Detection Of Critical Events Research Articles

Robust Detection of Critical Events in the Context of Railway Security Based on Multimodal Sensor Data Fusion.

Effective security surveillance is crucial in the railway sector to prevent security incidents, including vandalism, trespassing, and sabotage. This paper discusses the challenges of maintaining seamless surveillance over extensive railway infrastructure, considering both technological advances and the growing risks posed by terrorist attacks. Based on previous research, this paper discusses the limitations of current surveillance methods, particularly in managing information overload and false alarms that result from integrating multiple sensor technologies. To address these issues, we propose a new fusion model that utilises Probabilistic Occupancy Maps (POMs) and Bayesian fusion techniques. The fusion model is evaluated on a comprehensive dataset comprising three use cases with a total of eight real life critical scenarios. We show that, with this model, the detection accuracy can be increased while simultaneously reducing the false alarms in railway security surveillance systems. This way, our approach aims to enhance situational awareness and reduce false alarms, thereby improving the effectiveness of railway security measures.

Intelligent Video Surveillance System

Intelligent Video Surveillance Systems (IVSS) have emerged as critical tools for ensuring safety and security in various environments. This paper presents an IVSS capable of real-time detection and analysis of critical events, including falls and vehicle crashes. The proposed system employs state-of-the-art deep learning techniques, specifically the YOLO (You Only Look Once) object detection algorithm, to analyze video streams obtained from diverse sources such as surveil- lance cameras and recorded videos. For fall detection, the system utilizes the aspect ratio of detected persons’ bounding boxes to distinguish between falls and normal activities. Vehicle crashes are identified by detecting cars in each frame and assessing the proximity and intersection of their bounding boxes. The system operates in a continuous loop, processing video frames in real-time and generating alerts when critical events are detected. Experimental results demon- strate the effectiveness and reliability of the proposed IVSS in enhancing safety and security across different environments, including public spaces, workplaces, and transportation systems.

Early detection of critical urban events using mobile phone network data.

Network Signalling Data (NSD) have the potential to provide continuous spatio-temporal information about the presence, mobility, and usage patterns of cell phone services by individuals. Such information is invaluable for monitoring large urban areas and supporting the implementation of decision-making services. When analyzed in real time, NSD can enable the early detection of critical urban events, including fires, large accidents, stampedes, terrorist attacks, and sports and leisure gatherings, especially if these events significantly impact mobile phone network activity in the affected areas. This paper presents empirical evidence that advanced NSD can detect anomalies in mobile traffic service consumption, attributable to critical urban events, with fine spatial (a spatial resolution of a few decameters) and temporal (minutes) resolutions. We introduce two methodologies for real-time anomaly detection from multivariate time series extracted from large-scale NSD, utilizing a range of algorithms adapted from the state-of-the-art in unsupervised machine learning techniques for anomaly detection. Our research includes a comprehensive quantitative evaluation of these algorithms on a large-scale dataset of NSD service consumption for the Paris region. The evaluation uses an original dataset of documented critical or unusual urban events. This dataset has been built as a ground truth basis for assessing the algorithms' performance. The obtained results demonstrate that our framework can detect unusual events almost instantaneously and locate the affected areas with high precision, largely outperforming random classifiers. This efficiency and effectiveness underline the potential of NSD-based anomaly detection in significantly enhancing emergency response strategies and urban planning. By offering a proactive approach to managing urban safety and resilience, our findings highlight the transformative potential of leveraging NSD for anomaly detection in urban environments.

Dynamic semantic networks for exploration of creative thinking

Abstract Human creativity originates from brain cortical networks that are specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring creative thought. The primary advantage of using lexical databases such as WordNet for reproducible information-theoretic quantification of convergence or divergence of design ideas in creative problem solving is the simultaneous handling of both words and meanings, which enables interpretation of the constructed dynamic semantic networks in terms of underlying functionally active brain cortical regions involved in concept comprehension and production. In this study, the quantitative dynamics of semantic measures computed with a moving time window is investigated empirically in the DTRS10 dataset with design review conversations and detected divergent thinking is shown to predict success of design ideas. Thus, dynamic semantic networks present an opportunity for real-time computer-assisted detection of critical events during creative problem solving, with the goal of employing this knowledge to artificially augment human creativity.

