A data processing scheme based on CXML in mobile commerce

2017 ◽  
Vol 16 (4) ◽  
pp. 913-925
Author(s):  
Shengbo Shi ◽  
Runtong Zhang
Keyword(s):  
Data Processing ◽  
Mobile Commerce ◽  
Processing Scheme

scholarly journals Multi-group cross section library generation for HTGR lattice physics code XPZ

2021 ◽  
Vol 2048 (1) ◽  
pp. 012028
Author(s):  
Lerui Zhang ◽  
Ding She ◽  
Lei Shi ◽  
Richard Chambon ◽  
Alain Hébert
Keyword(s):  
Data Processing ◽  
Cross Section ◽  
Nuclear Data ◽  
Automatic Generation ◽  
Detailed Data ◽  
Processing Scheme ◽  
Processing Module ◽  
Complete Set ◽  
Evaluated Nuclear Data ◽  
High Temperature Gas

Abstract The XPZ code was previously developed for the lattice physics computation in High Temperature Gas-cooled Reactors (HTGRs), which adopted the multi-group cross section library converted from the existing open-source DRAGON library. In this paper, a new format of multi-group cross section library named XPZLIB has been implemented in XPZ code. XPZLIB is designed in binary and HDF5 formats, including detailed data contents for resonance, transport and depletion calculations. A new data-processing module named XPZR is developed based on NJOY-2016 to generate nuclide dependent XPZLIB from the most recent evaluated nuclear data, and besides, the PyNjoy-2016 system is developed for automatic generation of integrated XPZLIB including a complete set of nuclides. The new generated XPZLIB is presented with the XPZ code. Numerical results demonstrate the accuracy of the new library XPZLIB and the reliability of the data processing scheme. Moreover, the influence of different versions of ENDF/B data is investigated.

scholarly journals UAV and terrestrial laser scanner data processing for large scale topographic mapping

2020 ◽  
Vol 50 ◽  
pp. 63-73
Author(s):  
Ganbold Ulziisaikhan ◽  
Dash Oyuntsetseg
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Large Scale ◽  
Laser Scanner ◽  
Measurement Technology ◽  
Topographic Mapping ◽  
Terrestrial Laser Scanner ◽  
Scanner Data ◽  
Processing Scheme ◽  
Digital Elevation

Integrating spatial data from different sources results in visualization, which is the last step in the process of digital basic topographic map creation. Digital elevation model and visualization will used for geomorphological mapping, geospatial database, urban planning and etc. Large scale topographic mapping in the world countries is really a prominent challenge in geospatial industries today. The purpose of this work is to integrate laser scanner data with the ones generated by aerial photogrammetry from UAV, to produce detailed maps that can used by geodetic engineers to optimize their analysis. In addition, terrestrial - based LiDAR scans and UAV photogrammetric data were collected in Sharga hill in the north zone of Mongolia. In this paper, different measurement technology and processing software systems combined for topographic mapping in the data processing scheme. UTM (Universal Transverse Mercator) projected coordinate system calculated in WGS84 reference ellipsoid. Feature compilation involving terrestrial laser scanner data and UAV data will integrated to offer Digital Elevation Models (DEM) as the main interest of the topographic mapping activity. Used UAV generate high-resolution orthomosaics and detailed 3D models of areas where no data, are available. That result issued to create topographic maps with a scale of 1:1000 of geodetic measurements. Preliminary results indicate that discontinuity data collection from UAV closely matches the data collected using laser scanner.

scholarly journals In-Network Data Processing in Software-Defined IoT with a Programmable Data Plane

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ki-Wook Kim ◽  
Sung-Gi Min ◽  
Youn-Hee Han
Keyword(s):  
Data Processing ◽  
Data Aggregation ◽  
Sensor Nodes ◽  
Network Data ◽  
Packet Forwarding ◽  
Processing Scheme ◽  
Sensing Data ◽  
Iot Applications ◽  
Proposed Model ◽  
Data Plane

Making an SDN data plane flexible enough to satisfy the various requirements of heterogeneous IoT applications is very desirable in terms of software-defined IoT (SD-IoT) networking. Network devices with a programmable data plane provide an ability to dynamically add new packet- and data-processing procedures to IoT applications. The previously proposed solutions for the addition of the programmability feature to the SDN data plane provide extensibility for the packet-forwarding operations of new protocols, but IoT applications need a more flexible programmability for in-network data-processing operations (e.g., the sensing-data aggregation from thousands of sensor nodes). Moreover, some IoT models such as OMG DDS, oneM2M, and Eclipse SCADA use the publish-subscribe model that is difficult to represent using the operations of the existing message-centric data-plane models. We introduce a new in-network data-processing scheme for the SD-IoT data plane that defines an event-driven data-processing model that can express a variety of in-network data-processing cases in the SD-IoT environment. Also, the proposed model comprises a language for the programming of the data-processing procedures, while a flexible data-plane structure that can install and execute the programs at runtime is additionally presented. We demonstrate the flexibility of the proposed scheme by using sample programs in a number of example SD-IoT cases.

