Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This pap...Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.展开更多
Extreme temperature events can influence the natural environment and societal activities more so than mean temperature events. This study used daily data from 238 stations north of 60°N, obtained from the Global ...Extreme temperature events can influence the natural environment and societal activities more so than mean temperature events. This study used daily data from 238 stations north of 60°N, obtained from the Global Summary of the Day dataset for the period 1979~015, to investigate the trends of summertime extreme temperature. The results revealed most stations north of 60°N with trends of decrease in the number of cold days （nights） and increase in the number of warm clays （nights）. The regional average results showed trends of consistent decline （rise） of cold days and nights （warm days and nights） in Eurasia and Greenland. Similarly, the trends of the seasonal maximum and minimum values were most significant in these regions. In summer, of three indices considered （i.e., Arctic Oscillation, Arctic dipole, and E1 Nifi^Southem Oscillation）, the largest contributor to the trends of extreme temperature events was the Arctic dipole. Prevailing southerly winds in summer brought warm moist air across northern Eurasia and Greenland, conducive to increased numbers of warm days （nights） and decreased numbers of cold day （nights）. Moreover, we defined extreme events using different thresholds and found the spatial distributions of the trends were similar.展开更多
With the rapid development of the aviation industry,the development of intelligent manufacturing equipment represented by composite robots has been paid close attention by the aviation industry.Based on the analysis o...With the rapid development of the aviation industry,the development of intelligent manufacturing equipment represented by composite robots has been paid close attention by the aviation industry.Based on the analysis of the background and main structure function of composite robots,this paper focuses on the analysis of key technologies such as composite robot hardware design,visual sensing and planning system,integrated control of‘hands,feet,and eyes',multi-robot collaborative operation,and safety.The typical applications of composite robots in aviation intelligent manufacturing such as automatic drilling and connection of aircraft,aircraft surface spraying and finishing,parts handling,aircraft measurement,and inspection are presented.The development trends such as standardization of composite robots,integration of‘5G+cloud computing+AI',and fusion of intelligent sensors are proposed.展开更多
文摘Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.
基金supported by National Key R&D Program of China (Grant no.2017YFE0111700)Beijing Municipal Natural Science Foundation (Grant no.8182023)
文摘Extreme temperature events can influence the natural environment and societal activities more so than mean temperature events. This study used daily data from 238 stations north of 60°N, obtained from the Global Summary of the Day dataset for the period 1979~015, to investigate the trends of summertime extreme temperature. The results revealed most stations north of 60°N with trends of decrease in the number of cold days （nights） and increase in the number of warm clays （nights）. The regional average results showed trends of consistent decline （rise） of cold days and nights （warm days and nights） in Eurasia and Greenland. Similarly, the trends of the seasonal maximum and minimum values were most significant in these regions. In summer, of three indices considered （i.e., Arctic Oscillation, Arctic dipole, and E1 Nifi^Southem Oscillation）, the largest contributor to the trends of extreme temperature events was the Arctic dipole. Prevailing southerly winds in summer brought warm moist air across northern Eurasia and Greenland, conducive to increased numbers of warm days （nights） and decreased numbers of cold day （nights）. Moreover, we defined extreme events using different thresholds and found the spatial distributions of the trends were similar.
基金the National Key Research and Development Program of China(No.2022YFB4700400)。
文摘With the rapid development of the aviation industry,the development of intelligent manufacturing equipment represented by composite robots has been paid close attention by the aviation industry.Based on the analysis of the background and main structure function of composite robots,this paper focuses on the analysis of key technologies such as composite robot hardware design,visual sensing and planning system,integrated control of‘hands,feet,and eyes',multi-robot collaborative operation,and safety.The typical applications of composite robots in aviation intelligent manufacturing such as automatic drilling and connection of aircraft,aircraft surface spraying and finishing,parts handling,aircraft measurement,and inspection are presented.The development trends such as standardization of composite robots,integration of‘5G+cloud computing+AI',and fusion of intelligent sensors are proposed.
