Development Of Monitoring System Research Articles

Self-powered wireless structural sensors for long-term monitoring of bridges

The present work introduces the design of a self-powered wireless static and dynamic structural monitoring system developed by Wisepower Srl and deployed on some bridges along the S.S. 675 "Umbro-Laziale" (former "Civitavecchia - Orte" junction) since 2018. The most significant aspect of the developed monitoring system lies in the fact that it consists of a wireless network of MEMS accelerometers (noise spectral density in all axes: 22.5 μg/√Hz, and sensitivity: 3.9 μg/LSB) powered by energy harvesting systems, which can convert environmental energy (i.e. vibrations and light) into electrical energy collected in batteries, so extending the life and minimizing the maintenance cycles of the devices. Vibration-based energy harvesting is achieved through a non-linear resonator, which utilizes a wider spectrum of frequencies compared to traditional linear vibration energy harvesters owing to its non-linear dynamics. The efficiency of the system is further enhanced by the combination of solar panels, overcoming the limitations associated with traditional wiring or batteries, widening the lifespan of the electronics, and reducing the maintenance costs. The paper reports the analysis method and the data elaboration of some ambient acceleration records, validating the system for natural frequencies identification.

Design Science Research for Developing Risk Tourism Mapping Based on Visitor Sentiment Review

Real-time monitoring and assessment procedures at tourism sites are crucial. The lack of it may caused an operational incident. Therefore, this study aims to develop a design science research (DSR) methodology to produce an artifact in the form of a risk mapping system that can display a map of tourism sites in an urban area based on their risk group. The DSR approach has five stages to develop artifacts: explicate problem, define requirements, design and develop artifacts, demonstrate it, and evaluate it. The findings of this study propose suitable research strategies to develop artifacts based on relevant knowledge bases. It also discusses in depth the implementation of the explicate problem stage, which involves observing the root causes of operational accidents at tourism sites. It also finds that real-time monitoring of tourism sites can prevent accidents, and proposes utilizing reviews from visitors on Google Review, which can be categorized through text mining to identify risk factors based on Sapta Pesona's framework. This study provides valuable insights for developing monitoring systems in the field of tourism risk management using visitor review texts as an open-source resource.

The Current State of Artificial Intelligence Application in Urology.

Artificial intelligence (AI) is being used in many areas of healthcare, including disease diagnosis and personalized treatment and rehabilitation management. Medical AI research and development has primarily focused on diagnosis, prediction, treatment, and management as an aid to patient care. AI is being utilized primarily in the areas of personal healthcare and diagnostic imaging. In the field of urology, significant investments are being made in the development of urination monitoring systems in the field of personal healthcare and ureteral stricture and urinary stone diagnosis solutions in the field of diagnostic imaging. In addition, AI technology is also being applied in the field of neurogenic bladder to develop risk monitoring systems based on video and audio data. This paper examines the application of AI to urological diseases and discusses the current trends and future prospects of AI research.

Industrial Pollution Monitoring System Using LabVIEW and GSM

Abstract: This paper investigates how, in the face of growing industrialization, LabVIEW and GSM technology revolutionize pollution monitoring. Over the last ten years, there has been a 30% increase in LabVIEW's data capabilities and a 25% increase in GSM's remote access. It charts the development of monitoring systems and emphasizes how LabVIEW responds to pollution events 40% faster and how remote interventions can reduce downtime in GSM by 20%. When combined, they offer a 15% increase in anomaly detection accuracy.

Advance Multi-Gas Detection System for Optimal Safety

Indoor air quality is a growing concern, particularly the concentration of carbon dioxide (CO2) and other hazardous gases, which can pose significant health risks and diminish safety. Recognizing the critical nature of this issue, this paper detailsthe development of an advanced environmental monitoring system designed to detect elevated levels of CO2 and other potentially dangerous gases. Our approach synthesizes various research methods, including requirement gathering, literature review, and user surveys, to inform system conception and implementation. The resulting device integrates highly sensitive sensors for CO2, CO, LPG, smoke, and additional hazardous gas detection, all governed by a microcontroller at the system's nucleus. This technology is augmented by a comprehensive alarm strategy—encompassing audio alerts, text messages, emails, and mobile application notifications—together with a user-friendly display interface. The system is also endowed with Internet of Things (IoT) capabilities, offering users the convenience of real-time monitoring and control through a web dashboard and a dedicated mobile application. Moreover, the device boasts the ability to monitor environmental parameters like temperature and humidity, providing a holistic view of indoor air conditions. Key results indicate a significant improvement in detection response time and accuracy, with the enhanced communication features facilitating swift and effective alerts to users. These advancements mark a stride toward safer indoor environments, potentially mitigating the risks associated with poor air quality.

