The literature generally agrees that longer-horizon(over a month) predictions make more sense than short-horizon ones. However, it's an especially challenging task due to the lack of data(in unit of long horizon)a...The literature generally agrees that longer-horizon(over a month) predictions make more sense than short-horizon ones. However, it's an especially challenging task due to the lack of data(in unit of long horizon)and economic data have a low S/N ratio. We hypothesize that the stock trend is largely dictated by driving factors which are filtered by psychological factors and work on behavioral factors: representative indicators from these three aspects would be adequate in trend prediction. We then extend the Stepwise Regression Analysis(SRA)algorithm to constrained SRA(c SRA) to carry out a further feature selection and lag optimization. During modeling stage, we introduce the Deep Neural Network(DNN) model in stock prediction under the suspicion that economic interactions are too complex for shallow networks to capture. Our experiments indeed show that deep structures generally perform better than shallow ones. Instead of comparing to a kitchen sink model, where over-fitting can easily happen with a shortage of data, we turn around and use a model ensemble approach which indirectly demonstrates our proposed method is adequate.展开更多
Numerous studies deal with spatial analysis of green innovation(GI).However,researchers have paid limited attention to analyzing the multi-scale evolution patterns and predicting trends of GI in China.This paper seeks...Numerous studies deal with spatial analysis of green innovation(GI).However,researchers have paid limited attention to analyzing the multi-scale evolution patterns and predicting trends of GI in China.This paper seeks to address this research gap by examining the multi-scale distribution and evolutionary characteristics of GI activities based on the data from 337 cities in China during 2000-2019.We used scale variance and the two-stage nested Theil decomposition method to examine the spatial distribution and inequalities of GI in China at multiple scales,including regional,provincial,and prefectural.Additionally,we utilized the Markov chain and spatial Markov chain to explore the dynamic evolution of GI in China and predict its long-term development.The findings indicate that GI in China has a multi-scale effect and is highly sensitive to changes in spatial scale,with significant spatial differences of GI decreasing in each scale.Furthermore,the spatiotemporal evolution of GI is influenced by both geospatial patterns and spatial scales,exhibiting the“club convergence”effect and a tendency to transfer to higher levels of proximity.This effect is more pronounced on a larger scale,but it is increasingly challenging to transfer to higher levels.The study also indicates a steady and sustained growth of GI in China,which concentrates on higher levels over time.These results contribute to a more precise understanding of the scale at which GI develops and provide a scientific basis and policy suggestions for optimizing the spatial structure of GI and promoting its development in China.展开更多
Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-n...Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX.展开更多
Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the...Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.展开更多
The Longyangxia Gorge Key Water Control System is the first of the stairstep power sations along the Longyangxi-a-Qingtongxia river section. It has been playing an very important role in providing power, protecting fl...The Longyangxia Gorge Key Water Control System is the first of the stairstep power sations along the Longyangxi-a-Qingtongxia river section. It has been playing an very important role in providing power, protecting flood and ice run supplying and irrigation etc. in the northwestern China. Therefore, the study on trend prediction, variation on the flow into the Longyangxia Reservoir are of the great social and economic benefits. In the medium-and-long-range runoff forecast, all kinds of regression equation are often used for predicting future hydrologic regime. However, these regression models aren’t appropriate to super long -range runoff forecast because of the restricting on weather data and so on. So a new super long-range runoff forecast model don’t depend on Reai-time weather data and called “Period correcting for residual error series GM (1, 1) model” is presented based on analyzing for the relational hydrologic data and the variation on the flow into the Longyangxia Reservoir, and the forecast model was applied successfully to predict the recent and super long -term trends of the flow into the Longyangxia Reservoir. The results indicate that the annual flow into the Longyangxia Reservoir is in the ending minimum period of the runoff history. The runoff increasing is expected in for the coming years.展开更多
