In this paper,application examples of high-speed electrical machines are presented,and the machine structures are categorized.Key issues of design and control for the high-speed permanent magnet machines are reviewed,...In this paper,application examples of high-speed electrical machines are presented,and the machine structures are categorized.Key issues of design and control for the high-speed permanent magnet machines are reviewed,including bearings selection,rotor dynamics analysis and design,rotor stress analysis and protection,thermal analysis and design,electromagnetic losses analysis and reduction,sensorless control strategies,as well as comparison and selection of sine-wave and square-wave drive modes.Some challenges are also discussed,so that future studies could be focused.展开更多
Iron loss and copper loss are the significant parts of electrical loss of machines,which are the major parts particularly under high frequency condition.High-speed permanent magnet synchronous machines(HS-PMSM)have th...Iron loss and copper loss are the significant parts of electrical loss of machines,which are the major parts particularly under high frequency condition.High-speed permanent magnet synchronous machines(HS-PMSM)have the benefits of high power density,high efficiency and wide speed range.Which causes the calculation for iron loss and copper loss in whole operating range complex.By analyzing the components and influencing factors of iron loss and copper loss in stator,we have deduced the calculation formula of iron loss and copper loss in whole operating range based on the analytical solution and finite element approach(EFA)solution.According to the calculation solution,taking the influence of operating temperature on the iron loss and copper loss into account,we propose a temperature correction factor and establish the calculation method for the iron loss and copper loss with temperature influences.Finally,by the conductor transposition,we restrain the circulating current under high-frequency operating condition.展开更多
With the improvement of coal mining speed and mechanization level in China,traditional tunnel boring methods can no longer meet the actual needs.In order to solve the problems of low efficiency,high labor intensity,sl...With the improvement of coal mining speed and mechanization level in China,traditional tunnel boring methods can no longer meet the actual needs.In order to solve the problems of low efficiency,high labor intensity,slow tunnel boring speed,bad working environment and poor safety in traditional tunnel boring,on the basis of analyzing the development and application of coal roadway tunnel boring equipment at home and abroad,complete equipment for high-speed tunnel boring and bolting machines was developed by using the integrated technology of tunnel boring and bolting.The complete equipment for high-speed tunnel boring and bolting machines has the functions of tunnel boring and bolting synchronization,once-tunneling,negative pressure dust removal,digital guidance,independent cutting feed,digital cutting,safety monitoring and data interaction,which has the advantages of safety in use,reliability and efficiency.展开更多
Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wa...Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.展开更多
A numerical control (NC) tool path of digital CAD model is widely generated as a set of short line segments in machining. However, there are three shortcomings in the linear tool path, such as discontinuities of tange...A numerical control (NC) tool path of digital CAD model is widely generated as a set of short line segments in machining. However, there are three shortcomings in the linear tool path, such as discontinuities of tangency and curvature, huge number of line segments, and short lengths of line segments. These disadvantages hinder the development of high speed machining. To smooth the linear tool path and improve machining efficiency of short line segments, this paper presents an optimal feed interpolator based on G^2 continuous Bézier curves for the linear tool path. First, the areas suitable for fitting are screened out based on the geometric characteristics of continuous short segments (CSSs). CSSs in every area are compressed and fitted into a G^2 Continuous Bézier curve by using the least square method. Then a series of cubic Bézier curves are generated. However, the junction between adjacent Bézier curves is only G^0 continuous. By adjusting the control points and inserting Bézier transition curves between adjacent Bézier curves, the G^2 continuous tool path is constructed. The fitting error is estimated by the second-order Taylor formula. Without iteration, the fitting algorithm can be implemented in real-time environment. Second, the optimal feed interpolator considering the comprehensive constraints (such as the chord error constraint, the maximum normal acceleration, servo capacity of each axis, etc.) is proposed. Simulation and experiment are conducted. The results shows that the proposed method can generate smooth path, decrease the amount of segments and reduce machining time for machining of linear tool path. The proposed research provides an effective method for high-speed machining of complex 2-D/3-D profiles described by short line segments.展开更多
Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on f...Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.展开更多
