Real-time Monitoring System Research Articles

Investigation of Fiber Bragg Grating Sensor Measurability in Concrete Beams under Static Load Conditions

Recent advances in construction materials and architecture have enhanced the safety of modern structures, which now feature higher, lighter, and more unique designs. Bridges, as critical infrastructure, face increasing load demands due to rising traffic volumes, leading to accelerated structural deterioration. This necessitates more frequent inspections and maintenance, resulting in increased costs and potential operational disruptions. Real-time built-in monitoring systems are thus essential for both new and aging structures. Fiber Optic Sensor (FOS) technology, particularly Fiber Bragg Grating (FBG) sensors, offers a reliable and stable solution for long-term Structural Health Monitoring (SHM). This study evaluates the performance and applicability of FBG sensors (FBGs) in comparison to traditional strain gauges (SGs) for capturing strains, loads, deflections, and temperature in full-scale beams under static loads. Four large-scale reinforced concrete (RC) and CFRP-strengthened beams were tested. Nonlinear finite element analysis (NLFEA) using VecTor2 was employed to simulate the behavior of these beams under static loading. Data from FBGs were compared to those from collocated electrical SGs. The results demonstrated the reliability and flexibility of FBGs in monitoring structural performance. Notably, FBGs continued to provide accurate readings even after the specimens reached their maximum load, showcasing their endurance and resilience compared to conventional SGs.

Signal conditioning system for real-time monitoring of unbalanced mass and angular position in helicopter propeller rotor balancing

This paper outlines the design of a signal conditioning system developed to enable real-time monitoring of unbalanced mass and rotor angular position on a horizontal balancing machine. The system incorporates piezoelectric sensors with high sensitivity and a wide measurement range, allowing them to detect small variations in acceleration with great accuracy. Therefore, the proposed signal conditioning system relies on a lock-in amplifier, which can measure small signals even in the presence of considerable noise. The system achieved an error rate of 1% or less in the mass balance of a rotor at different speeds. These results revealed a consistent linear relationship between the unbalanced mass and the rotor velocity, which simplifies the interpretation and analysis of the data, allowing any variation in the signal to be directly related to changes in the unbalanced mass or rotor velocity, without any complications due to nonlinear effects.

Hybrid Duplex Medium Access Control Protocol for Tsunami Early Warning Systems in Underwater Networks

Tsunamis are devastating natural phenomena that cause extensive damage to both human life and infrastructure. To mitigate such impacts, tsunami early warning systems have been deployed globally. South Korea has also initiated a project to install a tsunami warning system to monitor its surrounding seas. To ensure reliable warning decisions, various types of data must be combined, but efficiently transmitting heterogeneous data poses a challenge due to the unique characteristics of underwater acoustic communication. Therefore, this paper proposes a Hybrid Duplex Medium Access Control (HDMAC) protocol designed for a tsunami warning system, with a specific focus on heterogeneous data transmission. HDMAC efficiently handles both seismic and environmental data by utilizing hybrid duplexing, which combines frequency duplex for seismic data with time duplex for environmental data. The protocol addresses the distinct transmission requirements for each data type by optimizing channel utilization through a group Automatic Repeat request (ARQ) scheme and packet size adjustment. Theoretical analysis predicts that HDMAC can achieve a channel utilization of up to 0.91 in smaller networks and 0.64 in larger networks. HDMAC is validated through simulations, and the simulation results closely match these predictions. The simulation results demonstrate the efficiency of HDMAC in supporting real-time submarine earthquake monitoring systems.

Design of a Real-Time Monitoring and Early Warning System for Engineering Safety Hazards Using Image Analysis Technology

Design of a Real-Time Monitoring and Early Warning System for Engineering Safety Hazards Using Image Analysis Technology

Raspberry Pi based IOT Weather Station

This paper presents a real-time system for monitoring and updating weather conditions online. The system tracks three key parameters: temperature, humidity, and rainfall. These values are displayed on an LCD screen and stored in a database. When the area is dry, the rainfall reading shows zero. If raindrops are detected, the rainfall value updates accordingly. As the temperature changes, it also reflects on the display. Users can check the weather status of a specific area from anywhere. We use an ARM-based Raspberry Pi 3 board, running the Raspbian operating system with a Linux kernel. The programming is done in Python, which is supported by the IDLE environment. This system provides users with a clear understanding of local weather conditions, making it a valuable tool for monitoring the climate in a specific area.

