning framework(FECA-LSMN)for efficient HGV trajectory prediction.The model first uses a Frequency Enhanced Channel Attention(FECA)module to facilitate the fusion of different HGV trajectory features,and then subsequently employs a Light Sampling-oriented Multi-Layer Perceptron Network(LSMN)based on simple MLP-based structures to extract long/shortterm HGV trajectory features for accurate trajectory prediction.Also,we employ a new data normalization method called reversible instance normalization(RevIN)to enhance the prediction accuracy and training stability of the network.Compared to other popular trajectory prediction models based on LSTM,GRU and Transformer,our FECA-LSMN model achieves leading or comparable performance in terms of RMSE,MAE and MAPE metrics while demonstrating notably faster computation time.The ablation experiments show that the incorporation of the FECA module significantly improves the prediction performance of the network.The RevIN data normalization technique outperforms traditional min-max normalization as well.展开更多
Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve ...Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.展开更多
A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,wh...A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.展开更多
[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimizati...A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM（1,1） model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM（1,1） model relatively.展开更多
One of the greatest challenges in the design of a gun is to balance muzzle velocity and recoil,especially for guns on aircrafts and deployable vehicles.To resolve the conflict between gun power and recoil force,a conc...One of the greatest challenges in the design of a gun is to balance muzzle velocity and recoil,especially for guns on aircrafts and deployable vehicles.To resolve the conflict between gun power and recoil force,a concept of rarefaction wave gun(RAVEN)was proposed to significantly reduce the weapon recoil and the heat in barrel,while minimally reducing the muzzle velocity.The main principle of RAVEN is that the rarefaction wave will not reach the projectile base until the muzzle by delaying the venting time of an expansion nozzle at the breech.Developed on the RAVEN principle,the purpose of this paper is to provide an engineering method for predicting the performance of a low-recoil gun with front nozzle.First,a two-dimensional two-phase flow model of interior ballistic during the RAVEN firing cycle was established.Numerical simulation results were compared with the published data to validate the reliability and accuracy.Next,the effects of the vent opening times and locations were investigated to determine the influence rules on the performance of the RAVEN with front nozzle.Then according to the results above,simple nonlinear fitting formulas were provided to explain how the muzzle velocity and the recoil force change with the vent opening time and location.Finally,a better vent venting opening time corresponding to the vent location was proposed.The findings should make an important contribution to the field of engineering applications of the RAVEN.展开更多
For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is ...For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is built. An analytical approximation to the probability density function (PDF) of the product's lifetime is derived in a closed form. The process and data of SSADT are analyzed to obtain the relation model of the observed data under each accelerated stress. The likelihood function for the population-based observed data is constructed. The population-based model parameters and its random coefficient prior values are estimated. According to the newly observed data of the target product in SSADT, an analytical approximation to the PDF of its residual lifetime (RL) is derived in accordance with its individual degradation characteristics. The parameter updating method based on Bayesian inference is applied to obtain the posterior value of random coefficient of the RL model. A numerical example by simulation is analyzed to verify the accuracy and advantage of the proposed model.展开更多
The state prediction based on the unscented Kalman filter （UKF） for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of a...The state prediction based on the unscented Kalman filter （UKF） for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval （Ci） is proposed to overcome the problem. The advantages of CI are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results.展开更多