Mangrove ecosystems have important ecological and economic values,especially their ability to store carbon.However,in recent years,human disturbance has accelerated mangrove degradation.Among them,the emission of poll...Mangrove ecosystems have important ecological and economic values,especially their ability to store carbon.However,in recent years,human disturbance has accelerated mangrove degradation.Among them,the emission of pollutants cannot be ignored.It is of great significance for carbon emission reduction and ecological protection to study the impacts of different pollutants on mangroves and their carbon stocks.Based on the remote sensing data of coastal areas south of the Yangtze River in China's Mainland,this paper builds the ensemble learning model Random Forest(RF)and Gradient Boosting Regression(GBR)to empirically analyse the relationship between industrial wastewater,industrial sulfur dioxide(SO2),PM2.5 and mangrove forests.The results show that the pollutant concentration of meteorological normalisation is more stable.The importance of pollutants presents regional heterogeneity.The area of mangroves in different cities and the corresponding total carbon stocks show different trends with the increase or decrease of pollutants,and there is a dynamic balance between urban pollutant discharge and mangrove growth in some cities.The research in this paper provides an analysis and explanation from the perspective of machine learning to explore the relationship between mangroves and pollutants and at the same time,provides scientific suggestions for the formulation of future pollutant emission policies in different cities.展开更多
Severe plastic deformation(SPD)-induced gradient nanostructured(GNS)metallic materials exhibit superior mechanical performance,especially the high strength and good ductility.In this study,a novel high-speed machining...Severe plastic deformation(SPD)-induced gradient nanostructured(GNS)metallic materials exhibit superior mechanical performance,especially the high strength and good ductility.In this study,a novel high-speed machining SPD technique,namely single point diamond turning(SPDT),was developed to produce effectively the GNS layer on the hexagonal close-packed(HCP)structural Mg alloy.The high-resolution transmission electron microscopy observations and atomistic molecular dynamics simulations were mainly performed to atomic-scale dissect the grain refinement process and corresponding plastic deformation mechanisms of the GNS layer.It was found that the grain refinement process for the formation of the GNS Mg alloy layer consists of elongated coarse grains,lamellar fine grains with deformation-induced-tension twins and contraction twins,ultrafine grains,and nanograins with the grain size of~70 nm along the direction from the inner matrix to surface.Specifically,experiment results and atomistic simulations reveal that these deformation twins are formed by gliding twinning partial dislocations that are dissociated from the lattice dislocations piled up at grain boundaries.The corresponding deformation mechanisms were evidenced to transit from the deformation twinning to dislocation slip when the grain size was below 2.45μm.Moreover,the Hall-Petch relationship plot and the surface equivalent stress along the gradient direction estimated by finite element analysis for the SPDT process were incorporated to quantitatively elucidate the transition of defo rmation mechanisms during the grain refinement process.Our findings have implications for the development of the facile SPD technique to construct high strength-ductility heterogeneous GNS metals,especially for the HCP metals.展开更多
To improve the efficiency of CNC machining, assumptive transit circular arc is used to contour two adjacent moves together on the comer to make smooth paths. The radios of transit circular arc can be adjusted with con...To improve the efficiency of CNC machining, assumptive transit circular arc is used to contour two adjacent moves together on the comer to make smooth paths. The radios of transit circular arc can be adjusted with contour accuracy, and the feed rate on the corner can be controlled through limiting the maximum feed rate of transit circular arc segment. A look-ahead algorithm for a series of moves is proposed for speed adjustment in advance, which avoids the occurrence of overload of cutting tool on the comer and reduces the servo track error of parts on the corner or of circular arc move. Equivalent trapezoidal velocity profile is used to analyze the speed of S-curve velocity profile and work out its accurate interpolation, which overcomes the disadvantage of looking up table to calculate feed rate approximately, hence high accuracy and fine surface quality can be obtained while the machining speed is high. The proposed methods can meet the requirements of real-time analysis of high-speed machining. The presented algorithm is effective and has been adopted by CNC system of newly developed high-speed milling machine.展开更多
Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper pr...Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.展开更多
We examine how machine learning models predict stock returns in the Korean market.By analyzing various firm characteristics and macroeconomic variables,we find that tree-based models outperform other machine learning ...We examine how machine learning models predict stock returns in the Korean market.By analyzing various firm characteristics and macroeconomic variables,we find that tree-based models outperform other machine learning approaches.This finding suggests that,in data-constrained contexts,moderately complex models outperform advanced methods that require extensive datasets.Using PFI,SHAP,and LIME,we consistently identify the 36-month momentum as the key predictor.PDP,ICE,and ALE analyses reveal threshold effects of 36-month momentum that diminish at higher return levels.Our findings underscore the value of ensemble-based methods in settings characterized by short data histories and heightened volatility.This study illustrates how multimethod interpretability can yield deeper economic insights,ultimately guiding more effective investment strategies and policy decisions.展开更多