The role of international organisations in the development of the financial monitoring system

The work aims to explore and highlight the crucial role of international organisations in developing the financial monitoring system. By setting global standards, providing capacity-building support, facilitating information sharing, conducting assessments, advocating policy measures, and promoting international coordination, these organisations contribute to preventing and detecting illicit financial activities.
 The methodological basis of the study involves a combination of qualitative research methods, a literature review, and an analysis of relevant documents and reports from international organisations. The author used the given methods to achieve the goal of the article: (1) literature review; (2) analysis of international organisation documents; (3) qualitative research methods, and (4) comparative analysis.
 Results. International organisations play a crucial role in setting global standards and guidelines for anti-money laundering and combating the financing of terrorism. These standards provide a framework for countries to develop their domestic legislation and establish effective financial monitoring systems. They provide extensive capacity-building support to countries, assisting them in implementing and enforcing financial monitoring standards. International organisations facilitate the exchange of information among countries, enabling them to identify and track illicit financial activities across borders.
 Conclusions. International organisations play a vital role in developing the financial monitoring system. They serve as platforms for cooperation, coordination, and information exchange among countries, helping to establish global standards, guidelines, and best practices. Their collective efforts contribute to preventing and detecting illicit financial activities, fostering integrity and stability in the global financial system.

On the formation and development of the State environmental monitoring system (State environmental monitoring)

The article talks about scientific and methodological issues of the development of state environmental monitoring. The authors have considered the structure of State environmental monitoring (State environmental monitoring) and the procedure for its implementation. The legal basis of state environmental monitoring systems.
 We present the work within the Framework of the national program of the unified system of state environmental monitoring (State environmental monitoring, SEM). The goals, objectives and procedure for the implementation of the SEM, its main subsystems.
 The scientists examined the role of the Ministry of Natural Resources and Ecology of the Russian Federation (Ministry of Natural Resources of Russia) and the Federal Service for Hydrometeorology and Environmental Monitoring (Roshydromet) in ensuring the functioning of state environmental monitoring and its information resources. The activities of the State Environmental Monitoring Data Fund (State environmental monitoring) and the Unified State Data Fund (USDF) are presented. The researchers have the work of the monitoring system using the state system of observations and control by Roshydromet of indicators of the content of pollutants in atmospheric air, surface waters, marine waters, soil, radiation monitoring, etc.
 The authors have considered the prospects for the development of state environmental monitoring in connection with the adoption by the Government of the Russian Federation of the State program "Environmental Protection" for 2012–2030.
 Ethics. This study did not require the conclusion of the Ethics Committee.

Understanding Enabling Factors for Community-Led Coral Reef Health Monitoring and Early Warning System through Participatory Action Research

Coral reefs are under pressure from climate change and various factors, putting them at risk of a decline in resilience. This heightened vulnerability increases the likelihood of reaching a tipping point with the next shock or stressor. Identifying early warning indicators for tipping points is crucial for proactive coral reef management. Therefore, this study aims to comprehend the coral reef health parameters considered important and feasible for collection by local stakeholders, as well as identify factors facilitating the implementation of a community-based monitoring and early warning system in a fishers-dominated community. The approach used was Participatory Action Research. The results reveal twelve priority parameters deemed necessary by the local community and feasible for collection by local stakeholders, including the local community, university, and non-governmental organization. The identified parameters are: coral bleaching, visibility, temperature, pH, dissolved oxygen, salinity, current, coral percent cover, fish community, macroalgal canopy cover and composition, benthic composition, and plankton. Interpretive structural model and MICMAC analysis show nine enabling factors supporting the development of a community-led coral reef health monitoring and early warning system. These factors include team motivation, training and team capacity, connection with government, connection with university, facilitation by NGOs, supporting regulations, operational funds, access to equipment, and operation and maintenance of equipment. Team motivation stands out as the most influential factor, with strong driving power and dependence, making it crucial to manage as actions on it will have ripple effects on other factors.

