Optimal zero-crossing group selection method of the absolute gravimeter based on improved auto-regressive moving average model

An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.

Deep Learning-Based Stock Price Prediction Using LSTM Model

The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.

Application of Seasonal Auto-regressive Integrated Moving Average Model in Forecasting the Incidence of Hand-foot-mouth Disease in Wuhan,China

Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.

Noise reduction of acoustic Doppler velocimeter data based on Kalman filtering and autoregressive moving average models

Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.

Influencing Factors and Prediction of Risk of Returning to Ecological Poverty in Liupan Mountain Region,China

China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.

Cyclic moving average control approach to cylinder pressure and its experimental validation

Cyclic variability is a factor adversely affecting engine performance. In this paper a cyclic moving average regulation approach to cylinder pressure at top dead center （TDC） is proposed, where the ignition time is adopted as the control input. The dynamics from ignition time to the moving average index is described by ARMA model. With this model, a one-step ahead prediction-based minimum variance controller （MVC） is developed for regulation. The performance of the proposed controller is illustrated by experiments with a commercial car engine and experimental results show that the controller has a reliable effect on index regulation when the engine works under different fuel injection strategies, load changing and throttle opening disturbance.

ARMA-GM combined forewarning model for the quality control

Three forecasting models are set up: the auto\|regressive moving average model, the grey forecasting model for the rate of qualified products P t, and the grey forecasting model for time intervals of the quality catastrophes. Then a combined forewarning system for the quality of products is established, which contains three models, judgment rules and forewarning state illustration. Finally with an example of the practical production, this modeling system is proved fairly effective.

山东省中医类医院卫生人力资源需求预测

目的了解山东省中医类医院卫生人力资源的现状,预测卫生人力资源未来的需求量并提出合理建议,以期为相关部门制定中医药人力资源规划提供依据和数据支持。方法运用差分自回归移动平均(auto-regressive moving average,ARIMA)模型、灰色系统预测模型(grey system forecasting model,GM)中的GM(1,1)模型以及两者的线性组合模型预测2021—2025年山东省中医类医院卫生人力资源需求量,比较不同模型预测的精准度。结果组合模型的系统误差小,预测效果最好;卫生技术人员、执业(助理)医师、中医类别执业(助理)医师、注册护士、药师(士)及中药师(士)2025年对应的人力资源预测值分别是107457人、43304人、22807人、51372人、5718人、3242人。结论山东省中医类别执业(助理)医师数量储备充足,但中药师(士)相对短缺,人才结构不合理,医护比有待优化。建议政府适当地增加中药师(士)的编制,促进执业(助理)医师与中药师(士)平衡发展;增加对中医类医院的财政拨款,加强人才引进力度,创新人才培养机制,优化山东省中医药人才结构;制定科学合理的排班制度,提高护士的社会地位,进一步优化医护比。

A Temporal Convolutional Network Based Hybrid Model for Short-term Electricity Price Forecasting

Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies.To improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency components.Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model.Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.

A Peridynamic Approach for the Evaluation of Metal Ablation under High Temperature

In this paper,the evaluations of metal ablation processes under high temperature,i.e.,the Al plate ablated by a laser and a heat carrier and the reactor pressure vessel ablated by a core melt,are studied by a novel peridynamic method.Above all,the peridynamic formulation for the heat conduction problem is obtained by Taylor’s expansion technique.Then,a simple and efficient moving boundary model in the peridynamic framework is proposed to handle the variable geometries,in which the ablated states of material points are described by an additional scalar field.Next,due to the automatic non-interpenetration properties of peridynamic method,a contact algorithm is established to determine the contact relationship between the ablated system and the additional heat carrier.In addition,the corresponding computational procedure is listed in detail.Finally,several numerical examples are carried out and the results verify the validity and accuracy of the present method.

