基于栅格的GIS三维空间数据模型

空间数据模型是描述GIS空间数据组织和进行空间数据库设计的理论基础，它反映了现实世界中空间实体之间的相互关系，是空间数据处理和空间数据挖掘的核心问题。文章论述了空间数据模型的表示形式，重点讨论了基于栅格的三维空间数据模型，指出了栅格矢量空间数据模型集成化的必要性以及集成化表示方法，给出了将矢量数据转换为栅格数据的欧氏距离变换与算法，为基于栅格空间数据的空间数据处理和空间数据挖掘奠定了基础。

区域源网荷储一体化调控管理模式

针对当前面临的“新能源+规模化储能”调控策略缺失,主动支撑能力不足以及多系统林立等共性问题,本文以全国首个“源网荷储”一体化项目为例,探讨一体化调控管理模式的应用。主要研究内容包括区域风光功率联合预测,电网友好型新能源电站的“网-源”协调关键机制,风光储场站协同优化控制保护,新能源快速一次调频和虚拟惯量支撑等技术创新研发,以及风光储场站一体化数据集控平台研发和“集控+直控”双调度模式创新。研究结果表明,一体化集控平台从可测、可控、可调、可支撑四个方面为源网荷储一体化运行提供数据支持,满足新能源电站一体化接入及风光储稳定运行的要求,实现了全景智慧、电网友好、风光储协同的目标。研究成果有助于加快推动源网荷储一体化建设,并为规范化的源网荷储一体化实施路径提供参考。

Irregular seismic data reconstruction based on exponential threshold model of POCS method

Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets （POCS） based image restoration method into the seismic data reconstruction field to interpolate irregularly missing traces. For entire dead traces, we transfer the POCS iteration reconstruction process from the time to frequency domain to save computational cost because forward and reverse Fourier time transforms are not needed. In each iteration, the selection threshold parameter is important for reconstruction efficiency. In this paper, we designed two types of threshold models to reconstruct irregularly missing seismic data. The experimental results show that an exponential threshold can greatly reduce iterations and improve reconstruction efficiency compared to a linear threshold for the same reconstruction result. We also analyze the anti- noise and anti-alias ability of the POCS reconstruction method. Finally, theoretical model tests and real data examples indicate that the proposed method is efficient and applicable.

Accessing Alignments of Ontologies via IoT Based on SKOS Data Model

Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic inferring,non-linear inverted index establishing,service composing) .There is a great deal of research about sensor ontology alignment dealing with the heterogeneity between the different sensor ontologies,but fewer solutions focus on exploiting syntaxes in a sensor ontology and the pattern of accessing alignments.Our solution infers alignments by extending structural subsumption algorithms to analyze syntaxes in a sensor ontology,and then combines the alignments with the SKOS model to construct the integration sensor ontology,which can be accessed via the IoT.The experiments show that the integration senor ontology in the SKOS model can be utilized via the IoT service,and the accuracy of our prototype,in average,is higher than others over the four real ontologies.

Evaluating the impact of spatio-temporal scale on CPUE standardization

This study focused on the quantitative evaluation of the impact of the spatio-temporal scale used in data collection and grouping on the standardization of CPUE(catch per unit effort).We used the Chinese squid-jigging fishery in the northwestern Pacific Ocean as an example to evaluate 24 scenarios at different spatio-temporal scales,with a combination of four levels of temporal scale(weekly,biweekly,monthly,and bimonthly)and six levels of spatial scale(longitude×latitude:0.5°×0.5°,0.5°×1°,0.5°×2°,1°×0.5°,1°×1°,and 1°×2°).We applied generalized additive models and generalized linear models to analyze the24 scenarios for CPUE standardization,and then the differences in the standardized CPUE among these scenarios were quantified.This study shows that combinations of different spatial and temporal scales could have different impacts on the standardization of CPUE.However,at a fine temporal scale(weekly)different spatial scales yielded similar results for standardized CPUE.The choice of spatio-temporal scale used in data collection and analysis may create added uncertainty in fisheries stock assessment and management.To identify a cost-effective spatio-temporal scale for data collection,we recommend a similar study be undertaken to facilitate the design of effective monitoring programs.