Energy Management Model for a Remote Microgrid Based on Demand-Side Energy Control

The internet of things is undergoing rapid expansion, transforming diverse industries by facilitating device connectivity and supporting advanced applications. In the domain of energy production, internet of things holds substantial promise for streamlining processes and enhancing efficiency. This research introduces a comprehensive monitoring and energy management model tailored for the University of Cuenca’s microgrid system, employing internet of things and ThingSpeak as pivotal technologies. The proposed approach capitalizes on intelligent environments and employs ThingSpeak as a robust platform for presenting and analyzing data. Through the integration of internet of things devices and sensors, the photovoltaic system’s parameters, including solar radiation and temperature, are monitored in real time. The collected data undergo analysis using sophisticated models and are presented visually through ThingSpeak, facilitating effective energy management and decision making. The developed monitoring system underwent rigorous testing in a laboratory microgrid setup, where the photovoltaic system is interconnected with other generation and storage systems, as well as the electrical grid. This seamless integration enhances visibility and control over the microgrid’s energy production. The results attest to the successful implementation of the monitoring system, highlighting its efficacy in improving the supervision, automation, and analysis of daily energy production. By leveraging internet of things technologies and ThingSpeak, stakeholders gain access to real-time data, enabling them to analyze performance trends and optimize energy resources. This research underscores the practical application of internet of things in enhancing the monitoring and management of energy systems with tangible benefits for stakeholders involved.

Brain response to errors in children who stutter

PurposeHeightened rates of social anxiety have been reported in adults who stutter (AWS), but it is unclear whether anxiety is heightened also in children who stutter (CWS). Objective neurophysiological responses such as the error-related negativity (ERN) have been associated with anxiety, and ERN was reported to be increased in AWS. In this study, we examined whether ERN and error positivity (Pe) are increased in CWS. We further characterized ERN associations with age and anxiety in CWS relative to children who do not stutter (CWNS). MethodsEEG data were recorded from twenty-four CWS and twenty-four matched CWNS aged 3–9 years as they performed a Go/No-Go task. Parent-reported anxiety, and child-reported speech-associated attitude measures were collected. Linear regression models tested the effects of age, group, and their interaction, and the effects of anxiety, group, and their interaction on ERN and Pe. ResultsContrary to expectations, no ERN or Pe difference were observed between CWS and CWNS. However, larger ERN amplitudes were associated with older age in CWS but not CWNS, suggesting altered development of the error monitoring system in CWS. Association of Pe with anxiety also differed between groups: smaller Pe amplitudes were associated with higher level of parent-reported child anxiety in CWNS but not in CWS. Neither anxiety nor self-reported communication attitude differed between groups. ConclusionsBrain responses to errors were overall comparable between CWS and CWNS. However, CWS differed in how error monitoring responses varied with age and with anxiety levels. More research is warranted to examine how these factors contribute to persistent stuttering.

Design and Development of A Digital Soil Temperature Monitoring System Based on The Internet of Things at North Sumatra Climatological Station

Soil temperature is a crucial parameter in monitoring and understanding climate and soil ecosystems. It plays a vital role in various environmental aspects, including agriculture, ecology, and geoscience. Monitoring soil temperature is necessary for planning and managing agriculture and natural resources. Currently, temporal observations of soil temperature by BMKG are limited, conducted only at 07:55, 13:55, and 18:55 local time. This limitation makes it difficult to perform detailed soil temperature analysis. This research was conducted to design a digital soil temperature monitoring device accessible via the internet. Seven DS18B20 sensors were used at depths of 0 cm, 2 cm, 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm, combined with an ESP8266 module using the Arduino system. The implementation of this design resulted in a real-time soil temperature monitoring system with data updates every 10 seconds. The observed data are displayed on a 20x4 LCD and sent to the cloud, making them accessible on the webpage http://monitoringsuhutanah.my.id. Calibration results indicate that the DS18B20 sensors used in this study provide accurate and consistent temperature measurements, with an average correction range of (-0.20) to 0.24, thus suitable for operational use. Field tests show that the digital data are accurate and correspond (linearly correlate) with conventional data. This is based on a correlation value of 0.7, while the RMSE values range from 0.5 to 2.18 and the bias ranges from (-0.69) to 0.08.

