Current Monitoring Systems Research Articles

European agricultural policy requires a stronger performance framework to achieve the Sustainable Development Goals

Non-technical summary Agriculture provides many benefits to people, such as producing food and creating jobs in rural areas, but it can also have negative impacts on the environment. We analysed existing monitoring indicators for the European Union's Common Agricultural Policy (CAP) to evaluate whether the CAP is effectively achieving multiple social and environmental goals. We found that the current CAP monitoring system is unable to balance many potentially competing goals because its indicators are biased towards a few objectives. We suggest the European Union and its Member States adopt a broader set of indicators covering clear targets when the policy is reformed after 2020.

Design and Simulation of an Intelligent Current Monitoring System for Urban Rail Transit

Many urban rail transit (URT) systems adopt the DC traction power supply system. Because of the impedance and incomplete ground insulation of the running track, it is inevitable for a part of the traction current to flow into the ground from the track, creating the stray current. This type of current causes great safety hazards to the metal structures in and near the URT system. Considering the power supply mode of the URT, this paper explores the different resistances in each power supply section under unilateral power supply and bilateral power supply. Then, the defects of the current discharge method were identified in the context of stray current protection. To solve these defects, the backpropagation neural network (BPNN) was adopted to build a discharge flow prediction model. On this basis, an intelligent current monitoring system was established for the URT. Finally, the authors simulated the impact of each factor on stray current, and verified the reliability and stability of the proposed monitoring system. Compared with predicted values and the actual values, the prediction agrees with the actual data very well.

Smart Current Monitoring System with Relay Breaker

Today, the high usage of electrical home appliances has increased the domestic energy consumption. To control the consumption of domestic energy, various techniques were developed to detect current flow and to measure current consumption. Usually, a current sensor was used as a detection device and then send the data wirelessly to a centre point in the house, which is placed near to the main entrance for monitoring and controlling purposes. Wireless technology can provide better connection and reducing the need for wired connection for all power sockets in a house. The usage of LCD is to display current usage for all electrical home appliances will be more useful especially for a multi-storey house. This smart current monitoring system, will help users to monitor and control their electrical home appliances current consumption. The system will monitor each power sockets and transmit the current usage data wirelessly to a centre point which helps in reducing the time to check all the appliances.

An overview of management and monitoring of harmful algal blooms in the northern part of the Persian Gulf and Oman Sea (Hormuzgan Province).

Algal bloom as a common phenomenon in the Persian Gulf and the Sea of Oman had catastrophic effects on environmental, social, economic, and human health aspects from 2008 to 2009. Since 2008, the Persian Gulf and the Sea of Oman Ecological Research Institute (PGSOERI) has monitored and managed algal blooms in the Hormuzgan coast along the northern Persian Gulf and the Sea of Oman. Management strategies have included regular monitoring of chlorophyll, water quality, and remote sensing. In this regard, relevant departments and the Regional Organization for Protection of Marine Environment (ROPME) collaborated with each other to prevent and forecast algal blooms. We reviewed historical and current monitoring, mitigation, and management systems of algal blooms in the Hormuzgan coast. In addition, complications and challenges of algal bloom monitoring and management were also discussed. Documenting algal bloom monitoring and research, improving forecasting and modeling of blooms, educating the public and fishermen, developing a cooperative monitoring framework, and controlling pollution input entering the ROPME region are the main challenges of algal bloom management in the Hormuzgan coast.

Real-time monitoring of the longitudinal strain of Continuous Welded Rail for safety improvement

Continuous welded rail maintenance plays a significant role in ensuring high levels of rail traffic and safety. Temperature variations, excessive alignment defects, decreased fastening system resistance and train braking (always in the same stretches and in the same direction) may result in rail buckling or rail breaks. The current traditional monitoring systems and procedures for continuous welded rail consist of programmed discontinuous diagnostic surveys that require personnel intervention on site. Moreover, these traditional systems often imply destructive and invasive operations on the track that may lead to interruption of railway operations. This paper proposes a Rail Strain Monitoring System (RSMS) that performs a real-time rail strain monitoring and allows rail inspection without personnel on site. Using strain gauges and temperature sensors, placed on the rail in specific measurement points, the proposed Rail Strain Monitoring System performs a multi-parameter check by measuring, at the same time, the temperature, the rail strain and the neutral temperature of the rail. The paper describes the mode of operation of the Rail Strain Monitoring System, the calibration procedure and the results from the field, and highlights the advantages of this system in comparison to other traditional monitoring systems. The safety improvement that can be achieved with the application of the Rail Strain Monitoring System is analysed. In particular, the reliability of the system is evaluated and compared to the human error probability in the traditional manual inspections. Finally, the reduction of derailment risk and related economic damages is estimated.

