Real-time Flow Data Research Articles

An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System

This study takes a sample of green storage monitoring data for corn from a biochemical energy enterprise, based on the enterprise’s original storage monitoring system while establishing a “green fortress” intending to achieve green and sustainable grain storage. This paper proposes a set of processing algorithms for real-time flow data from the storage system based on cluster analysis to detect abnormal storage conditions, achieve the goal of green grain storage and maximize benefits for the enterprises. Firstly, data from the corn storage monitoring system and the current status of research on data processing algorithms are analyzed. Our study summarizes the processing of re-al-time stream data together with the characteristics of the monitoring system and discusses the application of clustering analysis algorithms. The study includes an in-depth study of the green storage monitoring system data for corn and the processing requirements for real-time stream data. As the main novelty of this research, the optimization algorithm model is applied to the green storage monitoring system for maize and is validated. Finally, the processing results for the green storage monitoring data for maize are presented in graphical and textual formats.

Design of Pipeline Flowmeter Calibration System Based on PLC Control

In order to solve the problems of error and data storage and calculation in the calibration process of traditional pipeline flowmeter due to human factors, a calibration system based on PLC control was designed on the basis of a standard method. PLC controls the frequency converter through CAN bus, and then controls the motor to supply water to the calibration system. At the same time, the PLC extended IO modules are used to collect the real-time flow data of the standard meter and the tested meter. PLC and upper computer through Ethernet information exchange, and the use of the Kingview software to achieve the basic control of the PLC and the data display, data storage, data calculation. The test data show that when the errors of the standard table meets ±0.5%, the errors of the tested meter can meet ±1%, and the calibration efficiency is high and the data are accurate. Therefore, compared with the manual calibration method, the calibration system based on PLC control can better control the errors, and is more convenient for data storage and calculation, and has higher practical value.

InSight2: A Modular Visual Analysis Platform for Network Situational Awareness in Large-Scale Networks

The complexity and throughput of computer networks are rapidly increasing as a result of the proliferation of interconnected devices, data-driven applications, and remote working. Providing situational awareness for computer networks requires monitoring and analysis of network data to understand normal activity and identify abnormal activity. A scalable platform to process and visualize data in real time for large-scale networks enables security analysts and researchers to not only monitor and study network flow data but also experiment and develop novel analytics. In this paper, we introduce InSight2, an open-source platform for manipulating both streaming and archived network flow data in real time that aims to address the issues of existing solutions such as scalability, extendability, and flexibility. Case-studies are provided that demonstrate applications in monitoring network activity, identifying network attacks and compromised hosts and anomaly detection.

Real-time nanoleakage and the flow characteristics of calcium silicate root canal filling materials

This study aimed to investigate the real-time nanoleakage and flow characteristics of calcium silicate-based (Ca–Si) root canal filling materials. Extracted human teeth (n = 30) were decoronated and standardized in their inner and outer dimensions. After root canal enlargement, the roots were filled with gutta-percha (GP) and AH26 sealer, GP and EndoSeal MTA sealer, or Biodentine. The roots were connected to a Nanoflow device (IB Systems) under hydrostatic pressure (40 cm∙H2O) and fluid flow was traced through the filled roots. Data were detected at the nanoscale twice per second and automatically recorded in units of nL/s. Leakage was quantified as the mean slope until the curve plateaued over time, and all static flow intervals lasting longer than 1 s were analyzed to identify any increase in flow and duration. Data were statistically analyzed using the Kruskal-Wallis test. The calculated leakage values were 0.0670 ± 0.0516 nL/s for GP/AH26, 0.1397 ± 0.1579 nL/s for GP/EndoSeal MTA, and 0.0358 ± 0.0538 nL/s for Biodentine, with no statistically significant differences among the root filling materials (P > 0.05). An analysis of real-time flow data for 1000 s to identify spot trends and the overall tendency of flow until a plateau was reached revealed a stepwise increase in the roots filled with Ca–Si material, whereas the GP/AH26-filled roots showed a linear increase. Real-time measurements under hydrostatic pressure with the Nanoflow device enabled precise fluid flow tracing through the root canal filling material. In terms of nanoleakage, the tested root canal filling materials showed no significant differences, while the real-time flow patterns of roots filled with Ca–Si material showed different characteristics from those of GP/AH26-filled roots.

A Distributed Anomaly Detection System for In-Vehicle Network Using HTM

With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall.

