Recommender systems are rapidly transforming the digital world into intelligent information hubs.The valuable context information associated with the users’prior transactions has played a vital role in determining th...Recommender systems are rapidly transforming the digital world into intelligent information hubs.The valuable context information associated with the users’prior transactions has played a vital role in determining the user preferences for items or rating prediction.It has been a hot research topic in collaborative filtering-based recommender systems for the last two decades.This paper presents a novel Context Based Rating Prediction(CBRP)model with a unique similarity scoring estimation method.The proposed algorithm computes a context score for each candidate user to construct a similarity pool for the given subject user-item pair and intuitively choose the highly influential users to forecast the item ratings.The context scoring strategy has an inherent capability to incorporate multiple conditional factors to filter down the most relevant recommendations.Compared with traditional similarity estimation methods,CBRP makes it possible for the full use of neighboring collaborators’choice on various conditions.We conduct experiments on three publicly available datasets to evaluate our proposed method with random user-item pairs and got considerable improvement in prediction accuracy over the standard evaluation measures.Also,we evaluate prediction accuracy for every user-item pair in the system and the results show that our proposed framework has outperformed existing methods.展开更多
Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'preferences.However,existing methods still have two significant li...Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'preferences.However,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding process.To meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating prediction.To address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item representations.To exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among items.Moreover,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence graph.Extensive experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.展开更多
This paper examines the prediction of film ratings.Firstly,in the data feature engineering,feature construction is performed based on the original features of the film dataset.Secondly,the clustering algorithm is util...This paper examines the prediction of film ratings.Firstly,in the data feature engineering,feature construction is performed based on the original features of the film dataset.Secondly,the clustering algorithm is utilized to remove singular film samples,and feature selections are carried out.When solving the problem that film samples of the target domain are unlabelled,it is impossible to train a model and address the inconsistency in the feature dimension for film samples from the source domain.Therefore,the domain adaptive transfer learning model combined with dimensionality reduction algorithms is adopted in this paper.At the same time,in order to reduce the prediction error of models,the stacking ensemble learning model for regression is also used.Finally,through comparative experiments,the effectiveness of the proposed method is verified,which proves to be better predicting film ratings in the target domain.展开更多
This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women community.The major research problem is the lack of automated mod...This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women community.The major research problem is the lack of automated model to attain earlier prediction.Some existing model fails to give better prediction accuracy.Here,a novel clinical decision support system is framed to make the proper decision during a time of complexity.Multiple stages are followed in the proposed framework,which plays a substantial role in thyroid prediction.These steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(MS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier model.Various classifiers aremerged to handle the issues identified in others and intend to enhance the prediction rate.The proposed model provides superior outcomes and gives good quality prediction rate.The simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing approaches.The model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.展开更多
A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive researc...A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut.Our study aims to harness the power of available data to estimate a film’s early success rate.With the vast resources offered by the internet,we can access a plethora of movie-related information,including actors,directors,critic reviews,user reviews,ratings,writers,budgets,genres,Facebook likes,YouTube views for movie trailers,and Twitter followers.The first few weeks of a film’s release are crucial in determining its fate,and online reviews and film evaluations profoundly impact its opening-week earnings.Hence,our research employs advanced supervised machine learning techniques to predict a film’s triumph.The Internet Movie Database(IMDb)is a comprehensive data repository for nearly all movies.A robust predictive classification approach is developed by employing various machine learning algorithms,such as