High-Speed Permanent Magnet Electrical Machines - Applications, Key Issues and Challenges

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.

Development and Application of Complete Equipment for High-speed Tunnel Boring and Bolting Machines

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.

An Optimal Feed Interpolator Based on G^2 Continuous Bézier Curves for High-Speed Machining of Linear Tool Path

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.

High-Speed and Low-Noise Gear Finishing by Gear Grinding and Honing:A Review

Gears are important mechanical parts for transmitting power and play an increasingly important role in the machinery industry.The electric vehicle industry developed rapidly and became a vital development field of the automotive industry over the past few years.The conversion of energy increases the requirements for electric vehicle transmissions,which promotes the development of high-speed and low-noise transmission gear.This paper elaborates on the research progress of high-speed and low-noise gear finishing methods.Firstly,this review analyses the machining requirements,finishing,and specific machining technologies of high-speed and low-noise gears.The importance of gear grinding and honing in high-speed and low-noise gear machining is highlighted.Secondly,the applications of gear grinding and honing in gear modification,error compensation,and texture control are analyzed.Furthermore,the role of precision machining technology in improving gear performance is clarified.Finally,the design and processing methods of the modified tooth flank for gear modification are summarized.In addition,the influence of tooth surface texture on noise is described,and the texture change methods are explored.The well-known open problems of gear finishing are finally identified,and some new research interests are also pointed out.This review will provide valuable references for further research on gear finishing.

Adaptation of feed rate for 3-axis CNC high-speed machining

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.

Stock Type Prediction Based on Multiple Machine Learning Methods

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.

Comparative Analysis of Machine Learning Models for Stock Price Prediction: Leveraging LSTM for Real-Time Forecasting

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.

Calculation for Stator Loss of High-Speed Permanent Magnet Synchronous Machine in Torque-speed Envelope and Restraint Approach for Circulating Current in Windings

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.

Development of High-speed Machining Database with Case-based Reasoning

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.

Tool-path planning for free-form surface high-speed high-resolution machining using torus cutter

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.

Continuous prediction method of earthquake early warning magnitude for high-speed railway based on support vector machine

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.

Microstructure and cutting performance of CrTiAlN coating for high-speed dry milling

Using a closed field unbalanced magnetron sputtering system,the cemented carbide end mills were coated with a CrTiAlN hard coating,which consisted of a Cr adhesive layer,a CrN interlayer and a CrTiAlN top layer.The microstructure and mechanical properties of the coating were investigated by X-ray diffraction（XRD）,scanning electron microscopy（SEM）,transmission electron microscopy（TEM）,micro indentation and scratch test.The cutting performance of the coated end mills were conducted by high-speed dry milling hardened steel（P20,HRC 45）.The results indicates that the coating is composed of（Cr,Ti,Al）N columnar grains with nanolayers.The coating exhibits good adhesion to cemented carbide substrate and high microhardness of around 30 GPa.The coated end mills show significant improvement on tool life and much lower cutting force as compared to the uncoated ones.And the related mechanisms were discussed.

Predicting the daily return direction of the stock market using hybrid machine learning algorithms

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.

Forecasting Stock Prices with an Integrated Approach Combining ARIMA and Machine Learning Techniques ARIMAML

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.

Research Progress of Key Technology of High-Speed and High Precision Motorized Spindles

High speed machining and high precision machining are two tendencies of themanufacturing technology worldwide. The motorized spindle is the core component of the machine toolsfor achieving the high speed and high precise machining, which affects the general developmentlevel of the machine tools to a great extent. Progress of the key techniques is reviewed in thispaper, in which the high speed and high precision spindle bearings, the dynamical and thermalcharacteristics of spindles, the design technique of the high frequency motors and the drivers, theanti-electromagnetic damage technique of the motors, and the machining and assembling technique areinvolved. Finally, tha development tendencies of the motorized spindles are presented.

Stock Market Index Prediction Using Machine Learning and Deep Learning Techniques

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.

Analysis of Social Media Impact on Stock Price Movements Using Machine Learning Anomaly Detection

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.

A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID

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.

Stock Price Prediction Based on the Bi-GRU-Attention Model

The stock market, as one of the hotspots in the financial field, forms a data system with a huge volume of data and complex relationships between various factors, making stock price prediction an area of keen interest for further in-depth mining and research. Mathematical statistics methods struggle to deal with nonlinear relationships in practical applications, making it difficult to explore deep information about stocks. Meanwhile, machine learning methods, particularly neural network models and composite models, which have achieved outstanding results in other fields, are being applied to the stock market with significant results. However, researchers have found that these methods do not grasp the essential information of the data as well as expected. In response to these issues, researchers are exploring better neural network models and combining them with other methods to analyze stock data. Thus, this paper proposes the ABiGRU composite model, which combines the attention mechanism and bidirectional gated recurrent unit (GRU) that can effectively extract data features for stock price prediction research. Models such as LSTM, GRU, and Bi-LSTM are selected for comparative experiments. To ensure the credibility and representativeness of the research data, daily stock price indices of BYD are chosen for closing price prediction studies across different models. The results show that the ABiGRU model has a lower prediction error and better fitting effect on three index-based stock prices, enhancing the learning efficiency of the neural network model and demonstrating good prediction stability. This suggests that the ABiGRU model is highly adaptable for stock price prediction.

基于LSTM模型的股票价格预测

股票市场的价格波动被视为经济发展的晴雨表。对股票价格的精准预测一直是众多研究学者努力的方向。随着人工智能技术与大数据技术的不断应用与发展以及疫情防控期间国内经济变化和国际形势变换给股价带来的巨大波动,如何对股价进行精准预测变得越来越重要。本文根据股票市场的特点和LSTM(Long Short-Term Memory)递归神经网络的特性,对浦发银行(600000)股价进行预测。实验结果表明,LSTM模型预测股价,结果误差小,精准度高,具有良好的预测效果。