A microseismic monitoring system was used in the Donggua Shan underground copper mine, and its application was introduced. The spacial distribution of the seismic event was monitored effectively during mining with thi...A microseismic monitoring system was used in the Donggua Shan underground copper mine, and its application was introduced. The spacial distribution of the seismic event was monitored effectively during mining with this system. The distribution of the seismic intensity in different time periods and in the different mining districts was obtained via the clustering analysis of the monitored results, and the different intensity concentration districts of seismicity were compartmentalized. The various characteristics and waveforms of different vibrations in the underground mine were revealed with the help of the micro-seismic monitoring system. It was proved that the construction and application of the micro-seismic monitoring system in the mine not only realized the continuous monitoring of seismicity in the deep mine, but also settled an this system.展开更多
Sediment delivery ratio(SDR)for fluvial rivers was formulated with sediment rating curve.The observed data of SDR on flood event scale of the Lower Yellow River(LYR)were adopted to examine the formulation and to calib...Sediment delivery ratio(SDR)for fluvial rivers was formulated with sediment rating curve.The observed data of SDR on flood event scale of the Lower Yellow River(LYR)were adopted to examine the formulation and to calibrate the model parameters.A regression formula of SDR was then established and its 95%prediction interval was accordingly quantified to represent its overall uncertainties.Three types of factors including diversity of the incoming flow conditions,river self-regulation processes,and human activities were ascribed to the uncertainties.The following were shown:(1)With the incoming sediment coefficient(ISC)being a variable,it was not necessary to adopt the incoming flow discharge as the second variable in the formulation of SDR;and(2)ISC=0.003 and therefore SDR=2 might be a threshold for distinguishing the characteristics of sediment transport within the LYR.These findings would highlight sediment transport characteristics on the scale of flood event and contribute to uncertainty based analysis of water volume required for sediment transport and channel maintenance of the LYR.展开更多
Realizing autonomy is a hot research topic for automatic vehicles in recent years. For a long time, most of the efforts to this goal concentrate on understanding the scenes surrounding the ego-vehicle（autonomous vehi...Realizing autonomy is a hot research topic for automatic vehicles in recent years. For a long time, most of the efforts to this goal concentrate on understanding the scenes surrounding the ego-vehicle（autonomous vehicle itself）. By completing lowlevel vision tasks, such as detection, tracking and segmentation of the surrounding traffic participants, e.g., pedestrian, cyclists and vehicles, the scenes can be interpreted. However, for an autonomous vehicle, low-level vision tasks are largely insufficient to give help to comprehensive scene understanding. What are and how about the past, the on-going and the future of the scene participants？ This deep question actually steers the vehicles towards truly full automation, just like human beings. Based on this thoughtfulness, this paper attempts to investigate the interpretation of traffic scene in autonomous driving from an event reasoning view. To reach this goal, we study the most relevant literatures and the state-of-the-arts on scene representation, event detection and intention prediction in autonomous driving. In addition, we also discuss the open challenges and problems in this field and endeavor to provide possible solutions.展开更多
Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same g...Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.展开更多
总结论述了基于图论的故障诊断和定位技术、基于软件定义网络(Software Defined Network,SDN)架构的故障诊断和恢复技术、基于故障追踪的故障预测方法、基于事件驱动的故障预测方法。从故障诊断方法的优点、缺点进行对比分析,从故障预...总结论述了基于图论的故障诊断和定位技术、基于软件定义网络(Software Defined Network,SDN)架构的故障诊断和恢复技术、基于故障追踪的故障预测方法、基于事件驱动的故障预测方法。从故障诊断方法的优点、缺点进行对比分析,从故障预测的模型输入、应用范围、优点和不足等方面进行对比分析,对发展趋势进行分析总结,以实例验证了所总结的故障诊断和预测方法。展开更多
This article is a comment on the article by Jia et al,aiming at establishing a predictive model to predict the occurrence of preoperative gastric retention in endoscopic retrograde cholangiopancreatography preparation...This article is a comment on the article by Jia et al,aiming at establishing a predictive model to predict the occurrence of preoperative gastric retention in endoscopic retrograde cholangiopancreatography preparation.We share our perspectives on this predictive model.First,further differentiation in predicting the severity of gastric retention could enhance clinical outcomes.Second,we ponder whether this predictive model can be generalized to predictions of gastric retention before various endoscopic procedures.Third,large datasets and pro-spective clinical validation are needed to improve the prediction model.展开更多
Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock marke...Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily returns.However,major events’news also contains significant information regarding market drivers.An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market.This research proposes an efficient model for stock market prediction.The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and hospitality.We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock prediction.The LSTM has the advantage of analyzing relationship between time-series data through memory functions.The performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.展开更多
基金This work was financially supported by the National Key Technologies R & D Program of China (No.2004BA615A-04).