Applying high-speed machining technology in shop floor has many benefits, such as manufacturing more accurate parts with better surface finishes. The selection of the appropriate machining parameters plays a very impo...Applying high-speed machining technology in shop floor has many benefits, such as manufacturing more accurate parts with better surface finishes. The selection of the appropriate machining parameters plays a very important role in the implementation of high-speed machining technology. The case-based reasoning is used in the developing of high-speed machining database to overcome the shortage of available high-speed cutting parameters in machining data handbooks and shop floors. The high-speed machining database developed in this paper includes two main components: the machining database and the case-base. The machining database stores the cutting parameters, cutting tool data, work pieces and their materials data, and other relative data, while the case-base stores mainly the successfully solved cases that are problems of work pieces and their machining. The case description and case retrieval methods are described to establish the case-based reasoning high-speed machining database. With the case retrieval method, some succeeded cases similar to the new machining problem can be retrieved from the case-base. The solution of the most matched case is evaluated and modified, and then it is regarded as the proposed solution to the new machining problem. After verification, the problem and its solution are packed up into a new case, and are stored in the case-base for future applications.展开更多
Excellent surface integrity is an eternal pursuit in high performance manufacturing, with microstructure being a crucial component of the surface integrity dataset and a key factor controlling surface properties such ...Excellent surface integrity is an eternal pursuit in high performance manufacturing, with microstructure being a crucial component of the surface integrity dataset and a key factor controlling surface properties such as fatigue and creep. The multi-physical fields generated by thermomechanical loads during high-speed machining act on the processed surface layer, influencing the evolution of microstructures. To investigate the microstructural evolution mechanisms of ATI718plus during high-speed machining, cutting experiments and techniques such as Electron back scatter diffraction(EBSD), Transmission Kikuchi diffraction(TKD), and Precession electron diffraction(PED) is conducted to quantitatively analyze the microstructures in the chip shear zone and the machined surface. Subsequently, a combined finite element(FE) and cellular automata(CA) model is developed to simulate the microstructure evolution during the cutting process. The discontinuous dynamic recrystallization(DDRX) mechanism is employed to demonstrate the nucleation and growth of grains under the influence of multiple physical fields. The simulation and experimental results show similar dynamic recrystallization(DRX) grain sizes, indicating acceptable accuracy of the CA model in terms of DRX grain size. The comparison between experimental and simulation results confirms the occurrence of both continuous dynamic recrystallization(CDRX) and DDRX during the cutting process. The synergistic competition between CDRX induced grain lamellar refinement and DDRX induced grain growth emerge as the primary mechanism driving microstructural evolution. A layer of ultrafine grains, with a thickness within 20 μm, is formed on the machined surface. Results under different parameters demonstrate that the temperature has a more significant impact on the thickness of the ultrafine grain layer and the diameter of grains within the layer compared to the strain rate.展开更多
In CNC machining, two essential components decide the accuracy and machining time for a sculptured surface: one is the step-size interval, the other is the tool-path interval. Due to the limitation of the conventional...In CNC machining, two essential components decide the accuracy and machining time for a sculptured surface: one is the step-size interval, the other is the tool-path interval. Due to the limitation of the conventional method for calculating the tool-path interval, it cannot satisfy the machining requirement for high-speed and high-resolution machining. Accordingly, for high-speed and high-resolution machining, the current study proposes a new tool-path interval algorithm, plus a variable step-size algorithm for NURBS. Furthermore, a new type cutter, which can improve the cutting efficiency, is investigated in the paper. The transversal equation of the torus cutter onto the flat plan is given in this paper. The tool-path interval is calculated with the transversal equation and the proposed algorithm. The illustrated example shows that the redundant tool paths can be reduced because an accurate tool-path interval could be calculated.展开更多
Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learni...Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learning tools have been investigated for the past couple of decades.In recent years,recurrent neural networks(RNNs)have been observed to perform well on tasks involving sequence-based data in many research domains.With this motivation,we investigated the performance of long-short term memory(LSTM)and gated recurrent units(GRU)and their combination with the attention mechanism;LSTM+Attention,GRU+Attention,and LSTM+GRU+Attention.The methods were evaluated with stock data from three different stock indices:the KSE 100 index,the DSE 30 index,and the BSE Sensex.The results were compared to other machine learning models such as support vector regression,random forest,and k-nearest neighbor.The best results for the three datasets were obtained by the RNN-based models combined with the attention mechanism.The performances of the RNN and attention-based models are higher and would be more effective for applications in the business industry.展开更多
