Real-time Alarm Research Articles

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 dangerous action warning system based on graph convolution neural network

In order to solve a series of disadvantages of slow efficiency and high cost brought by the traditional human action recognition method based on manual feature extraction, in order to prove the feasibility of using graph convolution neural network and action recognition, a real-time dangerous action alarm system based on graph convolution neural network is designed and implemented. The system includes data collection, human posture estimation, action recognition, dangerous action alarm and other functions. The feasibility of using graph convolution neural network to realize real-time dangerous action alarm system is proved by the system. Even if the graph convolution neural network does not pass the training, it can extract good features only by using the original random initialization parameters. If certain annotation information is given, its effect can be greatly improved. After the system adopts the graph convolution neural network, the action recognition can be more accurate, more efficient, faster and more reliable than the traditional method.

Application of Intelligent Blood Temperature and Humidity Monitoring System in Blood Station

Objective: To explore the application of intelligent blood temperature and humidity monitoring system in cold chain management of blood station. Methods: Through the monitoring of fifty sets of cold-chain equipment in the central blood station in Hezhou for 6 months, the differences between the management of the automatic temperature and humidity monitoring system and the manual management were compared in terms of real-time recording, equipment alarm, data storage, historical data traceability and data analysis. Results: Temperature and humidity automatic monitoring system can automatically real-time acquisition, transmission, storage and alarm according to the required time interval; meanwhile, historical data can be quickly exported and traced, data and charts can be analyzed, and the alarm is real-time and effective. Conclusion: The system can effectively monitor the process of blood cold chain in blood stations and play a key role in ensuring blood quality. It can be popularized and used in blood stations.

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

A real-time prototype of a water level monitor and wide area early flood warning

This article presents a real-time prototype of a water level monitor and wide area early flood warning. The prototype consists of two systems: the first is designed to provide real-time display and alarm alerts on a lab view program; the second is designed to measure water levels and display results via applications on a smartphone in real-time using the Blynk application, as well as to alert users of impending floods via the LINE application using Internet of Things (IoT) technology. The target user in this research study is Sado Phong Community, Khao Kho District, Phetchabun Province. This case study was designed for a stream with a water depth of 200 cm and a three-tiered alarm system: 140 cm, 150 cm and 170 cm of water height. The study results showed that when simulating each set level increase, the prototype could display real-time water level changes and give an alarm in both systems. The error value of the meter for the system displayed in the lab view was ± 1.11%. In comparison, the error value for the system displayed via an application on a smartphone for ultrasonic sensor 1 was ± 0.60% and ultrasonic sensor 2 was ± 0.80%, including the ability to notify a group of community members via the LINE application.

Exploring the Risky Travel Area and Behavior of Car-hailing Service

Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.

Design of Refrigeration Control System for Parallel Refrigerator Unit Based on PLC

In order to improve the refrigeration control efficiency and reduce the temperature fluctuation in the cold storage. In this paper, the refrigeration control system of the parallel unit of the cold storage is designed based on the upper and lower computers. The lower computer uses PLC to monitor the field parameters and control the execution of the field equipment. The upper computer uses HMI to realize the real-time monitoring, parameter display and alarm function during the operation of the cold storage, and uses the slide valve to adjust the energy of the compressor. The results show that the control system runs reliably and meets the control requirements of large cold storage.

Sliding wire detection system of electric lock screw tool based on motor characteristics

This paper presents a sliding wire detection system of electric screw locking tool based on the characteristics of motor. The system can judge whether the screw has sliding wire through the current change of motor during normal operation, and realize the real-time detection and alarm of sliding wire. The system has the advantages of high sensitivity, low cost and high accuracy. It can be widely used in automatic assembly and other fields.

Research on small lightweight electromagnetic interference measurement system based on patrol unmanned aerial vehicle

Unmanned aerial vehicle (UAV) has been widely used in power system inspection. Most of the existing UAVs are equipped with visible and infrared cameras for fast full spectrum photography and fault monitoring of transmission lines. When the transmission line has defects such as aging, broken strand and serious corrosion, the traditional image recognition, infrared and other observation methods can not identify these defects well. In order to realize the rapid detection and judgment of broken strand, this paper analyzes the characteristics of electromagnetic interference of transmission line, and constructs the electromagnetic interference measurement system based on patrol UAV. The research results of this paper can realize the real-time detection and alarm of broken strand, and provide technical support for line inspection.