Support vector machine （SVM） is powerful to solve some problems such as nonlinear classification, function estimation and density estimation. To consider the chaotic fh （frequency hopping）-code＇s characters in ch...Support vector machine （SVM） is powerful to solve some problems such as nonlinear classification, function estimation and density estimation. To consider the chaotic fh （frequency hopping）-code＇s characters in chaotic dynamic system, the forecasting model of the support vector machine in combination with Takens＇ delay coordinate phase reconstruction of chaotic times is established and the least squares model for large-scale problems is used in local training for this model. Finally, a fh-code series generated by Logistic-Kent mapping is applied to verify the local prediction model. Simulation results show that the high accuracy and fault tolerant SVM model has an excellent performance in predicting the fh code, with a very low mean square error and a high relative coefficient.展开更多
Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was ...Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods.The dataset was examined with six widely accepted indices which are:the maximum tangential stress around the excavation boundary(MTS),uniaxial compressive strength(UCS)and uniaxial tensile strength(UTS)of the intact rock,stress concentration factor(SCF),rock brittleness index(BI),and strain energy storage index(EEI).Two boosting(AdaBoost.M1,SAMME)and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated.The available dataset was randomly divided into training set(2/3 of whole datasets)and testing set(the remaining datasets).Repeated 10-fold cross validation(CV)was applied as the validation method for tuning the hyper-parameters.The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles.According to 10-fold CV,the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1,SAMME algorithms and empirical criteria methods.展开更多
基金supported by Poongsan-KAIST Future Research Center Projectthe fund support provided by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(Grant No.2023R1A2C2005661)。
文摘This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
文摘The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are computationally expensive and scarce.We propose a novel hyper-gradient type method with a warm-start strategy to address this challenge.Particularly,we first use a Taylor expansion-based approach to obtain a good initial point.Then we apply a hyper-gradient descent method with an ex-plicit approximate hyper-gradient.We establish the convergence results of our algorithm theoretically.Furthermore,when the follower employs the least squares loss function,our method is shown to reach an e-stationary point by solving quadratic subproblems.Numerical experiments show our algorithms are empirically orders of magnitude faster than the state-of-the-art.
文摘Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to assess this complex toxicity endpoint and will be valuable for screening emerging pollutants as well as for m anaging new chemicals in China.Currently,there are few published DART prediction models in China,but many related research and development projects are in progress.In 2013,WU et al.published an expert rule-based DART decision tree(DT).This DT relies on known chemical structures linked to DART to forecast DART potential of a given chemical.Within this procedure,an accurate DART data interpretation is the foundation of building and expanding the DT.This paper excerpted case studies demonstrating DART data curation and interpretation of four chemicals(including 8-hydroxyquinoline,3,5,6-trichloro-2-pyridinol,thiacloprid,and imidacloprid)to expand the existing DART DT.Chemicals were first selected from the database of Solid Waste and Chemicals Management Center,Ministry of Ecology and Environment(MEESCC)in China.The structures of these 4 chemicals were analyzed and preliminarily grouped by chemists based on core structural features,functional groups,receptor binding property,metabolism,and possible mode of actions.Then,the DART conclusion was derived by collecting chemical information,searching,integrating,and interpreting DART data by the toxicologists.Finally,these chemicals were classified into either an existing category or a new category via integrating their chemical features,DART conclusions,and biological properties.The results showed that 8-hydroxyquinoline impacted estrous cyclicity,s exual organ weights,and embryonal development,and 3,5,6-trichloro-2-pyridinol caused central nervous system(CNS)malformations,which were added to an existing subcategory 8e(aromatic compounds with multi-halogen and nitro groups)of the DT.Thiacloprid caused dystocia and fetal skeletal malformation,and imidacloprid disrupted the endocrine system and male fertility.They both contain 2-chloro-5-methylpyridine substituted imidazolidine c yclic ring,which were expected to create a new category of neonicotinoids.The current work delineates a t ransparent process of curating toxicological data for the purpose of DART data interpretation.In the presence of sufficient related structures and DART data,the DT can be expanded by iteratively adding chemicals within the a pplicable domain of each category or subcategory.This DT can potentially serve as a tool for screening emerging pollutants and assessing new chemicals in China.