SOFTWARE IMPLEMENTATION OF ALGORITHMS FOR SELECTING THE OUTLINES OF OBJECTS IMAGES IN INTELLECTUAL VIDEO SURVEILLANCE SYSTEMS

The rapid advancement in digital image acquisition and transmission technologies poses challenges in processing vast volumes of video information streams. Images serve as crucial sources of decision-making information across a wide range of tasks. Key objectives addressed by intelligent video surveillance systems include object identification, trajectory determination, object speed measurement, and real-time detection of critical events for securing premises. Among the primary operations in intelligent video surveillance systems is the extraction of object contours from images, as contours contain essential information for object recognition based on their shape. This approach excludes internal image points, significantly reducing the amount of processed information and enabling real-time image analysis. Contour analysis encompasses methods for selecting, describing, and processing image contours to describe, store, compare, and search for objects based on their external contours. This methodology effectively addresses fundamental challenges in pattern recognition, such as transferring, rotating, and scaling object images. The paper discusses prominent contour analysis methods and presents software implementations of algorithms for extracting object outlines in intelligent video surveillance systems. A promising future direction involves synthesizing algorithms that extract object contours using a two-scale statistical image model.

Advanced IoT Pressure Monitoring System for Real-Time Landfill Gas Management.

This research presents a novel stand-alone device for the autonomous measurement of gas pressure levels on an active landfill site, which enables the real-time monitoring of gas dynamics and supports the early detection of critical events. The developed device employs advanced sensing technologies and wireless communication capabilities, enabling remote data transmission and access via the Internet. Through extensive field experiments, we demonstrate the high sampling rate of the device and its ability to detect significant events related to gas generation dynamics in landfills, such as flare shutdowns or blockages that could lead to hazardous conditions. The validation of the device's performance against a high-end analytical system provides further evidence of its reliability and accuracy. The developed technology herein offers a cost-effective and scalable solution for environmental landfill gas monitoring and management. We expect that this research will contribute to the advancement of environmental monitoring technologies and facilitate better decision-making processes for sustainable waste management.

Innovative Smart Drilling with Critical Event Detection and Material Classification

This work presents a cyber-physical drilling machine that incorporates technologies discovered in the fourth industrial revolution. The machine is designed to realize its state by detecting whether it hits or breaks through the workpiece, without the need for additional sensors apart from the position sensor. Such self-recognition enables the machine to adapt and shift the controllers that handle position, velocity, and force, based on the workpiece and the drilling environment. In the experiment, the machine can detect and switch controls that follow the drilling events (HIT and BREAKHTROUGH) within 0.1 and 0.5 s, respectively. The machine’s high visibility design is beneficial for classification of the workpiece material. By using a support-vector-machine (SVM) on thrust force and feed rate, the authors are seen to achieve 92.86% accuracy for classification of material, such as medium-density fiberboard (MDF), acrylic, and glass.

An Autonomous Continuous Bladder Irrigation System.

Introduction and Objective: Continuous bladder irrigation (CBI) is used in a variety of clinical settings, including post-transurethral surgery and the emergency department. Currently, CBI administration relies on nurses to diligently monitor and switch irrigation bags, as well as titrate the inflow rate based on effluent color. Inappropriate administration can result in discomfort to patients, clot urinary retention, repeat injury to the pathologic or surgical site, extended hospital stays, and even operative management. Our objective was to create an autonomous CBI system that decreases the incidence of disrupted irrigation flow and monitors the outflow to alert clinicians of critical events. Methods: 3D printing and off-the-shelf microcontrollers were used to design a device to fit the needs identified by stakeholders at our institution. An in vitro model of the bladder was created to test our design. The mechanical, electrical, and software subsystems were adjusted accordingly to meet our design requirements. Results: Our in vitro CBI model was able to simulate routine CBI administration with sudden bleeding. Bovine blood was used to simulate the bleeding events. A device was created that met identified stakeholder needs. Accurate detection of critical bleeding events, catheter blockage, and empty irrigation bags were achieved. The device responds to bleeding appropriately by increasing the irrigation rate. When the catheter is blocked, it stops the irrigation and alerts the nurse. Our system accurately titrated the irrigation rate to match a set outflow blood level parameter, conserving irrigation and minimizing nursing workload. Continuous monitoring of CBI effluent was recorded. Conclusions: We anticipate our device will decrease the cognitive load on nurses in busy clinical settings and improve workflow. Moreover, the detection of critical events will likely decrease patient morbidity. Continuous monitoring of the CBI outflow may prove to be a new clinical decision-making tool for ongoing hematuria. Clinical trial is pending.