An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into...An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.The applicability and validity of MWSVM for stock returns forecasting is confirmed through experiments on real-world stock data.展开更多
The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from ne...The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspec-tives.The meme stock mania of 2021 brought together stock traders and investors that were also active on social media.This mania was in good part driven by retail investors’discussions on investment strategies that occurred on social media plat-forms such as Reddit during the COVID-19 lockdowns.The stock trades by these retail investors were then executed using services like Robinhood.In this paper,machine learning models are used to try and predict the stock price movements of two meme stocks:GameStop($GME)and AMC Entertainment($AMC).Two sentiment metrics of the daily social media discussions about these stocks on Red-dit are generated and used together with 85 other fundamental and technical indi-cators as the feature set for the machine learning models.It is demonstrated that through the use of a carefully chosen mix of a meme stock’s fundamental indica-tors,technical indicators,and social media sentiment scores,it is possible to pre-dict the stocks’next-day closing prices.Also,using an anomaly detection model,and the daily Reddit discussions about a meme stock,it was possible to identify potential market manipulators.展开更多
Predicting stock price movement direction is a challenging problem influenced by different factors and capricious events. The conventional stock price prediction machine learning models heavily rely on the internal fi...Predicting stock price movement direction is a challenging problem influenced by different factors and capricious events. The conventional stock price prediction machine learning models heavily rely on the internal financial features, especially the stock price history. However, there are many outside-of-company features that deeply interact with the companies’ stock price performance, especially during the COVID period. In this study, we selected 9 COVID vaccine companies and collected their relevant features over the past 20 months. We added handcrafted external information, including COVID-related statistics and company-specific vaccine progress information. We implemented, evaluated, and compared several machine learning models, including Multilayer Perceptron Neural Networks with logistic regression and decision trees with boosting and bagging algorithms. The results suggest that the application of feature engineering and data mining techniques can effectively enhance the performance of models predicting stock price movement during the COVID period. The results show that COVID-related handcrafted features help to increase the model prediction accuracy by 7.3% and AUROC by 6.5% on average. Further exploration showed that with data selection the decision tree model with gradient, boosting algorithm achieved 70% in AUROC and 66% in the accuracy.展开更多
Stocks in the Chinese stock market can be divided into ST stocks and normal stocks, so to prevent investors from buying potential ST stocks, this paper first performs SMOTEENN oversampling data preprocessing for the S...Stocks in the Chinese stock market can be divided into ST stocks and normal stocks, so to prevent investors from buying potential ST stocks, this paper first performs SMOTEENN oversampling data preprocessing for the ST stock category, and selects 139 financial indicators and technical factor as predictive features. Then, it combines the Boruta algorithm and Copula entropy method for feature selection, effectively improving the machine learning model’s performance in ST stock classification, with the AUC values of the two models reaching 98% on the test set. In the model selection and optimization, this paper uses six major models, including logistic regression, XGBoost, AdaBoost, LightGBM, Catboost, and MLP, for modeling and optimizes them using the Optuna framework. Ultimately, XGBoost model is selected as the best model because its AUC value exceeds 95% and its running time is less. Finally, the XGBoost model is explained using the SHAP theory and the interaction between features is discovered, further improving the model’s accuracy and AUC value by about 0.6%, verifying the effectiveness of the model.展开更多
The research focuses on improving predictive accuracy in the financial sector through the exploration of machine learning algorithms for stock price prediction. The research follows an organized process combining Agil...The research focuses on improving predictive accuracy in the financial sector through the exploration of machine learning algorithms for stock price prediction. The research follows an organized process combining Agile Scrum and the Obtain, Scrub, Explore, Model, and iNterpret (OSEMN) methodology. Six machine learning models, namely Linear Forecast, Naive Forecast, Simple Moving Average with weekly window (SMA 5), Simple Moving Average with monthly window (SMA 20), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM), are compared and evaluated through Mean Absolute Error (MAE), with the LSTM model performing the best, showcasing its potential for practical financial applications. A Django web application “Predict It” is developed to implement the LSTM model. Ethical concerns related to predictive modeling in finance are addressed. Data quality, algorithm choice, feature engineering, and preprocessing techniques are emphasized for better model performance. The research acknowledges limitations and suggests future research directions, aiming to equip investors and financial professionals with reliable predictive models for dynamic markets.展开更多
基金The authors'team acknowledges the continuous and invaluable support from the Natural Science Foundation of China under the grants of 51577165,51690182,51377140,and 51077116.