To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,...To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,which is then fed into downstream learning modules.Co-SFM employs an upstream fusion module to incorporate multilevel data,thereby constructing a macro-plate-micro data structure.This configuration helps identify and integrate characteristics from different data levels,facilitating a deeper understanding of the internal links within the financial system.In the downstream model,Co-SFM uses a state-frequency memory network to mine hidden frequency information within stock prices,and the multifrequency patterns of sequential data are modeled.Empirical results show that Co-SFM s prediction accuracy for stock price trends is significantly better than that of other models.This is especially evident in multistep medium and long-term trend predictions,where integrating multilevel data results in notably improved accuracy.展开更多
Rail corrugation, as a prevalent type of rail damage in heavy railways, induces diseases in the track structure. In order to ensure the safe operation of trains, an improved whale optimization algorithm is proposed to...Rail corrugation, as a prevalent type of rail damage in heavy railways, induces diseases in the track structure. In order to ensure the safe operation of trains, an improved whale optimization algorithm is proposed to optimize the rail corrugation evolution trend prediction model of the least squares support vector machine (IPCA-ELWOA-LSSVM). The elite reverse learning combined with the Lévy flight strategy is introduced to improve the whale optimization algorithm. The improved WOA (ELWOA) algorithm is used to continuously optimize the kernel parameter σ and the normalization parameter γ in the LSSVM model. Finally, the improved prediction model is validated using data from a domestic heavy-duty railway experimental line database and compared with the prediction model before optimization and the other commonly used models. The experimental results show that the ELWOA-LSSVM prediction model has the highest accuracy, which proves that the proposed method has high accuracy in predicting the rail corrugation evolution trend.展开更多
Purpose:The study examines the synergy and hysteresis in the evolution of funding and its supported literature,depicts their temporal correlation mechanism,which aids in improving trend predictions.Design/methodology/...Purpose:The study examines the synergy and hysteresis in the evolution of funding and its supported literature,depicts their temporal correlation mechanism,which aids in improving trend predictions.Design/methodology/approach:The study uses the LDA model to identify topics in funding texts and supported papers.A cosine similarity algorithm was employed to estimate the nexus between topics and construct the topic evolution time series.Similarly,the hysteresis effect in topic evolution is analyzed based on topic popularity and content,leading to insights into their temporal correlation mechanism.Findings:The study finds that fund and sponsored paper topics exhibit strong collaboration with a noticeable lag in evolution.The fund topics significantly influence sponsored paper topics after a two-year lag.Moreover,the lag effect is inversely proportional to the topic’s similarity.Research limitations:We use the LDA model to determine the hysteresis effect in topic evolution despite its limitations in handling long-tail words and domain-specific vocabulary.Furthermore,the timing of the emergence of the focal topic in funds is undermined,affecting the findings.Practical implications:These findings enhance the accuracy and scientific validity of trend prediction.Estimating and identifying patterns can help technology managers anticipate future research hotspots,supporting informed decision-making and technology management.Originality/value:This study introduces a research framework to quantitatively and visually analyze the hysteresis effect,revealing the correlation and evolutionary patterns between fund research topics and their funded papers.展开更多
Mechanical vibration defect is the key factor leading to sudden failure of gas-insulated switchgear(GIS)equipment.It is important to realise effective prediction of the me-chanical vibration state development trend of...Mechanical vibration defect is the key factor leading to sudden failure of gas-insulated switchgear(GIS)equipment.It is important to realise effective prediction of the me-chanical vibration state development trend of GIS equipment in order to improve its active safety protection level.This paper carried out research on the accurate prediction method and experimental validation of the mechanical vibration state and its defect severity development trend for the GIS equipment.Firstly,the deep and shallow vibration feature parameters for different mechanical defect signals were jointly extracted by time-domain features and deep belief network methods.Secondly,a new prediction model,incorporating the attention mechanism and the bidirectional gated recurrent unit(BiGRU),was constructed with the deep and shallow vibration feature parameters as inputs.Finally,the prediction trend effectiveness was verified based on the real-type GIS mechanical simulation platform and the field operation GIS equipment.Results show that the deep and shallow vibration feature extraction method proposed in this paper can characterise the mechanical defect information more comprehensively.The new prediction method of the vibration state trend based on the attention-BiGRU model shows ideal accuracy,and the predicted vibration state development trend is highly consistent with the actual,with an average absolute error of 0.063.The root mean square error(ERMSE)value of the prediction method is<5%,which reduces the relative error value at least 37% compared with the traditional prediction models.This paper provides a valuable reference for the proactive defence of GIS mechanical failure.展开更多
Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission perf...Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.展开更多
In order to make trend analysis and prediction to acquisition data in amechanical equipment condition monitoring system, a new method of trend feature extraction andprediction of acquisition data is proposed which con...In order to make trend analysis and prediction to acquisition data in amechanical equipment condition monitoring system, a new method of trend feature extraction andprediction of acquisition data is proposed which constructs an adaptive wavelet on the acquisitiondata by means of second generation wavelet transform ( SGWT), Firstly, taking the vanishing momentnumber of the predictor as a constraint, the linear predictor and updater are designed according tothe acquisition data by using symmetrical interpolating scheme. Then the trend of the data isobtained through doing SGWT decomposition , threshold processing and SGWT reconstruction. Secondly,under the constraint of the vanishing moment number of the predictor, another predictor based on theacquisition data is devised to predict the future trend of the data using a non-symmetricalinterpolating scheme, A one-step prediction algorithm is presented to predict the future evolutiontrend with historical data. The proposed method obtained a desirable effect in peak-to-peak valuetrend analysis for a machine set in an oil refinery.展开更多
Deep learning technology has been widely applied in the finance industry, particularly in the study of stock price prediction. This paper focuses on the prediction accuracy and performance of long-term features and pr...Deep learning technology has been widely applied in the finance industry, particularly in the study of stock price prediction. This paper focuses on the prediction accuracy and performance of long-term features and proposes a Wide & Deep Asymmetrical Bidirectional Legendre Memory Units that captures long-term dependencies in time series through the immediate backpropagation of bidirectional recurrent modules and Legendre polynomial memory units. The proposed model achieves superior stock trend prediction capabilities by combining the memory and generalization capabilities of the Wide & Deep model. Experimental results on the daily trading data set of the constituents of the CSI 300 index demonstrate that the proposed model outperforms several baseline models in medium and long-term trend prediction.展开更多
Real-time drive cycles and driving trends have a vital impact on fuel consumption and emissions in a vehicle. To address this issue, an original and alternative approach which incorporates the knowledge about real-tim...Real-time drive cycles and driving trends have a vital impact on fuel consumption and emissions in a vehicle. To address this issue, an original and alternative approach which incorporates the knowledge about real-time drive cycles and driving trends into fuzzy logic control strategy was proposed. A machine learning framework called MC_FRAME was established, which includes two neural networks for self-learning and making predictions. An intelligent fuzzy logic control strategy based on the MC_FRAME was then developed in a hybrid electric vehicle system, which is called FLCS_MODEL. Simulations were conducted to evaluate the FLCS_MODEL using ADVISOR. The simulation results indicated that comparing with the default controller on the drive cycle NEDC, the FLCS_MODEL saves 12.25% fuel per hundred kilometers, with the HC emissions increasing by 22.7%, the CO emissions reducing by 16.5%, the NOx emissions reducing by 37.5% and with the PM emissions reducing by 12.9%. A conclusion can be drawn that the proposed approach realizes fewer fuel consumption and less emissions.展开更多
Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted...Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus,the input features used in the paper can be applied on Bitcoin price prediction.Furthermore,this approach allows investors to make better decisions regarding Bitcoin-related investments.展开更多
