Real-time Alarm Research Articles

All-in-one self-powered wind speed sensor with a wide start-up range and high output power

Anemometers play an important role in environmental monitoring in remote and unique locations, such as forests, islands, and mines. Self-powered wind speed sensors offer a solution for long-term reliable monitoring in unmanned environments. However, current self-powered wind speed sensors suffer from limited integration, limited start-up range, and insufficient output power. Therefore, an all-in-one self-powered wind speed sensor (ASWS-sensor) with a wide start-up range and high output power is proposed. The ASWS-sensor, based on triboelectric nanogenerator and electromagnetic generation technologies, features a unique dual-cup structure that integrates wind speed sensing and wind energy harvesting. This design enables wind speed detection across a broad range from 1.5 to 15 m/s and achieves a high output power of 1.18 W. To meet the long-term, reliable wind speed monitoring needs in coal mine tunnels, a real-time wind speed monitoring and alarm system is developed combining the ASWS-sensor and a master computer. Notably, in this system, the use of the FIR filtering algorithm effectively suppresses significant noise encountered during the collection of single-electrode triboelectric signals with a microcontroller, thereby simplifying the wind speed calculation process. The results show that when the wind speed exceeds 7 m/s, the system can collect, process, wirelessly transmit, analyze, and display wind speed-related data without the need for an external power supply. This demonstrates the excellent application potential of the ASWS-sensor in unmanned monitoring in remote and unique scenarios.

Driver Drowsiness Detection System for Accident Prevention

The abstract highlights the importance of driver drowsiness detection as a critical component of vehicle safety technology, with the goal of preventing accidents brought on by drowsy drivers. According to studies, driver fatigue possibly a factor in 20% of traffic accidents, underscoring the importance of developing efficient accident-avoidance strategies. The research focuses on a particular illustration of an automated tiredness detection system intended to improve driver safety by tracking unsafe driving practices. The primary objective of the research is to develop an automated system capable of accurate analysis the blink patterns of the driver's eyes. It detects modifications to the distance between the eyes and reflects eye blink events using an infrared-based eye blink sensor. The location of the visual is indicated by the sensor's output, which is high when the eye is closed and low when it is open. The project includes a circuit that, in the event that it detects indicators of tiredness, such closed eyes, while driving, sounds an alarm, namely a buzzer next to the driver. By offering a real-time alarm mechanism, this technology seeks to address the risks related to driver weariness and enhance overall road safety.

Automated Detection of Hazardous Areas in Railway Stabling Depots Utilizing Passive Infrared Motion Sensors

This research project aims to construct a motion sensor indication of hazardous railway depot areas to improve the safety of the crew. The project proposes a localized, solar-powered safety system that will govern the inherent dangers of railway station operations and avert possible accidents. The focus was developing and building a prototype incorporating motion sensor technology that uses visual and audio alerts to alert staff of impending dangers in real-time. This method would therefore design a system that consisted of a Passive Infrared (PIR) motion sensor, a siren alarm, a Light Emitting Diode (LED) indicator, and a manual push button that could activate the alarm, along with a Closed-Circuit Television (CCTV) camera for surveillance purposes and a wireless monitor to improve visibility. The prototype was seen to operate correctly in the experiment, where the motion sensor PIR easily sensed any movement and activated the siren alarm and the LED lights. The CCTV camera plus the wireless monitor supplemented and increased surveillance and visibility factors in the danger zone of safety. In this, this research provides a means of significant enhancement in terms of railway station safety that is through using a locally powered, solar motion sensor activation. The real-time detection and alarm of the system can potentially inform staff of potential dangers in time, thereby preventing accidents and creating a safer environment. Further research should be conducted to upgrade the capabilities and usability in different railway depot scenarios. This may involve evaluating different motion sensors, including additional safety devices, and making field tests to evaluate the system's effectiveness in real application cases.

High response H2S chemiresistive gas sensor based on multidimensional CuO/CeO2: The application for real-time portable alarm device