Effective Increasing of NPP Reliability Introducing the Active Monitoring System of Block Construction Stress-Strain State

All nuclear power facilities are unique facilities and are simultaneously identified according to STO 1.1.1.02.009.0873-2017 "Ensuring Safety in the Operation of Buildings and Structures of Nuclear Power Plants" as hazardous production facilities with an increased level of responsibility. In this connection, according to the authors, as well as international and domestic experts, the sixth protective barrier should be promptly introduced into the NPP safety system in the form of a system of active monitoring of irreplaceable load-bearing and enclosing structures of the reactor and turbine compartment. The paper substantiates the fact that the current monitoring systems do not allow reliable and effective assessment of the level of VAT of structures, as well as do not allow to assess the level of "residual" stresses, the presence of which in practice may also lead to pre-emergency and emergency situations. To solve this problem the article develops an innovative system of VAT monitoring of both metal and reinforced concrete bearing structures, which is completely devoid of shortcomings of traditional monitoring systems, and which also has a number of undeniable advantages. The proposed monitoring system is developed on the basis of the Forster effect, which is based on the dependence of the relative magnetic permeability of ferromagnetic media on the level of operating stresses in the material. The authors obtained patent No. 2295118 for the developed monitoring system, which testifies to the level of efficiency of innovative solution of this problem. In general, the use of the proposed system as the sixth barrier of protection will in practice prevent any external, pre-accident or emergency situation at the NPP with simultaneous increase of safety and reliability of operation of the NPP after the extension of its operation life.

Phase Discrimination in Marine Icing Using a Coplanar Capacitive Array

This paper describes the development of an array of coplanar capacitive sensors applied to marine icing. Current atmospheric icing monitoring systems consider single phase conditions in their operation. Marine icing conditions present a unique environment where the liquid water phase effects cannot be neglected and require a novel approach. We have conducted an initial proof of concept and propose a new icing monitoring system which can distinguish between the individual phases. A numerical model confirmed our initial hypothesis of the system’s ability to discriminate the multiphase domains based on the array of geometrically dissimilar capacitive sensors. In addition, we also developed a novel experimental technique based on a comparative study under constant conditions to eliminate the need for an independent ice accretion monitoring system normally required in sensor development. The new approach promises a better characteristic in marine icing monitoring systems or in similar applications where multiphase dielectric is present.

Research on Analytical Monitoring System for Single-Phase Electrical Appliances

In view of the limitations of the current domestic power monitoring system, in this paper an intelligent single-phase electrical appliances analysis and monitoring system have been designed.The system takes STM32 as the control core, and the electric energy metering circuit is used to collect signals. The learning and recognition functions of electrical appliances are designed.The whole single-phase electrical appliances analysis and monitoring system can be used to monitor the operation of one or more electrical appliances in the circuit and display specific electrical parameters.The system uses a special measurement chip RN8208G, and the current precision can reach 1 mA. It is reliable, suitable for long-term operation and not easy to make mistakes.In this paper, the design of a specific electrical monitoring system is novel and has broad prospects for development.

Enhanced IPFIX flow monitoring for VXLAN based cloud overlay networks

<span lang="EN-US">The demands for cloud computing services is rapidly growing due to its fast adoption and the migration of workloads from private data centers to cloud data centers. Many companies, small and large, prefer switching their data to the enterprise cloud environment rather than expanding their own data centers. As a result, the network traffic in cloud data centers is increasing rapidly. However, due to the dynamic resource provisioning and high-speed virtualized cloud networks, the traditional flow-monitoring systems is unable to provide detail visibility and information of traffic traversing the cloud overlay network environment. Hence, it does not fulfill the monitoring requirement of cloud overlay traffic. As the growth of cloud network traffic causes difficulties for the service providers and end-users to manage the traffic efficiently, an enhanced IPFIX flow monitoring mechanism for cloud overlay networks was proposed to address this problem. The monitoring mechanism provided detail visibility and information of overlay network traffic that traversed the cloud environment, which is not available in the current network monitoring systems. The experimental results showed that the proposed monitoring system able to capture overlay network traffic and segregated the tenant traffic based on virtual machines as compare to the standard monitoring system.</span>

Rectangular Array Electric Current Transducer with Integrated Fluxgate Sensors.

