ZTE Corporation, a leading global provider of telecommunications equipment and network solutions, launched the world’s fastest HSUPA data card at the 3GSM World Congress 2007 in Barcelona, Spain.
Constructing βmesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction （NWP） model. Without vertical velocity matching the βmesoscale weather system, co...Constructing βmesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction （NWP） model. Without vertical velocity matching the βmesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrom eteors. In this study, a method, basing on the threedimensional variational （3DVAR） assimilation technique, was developed to obtain reasonable structures of βmesoscale weather systems by assimilating radar data in a nextgeneration NWP system named GRAPES （the Global and Regional Assimilation and Prediction System） of China. Singlepoint testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity （taking Richardson’s equation as the observational operator） can greatly improve the vertical motion. Ex periments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further indepth study.展开更多
This paper introduced the theory and approaches of building driving forcemodels revealing the changes in land utilization level by integrating RS, GPS, and GIS technologiesbased on the example of Yuanmou County of Yun...This paper introduced the theory and approaches of building driving forcemodels revealing the changes in land utilization level by integrating RS, GPS, and GIS technologiesbased on the example of Yuanmou County of Yunnan Province. We first created the land utilizationtype database, natural driving forces for land utilization database, and human driving forces forland utilization database. Then we obtained the dependent and the independent variables of changesin land utilization level by exploring various data. Lastly we screened major factors affectingchanges in land utilization level by using the powerful spatial correlation analysis and maincomponent analysis module of GIS and obtained a multivariable linear regression model of thechangesin land utilization level by using GIS spatial regression analysis module.展开更多
The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier prediction.Regrettably,due to...The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier prediction.Regrettably,due to the highdimensionality nature of neural data and the least available samples,modelling an efficient computer diagnostic system is highly solicited.Learning approaches,specifically deep learning approaches,are essential in disease prediction.Deep Learning(DL)approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging.A novel 3D-Convolutional Neural Network(3D-CNN)architecture is proposed to predict AD with Magnetic resonance imaging(MRI)data.The proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary classification.The proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative(ADNI)data.The outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general approaches.The predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.Keras’experimentation is carried out,and the model’s superiority is compared with various advanced approaches for multi-level classification.The proposed model gives better prediction accuracy,precision,recall,and F-measure than other systems like Long Short Term Memory-Recurrent Neural Networks(LSTM-RNN),Stacked Autoencoder with Deep Neural Networks(SAE-DNN),Deep Convolutional Neural Networks(D-CNN),Two Dimensional Convolutional Neural Networks(2D-CNN),Inception-V4,ResNet,and Two Dimensional Convolutional Neural Networks(3D-CNN).展开更多
In this paper, we present a background and theory of the effect of Surface Acoustic Wave (SAW) Filter Module (SFM) in-band ripple on high data rate communications parameters such as the Error Vector Magnitude (EVM). I...In this paper, we present a background and theory of the effect of Surface Acoustic Wave (SAW) Filter Module (SFM) in-band ripple on high data rate communications parameters such as the Error Vector Magnitude (EVM). In addition, we present analyses and statements for the choice of unbalanced S-parameters set of the SFM over balanced S-parameters set of the SFM in measurements and Agilent’s Advance Design System (ADS) Ptolemy simulations. A test and measurement setup using Agilent’s equipment will be presented.展开更多
China is a great agricultural country with large population, limited soilresources and traditional farming mode, so the central government has been attaching greatimportance to the development of agriculture and put f...China is a great agricultural country with large population, limited soilresources and traditional farming mode, so the central government has been attaching greatimportance to the development of agriculture and put forward a new agricultural technologyrevolution ― the transformation from traditional agriculture to modern agriculture and fromextensive farming to intensive farming. Digital agriculture is the core of agriculturalinformatization. The enforcement of digital agriculture will greatly promote agricultural technologyrevolution, two agricultural transformations and its rapid development, and enhance China'scompetitive power after the entrance of WTO. To carry out digital agriculture, the frame system ofdigital agriculture is required to be studied in the first place. In accordance with the theory andtechnology of digital earth and in combination with the agricultural reality of China, this articleoutlines the frame system of digital agriculture and its main content arid technology support.展开更多
文摘ZTE Corporation, a leading global provider of telecommunications equipment and network solutions, launched the world’s fastest HSUPA data card at the 3GSM World Congress 2007 in Barcelona, Spain.