Innovative Risk Management: Identification, Assessment and Management of Risks in the Context of Innovative Project Management

"The article aims to solve the critical problem of risk management in the context of innovative projects, which affects the competitiveness and sustainability of organizations in the modern business environment. The purpose of the article is to highlight and develop an innovative approach to risk management in innovative projects to improve their success. The article examines the theoretical foundations of risk management in the context of innovative projects, including concepts, principles, and methods of risk management. The article identifies and analyzes the key risks that arise in the process of implementing innovative projects, including technological, financial, market and organizational risks. The article proposes the development of risk assessment methods and tools in the context of innovative projects, which may include the use of analytical methods, models, and approaches to assess the probability of risk occurrence and their potential impact on projects. The article offers effective methods of risk management in innovative projects the development of proactive measures to prevent risks, the determination of control points, the implementation of backup plans, and the development of a risk monitoring and control system. The article provides practical recommendations and examples of implementing innovative risk management in the real field of innovative project management. The authors examine various aspects of risk management and offer practical recommendations for managing them effectively. The results of the study will contribute to the improvement of risk management practices in organizations engaged in innovative activities and contribute to ensuring sustainable development and competitiveness."

Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.

A Short Review on Machine Learning in Space Science and Exploration

Machine learning is revolutionizing space exploration by tackling massive datasets, empowering astronauts, and driving scientific breakthroughs. From Deep Space 1's autonomous navigation to the James Webb Space Telescope's AI-assisted exoplanet discovery, Machine learning is transforming the present and shaping the future. With missions like NASA's Parker Solar Probe and the development of AI-powered monitoring systems and astro robots, the possibilities for unravelling the cosmos and democratizing space exploration are limitless. The future of space exploration lies in harnessing the power of ML to unlock the universe's secrets and make them accessible to all.

Tool wear segmentation in blanking processes with fully convolutional networks based digital image processing

The extend of tool wear significantly affects blanking processes and has a decisive impact on product quality and productivity. For this reason, numerous scientists have addressed their research to wear monitoring systems in order to identify or even predict critical wear at an early stage. Existing approaches are mainly based on indirect monitoring using time series, which are used to detect critical wear states via thresholds or machine learning models. Nevertheless, differentiation between types of wear phenomena affecting the tool during blanking as well as quantification of worn surfaces is still limited in practice. While time series data provides partial insights into wear occurrence and evolution, direct monitoring techniques utilizing image data offer a more comprehensive perspective and increased robustness when dealing with varying process parameters. However, acquiring and processing this data in real-time is challenging. In particular, high dynamics combined with increasing strokes rates as well as the high dimensionality of image data have so far prevented the development of direct image-based monitoring systems. For this reason, this paper demonstrates how high-resolution images of tools at 600 spm can be captured and subsequently processed using semantic segmentation deep learning algorithms, more precisely Fully Convolutional Networks (FCN). 125,000 images of the tool are taken from successive strokes. Selected images are labeled pixel by pixel according to their wear condition and used to train a FCN (U-Net). The approach presented offers the possibility of spatially monitoring wear in high-speed blanking operations in real-time.

Water Quality Control System Based on Web Application for Monitoring Shrimp Cultivation in Sidoarjo, East Java

Shrimp farming plays a crucial role to the Indonesian economy, but it is facing challenges from shifting weather patterns and global warming. This research focuses on the development and implementation of a web-based water quality monitoring system for shrimp farming to address these concerns. The research, conducted in collaboration with shrimp farmers in Sidoarjo, East Java, introduces PENS Aquaculture program, which is designed to efficiently monitor pH, salinity, and temperature. The system employs Internet ofThings (IoT) technology, which allows farmers to register several ponds, analyze water parameters, and receive real-time data through tables and graphs. The research takes a mixed-methods approach, integrating quantitative data from IoT devices with qualitative insights gathered through surveys and interviews with shrimp farmers. The study aims to evaluate the influence of IoT technology on shrimp pond quality and its contribution to the production. The findings show that PENS Aquaculture application is helpful in increasing shrimp farming efficiency, providing significant insights for the fisheries and cultural sectors.