Real-time monitoring of toxic and harmful hydrogen sulfide (H2S) is crucial in the extraction process of fossil fuels. However, in the process of detecting H2S, there will be difficulties in desorption, low response, poor anti-interference ability, and poor long-term stability. The multidimensional copper oxide (CuO) composite cerium oxide (CeO2) was synthesized by hydrothermal method and calcination techniques, and a gas sensor with easy desorption, high response, strong anti-interference ability, and excellent long-term stability for monitoring H2S was effectively prepared. Through various characterization tests such as X-ray diffraction (XRD) and scanning electron microscope (SEM), it was found that the composite material has multidimensional characteristics. When detecting 100 parts per million (ppm) of H2S at the optimal operating temperature of 180℃, the CuO/CeO2 gas sensor with the optimal ratio (nCu: nCe=1:1) showed an extremely high response (2010). At the same time, the gas sensor has extremely strong anti-interference ability and excellent long-term stability. In addition, these excellent H2S sensing properties are attributed to the excellent oxygen storage characteristics of CeO2 and the synergistic effect of CuO and CeO2. This provides more active sites for the absorption of H2S. Finally, to achieve real-time monitoring and alarm of H2S, a portable real-time monitoring and alarm device was made. When detecting different concentrations of H2S, it will flash corresponding color lights and give an alarm to achieve the monitoring function and ensure the safety and health of workers. The multi-dimensional CuO/CeO2 prepared has excellent gas sensing performance, providing a new method for H2S detection.

A Cloud Infrastructure for Health Monitoring in Emergency Response Scenarios.

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system's reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts.

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.

Design industrial 5.1 air quality monitoring system and develop smart city infrastructure

In order to meet the requirements of urban construction and further urbanization of the country, the author proposes an industrial 5.1 air quality monitoring system to develop smart city infrastructure. The author utilizes a wireless network of lighting nodes to solve the cost and positioning accuracy issues of perception nodes covering a large area, achieving real-time alarm of urban air quality status and location of pollution occurrence. The author also adopted the latest design concept of monitoring systems combined with cloud platform interfaces, breaking the closed design of traditional IoT systems and enabling better utilization of air quality data. The test results indicate that: The communication distance of CC2530 can be maintained at around 70m under normal power, while the spacing between urban street lights is approximately 30m, which fully meets the project requirements. After two days of testing, the system alarm function and various functions are running normally. ConclusionThe key parts of the system have been tested and simulated, and ideal results have been obtained.

Short-term epileptic seizures prediction based on cepstrum analysis and signal morphology

This article aims to provide and implement a patient-specific seizure (for Intervention Time (IT) detection) prediction algorithm using non-invasive data to develop warning devices to prevent further patient injury and reduce stress. Employing algorithms with high initial data volume and computations time to increase the accuracy is an important problem in prediction issues. Consequently, reduction of calculations is met by applying only two effective EEG signal channels without manual removal of artifacts by visual inspection as the algorithm’s input. Autoregression (AR) modeling and Cepstrum detect changes due to IT period. We carry out the goal of higher accuracy by increasing sensitivity to interictal epileptiform discharges or artifacts and reduce errors caused by them, taking advantage of the discrete wavelet transform and the comparison of two channels epochs by applying the median filter. Averaging and positive envelope methods are introduced to patient-specific thresholds become more differentiated as soon as possible and can be lead to sooner prediction. We examined this method on a mathematical model of adult epilepsy as well as on 10 patients with EEG data. The results of our experiments confirm that performance of the proposed approach in accuracy and average false prediction rate is superior to other algorithms. Simulation results have been shown the robustness of our proposed method to artifacts and errors, which is a step towards the development of real-time alarm devices by non-invasive techniques.

Exploring the Potential of Machine Learning Algorithms Associated with the Use of Inertial Sensors for Goat Kidding Detection.

The autonomous identification of animal births has a significant added value, since it enables for a prompt timely human intervention in the process, protecting the young and the mothers' health, without requiring continuous human surveillance. Wearable inertial sensors have been employed for a variety of animal monitoring applications, thanks to their low cost and the fact that they allow less invasive monitoring process. Alarms triggered by the occurrence of events must be generated close to the events to avoid delays caused by communication latency, which is why this type of mechanism is typically implemented at the network's edge and integrated with existing auxiliary mechanisms on the Internet. Although the detection of births in cattle has been carried out commercially for some years, there is no solution for small ruminants, especially goats, where the literature does not even report any attempts. The current work consisted of a first attempt at developing an automatic birth monitor using inertial sensing, as well as detection techniques based on Machine Learning, implemented in a network edge device to assure real-time alarm triggering. Thus, two concept drift detection techniques and seven kidding detection mechanisms were developed using data classification models. The work also includes the testing and comparison of learning results, both in terms of accuracy and of computational costs of the detection module, for algorithms implemented. The results revealed that, despite their simplicity, concept drift algorithms do not allow kidding detection, whereas classification-algorithm-based static learning models do, despite the unbalanced character of the dataset and its reduced size. The learning findings are quite promising in terms of computational cost and its suitability for deployment on edge devices. The algorithm demonstrates behavior changes four hours before kidding and allows for the identification of the kidding hour with an accuracy of 61%, as well as the capacity to improve the overall learning process with a larger dataset.

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.