In the context of 1905–1995 series from Nanjing and Hangzhou, study is undertaken of estab-lishing a predictive model of annual mean temperature in 1996–2005 to come over the Changjiang (Yangtze River) delta region ...In the context of 1905–1995 series from Nanjing and Hangzhou, study is undertaken of estab-lishing a predictive model of annual mean temperature in 1996–2005 to come over the Changjiang (Yangtze River) delta region through mean generating function and artificial neural network in combination. Results show that the established model yields mean error of 0.45°C for their abso-lute values of annual mean temperature from 10 yearly independent samples (1986–1995) and the difference between the mean predictions and related measurements is 0.156°C. The developed model is found superior to a mean generating function regression model both in historical data fit-ting and independent sample prediction. Key words Climate trend prediction. Mean generating function (MGF) - Artificial neural network (ANN) - Annual mean temperature (AMT)展开更多
目的系统分析1990—2021年全球及中美两国归因于空气污染的气管、支气管和肺癌(tracheal,bronchus,and lung cancer,TBL)疾病负担的时空分布特征与流行病学趋势,并基于预测模型评估2022—2031年疾病负担变化规律,为制定针对性的TBL防控...目的系统分析1990—2021年全球及中美两国归因于空气污染的气管、支气管和肺癌(tracheal,bronchus,and lung cancer,TBL)疾病负担的时空分布特征与流行病学趋势,并基于预测模型评估2022—2031年疾病负担变化规律,为制定针对性的TBL防控策略提供科学依据。方法基于全球疾病负担(global burden of disease,GBD)2021数据库,分析全球及中美两国1990—2021年归因于空气污染的TBL疾病负担数据,利用R Studio 4.3.2软件分析相应的变化趋势,并通过贝叶斯年龄-时期-队列(Bayesian age-period-cohort,BAPC)预测模型预测2022—2031年全球和中美两国归因于空气污染的TBL疾病负担状况。结果2021年,归因于空气污染的TBL死亡数和伤残调整寿命年数最高的国家是中国(21.14万例和489.47万人年),其次是美国(0.60万例和12.43万人年)。全球和中美两国归因于空气污染的TBL年龄标准化死亡率(age-standardized mortality rate,ASMR)和年龄标准化伤残调整寿命年率(age-standardized disability-adjusted life years rate,ASDR)呈下降趋势。1990—2021年中国归因于空气污染的TBL的ASMR和ASDR远高于美国和全球平均水平。从性别上看,1990—2021年全球和中美两国归因于空气污染的TBL男性患者的疾病负担远高于女性患者。BAPC预测模型显示,2022—2031年全球归因于空气污染的TBL的ASMR和ASDR呈上升趋势,中美两国呈下降趋势。结论近30年全球及中美空气污染相关TBL疾病负担持续下降,但中国仍显著高于全球平均水平。男性的疾病负担远超女性,男性及≥50岁群体为高危人群。未来10年全球疾病趋势或逆转上升,而中美有望连续下降,但针对高危人群的精准防控仍是关键挑战。展开更多
An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,mo...An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.展开更多
基金the National Natural Science Foundation of China(Nos.11501355 and 71571116)the Project of Knowledge Innovation Program of Shanghai Municipal Education Commission(No.15ZZ090)+2 种基金the 59th China Postdoctoral Sciences Foundation Funded Project(No.2016M591640)the Humanities and Social Sciences Research Project of Ministry of Education(No.15YJA790039)the National Social Science Foundation of China(No.15ZDA058)
文摘The literature generally agrees that longer-horizon(over a month) predictions make more sense than short-horizon ones. However, it's an especially challenging task due to the lack of data(in unit of long horizon)and economic data have a low S/N ratio. We hypothesize that the stock trend is largely dictated by driving factors which are filtered by psychological factors and work on behavioral factors: representative indicators from these three aspects would be adequate in trend prediction. We then extend the Stepwise Regression Analysis(SRA)algorithm to constrained SRA(c SRA) to carry out a further feature selection and lag optimization. During modeling stage, we introduce the Deep Neural Network(DNN) model in stock prediction under the suspicion that economic interactions are too complex for shallow networks to capture. Our experiments indeed show that deep structures generally perform better than shallow ones. Instead of comparing to a kitchen sink model, where over-fitting can easily happen with a shortage of data, we turn around and use a model ensemble approach which indirectly demonstrates our proposed method is adequate.
基金supported by the National Natural Science Foundation of China(Grant No.41971201).
文摘Numerous studies deal with spatial analysis of green innovation(GI).However,researchers have paid limited attention to analyzing the multi-scale evolution patterns and predicting trends of GI in China.This paper seeks to address this research gap by examining the multi-scale distribution and evolutionary characteristics of GI activities based on the data from 337 cities in China during 2000-2019.We used scale variance and the two-stage nested Theil decomposition method to examine the spatial distribution and inequalities of GI in China at multiple scales,including regional,provincial,and prefectural.Additionally,we utilized the Markov chain and spatial Markov chain to explore the dynamic evolution of GI in China and predict its long-term development.The findings indicate that GI in China has a multi-scale effect and is highly sensitive to changes in spatial scale,with significant spatial differences of GI decreasing in each scale.Furthermore,the spatiotemporal evolution of GI is influenced by both geospatial patterns and spatial scales,exhibiting the“club convergence”effect and a tendency to transfer to higher levels of proximity.This effect is more pronounced on a larger scale,but it is increasingly challenging to transfer to higher levels.The study also indicates a steady and sustained growth of GI in China,which concentrates on higher levels over time.These results contribute to a more precise understanding of the scale at which GI develops and provide a scientific basis and policy suggestions for optimizing the spatial structure of GI and promoting its development in China.
