Scientific data products and the data pre-processing subsystem of the Chang'e-3 mission

The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1） investigate the morphological features and geological structures at the landing site; （2） integrated in-situ analysis of minerals and chemical compositions; （3） integrated exploration of the structure of the lunar interior; （4） exploration of the lunar-terrestrial space environment, lunar sur- face environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System （GRAS） is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem （DPS） is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper de- scribes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies.

Study of inter-well interference in shale gas reservoirs by a robust production data analysis method based on deconvolution

In order to overcome the defects that the analysis of multi-well typical curves of shale gas reservoirs is rarely applied to engineering,this study proposes a robust production data analysis method based on deconvolution,which is used for multi-well inter-well interference research.In this study,a multi-well conceptual trilinear seepage model for multi-stage fractured horizontal wells was established,and its Laplace solutions under two different outer boundary conditions were obtained.Then,an improved pressure deconvolution algorithm was used to normalize the scattered production data.Furthermore,the typical curve fitting was carried out using the production data and the seepage model solution.Finally,some reservoir parameters and fracturing parameters were interpreted,and the intensity of inter-well interference was compared.The effectiveness of the method was verified by analyzing the production dynamic data of six shale gas wells in Duvernay area.The results showed that the fitting effect of typical curves was greatly improved due to the mutual restriction between deconvolution calculation parameter debugging and seepage model parameter debugging.Besides,by using the morphological characteristics of the log-log typical curves and the time corresponding to the intersection point of the log-log typical curves of two models under different outer boundary conditions,the strength of the interference between wells on the same well platform was well judged.This work can provide a reference for the optimization of well spacing and hydraulic fracturing measures for shale gas wells.

Product Data Management Implementation

On the basis of PDM(product data management) definition and its connotation, the factors to ensure implementation success are analyzed. The definition phase, analysis phase, design phase, build and test phase, and post production phase during PDM implementation are described. The implementation is divided into ten processes, which consist of the above different phases. The relationships between phases and processes are illustrated. Finally, a workflow is proposed to guide the implementing at a fixed price.

A simple data assimilation method for improving the MODIS LAI time-series data products based on the object analysis and gradient inverse weighted filter

A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and object analysis is proposed. The properties and quality control （QC） of MODIS LAI data products are introduced. Also, the gradient inverse weighted filter and object analysis are analyzed. An experiment based on the simple data assimilation method is performed using MODIS LAI data sets from 2000 to 2005 of Guizhou Province in China.

Research on the J2EE-based product database management system

The basic frame and the design idea of J2EE-based Product Data Management (PDM) system are presented. This paper adopts the technology of Object-Oriented to realize the database design and builds the information model of this PDM system. The integration key technology of PDM and CAD systems are discussed, the isomerous interface characteristics between CAD and PDM systems are analyzed, and finally, the integration mode of the PDM and CAD systems is given. Using these technologies, the integration of PDM and CAD systems is realized and the consistence of data in PDM and CAD systems is kept. Finally, the Product Data Management system is developed, which has been tested on development process of the hydraulic generator. The running process is stable and safety.

An improved data space inversion method to predict reservoir state fields via observed production data

A data-space inversion(DSI)method has been recently proposed and successfully applied to the history matching and production prediction of reservoirs.Based on Bayesian theory,DSI can directly and effectively obtain good posterior flow predictions without inversion of geological parameters of reservoir model.This paper presents an improved DSI method to fast predict reservoir state fields(e.g.saturation and pressure profiles)via observed production data.Firstly,a large number of production curves and state data are generated by reservoir model simulation to expand the data space of original DSI.Then,efficient history matching only on the observed production data is carried out via the original DSI to obtain related parameters which reflects the weight of the real reservoir model relative to prior reservoir models.Finally,those parameters are used to predict the oil saturation and pressure profiles of the real reservoir model by combining large amounts of state data of prior reservoir models.Two examples including conventional heterogeneous and unconventional fractured reservoir are implemented to test the performances of predicting saturation and pressure profiles of this improved DSI method.Besides,this method is also tested in a real field and the obtained results show the high computational efficiency and high accuracy of the practical application of this method.