文摘In this paper,application examples of high-speed electrical machines are presented,and the machine structures are categorized.Key issues of design and control for the high-speed permanent magnet machines are reviewed,including bearings selection,rotor dynamics analysis and design,rotor stress analysis and protection,thermal analysis and design,electromagnetic losses analysis and reduction,sensorless control strategies,as well as comparison and selection of sine-wave and square-wave drive modes.Some challenges are also discussed,so that future studies could be focused.
基金This work was supported by the National Natural Science Foundation of China(51677144).
文摘Iron loss and copper loss are the significant parts of electrical loss of machines,which are the major parts particularly under high frequency condition.High-speed permanent magnet synchronous machines(HS-PMSM)have the benefits of high power density,high efficiency and wide speed range.Which causes the calculation for iron loss and copper loss in whole operating range complex.By analyzing the components and influencing factors of iron loss and copper loss in stator,we have deduced the calculation formula of iron loss and copper loss in whole operating range based on the analytical solution and finite element approach(EFA)solution.According to the calculation solution,taking the influence of operating temperature on the iron loss and copper loss into account,we propose a temperature correction factor and establish the calculation method for the iron loss and copper loss with temperature influences.Finally,by the conductor transposition,we restrain the circulating current under high-frequency operating condition.
文摘With the improvement of coal mining speed and mechanization level in China,traditional tunnel boring methods can no longer meet the actual needs.In order to solve the problems of low efficiency,high labor intensity,slow tunnel boring speed,bad working environment and poor safety in traditional tunnel boring,on the basis of analyzing the development and application of coal roadway tunnel boring equipment at home and abroad,complete equipment for high-speed tunnel boring and bolting machines was developed by using the integrated technology of tunnel boring and bolting.The complete equipment for high-speed tunnel boring and bolting machines has the functions of tunnel boring and bolting synchronization,once-tunneling,negative pressure dust removal,digital guidance,independent cutting feed,digital cutting,safety monitoring and data interaction,which has the advantages of safety in use,reliability and efficiency.
基金supported by the National Natural Science Foundation of China(U2039209,U1534202,51408564)Natural Science Foundation of Heilongjiang Province(LH2021E119)the National Key Research and Development Program of China(2018YFC1504003).
文摘Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.
基金Supported by National Natural Science Foundation of China(Grant No.50875171)National Hi-tech Research and Development Program of China(863 Program,Grant No.2009AA04Z150)
文摘A numerical control (NC) tool path of digital CAD model is widely generated as a set of short line segments in machining. However, there are three shortcomings in the linear tool path, such as discontinuities of tangency and curvature, huge number of line segments, and short lengths of line segments. These disadvantages hinder the development of high speed machining. To smooth the linear tool path and improve machining efficiency of short line segments, this paper presents an optimal feed interpolator based on G^2 continuous Bézier curves for the linear tool path. First, the areas suitable for fitting are screened out based on the geometric characteristics of continuous short segments (CSSs). CSSs in every area are compressed and fitted into a G^2 Continuous Bézier curve by using the least square method. Then a series of cubic Bézier curves are generated. However, the junction between adjacent Bézier curves is only G^0 continuous. By adjusting the control points and inserting Bézier transition curves between adjacent Bézier curves, the G^2 continuous tool path is constructed. The fitting error is estimated by the second-order Taylor formula. Without iteration, the fitting algorithm can be implemented in real-time environment. Second, the optimal feed interpolator considering the comprehensive constraints (such as the chord error constraint, the maximum normal acceleration, servo capacity of each axis, etc.) is proposed. Simulation and experiment are conducted. The results shows that the proposed method can generate smooth path, decrease the amount of segments and reduce machining time for machining of linear tool path. The proposed research provides an effective method for high-speed machining of complex 2-D/3-D profiles described by short line segments.
文摘Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.
基金the Major Program of the National Fund of Philosophy and Social Science of China(Nos.21&ZD109).