基金National Natural Science Foundation of China(61921001,62201588,62022091)。
文摘The acquisition,analysis,and prediction of the radar cross section(RCS)of a target have extremely important strategic significance in the military.However,the RCS values at all azimuths are hardly accessible for non-cooperative targets,due to the limitations of radar observation azimuth and detection resources.Despite their efforts to predict the azimuth-dimensional RCS value,traditional methods based on statistical theory fails to achieve the desired results because of the azimuth sensitivity of the target RCS.To address this problem,an improved neural basis expansion analysis for interpretable time series forecasting(N-BEATS)network considering the physical model prior is proposed to predict the azimuth-dimensional RCS value accurately.Concretely,physical model-based constraints are imposed on the network by constructing a scattering-center module based on the target scattering-center model.Besides,a superimposed seasonality module is involved to better capture high-frequency information,and augmenting the training set provides complementary information for learning predictions.Extensive simulations and experimental results are provided to validate the effectiveness of the proposed method.
文摘With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.
文摘[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.
基金Supported by the Major Science and Technology Project of Jilin Province(20220301010GX)the International Scientific and Technological Cooperation(20240402071GH).
文摘In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel cells(PEMFC).Firstly,the Shapley additive explanations(SHAP)value method is used to select external characteristic parameters with high contributions as inputs for the data-driven approach.Next,a novel swarm optimization algorithm,the enhanced search ant colony optimization,is proposed.This algorithm improves the ant colony optimization(ACO)algorithm based on a reinforcement factor to avoid premature convergence and accelerate the convergence speed.Comparative experiments are set up to compare the performance differences between particle swarm optimization(PSO),ACO,and ENSACO.Finally,a data-driven method based on ENSACO-LSTM is proposed to predict the power degradation trend of PEMFCs.And actual aging data is used to validate the method.The results show that,within a limited number of iterations,the optimization capability of ENSACO is significantly stronger than that of PSO and ACO.Additionally,the prediction accuracy of the ENSACO-LSTM method is greatly improved,with an average increase of approximately 50.58%compared to LSTM,PSO-LSTM,and ACO-LSTM.
基金supported by the Aeronautical Science Foundation of China(2020Z023053002).
文摘High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.
文摘Hypersonic Glide Vehicles(HGVs)are advanced aircraft that can achieve extremely high speeds(generally over 5 Mach)and maneuverability within the Earth's atmosphere.HGV trajectory prediction is crucial for effective defense planning and interception strategies.In recent years,HGV trajectory prediction methods based on deep learning have the great potential to significantly enhance prediction accuracy and efficiency.However,it's still challenging to strike a balance between improving prediction performance and reducing computation costs of the deep learning trajectory prediction models.To solve this problem,we propose a new deep learning framework(FECA-LSMN)for efficient HGV trajectory prediction.The model first uses a Frequency Enhanced Channel Attention(FECA)module to facilitate the fusion of different HGV trajectory features,and then subsequently employs a Light Sampling-oriented Multi-Layer Perceptron Network(LSMN)based on simple MLP-based structures to extract long/shortterm HGV trajectory features for accurate trajectory prediction.Also,we employ a new data normalization method called reversible instance normalization(RevIN)to enhance the prediction accuracy and training stability of the network.Compared to other popular trajectory prediction models based on LSTM,GRU and Transformer,our FECA-LSMN model achieves leading or comparable performance in terms of RMSE,MAE and MAPE metrics while demonstrating notably faster computation time.The ablation experiments show that the incorporation of the FECA module significantly improves the prediction performance of the network.The RevIN data normalization technique outperforms traditional min-max normalization as well.
基金the National Natural Science Foundation of China (Grants No. 12072090 and No.12302056) to provide fund for conducting experiments。
文摘Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.
基金supported by National Natural Science Foundation of China(Grant No.12432018,12372346)the Innovative Research Groups of the National Natural Science Foundation of China(Grant No.12221002).
文摘A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金supported by the Key Project of National Social Science Foundation(12AZD111)the National Project for Education Science Planning(EFA110351)+2 种基金the Humanities and Social Science Foundation of Ministry of Education of China(12YJCZH207)the Key Project for Jiangsu Province Social Science Foundation(12DDA011)the Jiangsu College of Humanities and Social Sciences outside Campus Research Base:Chinese Development of Strategic Research Base for Internet of Things
文摘A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM（1,1） model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM（1,1） model relatively.