基金Supported by the National Key Research and Development Program (No.2019YFA0707201)the Key Work Program of Institute of Scientific and Technical Information of China (No.ZD2022-01,ZD2023-07)。
文摘Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX.
文摘Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.
基金Ninth-Five-Year"Key Project of the State Science and Technology Commission (96-912-01-02-05 ) and National NaturalScience Fou
文摘The Longyangxia Gorge Key Water Control System is the first of the stairstep power sations along the Longyangxi-a-Qingtongxia river section. It has been playing an very important role in providing power, protecting flood and ice run supplying and irrigation etc. in the northwestern China. Therefore, the study on trend prediction, variation on the flow into the Longyangxia Reservoir are of the great social and economic benefits. In the medium-and-long-range runoff forecast, all kinds of regression equation are often used for predicting future hydrologic regime. However, these regression models aren’t appropriate to super long -range runoff forecast because of the restricting on weather data and so on. So a new super long-range runoff forecast model don’t depend on Reai-time weather data and called “Period correcting for residual error series GM (1, 1) model” is presented based on analyzing for the relational hydrologic data and the variation on the flow into the Longyangxia Reservoir, and the forecast model was applied successfully to predict the recent and super long -term trends of the flow into the Longyangxia Reservoir. The results indicate that the annual flow into the Longyangxia Reservoir is in the ending minimum period of the runoff history. The runoff increasing is expected in for the coming years.
基金The National Natural Science Foundation of China(No.72173018).
文摘To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,which is then fed into downstream learning modules.Co-SFM employs an upstream fusion module to incorporate multilevel data,thereby constructing a macro-plate-micro data structure.This configuration helps identify and integrate characteristics from different data levels,facilitating a deeper understanding of the internal links within the financial system.In the downstream model,Co-SFM uses a state-frequency memory network to mine hidden frequency information within stock prices,and the multifrequency patterns of sequential data are modeled.Empirical results show that Co-SFM s prediction accuracy for stock price trends is significantly better than that of other models.This is especially evident in multistep medium and long-term trend predictions,where integrating multilevel data results in notably improved accuracy.
文摘Rail corrugation, as a prevalent type of rail damage in heavy railways, induces diseases in the track structure. In order to ensure the safe operation of trains, an improved whale optimization algorithm is proposed to optimize the rail corrugation evolution trend prediction model of the least squares support vector machine (IPCA-ELWOA-LSSVM). The elite reverse learning combined with the Lévy flight strategy is introduced to improve the whale optimization algorithm. The improved WOA (ELWOA) algorithm is used to continuously optimize the kernel parameter σ and the normalization parameter γ in the LSSVM model. Finally, the improved prediction model is validated using data from a domestic heavy-duty railway experimental line database and compared with the prediction model before optimization and the other commonly used models. The experimental results show that the ELWOA-LSSVM prediction model has the highest accuracy, which proves that the proposed method has high accuracy in predicting the rail corrugation evolution trend.