基金supported by the National Key Scientific and Technological Project (Grant No 2006BAC02B00)National Natural Science Foundation of China (Grant No40518001)
文摘Constructing βmesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction （NWP） model. Without vertical velocity matching the βmesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrom eteors. In this study, a method, basing on the threedimensional variational （3DVAR） assimilation technique, was developed to obtain reasonable structures of βmesoscale weather systems by assimilating radar data in a nextgeneration NWP system named GRAPES （the Global and Regional Assimilation and Prediction System） of China. Singlepoint testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity （taking Richardson’s equation as the observational operator） can greatly improve the vertical motion. Ex periments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further indepth study.
文摘This paper introduced the theory and approaches of building driving forcemodels revealing the changes in land utilization level by integrating RS, GPS, and GIS technologiesbased on the example of Yuanmou County of Yunnan Province. We first created the land utilizationtype database, natural driving forces for land utilization database, and human driving forces forland utilization database. Then we obtained the dependent and the independent variables of changesin land utilization level by exploring various data. Lastly we screened major factors affectingchanges in land utilization level by using the powerful spatial correlation analysis and maincomponent analysis module of GIS and obtained a multivariable linear regression model of thechangesin land utilization level by using GIS spatial regression analysis module.
文摘The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier prediction.Regrettably,due to the highdimensionality nature of neural data and the least available samples,modelling an efficient computer diagnostic system is highly solicited.Learning approaches,specifically deep learning approaches,are essential in disease prediction.Deep Learning(DL)approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging.A novel 3D-Convolutional Neural Network(3D-CNN)architecture is proposed to predict AD with Magnetic resonance imaging(MRI)data.The proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary classification.The proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative(ADNI)data.The outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general approaches.The predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.Keras’experimentation is carried out,and the model’s superiority is compared with various advanced approaches for multi-level classification.The proposed model gives better prediction accuracy,precision,recall,and F-measure than other systems like Long Short Term Memory-Recurrent Neural Networks(LSTM-RNN),Stacked Autoencoder with Deep Neural Networks(SAE-DNN),Deep Convolutional Neural Networks(D-CNN),Two Dimensional Convolutional Neural Networks(2D-CNN),Inception-V4,ResNet,and Two Dimensional Convolutional Neural Networks(3D-CNN).
文摘In this paper, we present a background and theory of the effect of Surface Acoustic Wave (SAW) Filter Module (SFM) in-band ripple on high data rate communications parameters such as the Error Vector Magnitude (EVM). In addition, we present analyses and statements for the choice of unbalanced S-parameters set of the SFM over balanced S-parameters set of the SFM in measurements and Agilent’s Advance Design System (ADS) Ptolemy simulations. A test and measurement setup using Agilent’s equipment will be presented.
文摘China is a great agricultural country with large population, limited soilresources and traditional farming mode, so the central government has been attaching greatimportance to the development of agriculture and put forward a new agricultural technologyrevolution ― the transformation from traditional agriculture to modern agriculture and fromextensive farming to intensive farming. Digital agriculture is the core of agriculturalinformatization. The enforcement of digital agriculture will greatly promote agricultural technologyrevolution, two agricultural transformations and its rapid development, and enhance China'scompetitive power after the entrance of WTO. To carry out digital agriculture, the frame system ofdigital agriculture is required to be studied in the first place. In accordance with the theory andtechnology of digital earth and in combination with the agricultural reality of China, this articleoutlines the frame system of digital agriculture and its main content arid technology support.
