Real-time Alarm System Research Articles

Design and implementation of a scalable IoT-based real-time environmental monitoring and alarm system: an experimental study

Digital transformation is crucial for organizations to survive, be competitive, and grow in the modern age. IoT is the key to enabling this transformation by connecting devices and optimizing processes. This paper presents the design and implementation of a generic IoT-based real-time environmental monitoring and alarm system. The platform is validated by applying it to a manufacturing plant scenario, where various sensors simulate industrial conditions. Scalable message distribution systems such as MQTT and Apache Kafka facilitate reliable data transmission. A microservice architecture is constructed for the backend services to ensure uninterrupted and high throughput services in the application domain. Instead of deploying a real WSN, traffic generation services were chosen to minimize costs, provide greater control and flexibility, and facilitate faster, scalable testing in a controlled environment. The platform also features an integrated alarm system with an event definition module, which allows users to define custom action rules. This flexible, scalable, and resilient architecture can be used across a wide range of application domains that require digital transformation. The experimental study demonstrates the platform's capabilities and great potential for broader IoT applications.

Real-time temperature monitoring and alarm system for biological samples storage based on FPGA

In the field of biosensing, high-quality biological samples are a prerequisite for precise analysis and detection. Strictly controlled storage temperature is crucial to ensure the stability of biological samples and achieve effective detection. Different biological samples require different temperature ranges for storage. Therefore, it is essential to design a temperature monitoring and alarm system capable of alarming based on different needs. This work presents a real-time temperature monitoring and alarm system based on the DS18B20 temperature sensor and FPGA chip. The system with simple structures has a wide temperature measurement range, an adjustable temperature alarm range, and a timely alarm function for various scenarios monitoring. This work demonstrates the temperature monitoring and alarm system for simple, accurate, and low-cost modes of temperature monitoring. The real-time temperature monitoring and alarm system will provide an effective means to strictly control the storage temperature of different biological samples, ensuring the stability and effectiveness of biological samples and the accuracy and sensitivity of biosensing and analysis.

Mapping the vulnerability of coastal areas of the Eastern region of Morocco (ERM) to oil spills risk

The objective of this study is to generate maps of coastal areas based on the Environmental Sensitivity Index (ESI) to assess oil spills risk. These maps have become a crucial tool for operational responders. ESI is a tool developed to evaluate the sensitivity of coastal environments and provides a method to assess the impact of oil spills systematically IPIECA. IMO. IOGP, (Juillet 2012). The establishment of the future Nador West Med oil transshipment port is expected to make maritime transport of hydrocarbons a significant source of pollution along the Eastern Region of Morocco (ERM), given the substantial traffic in these products. Through satellite imagery, it has been possible to observe and recognize the different geomorphological formations along the coast. In order to map these sites based on ESI index and describe their characteristics, it is essential to utilize Geographic Information System (GIS) and multi-criteria analysis methods to manage all relevant information. The produced maps provide valuable support to stakeholders in coastal management of oil spills response. Furthermore, ESI is used for coastal area management and preservation, particularly in identifying ecologically valuable regions. There are plans to implement a real-time alarm system to visualize coastal areas sensitivity in terms of the ESI index, guiding pollution control in the coastal areas.

Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases.

Telemedicine and remote patient monitoring (RPM) systems have been gaining interest and received adaptation in healthcare sectors since the COVID-19 pandemic due to their efficiency and capability to deliver timely healthcare services while containing COVID-19 transmission. These systems were developed using the latest technology in wireless sensors, medical devices, cloud computing, mobile computing, telecommunications, and machine learning technologies. In this article, a real-time remote patient monitoring system is proposed with an accessible, compact, accurate, and low-cost design. The implemented system is designed to an end-to-end communication interface between medical practitioners and patients. The objective of this study is to provide remote healthcare services to patients who need ongoing care or those who have been discharged from the hospital without affecting their daily routines. The developed monitoring system was then evaluated on 1177 records from MIMIC-III clinical dataset (aged between 19 and 99 years). The performance analysis of the proposed system achieved 88.7% accuracy in generating alerts with logistic regression classification algorithm. This result reflects positively on the quality and robustness of the proposed study. Since the processing time of the proposed system is less than 2 minutes, it can be stated that the system has a high computational speed and is convenient to use in real-time monitoring. Furthermore, the proposed system will fulfil to cover the lower doctor-to-patient ratio by monitoring patients from remote locations and aged people who reside in their residences.

