Rescue Operations Research Articles

White Paper on Adaptive Situational Awareness Enhancing Augmented Reality Interface Design on First Responders in Rescue Tasks

The advance in the development of augmented reality technologies has attracted interest in their applicability in rescue scenarios. The characteristics of the different missions covered by First Responders, as well as the different objectives they can cover in a rescue operation, condition the importance of the additional information they can receive in these rescue processes through technology. This white paper aims to analyze the difficulties encountered when converging on the design of an interface that is adaptable to the professional and contextual circumstances in a rescue task.

A Scoping Review on Role of Edge Computing in Search and Rescue Operations

A Scoping Review on Role of Edge Computing in Search and Rescue Operations

Emergent Technologies in Human Detection for Disaster Response: A Critical Review

In the face of natural disasters, the rapid detection of trapped victims is crucial for effective search and rescue (SAR) operations. This review critically examines the emergent technologies in human detection that are pivotal in disaster response. Current methodologies for human detection encompass a variety of techniques: Radar, Artificial intelligence (AI), Drones, Robotics, Gas sensors, Acoustic, Infrared, and Optical detection. These methods are specifically designed to detect human presence in disaster scenarios by leveraging principles from electromagnetic, acoustic, AI-based algorithms, gas sensors, and optical detection. However, these technologies face challenges and limitations. They often depend on environmental conditions, encounter obstacles that affect their penetration capabilities, and have limited spatial resolution due to complex backgrounds. This paper aims to provide a comprehensive overview of the state-of-the-art in human-detection technologies, assess their impact on SAR missions, and offer insights into future advancements that could further enhance the survivability of disaster-stricken individuals.

Application of IMU/GPS Integrated Navigation System Based on Adaptive Unscented Kalman Filter Algorithm in 3D Positioning of Forest Rescue Personnel.

Utilizing reliable and accurate positioning and navigation systems is crucial for saving the lives of rescue personnel and accelerating rescue operations. However, Global Navigation Satellite Systems (GNSSs), such as GPS, may not provide stable signals in dense forests. Therefore, integrating multiple sensors like GPS and Inertial Measurement Units (IMUs) becomes essential to enhance the availability and accuracy of positioning systems. To accurately estimate rescuers' positions, this paper employs the Adaptive Unscented Kalman Filter (AUKF) algorithm with measurement noise variance matrix adaptation, integrating IMU and GPS data alongside barometric altitude measurements for precise three-dimensional positioning in complex environments. The AUKF enhances estimation robustness through the adaptive adjustment of the measurement noise variance matrix, particularly excelling when GPS signals are interrupted. This study conducted tests on two-dimensional and three-dimensional road scenarios in forest environments, confirming that the AUKF-algorithm-based integrated navigation system outperforms the traditional Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Adaptive Extended Kalman Filter (AEKF) in emergency rescue applications. The tests further evaluated the system's navigation performance on rugged roads and during GPS signal interruptions. The results demonstrate that the system achieves higher positioning accuracy on rugged forest roads, notably reducing errors by 18.32% in the north direction, 8.51% in the up direction, and 3.85% in the east direction compared to the EKF. Furthermore, the system exhibits good adaptability during GPS signal interruptions, ensuring continuous and accurate personnel positioning during rescue operations.

Formal Modelling and Model Checking of a Flood Monitoring and Rescue System: A Case Study of Safety-Critical System