fine,medium,coarse,cosine,cubic,and weighted KNN.To determine the best model,the performance of each feature was evaluated based on composite metrics.Moreover,the significant influences of social media platforms were recognized including Twitter,Instagram,and Facebook on shaping individuals’opinions.A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms.The findings of this study demonstrate that the chosen algorithms offer more precise estimations,faster execution times,and higher accuracy rates when compared to previous research.By integrating the features of existing prediction models and social media sentiment analysis models,our proposed approach provides a remarkably accurate prediction of a movie’s success.This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release,allowing them to tailor their promotional activities accordingly.Furthermore,the adopted research lays the foundation for developing even more accurate prediction models,considering the ever-increasing significance of social media platforms in shaping individ-uals’opinions.In conclusion,this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films,opening new avenues for the film industry.展开更多
In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,t...In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.展开更多
In this paper, the method which can combine different seismic data with the different precision and completeness, even the palaeo-earthquake data, has been applied to estimate the yearly seismic moment rate in the sei...In this paper, the method which can combine different seismic data with the different precision and completeness, even the palaeo-earthquake data, has been applied to estimate the yearly seismic moment rate in the seismic region. Based on this, the predictable model of regional time-magnitude has been used in North China and Southwest China. The normal correlation between the time interval of the events and the magnitude of the last strong earthquake shows that the model is suitable. The value of the parameter c is less than the average value of 0.33 that is obtained from the events occurred in the plate boundary in the world. It is explained that the correlativity between the recurrence interval of the earthquake and the magnitude of the last strong event is not obvious. It is shown that the continental earthquakes in China are different from that occurred in the plate boundary and the recurrence model for the continental events are different from the one for the plate boundary events. Finally the seismic risk analysis based on this model for North China and Southwest China is given in this paper.展开更多
Online advertising click-through rate(CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent years.One of the hot topics in this area is...Online advertising click-through rate(CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent years.One of the hot topics in this area is the construction of feature interactions to facilitate accurate prediction.Factorization machine provides second-order feature interactions by linearly multiplying hidden feature factors.However,real-world data present a complex and nonlinear structure.Hence,second-order feature interactions are unable to represent cross information adequately.This drawback has been addressed using deep neural networks(DNNs),which enable high-order nonlinear feature interactions.However,DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original features.In this study,we propose an effective CTR prediction algorithm called CAN,which explicitly exploits the benefits of attention mechanisms and DNN models.The attention mechanism is used to provide rich and expressive low-order feature interactions and facilitate the optimization of DNN-based predictors that implicitly incorporate high-order nonlinear feature interactions.The experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature-and DNN-based predictors.展开更多
Real-time dynamic adjustment of the tunnel bore machine(TBM)advance rate according to the rockmachine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction.This ...Real-time dynamic adjustment of the tunnel bore machine(TBM)advance rate according to the rockmachine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction.This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network(TCN),based on TBM construction big data.The prediction model was built using an experimental database,containing 235 data sets,established from the construction data from the Jilin Water-Diversion Tunnel Project in China.The TBM operating parameters,including total thrust,cutterhead rotation,cutterhead torque and penetration rate,are selected as the input parameters of the model.The TCN model is found outperforming the recurrent neural network(RNN)and long short-term memory(LSTM)model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two.The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment.On the contrary,the influence of the cutterhead rotation and total thrust is moderate.The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.展开更多
