Search Results (135)

Intelligent unmanned clusters have played a crucial role in military reconnaissance, disaster rescue, border patrol, and other domains. Nevertheless, due to factors such as multipath propagation, electromagnetic interference, and frequency band congestion in high dynamic scenarios, unmanned cluster networks experience frequent topology changes and severe spectrum limitations, which hinder the provision of connected, elastic and autonomous network support for data interaction among unmanned aerial vehicle (UAV) nodes. To address the conflict between the demand for reliable data transmission and the limited network resources, this paper proposes an AODV routing protocol based on node energy consumption and mobility optimization (AODV-EM) from the perspective of network routing protocols. This protocol introduces two routing metrics: node energy based on node degree balancing and relative node mobility, to comprehensively account for both the balance of network node load and the stability of network links. The experimental results demonstrate that the AODV-EM protocol exhibits better performance compared to traditional AODV protocol in unmanned cluster networks with dense node distribution and high mobility, which not only improves the efficiency of data transmission, but also ensures the reliability and stability of data transmission. Full article

Maritime activities have become increasingly frequent with the deepening of economic globalization, highlighting the burgeoning significance of maritime rescue. However, in practical applications, UAVs for maritime rescue face numerous challenges, such as limited endurance and inadequate autonomous planning capabilities. To optimize flight routes and circumvent adverse sea conditions, an improved Pied Kingfisher Optimizer (IPKO) that incorporates refraction reverse learning, variable spiral search, and Cauchy mutation strategies was proposed. Comparative experiments conducted on CEC2005 and CEC2022 datasets with seven traditional algorithms demonstrate that the proposed algorithm exhibits superior precision and convergence speed. Subsequently, a path planning objective function was constructed based on trajectory cost and threat cost to simulate a 3D space for UAV maritime rescue missions, and the IPKO algorithm was applied to address the UAV path planning problem. The results showed that the total cost incurred by the IPKO algorithm decreased by 5.77% compared to the PKO algorithm and by 51.19% compared to the SCA algorithm. Finally, through UAV flight tests validating its practical applicability, it is ascertained that IPKO can enhance rescue efficiency in complex maritime rescue environments. Full article

This editorial discusses recent progress in data-driven intelligent modeling and optimization algorithms for industrial processes. With the advent of Industry 4.0, the amalgamation of sophisticated data analytics, machine learning, and artificial intelligence has become pivotal, unlocking new horizons in production efficiency, sustainability, and quality assurance. Contributions to this Special Issue highlight innovative research in advancements in work-sampling data analysis, data-driven process choreography discovery, intelligent ship scheduling for maritime rescue, process variability monitoring, hybrid optimization algorithms for economic emission dispatches, and intelligent controlled oscillations in smart structures. These studies collectively contribute to the body of knowledge on data-driven intelligent modeling and optimization, offering practical solutions and theoretical frameworks to address complex industrial challenges. Full article

Geological disasters, as a common occurrence, have a serious impact on social development in terms of their frequency of occurrence, disaster effects, and resulting losses. To effectively reduce the casualties, property losses, and social effects caused by various disasters, it is necessary to conduct real-time monitoring and early warning of various geological disaster risks. With the growing development of the information age, public attention to disaster relief, casualties, social impact effects, and other related situations has been increasing. Since social media platforms such as Weibo and Twitter contain a vast amount of real-time data related to disaster information before and after a disaster occurs, scientifically and effectively utilizing these data can provide sufficient and reliable information support for disaster relief, post-disaster recovery, and public appeasement efforts. As one of the techniques in natural language processing, the topic model can achieve precise mining and intelligent analysis of valuable information from massive amounts of data on social media to achieve rapid use of thematic models for disaster analysis after a disaster occurs, providing reference for post-disaster-rescue-related work. Therefore, this article first provides an overview of the development process of the topic model. Secondly, based on the technology utilized, the topic models were roughly classified into three categories: traditional topic models, word embedding-based topic models, and neural network-based topic models. Finally, taking the disaster data of “Dongting Lake breach” in Hunan, China as the research object, the application process and effectiveness of the topic model in urban geological disaster information mining were systematically introduced. The research results provide important references for the further practical innovation and expansion of the topic model in the field of disaster information mining. Full article