A Hierarchical Virus Immunization Method for Community Networks

Due to the enormous harm of virus propagation,research regarding virus immunizations still absolutely necessary.In comparison to current researches,a new virus immunization method the hierarchical virus immunization method(HVIM) for community networks is proposed.Based on the virus transmission dynamic model SusceptibleInfectious-Removed and SusceptibleRemoved(SIRSR),HVIM considered the influence of external factors on the spread of viruses and only needs a portion of the network structure to be able to carry out immunization.Another pro for HVIM is that it is scalable and suitable for parallel computing which is a requirement in the big data era.Finally,a simulation dataset and a real dataset were used to run experiments,and the results of simulation showed that HVIM obviously is superior to others on the aspect of immunity.

A Secure-Efficient Data Collection Algorithm Based on Self-Adaptive Sensing Model in Mobile Internet of Vehicles

Existing research on data collection using wireless mobile vehicle network emphasizes the reliable delivery of information.However,other performance requirements such as life cycle of nodes,stability and security are not set as primary design objectives.This makes data collection ability of vehicular nodes in real application environment inferior.By considering the features of nodes in wireless IoV,such as large scales of deployment,volatility and low time delay,an efficient data collection algorithm is proposed for mobile vehicle network environment.An adaptive sensing model is designed to establish vehicular data collection protocol.The protocol adopts group management in model communication.The vehicular sensing node in group can adjust network sensing chain according to sensing distance threshold with surrounding nodes.It will dynamically choose a combination of network sensing chains on basis of remaining energy and location characteristics of surrounding nodes.In addition,secure data collection between sensing nodes is undertaken as well.The simulation and experiments show that the vehicular node can realize secure and real-time data collection.Moreover,the proposed algorithm is superior in vehicular network life cycle,power consumption and reliability of data collection by comparing to other algorithms.

Developing a Risk Assessment Model for Typhoon-triggered Debris Flows

A methodology is developed for interactive risk assessment of physical infrastructure and spatially distributed response systems subjected to debris flows.The proposed framework is composed of three components,namely geotechnical engineering,geographical information systems and disaster management.With the integration of slope stability analysis,hazard scenario and susceptibility,geological conditions are considered as temporary static data,while meteorological conditions are treated as dynamic data with a focus on typhoons.In this research,the relevant parameters required for database building are defined,and the procedures for building the geological database and meteorological data sets are explained.Based on the concepts and data sets,Nantou and Hualien in Taiwan are used as the areas for case studies.

Factors Acquisition and Content Estimation of Farmland Soil Organic Carbon Based upon Internet of Things

Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things(IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.

An Action Research of Using Problem-Centered Double-Cycles Instructional Model to Implement ModeI-Elicitng Activity

The purpose of the study was to use the Problem-Centered Double-Cycles Instructional Model （PCDC-IM） （1995, Chang） to help the teacher （Lin） implement model-eliciting activity （MEA） in her mathematics classes. In this study, there were 31 students divided in 10 groups and engaged in the MEA ＂Who saved the oriental cherry trees＂. Data collections included the learning sheets, journals and debriefing forms of students, teaching and reflection journals, observation reports, and field notes of teachers, reflection journals and interview reports of researchers, video tapes and records of the classes and meetings. There were four parts of the teaching cycle in PCDC-IM, namely task, guidance, environment and analysis. We summarized developing and implementing the MEA in ＂Task＂ aspect, 5 principles and 5 kinds of teachers＇ roles in ＂Guidance＂ aspect, 2 elements and 5 features in ＂Environment＂ aspect and data in ＂Analysis＂ aspect. The findings showed that PCDC-IM was helpful for mathematics teachers who want to change their instruction from lecture into modeling teaching.

Effects of Reducing River Flow on Pulse Residence Time in Little Manatee River,USA

Residence time is an important indicator for river environmental management.In this paper,a 3D hydrodynamic model has been successfully applied to Little Manatee River to characterize the mixing and transport process and residence time.The model employs horizontal curvilinear orthogonal grids to represent the complex river system that consists of branches and bayous.The model has been satisfactorily calibrated and verified by using two continuous data sets.The data sets consist of hourly observations of all forcing boundaries,including freshwater inputs,tides,winds,salinity and temperatures at bay boundary,and air temperatures for model simulations.The data sets also consist of hourly observations of water levels,salinity,and temperature at several river stations.The calibrated and verified hydrodynamic model was used to predict residence time in the Little Manatee River.Under the minimum flow of 0.312 m3/s,the pulse residence time(PRT) is 108 days.Model simulations were also conducted for 17 flow scenarios.Empirical regression equations have been satisfactorily derived to correlate PRT to freshwater inflow.Correlation coefficient R2 is 0.982 for PRT.