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP.

To solve the problems of backward gas and coal dust explosion alarm technology and single monitoring means in coal mines, and to improve the accuracy of gas and coal dust explosion identification in coal mines, a sound identification method for gas and coal dust explosions based on MLP in coal mines is proposed, and the distributions of the mean value of the short-time energy, zero crossing rate, spectral centroid, spectral spread, roll-off, 16-dimensional time-frequency features, MFCC, GFCC, short-time Fourier coefficients of gas explosion sound, coal dust sound, and other underground sounds were analyzed. In order to select the most suitable feature vector to characterize the sound signal, the best feature extraction model of the Relief algorithm was established, and the cross-entropy distribution of the MLP model trained with the different numbers of feature values was analyzed. In order to further optimize the feature value selection, the recognition results of the recognition models trained with the different numbers of sound feature values were compared, and the first 35-dimensional feature values were finally determined as the feature vector to characterize the sound signal. The feature vectors are input into the MLP to establish the sound recognition model of coal mine gas and coal dust explosion. An analysis of the feature extraction, optimal feature extraction, model training, and time consumption for model recognition during the model establishment process shows that the proposed algorithm has high computational efficiency and meets the requirement of the real-time coal mine safety monitoring and alarm system. From the results of recognition experiments, the sound recognition algorithm can distinguish each kind of sound involved in the experiments more accurately. The average recognition rate, recall rate, and accuracy rate of the model can reach 95%, 95%, and 95.8%, respectively, which is obviously better than the comparison algorithm and can meet the requirements of coal mine gas and coal dust explosion sensing and alarming.

Research and Development of Environmental Monitoring Alarm and Automatic Flag Control System for Barracks

This paper proposes a real-time flag alarm system that can monitor air quality and automatically plant colored flags to inform the people in the barracks. This system automatically measures the local PM 2.5 concentrations with PM sensors; and automatically measures the temperature and humidity with temperature and humidity sensors, then converts the measured values into the grades of danger coefficients and the grades of AQI to plant or replace flags by automatic control. The danger coefficient grades are represented by four colored flags, namely, green, blue, yellow, and red; meanwhile, the AQI grades are represented by six colored flags, namely, green, yellow, orange, red, purple, and maroon. Moreover, this system displays all measured data and related information with electronic billboards to provide a reference for people participating in outdoor activities.

Mechanically flexible and flame retardant polyphenol-bridged casein/MXene composite for fire proofing repeatable contact/non-contact fire monitoring

Early fire warning sensors are critically vital to avoid the potential fire accidents for preventing huge life and economic losses, however, currently available fire warning materials work only under direct fire/smoke exposure or relatively high temperature with poor sensitivity. Herein, a biobased durable, reusable and highly sensitive fire warning composite (CTM) was developed using casein as the film-forming agent and dopant, MXene (Ti3C2Tx) as the conductive skeleton, and tannic acid as the crosslinking agent and free radical scavenger. Notably, the resultant CTM composite exhibits mechanically flexible properties, excellent thermal stability (T10% of 239.6 °C) and superb fire safety (total heat release (THR), total smoke production (TSP) and CO production rate of sensor are only 1.35 MJ/m2, 0.19 m2 and 0.42 mg/s, respectively). More importantly, the CTM presents reliable and rapid cycle warning responses under both direct flame attack and non-contact fire threat. Under continuous exposure to flame, it demonstrates an ultra-fast response time (982 ms), a long-time stable alert (93 s), and a repeatable warning ability. Under the impact of repetitively approaching flame, it exhibits consistent distance-dependent changes in resistance within 3 s, affording attractive accessibility to detect flame position. Moreover, this CTM composite can easily be coated on flexible textiles to develop a large-scale wireless real-time fire alarming system that can deliver the monitored signal to remote sites within only 3 s, thus it provides a potential application prospect in constructing smart fire-safe buildings.