Development and Application of a Production Data Analysis Model for a Shale Gas Production Well

This paper presents the development and application of a production data analysis software that can analyze and forecast the production performance and reservoir properties of shale gas wells.The theories used in the study were based on the analytical and empirical approaches.Its reliability has been confirmed through comparisons with a commercial software.Using transient data relating to multi-stage hydraulic fractured horizontal wells,it was confirmed that the accuracy of the modified hyperbolic method showed an error of approximately 4%compared to the actual estimated ultimate recovery(EUR).On the basis of the developed model,reliable productivity forecasts have been obtained by analyzing field production data relating to wells in Canada.The EUR was computed as 9.6 Bcf using the modified hyperbolic method.Employing the Pow Law Exponential method,the EUR would be 9.4 Bcf.The models developed in this study will allow in the future integration of new analytical and empirical theories in a relatively readily than commercial models.

A new product family mining method based on PLM database

Product family(PF) is the most important part of product platform. A new method is proposed to mine PF based on multi-space product data in PLM database. Product structure tree(PST) and bill of material(BOM) are used as the data source. A PF can be obtained by mining physics space, logic space and attribute space of product data. In this work, firstly, a PLM database is described, consisting of data organization form, data structure, and data characteristics. Then the PF mining method introduces the sequence alignment techniques used in bio-informatics, which mainly includes data pre-processing, regularization, mining algorithm and cluster analysis. Finally, the feasibility and effectiveness of the proposed method are verified by a case study of high and middle pressure valve, demonstrating a feasible method to obtain PF from PLM database.

Research on Product Data Management Based on Multi-agent System

PDM (product data management) is one kind of techniques based on software and database, which integrates information and process related to products. But it is not enough to perform the complication of PDM in enterprises. Then the mechanism to harmonize all kinds of information and process is needed. The paper introduces a novel approach to implement the intelligent monitor of PDM based on MAS (multi agent system). It carries out the management of information and process by MC (monitor center). The paper first puts forward the architecture of the whole system, then defines the structure of MC and its interoperation mode.

Statecharts for Distributed Product Data Management System Modelling

Product data management (PDM) has been accepted as an important tool for the manufacturing industries. In recent years, more and mor e researches have been conducted in the development of PDM. Their research area s include system design, integration of object-oriented technology, data distri bution, collaborative and distributed manufacturing working environment, secur ity, and web-based integration. However, there are limitations on their rese arches. In particular, they cannot cater for PDM in distributed manufacturing e nvironment. This is especially true in South China, where many Hong Kong (HK) ma nufacturers have moved their production plants to different locations in Pearl R iver Delta for cost reduction. However, they retain their main offices in HK. Development of PDM system is inherently complex. Product related data cover prod uct name, product part number (product identification), drawings, material speci fications, dimension requirement, quality specification, test result, log size, production schedules, product data version and date of release, special tooling (e.g. jig and fixture), mould design, project engineering in charge, cost spread sheets, while process data includes engineering release, engineering change info rmation management, and other workflow related to the process information. Accor ding to Cornelissen et al., the contemporary PDM system should contains manageme nt functions in structure, retrieval, release, change, and workflow. In system design, development and implementation, a formal specification is nece ssary. However, there is no formal representation model for PDM system. Theref ore a graphical representation model is constructed to express the various scena rios of interactions between users and the PDM system. Statechart is then used to model the operations of PDM system, Fig.1. Statechart model bridges the curr ent gap between requirements, scenarios, and the initial design specifications o f PDM system. After properly analyzing the PDM system, a new distributed PDM (DPDM) system is proposed. Both graphical representation and statechart models are constructed f or the new DPDM system, Fig.2. New product data of DPDM and new system function s are then investigated to support product information flow in the new distribut ed environment. It is found that statecharts allow formal representations to capture the informa tion and control flows of both PDM and DPDM. In particular, statechart offers a dditional expressive power, when compared to conventional state transition diagr am, in terms of hierarchy, concurrency, history, and timing for DPDM behavioral modeling.

