Development Of Monitoring System Research Articles

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

IoT Based Transformer Monitoring System using ESP-32

Abstract: The integration of Internet of Things (IoT) technology with power transformer monitoring systems presents a significant advancement in electrical grid management. This paper explores the development of an IoT-based transformer monitoring system utilizing the ESP-32 microcontroller. The primary objective is to enhance the reliability and efficiency of transformers by providing real-time data on their operational status and health. The system leverages the ESP-32's capabilities in wireless communication and data processing to monitor critical parameters such as temperature, load current, voltage, and oil levels. Data is transmitted to a centralized cloud platform for analysis and visualization, enabling predictive maintenance and rapid response to potential failures. The proposed system promises to reduce downtime and maintenance costs while ensuring the longevity and optimal performance of transformers. This research highlights the design, implementation, and testing phases of the monitoring system, demonstrating its effectiveness through various case studies and field trials.

Health monitoring system using ECG and PPG techniques

The demand for portable health monitoring systems has grown rapidly in recent years due to the increasing prevalence of chronic diseases and the need for continuous health monitoring. This article introduces the development and implementation of a portable health monitoring system using photo plethysmography (PPG) and electrocardiogram (ECG) technologies. The system integrates the Node MCU ESP8266 microcontroller, AD8232 ECG sensor, and MAX30100 sensor to measure vital signs such as heart rate and blood oxygen saturation level. The proposed system provides a cost-effective and efficient solution for real-time health monitoring, enabling early detection of abnormalities and timely intervention. The feasibility and performance of the system were proven through experimental tests.

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.

LoRa Network-based IoT Node for indoor environment monitoring

The concept of the Internet of Things (IoT), just by its name, presents itself as a network of physical objects, which are incorporated into a vast array of sensors, software, and any other type of technology, with the purpose of connecting and exchanging information with other devices and systems via the Internet. As we know it, the IoT is present in a wide range of devices ranging from simple household objects to complex tools spread across all sectors of industry. The means of communication used in IoT devices is based on Long Range (LoRa) technology. This technology stands out for its ability to operate with low energy consumption, which is vital for the use of IoT devices since they run on batteries. This paper will look at the development of an indoor space monitoring system based on an IoT Node made up of sensors supported by the LoRa network. To achieve the objectives, an in-depth study of the technology used in LoRa networks is carried out, followed by a presentation of the system's architecture and then the various tests carried out in indoor spaces to assess the functioning of both the sensors and the network itself. During the work, numerous tests were carried out over extended periods, which will be duly detailed throughout the document. These are used to ensure the stability of the monitoring system and gauge the environmental status of the space where it is installed.

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction

Wire-arc additive manufacturing (WAAM) is favored by the industry for its high material utilization rate and low cost. However, wire-arc additive manufacturing of lattice structures faces problems with forming accuracy such as broken rod and surface morphology defects, which cannot meet the industrial demand. This article innovatively combines the melt pool stress theory with visual perception algorithms to visually study the force balance of the near-suspended melt pool to predict the state of the melt pool. First, the method for melt pool segmentation was studied. The results show that the optimized U-net achieved high accuracy in melt pool segmentation tasks, with accuracies of 98.18%, MIOU 96.64%, and Recall 98.34%. In addition, a method for estimating melt pool force balance and predicting normal, sagging, and collapsing states of the melt pool is proposed. By combining experimental testing with computer vision technology, an analysis of the force balance of the melt pool during the inclined rod forming process was conducted, showing a prediction rate as high as 90% for the testing set. By using this method, monitoring and predicting the state of the melt pool is achieved, preemptively avoiding issues of broken rods during the printing process. This approach can effectively assist in adjusting process parameters and improving welding quality. The application of this method will further promote the development of intelligent unmanned WAAM and provide some references for the development of artificial intelligence monitoring systems in the manufacturing field.

Electronic Skin for Health Monitoring Systems: Properties, Functions, and Applications.