Biomarkers of alloimmune events in pediatric kidney transplantation.

Alloimmune events such as the development of de novo donor-specific antibody (dnDSA), T cell-mediated rejection (TCMR), and antibody-mediated rejection (ABMR) are the primary contributors to kidney transplant failure in children. For decades, a creatinine-based estimated glomerular filtration rate (eGFR) has been the non-invasive gold standard biomarker for detecting clinically significant alloimmune events, but it suffers from low sensitivity and specificity, especially in smaller children and older allografts. Many clinically "stable" children (based on creatinine) will have alloimmune events known as "subclinical acute rejection" (based on biopsy) that merely reflect the inadequacy of creatinine-based estimates for alloimmune injury rather than a distinct phenotype from clinical rejection with allograft dysfunction. The poor biomarker performance of creatinine leads to many unnecessary surveillance and for-cause biopsies that could be avoided by integrating non-invasive biomarkers with superior sensitivity and specificity into current clinical paradigms. In this review article, we will present and appraise the current state-of-the-art in monitoring for alloimmune events in pediatric kidney transplantation. We will first discuss the current clinical standards for assessing the presence of alloimmune injury and predicting long-term outcomes. We will review principles of biomarker medicine and the application of comprehensive metrics to assess the performance of a given biomarker against the current gold standard. We will then highlight novel blood- and urine-based biomarkers (with special emphasis on pediatric biomarker studies) that have shown superior diagnostic and prognostic performance to the current clinical standards including creatinine-based eGFR. Finally, we will review some of the barriers to translating this research and implementing emerging biomarkers into common clinical practice, and present a transformative approach to using multiple biomarker platforms at different times to optimize the detection and management of critical alloimmune events in pediatric kidney transplant recipients.

Integrating Wearable Sensors and Machine Learning for the Detection of Critical Events in Industry Workers.

The event where an industry worker experiences some sort of critical health problems on site, due to factors not strictly related to the job, poses a serious concern and is an issue of research. These events can be mitigated almost entirely if the workers' health is being monitored in real time by an occupational physician along with an artificial intelligence system that can foresee a health incident and act fast and efficiently. For this reason, we developed a framework of devices, systems, and algorithms which help the industry workers along with the industries to monitor such events and, if possible, minimize them. The aforementioned framework performs seamlessly and autonomously and creates a system where the health of the industry workers is being monitored in real time. In the proposed solution, the worker would wear a wrist sensor in the form of a smartwatch as well as a blood pressure device on the ear. These sensors can communicate directly with a cloud storage system to store sensor data, and then real-time data analysis can be performed. Subsequently, all results can be displayed in an interface operated by an occupational physician, and in case of a health issue event, the doctor and the worker will be notified.

Fuzzy-Based Expert Supervision System for Feedback Controlled Oxygenation.

Supervision of mechanical ventilation is currently still performed by clinical staff. With the increasing level of automation in the intensive care unit, automatic supervision is becoming necessary. We present a fuzzy-based expert supervision system applicable to automatic feedback control of oxygenation. An adaptive fuzzy limit checking and trend detection algorithm was implemented. A knowledge-based fuzzy logic system combines these outputs into a final score, which subsequently triggers alarms if a critical event is registered. The system was evaluated against annotated experimental data. An accuracy of 83 percent and a precision of 95 percent were achieved. The automatic detection of critical events during feedback control of oxygenation provides an additional layer of safety and assists in alerting clinicians in the case of abnormal behavior of the system. Clinical relevance - Automatic supervision is a necessary feature of physiological feedback systems to make them safer and more reliable in the future.