Rockfall alarm system for railway monitoring: Integrating seismic detection, localization, and characterization

Rockfalls pose a threat to human infrastructure below cliffs. Sensitive and reactive alarm systems are needed for rail traffic safety because small rockfalls ([Formula: see text]) impacting the railroad may cause train derailment. We develop a seismic processing workflow for rockfall early warning, powered by dense arrays deployed along the track. The method is evaluated by dropping rocks from a controlled height and triggering rockfalls on a cliff. We indicate that seismic arrays are highly sensitive to small impacts and are able to detect them, locate them, and estimate their magnitude. The detection can be performed in near real-time with a simple algorithm because small-scale rockfalls produce impulsive waveforms near the impact. Precise localization with matched field processing is able to track the trajectory of a rockfall. Impacts against the steel rails may be recognized by their source signature. The seismic amplitudes are related to the rockfall volume by the Hertz law, which may be used to estimate their volume. These results indicate the potential of seismic-driven near real-time rockfall alarm systems.

Sensortechnology for monitoring challenging behavior in nursing home residents with dementia

Background:Neuropsychiatric symptoms (NPS) are common in affected individuals and can be challenging for (in)formal caregivers. Therefore, they are also referred to as challenging behaviors (CBs). Sensor technology measuring context and behavior can be assistive to effectively manage CBs in an objective fashion. Sensors can help support healthcare professionals, such as nurses, by enabling remote monitoring and alarming on early-stage behavioral changes associated with CBs. This might/ will improve the quality of life (QoL) for both caregivers and clients living in a nursing homes (NH).In the project “MOnitoring Onbegrepen Gedrag bij Dementie met sensortechnologie” (MOOD-Sense), we aim to develop such a monitoring system. Our research focuses on two questions 1) How to develop and implement a monitoring system within the context of nursing homes with parameters on environment, physiology, and behavior, identify and process relevant precursors of challenging behavior with this monitoring system and 2) gain insight in which behaviors are challenging according to nurses and how they are described. This will be represented in an ontology such that sensor data can be translated into the same conceptual information.Methods:The first research question will be examined with a set of experiments in the field (in NH) with an iterative approach. Insights from previous experiments on usability and added value of sensors will be used to improve successive experiments. During each experiment, multiple participants (clients with dementia and CBs) are monitored with both ambient and wearable sensors. For the second research question a qualitative approach is employed, using focus groups (FG) and consensus methods. These FGs will be held amongst nursing staff who are involved in daily care tasks for people with dementia. Subsequently, consensus methods are used to align behavioral descriptors/labels.Results:early findings will be presented at the symposiumDiscussion:Within this project we expect to find precursors of challenging behavior in a personalized fashion based on nurse’s expert knowledge and sensor data. In order to develop a monitoring system that can be embedded within NH’s, real-time alarming, in-situ behavior recognition and trustworthiness are part of our technological requirements. Just-in-time interventions may then be deployed to prevent behavior escalation or the persistence of undesirable situations.

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.

APTSHIELD: A Stable, Efficient and Real-Time APT Detection System for Linux Hosts

Advanced Persistent Threat (APT) attack usually refers to the form of long-term, covert and sustained attack on specific targets, with an adversary using advanced attack techniques to destroy the key facilities of an organization. APT attacks have caused serious security threats and massive financial loss worldwide. Academics and industry thereby have proposed a series of solutions to detect APT attacks, such as dynamic/static code analysis, traffic detection, sandbox technology, endpoint detection and response (EDR), etc. However, existing defenses are failed to accurately and effectively defend against the current APT attacks that exhibit strong persistent, stealthy, diverse and dynamic characteristics due to the weak data source integrity, large data processing overhead and poor real-time performance in the process of real-world scenarios. To overcome these difficulties, in this paper we propose APTSHIELD, a stable, efficient and real-time APT detection system for Linux hosts. In the aspect of data collection, audit is selected to stably collect kernel data of the operating system so as to carry out a complete portrait of the attack based on comprehensive analysis and comparison of existing logging tools; In the aspect of data processing, redundant semantics skipping and non-viable node pruning are adopted to reduce the amount of data, so as to reduce the overhead of the detection system; In the aspect of attack detection, an APT attack detection framework based on ATT&CK model is designed to carry out real-time attack response and alarm through the transfer and aggregation of labels. Experimental results on both laboratory and Darpa Engagement show that our system can effectively detect web vulnerability attacks, file-less attacks and remote access trojan attacks, and has a low false positive rate, which adds far more value than the existing frontier work.

Electronic Antiretroviral Therapy Adherence Monitors and Associated Interventions Improve Adolescent-Caregiver Relationships and Self-Efficacy Among Adolescents and Young Adults with HIV in Uganda.