A Finite Element Model for Monitoring the Displacement of Pipelines in Landslide Regions by Discrete FBG Strain Sensors

This study investigates a system for monitoring displacements of underground pipelines in landslide-prone regions. This information is an important alarm indicator, not only to prevent the failure of the line itself but also to mitigate the direct consequences of landslides on buildings and infrastructures in the affected area. Specifically, a numerical processing tool coupled with a data acquisition system is proposed. The starting point is the measurement of axial strain at three points of discrete sections of the pipeline by Fiber Bragg grating sensors, used to approximate the trend of mean axial strain and bending curvatures along the pipe axis. A finite element analysis based on a 3D geometrically exact beam model is developed for computing the deformed configuration corresponding to the input strain field. After assigning the boundary conditions, a mixed iterative scheme is used for a quick solution to the nonlinear problem. Firstly, the tool is validated theoretically with benchmarks on beam-like structures undergoing large deflections. Then, experimental results are produced on a monitored pipe buried in a wedge of land subject to an artificial slide. The overall sensor-modeling system, with zero displacements far from the landslide as a boundary condition, provides a satisfactory displacement trend with a mean error of about 18% with just three effective monitored sections in the affected pipe stretch of 18 m. The acquisition and processing tool is implemented in a web application as a real-time alarm system.

Machine Learning Approach for Predictive Maintenance of the Electrical Submersible Pumps (ESPs).

Electrical submersible pumps (ESPs) are considered the second-most widely used artificial lift method in the petroleum industry. As with any pumping artificial lift method, ESPs exhibit failures. The maintenance of ESPs expends a lot of resources, and manpower and is usually triggered and accompanied by the reactive process monitoring of multivariate sensor data. This paper presents a methodology to deploy the principal component analysis and extreme gradient boosting trees (XGBoosting) in predictive maintenance in order to analyze real-time sensor data to predict failures in ESPs. The system contributes to an efficiency increase by reducing the time required to dismantle the pumping system, inspect it, and perform failure analysis. This objective is achieved by applying the principal component analysis as an unsupervised technique; then, its output is pipelined with an XGBoosting model for further prediction of the system status. In comparison to traditional approaches that have been utilized for the diagnosis of ESPs, the proposed model is able to identify deeper functional relationships and longer-term trends inferred from historical data. The novel workflow with the predictive model can provide signals 7 days before the actual failure event, with an F1-score more than 0.71 on the test set. Increasing production efficiencies through the proactive identification of failure events and the avoidance of deferment losses can be accomplished by means of the real-time alarming system presented in this work.

Mechanism of rainfall induced landslides in Yunnan Province using multi-scale spatiotemporal analysis and remote sensing interpretation

In order to explore the disaster mechanism of rainfall induced landslides in Yunnan Province, a new method is developed in the field of landslide research, which provides a strong basis for the study of rainfall induced landslide disaster mechanism in different regions. First, the mechanism of rainfall induced landslide initiation and the mechanism of rainfall impacting slope are introduced, and the temporal and spatial characteristics of landslide disasters in Yunnan Province are analyzed comprehensively. The influence of rainfall on landslide density is analyzed in 8 regions randomly, and a semi supervised feature selection algorithm based on manifold is proposed to optimize the remote sensing image of Zhaojiapo rainfall type landslide area in Yunnan Province. The results show that in the province the monthly rainfall days account for a large proportion from June to September, and the months with the largest number of landslides in the whole year are from June to August. The seasonal change of rainfall belt and the frequent occurrence time of landslide disasters and the spatial characteristics of landslide distribution and rainfall distribution basically belong to corresponding relationship; the greater the rainfall of 8 regions, the greater the probability of landslide occurrence, and the greater the landslide density, which belongs to the proportional relationship; the landform characteristics in the optimized remote sensing image can be clearly displayed, and the classification accuracy of the data is enhanced. The results show that based on multi-scale spatiotemporal analysis combined with optimized remote sensing technology, the cause of rainfall induced landslides in Yunnan Province is the change of landslide density caused by heavy rainfall, which eventually leads to disasters. It is conducive to scientifically understanding the inducement of rainfall induced landslides in Yunnan Province, provides a scientific basis for further disaster control and prevention, and provides a new possible method for landslide real-time prediction and alarm system.