Clinical feasibility of an advanced neonatal epidermal multiparameter continuous monitoring technology in a large public maternity hospital in Nairobi, Kenya

Clinically feasible multiparameter continuous physiological monitoring technologies are needed for use in resource-constrained African healthcare facilities to allow for early detection of critical events and timely intervention for major morbidities in high-risk neonates. We conducted a prospective clinical feasibility study of a novel multiparameter continuous physiological monitoring technology in neonates at Pumwani Maternity Hospital in Nairobi, Kenya. To assess feasibility, we compared the performance of Sibel’s Advanced Neonatal Epidermal (ANNE) technology to reference technologies, including Masimo’s Rad-97 pulse CO-oximeter with capnography technology for heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO2) measurements and Spengler’s Tempo Easy non-contact infrared thermometer for temperature measurements. We evaluated key performance criteria such as up-time, clinical event detection performance, and the agreement of measurements compared to those from the reference technologies in an uncontrolled, real-world setting. Between September 15 and December 15, 2020, we collected and analyzed 503 h of ANNE data from 109 enrolled neonates. ANNE’s up-time was 42 (11%) h more for HR, 77 (25%) h more for RR, and 6 (2%) h less for SpO2 compared to the Rad-97. However, ANNE’s ratio of up-time to total attached time was less than Rad-97’s for HR (0.79 vs 0.86), RR (0.68 vs. 0.79), and SpO2 (0.69 vs 0.86). ANNE demonstrated adequate performance in identifying high and low HR and RR and high temperature events; however, showed relatively poor performance for low SpO2 events. The normalized spread of limits of agreement were 8.4% for HR and 52.2% for RR and the normalized root-mean-square deviation was 4.4% for SpO2. Temperature agreement showed a spread of limits of agreement of 2.8 °C. The a priori-identified optimal limits were met for HR and temperature but not for RR or SpO2. ANNE was clinically feasible for HR and temperature but not RR and SpO2 as demonstrated by the technology’s up-time, clinical event detection performance, and the agreement of measurements compared to those from the reference technologies.

An efficient automatic accident detection system using inertial measurement through machine learning techniques for powered two wheelers

The two-wheeler accidents in most populated and developing countries have become vulnerable and six accidents happen every hour on average. This paper proposes an efficient automatic accident detection system that attempts to detect the occurrences of the accidents in powered two-wheelers (PTW) automatically using vehicle-dependent parameters and the physiological parameters of the rider in real-time. The proposed system builds an accident detection system in PTW using three steps namely, critical event detection system, accident detection system, and severity assessment system. The critical event detection system reads the accelerometer sensor values from the On-Board Diagnostic (OBD) unit mounted on the PTW and classifies the state of the vehicle as normal, fall-like, and fall through the enhanced decision tree algorithm. The enhanced decision tree algorithm uses a tanh function to calculate entropy values. The rules are extracted to fix the threshold by pruning the decision tree to identify the fall of the vehicle and the rider. Due to the unstable nature of PTW and the rider, a novel Adaptive Sequence Window algorithm (ASW) is proposed to substantiate and validate the occurrence of accidents based on the sequence of states identified. Once the accident is detected, the Decision Support System (DSS) running on the OBD mounted on the PTW decides the severity of the accident by combining the three parameters namely fall of the vehicle, fall of the rider, and pulse rate of the rider using the first-order predicate logic rules. The enhanced decision tree algorithm outperforms the other classifiers such as naïve Bayes, artificial neural network, and recurrent neural network with an accuracy of 99.8%. The OBD unit mounted on the PTW and the rider’s helmet is used to detect the occurrence of accidents automatically along with its severity with less time. The ASW algorithm enables the system to detect the fall of the vehicle and rider within five minutes and prevents false positives. Further, the information can be communicated based on the severity of the accident to the emergency medical service for quick response.

