Planning of automated data acquisition and process control systems (ADACS) /

"January 1980."

Solid state data acquisition and processing system (SSDAPS). Volume I: system summary and volume II: description, operation, and verification test report. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Software support system for data acquisition and analysis. Final report.

National Highway Traffic Safety Administration, East Liberty, Ohio

Supervisory Control and Data Acquisition (SCADA) in liquid pipelines.

"PB2005-917005."

Structural Data Acquisition Using Sensor Network

The development cost of any civil infrastructure is very high; during its life span, the civil structure undergoes a lot of physical loads and environmental effects which damage the structure. Failing to identify this damage at an early stage may result in severe property loss and may become a potential threat to people and the environment. Thus, there is a need to develop effective damage detection techniques to ensure the safety and integrity of the structure. One of the Structural Health Monitoring methods to evaluate a structure is by using statistical analysis. In this study, a civil structure measuring 8 feet in length, 3 feet in diameter, embedded with thermocouple sensors at 4 different levels is analyzed under controlled and variable conditions. With the help of statistical analysis, possible damage to the structure was analyzed. The analysis could detect the structural defects at various levels of the structure.

Some reflections on acquisition, processing and analysis of statistical data in forest soils

Beyond the classical statistical approaches (determination of basic statistics, regression analysis, ANOVA, etc.) a new set of applications of different statistical techniques has increasingly gained relevance in the analysis, processing and interpretation of data concerning the characteristics of forest soils. This is possible to be seen in some of the recent publications in the context of Multivariate Statistics. These new methods require additional care that is not always included or refered in some approaches. In the particular case of geostatistical data applications it is necessary, besides to geo-reference all the data acquisition, to collect the samples in regular grids and in sufficient quantity so that the variograms can reflect the spatial distribution of soil properties in a representative manner. In the case of the great majority of Multivariate Statistics techniques (Principal Component Analysis, Correspondence Analysis, Cluster Analysis, etc.) despite the fact they do not require in most cases the assumption of normal distribution, they however need a proper and rigorous strategy for its utilization. In this work, some reflections about these methodologies and, in particular, about the main constraints that often occur during the information collecting process and about the various linking possibilities of these different techniques will be presented. At the end, illustrations of some particular cases of the applications of these statistical methods will also be presented.

Upgrading a TCABR data analysis and acquisition system for remote participation using Java, XML, RCP and modern client/server communication/authentication

The TCABR data analysis and acquisition system has been upgraded to support a joint research programme using remote participation technologies. The architecture of the new system uses Java language as programming environment. Since application parameters and hardware in a joint experiment are complex with a large variability of components, requirements and specification solutions need to be flexible and modular, independent from operating system and computer architecture. To describe and organize the information on all the components and the connections among them, systems are developed using the extensible Markup Language (XML) technology. The communication between clients and servers uses remote procedure call (RPC) based on the XML (RPC-XML technology). The integration among Java language, XML and RPC-XML technologies allows to develop easily a standard data and communication access layer between users and laboratories using common software libraries and Web application. The libraries allow data retrieval using the same methods for all user laboratories in the joint collaboration, and the Web application allows a simple graphical user interface (GUI) access. The TCABR tokamak team in collaboration with the IPFN (Instituto de Plasmas e Fusao Nuclear, Instituto Superior Tecnico, Universidade Tecnica de Lisboa) is implementing this remote participation technologies. The first version was tested at the Joint Experiment on TCABR (TCABRJE), a Host Laboratory Experiment, organized in cooperation with the IAEA (International Atomic Energy Agency) in the framework of the IAEA Coordinated Research Project (CRP) on ""Joint Research Using Small Tokamaks"". (C) 2010 Elsevier B.V. All rights reserved.

Tokamak TCABR: Acquisition system, data analysis, and remote participation using MDSplus

Each plasma physics laboratory has a proprietary scheme to control and data acquisition system. Usually, it is different from one laboratory to another. It means that each laboratory has its own way to control the experiment and retrieving data from the database. Fusion research relies to a great extent on international collaboration and this private system makes it difficult to follow the work remotely. The TCABR data analysis and acquisition system has been upgraded to support a joint research programme using remote participation technologies. The choice of MDSplus (Model Driven System plus) is proved by the fact that it is widely utilized, and the scientists from different institutions may use the same system in different experiments in different tokamaks without the need to know how each system treats its acquisition system and data analysis. Another important point is the fact that the MDSplus has a library system that allows communication between different types of language (JAVA, Fortran, C, C++, Python) and programs such as MATLAB, IDL, OCTAVE. In the case of tokamak TCABR interfaces (object of this paper) between the system already in use and MDSplus were developed, instead of using the MDSplus at all stages, from the control, and data acquisition to the data analysis. This was done in the way to preserve a complex system already in operation and otherwise it would take a long time to migrate. This implementation also allows add new components using the MDSplus fully at all stages. (c) 2012 Elsevier B.V. All rights reserved.

Federal plan for the acquisition of water data.

Mode of access: Internet.

Fundamental understanding on the use of Bluetooth scanner as a complementary transport data

Literature is limited in its knowledge of the Bluetooth protocol based data acquisition process and in the accuracy and reliability of the analysis performed using the data. This paper extends the body of knowledge surrounding the use of data from the Bluetooth Media Access Control Scanner (BMS) as a complementary traffic data source. A multi layer simulation model named Traffic and Communication Simulation (TCS) is developed. TCS is utilised to model the theoretical properties of the BMS data and analyse the accuracy and reliability of travel time estimation using the BMS data.

The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector

Big Data and predictive analytics have received significant attention from the media and academic literature throughout the past few years, and it is likely that these emerging technologies will materially impact the mining sector. This short communication argues, however, that these technological forces will probably unfold differently in the mining industry than they have in many other sectors because of significant differences in the marginal cost of data capture and storage. To this end, we offer a brief overview of what Big Data and predictive analytics are, and explain how they are bringing about changes in a broad range of sectors. We discuss the “N=all” approach to data collection being promoted by many consultants and technology vendors in the marketplace but, by considering the economic and technical realities of data acquisition and storage, we then explain why a “n « all” data collection strategy probably makes more sense for the mining sector. Finally, towards shaping the industry’s policies with regards to technology-related investments in this area, we conclude by putting forward a conceptual model for leveraging Big Data tools and analytical techniques that is a more appropriate fit for the mining sector.

A network forensics tool for precise data packet capture and replay in cyber-physical systems

Network data packet capture and replay capabilities are basic requirements for forensic analysis of faults and security-related anomalies, as well as for testing and development. Cyber-physical networks, in which data packets are used to monitor and control physical devices, must operate within strict timing constraints, in order to match the hardware devices' characteristics. Standard network monitoring tools are unsuitable for such systems because they cannot guarantee to capture all data packets, may introduce their own traffic into the network, and cannot reliably reproduce the original timing of data packets. Here we present a high-speed network forensics tool specifically designed for capturing and replaying data traffic in Supervisory Control and Data Acquisition systems. Unlike general-purpose "packet capture" tools it does not affect the observed network's data traffic and guarantees that the original packet ordering is preserved. Most importantly, it allows replay of network traffic precisely matching its original timing. The tool was implemented by developing novel user interface and back-end software for a special-purpose network interface card. Experimental results show a clear improvement in data capture and replay capabilities over standard network monitoring methods and general-purpose forensics solutions.