Analyses on web-based product data management

This paper is concerned with the development of product data management （PDM） systems--WPDM systems based on web technologies. As a tool to integrate information, traditional PDM system has many benefits for the companies in such aspects as improving design productivity, better control over projects and so on. With the maturing of web technologies, the advantages of WPDM system are obvious. We will show these advantages in detail in Part 3. WPDM system is built on three-tier application model to provide security and flexibility, they are back-end, middle layer and front-end. The basic designs in each layer will be briefly introduced in Part 4. In the future, WPDM will be extended to integrate with other applications to provide a complete web-based engineering environment.

Establishment of Person and Enterprise Process Model in Product Data Management System

Before the implementation of product data management (PDM) system, person model and enterprise process model (EPM) must be firstly established. For the convenience of project management, all the related users must be allocated to the “Person User Role Group” net. Based on the person model and the direction of information flow, the EPM is established subsequently. The EPM consists of several release levels, in which the access controls are defined. The EPM procedure shows the blueprint of the workflow process structure. The establishment of person model and EPM in an enterprise has been instanced at the end of this paper.

A web product data management system based on Simple Object Access Protocol

A new web product data management architecture is presented. The three-tier web architecture and Simple Object Access Protocol (SOAP) are combined to build the web-based product data management (PDM) system which includes three tiers: the user services tier, the business services tier, and the data services tier. The client service component uses the server-side technology, and Extensible Markup Language (XML) web service which uses SOAP as the communication protocol is chosen as the business service component. To illustrate how to build a web-based PDM system using the proposed architecture, a case PDM system which included three logical tires was built. To use the security and central management features of the database, a stored procedure was recommended in the data services tier. The business object was implemented as an XML web service so that client could use standard internet protocols to communicate with the business object from any platform. In order to satisfy users using all sorts of browser, the server-side technology and Microsoft ASP.NET was used to create the dynamic user interface.

System View of Product Data Management and Its Application

A product data management system for a manufacturing enterprise is to make sure that the proper product data can be communicated to the right people at the right time.This paper describes a system analysis paradigm for data analysis in a product data management(PDM)development.Three aspects of the paradigm,i.e.,function,structure and behavior are rep- resented.The use of the paradigm explains why so many kinds of objects are necessary in a commercial database matrix and what models are available for developing a PDM application.As another result,a lot of models are derived from the analysis of product data system paradigm to model product data and PDM database definitions.

An Open Architecture and Implementation for Product Data Management

In this paper, an open architecture and its implementation for product data management are presented. The system architecture, product data definition model, and a set of components of the architecture are discussed in detail. Especially, the principle and some mechanism of one of the components, an object oriented database management system GH-EODB, are discussed in more detail. The architecture is extensible and adaptable.

The Value Insistence of the Cultural Production in the Big Data Era

The ultimate value of cultural production should be realizing human's comprehensive and free development,and deconstructing the ultimate value would result in human alienation.In the era of big data,every domain of human's social life,even the mode of thinking,has been transformed significantly.However,when the big data technology entirely penetrates the field of cultural production especially inducts the cultural production depending on the demand forecasting techniques,it would inevitably lead to a worry about value of the cultural production.This paper formulates that the cultural production's essence in the era of big data remains for the purpose of maximizing profit of commercial manipulation based on the modeling analysis of cultural production mechanism in the big data times.If the tendency is not corrected,the two main factors of cultural consumerism prevalence and the instrumental reason dictatorship will gradually deconstruct the ultimate value of cultural production and bring about the alienation of human being.For the sake of avoiding the trend,we should cope with two relationships:one is the people as a means and as a purpose;the other is the instrumental reason and the value rationality,finally giving rise to human's comprehensive and free development rather than human alienation.

Energy Efficiency Evaluation Based on Data Envelopment Analysis Integrated Analytic Hierarchy Process in Ethylene Production

Energy efficiency data from ethylene production equipment are of high dimension, dynamic and time sequential, so their evaluation is affected by many factors. Abnormal data from ethylene production are eliminated through consistency test, making the data consumption uniform to improve the comparability of data. Due to the limit of input and output data of decision making unit in data envelopment analysis(DEA), the energy efficiency data from the same technology in a certain year are disposed monthly using DEA. The DEA data of energy efficiency from the same technology are weighted and fused using analytic hierarchy process. The energy efficiency data from different technologies are evaluated by their relative effectiveness to find the direction of energy saving and consumption reduction.