The flood incidents are becoming more often and severe, thus extreme events require efficient and effective means of controlling and saving lives and property. The reason for this paper is to use Formal Modelling and Model Checking to analyse a new safety critical Flood Monitoring and Rescue System (FMRS) that shall form the basis for the efficient response to floods. Employing the TLA+ analysis, which outlines the FMRS’s dynamic behavior and operational specifications comprehensively. It is important to stress that in our work we address one of the most exciting directions of applying formal methods for the first time in collaboration with real-world safety-critical system designers and offer a powerful and transparent systematic approach to verifying safety-critical systems’ correctness, safety, and reliability. The TLA+ specifications are very carefully designed to represent multiple aspects of the FMRS, such as sensor systems, communication interfaces, as well as the rescue activity itself. To this end, we use model checking methodologies in order to assess the system’s compliance with the required safety properties, including timely detection of floods, correct delivery of data, and synchronization of rescue operations. The performed model checking demonstrates the presence of essential information about the system’s potential failure and weaknesses, which can be used for FMRS architecture improvement and development. Thus, this case shows that the best use of formal methods exist not as ad hoc methods for resolving some issues in the development of safety-critical systems, but a structured template that could be applied in other domains where high degree of assurance in the reliability of a system is needed. Besides the novel method for the better future of the field of formal verification, the proposal also sketches functional relevance of integrating the effective and efficient approaches for monitoring floods and emergency rescue operations in real-world contexts.

A methodology for appropriate withdrawal of tsunami warnings based on numerical simulations

This study performed large-scale numerical simulations for predicting the attenuation of tsunamis caused by the 2011 Tohoku, Japan, and 2010 Maule, Chile earthquakes, recorded at Japanese tide gauges. Tsunami amplitude waveforms were generated by computing the moving root-mean-square of the data for quantitative analyses. Sensitivity analysis showed that tsunami nonlinearity and computational grid intervals near the tide gauges significantly impact the prediction of tsunami attenuation. The predicted withdrawal times of tsunami warnings agreed with the observations; however, time discrepancies were observed for advisory withdrawals at several stations. Using the proposed method, we predicted the warning period of a great interplate earthquake in the Nankai Trough to be approximately one day. These findings can provide critical information for disaster prevention because the withdrawal of warnings is directly related to permission to enter coastal areas affected by the tsunami, whereas unnecessarily long warnings hinder rescue operations.

Penetrating Barriers: Noncontact Measurement of Vital Bio Signs Using Radio Frequency Technology.

The noninvasive measurement and sensing of vital bio signs, such as respiration and cardiopulmonary parameters, has become an essential part of the evaluation of a patient's physiological condition. The demand for new technologies that facilitate remote and noninvasive techniques for such measurements continues to grow. While previous research has made strides in the continuous monitoring of vital bio signs using lasers, this paper introduces a novel technique for remote noncontact measurements based on radio frequencies. Unlike laser-based methods, this innovative approach offers the advantage of penetrating through walls and tissues, enabling the measurement of respiration and heart rate. Our method, diverging from traditional radar systems, introduces a unique sensing concept that enables the detection of micro-movements in all directions, including those parallel to the antenna surface. The main goal of this work is to present a novel, simple, and cost-effective measurement tool capable of indicating changes in a subject's condition. By leveraging the unique properties of radio frequencies, this technique allows for the noninvasive monitoring of vital bio signs without the need for physical contact or invasive procedures. Moreover, the ability to penetrate barriers such as walls and tissues opens new possibilities for remote monitoring in various settings, including home healthcare, hospital environments, and even search and rescue operations. In order to validate the effectiveness of this technique, a series of experiments were conducted using a prototype device. The results demonstrated the feasibility of accurately measuring respiration patterns and heart rate remotely, showcasing the potential for real-time monitoring of a patient's physiological parameters. Furthermore, the simplicity and low-cost nature of the proposed measurement tool make it accessible to a wide range of users, including healthcare professionals, caregivers, and individuals seeking to monitor their own health.