The reliability of electrical connectors has critical impact on electronic systems. It is usually characterized by failure rate prediction value according to standard MIL-HDBK-217(or GJB-299 C in Chinese) in engineeri...The reliability of electrical connectors has critical impact on electronic systems. It is usually characterized by failure rate prediction value according to standard MIL-HDBK-217(or GJB-299 C in Chinese) in engineering practice. Given to their limitations and mislead results, a new failure rate prediction models needs to be presented. The presented model aims at the mechanism of increase of film thickness which leads to the increase of contact resistance. The estimated failure rate value can be given at different environmental conditions,and some of the factors affecting the reliability are taken into account. Accelerated degradation test(ADT) was conducted on GJB599 III series electrical connector. The failure rate prediction model can be simply formed and convenient to calculate the expression of failure rate changing with time at various temperature and vibration conditions. This model gives an objective assessment in short time, which makes it convenient to be applied to the engineering.展开更多
Modeling, simulation, and prediction of global energy indices remain veritable tools for econometric, engineering, analysis, and prediction of energy indices. Thus, this paper differentially modeled, simulated, and no...Modeling, simulation, and prediction of global energy indices remain veritable tools for econometric, engineering, analysis, and prediction of energy indices. Thus, this paper differentially modeled, simulated, and non-differentially predicated the global energy indices. The state-of-the-art of the research includes normalization of energy indices, generation of differential rate terms, and regression of rate terms against energy indices to generate coefficients and unexplained terms. On imposition of initial conditions, the solution to the system of linear differential equations was realized in a Matlab environment. There was a strong agreement between the simulated and the field data. The exact solutions are ideal for interpolative prediction of historic data. Furthermore, the simulated data were upgraded for extrapolative prediction of energy indices by introducing an innovative model, which is the synergy of deflated and inflated prediction factors. The innovative model yielded a trendy prediction data for energy consumption, gross domestic product, carbon dioxide emission and human development index. However, the oil price was untrendy, which could be attributed to odd circumstances. Moreover, the sensitivity of the differential rate terms was instrumental in discovering the overwhelming effect of independent indices on the dependent index. Clearly, this paper has accomplished interpolative and extrapolative prediction of energy indices and equally recommends for further investigation of the untrendy nature of oil price.展开更多
Ultrasonic-assisted chemical mechanical polishing(UA-CMP)can greatly improve the sapphire material removal and surface quality,but its polishing mechanism is still unclear.This paper proposed a novel model of material...Ultrasonic-assisted chemical mechanical polishing(UA-CMP)can greatly improve the sapphire material removal and surface quality,but its polishing mechanism is still unclear.This paper proposed a novel model of material removal rate(MRR)to explore the mechanism of sapphire UA-CMP.It contains two modes,namely two-body wear and abrasive-impact.Furthermore,the atomic force microscopy(AFM)in-situ study,computational fluid dynamics(CFD)simulation,and polishing experiments were conducted to verify the model and reveal the polishing mechanism.In the AFM in-situ studies,the tip scratched the reaction layer on the sapphire surface.The pit with a 0.22 nm depth is the evidence of two-body wear.The CFD simulation showed that abrasives could be driven by the ultrasonic vibration to impact the sapphire surface at high frequencies.The maximum total velocity and the air volume fraction(AVF)in the central area increased from 0.26 to 0.55 m/s and 20%to 49%,respectively,with the rising amplitudes of 1–3μm.However,the maximum total velocity rose slightly from 0.33 to 0.42 m/s,and the AVF was nearly unchanged under 40–80 r/min.It indicated that the ultrasonic energy has great effects on the abrasive-impact mode.The UA-CMP experimental results exhibited that there was 63.7%improvement in MRR when the polishing velocities rose from 40 to 80 r/min.The roughness of the polished sapphire surface was R_(a)=0.07 nm.It identified that the higher speed achieved greater MRR mainly through the two-body wear mode.This study is beneficial to further understanding the UA-CMP mechanism and promoting the development of UA-CMP technology.展开更多
In the era of intelligent economy, the click-through rate(CTR) prediction system can evaluate massive service information based on user historical information, and screen out the products that are most likely to be fa...In the era of intelligent economy, the click-through rate(CTR) prediction system can evaluate massive service information based on user historical information, and screen out the products that are most likely to be favored by users, thus realizing customized push of information and achieve the ultimate goal of improving economic benefits. Sequence modeling is one of the main research directions of CTR prediction models based on deep learning. The user's general interest hidden in the entire click history and the short-term interest hidden in the recent click behaviors have different influences on the CTR prediction