Application Of Monitoring System Research Articles

Unraveling Magnet Structural Defects in Permanent Magnet Synchronous Machines—Harmonic Diagnosis and Performance Signatures

Rare-earth-based permanent magnets (PMs) have a vital role in numerous sustainable energy systems, such as electrical machines (EMs). However, their production can greatly harm the environment and their supply chain monopoly presents economic threats. Alternative materials are emerging, but the use of rare-earth PMs remains dominant due to their exceptional performance. Damage to magnet structure can cause loss of performance and efficiency, and propagation of cracks in PMs can result in breaking. In this context, prolonging the service life of PMs and ensuring that they remain damage-free and suitable for re-use is important both for sustainability reasons and cost management. This paper presents a new harmonic content diagnosis and motor performance analysis caused by various magnet structure defects or faults, such as cracked or broken magnets. The proposed method is used for modeling the successive physical failure of the magnet structure in the form of crack formation, crack growth, and magnet breakage. A surface-mounted permanent magnet synchronous motor (PMSM) is studied using simulation in Ansys Maxwell software (Version 2023), and different cracks and PM faults are modeled using the two-dimensional finite element method (FEM). The frequency domain simulation results demonstrate the influence of magnet cracks and their propagation on EM performance measures, such as stator current, distribution of magnetic flux density, back EMF, flux linkage, losses, and efficiency. The results show strong potential for application in health monitoring systems, which could be used to reduce the occurrence of in-service failures, thus reducing the usage of rare-earth magnet materials as well as cost.

Electrode Droplet Microarray (eDMA): An Impedance Platform for Label-Free Parallel Monitoring of Cellular Drug Response in Nanoliter Droplets.

Label-free real-time monitoring of cellular behavior using impedance spectroscopy is important for drug development and toxicological assessments. Parallelization and miniaturization of such experiments are essential for increasing throughput and enabling experiments with low abundant stem or primary cells. Traditional methods are not miniaturized and require large volumes of reagents and number of cells, limiting their suitability for cost effective high-throughput screening of cells of limited availability. Here, the fabrication, optimization, and application of a bioelectrical signaling monitoring system - electrode droplet microarray (eDMA) are demonstrated. The eDMA platform is based on preparation of a hydrophilic-superhydrophobic patterns covering an array of individually addressable microelectrodes, which confines cells to individual microelectrodes, allowing for parallel, real-time, and label-free detection of cellular responses to drug treatments in nanoliter droplets. The real-time monitoring of cytotoxic effect of an anticancer drug is demonstrated over 48 h with real-time calculation of the half-inhibitory concentration (IC50) values through impedance spectroscopy. This demonstrates eDMA's ability to dynamically assess responses to various drugs in parallel at any given time point, which is crucial for functional personalized oncology. Specifically, the platform can be employed for monitoring anticancer drug toxicity using limited patient samples, where the miniaturization provided by eDMA is essential.

Establishment and application of infectious disease monitoring, early warning and disposal system

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan's health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN.

Wireless sensor networks (WSN) have found more and more applications in remote control and monitoring systems. Energy management in the network is crucial because all nodes in the WSN are energy constrained. Therefore, the design and implementation of WSN protocols that reduce energy depletion in the network is still an open scientific problem. In this paper, we propose a new clustering protocol that combines DEC (deterministic energy-efficient clustering) protocol with K-means clustering, called DEC-KM (deterministic energy-efficient clustering protocol with K-means). DEC is a very energy-efficient clustering protocol that outperforms its predecessors, such as LEACH and SEP. K-means ensures more effective clustering and shorter data transmission distances within the network. The shorter distances improve the network's lifetime and stability and reduce power consumption. Additional heuristic rules in DEC-KM ensure improved cluster head selection, taking into account node energy level and position and minimising the risk of premature cluster head exhaustion. The simulation results for the DEC-KM protocol using MATLAB show that cluster heads have shorter distances to nodes in cluster areas than for the original DEC protocol. The proposed protocol ensures reduced energy consumption, outperforms the standard DEC, and extends the stability period and lifetime of the network.