ERABiLNet: enhanced residual attention with bidirectional long short-term memory

Alzheimer’s Disease (AD) causes slow death in brain cells due to shrinkage of brain cells which is more prevalent in older people. In most cases, the symptoms of AD are mistaken as age-related stresses. The most widely utilized method to detect AD is Magnetic Resonance Imaging (MRI). Along with Artificial Intelligence (AI) techniques, the efficacy of identifying diseases related to the brain has become easier. But, the identical phenotype makes it challenging to identify the disease from the neuro-images. Hence, a deep learning method to detect AD at the beginning stage is suggested in this work. The newly implemented “Enhanced Residual Attention with Bi-directional Long Short-Term Memory (Bi-LSTM) (ERABi-LNet)” is used in the detection phase to identify the AD from the MRI images. This model is used for enhancing the performance of the Alzheimer’s detection in scale of 2–5%, minimizing the error rates, increasing the balance of the model, so that the multi-class problems are supported. At first, MRI images are given to “Residual Attention Network (RAN)”, which is specially developed with three convolutional layers, namely atrous, dilated and Depth-Wise Separable (DWS), to obtain the relevant attributes. The most appropriate attributes are determined by these layers, and subjected to target-based fusion. Then the fused attributes are fed into the “Attention-based Bi-LSTM”. The final outcome is obtained from this unit. The detection efficiency based on median is 26.37% and accuracy is 97.367% obtained by tuning the parameters in the ERABi-LNet with the help of Modified Search and Rescue Operations (MCDMR-SRO). The obtained results are compared with ROA-ERABi-LNet, EOO-ERABi-LNet, GTBO-ERABi-LNet and SRO-ERABi-LNet respectively. The ERABi_LNet thus provides enhanced accuracy and other performance metrics compared to such deep learning models. The proposed method has the better sensitivity, specificity, F1-Score and False Positive Rate compared with all the above mentioned competing models with values such as 97.49%.97.84%,97.74% and 2.616 respective;y. This ensures that the model has better learning capabilities and provides lesser false positives with balanced prediction.

Flying foxes optimization with reinforcement learning for vehicle detection in UAV imagery

Intelligent transportation systems (ITS) are globally installed in smart cities, which enable the next generation of ITS depending on the potential integration of autonomous and connected vehicles. Both technologies are being tested widely in various cities across the world. However, these two developing technologies are vital in allowing a fully automatic transportation system; it is necessary to automate other transportation and road components. Unmanned aerial vehicles (UAVs) or drones are utilized for many surveillance applications in the ITS. Detecting on-ground vehicles in drone images is significant for disaster rescue operations, traffic and parking management, and navigating uneven territories. This study presents a flying foxes optimization with deep learning-based vehicle detection and classification model on aerial images (FFODL-VDCAI) technique for ITS application. The main objective of the FFODL-VDCAI technique is to automate and accurately classify vehicles that exist in aerial images. Three primary processes are involved in the presented FFODL-VDCAI technique. Initially, the FFODL-VDCAI approach utilizes YOLO-GD (Ghost-Net and Depthwise convolution) for vehicle detection, where the YOLO-GD uses lightweight Ghost Net in place on the backbone network of YOLO-v4 and interchanges the conventional convolutional with depthwise separable convolutional and pointwise convolutional. Next, the FFO technique is used for hyperparameter tuning the Ghost Net technique. Finally, a deep Q-network (DQN) based reinforcement learning technique is used to classify detected vehicles effectively. A comprehensive simulation analysis of the FFODL-VDCAI methodology is conducted on the UAV image dataset. The performance validation of the FFODL-VDCAI methodology exhibited superior values of 96.15% and 92.03% under PSU and Stanford datasets concerning various aspects.

Deep learning with ensemble approach for early pile fire detection using aerial images

Wildfires rank among the world’s most devastating and expensive natural disasters, destroying vast forest resources and endangering lives. Traditional firefighting methods, reliant on ground crew inspections, have notable limitations and pose significant risks to firefighters. Consequently, drone-based aerial imaging technologies have emerged as a highly sought-after solution for combating wildfires. Recently, there has been growing research interest in autonomous wildfire detection using drone-captured images and deep-learning algorithms. This paper introduces a novel deep-learning-based method, distinct in its integration of infrared thermal, white, and night vision imaging to enhance early pile fire detection, thereby addressing the limitations of existing methods. The study evaluates the performance of machine learning algorithms such as random forest (RF) and support vector machines (SVM), alongside pre-trained deep learning models including AlexNet, Inception ResNetV2, InceptionV3, VGG16, and ResNet50V2 on thermal-hot, green-hot, and white-green-hot color images. The proposed approach, particularly the ensemble of ResNet50V2 and InceptionV3 models, achieved over 97% accuracy and over 99% precision in early pile fire detection on the FLAME dataset. Among the tested models, ResNet50V2 excelled with the thermal-fusion palette, InceptionV3 with the white-hot and green-hot fusion palettes, and VGG16 with a voting classifier on the normal spectrum palette dataset. Future work aims to enhance the detection and localization of pile fires to aid firefighters in rescue operations.