A web based tool for operational real-time flood forecasting using data assimilation to update hydraulic states

This article describes an operational flood forecasting system set up for the city of Dijon, France. This system assimilates real-time flow data at an hourly time step with the stationary Kalman filter to update hydraulic states. It uses a semi-distributed hydrologic model to integrate rainfall measurements and forecasts and provide discharge forecasts at several points on the watershed. It also offers powerful data management tools and an elaborated graphical interface available from any computer connected to the Internet. The hydrologic model was calibrated using a semi-distributed approach. Its simulation and forecasting performances are analyzed. The performances of the system on a recent flood event are also investigated.

A Promising Radar Altimetry Satellite System for Operational Flood Forecasting in Flood-Prone Bangladesh

Building on a recent suite of work that has demonstrated theoretical feasibility and operational readiness of a satellite altimeter based flood forecasting system, we recently put a progressively designed altimeter based transboundary flood forecasting system to the ultimate test of real-time operational delivery in Bangladesh. The JASON-2 satellite altimeter, which was in orbit at the time of writing this manuscript, was used as the flagship altimeter mission. This paper summarizes the entire process of designing the system, customizing the workflow, and putting the system in place for complete ownership by the Bangladesh stakeholder agency for a 100 day operational skill test spanning the period of June 1 2013 through Sept. 9, 2013. Correlation for most of the flood warning stations ranged between 0.95 to 0.80 during the 1 day to 8 days lead time range. The RMSE of forecast typically ranged between 0.75m to 1.5m at locations where the danger level relative to the river bed was more than an order higher (i.e., >20m). The RMSE of forecast at the 8 days lead time did not exceed 2m for upstream and mid-stream rivers inside Bangladesh. The RMSE of forecast at the 8 days lead time exceeded 2m at a few estuarine river locations affected by tidal effects, where danger level relative to river bed was smaller (i.e., <;20m). Such a satellite altimeter system, such as one based on the JASON-2 altimeter, is now poised to serve the entire inhabitants of the Ganges-Brahmaputra-Meghna river basins as well as 30 or more flood-prone downstream nations currently deprived of real-time flow data from upstream nations.

Assessing the Economic and Political Impacts of Climate Change on International River Basins Using Surface Wetness in the Zambezi and Mekong Basins

Many river basins will likely face higher hydrologic variability, including extreme floods and droughts, due to climate change, with economic and political consequences. Water treaties that govern international basins could face non-compliance among riparians and inter-state tensions as hydrologic variability increases. Accurate monitoring of water resources is essential to cope with these fluctuations in flow. This paper demonstrates a simple yet robust procedure — the Basist Wetness Index — to predict gauge station (actual water resources) measurements of surface wetness values derived from satellite data (for 1988-2013) and empirically derived flow distributions in two international river basins: Zambezi and Mekong. The paper further undertakes an economic analysis (applied to the Mekong), which identifies not only the economic costs and losses due to extreme flow events, but likewise showcases the benefits countries could potentially reap should they be able to make use of such flow data in real time. An illustrative application, using the wetness data and socio-political data, is also performed to highlight the utility of the procedure for research in the field of conflict and cooperation over water. The paper concludes that satellite data modeled with gauge station flow can help reduce the uncertainty inherent in negotiating international water issues. Moreover, the satellite observations can provide near real time monitoring of water resources, and provide valuable lead time for impending droughts and floods. Thus, the approach presented in the article can assist policy makers to devise more efficient and cooperative institutional apparatus.

Assessing the Economic and Political Impacts of Climate Change on International River Basins Using Surface Wetness in the Zambezi and Mekong Basins

Many river basins will likely face higher hydrologic variability, including extreme floods and droughts, due to climate change, with economic and political consequences. Water treaties that govern international basins could face non-compliance among riparians and inter-state tensions as hydrologic variability increases. Accurate monitoring of water resources is essential to cope with these fluctuations in flow. This paper demonstrates a simple yet robust procedure—the Basist Wetness Index—to predict gauge station (actual water resources) measurements of surface wetness values derived from satellite data (for 1988-2013) and empirically derived flow distributions in two international river basins: Zambezi and Mekong. The paper further undertakes an economic analysis (applied to the Mekong), which identifies not only the economic costs and losses due to extreme fl ow events, but likewise showcases the benefits countries could potentially reap should they be able to make use of such flow data in real time. An illustrative application, using the wetness data and socio-political data, is also performed to highlight the utility of the procedure for research in the field of conflict and cooperation over water. The paper concludes that satellite data modeled with gauge station flow can help reduce the uncertainty inherent in negotiating international water issues. Moreover, the satellite observations can provide near real time monitoring of water resources, and provide valuable lead time for impending droughts and floods. Thus, the approach presented in the article can assist policy makers to devise more efficient and cooperative institutional apparatus.