Real-time fire detection and alarm system using edge computing and cloud IoT platform

Fires often cause huge loss of personnel and property, hence it is very important to monitor fires and send alarms to users in real time. With the development of the Internet of Things (IoT) technology, intelligent edge devices can reduce delay in fire detection. However, there are still some problems with using edge devices for fire monitoring, such as the lack of data set of electrical fires in the city in the open source fire data set, the transmission capacity of edge devices is not enough to transmit massive detection data to the cloud, and the computing power of edge devices is not enough to run deep learning neural network models in real time. This paper proposes a real-time video fire monitoring and alarm system based on edge computing and cloud IoT platform. The intelligent edge device is implemented based on Nvidia Jetson Nano with object detection network YOLOv5s deployed for fire detection where YOLOv5s is accelerated by TensorRT and DeepStream. Then real-time message notification is performed with local server, and fire event and metadata can be further delivered to the Azure IoT platform. The experiment demonstrates that it is effective for real-time fire detection and message notification with our proposed system.

Real-time fire detection and alarm system using edge computing and cloud IoT platform

Real-time fire detection and alarm system using edge computing and cloud IoT platform

Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set.

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

Isolation of Primary Rat Hepatocytes with Multiparameter Perfusion Control.

Primary hepatocytes are widely used in basic research on liver diseases and for toxicity testing in vitro. The two-step collagenase perfusion procedure for primary hepatocyte isolation is technically challenging, especially in portal vein cannulation. The procedure is also prone to occasional contamination and variations in perfusion conditions due to difficulties in the assembly, optimization, or maintenance of the perfusion setup. Here, a detailed protocol for an improved two-step collagenase perfusion procedure with multiparameter perfusion control is presented. Primary rat hepatocytes were successfully and reliably isolated by taking the necessary technical precautions at critical steps of the procedure, and by reducing the operational difficulty and mitigating the variability of perfusion parameters through the adoption of a special intravenous catheter, standardized sterile disposable tubing, temperature control, and real-time monitoring and alarm system. The isolated primary rat hepatocytes consistently exhibit high cell viability (85%-95%), yield (2-5 x 108 cells per 200-300 g rat) and functionality (albumin, urea and CYP activity). The procedure was complemented by an integrated perfusion system, which is compact enough to be set up in the laminar flow hood to ensure aseptic operation.

Design and Realization of Monitoring and Alarm System for Intravenous Infusion

A real-time monitoring and alarm system for intravenous infusion in isolation wards is designed to reduce the number of contact between medical staff and patients during intravenous infusion. The system includes STM32L052K8 as lower computer to collect the speed and dosage of intravenous infusion, upper computer adopting LabVIEW to design the infusion monitoring interface and the communication via universal asynchronous receiver transmitter (UART). The experimental results show that the intravenous infusion monitoring system designed with STM32L052K8 and LabVIEW can realize real-time monitoring of drip, reduce the stay time of medical staff in isolation wards, and provide reference for hospital medical information.

Development of a cloud-based IoT monitoring system for Fish metabolism and activity in aquaponics

A cloud-based Internet of things monitoring system in aquaponics is proposed in this study. The system can measure water temperature, water depth, dissolved oxygen, and pH value. Moreover, three infrared distance sensors were attached to the aquarium glass at different heights to monitor the group activity of fish. Continuous water depth sensing in the rearing tank can be used to monitor the ebb-and-flow irrigation and calculate the flowrate of water circulation.By using the fish activity sensing to determine if the fish are at rest, the dissolved oxygen and water temperature at fish’s resting states are collected, then based on the established oxygen transfer rate model in this study, the systemic metabolic rate and fish’s locomotion-induce metabolic rate can be calculated through the daily regression analysis. Fish activity sensing is a proactive measuring method compared with the measuring of dissolved oxygen and pH value, it has the advantage of immediacy and can technically be used to cross check whether the reading of dissolved oxygen or pH sensor near feeding time is within a reasonable range.The measuring module incorporating sensors and sub-1 GHz communication can transmit data to a 1-km-long gateway module. Finally, the data are uploaded to ThingSpeak™, a cloud platform, through Wi-Fi. By using the data stored on the cloud, a real-time alarm system for indicating abnormalities is developed and a periodic regression analysis is conducted using the cloud-based programming of ThingSpeak™.

