Development Of Monitoring System Research Articles

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.

Acquisition Devices for Fetal Phonocardiography: A Scoping Review.

Timely and reliable fetal monitoring is crucial to prevent adverse events during pregnancy and delivery. Fetal phonocardiography, i.e., the recording of fetal heart sounds, is emerging as a novel possibility to monitor fetal health status. Indeed, due to its passive nature and its noninvasiveness, the technique is suitable for long-term monitoring and for telemonitoring applications. Despite the high share of literature focusing on signal processing, no previous work has reviewed the technological hardware solutions devoted to the recording of fetal heart sounds. Thus, the aim of this scoping review is to collect information regarding the acquisition devices for fetal phonocardiography (FPCG), focusing on technical specifications and clinical use. Overall, PRISMA-guidelines-based analysis selected 57 studies that described 26 research prototypes and eight commercial devices for FPCG acquisition. Results of our review study reveal that no commercial devices were designed for fetal-specific purposes, that the latest advances involve the use of multiple microphones and sensors, and that no quantitative validation was usually performed. By highlighting the past and future trends and the most relevant innovations from both a technical and clinical perspective, this review will represent a useful reference for the evaluation of different acquisition devices and for the development of new FPCG-based systems for fetal monitoring.

Structural health monitoring of an onshore steel wind turbine

The study presents the development of a structural monitoring system installed in a 45-m-high steel wind tower located in Italy. The installed monitoring system was composed by 16 strain gauges placed in the tower wall, in a pattern of four Wheatstone bridges at 45°, together with thermal couples, at 21 m from the ground (half-height of the tower). Moreover, several accelerometers were placed along the tower height (with one of them located next to the strain gauges). The wind velocity and directions were obtained directly from the turbine own monitoring system. Such a monitoring system was designed because, due to the decrement of the total height from the original design, the tower suffers from resonance problems. In fact, the investigated tower was originally designed with 65 m of height but then, to comply with local regulations, the height was decreased to the actual size. Therefore, to allow safe operation and avoid excessive fatigue due to the increased displacements, the velocity of the rotor has been manually limited causing an important reduction in the energy production. The results of the study show the importance of monitoring the resonance issue. The differences between the damage indexes obtained with two different working conditions are discussed: tower working with limited operational capacity and tower working at its maximum capacity (in resonance).

IoT-enabled Smart Weather Stations: Innovations, Challenges, and Future Directions

The evolution of the Internet of Things (IoT) has ushered in innovative approaches facilitated by breakthrough technologies. This paper presents a comprehensive review of recent advancements in smart weather stations, focusing on IoT-enabled solutions. The integration of Internet of Things (IoT) technologies has revolutionized traditional weather monitoring systems, enabling seamless data collection, analysis, and dissemination. Commercially available automated weather stations offer cost-effective solutions for comprehensive meteorological data collection. However, challenges such as limited local deployment and reliance on expensive options persist, hindering comprehensive monitoring efforts. The critical need for improved data collection methods is underscored to enhance the accuracy of weather forecasts and address evolving climatic conditions. Climate change impacts, including shifts in weather patterns and rising temperatures, highlight the importance of effective weather monitoring for agriculture, infrastructure, and national security. Additionally, the dependence on non-renewable energy sources for electricity generation emphasizes the environmental and economic implications of energy production. In response to these challenges, numerous IoT based smart weather station systems have been proposed by earlier researchers. The introduction of IoT-enabled smart weather stations represents a significant advancement in weather monitoring technology. These stations leverage IoT technologies to collect, analyse, and visualize meteorological data in real time. In order to identify the challenges and prospects in this area of technology, the purpose of this study is to present a thorough analysis of the suggested designs as well as to compare, evaluate, and assess the outcomes, contributing to the development of robust and efficient weather monitoring systems.

Development and evaluation of an artificial intelligence for bacterial growth monitoring in clinical bacteriology.