Design and Application of Intelligent Building Environment Monitoring System Based on Wireless Sensor Network

Design and Application of Intelligent Building Environment Monitoring System Based on Wireless Sensor Network

Design and Application of Oil Gathering Monitoring System based on IAAS Cloud Platform

Design and Application of Oil Gathering Monitoring System based on IAAS Cloud Platform

Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring.

Real-time data transmission and reliable operation are essential for a tsunami monitoring system to provide effective data. In this study, a novel real-time tsunami monitoring system is designed based on a submersible mooring system. This system is equipped with a data acquisition and tsunami wave identification algorithm, which can collect the measured data of the pressure sensor and detect a tsunami wave in real time. It adopts the combination design of underwater inductive coupling transmission and a redundant BeiDou communication device on the water surface to ensure the reliability of real-time data transmission. Compared with traditional tsunami monitoring buoys, it has the advantages of reliable communication, good concealment, high security, and convenient deployment, recovery, and maintenance. The results of laboratory and sea tests show that the system has high reliability of data transmission, stable overall operation of the system, and good application prospects in the field of real-time tsunami monitoring and early warning.

Optimizing Sensor Arrangement for Strain Mode-based Hull Monitoring System Application for Navy Ships

Optimizing Sensor Arrangement for Strain Mode-based Hull Monitoring System Application for Navy Ships

Improving unsupervised pedestrian re‐identification with enhanced feature representation and robust clustering

AbstractPedestrian re‐identification (re‐ID) is an important research direction in computer vision, with extensive applications in pattern recognition and monitoring systems. Due to uneven data distribution, and the need to solve clustering standards and similarity evaluation problems, the performance of unsupervised methods is limited. To address these issues, an improved unsupervised re‐ID method, called Enhanced Feature Representation and Robust Clustering (EFRRC), which combines EFRRC is proposed. First, a relation network that considers the relations between each part of the pedestrian's body and other parts is introduced, thereby obtaining more discriminative feature representations. The network makes the feature at the single‐part level also contain partial information of other body parts, making it more discriminative. A global contrastive pooling (GCP) module is introduced to obtain the global features of the image. Second, a dispersion‐based clustering method, which can effectively evaluate the quality of clustering and discover potential patterns in the data is designed. This approach considers a wider context of sample‐level pairwise relationships for robust cluster affinity assessment. It effectively addresses challenges posed by imbalanced data distributions in complex situations. The above structures are connected through a clustering contrastive learning framework, which not only improves the discriminative power of features and the accuracy of clustering, but also solves the problem of inconsistent clustering updates. Experimental results on three public datasets demonstrate the superiority of our method over existing unsupervised re‐ID methods.

CLINICAL SIGNIFICANCE OF FLASH MONITORING GLYCEMIA INDICATORS IN CHILDREN WITH TYPE 1 DIABETES MELLITUS

One of the basic principles of the treatment of type 1 diabetes mellitus, along with insulin therapy and diet therapy, is regular self-monitoring of glycemia. Glucose meters are most often used for daily self-monitoring, and in recent years devices for continuous and flash monitoring of glycemia have been used. The article discusses the clinical aspects of the application of a glycemic monitoring system with periodic scanning (flash monitoring). The principle of operation of the system is described, as well as the main parameters obtained when using this system – time in the target range, time above and below the target range and their sub-ranges, an indicator of glycemic variability and an indicator of glycemic control, their normative levels, as well as their practical significance and relationship with the clinical aspects of the course of diabetes mellitus and the prognosis of chronic complications. The results of major international and domestic studies on the effectiveness of the flash monitoring system in different age groups are presented. The advantages of the second generation of flash glycemia monitoring sensors are described, and the results of clinical studies of the accuracy of this monitoring system in both children and adult patients with diabetes mellitus are presented.

Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring.

Tool condition monitoring technology is an indispensable part of intelligent manufacturing. Most current research focuses on complex signal processing techniques or advanced deep learning algorithms to improve prediction performance without fully leveraging the end-to-end advantages of deep learning. The challenge lies in transforming multi-sensor raw data into input data suitable for direct model feeding, all while minimizing data scale and preserving sufficient temporal interpretation of tool wear. However, there is no clear reference standard for this so far. In light of this, this paper innovatively explores the processing methods that transform raw data into input data for deep learning models, a process known as an input paradigm. This paper introduces three new input paradigms: the downsampling paradigm, the periodic paradigm, and the subsequence paradigm. Then an improved hybrid model that combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was employed to validate the model's performance. The subsequence paradigm demonstrated considerable superiority in prediction results based on the PHM2010 dataset, as the newly generated time series maintained the integrity of the raw data. Further investigation revealed that, with 120 subsequences and the temporal indicator being the maximum value, the model's mean absolute error (MAE) and root mean square error (RMSE) were the lowest after threefold cross-validation, outperforming several classical and contemporary methods. The methods explored in this paper provide references for designing input data for deep learning models, helping to enhance the end-to-end potential of deep learning models, and promoting the industrial deployment and practical application of tool condition monitoring systems.

Establishment and application of real-time monitoring system for mine ventilation resistance

The monitoring of ventilation resistance in domestic coal mines is generally faced with two problems: huge workload and lagging monitoring data behind production, which is due to the backward monitoring technology. In recent years, with the progress of technology and equipment in the coal industry, ventilation resistance monitoring products have appeared in the market one after another, but they still face the following main problems: (1) the sensor measurement error needs to be further improved; (2) The monitoring scheme is relatively simple, and the use of absolute pressure multi-parameter monitoring faces two problems: measurement error and construction cost; (3) There is a lack of successful application cases of ventilation resistance monitoring in the industry, and the analysis and application of ventilation resistance monitoring data by the upper computer is insufficient. In view of the above problems, this paper studies the principle, monitoring scheme, system framework and practical application of ventilation resistance monitoring, aiming to solve the problem of real-time online monitoring and analysis of ventilation resistance.

Enhancing Paediatric Diabetes Management: How Artificial Intelligence is Revolutionising Care

Artificial intelligence (AI) is transforming paediatric diabetes management, offering innovative solutions for monitoring, treatment, and prediction. This mini-review explores how AI is being utilised to improve the care of children with diabetes mellitus, focusing on its application in glucose monitoring systems, predictive algorithms, and personalised treatment plans. The study synthesises recent advancements in AI technologies, examining their impact on enhancing the accuracy of diagnosis, reducing the burden on healthcare providers, and improving patient outcomes. Through a systematic review of the literature, key AI tools and models that have shown promise in paediatric diabetes care are identified. The findings highlight the potential of AI to revolutionise diabetes management, with implications for both clinical practice and future research. However, challenges remain in ensuring the ethical implementation and integration of these technologies into existing healthcare systems. The paper concludes with recommendations for advancing AI applications in this field, emphasising the need for continued innovation and collaboration between healthcare professionals and AI developers.

Cloud-edge-end-based aircraft assembly production quality monitoring system framework and applications

In recent years, the increasing complexity of quality control in aircraft assembly has necessitated the development of innovative and integrated solutions. This paper introduces a novel framework based on cloud-edge-end architecture to enhance quality monitoring systems in aircraft assembly production. The framework consists of five parts: cloud, edge, end, network, and the trained model. By leveraging the advantages of cloud computing, edge computing, and terminal devices, this framework aims to provide a comprehensive, real-time, and efficient quality monitoring solution. Key technologies within the cloud-edge-end framework are proposed, including data collection, storage, and transmission, deep learning methods based on Channel Attention Convolutional Neural Network (CACNN), and feedback mechanisms from the cloud layer to the lower layers. These innovations enable effective information sharing and utilization at various stages of the aircraft assembly process, providing operational managers with timely and accurate insights into assembly quality. A prototype system application has been implemented at an aircraft assembly site, facilitating process data monitoring and assembly status analysis. Experimental validation of the proposed CACNN algorithm has demonstrated its capability to monitor hole diameters online. Various case studies confirm the framework's effectiveness, showcasing substantial improvements in monitoring accuracy and operational efficiency.

