Monitoring System
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2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dehua Zeng

In this paper, real-time monitoring of acoustic emission power GIS equipment is studied and analyzed, and a real-time wireless sensing monitoring system is designed. The overall design of the wireless sensing monitoring system for acoustic emission power GIS equipment is as follows: the model of the system is constructed, the design of the system module function is proposed, the platform of the system is built, the hardware and software solutions are configured, the operating system is optimized, the graphics and data are input into the GIS distribution network system, the editing function is optimized, the fast positioning function of the equipment is improved, and the automatic generation of the system diagram is improved. By using ZigBee technology to establish a wireless sensor network and realize wireless transmission of monitoring signals, it can avoid many disadvantages, such as cumbersome cable laying, troublesome changes, inconvenient expansion, and daily maintenance in wired transmission, and save various resources and reduce monitoring costs, which is well worth promoting in the system. In this paper, for the actual situation of GIS equipment, combined with the characteristics of the wireless sensor network structure and the design needs of the monitoring system (the overall design of the monitoring system, OFS maximum response amplitude of 19.9 mV, to be reached 40 dB), the signal will be uploaded to the high-speed collection system, and the efficiency is increased by more than 10%. The feasibility test of the monitoring system, the test results, and the related data show that the UHF sensor can accurately collect the UHF signal, the wireless network formed by ZigBee can reliably transmit the signal, the monitoring interface is correctly displayed, and the whole monitoring system operates stably and meets the requirements of the initial design.


2021 ◽  
Author(s):  
Jixu Hou ◽  
Xiaofeng Xie ◽  
Qian Cai ◽  
Zhengjie Deng ◽  
Houqun Yang ◽  
...  

Abstract Dangerous driving, e.g., using mobile phone while driving, can result in serious traffic problem and threat to safely. To efficiently alleviate such problem, in this paper, we design a intelligent monitoring system to detect the dangerous behavior in driving. The monitoring system is combined by camera, terminal server, target detection algorithm and voice reminder. Furthermore, we applied an efficiently deep learning model, namely mobilenet combined with single shot multi-box detector (mobilenet-SSD), to identify the behavior of driver. To evaluate the performance of proposed system, we construct a dangerous driving dataset which consists of 6796 images. The experimental results show that the proposed system can achieve accuracy of 99% in 100 testing images. It can be used for real-time monitoring of the driver’s status.


Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1881
Author(s):  
Aziz Oukaira ◽  
Amrou Zyad Benelhaouare ◽  
Emmanuel Kengne ◽  
Ahmed Lakhssassi

The basic need common to all living beings is water. Less than 1% of the water on earth is fresh water and water use is increasing daily. Agricultural practices alone require huge amounts of water. The drip technique improved the efficiency of water use in irrigation and initiated the introduction and development of fertigation, the integrated distribution of water and fertilizer. The past few decades have seen extensive research being carried out in the area of development and evaluation of different technologies available to estimate/measure soil moisture to aid in various applications and to facilitate the use of drip irrigation for users and farmers. In this technology, plant moisture and temperature are accurately monitored and controlled in real time over roots in the form of droplets, by developing smart monitoring system to save water and avoid water waste using drip irrigation technology. Water is delivered to the roots drop by drop, which saves water as well as prevents plants from being flooded and decaying due to excess water released by irrigation methods such as flood irrigation, border irrigation, furrow irrigation, and control basin irrigation. Drip irrigation with an embedded intelligent monitoring system is one of the most valuable techniques used to save water and farmers’ time and energy. In this paper, we design an embedded monitoring system based in the integrated 65 nm CMOS technology in agricultural practices which would facilitate agriculture and enable farmers to monitor crops. Hence, to demonstrate the feasibility, a prototype was constructed and simulated with modelsim and validated with nclaunch the both tools from Cadence, as well as implementation on the FPGA board, was be performed.


2021 ◽  
pp. 414-419
Author(s):  
F. O. Ushanova ◽  
T. Yu. Demidova ◽  
M. Ya. Izmaylova

Introduction. Blood glucose monitoring is critical in maintaining glycemic control in women with GDM and in reducing adverse maternal and fetal outcomes. One of the tools that can help achieve optimal glycemic control during pregnancy is continuous glucose monitoring, which empowers clinicians to assess the characteristics of daily glycemic variability.The aim. Compare biweekly glycemic profiles and glycemic variability in pregnant women with GDM and in healthy pregnant women using the FreeStyle Libre flash glycemic monitoring system.Materials and methods. Analysis of the glycemic profile of 49 pregnant women aged 33.2 ± 6.1 years. The average gestational age of the women included in the study was 12.6 ± 6.4 weeks. Pregnant women were divided into 2 groups: 37 pregnant women with GDM and 12 healthy pregnant women. Each group underwent two-week glucose profile monitoring using the FreeStyle Libre continuous monitoring system. Statistical analysis was carried out using Microsoft Office Excel 2016, STATISTICA 10 programs (developed by StatSoft.Inc), EasyGV, version 9.Results. The average glycemic level in the groups was 4.724 ± 0.37 mmol/L vs 4.24 ± 0.34 mmol/L, respectively (p˂0.001). Comparative analysis of GV parameters in groups of pregnant women with and without GDM: SD – 0.908 vs 0.7213 (p˂0.05); LI – 1.5 vs 0.8 (p˂0.05); HBGI – 0.503 vs 0.42 (p˂0.05); J-index – 10.343 vs 7.9870 (p˂0.001); MOOD – 0.956 vs 0.7992 (p˂0.05); MAGE  – 2.326  vs 1.8042  (p˂0.05); ADDR  – 2.216  vs 0.4210  (p˂0.05); MAG  – 4.612  vs 2.6163  (p˂0.001), respectively. The CONGA index did not show a statistically significant difference in both groups: 3.95 vs 3.7 (p = 0.5).Conclusions. Flash-glycemic monitoring can be used to obtain more detailed information about the glycemic profile, especially when it is difficult to assess the degree of GDM compensation. Continuous glucose monitoring can facilitate the optimization of glycemic control and provide a basis for treatment decisions. 


Author(s):  
Dr. Hirakjyoti Sarma ◽  
◽  
Dimpal Huzuri ◽  
Dr. Manoj Kumar Deka ◽  
◽  
...  

This paper approaches an IoT based vehicle health monitoring system that is embedded for detecting the condition of a vehicle by monitoring the internal parameters such as heating rate, engine oil level and status of the CO of the vehicle. It is a real time vehicle health monitoring system is designed and developed to detect and identify the actuator and sensor faults created by automatically or manually by the user of the vehicle. Actually, Vehicles need repair after a certain interval of time and if are not repaired at fixed intervals, it can lead to loss of life of the persons travelling on it and there are many key issues which can affect the vehicle. So, the primary objective of this system is developing an IoT based embedded system that can detect the internal condition of a vehicle by evaluating the various parameters that are used to examine in the vehicle’s current health condition. In fact, this is a real time evaluation system that can be used for rapid condition screening. As a result, it provides all reliable information about the vehicle conditions. This IoT based system claims that it can detect and identify actuator and sensor faults with almost minimal detection latency even after lots of disturbances and uncertainties.


2021 ◽  
Vol 12 ◽  
Author(s):  
Carla L. Schwan ◽  
Sara Lomonaco ◽  
Leonardo M. Bastos ◽  
Peter W. Cook ◽  
Joshua Maher ◽  
...  

Non-typhoidal Salmonella enterica is a pathogen of global importance, particularly in low and middle-income countries (LMICs). The presence of antimicrobial resistant (AMR) strains in market environments poses a serious health threat to consumers. In this study we identified and characterized the genotypic and phenotypic AMR profiles of 81 environmental S. enterica strains isolated from samples from informal markets in Cambodia in 2018–2019. AMR genotypes were retrieved from the NCBI Pathogen Detection website (https://www.ncbi.nlm.nih.gov/pathogens/) and using ResFinder (https://cge.cbs.dtu.dk/services/) Salmonella pathogenicity islands (SPIs) were identified with SPIFinder (https://cge.cbs.dtu.dk/services/). Susceptibility testing was performed by broth microdilution according to the Clinical and Laboratory Standards Institute (CLSI) standard guidelines M100-S22 using the National Antimicrobial Resistance Monitoring System (NARMS) Sensititre Gram Negative plate. A total of 17 unique AMR genes were detected in 53% (43/81) of the isolates, including those encoding tetracycline, beta-lactam, sulfonamide, quinolone, aminoglycoside, phenicol, and trimethoprim resistance. A total of 10 SPIs (SPI-1, 3–5, 8, 9, 12–14, and centisome 63 [C63PI]) were detected in 59 isolates. C63PI, an iron transport system in SPI-1, was observed in 56% of the isolates (n = 46). SPI-1, SPI-4, and SPI-9 were present in 13, 2, and 5% of the isolates, respectively. The most common phenotypic resistances were observed to tetracycline (47%; n = 38), ampicillin (37%; n = 30), streptomycin (20%; n = 16), chloramphenicol (17%; n = 14), and trimethoprim-sulfamethoxazole (16%; n = 13). This study contributes to understanding the AMR genes present in S. enterica isolates from informal markets in Cambodia, as well as support domestic epidemiological investigations of multidrug resistance (MDR) profiles.


2021 ◽  
Vol 3 (3) ◽  
pp. 192-204
Author(s):  
R. Rajesh Sharma

Transformers are one of the primary device required for an AC (Alternating Current) distribution system which works on the principle of mutual induction without any rotating parts. There are two types of transformers are utilized in the distribution systems namely step up transformer and step down transformer. The step up transformers are need to be placed at some regular distances for reducing the line losses happening over the electrical transmission systems. Similarly the step down transformers are placed near to the destinations for regulating the electricity power for the commercial usage. Certain regular check-ups are must for a distribution transformer for increasing its operational life time. The proposed work is designed to regularize such health check-ups using IoT sensors for making a centralized remote monitoring system.


2021 ◽  
Author(s):  
MAZNAH BINTI ILIYAS AHMAD ◽  
Yusri Yusof ◽  
Mohammad Sukri Mustapa ◽  
Md Elias Daud ◽  
Kamran Latif ◽  
...  

Abstract Smart factories are focusing on bridging the gap between physical to cyber-physical systems through software applications. This article proposed an efficient and sufficient data acquisition, storing and processing real-time monitoring information, response, and feedback in milling process monitoring. A methodology to enable integration between service oriented IoT based monitoring with interpreted information for open Architecture CNC system was presented. The proposed system comprises four main layers: the perception layer, communication layer, application layer, and CNC machine. The developed system was validated through two case studies. First, the developed system successfully enabled data flow through the validation, from CNC machine back to CNC machine. Secondly, the reading of temperature, vibration and electric current monitoring is higher for the worn cutting tool than the new cutting tool. Third, the percentage difference between new and worn cutting tools for temperature monitoring is up to 3.38 %, and for vibration monitoring, it is up to 78.93 %. Fourth, the electric current reading is proportional to cutting force as the reading of electric current on cutting insert is higher than reading before cutting tool insert with percentage differences more than 8.33% up to 20%. Based on the findings, it was summarized that the developed integrated monitoring system is feasible enough based on the performance and highly sensitive to any changes that occurred during the machining process, specifically on the cutting tool condition. In the future, this integrated monitoring system could be applied to other Open CNC machine-based plug and play.


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