Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal...Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.展开更多
Through systematically summarizing the observational data of earth resistivity during 26 years from nearly a hundred stations in China, the author found that the pattern of the earth resistivity (ρs) tendency variati...Through systematically summarizing the observational data of earth resistivity during 26 years from nearly a hundred stations in China, the author found that the pattern of the earth resistivity (ρs) tendency variations,based on monthly average data, could be divided into five types, three types of which were defined as anomalous variation, which have different qualitative and quantitative characteristics and different relations with earthquakes as well.The first type of tendency variation called “funnel” is related to strong earthquakes, the Second type called “scoop” has good corresponding relation with moderate earthquakes, and the third type called “tilt” has no relation with earthquakes. Preliminary discussions about the relations between the three types of ρs tendency variation patterns and earthquakes are made in this paper, according to the experimental results of pressed rocks. It is concluded that the different patterns of ρs tendency variation actually reflect the different stress conditions of underground soil-rock layers: the “funnel” type reflects high stress status, the “scoop” type shows moderate stress condition and the “tilt” type is related to stress relief. All of such knowledges mentioned above are very useful in making accurate medium-term earthquake prediction.展开更多
Candlestick charts display the high,low,opening,and closing prices in a specific period.Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate.These patterns capture i...Candlestick charts display the high,low,opening,and closing prices in a specific period.Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate.These patterns capture information on the candles.According to Thomas Bulkowski’s Encyclopedia of Candlestick Charts,there are 103 candlestick patterns.Traders use these patterns to determine when to enter and exit.Candlestick pattern classification approaches take the hard work out of visually identifying these patterns.To highlight its capabilities,we propose a two-steps approach to recognize candlestick patterns automatically.The first step uses the Gramian Angular Field(GAF)to encode the time series as different types of images.The second step uses the Convolutional Neural Network(CNN)with the GAF images to learn eight critical kinds of candlestick patterns.In this paper,we call the approach GAF-CNN.In the experiments,our approach can identify the eight types of candlestick patterns with 90.7%average accuracy automatically in real-world data,outperforming the LSTM model.展开更多
Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are s...Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are starting to use deep learning to tackle TSC tasks.Quantum neural networks(QNN)have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing,but research using quantum neural networks to handle TSC tasks has not received enough attention.Therefore,we proposed a learning framework based on multiple imaging and hybrid QNN(MIHQNN)for TSC tasks.We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN.We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging.Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks.We tested our method on several standard datasets and achieved significant results compared to several current TSC methods,demonstrating the effectiveness of MIHQNN.This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.展开更多
基金the Frontier Program of the Knowledge Innovation Program of Chinese Academy of Sciences
文摘Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.
文摘Through systematically summarizing the observational data of earth resistivity during 26 years from nearly a hundred stations in China, the author found that the pattern of the earth resistivity (ρs) tendency variations,based on monthly average data, could be divided into five types, three types of which were defined as anomalous variation, which have different qualitative and quantitative characteristics and different relations with earthquakes as well.The first type of tendency variation called “funnel” is related to strong earthquakes, the Second type called “scoop” has good corresponding relation with moderate earthquakes, and the third type called “tilt” has no relation with earthquakes. Preliminary discussions about the relations between the three types of ρs tendency variation patterns and earthquakes are made in this paper, according to the experimental results of pressed rocks. It is concluded that the different patterns of ρs tendency variation actually reflect the different stress conditions of underground soil-rock layers: the “funnel” type reflects high stress status, the “scoop” type shows moderate stress condition and the “tilt” type is related to stress relief. All of such knowledges mentioned above are very useful in making accurate medium-term earthquake prediction.
基金Jun-Hao Chen and Yun-Cheng Tsai are supported in part by the Ministry of Science and Technology of Taiwan under grant 108-2218-E-002-050-.
文摘Candlestick charts display the high,low,opening,and closing prices in a specific period.Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate.These patterns capture information on the candles.According to Thomas Bulkowski’s Encyclopedia of Candlestick Charts,there are 103 candlestick patterns.Traders use these patterns to determine when to enter and exit.Candlestick pattern classification approaches take the hard work out of visually identifying these patterns.To highlight its capabilities,we propose a two-steps approach to recognize candlestick patterns automatically.The first step uses the Gramian Angular Field(GAF)to encode the time series as different types of images.The second step uses the Convolutional Neural Network(CNN)with the GAF images to learn eight critical kinds of candlestick patterns.In this paper,we call the approach GAF-CNN.In the experiments,our approach can identify the eight types of candlestick patterns with 90.7%average accuracy automatically in real-world data,outperforming the LSTM model.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.61772295 and 61572270)the PHD foundation of Chongqing Normal University (Grant No.19XLB003)Chongqing Technology Foresight and Institutional Innovation Project (Grant No.cstc2021jsyjyzysbAX0011)。
文摘Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are starting to use deep learning to tackle TSC tasks.Quantum neural networks(QNN)have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing,but research using quantum neural networks to handle TSC tasks has not received enough attention.Therefore,we proposed a learning framework based on multiple imaging and hybrid QNN(MIHQNN)for TSC tasks.We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN.We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging.Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks.We tested our method on several standard datasets and achieved significant results compared to several current TSC methods,demonstrating the effectiveness of MIHQNN.This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.