results, which are highly important. In terms of capturing the user's general interest, existing models paid more attention to the relationships between item embedding vectors(point-level), while ignoring the relationships between elements in item embedding vectors(union-level). The Lambda layer-based Convolutional Sequence Embedding(LCSE) model proposed in this paper uses the Lambda layer to capture features from click history through weight distribution, and uses horizontal and vertical filters on this basis to learn the user's general preferences from union-level and point-level. In addition, we also incorporate the user's short-term preferences captured by the embedding-based convolutional model to further improve the prediction results. The AUC(Area Under Curve) values of the LCSE model on the datasets Electronic, Movie & TV and MovieLens are 0.870 7, 0.903 6 and 0.946 7, improving 0.45%, 0.36% and 0.07% over the Caser model, proving the effectiveness of our proposed model.展开更多
With poor irrigation water quality,cultivation difficulties are certainly expected to rise.This will cause a severe reduction in crops yield unless a strong strategy is followed to control and sustain high yielding ca...With poor irrigation water quality,cultivation difficulties are certainly expected to rise.This will cause a severe reduction in crops yield unless a strong strategy is followed to control and sustain high yielding capacity under particular circumstances.Water salinity presented in the form of water electrical conductivity(EC),has been presented in this study as one of the parameters that significantly participated in decreasing the quality of irrigation water in Al-Hassa oasis at Kingdom of Saudi Arabia.The sharing factors in quantifying water EC and its distribution spacewise has been examined by applying the frequency ratio(FR)technique(spatial autocorrelation)between salinity status and water measured elements,specifically,chlorine(Cl^(-)),sodium(Na^(+)),calcium(Ca^(2+)),potassium(K^(+))and magnesium(Mg^(2+)).A threshold salinity value of(EC≥2.0 dS/m)was identified as a break-line for classifying the well-water sources that non-valid for irrigating vegetables grown in the area.A statistical correlation among the examined parameters and EC was conducted using the statistical package for social sciences(SPSS),and compared to the applied FR technique.A dosage of Cl^(-) in irrigation water was observed to be the most significant candidate that raised EC,proved by an R^(2) of 63%.However,the FR technique has shown the validity in analyzing the spatial distribution of water measured variables;in addition to nominating the variable that had the higher association portion,which was assessed to be Na^(+),followed by Cl^(-) with prediction rates of 4.22 and 3.22,respectively.展开更多
基金This work is supported by National Natural Science Foundation of China(No.61672133)Sichuan Science and Technology Program(No.2019YFG0535)the 111 Project(No.B17008).
文摘Recommender systems are rapidly transforming the digital world into intelligent information hubs.The valuable context information associated with the users’prior transactions has played a vital role in determining the user preferences for items or rating prediction.It has been a hot research topic in collaborative filtering-based recommender systems for the last two decades.This paper presents a novel Context Based Rating Prediction(CBRP)model with a unique similarity scoring estimation method.The proposed algorithm computes a context score for each candidate user to construct a similarity pool for the given subject user-item pair and intuitively choose the highly influential users to forecast the item ratings.The context scoring strategy has an inherent capability to incorporate multiple conditional factors to filter down the most relevant recommendations.Compared with traditional similarity estimation methods,CBRP makes it possible for the full use of neighboring collaborators’choice on various conditions.We conduct experiments on three publicly available datasets to evaluate our proposed method with random user-item pairs and got considerable improvement in prediction accuracy over the standard evaluation measures.Also,we evaluate prediction accuracy for every user-item pair in the system and the results show that our proposed framework has outperformed existing methods.
基金supported in part by National Natural Science Foundation of China(U21B2015,61972300)in part by Young Scientists Fund of the National Natural Science Foundation of China(62202356)+1 种基金in part by Young Talent Fund of Association for Science and Technology in Shaanxi(20220113)in part by Intelligent Financial Software Engineering New Technology Joint Laboratory Project(99901220858)。
文摘Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'preferences.However,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding process.To meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating prediction.To address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item representations.To exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among items.Moreover,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence graph.Extensive experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.
基金Supported by the Scientific Research Foundation of Liaoning Provincial Department of Education(No.LJKZ0139).