基金supported by National Natural Science Foundation of China(No.72104110 No.72274113)Basic Science(Natural Science)Research Projects in Higher Education Institutions in Jiangsu Province(No.22KJB630011)+2 种基金General Project of Philosophy and Social Sciences Research in Jiangsu Universities(No.2022SJYB0253)Taishan Scholar Foundation of Shandong province of China(tsqn202103069)Shandong Provincial Natural Science Foundation(No.ZR202111130115)。
文摘Purpose:The study examines the synergy and hysteresis in the evolution of funding and its supported literature,depicts their temporal correlation mechanism,which aids in improving trend predictions.Design/methodology/approach:The study uses the LDA model to identify topics in funding texts and supported papers.A cosine similarity algorithm was employed to estimate the nexus between topics and construct the topic evolution time series.Similarly,the hysteresis effect in topic evolution is analyzed based on topic popularity and content,leading to insights into their temporal correlation mechanism.Findings:The study finds that fund and sponsored paper topics exhibit strong collaboration with a noticeable lag in evolution.The fund topics significantly influence sponsored paper topics after a two-year lag.Moreover,the lag effect is inversely proportional to the topic’s similarity.Research limitations:We use the LDA model to determine the hysteresis effect in topic evolution despite its limitations in handling long-tail words and domain-specific vocabulary.Furthermore,the timing of the emergence of the focal topic in funds is undermined,affecting the findings.Practical implications:These findings enhance the accuracy and scientific validity of trend prediction.Estimating and identifying patterns can help technology managers anticipate future research hotspots,supporting informed decision-making and technology management.Originality/value:This study introduces a research framework to quantitatively and visually analyze the hysteresis effect,revealing the correlation and evolutionary patterns between fund research topics and their funded papers.
基金National Key R&D Program of China,Grant/Award Numbers:2022YFB2403700,2022YFB2403705Natural Science Foundation of Chongqing,Grant/Award Number:CSTB2022NSCQ-MSX1247。
文摘Mechanical vibration defect is the key factor leading to sudden failure of gas-insulated switchgear(GIS)equipment.It is important to realise effective prediction of the me-chanical vibration state development trend of GIS equipment in order to improve its active safety protection level.This paper carried out research on the accurate prediction method and experimental validation of the mechanical vibration state and its defect severity development trend for the GIS equipment.Firstly,the deep and shallow vibration feature parameters for different mechanical defect signals were jointly extracted by time-domain features and deep belief network methods.Secondly,a new prediction model,incorporating the attention mechanism and the bidirectional gated recurrent unit(BiGRU),was constructed with the deep and shallow vibration feature parameters as inputs.Finally,the prediction trend effectiveness was verified based on the real-type GIS mechanical simulation platform and the field operation GIS equipment.Results show that the deep and shallow vibration feature extraction method proposed in this paper can characterise the mechanical defect information more comprehensively.The new prediction method of the vibration state trend based on the attention-BiGRU model shows ideal accuracy,and the predicted vibration state development trend is highly consistent with the actual,with an average absolute error of 0.063.The root mean square error(ERMSE)value of the prediction method is<5%,which reduces the relative error value at least 37% compared with the traditional prediction models.This paper provides a valuable reference for the proactive defence of GIS mechanical failure.
基金Fundamental Research Funds for the Central UniversitiesNo.19lgzd09+2 种基金Guangdong Special Support ProgramPearl River S&T Nova Program of GuangzhouNo.201806010187
文摘Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.
文摘In order to make trend analysis and prediction to acquisition data in amechanical equipment condition monitoring system, a new method of trend feature extraction andprediction of acquisition data is proposed which constructs an adaptive wavelet on the acquisitiondata by means of second generation wavelet transform ( SGWT), Firstly, taking the vanishing momentnumber of the predictor as a constraint, the linear predictor and updater are designed according tothe acquisition data by using symmetrical interpolating scheme. Then the trend of the data isobtained through doing SGWT decomposition , threshold processing and SGWT reconstruction. Secondly,under the constraint of the vanishing moment number of the predictor, another predictor based on theacquisition data is devised to predict the future trend of the data using a non-symmetricalinterpolating scheme, A one-step prediction algorithm is presented to predict the future evolutiontrend with historical data. The proposed method obtained a desirable effect in peak-to-peak valuetrend analysis for a machine set in an oil refinery.
文摘Deep learning technology has been widely applied in the finance industry, particularly in the study of stock price prediction. This paper focuses on the prediction accuracy and performance of long-term features and proposes a Wide & Deep Asymmetrical Bidirectional Legendre Memory Units that captures long-term dependencies in time series through the immediate backpropagation of bidirectional recurrent modules and Legendre polynomial memory units. The proposed model achieves superior stock trend prediction capabilities by combining the memory and generalization capabilities of the Wide & Deep model. Experimental results on the daily trading data set of the constituents of the CSI 300 index demonstrate that the proposed model outperforms several baseline models in medium and long-term trend prediction.