Impacts of Large-demand Customer on Water Distribution System

Abstract In water utilities’ practical applications, this high demand in one place induces low pressures and sometimes service interruptions into adjacent area. Moreover, the resulting high velocity causes disturbance of the material deposited in the distribution main. In this study, real-time flow data in combination with pressure and turbidity was observed in NS City of Korea. The experimental results showed that the intensive water use of large-demand customer influences on pressure drop by the magnitude of 0.5 kg/cm2 and on instantaneous values of turbidity reaching 15 times of average turbidity.

Integrating hydrograph modeling with real-time flow monitoring to generate hydrograph-specific sampling schemes

Automated sample collection for water quality research and evaluation generally is performed by simple time-paced or flow-weighted sampling protocols. However, samples collected on strict time-paced or flow-weighted schemes may not adequately capture all elements of storm event hydrographs (i.e., rise, peak, and recession). This can result in inadequate information for calculating chemical mass flux over storm events. In this research, an algorithm was developed to guide automated sampling of hydrographs based on storm-specific information. A key element of the new “hydrograph-specific sampling scheme” is the use of a hydrograph recession model for predicting the hydrograph recession curve, during which flow-paced intervals are calculated for scheduling the remaining samples. The algorithm was tested at a tile drained Midwest agricultural site where real-time flow data were processed by a programmable datalogger that in turn activated an automated sampler at the appropriate sampling times to collect a total of twenty samples during each storm event independent of the number of sequential hydrographs generated. The utility of the algorithm was successfully tested with hydrograph data collected at both a tile drain and agricultural ditch, suggesting the potential for general applicability of the method. This sampling methodology is flexible in that the logic can be adapted for use with any hydrograph recession model; however, in this case a power law equation proved to be the most practical model.

Self-priming Hemodynamic Reservoir and Inline Flow Meter for a Cardiopulmonary Bypass Simulation

Simulator exercises are used at Midwestern University to augment academic and laboratory training toward consolidating particular skills, increasing situation awareness, and preparing the student for practice within the team environment of an operating room. This paper describes an enhanced cardiopulmonary bypass simulator consisting of a self-priming hemodynamic reservoir that includes an inline flow meter. A typical cardiopulmonary bypass adult perfusion circuit was assembled using a roller pump console and integrated oxygenator/heat exchanger/reservoir and primed with 2 liters of water. For patient simulation, a soft-sided reservoir bag was mounted onto an inclined platform. A 1-liter soft-sided bag was placed just above the reservoir, providing an overflow reservoir. The priming line extended to the head of the mannequin. The arterial, venous, and suction lines extended through the open chest. The primed perfusion circuit was connected to ports on the filled reservoir bag. To test the patient simulation, the arterial pump output was adjusted to flow rates ranging from 1–7 liters per minute, with a complete interruption (to zero flow) between each test run. An inline flow meter was added to the bypass circuit and an analog to digital converter board was used to pass flow data into the computer-based simulation program. The use of an inclined hemodynamic reservoir bag proved to be self-priming and functional without problems over a wide range of flows tested. By including a reservoir with the mannequin, plus processing and displaying real-time flow data using the CPB-Sim simulation program, a higher fidelity and more realistic simulation experience was created.

Self-gated Fourier velocity encoding

Self-gating is investigated to improve the velocity resolution of real-time Fourier velocity encoding measurements in the absence of a reliable electrocardiogram waveform (e.g., fetal magnetic resonance or severe arrhythmia). Real-time flow data are acquired using interleaved k-space trajectories which share a common path near the origin of k-space. These common data provide a rapid self-gating signal that can be used to combine the interleaved data. The combined interleaves cover a greater area of k-space than a single real-time acquisition, thereby providing higher velocity resolution for a given aliasing velocity and temporal resolution. For example, this approach provided velocity spectra with a temporal resolution of 19 ms and velocity resolution of 22 cm/s over an 818 cm/s field-of-view. The method was validated experimentally using a computer-controlled pulsatile flow apparatus and applied in vivo to measure aortic-valve flow in a healthy volunteer.