Electronic skin (e-skin), a skin-like wearable electronic device, holds great promise in the fields of telemedicine and personalized healthcare because of its good flexibility, biocompatibility, skin conformability, and sensing performance. E-skin can monitor various health indicators of the human body in real time and over the long term, including physical indicators (exercise, respiration, blood pressure, etc.) and chemical indicators (saliva, sweat, urine, etc.). In recent years, the development of various materials, analysis, and manufacturing technologies has promoted significant development of e-skin, laying the foundation for the application of next-generation wearable medical technologies and devices. Herein, the properties required for e-skin health monitoring devices to achieve long-term and precise monitoring and summarize several detectable indicators in the health monitoring field are discussed. Subsequently, the applications of integrated e-skin health monitoring systems are reviewed. Finally, current challenges and future development directions in this field are discussed. This review is expected to generate great interest and inspiration for the development and improvement of e-skin and health monitoring systems.

Development of multi-sensors fusion monitoring system for shaft wall deformation

Ensuring shaft safety is crucial for underground coal mining. The prevailing method for monitoring shaft deformation involves manual measurements conducted regularly, necessitating numerous surveyors to descend into the shaft. This approach is laborious and time-consuming, and its reliance on human intervention significantly affects accuracy. An emerging alternative employs an enhanced laser slam scanner comprising laser ranging and inertial navigation angle measurement modules. The accuracy of the inertial navigation angle often requires refinement through laser point cloud registration to mitigate errors. However, existing research has not explored the utilization of a single inertial navigation sensor within a shaft, which potentially influences measurement outcomes. To investigate the impact of inertial navigation angle measurement errors from lidar scanners on shaft interior measurements and propose corresponding error reduction methods, this study developed a multisensor fusion system for monitoring shaft wall deformations. Three experiments were conducted to assess the accuracy of this monitoring system: a laboratory experiment, a simulated experiment, and a real shaft experiment. The results demonstrated that the maximum mean absolute errors (MAEs) in the laboratory and simulated shaft experiments were 0.06 cm and 0.30 cm, respectively, validating the reliability of the point cloud processing algorithm. In the real shaft experiment, the maximum MAE was 0.48 cm, confirming the measurement accuracy of the monitoring system. Compared to manual measurements, the system reduced the measurement time by 48% and streamlined the process from 12 to 3 steps. These findings highlight the monitoring system’s centimeter-level accuracy, efficiency in reducing measurement time, simplification of the measurement process, and elimination of the need for human entry into the shaft.

Telemetry to solve dynamic analysis of a distributed system

In the modern software development world, implementing distributed solutions has become quite common due to the flexibility it brings to big companies. The downside is that when developing such systems, especially in many teams, global design problems may not be obvious and lead to a slowdown in the development process or even problems with the location of errors or degradation of overall system performance. In addition, the timely reaction to system degradation is complicated by the distributed nature of the architecture; while manually configuring rules for reporting problematic situations can be time-consuming and still incomplete, automatic detection of possible system anomalies will give engineers (especially Software Reliability Engineers) the focus on problems. For this reason, applications that can dynamically analyse the system for problems have great potential. Currently, the topic of using telemetry for system analysis is actively studied and gaining traction, so further research is valuable. The work aims to theoretically and practically prove the possibility of using telemetry to analyse a distributed information system and detect harmful architectural practices and anomalous events. To do this, firstly, a detailed overview of the problems related to the topic and the feasibility of using telemetry is provided; the next section briefly describes the history of the development of monitoring systems and the key points of the latest OpenTelemetry standard, reviews popular application performance monitoring systems, and defines innovative features to be further researched. The main part includes an explanation of the approach used to collect and process telemetry, a reasoning behind the usage of Neo4j as a data storage solution, a practical overview of graph theory algorithms that help in the analysis of the collected data, and a description outlining how the PCA algorithm is employed to detect unusual situations in the whole system instead of individual metrics. The results provide an example of using the software presented with Neo4j Bloom to visualise and analyse the data collected over several hours from the OpenTelemetry Demo test system. The last section contains additional remarks on the results of the study.

