[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanc...Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.展开更多
In this paper, we calculated the seismic pattern of instrumental recorded small and moderate earthquakes near the epicenter of the 1303 Hongtong M=8 earthquake, Shanxi Province. According to the spatial distribution o...In this paper, we calculated the seismic pattern of instrumental recorded small and moderate earthquakes near the epicenter of the 1303 Hongtong M=8 earthquake, Shanxi Province. According to the spatial distribution of small and moderate earthquakes, 6 seismic dense zones are delineated. Temporal distribution of ML2 earthquakes since 1970 in each seismic dense zone has been analyzed. Based on temporal distribution characteristics and historical earthquake activity, three types of seismicities are proposed. The relationship between seismic types and crustal medium is analyzed. The mechanism of three types is discussed. Finity of strong earthquake recurrence is pro-posed. Seismic hazard in mid-long term and diversity of earthquake disaster in Shanxi seismic belt are discussed.展开更多
In the estimation of seismic tendency, using Gutenberg-Richters b-value and using Hurst exponent are two com-monly used methods. Based on the fractal geometry of earthquake time series, we point out that these two met...In the estimation of seismic tendency, using Gutenberg-Richters b-value and using Hurst exponent are two com-monly used methods. Based on the fractal geometry of earthquake time series, we point out that these two methods correlate to each other. In the perspective of fractional Brownian motion (FBM), an earthquake sequence with b>3/4 and that with b<3/4 have different dynamic properties.展开更多
One day,can we foresee earthquakes? This question always comes back from every telluric disaster,and the seismology is well annoyed to answer it.The destructtion of the city of Kobe in Japan,on January 17th 1995,arous...One day,can we foresee earthquakes? This question always comes back from every telluric disaster,and the seismology is well annoyed to answer it.The destructtion of the city of Kobe in Japan,on January 17th 1995,aroused deep debates upon the research policy on earthquakes.This disaster obviously shows our well limited capacity to take up the challenge of the prediction of earthquakes because,finally,it is indeed in Japan where authorities invest most in展开更多
By processing and analyzing geodetic data of vertical deformation, fault deformation and horizontal deformation by GPS in Gansu Ningxia Qinghai area and by comparing them with geological structures and many medium to ...By processing and analyzing geodetic data of vertical deformation, fault deformation and horizontal deformation by GPS in Gansu Ningxia Qinghai area and by comparing them with geological structures and many medium to strong earthquake activities in this area, some features of recent tectonic deformation anomaly and the development of medium to strong earthquakes are studied. The results show that: ①Near the main faults tectonic deformations are relatively large. The amount of vertical movement and the deformation status evolve with time. The horizontal movement and deformation show obvious compressional strike slip character. ②The dominant stress of tectonic deformation and seismic development in this area comes from the persistent northeastward compression of Qinghai Tibet block;The time spatial distribution evolution of tectonic deformation and seismic activities are closely related to dynamic evolution of block motion and regional tectonic stress field. ③The abnormal uplift and high gradient deformation belts and remarkable fault deformation anormaly on the borders of regional tectonic blocks are indicators of developing moderate to strong earthquakes but earthquakes may not necessarily take place in the position of maxium deformation, it usually occurred in the region where fault deformation anormaly shows “trend accumulation acceleration turn ” variation character or nearby. On the basis of above study, a preliminary prediction for strong earthquake risk in this area is given.展开更多
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金Project(2012CB725403)supported by the National Basic Research Program of ChinaProjects(71210001,51338008)supported by the National Natural Science Foundation of ChinaProject supported by World Capital Cities Smooth Traffic Collaborative Innovation Center and Singapore National Research Foundation Under Its Campus for Research Excellence and Technology Enterprise(CREATE)Programme
文摘Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.
基金Key Science Research Project (100501-05-09) from China Earthquake Administration during the tenth Five-year Plan.
文摘In this paper, we calculated the seismic pattern of instrumental recorded small and moderate earthquakes near the epicenter of the 1303 Hongtong M=8 earthquake, Shanxi Province. According to the spatial distribution of small and moderate earthquakes, 6 seismic dense zones are delineated. Temporal distribution of ML2 earthquakes since 1970 in each seismic dense zone has been analyzed. Based on temporal distribution characteristics and historical earthquake activity, three types of seismicities are proposed. The relationship between seismic types and crustal medium is analyzed. The mechanism of three types is discussed. Finity of strong earthquake recurrence is pro-posed. Seismic hazard in mid-long term and diversity of earthquake disaster in Shanxi seismic belt are discussed.
文摘In the estimation of seismic tendency, using Gutenberg-Richters b-value and using Hurst exponent are two com-monly used methods. Based on the fractal geometry of earthquake time series, we point out that these two methods correlate to each other. In the perspective of fractional Brownian motion (FBM), an earthquake sequence with b>3/4 and that with b<3/4 have different dynamic properties.
文摘One day,can we foresee earthquakes? This question always comes back from every telluric disaster,and the seismology is well annoyed to answer it.The destructtion of the city of Kobe in Japan,on January 17th 1995,aroused deep debates upon the research policy on earthquakes.This disaster obviously shows our well limited capacity to take up the challenge of the prediction of earthquakes because,finally,it is indeed in Japan where authorities invest most in
文摘By processing and analyzing geodetic data of vertical deformation, fault deformation and horizontal deformation by GPS in Gansu Ningxia Qinghai area and by comparing them with geological structures and many medium to strong earthquake activities in this area, some features of recent tectonic deformation anomaly and the development of medium to strong earthquakes are studied. The results show that: ①Near the main faults tectonic deformations are relatively large. The amount of vertical movement and the deformation status evolve with time. The horizontal movement and deformation show obvious compressional strike slip character. ②The dominant stress of tectonic deformation and seismic development in this area comes from the persistent northeastward compression of Qinghai Tibet block;The time spatial distribution evolution of tectonic deformation and seismic activities are closely related to dynamic evolution of block motion and regional tectonic stress field. ③The abnormal uplift and high gradient deformation belts and remarkable fault deformation anormaly on the borders of regional tectonic blocks are indicators of developing moderate to strong earthquakes but earthquakes may not necessarily take place in the position of maxium deformation, it usually occurred in the region where fault deformation anormaly shows “trend accumulation acceleration turn ” variation character or nearby. On the basis of above study, a preliminary prediction for strong earthquake risk in this area is given.