Autoregressive moving average model for matrix time series

In the paper,the autoregressive moving average model for matrix time series(MARMA)is inves-tigated.The properties of the MARMA model are investigated by using the conditional least square estimation,the conditional maximum likelihood estimation,the projection theorem in Hilbert space and the decomposition technique of time series,which include necessary and suf-ficient conditions for stationarity and invertibility,model parameter estimation,model testing and model forecasting.

Stage separation of recoverable liquid launch vehicle by using moving pulsating ball analogue for propellant sloshing

In the process of stage separation of recoverable liquid launch vehicles,because of the large amount of residual fuel in the storage tanks,the influence of liquid sloshing on separation safety must be considered.Considering calculation simplicity and operation practicability,the Moving Pulsating Ball Model(MPBM)of large amplitude liquid sloshing is introduced into the calculation of launch vehicle stage separation.Combining the dynamic equation of the model with the energy relationship during"breathing movement",the formula calculating the force of liquid on the rigid body is derived.Compared with the calculations of commercial CFD calculation software,the accuracy of MPBM model is verified.Then,all the external forces and moments are applied to the rigid body of the stages,so that the translational and rotational dynamic equations of the stages are obtained respectively.According to the relative position of the two stages,the geometric shape of the interstage section and the engine of the second stage,the minimum clearance in the separation process can be decided to guarantee that the separation process is safe.

Mouthguard Thermoforming Method to Decrease Palatal Thickness While Maintaining Labial and Buccal Thickness

Wearing a mouthguard reduces the risk of sports-related injuries, but a more comfortable design is required in order to increase the wearing rate. The aim of this study was to investigate a thermoforming method that decreases palatal thickness while maintaining labial and buccal thickness. Mouthguards were fabricated from an ethylene-vinyl acetate sheet (thickness: 4.0 mm) by using a vacuum forming machine. Four working models were prepared: 1) the anterior height was 25-mm and the posterior height was 20-mm (model A), 2) model A with the palate trimmed (model B), 3) heights 5 mm greater than model A (model C), and 4) model C with the palate trimmed (model D). The two forming conditions were as follows: 1) The sheet was formed when it sagged 15 mm below the level of the sheet frame at the top of the post under ordinary use (control);2) The sheet frame at the top of the post was lowered and the sheet covered the model when it sagged by 15 mm. The rear side of the model was pushed to move the model forward 20 mm, and then the sheet was formed (MP). Differences in mouthguard thickness due to forming conditions and model forms were analyzed by two-way analysis of variance and Bonferroni’s multiple comparison tests. Difference in forming conditions was similar for all model forms;for the MP, the thickness of the incisal edge, labial surface, cusp and buccal surface were greater, and the palatal surface was thinner than the control. On the labial and buccal surface, the thickness difference due to the model form was observed only for the MP, and models A and B were thicker than models C and D. The palatal thickness tended to be thin in the models with the trimmed palate. This study suggested that the labial and buccal thickness of the mouthguard can be maintained, and the palatal thickness can be decreased by using the model with the palate trimmed with the forming method in which the model position is moved forward immediately before the vacuum formation.

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

Multi-objective optimization scheduling for new energy power system considering energy storage participation

For the low utilization rate of photovoltaic power generation,taking a new energy power system constisting of concentrating solar power(CSP),photovoltaic power(PP)and battery energy storage system as an example,a multi-objective optimization scheduling strategy considering energy storage participation is proposed.Firstly,the new energy power system model is established,and the PP scenario generation and reduction frame based on the autoregressive moving average model and Kantorovich-distance is proposed.Then,based on the optimization goal of the system operation cost minimization and the PP output power consumption maximization,the multi-objective optimization scheduling model is established.Finally,the simulation results show that introducing energy storage into the system can effectively reduce the system operation cost and improve the utilization efficiency of PP.

Exponential Bounds for Ruin Probability in Two Moving Average Risk Models with Constant Interest Rate

The authors consider two discrete-time insurance risk models. Two moving average risk models are introduced to model the surplus process, and the probabilities of ruin are examined in models with a constant interest force. Exponential bounds for ruin probabilities of an infinite time horizon are derived by the martingale method.

