Current Monitoring Systems Research Articles

Analysis of Surface Water Quality in Upstream Province of Vietnamese Mekong Delta Using Multivariate Statistics

The study employed different statistical approaches to assess surface water quality in the upstream region of the Vietnamese Mekong Delta. The dataset included seven parameters (i.e., temperature, pH, total suspended solids (TSS), five-day biological oxygen demand (BOD5), chemical oxygen demand (COD), ammonium nitrogen (NH4+-N) and coliform) at seventy-three locations. Cluster analysis (CA) and principal component analysis (PCA) were applied to analyze spatial variations in surface water quality and recognize the important parameters. The findings revealed that surface water quality was deteriorated by organic matters (high BOD5 and COD), nutrients and microorganisms. Particularly, urban areas were found to be more polluted than the other areas. The PCA results indicated that three potential water pollution sources, including industry, urban and tourism, could explain 87.03% of the total variance. Coliform was identified as the leading latent factor that controls surface water quality in the study area. CA grouped the sampling locations into 11 groups, in which the groups of the baseline monitoring sites and large rivers had better water quality. The results indicated a significant impact of anthropogenic activities (especially, urban and tourism practices) in surface water quality degradation. Moreover, CA suggested that the numbers of the sampling sites could be reduced from 73 to 58 locations, lowering 20.54% of the monitoring cost. Thus, the study recommends scrutinizing the current surface water quality monitoring system to be more economic and urgently implementing appropriate solutions to mitigate coliform pollution in the smaller water bodies.

NB-IoT-based health monitoring system for COVID-19

<p>To address the problems of complex networking, low transmission rate and poor reliability of current health monitoring systems using Wi-Fi and Bluetooth transmission, an NB-IoT-based health monitoring system for COVID-19 is proposed. The system uses the STM32F103RET6 as the main controller, and the smart wearable device uses the NB-IoT information transmission method to transmit data to the software client platform with the OneNET cloud platform to realize the remote monitoring of key medical parameters of COVID-19. The test results show that the system is stable and reliable in data transmission, reducing the development cost and power consumption of existing health monitoring terminals, and has certain practical value and market prospects.</p>

Development of a crop water use monitoring system using electromagnetic induction survey

Monitoring soil water use at the plot level is essential for studying the plant’s response to drought or irrigation. However, the current soil monitoring system using moisture sensors is impractical. Soil electromagnetic induction (EMI) surveys can be a rapid, convenient, and non-invasive solution for monitoring soil moisture changes at the plot level. This study developed a crop water use monitoring system for the plot level using an EMI instrument that collects measurements at 0, 20, 40, 60, and 80 cm above the ground surface. The EMI data were converted to bulk soil electrical conductivity (σ) to the depth 1.5 m via an inversion technique and correlated with measured volumetric moisture contents. The EMI system was first tested for variation in ambient temperature. Within the temperature range of 25.5–34 °C, the standard deviation varied between 0.84 and 3.45 mS m−1 with coefficient of variations 1.54–9.26%. The system was then tested on a field trial at Narrabri, New South Wales, Australia, to measure the water use of chickpea genotypes grown over a season. The total water use of chickpeas obtained from EMI surveys correlated highly with values obtained from the Neutron Moisture Meter (NMM) (R2 = 0.73). Time-series of water content, as measured by the EMI survey, could differentiate the water use of different genotypes. In conclusion, the EMI survey is an efficient technique for monitoring crop water use.

Implementation of Microcontroller-Based Current Speed Monitoring System

In this research, the designed system consists of two parts. First, the hardware consists of a power supply, display, optocoupler sensor, LCD and mechanical measuring instrument. The microcontroller used is ATMega 8535 which serves as a controller. The software used on the microcontroller chip functions to read, process data received from the input port and send the processed data through the output port. On the ATMega8535 microcontroller, the system has been designed with an accuracy rate of 97%, precision 96%, error 3.8%, the resolution of the measuring instrument can be used as a measurement reference. Additional software used is a delogger, which is set to read the river current velocity every minute, which is stored every 10 minutes.

Evaluation of the self-induced vibrations modes of bridge superstructure during monitoring

In the last 20–30 years, bridge condition monitoring during the operational cycle has become increasingly common on the Russian highway network. The monitoring ensures control of the bridge on an ongoing basis in a continuous mode. The task facing the monitoring systems developers is to expand the range of the parameters that can be used in real-time to monitor the bridge’s condition and the safe conditions for its operation. One way is to use indirect parameters obtained as a direct data processing result. Appropriate algorithms for converting information recorded by the system sensors and obtaining new parameters are needed. The most important design characteristics are dynamic characteristics, which include the frequencies and amplitudes of the self-induced vibration modes, and vibration decrements. This article discusses a technique for estimating the dynamic bridge characteristics, namely, the self-induced vibration modes frequencies and amplitudes using experimental data. A dynamic system mathematical model that performs self-induced vibration is considered in the system form with one input signal and n output signals. Formulas for the model frequency response and its components are given: the amplitude and phase response of the system. To calculate the frequency response, power spectra and cross-power spectra are needed, which are obtained using the Fourier transforms of the signal. The article shows how, to reduce the random error, to estimate the mutual spectrum by dividing the realizations into several adjacent intervals (segments) of length T each. The final spectrum (periodogram) is obtained as the arithmetic mean of the segment spectra. Monitoring systems using accelerometers that measure the structure linear accelerations of the record the output signals. One of the accelerometers is considered an input signal source. According to the proposed mathematical model, the self-induced vibration frequency response of the system characterizes the amplitudes and signs of the structure displacements at the accelerometers’ location at different frequencies. The mathematical apparatus considered by the authors is applied to the data obtained on a real object: a bridge crossing over the Volga river on the highway Nizhny Novgorod-Shakhunya-Kirov in the Nizhny Novgorod region — Borsky Bridge. The monitoring of the bridge state stream crossing the Volga by its purpose is control and research, in terms of the form of information presentation over time — continuous, in terms of the speed and synchronism of polling sensors — dynamic. The monitoring purpose is to monitor the bridge structure operation and its operating conditions, including the technical control of the stress-strain state (SSS) parameters. The bridge characteristics and the current monitoring system are given. The results of numerical calculations of the arch span for vibrations are presented. The authors performed periodogram calculations using the mathematical package MathCad using the signals of two accelerometers. To estimate the frequencies and amplitudes of the span structure vibration modes using monitoring data, periodogram calculations were performed using the SpektrKatKross program that implements the proposed algorithm. The correspondence results of the program to the results of the calculations by the program MathCad are shown. The calculated and experimental vibration modes of the span are close. As a result of using the proposed mathematical model, the reliability of this comparison is ensured both in terms of frequencies and in terms of amplitudes.

A review of Ghana’s planting for food and jobs program: implementation, impacts, benefits, and costs

Farm input subsidies are widely used in Sub-Saharan African countries as a response to low adoption of fertilizers and seeds. While subsidy programs traditionally focused on helping farmers access inputs, new generation market smart subsidies additionally emphasize careful targeting, development of input supply systems, and complementary production and marketing support mechanisms. Ghana’s Planting for Food and Jobs (PFJ) initiative, launched in 2017, is one example of such an evolved subsidy program; yet, despite its scale and prominence, the current government monitoring and evaluation system is not well equipped to accurately assess its impacts. This paper triangulates evidence from multiple public sources and independent evaluations to develop a simple and effective impact assessment model for PFJ that can easily be adopted by the government. It can also be adapted to other contexts with minimal adjustment. Model results reveal that maize and rice production levels are more than 40 percent higher than they would have been in the absence of PFJ, thus contributing significantly to food and calorie availability in Ghana. However, there is much room for efficiency improvements that would increase the return on investment-currently, program benefits roughly equal public and private costs of the program. In this regard, several recommendations are made relating to beneficiary targeting, crowding out of commercial input sales, input use efficiency, marketing support to farmers, and improvements in the monitoring and evaluation system, all of which have relevance for other countries implementing or considering similar programs.

Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling.

This paper presents the implementation of a measurement system that uses a four microphone array and a data-driven algorithm to estimate depth of cut during end milling operations. The audible range acoustic emission signals captured with the microphones are combined using a spectral subtraction and a blind source separation algorithm to reduce the impact of noise and reverberation. Afterwards, a set of features are extracted from these signals which are finally fed into a nonlinear regression algorithm assisted by machine learning techniques for the contactless monitoring of the milling process. The main advantages of this algorithm lie in relatively simple implementation and good accuracy in its results, which reduce the variance of the current noncontact monitoring systems. To validate this method, the results have been compared with the values obtained with a precision dynamometer and a geometric model algorithm obtaining a mean error of 1% while maintaining an STD below 0.2 mm.

The role of big data in network public opinion within the colleges and universities

The online public opinion of colleges and universities, which utilizes social opinion, is a type of college student’s individualism that exists on campus and is a societal hot concern. With the advancement of Internet skills and uses, public perception of the network has changed significantly in terms of data quantity, sophistication, and production speed. The current public opinion assessment and monitoring system is hard to adapt to the variations occurring in this situation. It must be created under the guise of big data investigation. Furthermore, with the use of social opinion by colleges and universities, the problem of online public opinion is becoming more serious. The academic community has also been very interested in the college's early warning research on online public opinion. This manuscript examines the role of big data in guiding network public opinion in colleges as well as in universities. It is based on the analysis of the current state of college online public opinion. Our proposed model builds multi-level and comprehensive guidance metrics for new media through dynamic monitoring. It will help colleges and universities prevent early warning, processing, and analysis of online public opinion. Besides, it will promote the professionalism of online public opinion management in colleges and universities accurately and effectively.

Attendance Management System Using Facial Recognition

The main purpose of this project is to build a face recognition based attendance management system for any institution or organization to enhance and upgrade the current attendance monitoring system in to a more efficient and effective one. The current system has lot of ambiguity that causes inaccurate and inefficient attendance record. Problems of attendance monitoring and management that exists in the current system can be resolved using current emerging technologies. The technology working behind this proposed project will be the face recognition system. The human face is one of the natural traits that can uniquely identify an individual. Possibilities for a human face to change or being duplicated is low. In this project, databases of human face images will be created to use them in the recognizer algorithm. During the attendance human faces will be compared against the stored data to seek for identity. When an individual is identified, attendance will be recorded automatically by saving the necessary information in to an excel sheet database. Further the saved attendance information can be used for monitoring and management purposes.

Towards observation- and atmospheric model-based early warning systems for meteotsunami mitigation: A case study of Korea

A traveling air pressure disturbance of several hPa over a short period (5–10 min) can generate tsunami-like waves in coastal areas owing to a multi-resonant mechanism. In recent years, pressure-forced meteotsunamis have been regularly reported over the Korean Peninsula. However, the Korean meteotsunami early warning system does not always provide sufficient lead time. The early warning system comprises two-segmented zones for tracking the intensity and propagation of air pressure disturbances via 89 meteorological stations in the precaution (offshore islands) and warning zones (coastline and inland areas). To address the lead time problem in the current observation-based monitoring system, we used an atmospheric model with a horizontal resolution of 1.5 km, the Korea Meteorological Administration's local data assimilation and prediction system (LDAPS). The ability of the LDAPS to detect air pressure disturbances was tested for a widespread and destructive meteotsunami event that occurred on March 4, 2018. The detection capability of significant air pressure disturbances (>1.5 hPa/10 min) was 67% in the precaution zone, and the propagation pattern (direction, speed, and spatial scale) was reasonably consistent with the monitoring results. Based on LDAPS prediction and the established observation-based monitoring system, monitoring operators can determine the potential meteotsunami risk from the open Yellow Sea beyond the observation system with sufficient lead time. This case study contributes to developing observation- and atmospheric model-based early warning systems for meteotsunami mitigation. An early warning of tsunamigenic disturbances in the Korean Peninsula is expected to be provided with the LDAPS-based modeling system using a high-resolution atmosphere-ocean coupled model shortly.

IoT based Substation Monitoring and Control

Abstract: The Smart Voltage and Current Monitoring System (SVCMS) approach is utilized in this undertaking. It shows a solitary stage electrical design that uses voltage and flow from the sensor by means of a microcontroller and afterward remotely conveys the expected information to the outcomes screen through another PC. It is vital to utilize the Internet of Things (IoT) to upgrade the idea of the power with substation observing and control discretionary change. Accordingly, it is basic to have a perception framework that can distinguish and channel. The Arduino Uno is utilized as a microcontroller in the coordinated Smart Voltage and Current Monitoring System (SVCMS) set up to ascertain the voltage and current sensor results. It then sends this data, after assessment, to the end client's PC contraption utilizing the IoT module. The Arduino Uno controller and IoT modules are a microcontroller and far off devices, separately. In light of the heap got by the electrical machines in the event that the heap is high, the power conveyed is to be limited or the power must be closed down. In the olden days there should be a one person at the substation to look for a faults if anything occurred and now due to the increase in the tremendous technology there are many new technologies introduced into the life cycle of the of the human beings. In that one of the technology that helps to reduce the efforts of the human beings is Internet of Things(IoT) that uses the Wi-Fi module that helps to connect the hardware and the software. The data collected will be shared to the ThingSpeak platform and it will displayed by using the graphs for each parameter different bar graphs are used. Keywords: Arduino Uno, LCD display, Servo Motor, wi-fi module, Thing view App, Internet of Things, Substation Monitoring and Control

Security Assessment of Coal Mine Power Grid Voltage Based on an Improved AHP-FCE

The voltage level of a coal mine power grid directly affects the starting and normal operation of the electric motor, but the current grid monitoring system cannot use real-time grid structure parameters and power flow distribution data to conduct early warning research on the running status and voltage level of the power grid, so it cannot eliminate hidden danger in the operation of the power grid. Therefore, it is necessary to accurately and effectively evaluate the voltage safety level of the coal mine power grid. In this paper, an improved AHP-FCE method is proposed to evaluate the voltage safety level of a coal mine power grid. By replacing the comparison judgment matrix with the interval judgment matrix, the objectivity of the weight vector is improved while retaining expert subjectivity. According to the real-time power flow distribution data of the power grid monitoring system, the current voltage safety level of the power grid is evaluated. The results show that the improved AHP-FCE method can accurately evaluate the current grid voltage level, analyze the voltage development trend, give early warnings regarding coal mine grid voltage operation, and improve the monitoring function of the power monitoring system.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

The application and development of a wide-area measurement system (WAMS) has enabled many applications and led to several requirements based on dynamic measurement data. Such data are transmitted as big data information flow. To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS, this study performs real-time traffic monitoring and analysis of the data network of a power information system, and establishes corresponding network optimization strategies to solve existing transmission problems. This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy, covering the optimization in three progressive levels: the underlying communication protocol, source data, and transmission process. Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

Occurrence of Odor and Suggestions for a Comprehensive Management System in Ulsan, South Korea

In Ulsan, large-scale industrial facilities emit large amounts of various odors and hazardous air pollutants. This study investigated the current status of odor problems in Ulsan and suggested a comprehensive management system. Owing to the geographical conditions, weather conditions, major industrial complexes, and management of emission facilities, complaints about odor occur mainly in summer. The city authority responds to odor problems by preparing comprehensive measures to prevent odors and introducing unmanned sampling devices, real-time monitors, and a mobile monitoring system. Major odor substances and pollution characteristics can be identified through these efforts, but information on specific odor substances, complex odors, emission sources, and transport pathways is lacking. Therefore, it is necessary to upgrade the current monitoring system and establish a comprehensive management system to solve this problem.

A Convolutional Neural Network (CNN) classification to identify the presence of pores in powder bed fusion images

This study aims to detect seeded porosity during metal additive manufacturing by employing convolutional neural networks (CNN). The study demonstrates the application of machine learning (ML) in in-process monitoring. Laser powder bed fusion (LPBF) is a selective laser melting technique used to build complex 3D parts. The current monitoring system in LPBF is inadequate to produce safety-critical parts due to the lack of automated processing of collected data. To assess the efficacy of applying ML to defect detection in LPBF by in-process images, a range of synthetic defects have been designed into cylindrical artefacts to mimic porosity occurring in different locations, shapes, and sizes. Empirical analysis has revealed the importance of accurate labelling strategies required for data-driven solutions. We formulated two labelling strategies based on the computer-aided design (CAD) file and X-ray computed tomography (XCT) scan data. A novel CNN was trained from scratch and optimised by selecting the best values of an extensive range of hyper-parameters by employing a Hyperband tuner. The model’s accuracy was 90% when trained using CAD-assisted labelling and 97% when using XCT-assisted labelling. The model successfully spotted pores as small as 0.2mm. Experiments revealed that balancing the data set improved the model’s precision from 89% to 97% and recall from 85% to 97% compared to training on an imbalanced data set. We firmly believe that the proposed model would significantly reduce post-processing costs and provide a better base model network for transfer learning of future ML models aimed at LPBF micro-defects detection.

Sistem Monitoring Panel Surya Berbasis Android Secara Real-Time

This Android-Based Solar Panel Current and Voltage Monitoring System is intended to be implemented properly by companies or factories where the monitoring system can be carried out in real time and anywhere using Android. This prototype is made using a 30WP solar panel and uses a buck converter as a voltage reducer that enters from the panel which then the current and voltage will be read by the INA219 sensor which is then processed by Arduino so that it can be displayed by Blynk on Android which is of course connected to each other by the internet. This design is also designed when there is an overvoltage there will be a danger alarm notification via a buzzer that sounds. This test is carried out when in charge and discharge conditions which will later be displayed on the blynk when the battery voltage is low or the battery is full so that the charge and discharge process can be carried out by pressing the on or off button on the blynk so that the load will be in two conditions, namely on and off. The battery is in low condition if the voltage is less than 12.3 volts then there will be a notification tone on the blynk, while the battery is in full condition, namely at a voltage of 13.5 volts it will also display a notification which can be displayed by the blynk.

Design of Centralized Heating Monitoring System Based on Wireless Sensor Networks

Current centralized heating monitoring system has realized the collection and control of working condition data in heating power stations, but there are still some shortcomings, such as the inability to collect data on the working conditions of user sides, and the inability to meet the further demand of heating enterprises for the refinement of heating network monitoring data. A wireless sensor network is a fully distributed sensor system with no central node, which can intensively deploy many sensor nodes to monitoring area through random placement, and integrates sensors, data processing and communication modules to form a self-organized network system. Therefore, in order to realize the intelligence of heating system and improve the flexibility of node data collection, the monitoring system can use wireless sensor network technology to realize wireless collection of node data, and display the collected data on a man-machine interface in real time. On the basis of research results from previous scholars, this paper expounds the research status and significance of centralized heating monitoring system design, elaborates the development background, current status and future challenges of wireless sensor network technology, introduces the methods and principles of wireless network communication protocol and heating and heat balance flow analysis, proposes a structural model of a centralized heating monitoring system based on wireless sensor networks, carries out the design of perception and convergence nodes, analyzes the layout of wireless sensor networks, explores the design scheme of centralized heating monitoring system based on wireless sensor networks, conducts the hardware and software design of the monitoring system, implements the software testing and hardware debugging of the centralized heating monitoring system, and finally discusses the relationship between data transfer related tasks and task scheduling. The study results show that the application of the centralized heating monitoring system based on the wireless sensor networks can not only more conveniently monitor, control and manage the entire heating networks, but also make full use of the centralized monitoring and quantitative management functions of the wireless sensor networks. This achieves dynamic tracking and monitoring of heating operation, real-time diagnosis of hidden dangers in heating operation, and safe, normal and energy-saving operation of the centralized heating system. The study results of this paper provide a reference for further researches on the design of centralized heating monitoring system based on wireless sensor networks.

Application of Key Technologies of Distributed Storage Based on the Internet of Things in Urban Fire Protection

Due to the rapid development of science and technology in the current era, fires occur more frequently, and the relationship between various economic activities and things is becoming more and more frequent. The need for real-time monitoring and remote monitoring of various firefighting facilities in buildings has become very urgent; this is a task that must be put on the agenda. The existing urban fire remote monitoring system has fewer intelligent networks, so it has high requirements for the firefighters on duty in the fire control room, which is no longer sufficient for the firefighting needs of the modern society. This paper proposes a wireless city fire remote monitoring system based on Internet of Things technology, NB-IoT technology, and cloud computing technology and studies core technologies such as designing wireless monitoring nodes at the perception layer. In the urban fire protection environment of distributed storage, several important theories, key technologies, and related algorithms are being studied in detail. After a variety of experimental verification results, the spatial data engine and adaptive spatial data model of the metadata database are developed. This provides a distributed storage virtual city geographic environment, multilevel, multiregional design and development of a simulated prototype platform and storage, management, sharing, and visualization of urban geospatial data. After technical analysis, a visualization framework was installed, which has the characteristics of global vector grid integration and distributed spatial data. Through the research on the key technology of distributed storage of the Internet of Things, this paper applies it to urban fire protection and promotes the intelligent development of urban fire protection. The application of IoT storage technology in urban fire protection can solve the shortcomings of the current urban fire remote monitoring system and automatic fire alarm system. Wireless city fire remote monitoring system, real-time collection, and transmission and storage of working status information of various fire facilities in the building are provided. The functions of real-time monitoring, real-time alarm, real-time search query, record query, maintenance management, route guidance, and user management are also realized.

Cloud cover and its impact on Brazil's deforestation satellite monitoring program: Evidence from the cerrado biome of the Brazilian Legal Amazon

The creation of a satellite rapid alert programme (DETER-A) in 2004 was a cornerstone of the Brazilian government's strategy to reduce deforestation. This programme allowed authorities to detect and respond rapidly to periods of deforestation. Due to the fact that the policy instrument was based on multispectral remote radar, weather-related obstacles posed a continuous impediment to the study of deforestation. This paper investigates to what extent cloud cover has reduced the effectiveness of the DETER-A program to detect deforestation. To test this hypothesis, survival model analysis is undertaken on satellite data derived measures of local deforestation. The emphasis is on the state of Maranhão, which is separated into two areas by an arbitrary line of demarcation (Legal Amazon delimitation) where the forest on one side is covered by the satellite monitoring program while the other is not. The results suggest that following the implementation of the satellite monitoring program, there was more deforestation in those years with more cloud persistence in the area covered by the program. Counterfactual simulations indicate that the absence of clouds would have prevented deforestation equivalent to almost 7% of the study region, which is equivalent to 73 million tonnes of CO2 with a value of US$ 366 million. If the current monitoring system was replaced with the experimental policy instrument DETER-C/DETER INTENSO, cloud cover would be less of an impediment to deforestation detection.

Organic Horticulture and Bioherbicides: A Review

Weeds are one of the most problematic, time-consuming, and expensive production issues for organic horticulturists. The need for novel bioherbicides to manage weeds has increased as organic agriculture has risen in importance. Pathogens, cosmetic compounds, and natural material extracts might all be made to produce bio herbicides. Two types of infectious pathogens that can be used as bio-herbicides are dangerous fungus and microbes. Herbicidal activity has been shown in byproducts of accepted birthplaces such as Dried-DistillersGrains with Soluble(DDGS), Corn-Gluten-Meal(CGM), and Mustard-Seed-Meals(MSMs). Bioherbicide potential has also been discovered in certain essential oil extracts. The effectiveness of a bio-herbicide is the primary restraining element in its application, and it may be influenced by variables like as humidity and moisture, application technique, bioherbicide spectrum, and formulation type. In addition to effectiveness, bioherbicide usage is limited by price and worries about possible human health risks. Because incorporating bioherbicide technology into current weed monitoring systems might well help manage resistance mechanisms, benefit from economies of scale, and increase crop yields, continued studies should concentrate on the improvement of more cost-effective and convenient bio-herbicides for pest management, as well as the efficiency of manufacturing techniques and traditional influences with the use of frontrunner bio-herbicides.