Migration Activity of Spodoptera litura (Lepidoptera: Noctuidae) between China and the South-Southeast Asian Region.

The common cutworm, Spodoptera litura (F.), feeds on a wide variety of food and cash crops and is one of the most widespread and destructive agricultural pests worldwide. Migration is the biological basis of its regional population outbreaks but the seasonal movement of this pest between east and south Asia regions remains unknown. In this study, searchlight traps were used to monitor the seasonal migration of S. litura from 2019 to 2023 in Ruili City (Yunnan, China), located along the insect migratory route between China and the south Asia region. The results showed that migratory activity could occur throughout the year, with the main periods found in spring (April-May) and autumn (October-December). The ovarian development and mating status of the trapped females indicated that most individuals were in the middle or late stages of migration and that Ruili City was located in the transit area of the long-distance migration of the pest. In the migration trajectory simulation, populations of S. litura moved from northeast India, Bangladesh, and northern Myanmar to southwestern China along the southern margin of the Himalayas in spring and returned to the south Asia region in autumn. Our findings clarify the seasonal migration patterns of S. litura in China and South Asia and facilitate the development of regional cross-border monitoring and management systems for this pest.

Breaking Barriers: An Innovative Tool to Assess the National and City-Level Physical Activity Policy Development to Practice Disconnect.

Population-level physical activity increases are improbable without intersectoral collaboration across government levels and sectors to develop and implement physical activity promotion policies. This study aims to provide information about the development of the Interaction between National and Local Government Levels in Development and Implementation of Physical Activity Policies Tool (INTEGRATE PA-Pol). A framework was created to examine the development and implementation of national and subnational physical activity policies and the (mis)alignment between government levels. The work was conducted in 3 phases: (1)a scoping review was carried out to identify local government physical activity promotion policies and instruments for assessing them, (2)an expert group designed 6 questionnaires, and (3)cognitive response testing was employed for validity testing and item modification with a panel of research and policy experts. The INTEGRATE PA-Pol Tool consists of 6 questionnaires assessing how national and subnational governments collaborate to develop and implement physical activity promotion policies. This tool can assist in better understanding the development and implementation of a public policy monitoring system that will allow for benchmarking and priority setting to comprehend how physical activity promotion policies are designed and executed.

Development and application of wellbore flow assurance monitoring system for combustible ice production in deepwater

The commercial development of combustible ice, as the most promising strategy energy, requires not only efficient gas production technology but also a safe flow assurance system. For the depressurization development of combustible ice in the sea area, the issues of wellbore fluid carrying sand, hydrate re-formation in the wellbore, and instability of wellbore flow, are the key problems leading to flow obstacles in the wellbore and affecting the normal production. Therefore, some measures must be taken to effectively monitor and analyze the flow situation in the wellbore during the development of combustible ice in the sea. In this paper, a Wellbore Flow Assurance Monitoring System (WFAMS) is developed to display the fluid flow conditions in the wellbore in real-time during the gas hydrate production, including temperature, pressure, flow rate, the carrying sand amount, etc. Furthermore, the system is capable of online monitoring for potential plug risks, including sand plugs and hydrate blockages caused by gas hydrate formation, as well as assessing flow stability. Additionally, it can provide valuable information, such as production adjustment data and the required injection dosage of thermodynamic inhibitors to prevent hydrate formation. In the 2020 South China Sea production test, this system was used for depressurization production and successfully predicted wellbore flow conditions. This enabled field engineers to monitor flow risks and adjust production plans in a timely manner, ensuring smooth test progress.

