In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandem...In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic(GPCP).In this article,the authors use the ensemble empirical mode decomposition(EEMD)model and autoregressive moving average(ARMA)model to improve the prediction results of GPCP.In addition,the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease,whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model.Judging from the results,the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP.For countries such as El Salvador with a small number of cases,the absolute values of the relative errors of prediction become smaller.Therefore,this article concludes that this method is more effective for improving prediction results and direct prediction.展开更多
To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was...To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in Shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction.展开更多
Erhai Lake is the second largest freshwater lake on the Yunnan Plateau, Southwest China. In recent decades, a number of exotic fish species have been introduced into the lake and the fish community has changed conside...Erhai Lake is the second largest freshwater lake on the Yunnan Plateau, Southwest China. In recent decades, a number of exotic fish species have been introduced into the lake and the fish community has changed considerably. We evaluated the status of the fish community based on surveys with multimesh gillnet, trap net, and benthic fyke-net between May 2009 and April 2012. In addition, we evaluated the change in the community using historical data （1952-2010） describing the fish community and fishery harvest. The current fish community is dominated by small-sized fishes, including Pseudorasbora parva, Rhinogobius giurinus, Micropercops swinhonis, Hemiculter leucisculus, and Rhinogobius cliffordpopei. These accounted for 87.7% of the 22 546 total specimens collected. Omnivorous and carnivorous species dominated the community. A canonical correspondence analysis （CCA） plot revealed that the distribution of fishes in the lake is influenced by aquatic plants, water temperature, pH, and season. The abundance of indigenous species has declined sharply, and a majority of endemic species have been extirpated from the lake （a decrease from seven to two species）. In contrast, the number of exotic species has increased since the 1960s to a total of 22 at present. The fishery harvest decreased initially following the 1960s, but has since increased due to the introduction of non-native fish and stocking of native fish. The fishery harvest was significantly correlated with total nitrogen, not total phosphorus, during the past 20 years. Based on our results, we discuss recommendations for the restoration and conservation of the fish resources in Erhai Lake.展开更多
This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is intro...This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.展开更多
As an important part of land use/cover change(LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ...As an important part of land use/cover change(LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ′top-down′ decision-making behaviors, here are two global historical land-use datasets, generally referred as the Sustainability and the Global Environment datasets(SAGE datasets) and History Database of the Global Environment datasets(HYDE datasets). However, at the regional level, these global datasets have coarse resolutions and inevitable errors. Considering various factors that influenced cropland distribution, including cropland connectivity and the limitation of natural and human factors, this study developed a reconstruction model of historical cropland based on constrained Cellular Automaton(CA) of ′bottom-up′. Then, an available labor force index is used as a proxy for the amount of cropland to inspect and calibrate these spatial patterns. Applied the reconstruction model to Shandong Province, we reconstructed its spatial distribution of cropland during 8 periods. The reconstructed results show that: 1) it is properly suitable for constrained CA to simulate and reconstruct the spatial distribution of cropland in traditional cultivated region of China; 2) compared with ′SAGE datasets′ and ′HYDE datasets′, this study have formed higher-resolution Boolean spatial distribution datasets of historical cropland with a more definitive concept of spatial pattern in terms of fractional format.展开更多
基金This work was jointly supported by the National Natural Science Foundation of China[grant numbers 41521004 and 41875083]the Gansu Provincial Special Fund Project for Guiding Scientific and Technological Innovation and Development[grant number 2019ZX-06].
文摘In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic(GPCP).In this article,the authors use the ensemble empirical mode decomposition(EEMD)model and autoregressive moving average(ARMA)model to improve the prediction results of GPCP.In addition,the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease,whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model.Judging from the results,the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP.For countries such as El Salvador with a small number of cases,the absolute values of the relative errors of prediction become smaller.Therefore,this article concludes that this method is more effective for improving prediction results and direct prediction.
基金Project(2011AA010101) supported by the National High Technology Research and Development Program of China
文摘To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in Shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction.
基金Supported by the National Special Research Fund for Non-Profit Sector(Agriculture)(No.200903048-04)the Major Science and Technology Program for Water Pollution Control and Treatment of China(No.2012ZX07105-004)the State Key Program of National Natural Science Foundation of China(No.30830025)
文摘Erhai Lake is the second largest freshwater lake on the Yunnan Plateau, Southwest China. In recent decades, a number of exotic fish species have been introduced into the lake and the fish community has changed considerably. We evaluated the status of the fish community based on surveys with multimesh gillnet, trap net, and benthic fyke-net between May 2009 and April 2012. In addition, we evaluated the change in the community using historical data （1952-2010） describing the fish community and fishery harvest. The current fish community is dominated by small-sized fishes, including Pseudorasbora parva, Rhinogobius giurinus, Micropercops swinhonis, Hemiculter leucisculus, and Rhinogobius cliffordpopei. These accounted for 87.7% of the 22 546 total specimens collected. Omnivorous and carnivorous species dominated the community. A canonical correspondence analysis （CCA） plot revealed that the distribution of fishes in the lake is influenced by aquatic plants, water temperature, pH, and season. The abundance of indigenous species has declined sharply, and a majority of endemic species have been extirpated from the lake （a decrease from seven to two species）. In contrast, the number of exotic species has increased since the 1960s to a total of 22 at present. The fishery harvest decreased initially following the 1960s, but has since increased due to the introduction of non-native fish and stocking of native fish. The fishery harvest was significantly correlated with total nitrogen, not total phosphorus, during the past 20 years. Based on our results, we discuss recommendations for the restoration and conservation of the fish resources in Erhai Lake.
基金Supported by the Shandong Natural Science Foundation(ZR2013BL008)
文摘This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.
基金Under the auspices of National Basic Research Program of China(No.2011CB952001)National Natural Science Foundation of China(No.41340016,412013860)
文摘As an important part of land use/cover change(LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ′top-down′ decision-making behaviors, here are two global historical land-use datasets, generally referred as the Sustainability and the Global Environment datasets(SAGE datasets) and History Database of the Global Environment datasets(HYDE datasets). However, at the regional level, these global datasets have coarse resolutions and inevitable errors. Considering various factors that influenced cropland distribution, including cropland connectivity and the limitation of natural and human factors, this study developed a reconstruction model of historical cropland based on constrained Cellular Automaton(CA) of ′bottom-up′. Then, an available labor force index is used as a proxy for the amount of cropland to inspect and calibrate these spatial patterns. Applied the reconstruction model to Shandong Province, we reconstructed its spatial distribution of cropland during 8 periods. The reconstructed results show that: 1) it is properly suitable for constrained CA to simulate and reconstruct the spatial distribution of cropland in traditional cultivated region of China; 2) compared with ′SAGE datasets′ and ′HYDE datasets′, this study have formed higher-resolution Boolean spatial distribution datasets of historical cropland with a more definitive concept of spatial pattern in terms of fractional format.