文摘This paper examines the prediction of film ratings.Firstly,in the data feature engineering,feature construction is performed based on the original features of the film dataset.Secondly,the clustering algorithm is utilized to remove singular film samples,and feature selections are carried out.When solving the problem that film samples of the target domain are unlabelled,it is impossible to train a model and address the inconsistency in the feature dimension for film samples from the source domain.Therefore,the domain adaptive transfer learning model combined with dimensionality reduction algorithms is adopted in this paper.At the same time,in order to reduce the prediction error of models,the stacking ensemble learning model for regression is also used.Finally,through comparative experiments,the effectiveness of the proposed method is verified,which proves to be better predicting film ratings in the target domain.
文摘This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women community.The major research problem is the lack of automated model to attain earlier prediction.Some existing model fails to give better prediction accuracy.Here,a novel clinical decision support system is framed to make the proper decision during a time of complexity.Multiple stages are followed in the proposed framework,which plays a substantial role in thyroid prediction.These steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(MS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier model.Various classifiers aremerged to handle the issues identified in others and intend to enhance the prediction rate.The proposed model provides superior outcomes and gives good quality prediction rate.The simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing approaches.The model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.
文摘A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut.Our study aims to harness the power of available data to estimate a film’s early success rate.With the vast resources offered by the internet,we can access a plethora of movie-related information,including actors,directors,critic reviews,user reviews,ratings,writers,budgets,genres,Facebook likes,YouTube views for movie trailers,and Twitter followers.The first few weeks of a film’s release are crucial in determining its fate,and online reviews and film evaluations profoundly impact its opening-week earnings.Hence,our research employs advanced supervised machine learning techniques to predict a film’s triumph.The Internet Movie Database(IMDb)is a comprehensive data repository for nearly all movies.A robust predictive classification approach is developed by employing various machine learning algorithms,such as fine,medium,coarse,cosine,cubic,and weighted KNN.To determine the best model,the performance of each feature was evaluated based on composite metrics.Moreover,the significant influences of social media platforms were recognized including Twitter,Instagram,and Facebook on shaping individuals’opinions.A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms.The findings of this study demonstrate that the chosen algorithms offer more precise estimations,faster execution times,and higher accuracy rates when compared to previous research.By integrating the features of existing prediction models and social media sentiment analysis models,our proposed approach provides a remarkably accurate prediction of a movie’s success.This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release,allowing them to tailor their promotional activities accordingly.Furthermore,the adopted research lays the foundation for developing even more accurate prediction models,considering the ever-increasing significance of social media platforms in shaping individ-uals’opinions.In conclusion,this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films,opening new avenues for the film industry.
文摘In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.
文摘In this paper, the method which can combine different seismic data with the different precision and completeness, even the palaeo-earthquake data, has been applied to estimate the yearly seismic moment rate in the seismic region. Based on this, the predictable model of regional time-magnitude has been used in North China and Southwest China. The normal correlation between the time interval of the events and the magnitude of the last strong earthquake shows that the model is suitable. The value of the parameter c is less than the average value of 0.33 that is obtained from the events occurred in the plate boundary in the world. It is explained that the correlativity between the recurrence interval of the earthquake and the magnitude of the last strong event is not obvious. It is shown that the continental earthquakes in China are different from that occurred in the plate boundary and the recurrence model for the continental events are different from the one for the plate boundary events. Finally the seismic risk analysis based on this model for North China and Southwest China is given in this paper.
文摘Online advertising click-through rate(CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent years.One of the hot topics in this area is the construction of feature interactions to facilitate accurate prediction.Factorization machine provides second-order feature interactions by linearly multiplying hidden feature factors.However,real-world data present a complex and nonlinear structure.Hence,second-order feature interactions are unable to represent cross information adequately.This drawback has been addressed using deep neural networks(DNNs),which enable high-order nonlinear feature interactions.However,DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original features.In this study,we propose an effective CTR prediction algorithm called CAN,which explicitly exploits the benefits of attention mechanisms and DNN models.The attention mechanism is used to provide rich and expressive low-order feature interactions and facilitate the optimization of DNN-based predictors that implicitly incorporate high-order nonlinear feature interactions.The experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature-and DNN-based predictors.