Highly durable yarn-based strain sensor with enhanced underwater monitoring capabilities and rapid drowning detection for safety applications

Flexible strain sensors have garnered significant attention for their outstanding performance in fields such as telemedicine, motion management, and human–machine interfaces. However, in the demanding marine environments, these advanced sensors face challenges related to mechanical weakness, stability, and durability under wet conditions, which are critical for expanding their range of practical applications. In this study, a stable and durable yarn sensor was developed specifically tailored for underwater activity monitoring. This was achieved by using polydopamine to immobilize silver nanoparticles, thereby creating a highly conductive layer, followed by applying an impregnation coating to establish a hydrophobic layer. The resulting composite structure featured a micro-convex layer of silver nanoparticles, which provided a durable hydrophobic surface resistant to mechanical wear and capable of maintaining its electrical properties even under high-velocity water impacts. More significantly, a FWAS was engineered, which is activated by pre-stretching the yarn sensor using a water-soluble vinylon yarn. The FWAS automatically alters its resistance within 1.3 s upon detecting someone falling into the water, thereby sending out an accurate distress signal to the external world by transmitting Morse code signals via gesture movements. This innovation provides a straightforward and effective approach for timely rescue operations from drowning, demonstrating the versatility of yarn-based flexible strain sensors for use not only in air but also in underwater scenarios.

Genetic Algorithm-based Optimization Model for Emergency Shelters Location Allocation in Public Health Emergency Situations

Deploying emergency resources after a disaster is crucial for reducing the event’s impact. A multi-objective (MO) resource allocation (RA) method is developed to achieve efficient distribution of relief items and the best choice of transportation routes, taking into account the variable and persistent nature of rescue operations. The MO Cellular Genetical Approach (MOC-GA) addresses the concept by incorporating auxiliary populations and neighborhood architecture into the cellular automaton. The comparative experiment conclusively demonstrates that MOC-GA performs favorably compared to Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and Dragon Fly Optimisation (DFO) in several metrics such as the Pareto Front (PF), hypervolume, mean objective function value, and PF ratios. The findings demonstrate that MOC-GA effectively addresses the MO dynamical emergency RA approach, offering decision-makers a more comprehensive range of superior and diversified candidate rescue strategies than alternative methods. This research analyzes the existing rescue methods and proposes a theoretical rescue strategy to aid decision-makers in making scientifically informed decisions.

Damage assessment of self‐centering rocking piers using an input energy‐based damage prediction model coupled with self‐centering index

AbstractThe immediate functionality of bridges following severe earthquakes is vital for uninterrupted rescue operations. Regarding the significance of resiliency in bridges, post‐tensioned (PT) rocking piers with low residual displacements and minimal damages have developed over the past few decades. The rocking mechanism at two ends of the pier avoids bending moments and excessive flexural damage. The self‐centering (SC) capacity in this system is provided through post‐tensioning forces. Concerning optimum seismic design and retrofit purposes, it is essential to predict the actual degree of seismic damage and SC capacity of PT rocking systems after seismic hazards. In this case, a self‐centering index (SI) is proposed to evaluate the SC capacity when piers are subjected to cyclic and seismic loadings. This SI, when used in co‐operation with a viable damage prediction model, predicts whether or not the piers remain reparable under cyclic or seismic loading scenarios. After comparing a number of energy‐based damage indices, all of which consider the cumulative hysteresis energy, with the input energy‐based damage index (IEB‐DI), the latter was calibrated against observed damages under cyclic loading tests. This DI was chosen as the most suitable damage prediction model and was considered to be simply applicable after time history analysis. In this study, the seismic performance of a seismic‐resistant dual system, consisting of three RC bents along with an SC bent, was evaluated using the aforementioned damage limit states and the introduced SI. The damage predictions of the monolithic bridge, as the reference model, were compared with the estimated damage to the dual bridge. The results show that the joint application of the IEB‐DI and the proposed SI in predicting the performance level of SC rocking piers results in a comprehensive damage prediction model.