In clinical bacteriology laboratories, reading and processing of sterile plates remain a significant part of the routine workload (30%-40% of the plates). Here, an algorithm was developed for bacterial growth detection starting with any type of specimens and using the most common media in bacteriology. The growth prediction performance of the algorithm for automatic processing of sterile plates was evaluated not only at 18-24 h and 48 h but also at earlier timepoints toward the development of an early growth monitoring system. A total of 3,844 plates inoculated with representative clinical specimens were used. The plates were imaged 15 times, and two different microbiologists read the images randomly and independently, creating 99,944 human ground truths. The algorithm was able, at 48 h, to discriminate growth from no growth with a sensitivity of 99.80% (five false-negative [FN] plates out of 3,844) and a specificity of 91.97%. At 24 h, sensitivity and specificity reached 99.08% and 93.37%, respectively. Interestingly, during human truth reading, growth was reported as early as 4 h, while at 6 h, half of the positive plates were already showing some growth. In this context, automated early growth monitoring in case of normally sterile samples is envisioned to provide added value to the microbiologists, enabling them to prioritize reading and to communicate early detection of bacterial growth to the clinicians.

Multi-open-path laser dispersion spectroscopy combined with Bayesian state estimation for localizing and quantifying methane emissions

A global effort towards improved quantitative understanding of greenhouse gas emissions is taking pace. This includes developing source identification, quantification, and apportionment in an attempt to understand global budget and trends, but also developing monitoring systems making emission reduction commitment verifiable. In this context, we demonstrate a novel approach to continuous methane emission monitoring at the spatial scale of an industrial facility. By combining multi-directional measurements of path-integrated methane concentrations with Bayesian state estimation, we show a realistic tomographic gas plume reconstruction, its evolution in time, and the associated estimation of the source map. The method is validated using measurements from controlled methane releases over a domain of area 120×40 m2. For the first demonstration, a two dimensional geometry has been used in the gas flow model; nevertheless, sources are located within 3–12 meters, and mass emission rates are estimated within <30% for 80% of the cases.

Photosvaltable Characteristics Monitoring System Using the Internet of Things (IOT)

&#x0D; &#x0D; &#x0D; &#x0D; Continuous monitoring of solar panel performance is essential to ensure effective and efficient generation of electrical energy. Developing a monitoring system such as an I-V characteristic curve tracker enables real-time monitoring and identification of potential problems or efficiency improvements. The system utilizes a circuit switching system for accurate and continuous measurement, providing insight into how the solar panel responds to changing environmental and load conditions. The data recorded by temperature, light intensity, current, and voltage sensors on the solar panel provides valuable information about its condition and the environmental factors affecting it. Utilizing IoT technology allows real-time monitoring of the data, accessible via a web page and presented graphically. The data is also stored in Excel format for further analysis, so users can access and monitor continuously as long as they are connected to the internet. From the experimental results, the Voc of the solar panel ranged from 18.7 V to 20.42 V, and Isc between 0.0727 A to 0.2145 A, which were tested under solar irradiation ranging from 100 to 500 W/m². This research demonstrates the importance of continuous monitoring and the effectiveness of the developed monitoring system in providing real-time data for analysis and optimization of solar panel performance. The system has been tested to meet the IEC No. 13B-23 standard for accuracy classes 0.2 and 0.5. A comparison between manual testing and sensor testing shows that despite the difference in accuracy levels, the sensor measurements have a relatively low average error (1.04% for voltage and 0.64% for current) compared to manual measurements, indicating that the sensor is reliable in providing accurate data. This demonstrates the reliability and effectiveness of using the sensor in the solar panel characteristics monitoring system.&#x0D; &#x0D; &#x0D; &#x0D;

Development and implementation of real-time staff performance monitoring and evaluation system based on Global Positioning System (GPS)

This work presents the development and implementation of a Real-Time Staff Performance Monitoring and Evaluation System (RT-SPMES) using Global Positioning System (GPS) technology. In line with the dynamism of the workplace, due to rapid changes in technology, existing staff performance appraisal systems have fallen short of expectations due to their manual nature. Hence, the need arises for a more robust approach to monitoring and evaluating employee work performance. To achieve this, this work carried out an exhaustive review of existing performance evaluation methods within the academic and a conceptual architecture was developed. A real-time monitoring and evaluation system was developed, using PHP programming language, Hyper Text Markup Language and Cascading Styles Sheet. The system is hosted on Azure Cloud infrastructure. Through sufficient testing of the developed system developed in this work, it was asserted effectively monitors employee performance in real-time, paving the way for an objective appraisal process devoid of bias and manipulation.