Many adolescents and young adults with HIV (AYWH) struggle with antiretroviral therapy (ART) adherence and experience poorer outcomes than adults. Relevant factors include forgetfulness and poor self-efficacy related to their evolving neurobiology. We qualitatively explored experiences of AYWH-caregivers dyads using real-time ART adherence monitors and associated reminder functions in the home setting. As part of an implementation science-oriented study, AYWH used the Wisepill adherence monitor for 3 months. AYWH could also opt for short message service (SMS) self-reminders, a self-selected social supporter for delayed or missed doses, or an alarm reminder. We conducted in-depth interviews with randomly selected AYWH-caregiver dyads regarding their experience using the monitor. Qualitative data were analyzed using inductive content analysis. We completed 15 AYWH-caregiver dyad interviews. Of the AYWH, 67% were female, mean age was 16 years, 56% lived with their biological mother, and 86% were virologically suppressed. AYWH and their caregivers generally found the adherence monitors acceptable, though some had privacy concerns. AYWH felt the monitors helped them take charge of their medication, largely through the real-time alarm and SMS reminders; this took the burden of adherence reminders away from the caregivers, improving strained AYWH-caregiver relationships. Two adolescents reported rebound poor adherence after monitor withdrawal. ART adherence monitors and associated tools were largely acceptable to AYWH and their caregivers in home settings. The intervention helped improve AYWH self-efficacy and alleviated burden from some AYWH-caregiver relationships. Rebound poor adherence suggests the need for on-going support and/or other means to achieve intrinsic mechanisms for sustained adherence. Clinical Trial Registration number: NCT03825952.

Machine Learning Software for the Detection of Violence from CCTV Live Footage

Automated CCTV analytics have become a potent resource for boosting public safety. The software program, which has sophisticated video analytics capabilities, isintroduced in this paper. The software program can instantly identify situations and notify concerned agencies such as the police and medical staff. Software processes video in three different ways: live streaming from external links, video uploaded from a computer, and real-time video from security cameras. Using past data for analysis, it scans CCTV feeds from public spaces for instances of violent crime, theft, burglary, and other crimes. The software’s key features are real-time CCTV stream analysis, alarm and report generatingand database updatingfor prompt response by the nearest police station.This software has the potential to significantly improve public safety measures by enabling proactive incident detection and real-time response, which will improve law enforcement operations.The software Program boasts key features such as real-time CCTV stream analysis, the instantaneous generation of alarms and reports, and seamless database updates accessible to the nearest police station for immediate response. This software's potential to significantly enhance public safety measures lies in its capacity to enable proactive incident detection and real-time responses, promising to optimize law enforcement operations and overall security.

Fire Accident Risk Analysis of Lithium Battery Energy Storage Systems during Maritime Transportation

The lithium battery energy storage system (LBESS) has been rapidly developed and applied in engineering in recent years. Maritime transportation has the advantages of large volume, low cost, and less energy consumption, which is the main transportation mode for importing and exporting LBESS; nevertheless, a fire accident is the leading accident type in the transportation process of LBESS. This paper applied fault tree analysis and Bayesian network methods to evaluate the fire accident risk of LBESS in the process of maritime transportation. The Bayesian network was constructed via GeNIe 2.3 software, and the probability of LBESS fire accidents during maritime transportation was calculated based on the probability of basic events occurring. The results showed that an unsuitable firefighting system for putting out lithium battery fires, high humidity, and monitoring equipment without a real-time alarm function have the most significant influence on the occurrence of LBESS fire accidents during maritime transportation. The research work of this paper provides a theoretical basis for the risk assessment of LBESS during maritime transportation.

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.

MFM-based alarm root-cause analysis and ranking for nuclear power plants

Alarm flood due to abnormality propagation is the most difficult alarm overloading problem in nuclear power plants (NPPs). Root-cause analysis is suggested to help operators in understand emergency events and plant status. Multilevel Flow Modeling (MFM) has been extensively applied in alarm management by virtue of the capability of explaining causal dependencies among alarms. However, there has never been a technique that can identify the actual root cause for complex alarm situations. This paper presents an automated root-cause analysis system based on MFM. The causal reasoning algorithm is first applied to identify several possible root causes that can lead to massive alarms. A novel root-cause ranking algorithm can subsequently be used to isolate the most likely faults from the other root-cause candidates. The proposed method is validated on a pressurized water reactor (PWR) simulator at HAMMLAB. The results show that the actual root cause is accurately identified for every tested operating scenario. The automation of root-cause identification and ranking affords the opportunity of real-time alarm analysis. It is believed that the study can further improve the situation awareness of operators in the alarm flooding situation.