The Role Of Animal Police In The Process Of Disclosing A Crime (Study At The K-9 Unit Of Bali Regional Police)

The K-9 Unit of the Bali Regional Police is an elite unit specializing in using sniffer dogs to assist in various police operations, ranging from apprehending criminals and detecting narcotics, to search and rescue (SAR) operations. The ability of sniffer dogs to detect narcotics and explosives is crucial in crime prevention and control efforts. In Bali, an international tourist destination, the threat of narcotics smuggling is very high. Specially trained sniffer dogs can quickly identify the presence of these illegal substances in various locations, such as airports, seaports, and entertainment venues. This study aims to identify and analyze the role of the K-9 Unit of the Bali Regional Police in the process of crime disclosure. Through a qualitative approach with a case study method, data were collected through interviews, observations, and literature studies. Through methods of observation, interviews, and literature study, this research is expected to provide a comprehensive and in-depth overview of the role of the K-9 Unit of the Bali Regional Police. The K-9 Unit of the Bali Regional Police has a significant contribution to disclosing crimes by providing technical assistance using sniffer dogs, which have proven effective in crime scene investigation and have a direct impact on solving criminal cases. Factors affecting the effectiveness of this unit include public demand, environmental conditions, and the availability of human and animal resources. To optimize its role, it is necessary to enhance training, facilities, coordination with related parties, and regular evaluation of the unit's performance.

Performance analysis of fire protective clothing: a review

Firefighters in such serious environments (i.e., hazardous flames and radiant heat, high air temperatures, hot chemicals and liquids) must wear fire protective clothing during fire extinguishing and rescue operations. Many studies in the field of fire protective clothing have been conducted to evaluate the protective, mechanical and comfort performance of fire protective clothing. A comprehensive review of the literature has been undertaken to provide a better understanding of single-layer and multilayer fire protective clothing with protective, mechanical and comfort performance. Factors affecting the durability of protective clothing in the light of their expected retirement age and a number of significant standards for laboratory and manikin testing are discussed. The comfort of wearing clothing is another aspect that needs special consideration when designing a multilayered fire protective suit. A number of emerging technologies have been outlined to address the various challenges of the present day.

Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces

Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces.

A Review of Multi-UAV Task Allocation Algorithms for a Search and Rescue Scenario

Unmanned aerial vehicles (UAVs) play a crucial role in enhancing search and rescue (SAR) operations by accessing inaccessible areas, accomplishing challenging tasks, and providing real-time monitoring and modeling in situations where human presence is unsafe. Multi-UAVs can collaborate more efficiently and cost-effectively than a single large UAV for performing SAR operations. In multi-UAV systems, task allocation (TA) is a critical and complex process involving cooperative decision making and control to minimize the time and energy consumption of UAVs for task completion. This paper offers an exhaustive review of both static and dynamic TA algorithms, confidently assessing their strengths, weaknesses, and limitations. It provides valuable insights into addressing research questions related to specific UAV operations in SAR. The paper rigorously discusses outstanding issues and challenges and confidently presents potential directions for the future development of task assignment algorithms. Finally, it confidently highlights the challenges of multi-UAV dynamic TA methods for SAR. This work is crucial for gaining a comprehensive understanding of multi-UAV dynamic TA algorithms and confidently emphasizes critical open issues and research gaps for future SAR research and development, ensuring that readers feel informed and knowledgeable.