5G SMART DIABETES TOWARDS PERSONALIZED DIABETES DIAGNOSIS WITH HEALTH CARE BIGDATA CLOUD

Recent advances in wireless networking and big data technologies, such as 5G networks, medical big data analytics, and the Internet of Things, along with recent developments in wearable computing and artificial intelligence, are enabling the development and implementation of innovative diabetes monitoring systems and applications. Due to the life-long and systematic harm suffered by diabetes patients, it is critical to design effective methods for the diagnosis and treatment of diabetes. Based on our comprehensive investigation, this article classifies those methods into Diabetes 1.0 and Diabetes 2.0, which exhibit deficiencies in terms of networking and intelligence. Thus, our goal is to design a sustainable, cost-effective, and intelligent diabetes diagnosis solution with personalized treatment. In this article, we first propose the 5G-Smart Diabetes system, which combines the state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes. Then we present the data sharing mechanism and personalized data analysis model for 5G-Smart Diabetes. Finally, we build a 5G-Smart Diabetes testbed that includes smart clothing, smartphone, and big data clouds. The experimental results show that our system can effectively provide personalized diagnosis and treatment suggestions to patients. Key Words: Monitoring, Diabetes, Wearable, Big Data, Smart Systems, Machine Learning

The applicability of automated marine clay gully delineation using deep learning in Norway

AbstractGullies and ravines are common landforms in raised marine fine‐grained deposits in Norway. Gullies in marine clay are significant landforms indicative of soil erosion and natural hazards and are of high conservation value. As a result of the substantial impact of human intervention over the past century, marine clay gullies are now red‐listed. To monitor the condition of these landforms, we need to improve our understanding of their spatial extent, complexity and morphology. We explore the applicability of automated approaches that use a methodology of combining deep learning (DL), fully convolutional neural networks (FCNNs) and an unmodified U‐Net model with ArcPy libraries and ground truth data to derive a high‐resolution map of gullies in raised marine fine‐grained deposits. Predictors used comprise solely terrain derivatives to broaden the usage of the pre‐trained model to other regions. Our best model achieved a precision score of 0.82 and a recall of 0.75. We find that our pre‐trained model can successfully predict gullies, also in blind‐test areas. The model performs better in regions with similar geological settings, scoring a length‐weighted overlap of >70% with reference datasets. The novelty of this study is that we demonstrate that the model's applicability for mapping routines increases when we post‐process the predictions by eliminating noise, especially by using the predictions derived from ensembled models. We, therefore, conclude that the pre‐trained models can effectively be used to supplement the geomorphological mapping of marine clay gullies in Norway. The outcome of this research contributes towards mapping the spatial extent and condition of red‐listed landforms in Norway, as well as the development of monitoring systems for future landscape change.

Research on Online Monitoring Technology and Filtration Process of Inclusions in Aluminum Melt.

Online monitoring and real-time feedback on inclusions in molten metal are essential for metal quality control. However, existing methods for detecting aluminum melt inclusions face challenges, including interference, prolonged processing times, and latency. This paper presents the design and development of an online monitoring system for molten metal inclusions. Initially, the system facilitates real-time adjustment of signal acquisition parameters through a multiplexer. Subsequently, it employs a detection algorithm capable of swiftly extracting pulse peaks, with this task integrated into our proprietary host computer software to ensure timely detection and data visualization. Ultimately, we developed a monitoring device integrated with this online monitoring system, enabling the online monitoring of the aluminum alloy filtration process. Our findings indicate that the system can accurately measure the size and concentration of inclusions during the filtration process in real time, offering enhanced detection speed and stability compared to the industrial LiMCA CM (liquid metal cleanliness analyzer continuous monitoring) standard. Furthermore, our evaluation of the filtration process demonstrates that the effectiveness of filtration significantly improves with the increase in inclusion sizes, and the synergistic effect of combining CFF (ceramic foam filter) and MCF (metallics cartridge filter) filtration methods exceeds the performance of the CFF method alone. This system thus provides valuable technical support for optimizing filtration processes and controlling inclusion quality.

Development and Research of Safety Video Monitoring System for Loading and Unloading of Chemical Products in Railway Transportation

As the important position of chemical railroad transportation in modern logistics is becoming more and more prominent, its safety problem has attracted wide attention. To improve the safety of transporting chemicals by rail, this paper strives to develop an advanced safety monitoring system. The main background of this paper is the serious potential results of chemical products railroad transportation accidents which require an effective monitoring method to prevent. The purpose of this research is to design and realize an efficient and reliable video monitoring system to monitor the loading and unloading of chemicals transported by railroads in real-time. To achieve this goal, we adopted comprehensive methods, including video image recognition technology, data analysis technology, communication network technology, etc., which have significant meaning in preventing railroad accidents of chemical product transportation and safeguarding people’s life and property.