文摘A microseismic monitoring system was used in the Donggua Shan underground copper mine, and its application was introduced. The spacial distribution of the seismic event was monitored effectively during mining with this system. The distribution of the seismic intensity in different time periods and in the different mining districts was obtained via the clustering analysis of the monitored results, and the different intensity concentration districts of seismicity were compartmentalized. The various characteristics and waveforms of different vibrations in the underground mine were revealed with the help of the micro-seismic monitoring system. It was proved that the construction and application of the micro-seismic monitoring system in the mine not only realized the continuous monitoring of seismicity in the deep mine, but also settled an this system.
基金supported by the Ministry of Science and Technology (Grant No.2006BAB06B04)the National Natural Science Foundation of China(Grant No.50725930)
文摘Sediment delivery ratio(SDR)for fluvial rivers was formulated with sediment rating curve.The observed data of SDR on flood event scale of the Lower Yellow River(LYR)were adopted to examine the formulation and to calibrate the model parameters.A regression formula of SDR was then established and its 95%prediction interval was accordingly quantified to represent its overall uncertainties.Three types of factors including diversity of the incoming flow conditions,river self-regulation processes,and human activities were ascribed to the uncertainties.The following were shown:(1)With the incoming sediment coefficient(ISC)being a variable,it was not necessary to adopt the incoming flow discharge as the second variable in the formulation of SDR;and(2)ISC=0.003 and therefore SDR=2 might be a threshold for distinguishing the characteristics of sediment transport within the LYR.These findings would highlight sediment transport characteristics on the scale of flood event and contribute to uncertainty based analysis of water volume required for sediment transport and channel maintenance of the LYR.
基金supported by National Key R&D Program Project of China(No.2016YFB1001004)National Natural Science Foundation of China(Nos.61751308,61603057,61773311)+1 种基金China Postdoctoral Science Foundation(No.2017M613152)Collaborative Research with MSRA
文摘Realizing autonomy is a hot research topic for automatic vehicles in recent years. For a long time, most of the efforts to this goal concentrate on understanding the scenes surrounding the ego-vehicle（autonomous vehicle itself）. By completing lowlevel vision tasks, such as detection, tracking and segmentation of the surrounding traffic participants, e.g., pedestrian, cyclists and vehicles, the scenes can be interpreted. However, for an autonomous vehicle, low-level vision tasks are largely insufficient to give help to comprehensive scene understanding. What are and how about the past, the on-going and the future of the scene participants？ This deep question actually steers the vehicles towards truly full automation, just like human beings. Based on this thoughtfulness, this paper attempts to investigate the interpretation of traffic scene in autonomous driving from an event reasoning view. To reach this goal, we study the most relevant literatures and the state-of-the-arts on scene representation, event detection and intention prediction in autonomous driving. In addition, we also discuss the open challenges and problems in this field and endeavor to provide possible solutions.
基金funded by the Fujian Province Science and Technology Plan,China(Grant Number 2019H0017).
文摘Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.
文摘总结论述了基于图论的故障诊断和定位技术、基于软件定义网络(Software Defined Network,SDN)架构的故障诊断和恢复技术、基于故障追踪的故障预测方法、基于事件驱动的故障预测方法。从故障诊断方法的优点、缺点进行对比分析,从故障预测的模型输入、应用范围、优点和不足等方面进行对比分析,对发展趋势进行分析总结,以实例验证了所总结的故障诊断和预测方法。
基金Supported by National Natural Science Foundation of China,No.82170675.
文摘This article is a comment on the article by Jia et al,aiming at establishing a predictive model to predict the occurrence of preoperative gastric retention in endoscopic retrograde cholangiopancreatography preparation.We share our perspectives on this predictive model.First,further differentiation in predicting the severity of gastric retention could enhance clinical outcomes.Second,we ponder whether this predictive model can be generalized to predictions of gastric retention before various endoscopic procedures.Third,large datasets and pro-spective clinical validation are needed to improve the prediction model.
基金supported by X-mind Corps program of National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(No.2019H1D8A1105622)the Soonchunhyang University Research Fund.
文摘Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily returns.However,major events’news also contains significant information regarding market drivers.An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market.This research proposes an efficient model for stock market prediction.The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and hospitality.We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock prediction.The LSTM has the advantage of analyzing relationship between time-series data through memory functions.The performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.