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

무인 점포 실시간 이상 행동 감지를 위한 Transformer 기반지능형 CCTV 시스템

The rapid expansion of unmanned retail stores has raised critical security concerns, thereby necessitating the development and implementation of robust protective measures. The absence of real-time monitoring systems in these environments has heightened the vulnerability to risks such as theft and property damage. Although closed-circuit television (CCTV) systems have been deployed to retrospectively investigate criminal activities, these systems are often insufficient in preventing incidents. This study introduces a Transformer-based intelligent CCTV system designed for the real-time detection of anomalous behaviors within unmanned retail environments. Unlike conventional systems that rely on basic machine learning models, our proposed system leverages human joint position data extracted from CCTV footage to classify a range of anomalous behaviors, including theft, falls, and property damage. Additionally, extensive hyperparameter optimization was performed to maximize the model's effectiveness in these specific environments. Our System enhances the system's usability by enabling real-time identification of anomalous behavior, complete with location data, timestamps, and corresponding video frame sequences.

Artificial Neural Network-based Model for Predicting Cardiologists’ Over-apron Dose in CATHLABs

Abstract Aim: The radiation dose that cardiologists receive in the catheterization laboratory is influenced by various factors. Handling high-stress tasks in interventional cardiology departments may cause physicians to overlook the use of dosimeters. Therefore, it is essential to develop a model for predicting cardiologists’ radiation exposure. Materials and Methods: This study developed an artificial neural network (ANN) model to predict the over-apron radiation dose received by cardiologists during catheterization procedures, using dose area product (DAP) values. Leveraging a validated Monte Carlo simulation program, we generated data from simulations with varying spectra (70, 81, and 90 kVp) and tube orientations, resulting in 125 unique scenarios. We then used these data to train a multilayer perceptron neural network with four input features: DAP, energy spectrum, tube angulation, and the resulting cardiologist’s dose. Results: The model demonstrated high predictive accuracy with a correlation coefficient (R-value) of 0.95 and a root mean square error (RMSE) of 3.68 μSv, outperforming a traditional linear regression model, which had an R-value of 0.48 and an RMSE of 18.15 μSv. This significant improvement highlights the effectiveness of advanced techniques such as ANNs in accurately predicting occupational radiation doses. Conclusion: This study underscores the potential of ANN models for accurate radiation dose prediction, enhancing safety protocols, and providing a reliable tool for real-time exposure assessment in clinical settings. Future research should focus on broader validation and integration into real-time monitoring systems.

A Support Vector Machine-Based Intelligent System for Real-Time Structural Health Monitoring of Port Tower Cranes

A Support Vector Machine-Based Intelligent System for Real-Time Structural Health Monitoring of Port Tower Cranes

A programmable platform for probing cell migration and proliferation.

The advent of advanced robotic platforms and workflow automation tools has revolutionized the landscape of biological research, offering unprecedented levels of precision, reproducibility, and versatility in experimental design. In this work, we present an automated and modular workflow for exploring cell behavior in two-dimensional culture systems. By integrating the BioAssemblyBot® (BAB) robotic platform and the BioApps™ workflow automater with live-cell fluorescence microscopy, our workflow facilitates execution and analysis of in vitro migration and proliferation assays. Robotic assistance and automation allow for the precise and reproducible creation of highly customizable cell-free zones (CFZs), or wounds, in cell monolayers and "hands-free," schedulable integration with real-time monitoring systems for cellular dynamics. CFZs are designed as computer-aided design models and recreated in confluent cell layers by the BAB 3D-Bioprinting tool. The dynamics of migration and proliferation are evaluated in individual cells using live-cell fluorescence microscopy and an in-house pipeline for image processing and single-cell tracking. Our robotics-assisted approach outperforms manual scratch assays with enhanced reproducibility, adaptability, and precision. The incorporation of automation further facilitates increased flexibility in wound geometry and allows for many experimental conditions to be analyzed in parallel. Unlike traditional cell migration assays, our workflow offers an adjustable platform that can be tailored to a wide range of applications with high-throughput capability. The key features of this system, including its scalability, versatility, and the ability to maintain a high degree of experimental control, position it as a valuable tool for researchers across various disciplines.