An Improved High-Intelligence Method of Gas and Oil Pipeline Prewarning System in Real Soil Environment

With the development of technology, the total extent of global pipeline transportation is also increased. However, the traditional long-distance optical fiber prewarning system has poor real-time performance and high false alarm rate when recognizing events threatening pipeline safety. The same vibration signal would vary greatly when collected in different soil environments and this problem would reduce the signal recognition accuracy of the prewarning system. In this paper, we studied this effect theoretically and analyzed soil vibration signals under different soil conditions. Then we studied the signal acquisition problem of long-distance gas and oil pipeline prewarning system in real soil environment. Ultimately, an improved high-intelligence method was proposed for optimization. This method is based on the real application environment, which is more suitable for the recognition of optical fiber vibration signals. Through experiments, the method yielded high recognition accuracy of above 95%. The results indicate that the method can significantly improve signal acquisition and recognition and has good adaptability and real-time performance in the real soil environment.

The Zipperator! A Novel Model to Simulate Penile Zipper Entrapment.

The Zipperator! A Novel Model to Simulate Penile Zipper Entrapment.

Parallel Coupling of Ion Mobility Spectrometry and Ion Trap Mass Spectrometry for the Real-Time Alarm Triggering and Identification of Hazardous Chemical Leakages.

Hazardous chemical leakages involved in chemical terrorist attacks and chemical industrial accidents have been posing severe threats to human health and the environment. Vehicle-mounted mass spectrometry (MS) has been developed for continuous, on-road measurements to map the spatial and temporal distributions of hazardous chemicals. However, the detection of chemicals with small temporal scales and spatial scales is always challenging. In this study, a parallel coupling apparatus combining the techniques of ion mobility spectrometry and ion trap MS (p-IMS-ITMS) was developed to improve the detection rate and the time response capability of a stand-alone ITMS system for short time-span chemical tracking. A workflow was also proposed along with the apparatus, where the ITMS system can be triggered, as chemical suspects were discovered with the IMS system. The sampling positions of the ITMS system were investigated and optimized. In addition, a strategy was proposed to diminish the time span of samples from 1.5 to 0.5 s for evaluating the performances of the p-IMS-ITMS system. The detection rate of the stand-alone ITMS system was measured to be only 9.5, 32, and 87.5% for the time span of 0.5, 1, and 1.5 s, respectively. By comparison, the detection rates of the p-IMS-ITMS system were 99.5, 100, and 100%, where the detection rate was increased by a factor of 10 for 0.5 s time span. Moreover, the addition of an IMS system could provide temporal patterns of hazardous chemicals with a resolution of 33 ms. Finally, the potential of the p-IMS-ITMS system for environmental navigation monitoring and assessment was further demonstrated by detecting the leakages of dimethyl methyl phosphonate and dipropylene glycol monomethyl ether.

Electro-Blown Spun Silk/Graphene Nanoionotronic Skin for Multifunctional Fire Protection and Alarm.

Artificial protective skins are widely used in artificial intelligence robots, such as humanoid robots, mobile manipulation robots, and automatic probe robots, but their safety in use, especially flame retardancy, is rarely considered. As many artificial skins are designed for use in flammable or even explosive environments, flammability is a significant concern. Herein, a flame-retardant silk/graphene nanoionotronic (SGNI) skin is developed by using a rationally designed high-throughput electro-blown spinning technique, with a more efficient production efficiency than electrospinning. These flame retardant SGNI skins combine the advantages of nanofibrous and ionotronic materials, and they are sustainable, conductive, highly porous, mechanically robust, highly stretchable, self-adhesive, and humidity- and temperature-sensitive. These merits support the assembly of SGNI skins into a fire alarm system, with real-time alarm (response in 2 s) to mobile phones, clouds, and a central control system. The concept that combines a flame retardant and fire alarm material into an intelligent skin may provide potential solutions toward the design of protective skins for robotics and human-machine interactions.

Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm.

AimsThis work attempts to develop a standalone heart rhythm alerting system for the intensive care unit (ICU), where life-threatening arrhythmias have to be identified/alerted more precisely and more instantaneously (i.e. with lower latency) than existing bedside monitors. Methods and resultsWe use the dataset from the PhysioNet 2015 Challenge, which contains records that led to true and false arrhythmic alarms in the ICU. These records have been re-annotated as one of eight classes, namely (i) asystole, (ii) extreme bradycardia, (iii) extreme tachycardia, (iv) ventricular fibrillation (VF), (v) ventricular tachycardia (VT), (vi) normal sinus rhythm, (vii) sinus tachycardia, and (viii) noise/artefacts. Arrhythmia-specific features and features that measure the signal quality were extracted from all the records. To improve VF detection, an improved, over an existing, single-lead R-wave detection was developed that takes into account the R-waves detected in all electrocardiographic (ECG) leads. To avoid false R-wave detection due to pacing spikes, ECG signals were filtered with a low pass filter prior to R-wave detection, while the raw signals were used for feature extraction. Random forest was used as the classifier, and 10-time five-fold cross-validation, resulted in a macro-average sensitivity of 81.54%.ConclusionsIn conclusion, comparing with the bedside monitors used in the PhysioNet 2015 competition, we find that our method achieves higher positive predictive values for asystole, extreme bradycardia, VT, and VF; furthermore, our method is able to alert the presence of arrhythmia instantaneously, i.e. up to 4 s earlier.

Analysis of In Situ Optical Signals during Laser Metal Deposition of Aluminum Alloys

During laser metal deposition (LMD) of thin-walled aluminum alloy structures, the deposition height and width is hard to keep stable because of the special properties of aluminum alloys, such as high reflectivity to laser beams, low viscosity, and high thermal conductivity. Monitoring the LMD process allows for a better comprehension and control of this process. To investigate the characteristics of the aluminum alloy LMD process, three real-time coaxial optical sensors sensitive to visible light, infrared light, and back-reflected lasers ere used to monitor the aluminum alloy LMD process. Thin-walled parts were deposited with different laser power, and the characteristics of the three in situ signals are analyzed. The results show that there exists high linear correlation between reflected laser and accumulated deposition height. A laser reflection model was built to explain the correlation. Besides, the infrared light is linearly correlated with deposition width. Overall, the results of this study show that the optical signals are able to reflect the deposition height and width simultaneously. Infrared light signals and reflected laser signals have the potential to serve as the input of online feedback geometry control systems and real-time defect alarm systems of the LMD process.

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.

Design of thermal protection system for Marine Motor

The abnormal heat fault of marine motor frequently occurs for the bad working environment. And the navigation safety and reliability of the ship had been affected seriously. The heat transfer path of the motor is analyzed theoretically from the point of view of heat transfer in order to solve this problem. The relationship between the surface temperature and the internal temperature is established. The surface temperature of the motor is measured in real time by the temperature acquisition module. And the measurement results are transmitted to the host computer through RS485 communication interface. The inner and outer surface temperature quantitative relationship is used to judge whether the inner temperature of the motor is abnormal or not, and the real-time alarm is carried out to realize the thermal protection of the motor.

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.

Real-time monitoring implementation of PV/T façade system based on IoT

PV/T façade system is a novel style for solar renewable energy applications, which the system can improve the efficiency of the comprehensive utilization of solar energy. In order to research the performance of PV/T façade system, a modular remote monitoring system based on the Internet of Things and cloud platform technology is proposed, built, deployed as experimental prototype in this paper. The system collects temperature, pressure and wind speed through two RS485 RTU modules, and transmits data to database on the cloud platform via 4G network. The monitoring system allows authorized users to access real-time data or alarm information in a remote way via a website or mobile application. Besides the PV/T System operating parameters, meteorological data are also monitored to ensure a more complete analysis of performance. The proposed modular architecture can be built easily and rapidly, and onsite experimental test shows the stability of the monitoring system, which makes the system flexible enough for potential future modifications.

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.