Novel rectangular yokeless current transducer with the range 400 A using 16 microfluxgate sensors around the busbar conductor is presented in this paper. Compared to yokeless transducers utilizing the differential pair of magnetic sensors, our solution has much better suppression of the external currents (lower crosstalk). Compared to industrial transducers with yoke, the new transducer has 15-times lower noise, 7-times better temperature stability, and same crosstalk. Sensor design and design of current monitoring system is presented together with the results of long-term field tests. Crosstalk error is examined in dependence on the number of the operating sensors and external current position.

The New Eddy Current Type Septum Magnets for Upgrading of Fast Extraction in Main Ring of J-PARC

The J-PARC Main Ring (MR) is currently working on upgrading the machine power reach to 750 kW by increasing the beam repetition rate from 0.4 Hz to 0.78 Hz. This new operation mode is referred as ”1 Hz” operation. The Fast eXtraction (FX) system in MR needs to upgrade to realize 1 Hz operation, and the improvement will be completed in 2021. The present FX Low-Field Septa (FX LF-Septa) which are conventional ones will be replaced with the new septa which is induced eddy current type (FX Eddy-Septa). The first FX Eddy-Septa system, which included the magnets and short pulse power supply (PS), was constructed in 2014. We have been studying the FX Eddy-Septa system to evaluate the stability of the output pulsed current and magnetic gap field since 2014. In winter of 2018, the 1 Hz operation test by using the two Main-chargers was conducted without any problem. The stability of the output pulsed current was 10 ppm. The leakage field was measured along the circulating beam line, and we found that the level of leakage field was still larger than our requirement. The PS has still several issues which must be solved. The switching time of the Main-charger to the Sub-charger must be reduced, the jitter of time lag between two discharge triggers must be eliminated, and the disagreement between the two current monitor systems must be solved.

Research on Network Active Detection Technology of Power Monitoring System Based on Machine Learning

There are still many security risks in the current power monitoring system network, and lack of independent learning ability. Based on this, a network learning based power detection system network active detection technology is proposed. The scheme analyzes and processes the data, uses feature engineering to process the features, and improves the common filtering method by using the feature correlation matrix in feature selection. Model training and prediction based on threshold-enhanced k-means semi-supervised algorithm. The experimental results show that the program has a certain accuracy rate, has certain self-learning ability, and has certain feasibility.

Pengesanan Logam Berat daripada Sungai Tercemar Menggunakan Enzim Penurun Molybdenum

Heavy metals contamination in Malaysia River has attracted concern among Malaysians. The issue arises when the Department of Environmental Malaysia in 2015 claimed the number of polluted river has increased from year 2000 to 2015. Therefore, the monitoring method is required in order to screen the polluted area. Current monitoring systems involved analytical methods such as Atomic Absorption Spectrophotometer (AAS) and Inductively Coupled Plasma (ICP) are costly, thus, the development of effective method and cost effective in screening heavy metal contamination is required. Molyhdenum-reducing enzyme has a potential to detect heavy metals contamination in river water samples. The enzyme extracted from locally isolated bacteria labeled as Bacilus sp. strain A.rzi was partially purified based on ion exchange chromatography using High Performance Liquid Chromatography (HPLC). The screening for heavy metals inhibition towards Mo-reducing enzyme was based on colorimetric analysis. Data for IC50 determined using statistical analysis (one phase decay plot) showed. Significant inhibition with three heavy metals (copper, lead and mercury) towards Mo-reducing enzyme. Copper showed the highest IC50 value of 0.25 mgL-1 followed by mercury and lead at 0.35 mgL-1 and 0.49 mgL-1 respectively. Screening for river water samples showed that Mo-reducing enzyme positively reacted to heavy metals in water samples. The Colorimetric based assay also showed heavy metals present in river water samples had inhibited Mo-reducing enzyme activity. The enzyme could reduce heavy metals to form molybdenum blue and was observed during reaction. Due to this unique feature, Mo-reducing enzyme can be proposed as a bioassay method for detecting heavy metal in river samples.