基金Supports from National Natural Science Foundation of China(Grant No.11902069)Sichuan University,State Key Lab Hydraul&Mt River Engn(No.SKHL1915)+2 种基金and the Research Project of China Railway First Survey and Design Institute Group Co.,Ltd(No.19-15 and No.20-17-1)are also acknowledgedsupported by the 111 Project(B17009)under the framework of Sino-Franco Joint Research Laboratory on Multiphysics and Multiscale Rock Mechanics.
文摘Real-time dynamic adjustment of the tunnel bore machine(TBM)advance rate according to the rockmachine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction.This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network(TCN),based on TBM construction big data.The prediction model was built using an experimental database,containing 235 data sets,established from the construction data from the Jilin Water-Diversion Tunnel Project in China.The TBM operating parameters,including total thrust,cutterhead rotation,cutterhead torque and penetration rate,are selected as the input parameters of the model.The TCN model is found outperforming the recurrent neural network(RNN)and long short-term memory(LSTM)model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two.The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment.On the contrary,the influence of the cutterhead rotation and total thrust is moderate.The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.
文摘The reliability of electrical connectors has critical impact on electronic systems. It is usually characterized by failure rate prediction value according to standard MIL-HDBK-217(or GJB-299 C in Chinese) in engineering practice. Given to their limitations and mislead results, a new failure rate prediction models needs to be presented. The presented model aims at the mechanism of increase of film thickness which leads to the increase of contact resistance. The estimated failure rate value can be given at different environmental conditions,and some of the factors affecting the reliability are taken into account. Accelerated degradation test(ADT) was conducted on GJB599 III series electrical connector. The failure rate prediction model can be simply formed and convenient to calculate the expression of failure rate changing with time at various temperature and vibration conditions. This model gives an objective assessment in short time, which makes it convenient to be applied to the engineering.
文摘Modeling, simulation, and prediction of global energy indices remain veritable tools for econometric, engineering, analysis, and prediction of energy indices. Thus, this paper differentially modeled, simulated, and non-differentially predicated the global energy indices. The state-of-the-art of the research includes normalization of energy indices, generation of differential rate terms, and regression of rate terms against energy indices to generate coefficients and unexplained terms. On imposition of initial conditions, the solution to the system of linear differential equations was realized in a Matlab environment. There was a strong agreement between the simulated and the field data. The exact solutions are ideal for interpolative prediction of historic data. Furthermore, the simulated data were upgraded for extrapolative prediction of energy indices by introducing an innovative model, which is the synergy of deflated and inflated prediction factors. The innovative model yielded a trendy prediction data for energy consumption, gross domestic product, carbon dioxide emission and human development index. However, the oil price was untrendy, which could be attributed to odd circumstances. Moreover, the sensitivity of the differential rate terms was instrumental in discovering the overwhelming effect of independent indices on the dependent index. Clearly, this paper has accomplished interpolative and extrapolative prediction of energy indices and equally recommends for further investigation of the untrendy nature of oil price.
基金This work was supported by the National Natural Science Foundation of China(Nos.51865030 and 52165025).