Smart spirits: an integrated IoT-based liquor and health monitoring system

This project entails the development of a comprehensive health monitoring system utilizing Arduino Uno, a range of sensors (including alcohol, pulse, and DHT11), an LCD display, and a GSM module for data transmission to the Thingspeak application. Its objective is to monitor vital health parameters such as temperature, humidity, pulse rate, and alcohol levels in real-time, accessible via the Thingspeak mobile app. Key components include an Arduino Uno microcontroller, DHT11 sensor for temperature and humidity, pulse sensor for heart rate, alcohol sensor for detecting alcohol levels, LCD display for immediate feedback, and a GSM module for remote data transmission. The user-friendly Thingspeak app serves as an interface for seamless health metric monitoring. The system operates by collecting sensor data, displaying it locally on the LCD screen, and simultaneously transmitting it to the Thingspeak app via GSM communication. Users can then access the app to visualize and monitor their health metrics in real-time, allowing for prompt interventions or medical assistance if needed. The code implementation integrates sensor readings, LCD display control, and Thingspeak functionality. Leveraging Thingspeak's cloud-based platform enhances usability and accessibility, making remote health monitoring feasible. This system offers a versatile solution for individuals looking to efficiently track and manage their well-being, thanks to the integration of Arduino Uno, sensors, Thingspeak features, and GSM communication, making it suitable for both local and remote health monitoring applications.

Advancements in Mobile-Based Air Pollution Detection: From Literature Review to Practical Implementation

The aim of this paper is to investigate the effectiveness of combining mobile technologies and sensors to detect harmful particles in the air and address the problem of air pollution caused by traffic and exhaust emissions. The paper contains a systematic literature review of information technology research related to pollution detection in order to point out the main obstacles in the field and propose solutions. Furthermore, the paper presents the development of an Android smartphone-based real-time monitoring system that utilizes an external analog sensor board to acquire and evaluate physical measurements. The proposed system is calibrated for CO gas measurements, and it is compared with a commercial gas analyzer instrument. Results imply that the developed system is capable of detecting concentration levels in the air and that the accuracy is within the range of the industrial device’s accuracy.

Web-Based Monitoring and Synchronization of Lecturers SKS Information System (Case Study of Muhammadiyah University Cirebon)

Institutions of higher education are increasingly embracing technology to enhance administrative processes and improve efficiency. This study presents a case study conducted at Muhammadiyah University Cirebon, focusing on the development and implementation of a web-based monitoring and synchronization system for lecturer SKS (Sistem Kredit Semester) information. The system aims to streamline the process of monitoring lecturer workload and credit distribution, thereby optimizing academic resource allocation. Through a combination of qualitative and quantitative research methods, including interviews, surveys, and system evaluation, this study examines the effectiveness and user satisfaction of the implemented system. Findings indicate significant improvements in administrative efficiency, transparency, and data accuracy. Moreover, feedback from users demonstrates a high level of satisfaction with the system's usability and functionality. The study contributes to the body of knowledge on the application of web-based systems in academic institutions and provides insights for similar implementations in other contexts.

Development of Low-cost Wireless Monitoring System and Its Implementation on Computer Numerical Control Machine Tool

Development of Low-cost Wireless Monitoring System and Its Implementation on Computer Numerical Control Machine Tool

Advanced Monitoring for Auxiliary Transformer Failures and Alerting System

Auxiliary transformers are critical components in power distribution systems, and their failure can lead to costly downtime and potential damage to electrical infrastructure. This paper proposes the development of a real-time alert system will be designed to monitor auxiliary transformer health and promptly detect anomalies and failures. The development of a transformer health monitoring system involves the utilization of Arduino-based embedded systems to interface with temperature, ultrasonic, and current sensors. The system aims to monitor essential parameters such as temperature, oil level, and current values. In case of abnormalities, it triggers immediate alerts through GSM messaging and implements protective measures by controlling the load using relays and a buzzer for audible alerts. Key Words: Auxiliary Transformer, GSM Technology, Arduino, Temperature Sensor, Ultrasonic Sensor, Current Sensor.