Real-time Fishpond Water Temperature Monitoring with Internet of Things (IoT) Technology

The quality of pond water is an important factor in ensuring the health and sustainability of the organisms within it. Internet of Things (IoT) technology has enabled the development of sophisticated and effective monitoring systems to monitor water quality in real-time. One important parameter that needs to be monitored is the temperature of the pond water, as unstable temperatures can harm aquatic organisms. To obtain good quality and disease-free fish at normal times, the optimal temperature for catfish fry is around 28˚C - 30˚C. Manual monitoring or using thermometer measuring instruments is still very traditional and limited to the ability of supervisors to carry out continuous monitoring and may cause delays in detecting significant temperature changes. This research aims to analyze the monitoring system of pool water quality based on temperature by utilizing IoT technology. The proposed system uses a DS18B20 temperature sensor that is wirelessly connected to the IoT network. The temperature data collected in real time will be uploaded to Firebase for storage and further analysis. Through the development of this IoT-based pond water quality monitoring system, it is expected to increase efficiency and reliability in monitoring pond water quality. From the monitoring conducted for approximately 24 hours, the temperature in the fishpond is 28°-30°. After analyzing with direct monitoring, the good temperature is at night at 18.00-00.00 WIB. In conjunction with a standard thermometer, resulted in an average temperature sensor error rate of less than 5%, highlighting the reliability of the IoT technology in real-time environmental monitoring.

Smartphone-interfaced flow injection amperometric system for enzyme-free glucose detection using a palladium-PANI/carbon microsphere@carbon nanotubes modified electrode

Integrating a flow injection system with an electrochemical sensor controlled by a smartphone enables rapid, precise real-time monitoring and reduces analyte consumption, contamination and costs. An enzyme-free electrochemical sensor is presented for the monitoring of glucose, an important indicator of diabetes. The sensor integrates a smartphone and a flow injection amperometric system. The flow cell is fabricated from acrylic plastic and includes an electrochemical three-electrode cell that comprises a working electrode modified with a catalyst of palladium-polyaniline/carbon microspheres@carbon nanotubes (Pd-PANI/CM@CNTs/E) for the detection of glucose oxidation. In the optimal condition, the proposed system produced good analytical performances. The sensor determined glucose linearly in a range from 0.020 to 100 mM, with a sensitivity of 51.51 μA mM−1 cm−2, and a detection limit (LOD) of 0.0071 mM. The developed system also exhibited good repeatability, reproducibility, operational stability, and anti-interference properties, and accurately determined glucose levels in real human serum samples. This sensing platform provides an alternative tool for the development of a continuous glucose monitor system for clinical applications.

Development of a Network Monitoring System

Development of a Network Monitoring System

A framework for the practical development of condition monitoring systems with application to the roller compactor

Implementing a condition-based maintenance strategy requires an effective condition monitoring (CM) system that can be complicated to develop and even harder to maintain. In this paper, we review the main complexities of developing condition monitoring systems and introduce a four-stage framework that can address some of these difficulties. The framework achieves this by first using process knowledge to create a representation of the process condition. This representation can be broken down into simpler modules, allowing existing monitoring systems to be mapped to their corresponding module. Data-driven models such as machine learning models could then be used to train the modules that do not have existing CM systems. Even though data-driven models tend to not perform well with limited data, which is commonly the case in the early stages of pharmaceutical process development, application of this framework to a pharmaceutical roller compaction unit shows that the machine learning models trained on the simpler modules can make accurate predictions with novel fault detection capabilities. This is attributed to the incorporation of process knowledge to distill the process signals to the most important ones vis-à-vis the faults under consideration. Furthermore, the framework allows the holistic integration of these modular CM systems, which further extend their individual capabilities by maintaining process visibility during sensor maintenance.