文摘Ultrasonic-assisted chemical mechanical polishing(UA-CMP)can greatly improve the sapphire material removal and surface quality,but its polishing mechanism is still unclear.This paper proposed a novel model of material removal rate(MRR)to explore the mechanism of sapphire UA-CMP.It contains two modes,namely two-body wear and abrasive-impact.Furthermore,the atomic force microscopy(AFM)in-situ study,computational fluid dynamics(CFD)simulation,and polishing experiments were conducted to verify the model and reveal the polishing mechanism.In the AFM in-situ studies,the tip scratched the reaction layer on the sapphire surface.The pit with a 0.22 nm depth is the evidence of two-body wear.The CFD simulation showed that abrasives could be driven by the ultrasonic vibration to impact the sapphire surface at high frequencies.The maximum total velocity and the air volume fraction(AVF)in the central area increased from 0.26 to 0.55 m/s and 20%to 49%,respectively,with the rising amplitudes of 1–3μm.However,the maximum total velocity rose slightly from 0.33 to 0.42 m/s,and the AVF was nearly unchanged under 40–80 r/min.It indicated that the ultrasonic energy has great effects on the abrasive-impact mode.The UA-CMP experimental results exhibited that there was 63.7%improvement in MRR when the polishing velocities rose from 40 to 80 r/min.The roughness of the polished sapphire surface was R_(a)=0.07 nm.It identified that the higher speed achieved greater MRR mainly through the two-body wear mode.This study is beneficial to further understanding the UA-CMP mechanism and promoting the development of UA-CMP technology.
基金Supported by the National Natural Science Foundation of China (62272214)。
文摘In the era of intelligent economy, the click-through rate(CTR) prediction system can evaluate massive service information based on user historical information, and screen out the products that are most likely to be favored by users, thus realizing customized push of information and achieve the ultimate goal of improving economic benefits. Sequence modeling is one of the main research directions of CTR prediction models based on deep learning. The user's general interest hidden in the entire click history and the short-term interest hidden in the recent click behaviors have different influences on the CTR prediction results, which are highly important. In terms of capturing the user's general interest, existing models paid more attention to the relationships between item embedding vectors(point-level), while ignoring the relationships between elements in item embedding vectors(union-level). The Lambda layer-based Convolutional Sequence Embedding(LCSE) model proposed in this paper uses the Lambda layer to capture features from click history through weight distribution, and uses horizontal and vertical filters on this basis to learn the user's general preferences from union-level and point-level. In addition, we also incorporate the user's short-term preferences captured by the embedding-based convolutional model to further improve the prediction results. The AUC(Area Under Curve) values of the LCSE model on the datasets Electronic, Movie & TV and MovieLens are 0.870 7, 0.903 6 and 0.946 7, improving 0.45%, 0.36% and 0.07% over the Caser model, proving the effectiveness of our proposed model.
基金This research was financially supported by the Deanship of Scientific Research at King Faisal University under Nasher Track(Grant No.186240).
文摘With poor irrigation water quality,cultivation difficulties are certainly expected to rise.This will cause a severe reduction in crops yield unless a strong strategy is followed to control and sustain high yielding capacity under particular circumstances.Water salinity presented in the form of water electrical conductivity(EC),has been presented in this study as one of the parameters that significantly participated in decreasing the quality of irrigation water in Al-Hassa oasis at Kingdom of Saudi Arabia.The sharing factors in quantifying water EC and its distribution spacewise has been examined by applying the frequency ratio(FR)technique(spatial autocorrelation)between salinity status and water measured elements,specifically,chlorine(Cl^(-)),sodium(Na^(+)),calcium(Ca^(2+)),potassium(K^(+))and magnesium(Mg^(2+)).A threshold salinity value of(EC≥2.0 dS/m)was identified as a break-line for classifying the well-water sources that non-valid for irrigating vegetables grown in the area.A statistical correlation among the examined parameters and EC was conducted using the statistical package for social sciences(SPSS),and compared to the applied FR technique.A dosage of Cl^(-) in irrigation water was observed to be the most significant candidate that raised EC,proved by an R^(2) of 63%.However,the FR technique has shown the validity in analyzing the spatial distribution of water measured variables;in addition to nominating the variable that had the higher association portion,which was assessed to be Na^(+),followed by Cl^(-) with prediction rates of 4.22 and 3.22,respectively.