Adaptive and low-cost resource synchronization based on data distribution service in high dynamic networks

In disaster-stricken areas monitoring, management, search, and rescue operations, unmanned aerial vehicles (UAVs) play a crucial role in disaster management and emergency communication due to their high mobility. To efficiently coordinate and plan UAVs and their carried sensor and base station resources, synchronization is essential to establish consistency, laying the foundation for high-level demands. In such scenarios, synchronization relies on request–response (RR) or publish–subscribe (PS) forms of information exchange. Existing research in the field typically focuses on higher-level applications and selects either RR or PS synchronization, thereby overlooking the potential advantages that could be gained from combining both methods to meet synchronization requirements. We propose a resource synchronization method based on the Data Distribution Service (DDS) and a linear time complexity subscription mechanism tailored to specific query demands, which considers the pros and cons of the above two information exchange forms and the bottom-layer network topology. Experimental results using open-source simulation tools demonstrate that the proposed method adapts to scene requirements and decreases bandwidth by at least 21.2% and packet rate by at least 3.7% compared to different baseline methods across three topologies, while satisfying delay and query success rate requirements. Furthermore, the method maintains robust performance in the face of dynamic changes in network topology, showcasing its robustness.

Experimental study on the effect of longitudinal ventilation on spill fire characteristics in sloped tunnels

A series of spill fire tests were carried out in a reduced-scale tunnel (6 m × 0.6 m × 0.45 m), considering different discharge rates (60–140 ml/min), ventilation rates (0∼1.26 m/s), and tunnel slopes (3 %, 5 %, 7 %). Some crucial characteristic parameters during the quasi-steady combustion stage of tunnel spill fires were analyzed, including the diffusion shape of the liquid fuel, mass burning rate per unit area, flame morphology and plume temperature, and the critical ventilation velocity. The results show that as the tunnel slope increases, the diffusion shape of the liquid fuel transitions from circular to an ellipse shape and then to rectangular or linear shape. Moreover, the mass burning rate per unit area decreases with increasing ventilation rates at low ventilation (V < 0.5 m/s), while it remains almost constant at high ventilation rates (V ≥ 0.5 m/s). This could be attributed to variations in flame tilt behavior under different ventilation conditions. Predictive models for the flame tilt angle and the vertical temperature distribution of tunnel spill fires under the effect of ventilation were further established and verified. Furthermore, the critical ventilation velocity of tunnel spill fires remains essentially unchanged as the discharge rate (or fire source power) increases. This phenomenon was explained by analyzing the resistance of the spill fire plume and the buoyancy generated by the smoke temperature. This work could serve as a critical reference for assessing disaster risks and executing emergency rescue operations during tunnel spill fires.

Post-Processing Maritime Wind Forecasts from the European Centre for Medium-Range Weather Forecasts around the Korean Peninsula Using Support Vector Regression and Principal Component Analysis

Accurate wind data are crucial for successful search and rescue (SAR) operations on the sea surface in maritime accidents, as survivors or debris tend to drift with the wind. As maritime accidents frequently occur outside the range of wind stations, SAR operations heavily rely on wind forecasts generated by numerical models. However, numerical models encounter delays in generating results due to spin-up issues, and their predictions can sometimes exhibit inherent biases caused by geographical factors. To overcome these limitations, we reviewed the observations for the first 24 h of the 72-hour forecast from the ECMWF and then post-processed the forecast for the remaining 48 h. By effectively reducing the dimensionality of input variables comprising observation and forecast data using principal component analysis, we improved wind predictions with support vector regression. Our model achieved an average RMSE improvement of 16.01% compared to the original forecast from the ECMWF. Furthermore, it achieved an average RMSE improvement of 5.42% for locations without observation data by employing a model trained on data from the nearest wind station and then applying an adaptive weighting scheme to the output of that model.