BIG DATA AND CHRONIC DISEASE MANAGEMENT THROUGH PATIENT MONITORING AND TREATMENT WITH DATA ANALYTICS

This systematic review investigates the role of predictive analytics in chronic disease management, focusing on its capacity to predict disease progression, reduce hospitalizations, and enhance patient outcomes. A total of 35 peer-reviewed articles were analyzed to evaluate how predictive models, utilizing data from electronic health records (EHRs), wearable devices, and real-time monitoring systems, are applied in managing chronic diseases such as diabetes and cardiovascular conditions. The findings indicate that predictive models have significantly improved early disease detection accuracy, with studies showing improved forecasting of heart failure exacerbations and notable reductions in hospital readmissions due to timely interventions. Additionally, many studies reported a reduction in healthcare utilization through predictive analytics-driven early interventions. However, challenges regarding data quality and model accuracy were frequently cited, particularly concerning data integration and harmonization across various healthcare systems. Ethical and privacy concerns, including data security and algorithmic bias, were also highlighted, underscoring the need to address these issues for responsible and equitable use of predictive analytics. This review affirms the growing impact of predictive analytics in chronic disease management, while calling for advancements in data management and ethical frameworks to maximize its potential.

Employee’s Security and Health Augmentation in the Hotel Sector

The vibrant and quick-paced environment that defines the hotel sector is defined by a strong emphasis on employee welfare. This study examines the various aspects of worker health and safety in the context of the hospitality sector with the goal of identifying problems, prevalent behaviors, and appropriate remedial measures. The study uses a mixed-methods approach, combining quantitative analysis of safety event data with qualitative interviews with important industry players. In-depth interviews with hotel managers, staff members, and safety specialists are conducted as part of the qualitative component to elucidate the myriad difficulties faced by hotel staff in upholding their health and safety. The effects of irregular work schedules and sleep patterns, workplace stress, and ergonomic considerations all emerge as major themes. The study also examines how well the current training and safety practices work, looking for flaws and potential improvement areas. The study examines historical safety event data from a wide range of hotels on the quantitative front. The goal of this analysis is to spot trends, prevalent accident causes, and geographic regions with a higher prevalence of safety concerns. The results offer a data-driven framework for creating focused interventions and preventive actions. The research also examines how technology can support initiatives to increase employee safety in hotels. We investigate the possibilities for real-time monitoring systems, wearable technology, and enhanced training simulations to improve safety awareness and reactivity. A series of suggestions for enhancing employee health and safety in the hotel business are provided as part of the study's conclusion. The incorporation of technical solutions, specialized training programs, and changes to policy are all included in these suggestions. The hotel business may cultivate a safer and healthier work environment by resolving the issues mentioned, enhancing overall employee well-being and assisting in the industry's long-term success.

Cybersecurity in Tanzanian Maritime Operations: Exploring Global Best Practices and Their Local Adaptation Using the Cybersecurity Capability Maturity Model (C2M2)