Data Stream Processing for Packet-Level Analytics.

One of the most challenging tasks for network operators is implementing accurate per-packet monitoring, looking for signs of performance degradation, security threats, and so on. Upon critical event detection, corrective actions must be taken to keep the network running smoothly. Implementing this mechanism requires the analysis of packet streams in a real-time (or close to) fashion. In a softwarized network context, Stream Processing Systems (SPSs) can be adopted for this purpose. Recent solutions based on traditional SPSs, such as Storm and Flink, can support the definition of general complex queries, but they show poor performance at scale. To handle input data rates in the order of gigabits per seconds, programmable switch platforms are typically used, although they offer limited expressiveness. With the proposed approach, we intend to offer high performance and expressive power in a unified framework by solely relying on SPSs for multicores. Captured packets are translated into a proper tuple format, and network monitoring queries are applied to tuple streams. Packet analysis tasks are expressed as streaming pipelines, running on general-purpose programmable network devices, and a second stage of elaboration can process aggregated statistics from different devices. Experiments carried out with an example monitoring application show that the system is able to handle realistic traffic at a 10 Gb/s speed. The same application scales almost up to 20 Gb/s speed thanks to the simple optimizations of the underlying framework. Hence, the approach proves to be viable and calls for the investigation of more extensive optimizations to support more complex elaborations and higher data rates.

On the Use of Cameras for the Detection of Critical Events in Sensors-Based Emergency Alerting Systems

The adoption of emergency alerting systems can bring countless benefits when managing urban areas, industrial plants, farms, roads and virtually any area that is subject to the occurrence of critical events, supporting in rescue operations and reducing their negative impacts. For such systems, a promising approach is to exploit scalar sensors to detect events of interest, allowing for the distributed monitoring of different variables. However, the use of cameras as visual sensors can enhance the detection of critical events, which can be employed along with scalar sensors for a more comprehensive perception of the environment. Although the particularities of visual sensing may be challenging in some scenarios, the combination of scalar and visual sensors for the early detection of emergency situations can be valuable for many scenarios, such as smart cities and industry 4.0, bringing promising results. Therefore, in this article, we extend a sensors-based emergency detection and alerting system to also exploit visual monitoring when identifying critical events. Implementation and experimental details are provided to reinforce the use of cameras as a relevant sensor unit, bringing promising results for emergencies management.

Using artificial intelligence to detect crisis related to events: Decision making in B2B by artificial intelligence

Artificial Intelligence (AI) could be an important foundation of competitive advantage in the market for firms. As such, firms use AI to achieve deep market engagement when the firm's data are employed to make informed decisions. This study examines the role of computer-mediated AI agents in detecting crises related to events in a firm. A crisis threatens organizational performance; therefore, a data-driven strategy will result in an efficient and timely reflection, which increases the success of crisis management. The study extends the situational crisis communication theory (SCCT) and Attribution theory frameworks built on big data and machine learning capabilities for early detection of crises in the market. This research proposes a structural model composed of a statistical and sentimental big data analytics approach. The findings of our empirical research suggest that knowledge extracted from day-to-day data communications such as email communications of a firm can lead to the sensing of critical events related to business activities. To test our model, we use a publicly available dataset containing 517,401 items belonging to 150 users, mostly senior managers of Enron during 1999 through the 2001 crisis. The findings suggest that the model is plausible in the early detection of Enron's critical events, which can support decision making in the market.

Simulation-based training of critical events during cardiopulmonary bypass: importance of a critical events checklist.