文摘Mangrove ecosystems have important ecological and economic values,especially their ability to store carbon.However,in recent years,human disturbance has accelerated mangrove degradation.Among them,the emission of pollutants cannot be ignored.It is of great significance for carbon emission reduction and ecological protection to study the impacts of different pollutants on mangroves and their carbon stocks.Based on the remote sensing data of coastal areas south of the Yangtze River in China's Mainland,this paper builds the ensemble learning model Random Forest(RF)and Gradient Boosting Regression(GBR)to empirically analyse the relationship between industrial wastewater,industrial sulfur dioxide(SO2),PM2.5 and mangrove forests.The results show that the pollutant concentration of meteorological normalisation is more stable.The importance of pollutants presents regional heterogeneity.The area of mangroves in different cities and the corresponding total carbon stocks show different trends with the increase or decrease of pollutants,and there is a dynamic balance between urban pollutant discharge and mangrove growth in some cities.The research in this paper provides an analysis and explanation from the perspective of machine learning to explore the relationship between mangroves and pollutants and at the same time,provides scientific suggestions for the formulation of future pollutant emission policies in different cities.
基金financially supported by the National Natural Science Foundation of China(Nos.51701171 and 51971187)the Partner State Key Laboratories in Hong Kong from the Innovation and Technology Commission(ITC)of the Government of the Hong Kong Special Administration Region(HKASR),Chinafinancial support from the PolyU Research Office(Project Code:1-BBXA)。
文摘Severe plastic deformation(SPD)-induced gradient nanostructured(GNS)metallic materials exhibit superior mechanical performance,especially the high strength and good ductility.In this study,a novel high-speed machining SPD technique,namely single point diamond turning(SPDT),was developed to produce effectively the GNS layer on the hexagonal close-packed(HCP)structural Mg alloy.The high-resolution transmission electron microscopy observations and atomistic molecular dynamics simulations were mainly performed to atomic-scale dissect the grain refinement process and corresponding plastic deformation mechanisms of the GNS layer.It was found that the grain refinement process for the formation of the GNS Mg alloy layer consists of elongated coarse grains,lamellar fine grains with deformation-induced-tension twins and contraction twins,ultrafine grains,and nanograins with the grain size of~70 nm along the direction from the inner matrix to surface.Specifically,experiment results and atomistic simulations reveal that these deformation twins are formed by gliding twinning partial dislocations that are dissociated from the lattice dislocations piled up at grain boundaries.The corresponding deformation mechanisms were evidenced to transit from the deformation twinning to dislocation slip when the grain size was below 2.45μm.Moreover,the Hall-Petch relationship plot and the surface equivalent stress along the gradient direction estimated by finite element analysis for the SPDT process were incorporated to quantitatively elucidate the transition of defo rmation mechanisms during the grain refinement process.Our findings have implications for the development of the facile SPD technique to construct high strength-ductility heterogeneous GNS metals,especially for the HCP metals.
基金Sponsored by the National Excellent Young Teacher Encouragement Plan of China
文摘To improve the efficiency of CNC machining, assumptive transit circular arc is used to contour two adjacent moves together on the comer to make smooth paths. The radios of transit circular arc can be adjusted with contour accuracy, and the feed rate on the corner can be controlled through limiting the maximum feed rate of transit circular arc segment. A look-ahead algorithm for a series of moves is proposed for speed adjustment in advance, which avoids the occurrence of overload of cutting tool on the comer and reduces the servo track error of parts on the corner or of circular arc move. Equivalent trapezoidal velocity profile is used to analyze the speed of S-curve velocity profile and work out its accurate interpolation, which overcomes the disadvantage of looking up table to calculate feed rate approximately, hence high accuracy and fine surface quality can be obtained while the machining speed is high. The proposed methods can meet the requirements of real-time analysis of high-speed machining. The presented algorithm is effective and has been adopted by CNC system of newly developed high-speed milling machine.
文摘Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT,Ministry of Science and ICT)[RS-2025-005183388]supported by the"Regional Innovation System&Education(RISE)"through the Seoul RISE Center,funded by the Ministry of Education(MOE)and the Seoul Metropolitan Government(2025-RISE-01-018-01).
文摘We examine how machine learning models predict stock returns in the Korean market.By analyzing various firm characteristics and macroeconomic variables,we find that tree-based models outperform other machine learning approaches.This finding suggests that,in data-constrained contexts,moderately complex models outperform advanced methods that require extensive datasets.Using PFI,SHAP,and LIME,we consistently identify the 36-month momentum as the key predictor.PDP,ICE,and ALE analyses reveal threshold effects of 36-month momentum that diminish at higher return levels.Our findings underscore the value of ensemble-based methods in settings characterized by short data histories and heightened volatility.This study illustrates how multimethod interpretability can yield deeper economic insights,ultimately guiding more effective investment strategies and policy decisions.