Modeling and Analysis of Data Dependencies in Business Process for Data-Intensive Services

With the growing popularity of data-intensive services on the Internet, the traditional process-centric model for business process meets challenges due to the lack of abilities to describe data semantics and dependencies, resulting in the inflexibility of the design and implement for the processes. This paper proposes a novel data-aware business process model which is able to describe both explicit control flow and implicit data flow. Data model with dependencies which are formulated by Linear-time Temporal Logic(LTL) is presented, and their satisfiability is validated by an automaton-based model checking algorithm. Data dependencies are fully considered in modeling phase, which helps to improve the efficiency and reliability of programming during developing phase. Finally, a prototype system based on j BPM for data-aware workflow is designed using such model, and has been deployed to Beijing Kingfore heating management system to validate the flexibility, efficacy and convenience of our approach for massive coding and large-scale system management in reality.

Usage of 3D Point Cloud Data in BIM （Building Information Modelling）： Current Applications and Challenges

BIM （building information modelling） has gained wider acceptance in the A/E/C （architecture/engineering/construction） industry in the US and internationally. This paper presents current industry approaches of implementing 3D point cloud data in BIM and VDC （virtual design and construction） applications during various stages of a project life cycle and the challenges associated with processing the huge amount of 3D point cloud data. Conversion from discrete 3D point cloud raster data to geometric/vector BIM data remains to be a labor-intensive process. The needs for intelligent geometric feature detection/reconstruction algorithms for automated point cloud processing and issues related to data management are discussed. This paper also presents an innovative approach for integrating 3D point cloud data with BIM to efficiently augment built environment design, construction and management.

交通数据资源分析视图应用研究

文章论述了在交通数据资源分析利用中多维分析视图的构建方案。从交通数据资源现状分析及数据分析利用的需求出发,通过分析主题、维度等概念,结合浙江交通行政处罚监管系统的数据分析应用场景,详细阐述了一个完整的交通数据资源多维分析综合视图及其工作机制,提出了针对交通数据资源数据模型集以及模型配置文件设计等具体实现成果,对交通行业数据资源增值利用进行了应用研究。

Framework Design of Eco-Technology Evaluation Platform and Integration System

Global ecological degradation is a matter of enormous concern. In the early 20 st century, the United States, Europe and China began to apply eco-technology to ecosystem management and restoration in order to slow down or stop ecological degradation. To date, there has been neither a systematic summary and scientific evaluation, nor is there a unified platform to describe ecological degradation problems in different areas and existing eco-technologies. These shortcomings have hindered the popularization and application of technologies. This study intends to build an eco-technology evaluation platform and integration system that brings together heterogeneous data from multiple sources. The key technology of the eco-technology evaluation platform and integration system is information integration technology. We will establish a metadata engine based on metadata storage to achieve access to and integration of metadata and heterogeneous data sources. The information integration mode based on a metamodel addresses information heterogeneity at four levels： system, syntax, structure and semantics. We develop the framework for an eco-technology evaluation platform and integration system to integrate ecotechnology databases, eco-technology evaluation model databases, eco-technology evaluation parameter databases and spatial databases of ecological degradation and eco-technology with metadata and metamodel integration mode. This system can support functions for the query and display of global and typical ecological degradation and the query, display, evaluation and prioritization of eco-technologies, which can realize the visualization of global and Chinese ecological degradation and eco-technology evaluation and prioritization. This system will help government decision makers and relevant departments to understand ecological degradation and the effects of ecotechnology implementation.

Predicting Soil Moisture Characteristic Curves from Continuous Particle-Size Distribution Data