The increasing integration of digital technologies in maritime operations has significantly enhanced efficiency in cargo tracking, port management, and communication systems. However, this digital transformation also introduces substantial cybersecurity risks, particularly in developing regions like Tanzania, where technological infrastructure and specialized skills may lag behind global standards. This study evaluates the cybersecurity readiness of three key Tanzanian maritime organizations Tanzania Ports Authority (TPA), Tanzania Shipping Agency Corporation (TASAC), and SINOTASHIP using the Cybersecurity Capability Maturity Model (C2M2). Through a detailed assessment across ten C2M2 domains, the study identifies existing strengths and critical gaps in areas such as risk management, asset management, and incident response. The findings reveal that while basic cybersecurity practices exist, they are largely reactive, with most domains scoring between Level 1 (Initial) and Level 3 (Defined). This lack of advanced, proactive measures poses significant risks to Tanzania's maritime infrastructure, particularly given the strategic role of ports like Dar es Salaam in regional trade. The study highlights the need for tailored improvements, including enhanced asset management, continuous workforce training, and real-time monitoring systems, to bridge the gap between global standards and local practices. By implementing these measures, Tanzanian maritime operations can strengthen their resilience against cyber threats, ensuring secure and efficient port operations in an increasingly interconnected world.

Heart Rate and Oxygen Saturation Internet of Things System (HROS-IoT) Uses Fuzzy Logic

Emergencies in hospitals, including in-hospital cardiac arrest (IHCA), necessitate effective response systems for monitoring vital signs such as heart rate and oxygen saturation. This study aims to develop and evaluate a Heart Rate and Oxygen Saturation Internet of Things (HROS-IoT) system utilizing the MAX30102 sensor and ESP32 microcontroller for real-time health monitoring. The system transmits data to the Blynk application via WiFi, enabling remote monitoring. Testing involved comparing the HROS-IoT system's performance against the commercial LK87 oximeter in measuring heart rate and oxygen saturation before and after meals with five participants. Results indicated that the HROS-IoT system produced heart rate measurements with an average error of 5.2 BPM before meals and 11.3 BPM after meals. Oxygen saturation readings showed an average error of 1% before meals and after meals. Despite minor discrepancies influenced by individual physiological differences and environmental conditions, the HROS-IoT system consistently delivered reliable data. The system's real-time monitoring capability and remote data access enhance proactive health management in hospitals. This study demonstrates the potential of the HROS-IoT system to improve patient outcomes and safety, suggesting further refinements for better accuracy and integration into healthcare settings.

Design of Exterior Orientation Parameters Variation Real-Time Monitoring System in Remote Sensing Cameras

The positional accuracy of satellite imagery is essential for remote sensing cameras. However, vibrations and temperature changes during launch and operation can alter the exterior orientation parameters of remote sensing cameras, significantly reducing image positional accuracy. To address this issue, this article proposes an exterior orientation parameter variation real-time monitoring system (EOPV-RTMS). This system employs lasers to establish a full-link active optical monitoring path, which is free from time and space constraints. By simultaneously receiving star and laser signals with the star tracker, the system monitors changes in the exterior orientation parameters of the remote sensing camera in real time. Based on the in-orbit calibration geometric model, a new theoretical model and process for the calibration of exterior orientation parameters are proposed, and the accuracy and effectiveness of the system design are verified by ground experiments. The results indicate that, under the condition of a centroid extraction error of 0.1 pixel for the star tracker, the EOPV-RTMS achieves a measurement accuracy of up to 0.6″(3σ) for a single image. Displacement variation experiments validate that the measurement error of the system deviates by at most 0.05″ from the theoretical calculation results. The proposed EOPV-RTMS provides a new design solution for improving in-orbit calibration technology and image positional accuracy.

Estimation of Hydraulic and Water Quality Parameters Using Long Short-Term Memory in Water Distribution Systems

Predicting essential water quality parameters, such as discharge, pressure, turbidity, temperature, conductivity, residual chlorine, and pH, is crucial for ensuring the safety and efficiency of water supply systems. This study employs long short-term memory (LSTM) networks to address the challenge of capturing temporal dependencies in these complex processes. Our approach, using a robust LSTM-based model, has demonstrated significant predictive accuracy, as evidenced by substantial R-squared values (e.g., 0.86 for discharge and 0.97 for conductivity). These models have proven particularly effective in handling non-linear patterns and time-series data, which are prevalent in water quality metrics. The results indicate the potential for LSTMs not only to enhance the real-time monitoring of water systems but also to aid in the strategic planning and management of water supply systems. This study’s findings can serve as a basis for further research into the integration of AI in environmental engineering, particularly for predictive tasks in complex, dynamic systems.