Real-time detection of wildfire risk caused by powerline vegetation faults using advanced machine learning techniques

Wildfires, also known as bushfires, happened more and more frequently in the last decades. Especially in countries like Australia, the dry and warm climate there make bushfire become one of the most frequent local hazards. Among different kinds of causes of bushfires, overhead powerline vegetation fault is one of the most common causes that relate to human activities. Reducing the bushfire risk from this perspective has attracted many scholars to study efficient strategies and systems. However, most of them started their research from the angle of powerline faults, while limited literature has explored the characteristics of the vegetations and their ignition features. The objective of this study is to explore and discover the numerical patterns from the contact to the ignition process between different upper story vegetations and the powerlines. Those patterns can not only help provide real-time warnings of bushfire caused by powerline vegetation faults but also avoid false alarm. To achieve this, we collected the voltage and current records of 188 ignition field tests that simulated the powerline vegetation faults. To explore the numerical patterns behind and develop a real-time alarming system, this study proposed a machine learning-based model, namely Hybrid Step XGBoost. According to the tests, the model could identify the safe contacts or the danger contacts between the powerlines and the upper story vegetation with an accuracy of 98.17%. Its performance also surpassed some advanced deep learning networks in our experiments.

A Study of Human Response in the Blind Zone of Earthquake Early Warning

The potential for seismic hazard in Taiwan is quite high. In addition to the comprehensive reinforcement of the earthquake resistance of old construction, a short period of time can be immediately is the implementation of earthquake prevention and rescue education. Because of the blind zone of the existing strong earthquake real-time alarm system, the response time is limited, so the application of the message will have better efficiency in an automated way. As regards the emergency response of schoolchildren and the population in the blind zone of strong earthquake early warning, it is suggested that people should be using methods in line with local circumstances. In order to understand and make good use of the vibration characteristics of seismic waves, this study analyzes and illustrates them with several practical examples, including (1) 20100304 Jiasian earthquake with local magnitude (ML) of 6.4,(2) 20130327 Nantou earthquake with local magnitude (ML) of 6.2, as well as (3) 19990921 Chi-Chi earthquake with moment magnitude (Mw) of 7.6. The results of this study show that the human body's feelings are one of the most direct, real-time strong earthquake warning messages, especially in the blind zone of earthquake early warning where it is too late to provide seismic information. Judging by the obvious vibrations up and down by the P wave, it is advisable for the people on the first floor of the old low earthquake-resistant premises to seize the opportunity and suggest a time take an immediate approach to outdoor escape response. While fear and panic may be an obstacle to escape, reflex movements can overcome fear, and reflex movements can be developed through regular drills. Accordingly, people will make a correct seismic response immediately within the earthquake, in a short period of time without panic of the emergency evacuation to reach a safe location.

Application and Research of Digital Twin Technology in Safety and Health Monitoring of the Elderly in Community

In this paper, through the research of digital twin technology, combined with the application of vision sensor, artificial intelligence chip and deep learning algorithm technology, the real-time monitoring and alarm system of elderly fall and abnormal posture based on digital twin technology is developed. The system collects the data of the posture and behavior of the elderly, and then presents them in the cloud by digital mapping after the calculation and analysis of artificial intelligence chip. Once the safety threshold is deviated, the alarm can be activated to avoid or mitigate the injury caused by the fall of the elderly. Through product validation and trial operation of Tianbao Nursing Home in Hongkou District of Shanghai, the user can set alarm thresholds in different time periods and regions, thus achieving the preset purpose of the product.

PMS: Intelligent Pollution Monitoring System Based on the Industrial Internet of Things for a Healthier City

Health is one of the most important considerations for everyone. However, environmental pollution damages people's health seriously, especially those who live in industrial cities. Gas waste, water waste, and waste residue seriously endanger the health of people. Therefore, pollution regulation is one of the keys to improving people's health. In this article, we propose an intelligent pollution monitoring system (PMS) based on the Industrial Internet of Things to prevent direct emission of harmful substances and to build healthier cities. The PMS has three main parts: 1. Real-time pollutant monitoring equipment, which can monitor harmful substances in the gas and water in real time, and further cannot be damaged easily 2. A real-time alarm system, which can send alerts immediately after detecting harmful substances 3. An intelligent and fast response system, which can act to respond to an alarm The experimental results show that the proposed PMS can improve the efficiency of responses to pollution and increase people's health.