文摘Real-time drive cycles and driving trends have a vital impact on fuel consumption and emissions in a vehicle. To address this issue, an original and alternative approach which incorporates the knowledge about real-time drive cycles and driving trends into fuzzy logic control strategy was proposed. A machine learning framework called MC_FRAME was established, which includes two neural networks for self-learning and making predictions. An intelligent fuzzy logic control strategy based on the MC_FRAME was then developed in a hybrid electric vehicle system, which is called FLCS_MODEL. Simulations were conducted to evaluate the FLCS_MODEL using ADVISOR. The simulation results indicated that comparing with the default controller on the drive cycle NEDC, the FLCS_MODEL saves 12.25% fuel per hundred kilometers, with the HC emissions increasing by 22.7%, the CO emissions reducing by 16.5%, the NOx emissions reducing by 37.5% and with the PM emissions reducing by 12.9%. A conclusion can be drawn that the proposed approach realizes fewer fuel consumption and less emissions.
基金This study was supported by a National Research Foundation of Korea(NRF)(http://nrf.re.kr/eng/index)grant funded by the Korean government(NRF-2020R1A2C1014957).
文摘Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus,the input features used in the paper can be applied on Bitcoin price prediction.Furthermore,this approach allows investors to make better decisions regarding Bitcoin-related investments.
文摘In the context of 1905–1995 series from Nanjing and Hangzhou, study is undertaken of estab-lishing a predictive model of annual mean temperature in 1996–2005 to come over the Changjiang (Yangtze River) delta region through mean generating function and artificial neural network in combination. Results show that the established model yields mean error of 0.45°C for their abso-lute values of annual mean temperature from 10 yearly independent samples (1986–1995) and the difference between the mean predictions and related measurements is 0.156°C. The developed model is found superior to a mean generating function regression model both in historical data fit-ting and independent sample prediction. Key words Climate trend prediction. Mean generating function (MGF) - Artificial neural network (ANN) - Annual mean temperature (AMT)
文摘目的系统分析1990—2021年全球及中美两国归因于空气污染的气管、支气管和肺癌(tracheal,bronchus,and lung cancer,TBL)疾病负担的时空分布特征与流行病学趋势,并基于预测模型评估2022—2031年疾病负担变化规律,为制定针对性的TBL防控策略提供科学依据。方法基于全球疾病负担(global burden of disease,GBD)2021数据库,分析全球及中美两国1990—2021年归因于空气污染的TBL疾病负担数据,利用R Studio 4.3.2软件分析相应的变化趋势,并通过贝叶斯年龄-时期-队列(Bayesian age-period-cohort,BAPC)预测模型预测2022—2031年全球和中美两国归因于空气污染的TBL疾病负担状况。结果2021年,归因于空气污染的TBL死亡数和伤残调整寿命年数最高的国家是中国(21.14万例和489.47万人年),其次是美国(0.60万例和12.43万人年)。全球和中美两国归因于空气污染的TBL年龄标准化死亡率(age-standardized mortality rate,ASMR)和年龄标准化伤残调整寿命年率(age-standardized disability-adjusted life years rate,ASDR)呈下降趋势。1990—2021年中国归因于空气污染的TBL的ASMR和ASDR远高于美国和全球平均水平。从性别上看,1990—2021年全球和中美两国归因于空气污染的TBL男性患者的疾病负担远高于女性患者。BAPC预测模型显示,2022—2031年全球归因于空气污染的TBL的ASMR和ASDR呈上升趋势,中美两国呈下降趋势。结论近30年全球及中美空气污染相关TBL疾病负担持续下降,但中国仍显著高于全球平均水平。男性的疾病负担远超女性,男性及≥50岁群体为高危人群。未来10年全球疾病趋势或逆转上升,而中美有望连续下降,但针对高危人群的精准防控仍是关键挑战。
基金Supported by the China Youth Program of National Natural Science Foundation(42002134)The 14th Special Support Program of China Postdoctoral Science Foundation(2021T140735).
文摘An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.