Lipophilic Toxins in Galicia (NW Spain) between 2014 and 2017: Incidence on the Main Molluscan Species and Analysis of the Monitoring Efficiency.

Galicia is an area with a strong mussel aquaculture industry in addition to other important bivalve mollusc fisheries. Between 2014 and 2017, 18,862 samples were analyzed for EU regulated marine lipophilic toxins. Okadaic acid (OA) was the most prevalent toxin and the only single toxin that produced harvesting closures. Toxin concentrations in raft mussels were generally higher than those recorded in other bivalves, justifying the use of this species as an indicator. The Rías of Pontevedra and Muros were the ones most affected by OA and DTX2 and the Ría of Ares by YTXs. In general, the outer areas of the Rías were more affected by OA and DTX2 than the inner ones. The OA level reached a maximum in spring, while DTX2 was almost entirely restricted to the fall–winter season. YTXs peaked in August–September. The toxins of the OA group were nearly completely esterified in all the bivalves studied except mussels and queen scallops. Risk of intoxication with the current monitoring system is low. In less than 2% of cases did the first detection of OA in an area exceed the regulatory limit. In no case, could any effect on humans be expected. The apparent intoxication and depuration rates were similar and directly related, suggesting that the rates are regulated mainly by oceanographic characteristics.

Damage detection in a suspension bridge using modal flexibility method

Advances in structural engineering and material technology have resulted in increasing use of suspension bridges in infrastructure systems to satisfy the modern transportation requirements. However, during the long life of a suspension bridge, its main cables and hangers could suffer severe corrosion and fatigue leading to failure of at least a part of the bridge. There is a need for a simple and reliable procedure to detect and locate such damage at its onset so that appropriate retrofitting can be carried out to prevent the failure of these important structural elements. Current Structural Health Monitoring (SHM) systems are integrated with a variety of damage detection methods, which are global and local in nature. Limitations in local methods necessitate the non-destructive and global techniques for damage diagnosis leading to continuous development in vibration based damage detection (VBDD) methods. In this context, this paper develops and applies mode shape component specific damage indices namely; vertical damage index (DIV) and lateral damage index (DIL) based on the Modal Flexibility (MF) concept to detect and locate damage in the main cables and hangers of a suspension bridge. The application of these damage indices is illustrated under single, multiple and complex damage scenarios. Effects of noise in the modal data and higher order vibration modes on the damage detection capability are also captured. Results confirm the applicability of the proposed vertical damage index DIV to accurately detect damage in real suspension bridges using only the first few modes. Bridge engineers can use the proposed technique to monitor the health of suspension bridges and prevent their unexpected failure.

Beyond Earthworm: Keeping the Promise

AbstractThe founding of the Advanced National Seismic System (ANSS) vision was originally presented in U.S. Geological Survey Circular 1188 (U.S. Geological Survey [USGS], 1999), after many years of discussions and workshops, described in detail by Filson and Arabasz (2016). Much has been accomplished in the ensuing two decades. Disparate and sometimes divergent developments that had been previously explored at individual private and public universities were finally centralized with increased efficiency and coherency of effort. The stated mission of the ANSS is to “… provide accurate and timely data and information products for seismic events, including their effects on buildings and structures, employing modern monitoring methods and technologies.” In this article, an approach (xQuake) is proposed that does not interfere in any way with the mission of the National Earthquake Information Center and ANSS but instead restores much of the community focus and international collaboration that has been lost over the past two decades. xQuake uses an executable graph framework in a pipeline architecture; this framework can be seamlessly integrated into current ANSS quake monitoring systems. This new approach incorporates modern approaches to computer analytics, including multitopic Kafka exchange rings, cloud computing, a self-configuring phase associator, and machine learning. The xGraph system is free for noncommercial use, open source, hardware agnostic (Windows, Linux, Mac), with no requirement for commercial datastores.

Research on Transmission Lines Early Warning Technology Based on Deep Learning

At the present stage, high-voltage transmission lines are distributed in long distance chains, with large space span, complicated meteorological and geographical environment. The operating environment of the transmission lines is poor. Hence, manual patrol and maintenance are difficult. Various faults are very likely to affect the safe and stable operation of the power grid system. Therefore, this paper proposes a transmission lines early warning method based on Deep Learning. Through object detection technology of Deep Learning, intrusion objects in the monitoring screen are automatically identified, at same time, their positions and types are marked. The experimental results show that this method has high accuracy and is suitable for the current power grid monitoring system.