文摘Applying high-speed machining technology in shop floor has many benefits, such as manufacturing more accurate parts with better surface finishes. The selection of the appropriate machining parameters plays a very important role in the implementation of high-speed machining technology. The case-based reasoning is used in the developing of high-speed machining database to overcome the shortage of available high-speed cutting parameters in machining data handbooks and shop floors. The high-speed machining database developed in this paper includes two main components: the machining database and the case-base. The machining database stores the cutting parameters, cutting tool data, work pieces and their materials data, and other relative data, while the case-base stores mainly the successfully solved cases that are problems of work pieces and their machining. The case description and case retrieval methods are described to establish the case-based reasoning high-speed machining database. With the case retrieval method, some succeeded cases similar to the new machining problem can be retrieved from the case-base. The solution of the most matched case is evaluated and modified, and then it is regarded as the proposed solution to the new machining problem. After verification, the problem and its solution are packed up into a new case, and are stored in the case-base for future applications.
基金supported by National Natural Science Foundation of China(Nos.92160301,92360309)Science Center for Gas Turbine Project(Grant No.P2022-AB-Ⅳ-001-002)+1 种基金Shaanxi Provincial Key Research and Development Program(No.2021ZDLGY10-06)Innovation Capability Support Program of Shaanxi(Program No.2022TD-60).
文摘Excellent surface integrity is an eternal pursuit in high performance manufacturing, with microstructure being a crucial component of the surface integrity dataset and a key factor controlling surface properties such as fatigue and creep. The multi-physical fields generated by thermomechanical loads during high-speed machining act on the processed surface layer, influencing the evolution of microstructures. To investigate the microstructural evolution mechanisms of ATI718plus during high-speed machining, cutting experiments and techniques such as Electron back scatter diffraction(EBSD), Transmission Kikuchi diffraction(TKD), and Precession electron diffraction(PED) is conducted to quantitatively analyze the microstructures in the chip shear zone and the machined surface. Subsequently, a combined finite element(FE) and cellular automata(CA) model is developed to simulate the microstructure evolution during the cutting process. The discontinuous dynamic recrystallization(DDRX) mechanism is employed to demonstrate the nucleation and growth of grains under the influence of multiple physical fields. The simulation and experimental results show similar dynamic recrystallization(DRX) grain sizes, indicating acceptable accuracy of the CA model in terms of DRX grain size. The comparison between experimental and simulation results confirms the occurrence of both continuous dynamic recrystallization(CDRX) and DDRX during the cutting process. The synergistic competition between CDRX induced grain lamellar refinement and DDRX induced grain growth emerge as the primary mechanism driving microstructural evolution. A layer of ultrafine grains, with a thickness within 20 μm, is formed on the machined surface. Results under different parameters demonstrate that the temperature has a more significant impact on the thickness of the ultrafine grain layer and the diameter of grains within the layer compared to the strain rate.
文摘In CNC machining, two essential components decide the accuracy and machining time for a sculptured surface: one is the step-size interval, the other is the tool-path interval. Due to the limitation of the conventional method for calculating the tool-path interval, it cannot satisfy the machining requirement for high-speed and high-resolution machining. Accordingly, for high-speed and high-resolution machining, the current study proposes a new tool-path interval algorithm, plus a variable step-size algorithm for NURBS. Furthermore, a new type cutter, which can improve the cutting efficiency, is investigated in the paper. The transversal equation of the torus cutter onto the flat plan is given in this paper. The tool-path interval is calculated with the transversal equation and the proposed algorithm. The illustrated example shows that the redundant tool paths can be reduced because an accurate tool-path interval could be calculated.
基金supported by NRPU Project No.20-16091awarded by Higher Education Commission,PakistanThe title of the project is“University Education and Occupational Skills Mismatch (A Case Study of SMEs in Khyber Pakhtunkhwa)”,by the National Natural Science Foundation of China (Grant No.61370073)the National High Technology Research and Development Program of China,the project of Science and Technology Department of Sichuan Province (Grant No.2021YFG0322).