Bootstrapping Data Envelopment Analysis of Efficiency and Productivity of County Public Hospitals in Eastern, Central, and Western China after the Public Hospital Reform

China implemented the public hospital reform in 2012. This study utilized bootstrapping data envelopment analysis（DEA） to evaluate the technical efficiency（TE） and productivity of county public hospitals in Eastern, Central, and Western China after the 2012 public hospital reform. Data from 127 county public hospitals（39, 45, and 43 in Eastern, Central, and Western China, respectively） were collected during 2012–2015. Changes of TE and productivity over time were estimated by bootstrapping DEA and bootstrapping Malmquist. The disparities in TE and productivity among public hospitals in the three regions of China were compared by Kruskal–Wallis H test and Mann–Whitney U test. The average bias-corrected TE values for the four-year period were 0.6442, 0.5785, 0.6099, and 0.6094 in Eastern, Central, and Western China, and the entire country respectively, with average non-technical efficiency, low pure technical efficiency（PTE）, and high scale efficiency found. Productivity increased by 8.12%, 0.25%, 12.11%, and 11.58% in China and its three regions during 2012–2015, and such increase in productivity resulted from progressive technological changes by 16.42%, 6.32%, 21.08%, and 21.42%, respectively. The TE and PTE of the county hospitals significantly differed among the three regions of China. Eastern and Western China showed significantly higher TE and PTE than Central China. More than 60% of county public hospitals in China and its three areas operated at decreasing return scales. There was a considerable space for TE improvement in county hospitals in China and its three regions. During 2012–2015, the hospitals experienced progressive productivity; however, the PTE changed adversely. Moreover, Central China continuously achieved a significantly lower efficiency score than Eastern and Western China. Decision makers and administrators in China should identify the causes of the observed inefficiencies and take appropriate measures to increase the efficiency of county public hospitals in the three areas of China, especially in Central China.

Kenyan Counties Geospatial Data Knowledge to Monitor Crop Production

Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, resulting in reduced harvests and sometimes losses for farmers. Better availability of information such as weather patterns, suitable crops, nutrient requirements based on soil types and conditions would greatly alleviate these challenges. While geospatial information is being developed and improved continuously by researchers, its accessibility and use by the counties has not been established and cannot be identified as contributing to better crop production outcomes. The aim of this study, therefore, was to assess the awareness and status of geospatial data availability and use for crop production, and the level of the relevant capacities, both human and infrastructural, in selected Counties of Kenya. A survey was conducted in the four counties of Vihiga, Kilifi, Wajir and Nyeri and key informant interviews were conducted with both management and technical County Agricultural Officers, as well as sub-county agricultural extension officers. From the results of the survey, out of the four counties, only one has adequate infrastructure in terms of hard-ware, software and connectivity to conduct useful geospatial data acquisition and processing. While most indicated awareness of the existence of geospatial data, limited resources, low skills and knowledge have restricted any meaningful sourcing of and access to data, with only 38% moderately or highly skilled in acquisition, 48% in processing and 57% in interpretation and use of geospatial data. The study concludes that moderate skills and capacities available within the counties have considerable potential to make use the available geospatial data to inform farmers accordingly and improve their farming outcomes.

Agricultural Total Factor Productivity and Income Gap between Urban and Rural Residents--An Empirical Study Based on Provincial Panel Data

Taking the relevant data of 27 provinces in China during 2013 and 2017 as samples,this paper firstly measured the agricultural total factor productivity( TFP) using Malmquist index method. Then,it built the panel data model,and empirically tested the impacts of agricultural TFP on the income gap between urban and rural residents. The results show that the improvement in agricultural TFP can promote the narrowing of the income gap between urban and rural residents,and the factors such as urbanization level and industrial structure also have significant impacts on the income gap between urban and rural residents. On the basis of these,it came up with recommendations,including increasing agricultural human capital investment and establishing agricultural production research institutions.