A biocompatible triboelectric nanogenerator-based edible electronic skin for morse code transmitters and smart healthcare applications

Skin-inspired electronic devices have gained immense interest due to their application in prosthetics, healthcare, robotics, and humanmachine interactions. Mimicking human skin and incorporating multifunctional properties such as stretchability, flexibility, biocompatibility, biodegradability, and paving the way for self-powered electronic skin (e-skin) is highly desirable. The present work reports the fabrication of biocompatible triboelectric nanogenerator (TENG)-based edible electronic skins made of a biocompatible chitosan film. The fabricated chitosan film was engineered using plasticizers to enhance its flexibility and mechanical strength. To make the entire device biocompatible, edible gold leaf, which is used as a decorative layer in the sweets of the Indian subcontinent, has been used as an electrode. Both the chitosan film and the edible gold electrode were studied for their biocompatibility by immersion in acetic acid and H2O2, where the films were completely dissolved in 1 h and 50 h, respectively. The e-skins were used as the active layer in the triboelectric nanogenerator by working under both vertical contact separation and single electrode modes of triboelectrification. Furthermore, the fabricated device has been used for real-time applications in self-powered sleep monitoring systems as well as for wearable Morse code transmitters. The electrical response and performance in real-time applications prove that the developed e-skin-based device is a promising candidate for future wearable applications in health care, humanmachine interactions and haptics.

Micro‐3D Printed Conductive Polymer Composite via Two‐Photon Polymerization for Sensing Applications

AbstractTwo‐photon Polymerization (2PP) process for high‐resolution 3D printing presents an opportunity to design micro‐scale structures with a high surface‐to‐volume ratio for highly responsive devices. However, these acrylate or thiol‐based resins are electrically insulating and non‐functional in nature, therefore limiting their widespread application in biosensing and biotechnology. Here, a novel conductive polymeric composite resin to print conductive 3D micro‐structures via the 2PP technique is developed and its application in sensing are demonstrated. The composite consists of acrylate‐based 2PP resin and Poly(3,4‐ethylenedioxythiophene)‐poly(styrenesulfonate) (PEDOT:PSS), a conductive and biocompatible organic semiconductor The PEDOT:PSS incorporation in resin through Raman and X‐ray photoelectron spectroscopy (XPS) is studied. An electrical conductivity of 3.5 × 102 S cm−1 in a 20 µm long and 10 µm high 3D printed micro‐structure which is suitable for electronic applications is achieved. An ultra‐fast micro‐3D printed humidity sensor with a response and recovery time of 0.15 and 0.3 s respectively is demonstrated. The printed sensors show high sensitivity in humidity levels of 0–80%RH. As a proof of concept, the real‐time respiration of a human body is recorded, implying a potential application in health monitoring systems.

A portable self-powered biosensor for monitoring artistic gymnastics techniques

The development of self-powered smart biosensors has attracted great attention due to their potential applications in human motion monitoring and energy collection systems. Here, a newly designed triboelectric nanogenerator (PSP-TENG) composed of the PU sponge, polytetrafluoroethylene, and polydimethylsiloxane for monitoring rear leg rotation motion in artistic gymnastics has been developed. The PSP-TENG can monitor the buffering and stretching time of rear leg rotation and identify the angle between the hip, knee, and ankle during the stretching stage. Furthermore, the PSP-TENG has the function of converting measured signals into power signals, which can convert collected human motion mechanical energy into electrical energy to power wearable devices. The development of PSP-TENG has made contributions to the field of human motion monitoring, providing ideas for new energy and technologies to improve the environment and promote a virtuous ecological cycle.