Intelligent Grounding Current Monitoring System for Iron Core of Power Transforme

In the process of stable operation of power transformer, one-point grounding of iron core should be ensured. When two or more points of grounding occur, it will cause transformer operation failure, even burning iron core, affecting the safe operation of the power grid. At present, the monitoring of transformer grounding point mostly adopts the current transformer monitoring mode, only after uploading the detection data to the server, the comparison prompts that manual intervention is needed in the later stage, which increases the processing time and increases the risk of power grid problems. From the point of view of intelligent monitoring, this paper designs an intelligent transformer core grounding monitoring system. When the fault of multi-point grounding is detected, the resistance value of current limiting resistance is calculated immediately according to the magnitude of current, and the series resistance is introduced to limit the current of grounding circuit. At the same time, the fault alarm is given, indicating that manual intervention greatly reduces the occurrence of transformer faults. The processing time reduces the probability of deterioration in case of failure and improves the stability of transformer operation.

Population-level surveillance of antibiotic resistance in Escherichia coli through sewage analysis.

IntroductionThe occurrence of antibiotic resistance in faecal bacteria in sewage is likely to reflect the current local clinical resistance situation.AimThis observational study investigated the relationship between Escherichia coli resistance rates in sewage and clinical samples representing the same human populations.Methods E. coli were isolated from eight hospital (n = 721 isolates) and six municipal (n = 531 isolates) sewage samples, over 1 year in Gothenburg, Sweden. An inexpensive broth screening method was validated against disk diffusion and applied to determine resistance against 11 antibiotics in sewage isolates. Resistance data on E. coli isolated from clinical samples from corresponding local hospital and primary care patients were collected during the same year and compared with those of the sewage isolates by linear regression.Results E. coli resistance rates derived from hospital sewage and hospital patients strongly correlated (r2 = 0.95 for urine and 0.89 for blood samples), as did resistance rates in E. coli from municipal sewage and primary care urine samples (r2 = 0.82). Resistance rates in hospital sewage isolates were close to those in hospital clinical isolates while resistance rates in municipal sewage isolates were about half of those measured in primary care isolates. Resistance rates in municipal sewage isolates were more stable between sampling occasions than those from hospital sewage.ConclusionOur findings provide support for development of a low-cost, sewage-based surveillance system for antibiotic resistance in E. coli, which could complement current monitoring systems and provide clinically relevant antibiotic resistance data for countries and regions where surveillance is lacking.

Analysis of a Unique Postmarket Surveillance Dataset That a Glucose Test-Strip Demonstrates no Evidence of Interference and Robust Clinical Accuracy Irrespective of the Prescription Medication Status of a Large Cohort of Patients With Diabetes

Despite a marked increase in polypharmacy in patients with diabetes there have been no thorough evaluations of the impact of polypharmacy on the accuracy of any current blood glucose monitoring (BGM) system. This study evaluated the accuracy of a BGM test-strip with respect to polypharmacy using a large clinical registry dataset. Medication profiles were analyzed for 830 subjects (334 with type 1 [T1D] and 496 with type 2 diabetes [T2D]) attending three hospitals. Blood samples were analyzed to determine clinical accuracy of the BGM test-strip compared to a laboratory comparator. Across the 830 subjects, 473 different medications (41 diabetes and 432 nondiabetes) were recorded. Patients took on average 6.5 (n = 1-23) individual medications and 4 (n = 1-11) unique classes of medication. Clinical accuracy to EN ISO 15197:2015 criteria was met irrespective of increasing average number of individual medication, categorized from 1 to 4, 5 to 8, 9 to 12, and >12 taken per subject (97.7%, 98.4%, 98.1%, and 98.5%, respectively). Clinical accuracy to EN ISO 15197:2015 criteria was also met across 15 classes of medication using the combined dataset (98.1%; 13 003/13 253). Surveillance error grid analysis showed 98.8% (13 079/13 232) of readings presented no clinical risk. No individual class or combination of medication classes impacted clinical accuracy of the BGM test-strip. This comprehensive analysis for this specific test-strip platform demonstrated no evidence of interference and robust clinical accuracy of this test strip, irrespective of the prescription medication status of patients with diabetes.