基金supported by the National Natural Science Foundation of China(Grant No.11502114)the Fundamental Research Funds for the Central Universities(Grant No.30918011323)
文摘One of the greatest challenges in the design of a gun is to balance muzzle velocity and recoil,especially for guns on aircrafts and deployable vehicles.To resolve the conflict between gun power and recoil force,a concept of rarefaction wave gun(RAVEN)was proposed to significantly reduce the weapon recoil and the heat in barrel,while minimally reducing the muzzle velocity.The main principle of RAVEN is that the rarefaction wave will not reach the projectile base until the muzzle by delaying the venting time of an expansion nozzle at the breech.Developed on the RAVEN principle,the purpose of this paper is to provide an engineering method for predicting the performance of a low-recoil gun with front nozzle.First,a two-dimensional two-phase flow model of interior ballistic during the RAVEN firing cycle was established.Numerical simulation results were compared with the published data to validate the reliability and accuracy.Next,the effects of the vent opening times and locations were investigated to determine the influence rules on the performance of the RAVEN with front nozzle.Then according to the results above,simple nonlinear fitting formulas were provided to explain how the muzzle velocity and the recoil force change with the vent opening time and location.Finally,a better vent venting opening time corresponding to the vent location was proposed.The findings should make an important contribution to the field of engineering applications of the RAVEN.
基金supported by the National Defense Foundation of China(71601183)
文摘For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is built. An analytical approximation to the probability density function (PDF) of the product's lifetime is derived in a closed form. The process and data of SSADT are analyzed to obtain the relation model of the observed data under each accelerated stress. The likelihood function for the population-based observed data is constructed. The population-based model parameters and its random coefficient prior values are estimated. According to the newly observed data of the target product in SSADT, an analytical approximation to the PDF of its residual lifetime (RL) is derived in accordance with its individual degradation characteristics. The parameter updating method based on Bayesian inference is applied to obtain the posterior value of random coefficient of the RL model. A numerical example by simulation is analyzed to verify the accuracy and advantage of the proposed model.
基金supported by the National Natural Science Foundation of China(60574088608740536103406)
文摘The state prediction based on the unscented Kalman filter （UKF） for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval （Ci） is proposed to overcome the problem. The advantages of CI are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results.
基金the National Natural Science Foundation of China(10577007)Special Fund of Anti-InterferenceTechnology in Tactical Communication Defend Lab(51434020105ZS04).
文摘Support vector machine （SVM） is powerful to solve some problems such as nonlinear classification, function estimation and density estimation. To consider the chaotic fh （frequency hopping）-code＇s characters in chaotic dynamic system, the forecasting model of the support vector machine in combination with Takens＇ delay coordinate phase reconstruction of chaotic times is established and the least squares model for large-scale problems is used in local training for this model. Finally, a fh-code series generated by Logistic-Kent mapping is applied to verify the local prediction model. Simulation results show that the high accuracy and fault tolerant SVM model has an excellent performance in predicting the fh code, with a very low mean square error and a high relative coefficient.
基金Projects(41807259,51604109)supported by the National Natural Science Foundation of ChinaProject(2020CX040)supported by the Innovation-Driven Project of Central South University,ChinaProject(2018JJ3693)supported by the Natural Science Foundation of Hunan Province,China。
文摘Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods.The dataset was examined with six widely accepted indices which are:the maximum tangential stress around the excavation boundary(MTS),uniaxial compressive strength(UCS)and uniaxial tensile strength(UTS)of the intact rock,stress concentration factor(SCF),rock brittleness index(BI),and strain energy storage index(EEI).Two boosting(AdaBoost.M1,SAMME)and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated.The available dataset was randomly divided into training set(2/3 of whole datasets)and testing set(the remaining datasets).Repeated 10-fold cross validation(CV)was applied as the validation method for tuning the hyper-parameters.The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles.According to 10-fold CV,the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1,SAMME algorithms and empirical criteria methods.