Prediction of Errors in the Field of Additive Manufacturing Technology

Additive manufacturing, also known as 3D printing, allows the formation of complex geometric structures layer by layer. Predicting errors in this process may help identify potential problems in a timely manner and minimise waste. A human may detect an additive manufacturing error, but cannot provide continuous monitoring or real-time correction. The article is focused on the design of a camera system design for online monitoring of the 3D printing process with the task of detecting process errors arising during 3D printing of objects. The article describes the methodology for tracking the occurrence of process errors in 3D printing, which are identified in the OctoPrint Nexus AI plug-in environment for the subsequent application of a suitable solution to minimize the occurrence of defects. The application of a real-time process monitoring system including the ability to correctly predict anomalous behaviour in the context of artificial intelligence has proven to be an appropriate solution to that particular problem.

Development and Validation of Low-Cost Indoor Air Quality Monitoring System for Swine Buildings.

The optimal indoor environment is associated with comfortable temperatures along with favorable indoor air quality. One of the air pollutants, particulate matter (PM), is potentially harmful to animals and humans. Most farms have monitoring systems to identify other hazardous gases rather than PM due to the sensor cost. In recent decades, the application of environmental monitoring systems based on Internet of Things (IoT) devices that incorporate low-cost sensors has elevated extensively. The current study develops a low-cost air quality monitoring system for swine buildings based on Raspberry Pi single-board computers along with a sensor array. The system collects data using 11 types of environmental variables along with temperature, humidity, CO2, light, pressure, and different types of gases, namely PM1, PM2.5, and PM10. The system is designed with a central web server that provides real-time data visualization and data availability through the Internet. It was tested in actual pig barns to ensure stability and functionality. In addition, there was a collocation test conducted by placing the system in two different pig barns to validate the sensor data. The Wilcoxon rank sum test demonstrates that there are no significant differences between the two sensor datasets, as all variables have a p-value greater than 0.05. However, except for carbon monoxide (CO), none of the variables exhibit correlation exceeding 0.5 with PM concentrations. Overall, a scalable, portable, non-complex, low-cost air quality monitoring system was successfully developed within a cost of USD 94.

ANALYSIS OF THE PROSPECTS OF APPLICATION OF NATURAL GAS LEAK DETECTION METHODS TO INCREASE THE EFFICIENCY OF ATMOSPHERIC AIR MONITORING AT HTS FACILITIES

The activities of the oil and gas industry enterprises, in particular the objects of gas transportation systems (GTS), are accompanied by a significant anthropogenic impact on the environment. The atmospheric air itself is significantly affected, since many technological processes are accompanied by emissions of natural gas (organized, unorganized and emergency) and, as a result, a significant amount of volatile substances, many of which are dangerous and harmful, enter the atmosphere. There is a wide range of methods for detecting gas leaks from elements of gas transportation systems. Ways of classifying methods directly depend on the features that form the basis of their distribution. However, the insufficient definition of the classification features, their "one-sidedness" do not allow a full assessment of the shortcomings and advantages of certain methods and means, and, therefore, the possibility of their application to increase the effectiveness of atmospheric air monitoring at HTS facilities in order to ensure environmental safety. The main tasks solved in this article are the formulation of the optimal classification of existing methods of controlling the presence of gas leaks in terms of applicability in environmental monitoring systems of atmospheric air; analysis of their advantages and disadvantages; substantiation of the expediency and perspective of using these means and methods to ensure constant monitoring of the state of atmospheric air at GTS facilities. A classification of methods and means of control is proposed based on the level of application of technical means, according to which all methods are divided into organoleptic, hardware and computational (software-mathematical). According to the results of the analysis of the data of the groups, it was determined that, from the point of view of the prospects of application in the atmospheric air quality monitoring systems at the GTS facilities, hardware means, namely gas analyzers, have the most advantages.