Mapping the NGSI-LD Context Model on Top of a SPARQL Event Processing Architecture: Implementation Guidelines

NGSI-LD is an open specification released by ETSI which proposes an information model and an API for an easy to use and standard management of context information. The NGSI-LD information model is framed within an ontology and adopts JSON-LD as serialization format for context information. This paper presents an approach to the implementation of the NGSI-LD specification over a SPARQL Event Processing Architecture. This work is being developed within the European-Brasilian H2020 SWAMP project focused on implementing an Internet of Things platform providing services for smart water management in agriculture

Standard Information Model for Meta-Data

This document provides a detailed and explanatory description of the Standard Information Model for Meta-Data (SIM) which constitutes an intrinsic part of the Spatial Information Platform (SIP) of the EU FP7 project SWITCH-ON (Sharing Water-related Information to Tackle Changes in the Hydrosphere – for Operational Needs). Although widely adopted information models for the description of data and services do exist (e.g. ISO19115 (2003) and ISO 19119 (2005), the Standard Information Model of the SIP is not solely based on one of these standards. Instead of defining one fixed information model that is based on a selection of particular meta(data) standards or profiles, the Standard Information Model of the SIP has been tailored to the actual information needs of the SIP, auxiliary services, and tools as well as its end users (product developers and researchers working in the virtual water-science lab). Thereby, the concepts of the CKAN (Comprehensive Knowledge Archive Network) domain model as well as support for meta- (data) standards like Dublin Core, ISO 19115, etc., have been considered in the design of the SIM. The design of the SIM follows therefore a graduated approach with the following three different levels of increasing extensibility and flexibility: Relational Model The relational model defines the outline for an object relational database model and supports the core business processes of the SIP. Dynamic Tag Extensions Dynamic tag extensions augment the relational model by user definable code lists and thus provide a simple yet powerful extension mechanism. Dynamic Content Extensions Dynamic Content Extensions form a mechanism to dynamically inject complex structured or semi-structured content in the SIM without the need to change the relational model

Integrated Decision Support System for Prognostic and Diagnostic Analyses of Water Distribution System Failures

This paper presents an innovative decision support system (DSS) for prognostic and diagnostic analyses of water distribution system (WDS) failures. The framework of the DSS is based on four novel models developed and published by the authors of this paper. The four models include reliability assessment model, leakage potential model, leakage detection model, and water quality failure potential model. Information obtained from these models together with external information such as customer complaints, lab test results (if any), and historical information are integrated using Dempster-Shafer (D-S) theory to evaluate prognostic and diagnostic capabilities of the DSS. The prognostic capabilities of the DSS provide hydraulic and water quality states of a WDS whereas the diagnostic capabilities of the DSS help to identify the failure location with minimal time after the occurrence and will help to reduce false positive and false negative predictions. The framework has ‘unique’ capacity to bring the modeling information (hydraulic and Quality), consumer complaints, historical failure data, and laboratory test information under a single platform to perform a prognostic and diagnostic investigation of WDS failures (hydraulic and Quality). The proof of concept of the DSS has been demonstrated using data used in published four articles. The outcomes of this research widely addressed the uncertainties associated with WDS which improves the efficiency and effectiveness of diagnosis and prognosis analyses of WDS. It is expected that the developed integrated framework will help municipalities to make informed decisions to increase the safety, reliability and the security of public health.Natural Sciences and Engineering Research Council of Canada (NSERC-SPG (Strategic Project Grants)

Estimating Crop Stomatal Conductance Through High-Throughput Plant Phenotyping

During photosynthesis and transpiration, crops exchange carbon dioxide and water with the atmosphere through stomata. When a crop experiences water stress, stomata are closed to reducing water loss. However, the closing of stomata also negatively affects the photosynthetic efficiency of the crop and leads to lower yields. Stomatal conductance (gs) quantifies the degree of stomatal opening and closing by using the rate of gas exchange between the crop and the atmosphere, which helps to understand the water status of the crop for better irrigation management. Unfortunately, gs measurement typically requires contact measuring instruments and manual collection in the field, which is time-consuming and labor-intensive. Thus, this study estimates gs in two ways. Firstly, plant phenotypic data and weather information were used to estimate gs for various types of crops. The plant phenotypic data were extracted from images captured by a thermal infrared camera, a multispectral camera, and a visible and near-infrared spectrometer integrated on field phenotyping platform. Weather information was obtained from a field weather station. The random forest regression (RFR) model performed the best with R2 of 0.69 and RMSE of 0.135 mol*m-2 *s-1 , while the model using weather parameters alone had R2 of 0.58 and RMSE of 0.161, and the model using phenotypic data alone had R2 values of 0.59 and RMSE of 0.158 mol*m-2 *s-1 . The results indicated that there was a complementary relationship between plant phenotypic data and weather information in estimating gs. The second aspect of the study was to estimate maize and soybean gs directly from near-infrared, thermal-infrared and RGB (Red Green Blue) images collected by the same platform. The results showed that the convolutional neural network (CNN) model outperformed the other models with an R2 of 0.52. In addition, adding soil moisture as a variable to the model improved its accuracy, which decreased the RMSE from 0.147 to 0.137 mol*m-2 *s-1 . This study highlights the potential of estimating gs from remote sensing and field phenotyping platforms to help growers obtain information about the water status of crops and plan irrigation more efficiently. Advisor: Yufeng G