Development of A Tool Condition Monitoring System for Flank Wear in Turning Process Using Machine Learning

Computer-numerical control (CNC) machining has become the norm in most manufacturing businesses. In order to maximize machining efficiency, prevent unintended damage, and maintain product quality all at once, tool wear measurement is essential. Wear on the tools is typically an inevitable side effect of machining. Because it endangers the machining process, flank wear, the most frequent type of tool wear, should be avoided. This study's objective is to fill this growing need by developing a system that can use machine learning to monitor tool condition during the turning process using MATLAB. The regression method with boosted decision trees and SVR with Gaussian kernels are applied to predict the flank wear based on the vibration signal and cutting parameters. This study found that the regression with boosted decision tree method has a lower mean average percentage error, 6.43%, while SVR is 10.11%. Plus, R2 for regression is slightly better than SVR. It shows that the system successfully produced an accurate prediction of flank wear.

ML-Based Identification of Neuromuscular Disorder Using EMG Signals for Emotional Health Application

Abstract: The electromyogram (EMG), also known as an EMG, is used to assess nerve impulses in motor nerves, sensory nerves, and muscles. EMS is a versatile tool used in various biomedical applications. It is commonly employed to determine physical health, but it also finds utility in evaluating emotional well-being, such as through facial electromyography. Classification of EMG signals has attracted the interest of scientists since it is crucial for identifying neuromuscular disorders (NMDs). Recent advances in the miniaturization of biomedical sensors enable the development of medical monitoring systems. This paper presents a portable and scalable architecture for machine learning modules designed for medical diagnostics. In particular, we provide a hybrid classification model for NMDs. The proposed method combines two supervised machine learning classifiers with the discrete wavelet transform (DWT). During the online testing phase, the class label of an EMG signal is predicted using the classifiers’ optimal models, which can be identified at this stage. The simulation results demonstrate that both classifiers have an accuracy of over 98%. Finally, the proposed method was implemented using an embedded CompactRIO-9035 real-time controller.

Development of a Three-dimensional Stress Monitoring System for Coal Rock and its Application in a Mine

Development of a Three-dimensional Stress Monitoring System for Coal Rock and its Application in a Mine

Carbon dioxide and trace oxygen concentrations impact growth and product formation of the gut bacterium Phocaeicola vulgatus

BackgroundThe promising yet barely investigated anaerobic species Phocaeicola vulgatus (formerly Bacteroides vulgatus) plays a vital role for human gut health and effectively produces organic acids. Among them is succinate, a building block for high-value-added chemicals. Cultivating anaerobic bacteria is challenging, and a detailed understanding of P. vulgatus growth and metabolism is required to improve succinate production. One significant aspect is the influence of different gas concentrations. CO2 is required for the growth of P. vulgatus. However, it is a greenhouse gas that should not be wasted. Another highly interesting aspect is the sensitivity of P. vulgatus towards O2. In this work, the effects of varying concentrations of both gases were studied in the in-house developed Respiratory Activity MOnitoring System (RAMOS), which provides online monitoring of CO2, O2, and pressure under gassed conditions. The RAMOS was combined with a gas mixing system to test CO2 and O2 concentrations in a range of 0.25-15.0 vol% and 0.0-2.5 vol%, respectively.ResultsChanging the CO2 concentration in the gas supply revealed a CO2 optimum of 3.0 vol% for total organic acid production and 15.0 vol% for succinate production. It was demonstrated that the organic acid composition changed depending on the CO2 concentration. Furthermore, unrestricted growth of P. vulgatus up to an O2 concentration of 0.7 vol% in the gas supply was proven. The viability decreased rapidly at concentrations larger than or equal to 1.3 vol% O2.ConclusionsThe study showed that P. vulgatus requires little CO2, has a distinct O2 tolerance and is therefore well suited for industrial applications.