Evaluation of critical events training for clinical perfusionists is necessary to improve this educational approach. Critical events checklists are effective in reducing clinical complications, but should be tested in a simulation environment first. Individual behavior and stress response of clinical perfusionists during simulated critical events on cardiopulmonary bypass have not been evaluated yet. This study focuses on the evaluation of critical events training and critical events checklists in simulated cardiopulmonary bypass. A total of 19 clinical perfusionists from a single hospital took part in two simulated critical event scenarios. Clinical perfusionist behavior and physiological responses were recorded using eye tracking, heart rate variability, video, and audio. In addition, workloads were determined and participants were interviewed. Relevant areas of interest were identified for each simulation phase. During critical event detection and subsequent decision-making, areas of interest hits and fixation durations varied with the use of a critical events checklist. Times to decision were shorter, decision quality was higher, and temporal workload was increased when the checklist was used. Evaluation of selected heart rate variability measures revealed a good correlation with pupil diameters. Evaluation of critical events during simulated cardiopulmonary bypass shows that the scenario is realistic and relevant for clinical practice. Integrating a critical events checklist improves the probability of correct decision-making and shortens the correct decision time. Temporal workload is increased when using a checklist. Eye tracking and heart rate variability are well suited to evaluate participants' behaviors and stress levels. All participants welcomed simulation training for critical incidents.

Continuous, Lateralized Auditory Stimulation Biases Visual Spatial Processing.

Sounds in our environment can easily capture human visual attention. Previous studies have investigated the impact of spatially localized, brief sounds on concurrent visuospatial attention. However, little is known on how the presence of a continuous, lateralized auditory stimulus (e.g., a person talking next to you while driving a car) impacts visual spatial attention (e.g., detection of critical events in traffic). In two experiments, we investigated whether a continuous auditory stream presented from one side biases visual spatial attention toward that side. Participants had to either passively or actively listen to sounds of various semantic complexities (tone pips, spoken digits, and a spoken story) while performing a visual target discrimination task. During both passive and active listening, we observed faster response times to visual targets presented spatially close to the relevant auditory stream. Additionally, we found that higher levels of semantic complexity of the presented sounds led to reduced visual discrimination sensitivity, but only during active listening to the sounds. We provide important novel results by showing that the presence of a continuous, ongoing auditory stimulus can impact visual processing, even when the sounds are not endogenously attended to. Together, our findings demonstrate the implications of ongoing sounds on visual processing in everyday scenarios such as moving about in traffic.

Fault Diagnostics in PEMFC Stacks by Evaluation of Local Performance and Cell Impedance Analysis

AbstractStarvation, flooding, and dry‐out phenomena occur in polymer electrolyte membrane fuel cells (PEMFCs), due to heterogeneous local conditions, material inhomogeneity, and uneven flow distribution across the single cell active area and in between the individual cells. The impact of the load level and air feed conditions on the performance was identified for individual single cells within a 10‐cell stack. Analysis of the current density distribution across the active area at the cell level was correlated with electrochemical impedance spectroscopy to enable operando fault diagnostic without any impact of the applied analytical tools on the single cell behavior. Moreover, the combination of both technologies allows in‐depth analysis of fault mechanisms in fuel cell single cells with improved sensitivity. Current density distribution and the quantitative assessment of the performance homogeneity demonstrated high sensitivity to small humidity changes and allow the detection of critical events, such as dry‐out in single cells. Impedance analysis is more sensitive regarding polarization and diffusion limitations and allows detection of cell flooding. The combination of both techniques is required for reliable identification of air starvation faults.

A prioritization approach for optimization of multiple concurrent sensing applications in smart cities

The development of more efficient communication technologies and the affordability of powerful hardware platforms have brought the smart cities prospects to a more realistic setting. Actually, this promising scenario has fostered the development of different monitoring and control applications aimed at improving the quality of life in modern urban areas, often implemented through sensors and actuators networks. Nevertheless, multiple concurrent sensing applications may have different levels of criticality according to the impact of the performed tasks for the protection of people’s integrity and thus they should be treated in different ways. This article then proposes a prioritization approach to guide the development of sensors-based applications in smart city scenarios, which will be differentiated based on their monitoring objectives, some contextual prioritization parameters and the detection of critical events. This approach will also define security mechanisms to ensure that all sensor nodes will follow the defined prioritization policies for the considered smart cities. Besides simulation results, the proposed definitions and mechanisms were completely implemented for additional validation, resulting in a set of codes to facilitate the construction of practical sensor applications based on the proposed approach.