scholarly journals Preliminary assessment of the pragmatic value of information in the classification problem based on deep neural networks

2021 ◽  
Vol 16 (93) ◽  
pp. 9-20
Author(s):  
Valery P. Meshalkin ◽  
◽  
Maxim I. Dli ◽  
Andrey Yu. Puchkov ◽  
Ekaterina I. Lobaneva ◽  
...  
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Data Processing ◽  
Value Of Information ◽  
Learning Algorithm ◽  
The State ◽  
Preliminary Assessment ◽  
Processing Scheme ◽  
Input Information ◽  
Controlled Object

A method is proposed for preliminary assessment of the pragmatic value of information in the problem of classifying the state of an object based on deep recurrent networks of long short-term memory. The purpose of the study is to develop a method for predicting the state of a controlled object while minimizing the number of used prognostic parameters through a preliminary assessment of the pragmatic value of information. This is an especially urgent task under conditions of processing big data, characterized not only by significant volumes of incoming information, but also by information rate and multiformatness. The generation of big data is now happening in almost all areas of activity due to the widespread introduction of the Internet of Things in them. The method is implemented by a two-level scheme for processing input information. At the first level, a Random Forest machine learning algorithm is used, which has significantly fewer adjustable parameters than a recurrent neural network used at the second level for the final and more accurate classification of the state of the controlled object or process. The choice of Random Forest is due to its ability to assess the importance of variables in regression and classification problems. This is used in determining the pragmatic value of the input information at the first level of the data processing scheme. For this purpose, a parameter is selected that reflects the specified value in some sense, and based on the ranking of the input variables by the level of importance, they are selected to form training datasets for the recurrent network. The algorithm of the proposed data processing method with a preliminary assessment of the pragmatic value of information is implemented in a program in the MatLAB language, and it has shown its efficiency in an experiment on model data.

scholarly journals Data Processing Scheme Based on Blockchain

2020 ◽  
Author(s):  
Jinhua Fu ◽  
Mixue Xu ◽  
Yongzhong Huang ◽  
Xueming Si ◽  
Chao Yuan
Keyword(s):  
Data Processing ◽  
Data Sharing ◽  
High Performance ◽  
Joint Venture ◽  
White Paper ◽  
Distribution Data ◽  
Processing Scheme ◽  
Electronic Evidence ◽  
Supply Chain Finance ◽  
A Chain

Abstract In the white paper written on Bitcoin, a chain of blocks was proposed by Satoshi Nakamoto. Since then, blockchain has been rapidly developed. Blockchain is not only limited to the field of cryptocurrency but also has been extensively applied to the Internet of Things (IoT), supply chain finance, electronic evidence storage, data sharing, and e-government fields. Both the public chain and the alliance chain have been extensively developed. In the data processing field, blockchain has a particularly good application potential. The Square Kilometre Array (SKA) is a proposal consisting of a joint venture of more than ten countries, resulting in the world’s largest synthetic aperture radio telescope. In the SKA, the processing scale of the data is large, and it consists of several data processing nodes. The data will be processed in the cloud computing mode. Taking the SKA under consideration, this report proposes a data processing scheme based on blockchain for the anti-counterfeiting, anti-tampering and traceability of data. Furthermore, the authenticity and integrity of the data are assured. The primary aspects include data distribution, data operation and data sharing, which correspond to the data reception, data algorithm processing and result sharing of data operation in the SKA. With this process, the integrity, reliability and authenticity of the data are guaranteed. Additionally, smart contracts, homomorphic hashing, secure containers, aggregate signatures and one-way encrypted channels are implemented to ensure the intelligence, security and high performance of the process.

A Novel Efficient Big Data Processing Scheme for Feature Extraction in Electrical Discharge Machining

2019 ◽  
Vol 4 (2) ◽  
pp. 910-917
Author(s):  
Chao-Chun Chen ◽  
Min-Hsiung Hung ◽  
Benny Suryajaya ◽  
Yu-Chuan Lin ◽  
Haw-Ching Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Data Processing ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Big Data Processing ◽  
Processing Scheme
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