Belt rotation in pipe conveyors: Development of an overlap monitoring system using digital twins, industrial Internet of things, and autoregressive language models

Belt rotation is a critical failure mode in pipe conveyors. However, overlap position monitoring remains unexplored. Existing monitoring solutions are expensive and lack scalability, primarily relying on detecting angle variations beyond predefined thresholds. We developed a digital twin of a low-cost test rig for measuring belt rotation using 3D prototyping, infrared distance sensors, and a 3-axis accelerometer. We implemented a controller-responder industrial Internet of Things network using ESP-NOW protocol. Applying a minimum viable prompt, we fine-tuned an autoregressive language model for predicting the overlap position. We then developed a software application for cloud computing and local storage of measurements. Additionally, we developed a human–machine interface for future real-case applications. The bias and precision for the predicted angle (0.2294° ± 11.8822°), angular speed (0.1630 ± 0.7344 RPM), number of laps (0.0001336 ± 0.0448 laps), diameter (–0.1984 ± 6.3743 mm), and outer-edge side (100 % agreement) were suitable for the intended use.

Multiple-state pattern encrypted superhydrophobic-icephobic colorimetric sensing film for anticounterfeiting and food freshness monitoring

Exploring high-sensitive and convenient anticounterfeiting as well as food freshness monitoring technologies to combat food safety is urgently needed but remains a great challenge. Herein, the color dynamic response and transparency encryption features of colorimetric label with heteronetwork structure can effectively cope with the above problems. The colorimetric label demonstrates excellent superhydrophobic with water contact angle of 156.2° and roll-off angle of 8.9°, and icephobic that delays the freezing time of 40 μL water droplets from 38 s to 162 s and reduces the bonding strength of ice from 249.00 kPa to 15.20 kPa. Furthermore, the colorimetric label exhibits a colorimetric shift of 126 nm and a detection limit of 0.8 ppm for NH3, making it successfully applicable for shrimp freshness monitoring. Notably, a locking encryption system featuring multiple logics can be achieved by combining the colorimetric properties and transparency changes of the colorimetric tags. This work proposes an ingenious, cost-effective, and convenient dual system for anticounterfeiting and food freshness monitoring, which inspired the development of an intelligent monitoring system for food quality, and has significant implications for enhancing food safety.

INTELLIGENT MONITORING SYSTEM FOR REAL-TIME OPTIMIZATION OF ULTRA-PRECISION MANUFACTURING PROCESSES

In the realm of ultra-precision manufacturing, the minutiae of process control and material handling are paramount to achieving the highest levels of product quality and manufacturing efficiency. The industry faces a significant challenge: maintaining and enhancing the precision of manufacturing processes in real-time to ensure optimal output quality while minimizing waste and energy consumption. This challenge is compounded by the increasing complexity of products and the materials used, requiring ever more precise and adaptive manufacturing techniques. The importance of addressing this challenge cannot be overstated, as it directly impacts the competitiveness, sustainability, and innovation capacity of manufacturing firms. The development of an intelligent monitoring system that integrates advanced precision metrology techniques presents a promising solution to this problem. Such a system would enable real-time optimization of manufacturing processes, leveraging cutting-edge technologies and data analytics to dynamically adjust parameters for optimal performance. By doing so, it would not only enhance the precision and efficiency of manufacturing operations but also contribute significantly to sustainable manufacturing practices by reducing waste and energy consumption. This concept paper proposes the development of this intelligent monitoring system, outlining its potential impact on the ultra-precision manufacturing industry.&#x0D; Keywords: Ultra Precision, Monitoring, Processing, Real Time.

Smart Industrial Safety Monitoring and Alert System using Raspberry Pi

The rapid advancement of Internet of Things (IoT) technology has enabled the development of systems for automatic monitoring and alerting in industrial settings. Industrial accidents, such as gas leaks or fires, can lead to significant setbacks. To address this, we propose a system utilizing IoT concepts to monitor industrial applications and generate alerts or alarms to prevent mishaps. Our system employs flame and gas sensors to detect fires and gas leaks, along with other sensors such as temperature detectors, PIR sensors for trespass detection, and current and voltage monitoring. Data from these sensors are continuously scanned and transmitted online for real-time monitoring. The system, implemented using Raspberry Pi and interfaced with an LCD screen, provides a comprehensive approach to industrial safety, aiming to reduce both human and economic losses. Key Words: Internet of Things (IoT), industrial safety, sensors, Raspberry Pi, fault monitoring, Wi-Fi module