A study on the introduction of artificial intelligence technology in the water treatment process

Thesis(Master) --KDI School:Master of Public Mangement,2020.Today, we stand in front of a huge wave of change named the "Fourth industrial revolution." Key technologies of the Fourth Industrial Revolution include artificial intelligence, the Internet of Thing (IoT), cloud computing, big data analysis, etc. These technologies will lead to an intelligent information society, and platform services will change every aspect of society from economic and work. This paper proposes several introductions of Artificial Intelligence Technology to improve water management. AI Technology secure a leadership position in the unfolding revolution and expedite the realization of an intelligent information company. K-water has to secure innovative technologies in advance as the foster related industries and upgrade services in order to generate new value and ensure the competitiveness of its intelligent water system. The K-water should take significant steps to thoroughly prepare for the coming Fourth Industrial Revolution, such as Artificial Intelligence-based autonomous Water Purification Plant with developing a creative water treatment process. The artificial intelligence system will be able to secure technological competitiveness in the water industry and secure future growth engines in the water industry by securing intelligence information technology, which is key to the fourth industrial revolution.Ⅰ. Introduction Ⅱ. Review of Literature and Cases III. Analysis of AI Technology Application in Water Treatment Ⅳ. Recommendation for the Standard Model of Artificial Intelligence Ⅴ. ConclusionmasterpublishedSeong Il, JEONG

LAGOVirtual: A Collaborative Environment for the Large Aperture GRB Observatory

We present the LAGOVirtual Project: an ongoing project to develop platform to collaborate in the Large Aperture GRB Observatory (LAGO). This continental-wide observatory is devised to detect high energy (around 100 GeV) component of Gamma Ray Bursts, by using the single particle technique in arrays of Water Cherenkov Detectors (WCD) at high mountain sites (Chacaltaya, Bolivia, 5300 m a.s.l., Pico Espejo, Venezuela, 4750 m a.s.l., Sierra Negra, Mexico, 4650 m a.s.l). This platform will allow LAGO collaboration to share data, and computer resources through its different sites. This environment has the possibility to generate synthetic data by simulating the showers through AIRES application and to store/preserve distributed data files collected by the WCD at the LAGO sites. The present article concerns the implementation of a prototype of LAGO-DR adapting DSpace, with a hierarchical structure (i.e. country, institution, followed by collections that contain the metadata and data files), for the captured/simulated data. This structure was generated by using the community, sub-community, collection, item model; available at the DSpace software. Each member institution-country of the project has the appropriate permissions on the system to publish information (descriptive metadata and associated data files). The platform can also associate multiple files to each item of data (data from the instruments, graphics, postprocessed-data, etc.).Comment: Second EELA-2 Conference Choroni, Venezuela, November 25th to 27th 200

Design and construction of a novel tribometer with on-line topography and wear measurement

We present a novel experimental platform that links topographical and material changes with the friction and wear behavior of oil-lubricated metal surfaces. This concept combines state-of-the-art methods for the analysis of the surface topography on the micro- and nano-scale with the online measurement of wear. At the same time, it allows for frictional and lateral force detection. Information on the topography of one of the two surfaces is gathered in-situ with a 3D holography microscope at a maximum frequency of 15 fps and higher resolution images are provided at defined time intervals by an atomic force microscope (AFM). The wear measurement is conducted on-line by means of radio nuclide technique (RNT). The quantitative measurement of the lateral and frictional forces is conducted with a custom-built 3D force sensor. The surfaces can be lubricated with an optically transparent oil or water. The stability and precision of the setup have been tested in a model experiment. The results show that the exact same position can be relocated and examined after each load cycle. Wear and topography measurements were performed with a radioactive labeled iron pin sliding against an iron plate

Testing and Analyzing Surface Water Quality of Yue-Guan Canal in Yueqing, China

The rapid industrialization in eastern China has made great contributions to the growth of the country’s economy while also degrading the environmental quality. Yet the government is reluctant to release the environmental information to the public, resulting in a barrier for concerned citizens to participate. This study focuses on the water quality of Yue-Guan Canal in Yueqing, Zhejiang, where private rural enterprises have mushroomed since the 1980s. The objective of this study is to 1) test the water quality of the canal based on pH, total dissolved solids (TDS), copper (Cu), ammonia nitrogen (NH3-N) and hexavalent chromium (Cr VI); 2) determine the relation between water quality and land use and land cover (LULC), and model the water quality of the whole canal based on LULC; and 3) make the information available to the public via an online geographic information system (GIS) platform. I chose six sampling sites along this 27 km canal, and collected samples on 3 workdays and 3 non-workdays in December 2012 and January 2013. I found an excess of NH3-N made the water samples of all sites fail to meet the national standards for Class V water. I created regression models for pH, TDS and Cu in relation to LULC, but all resulted in low R2 values, which may suggest point sources contribute more pollutants than non-point sources. I published the map and the data as a web application for the public