文摘Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learning tools have been investigated for the past couple of decades.In recent years,recurrent neural networks(RNNs)have been observed to perform well on tasks involving sequence-based data in many research domains.With this motivation,we investigated the performance of long-short term memory(LSTM)and gated recurrent units(GRU)and their combination with the attention mechanism;LSTM+Attention,GRU+Attention,and LSTM+GRU+Attention.The methods were evaluated with stock data from three different stock indices:the KSE 100 index,the DSE 30 index,and the BSE Sensex.The results were compared to other machine learning models such as support vector regression,random forest,and k-nearest neighbor.The best results for the three datasets were obtained by the RNN-based models combined with the attention mechanism.The performances of the RNN and attention-based models are higher and would be more effective for applications in the business industry.
基金the Hunan Natural Science Foundation(No. 09JJ3129)the Hunan Key Social Science Foundation (No. 09ZDB04)the Hunan Social Science Foundation (No. 08JD28)
文摘An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.The applicability and validity of MWSVM for stock returns forecasting is confirmed through experiments on real-world stock data.
文摘The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspec-tives.The meme stock mania of 2021 brought together stock traders and investors that were also active on social media.This mania was in good part driven by retail investors’discussions on investment strategies that occurred on social media plat-forms such as Reddit during the COVID-19 lockdowns.The stock trades by these retail investors were then executed using services like Robinhood.In this paper,machine learning models are used to try and predict the stock price movements of two meme stocks:GameStop($GME)and AMC Entertainment($AMC).Two sentiment metrics of the daily social media discussions about these stocks on Red-dit are generated and used together with 85 other fundamental and technical indi-cators as the feature set for the machine learning models.It is demonstrated that through the use of a carefully chosen mix of a meme stock’s fundamental indica-tors,technical indicators,and social media sentiment scores,it is possible to pre-dict the stocks’next-day closing prices.Also,using an anomaly detection model,and the daily Reddit discussions about a meme stock,it was possible to identify potential market manipulators.
文摘Predicting stock price movement direction is a challenging problem influenced by different factors and capricious events. The conventional stock price prediction machine learning models heavily rely on the internal financial features, especially the stock price history. However, there are many outside-of-company features that deeply interact with the companies’ stock price performance, especially during the COVID period. In this study, we selected 9 COVID vaccine companies and collected their relevant features over the past 20 months. We added handcrafted external information, including COVID-related statistics and company-specific vaccine progress information. We implemented, evaluated, and compared several machine learning models, including Multilayer Perceptron Neural Networks with logistic regression and decision trees with boosting and bagging algorithms. The results suggest that the application of feature engineering and data mining techniques can effectively enhance the performance of models predicting stock price movement during the COVID period. The results show that COVID-related handcrafted features help to increase the model prediction accuracy by 7.3% and AUROC by 6.5% on average. Further exploration showed that with data selection the decision tree model with gradient, boosting algorithm achieved 70% in AUROC and 66% in the accuracy.
文摘Stocks in the Chinese stock market can be divided into ST stocks and normal stocks, so to prevent investors from buying potential ST stocks, this paper first performs SMOTEENN oversampling data preprocessing for the ST stock category, and selects 139 financial indicators and technical factor as predictive features. Then, it combines the Boruta algorithm and Copula entropy method for feature selection, effectively improving the machine learning model’s performance in ST stock classification, with the AUC values of the two models reaching 98% on the test set. In the model selection and optimization, this paper uses six major models, including logistic regression, XGBoost, AdaBoost, LightGBM, Catboost, and MLP, for modeling and optimizes them using the Optuna framework. Ultimately, XGBoost model is selected as the best model because its AUC value exceeds 95% and its running time is less. Finally, the XGBoost model is explained using the SHAP theory and the interaction between features is discovered, further improving the model’s accuracy and AUC value by about 0.6%, verifying the effectiveness of the model.
文摘The research focuses on improving predictive accuracy in the financial sector through the exploration of machine learning algorithms for stock price prediction. The research follows an organized process combining Agile Scrum and the Obtain, Scrub, Explore, Model, and iNterpret (OSEMN) methodology. Six machine learning models, namely Linear Forecast, Naive Forecast, Simple Moving Average with weekly window (SMA 5), Simple Moving Average with monthly window (SMA 20), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM), are compared and evaluated through Mean Absolute Error (MAE), with the LSTM model performing the best, showcasing its potential for practical financial applications. A Django web application “Predict It” is developed to implement the LSTM model. Ethical concerns related to predictive modeling in finance are addressed. Data quality, algorithm choice, feature engineering, and preprocessing techniques are emphasized for better model performance. The research acknowledges limitations and suggests future research directions, aiming to equip investors and financial professionals with reliable predictive models for dynamic markets.