A data-driven method to predict future bottlenecks in a remanufacturing system with multi-variant uncertainties

The remanufacturing system is remolding the manufacturing industry by bringing scrapped products back to such a condition that reintegrated performance is just as good as new.The remanufacturing environment is featured by a far deeper level of uncertainty than new manufacturing,such as probabilistic routing files,and highly variable processing time.The stochastic disturbances result in the production bottlenecks,which constrain the productivity of the job shop.The uncertainties in the remanufacturing process cause the bottlenecks to shift when the workshop is processing.Considering this outstanding problem,many researchers try to optimize the production process to mitigate dynamic bottlenecks toward a balanced state.This paper proposes a data-driven method to predict bottlenecks in the remanufacturing system with multi-variant uncertainties.Firstly,discrete event simulation technology is applied to establish a simulation model of the remanufacturing production line and calculate the bottleneck index to identify bottlenecks.Secondly,a data-driven method,auto-regressive moving average(ARMA)model is employed to predict the bottlenecks in the system based on real-time data captured by the Arena software.Finally,the proposed prediction method is verified on real data from the automobile engine remanufacturing production line.

Fusing moving average model and stationary wavelet decomposition for automatic incident detection:case study of Tokyo Expressway

Traffic congestion is a growing problem in urban areas all over the world. The transport sector has been in full swing event study on intelligent transportation system for automatic detection. The functionality of automatic incident detection on expressways is a primary objective of advanced traffic management system. In order to save lives and prevent secondary incidents, accurate and prompt incident detection is necessary. This paper presents a methodology that integrates moving average （MA） model with stationary wavelet decomposition for automatic incident detection, in which parameters of layer coefficient are extracted from the difference between the upstream and downstream occupancy. Unlike other wavelet-based method presented before, firstly it smooths the raw data with MA model. Then it uses stationary wavelet to decompose, which can achieve accurate reconstruction of the signal, and does not shift the signal transfer coefficients. Thus, it can detect the incidents more accurately. The threshold to trigger incident alarm is also adjusted according to normal traffic condition with con- gestion. The methodology is validated with real data from Tokyo Expressway ultrasonic sensors. Ex- perimental results show that it is accurate and effective, and that it can differentiate traffic accident from other condition such as recurring traffic congestion.

Time-series analysis of monthly rainfall data for the Mahanadi River Basin, India

Time series analysis has two goals, modeling random mechanisms and predicting future series using historical data. In the present work, a uni-variate time series autoregressive integrated moving average （ARIMA） model has been developed for （a） simulating and forecasting mean rainfall, obtained using Theissen weights; over the Mahanadi River Basin in India, and （b） simula^ag and forecasting mean rainfall at 38 rain-gauge stations in district towns across the basin. For the analysis, monthly rainfall data of each district town for the years 1901-2002 （102 years） were used. Theissen weights were obtained over the basin and mean monthly rainfall was estimated. The trend and seasonality observed in ACF and PACF plots of rainfall data were removed using power transformation （a=0.5） and first order seasonal differencing prior to the development of the ARIMA model. Interestingly, the AR1MA model （1,0,0）（0,1,1）12 developed here was found to be most suitable for simulating and forecasting mean rainfall over the Mahanadi River Basin and for all 38 district town rain-gauge stations, separately. The Akaike Information Criterion （AIC）, good- ness of fit （Chi-square）, R2 （coefficient of determination）, MSE （mean square error） and MAE （mea absolute error） were used to test the validity and applicability of the developed ARIMA model at different stages. This model is considered appropriate to forecast the monthly rainfall for the upcoming 12 years in each district town to assist decision makers and policy makers establish priorities for water demand, storage, distribution, and disaster management.

Damage Localization of Marine Risers Using Time Series of Vibration Signals

Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average(ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive(AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect.