Current Monitoring Systems Research Articles

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends.

Linear infrastructures, such as railways, tunnels, and pipelines, play essential roles in economic and social development worldwide. However, under the influence of geohazards, earthquakes, and human activities, linear infrastructures face the potential risk of damage and may not function properly. Current monitoring systems for linear infrastructures are mainly based on non-contact detection (InSAR, UAV, GNSS, etc.) and geotechnical instrumentation (extensometers, inclinometers, tiltmeters, piezometers, etc.) techniques. Regarding monitoring sensitivity, frequency, and coverage, most of these methods have some shortcomings, which make it difficult to perform the accurate, real-time, and comprehensive monitoring of linear infrastructures. Distributed acoustic sensing (DAS) is an emerging sensing technology that has rapidly developed in recent years. Due to its unique advantages in long-distance, high-density, and real-time monitoring, DAS arrays have shown broad application prospects in many fields, such as oil and gas exploration, seismic observation, and subsurface imaging. In the field of linear infrastructure monitoring, DAS has gradually attracted the attention of researchers and practitioners. In this paper, recent research and the development activities of applying DAS to monitor different types of linear infrastructures are critically reviewed. The sensing principles are briefly introduced, as well as the main features. This is followed by a summary of recent case studies and some critical problems associated with the implementation of DAS monitoring systems in the field. Finally, the challenges and future trends of this research area are presented.

Design of a wearable multi-point temperature monitoring system – practical application in waist temperature monitoring

PurposeHuman skin temperature data can provide reference advice for diagnosing many diseases, but current wearable skin temperature monitoring systems have few points and may miss temperature information in critical areas. This paper aims to develop a wearable system that can be used for local temperature monitoring and restore better the actual state of local human temperature by exploring ways to extend more temperature measurement points. This will provide better assistance in developing medical targeting and intelligent clothing.Design/methodology/approachThe temperature measurement system contains modules for temperature monitoring, digital-to-analog conversion and regulated power supply, enabling fast reading of 12 channels of monitoring data. The microprocessor unit is the STM32F407. The instructions and control modes are written in C. The waist area was chosen as the monitoring area because it is susceptible to temperature, and many diseases are associated with skin temperature. Twelve points, including temperature-related acupuncture points and heat-sensitive points, were selected for testing and the data results agreed well with the infrared imaging results.FindingsThe waist is selected as the monitoring object, and an easy-to-wear waist temperature monitoring belt is designed to verify the application value of the system. The development of the system provides reference suggestions for the exploration of multi-point temperature monitoring systems and the integration capabilities of temperature measurement modules in wearable multifunctional systems.Practical implicationsIn addition to waist temperature monitoring, the wearable temperature measurement system developed in this study can also be applied to other body parts. In addition, the system can be efficiently and effectively combined with various garments, making it a useful tool for researching human skin temperature.Originality/valueThe wearable temperature monitoring system designed in this paper extends the number of temperature test points to 12. The number of test points covers as many localized body areas as possible to indeed reproduce the temperature distribution of the human body. In addition, the selection of test points combines medically relevant body points with physiologically relevant heat-sensitive areas, which makes the temperature measurement data more valuable.

Application of Building Electrical Residual Current Electrical Fire Monitoring System

Application of Building Electrical Residual Current Electrical Fire Monitoring System

Recent Advances in Nondestructive Method and Assessment of Corrosion Undercoating in Carbon–Steel Pipelines

Carbon–steel pipelines have mostly been utilized in the oil and gas (OG) industry owing to their strength and cost-effectiveness. However, the detection of corrosion under coating poses challenges for nondestructive (ND) pipeline monitoring techniques. One of the challenges is inaccessibility because of the pipeline structure, which leads to undetected corrosion, which possibly leads to catastrophic failure. The drawbacks of the existing ND methods for corrosion monitoring increase the need for novel frameworks in feature extraction, detection, and characterization of corrosion. This study begins with the explanations of the various types of corrosion in the carbon–steel pipeline in the OG industry and its prevention methods. A review of critical sensors integrated with various current ND corrosion monitoring systems is then presented. The importance of acoustic emission (AE) techniques over other ND methods is explained. AE data preprocessing methods are discussed. Several AE-based corrosion detection, prediction, and reliability assessment models for online pipeline condition monitoring are then highlighted. Finally, a discussion with future perspectives on corrosion monitoring followed by the significance and advantages of the emerging AE-based ND monitoring techniques is presented. The trends and identified issues are summarized with several recommendations for improvement in the OG industry.

Projected network performance for next-generation xenon monitoring systems

Next-generation radioxenon monitoring systems are reaching maturity and are expected to improve certain aspects of performance in verifying the absence of nuclear tests. To predict the improvement in detecting and locating nuclear test releases, thousands of releases all over the globe were simulated and the global detection probability was calculated for the single xenon isotope 133Xe. This was done for the International Monitoring System network of noble gas samplers as it currently exists (25 certified stations), and how it would be for potential future network sizes of 39 and 79 stations. The probability of detection was calculated for releases ranging from 1010 Bq to 1016 Bq of 133Xe using 10 d of atmospheric transport and presented as coverage maps and global integrals for both current and next-generation monitoring systems. Similarly, the number of detecting stations and the number of detecting samples were tabulated to elucidate the possibilities for enhanced location capability. Improvements in global detection coverage are maximized at different release sizes in a way that depends on the station density. For example, for releases of 3 × 1014 Bq and 39 stations, the detection probability would rise from 60% to 70% with next-generation systems, while for releases of 1013 Bq and 79 stations, it would rise from 37% to 52%. Achieving an average of two detecting stations would require a 1015 Bq release for a 39-station network and a 1014 Bq release for a 79-station network. The largest impact of using next-generation systems may be the confidence, detection redundancy, and location capability that arise from obtaining multiple samples associated with a single release event.

A Monitoring System Based on NB-IoT and BDS/GPS Dual-Mode Positioning

Monitoring system is widely used to detect the environment parameters such as temperature, humidity and position information in cold chain logistic, modern agriculture, hospital and so on. Poor position precision, high communication cost, high packet loss rate are the main problems in current monitoring system. To solve these problems, the paper presents a new monitoring system based on Narrow Band Internet of Things (NB-IoT) and BeiDou system/Global System Position (BDS/GPS) dual-mode positioning. Considering the position precision, a dual-mode positioning circuit based on at6558 is designed, and the calculation formula of the positioning information of the monitored target has been derived. Subsequently, a communication network based on wh-nb75-ba NB-IoT module is designed after compared with the LoRa technology. According to the characteristics of high time correlation of sensor data, an adaptive optimal zero suppression (AOZS) compression algorithm is proposed to improve the efficiency of data transmission. Experiments prove the feasibility and effectiveness of the system from the aspects of measurement accuracy, positioning accuracy and communication performance. The temperature and humidity error are less than 1 °C and 5% RH respectively with the selected sensor chips. The position error is decided by several factors, including the number of satellites used for positioning, the monitored target moving speed and NB-IoT module lifetime period. When the monitored target is stationary, the positioning error is about 2 m, which is less than that of the single GPS or BDS mode. When the monitored target moves, the position error will increase. But the error is still less than that of the single GPS or BDS mode. Then the AOZS compression algorithm is used in actually experiment. The compression ratio (CR) of it is about 10% when the data amount increasing. In addition, the packet loss rate test experiment proves the high reliability of the proposed system.

Projected network performance for multiple isotopes using next-generation xenon monitoring systems

Since about 2000 (Bowyer et al., 1998), radioxenon monitoring systems have been under development and testing for the verification of the Comprehensive Nuclear Test-Ban Treaty (CTBT). Operation of the systems since then has resulted in development of a next-generation of systems that are nearly ready for operational deployment. By 2010, the need to screen out civilian sources was well known (Auer et al., 2010; Saey, 2009), and isotopic ratio approaches were soon considered (Kalinowski and Pistner, 2006) to identify specific sources. New generation systems are expected to improve the ability to verify the absence of nuclear tests by using isotopic ratios when multiple isotopes are detected. In this work, thousands of releases were simulated to compute the global detection probability of 131mXe, 133mXe, 133Xe, and 135Xe at 39 noble gas systems in the International Monitoring System (IMS) for both current and next-generation systems. Three release scenarios are defined at 1 h, 1 d, and 10 d past a 1 kt TNT equivalent 235U explosion event. Multiple cases using from one part in a million to the complete release of the xenon isotopic activity are evaluated for each scenario.Coverage maps and global integrals comparing current and next-generation monitoring systems are presented showing that next-generation noble gas systems will create measurable improvements in the IMS. The global detection probability for 133Xe is shown to be strong in all scenarios, but only modestly improved by next-generation equipment. However, the detection probability for 131mXe and 133mXe increased to about 50% in different scenarios, providing a second detectable isotope for many events. As anticipated from shorter sampling intervals, the expected number of detecting samples roughly doubled and the expected number of detecting stations rose by approximately 50% for all release scenarios. Thus, it might be anticipated that future events would consist of multiple 133Xe detections and one or more second isotope detections. Signals of this nature should increase detection confidence, tighten release location estimates, improve rejection of civilian signals, and lessen the impacts from individual systems being offline for maintenance or repair reasons.

Supervised contrastive learning-guided prototypes on axle-box accelerations for railway crossing inspections

Increasing demands on railway structures have led to a need for new cost-effective maintenance strategies in recent years. Current dynamic railway track monitoring systems are usually based on the analysis of axle-box accelerations to automatically detect track singularities and defects. These methods rely on hand-crafted feature extraction and classifiers for different tasks. However, the low performance shown in previous literature makes it necessary to complement these analyses with in-situ inspections. Very recent works have proposed the use of deep learning systems that allow extracting more generalizable features from time–frequency spectrograms. However, the lack of specific public domain datasets and the finite number of track singularities in a railway structure have limited the development of deep learning based systems. In this paper, we propose a method capable of outstanding in low-data scenarios. In particular, we explore the use of supervised contrastive learning to cluster class embeddings nearly in the encoder latent space, which is used during inference for prototypical distance-based class assignment. We provide comprehensive experiments demonstrating the performance of our method in comparison to previous literature for detecting worn-out crossings.

Design of Intelligent Information Monitoring System for Distribution Network and Adjustment of Alarm Threshold

In the current distribution network information monitoring system, there are many false alarm information, which forms redundant interference to the fault alarm threshold, and it is difficult to ensure the alarm accuracy of the monitoring system. The distribution network intelligent information monitoring system and the alarm threshold adjustment method based on machine learning are designed, with the physical layer of the system designed to collect the operation status information of each line and equipment of the distribution network according to various sensors, and transfer it to the data layer. The data layer extracts, processes, and classifies the received information, stores it in the database, obtains the abnormal information in the information base, and adjusts the alarm threshold based on the fuzzy clustering method in machine learning, realizing intelligent monitoring of distribution network. The test results show that the detection performance of abnormal information is good, that the abnormal information in the data can be obtained accurately, and that the clustering of the target category of abnormal information can be completed according to the eigenvalue, and has a good threshold adaptive adjustment ability, to maximize the balance between human, machine, and power grid operation state in the process of distribution network monitoring information, ensure real-time and reliable monitoring and alarm results.

Revolutionizing Waste Management in Smart Cities: An IoT-Powered Solution

Garbage monitoring of collection is a crucial issue for most cities today, and it has become a significant concern in urban areas. The current garbage monitoring and management system is incredibly inefficient, which raises the price of collection and transportation. We propose to design a " Smart Waste Management System for Smart Cities Using IOT" to prevent situations where dust bins are seen overflowing due to poor monitoring and collection, which can cause long-term issues including unpleasant odor and hazardous infections. As its primary microprocessor, NODEMCU is used in this intelligent waste management system. It measures the amount of waste in the trashcan using the ultrasonic sensor as a level detector. The ability to detect waste volume not only contributes to the development of a smart city but also helps in the development of waste reduction and recycling strategies that are more effective and environmentally beneficial as taken from references.

Human recognition with the optoelectronic reservoir-computing-based micro-Doppler radar signal processing.

Current perception and monitoring systems, such as human recognition, are affected by several environmental factors, such as limited light intensity, weather changes, occlusion of targets, and public privacy. Human recognition using radar signals is a promising direction to overcome these defects; however, the low signal-to-noise ratio of radar signals still makes this task challenging. Therefore, it is necessary to use suitable tools that can efficiently deal with radar signals to identify targets. Reservoir computing (RC) is an efficient machine learning scheme that is easy to train and demonstrates excellent performance in processing complex time-series signals. The RC hardware implementation structure based on nonlinear nodes and delay feedback loops endows it with the potential for real-time fast signal processing. In this paper, we numerically study the performance of the optoelectronic RC composed of optical and electrical components in the task of human recognition with noisy micro-Doppler radar signals. A single-loop optoelectronic RC is employed to verify the application of RC in this field, and a parallel dual-loop optoelectronic RC scheme with a dual-polarization Mach-Zehnder modulator (DPol-MZM) is also used for performance comparison. The result is verified to be comparable with other machine learning tools, which demonstrates the ability of the optoelectronic RC in capturing gait information and dealing with noisy radar signals; it also indicates that optoelectronic RC is a powerful tool in the field of human target recognition based on micro-Doppler radar signals.

IOT Based Waste Management System Using Node MCU

This project presents a novel approach to provide illustrative model of the Waste Management systems using IoT Waste Management System. Garbage segregation and monitoring is an essential issue for most of the cities in India. The current garbage monitoring and management system are highly ineffective contributing to high transportation and collection costs. Due to poor monitoring and collection, garbage bins are seen overflowing in certain areas which can lead to long-term problems such as bad odor and harmful diseases.To overcome these problems “NodeMcu based smart garbage monitoring system” can be introduced as an effective solution. This smart garbage monitoring system employs NodeMcu as its main microcontroller. It uses the ultrasonic sensor as a level detector to detect the amount of garbage in the bin. And also moisture sensor is used to segregate dry and wet waste. Software Tools:  Arduino IDE  C-Language Key Words: Waste Mangement segregation, Node MCU,Segregation.

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.