Soil moisture characteristic curve （SMC） is a fundamental soil property and its direct measurement is tedious and time consuming. Therefore, various indirect methods have been developed to predict SMC from particle-size distribution （PSD）. However, the majority of these methods often yield intermittent SMC data because they involve estimating individual SMC points. The objectives of this study were 1） to develop a procedure to predict continuous SMC from a limited number of experimental PSD data points and 2） to evaluate model predictions through comparisons with measured values. In this study, an approach that allowed predicting SMC from the knowledge of PSD, parameterized by means of the closed-form van Genuchten model （VG）, was used. Through using Mohammadi and Vanclooster （MV） model, the parameters obtained from fitting of VG to PSD data were applied to predict SMC curves. Since the residual water content （Or） could not be obtained through fitting of VG-MV integrated model to PSD data, we also examined and compared four different methods estimating 0r. Results showed that the proposed equation （MV-VG integrated model） provided an excellent fit to all the PSD data and the model could adequately predict SMC as measured in forty-two soils sampled from different regions of Iran. For all soils, the method in which Or Was obtained through parameter optimization procedure provided the best overall predictions of SMC. The two methods estimating Or with Campbell and Shiozawa （CS） model resulted in less accuracy than the optimization procedure. Furthermore, the proposed model underestimated the moisture content in the dry range of SMC when the value of 0r was assumed to equal zero. 0r could be attributed to the incomplete desorption of water coated on soil particles and the accurate estimation of 0r was critical in prediction of SMC, especially for fine-textured soils at high suction heads. It could be concluded that the advantages of our approach were the continuity, robustness, and independency of model performance on soil type, allowing to improve predictions of SMC from PSD at the field and watershed scales.

Survival analysis of car travel time near a bus stop in developing countries

This paper presents an important investigation into car travel time affected by mixed traffic flow near a bus stop on the basis of survival analysis theory.Travel time data associated with mixed traffic characteristics near a bus stop were collected by video cameras.A hazard-based duration model was introduced to analyze the effects of mixed traffic flow on car travel time.The results indicate that mixed traffic flow impacts car travel time significantly.And the presence of bus berthing violation would delay car travel time.The proposed model can be used to forecast temporal shifts in car travel time due to changes in mixed traffic flow.The influential factors related to mixed traffic flow should be given full consideration in the planning and designing of bus stops in developing countries.

Climate effects of the GlobeLand30 land cover dataset on the Beijing Climate Center climate model simulations

Land cover is one of the most basic input elements of land surface and climate models. Currently, the direct and indirect effects of land cover data on climate and climate change are receiving increasing attentions. In this study, a high resolution(30 m) global land cover dataset(Globe Land30) produced by Chinese scientists was, for the first time, used in the Beijing Climate Center Climate System Model(BCC_CSM) to assess the influences of land cover dataset on land surface and climate simulations. A two-step strategy was designed to use the Globe Land30 data in the model. First, the Globe Land30 data were merged with other satellite remote sensing and climate datasets to regenerate plant functional type(PFT) data fitted for the BCC_CSM. Second, the up-scaling based on an area-weighted approach was used to aggregate the fine-resolution Globe Land30 land cover type and area percentage with the coarser model grid resolutions globally. The Globe Land30-based and the BCC_CSM-based land cover data had generally consistent spatial distribution features, but there were some differences between them. The simulation results of the different land cover type dataset change experiments showed that effects of the new PFT data were larger than those of the new glaciers and water bodies(lakes and wetlands). The maximum value was attained when dataset of all land cover types were changed. The positive bias of precipitation in the mid-high latitude of the northern hemisphere and the negative bias in the Amazon, as well as the negative bias of air temperature in part of the southern hemisphere, were reduced when the Globe Land30-based data were used in the BCC_CSM atmosphere model. The results suggest that the Globe Land30 data are suitable for use in the BCC_CSM component models and can improve the performance of the land and atmosphere simulations.

THE DATA ENVELOPMENT ANALYSIS MODEL WITH INTERSECTION FORM PRODUCTION POSSIBILITY SET

The data envelopment analysis （DEA） model is used to evaluate the relative economic efficiency of a given set of decision making units （DMUs）. In this paper, the DEA production possibility set is transferred from the conventional sum form into the intersection form which is represented by a linear inequality system. Although it is time consuming to obtain the intersection form of the production possibility set, it suggests a new angle to investigate the properties of DMUs and to extend the DEA research further beyond the efficiency measurement. Following the intersection form, the analytical formula of the efficiency indicator and projection is given. Various aspects of technical efficiency, returns to scale and evidence of congestion of the DMUs are studied. The relationship between the weak DEA efficiency and the weak Pareto solution is discussed. Finally, a procedure for DMU grouping is proposed to help the decision makers for better resource reallocation and strategy adjustment.

Model-Based Integrated Methods for Quantitative Estimation of Soil Salinity from Hyperspectral Remote Sensing Data:A Case Study of Selected South African Soils

Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.