Development Of Monitoring System Research Articles

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.

Rancang Bangun Sistem Monitoring Cuaca Berbasis Google Spreadsheet Untuk Nelayan Di Wilayah Pesisir Pantai

Coastal areas are vital areas for fishing activities, and the weather in this area plays a very important role in the safety and efficiency of fishermen's operations. Fishermen on the coast often face the challenges of weather that is changeable and can be very extreme, including strong winds that can cause high waves and unsafe sea conditions. Therefore, the development of a Google spreadsheet-based weather monitoring system for fishermen in coastal areas is very relevant and strategic to support safer and more productive fishing activities. This research aims to make it easier for fishermen to monitor the weather in coastal areas in real time. Testing is carried out by running the system for observation for a predetermined time duration. This research uses an anemometer, DHT11 and raindrop sensors connected to a NodeMCU ESP32 microcontroller to send data to a Google spreadsheet, with readings of wind speed, temperature, air humidity and rain values. This research was carried out at the Nusantara Prigi fishing port, Trenggalek, challenging the work of anemometer sensors, DHT11 and raindrop sensors. Testing of tools and data obtained by comparing data on wind speed, temperature and air humidity using a digital anemometer and digital thermometer hygrometer humidity. The results of this research are that a Google spreadsheet-based weather monitoring design for fishermen in coastal areas can monitor wind speed, air humidity, temperature and detect rain online using a spreadsheet application and functions well with an average error accuracy of 3%.

Cleaned Meta Pseudo Labels-Based Pet Behavior Recognition Using Time-Series Sensor Data.

With the increasing number of households owning pets, the importance of sensor data for recognizing pet behavior has grown significantly. However, challenges arise due to the costs and reliability issues associated with data collection. This paper proposes a method for classifying pet behavior using cleaned meta pseudo labels to overcome these issues. The data for this study were collected using wearable devices equipped with accelerometers, gyroscopes, and magnetometers, and pet behaviors were classified into five categories. Utilizing this data, we analyzed the impact of the quantity of labeled data on accuracy and further enhanced the learning process by integrating an additional Distance Loss. This method effectively improves the learning process by removing noise from unlabeled data. Experimental results demonstrated that while the conventional supervised learning method achieved an accuracy of 82.9%, the existing meta pseudo labels method showed an accuracy of 86.2%, and the cleaned meta pseudo labels method proposed in this study surpassed these with an accuracy of 88.3%. These results hold significant implications for the development of pet monitoring systems, and the approach of this paper provides an effective solution for recognizing and classifying pet behavior in environments with insufficient labels.

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.

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.

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.

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.

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.

AI-Driven Privacy in Elderly Care: Developing a Comprehensive Solution for Camera-Based Monitoring of Older Adults

The need for privacy in elderly care is crucial, especially where constant monitoring can intrude on personal dignity. This research introduces the development of a unique camera-based monitoring system designed to address the dual objectives of elderly care: privacy and safety. At its core, the system employs an AI-driven technique for real-time subject anonymization. Unlike traditional methods such as pixelization or blurring, our proposed approach effectively removes the subject under monitoring from the scene, replacing them with a two-dimensional avatar. This is achieved through the use of YOLOv8, which facilitates accurate real-time person detection and pose estimation. Furthermore, the proposed system incorporates a fall detection algorithm that utilizes a residual causal convolutional network together with motion features of persons to identify emergency situations and promptly notify caregivers in the event of a fall. The effectiveness of the system is evaluated to emphasize its advanced privacy protection technique and fall detection capabilities using several metrics. This evaluation demonstrates the system’s proficiency in real-world applications and its potential to enhance both safety and privacy in elderly care environments.

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.

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.

The Evolving International Climate Change Regime: Mitigation, Adaptation, Reflection

The complex international regime for climate change has evolved over the past three decades, from the Framework Convention on Climate Change and the Kyoto Protocol through the Paris Agreement and beyond. We assess this evolution from the 1990s to the 2020s, and its potential future evolution from the 2020s to the 2050s, across three main policy strategies: mitigation, adaptation, and reflection. In its first three decades, the regime has focused predominantly on the mitigation of net emissions and on engaging all major emitting countries in that effort. More recently, as progress on mitigation has been slow and as the impacts of climate change have risen around the world, the regime has begun to address adaptation. The next three decades may see the rise of a third strategy, reflection, if actors (collectively or unilaterally) perceive an urgent need to alleviate peak climate damages through fast-acting but controversial and risky climate interventions known as sunlight reflection methods or solar radiation modification (SRM). Several major international groups have recently issued reports on SRM, yet the international climate change regime has not yet constructed a governance regime for assessment or management of SRM. We recommend and outline comprehensive risk-risk tradeoff analyses of SRM to help avoid harmful countervailing risks. We suggest the development of an adaptive governance regime, starting early and embracing iterative and inclusive learning and updating over time. We urge that among the first key steps should be the development of a transparent international monitoring system for SRM. Such a monitoring system could provide early warning and help deter any unilateral SRM, assess the intended and unintended global and regional impacts of any research or eventual deployment of SRM, foster collective deliberation and reduce the risk of international conflict over SRM, help attribute adverse side effects of SRM to assist those adversely affected, and aid learning to improve the system adaptively over time. Thus, any reflection (of sunlight) should involve ongoing reflection (analysis and revision). Such an SRM monitoring regime is needed before SRM might be deployed, and can be developed at the same time that the focus of current efforts remains on mitigation and adaptation.

Development of WebGIS-Based Monitoring System for Key Performance Indicators of Muhammadiyah Regional Board in Central Java

Development of WebGIS-Based Monitoring System for Key Performance Indicators of Muhammadiyah Regional Board in Central Java

Основные аспекты цифрового развития общества

The digital development of society is an urgent and important topic in the modern world, as technologies and digital tools play an increasingly important role in our daily lives. This research topic is an urgent phenomenon that requires attention and study for the effective implementation of digital technologies in various spheres of life. The purpose of the work is to study the main aspects of the digital development of society, their use and impact on people’s lives. To achieve the goal, the following were studied: the development of digital technologies and devices, the creation of digital infrastructure, the use of digital technologies to increase productivity and efficiency in various fields, the use of digital technologies to ensure a decent human life, as well as opportunities and risks. Thus, the relevance of this work lies in describing the impact of digitalization on all processes taking place in society as a whole. The paper reflects the directions of the use of the Internet and the use of digital technologies in organizations, identifies the share of the use of digital technologies to increase productivity and efficiency in agriculture and higher education, notes the development of electronic services for public services in the field of public administration and social services, improving the efficiency of the health system, the introduction of electronic voting and digital identification, the development of data monitoring and analysis systems and more. It is noted that along with the possibilities of digitalization, risks arise. Thus, digitalization has affected the economy, education, healthcare, government, cultural and entertainment development, and security.

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.