Ultra-High Sensitivity Real-Time Monitoring of Landslide Surface Deformation via Triboelectric Nanogenerator.

Monitoring surface deformation is crucial for the early warning of landslides, facilitating timely preventive measures. Triboelectric nanogenerator (TENG) demonstrates great potential for self-powered distributed monitoring in remote and power-scarce landslide areas. However, landslides deform typically at a rate of a few millimeters per day (mm d-1), making it challenging for TENG to directly monitor the deformation process. Herein, a method for monitoring surface deformation of landslides by constructing an ultra-low-speed triboelectric displacement sensor (US-TDS) is reported. Utilizing a force storage-release device and an accelerator, the US-TDS can produce obvious sensing signals at a linear input speed of 4.32mm d-1. The coefficient of determination (R2) for the fitting curve of the pulse signals within the speed range of 21.6 to 129.6mm d-1 reaches 0.999. Moreover, US-TDS can detect deformation displacement as small as 0.0382mm. The stability of US-TDS displacement measurements is confirmed at a speed of 108mm d-1, with relative errors under 1%. Ultimately, a real-time monitoring and early warning system for landslide surface deformation is constructed and verified through a combination of indoor simulations and outdoor experiments. This work provides a feasible solution for the scientific monitoring and early warning of the landslide development.

Landslide Monitoring and Warning System Based on Wireless Sensor Network

Landslides are common in Indonesia, often caused by factors like heavy rainfall and high soil moisture levels. The Batubrak mini hydro power plant intake area in Lampung province of Indonesia has experienced landslides, prompting the need for an early warning system. This study presents the development of a cost-effective and efficient real-time landslide monitoring and warning system. The system incorporates sensors to measure accelerometer and vibration parameters, enabling real-time data display through a wireless sensor network and SCADA software. Early warnings are issued when triggers are activated. Three sensor nodes were deployed in the intake area for testing, demonstrating their ability to detect, collect, process, and transmit accelerometer and vibration data to the gateway node.

A polydiacetylene (PDA)-based colorimetric sensor for on-site cyanide anion monitoring integrated with a lateral flow assay platform

An advanced sensing platform for the on-site and real-time monitoring of cyanide (CN−) pollution was developed, addressing the key challenges of enhancing the selectivity and sensitivity, whilst simplifying the sensing process. This platform features a novel colorimetric sensor (PDA-BMN) created by incorporating a receptor for CN− recognition within a polydiacetylene (PDA) conjugated polymer system. The interaction of CN− with the receptor distorted the π-conjugated backbone, causing a distinct color change from blue to orange, with a limit of detection (LOD) of 0.55 µM, thereby highlighting its high sensitivity. The PDA-BMN system was integrated into a lateral flow assay (LFA) strip to facilitate on-site monitoring. The LFA strip was strategically designed with a partially pressed zone to reduce the fluid flow rate, enhance the reaction time, and increase the probability of interaction with the PDA-BMN sensor. This innovation resulted in a significant colorimetric transition, enabling the rapid and sensitive detection of CN− without the requirement for analytical instruments. This study demonstrates the effectiveness of combining PDA and LFA systems for real-time environmental monitoring, offering a robust, efficient, and user-friendly solution for detecting CN− pollution.

CONTEMPORARY CHALLENGES IN CIVIL PROTECTION AND DEFENSE

The addressed problem is the need to enhance public policies and regulations in disaster management in Brazil, due to the increasing frequency and severity of these events. The hypothesis posits that integrating public policies with emerging technologies and community participation can significantly improve disaster management. The methodology involves a literature review of existing legislation, case study analysis, and the implementation of real-time hydrometeorological monitoring systems. Key results indicate that, despite advancements in prevention and mitigation policies, significant gaps remain in inter-institutional coordination and practical application of regulations. The main conclusion is that effective integration of public policies and emerging technologies is essential for risk reduction and the effective protection of vulnerable communities.