Natural disasters cause extensive infrastructure and significant economic losses, hindering sustainable development and impeding social and economic progress. More importantly, they jeopardize community well-being by causing injuries, damaging human health, and resulting in loss of life. Furthermore, communities often experience delayed disaster response. Aggravating the situation, the frequency and impact of disasters have been continuously increasing. Therefore, fast and effective disaster response management is paramount. To achieve this, disaster managers must proactively safeguard communities by developing quick and effective disaster management strategies. Disruptive technologies such as artificial intelligence (AI), machine learning (ML), and robotics and their applications in geospatial analysis, social media, and smartphone applications can significantly contribute to expediting disaster response, improving efficiency, and enhancing safety. However, despite their significant potential, limited research has examined how these technologies can be utilized for disaster response in low-income communities. The goal of this research is to explore which technologies can be effectively leveraged to improve disaster response, with a focus on low-income communities. To this end, this research conducted a comprehensive review of existing literature on disruptive technologies, using Covidence to simplify the systematic review process and NVivo 14 to synthesize findings. Full article

The prediction of fire growth is crucial for effective firefighting and rescue operations. Recent advancements in vision-based techniques using RGB vision and infrared (IR) thermal imaging data, coupled with artificial intelligence and deep learning techniques, have shown promising solutions to be applied in the detection of fire and the prediction of its behavior. This study introduces the use of Convolutional Long Short-term Memory (ConvLSTM) network models for predicting room fire growth by analyzing spatiotemporal IR thermal imaging data acquired from full-scale room fire tests. Our findings revealed that SwinLSTM, an enhanced version of ConvLSTM combined with transformers (a deep learning architecture based on a new mechanism called multi-head attention) for computer vision purposes, can be used for the prediction of room fire flashover occurrence. Notably, transformer-based ConvLSTM deep learning models, such as SwinLSTM, demonstrate superior prediction capability, which suggests a new vision-based smart solution for future fire growth prediction tasks. The main focus of this work is to perform a feasibility study on the use of a pure vision-based deep learning model for analysis of future video data to anticipate behavior of fire growth in room fire incidents. Full article

Known for their compact size, mobility, and off-road capabilities, motorcycles are increasingly used for logistics, emergency rescue, and reconnaissance. However, due to their two-wheeled nature, motorcycles are susceptible to instability, heightening the risk of tipping during cornering. This study includes some research and exploration into the following aspects: (1) The design of a front-wheel steering self-balancing controller. It achieves self-balance during motion by adjusting the front-wheel steering angle through manipulation of handlebar torque. (2) Trajectory tracking control based on preview control theory. It establishes a proportional relationship between lateral deviation and lean angle, as determined by path preview. The desired lean angle then serves as input for the self-balancing controller. (3) A pre-braking controller for enhanced active safety. To prevent lateral slide on wet and slippery surfaces, the controller is designed considering the motorcycle’s maximum braking deceleration. These advancements were validated via a joint BikeSim and Matlab/Simulink simulation, which included scenarios such as double lane changes and 60 m-radius turns. The results demonstrate that the intelligent motorcycle equipped with the proposed control algorithm tracks trajectories and maintains stability effectively. Full article

Drowning is among the most common reasons for children’s death aged one to fourteen around the globe, ranking as the third leading cause of unintentional injury death. With rising populations and the growing popularity of swimming pools in hotels and villas, the incidence of drowning has accelerated. Accordingly, the development of systems for detecting and preventing drowning has become increasingly critical to provide safe swimming settings. In this paper, we propose a comprehensive review of recent existing advancements in automated drowning detection and prevention systems. The existing approaches can be broadly categorized according to their objectives into two main groups: detection-based systems, which alert lifeguards or parents to perform manual rescues, and detection and rescue-based systems, which integrate detection with automatic rescue mechanisms. Automatic drowning detection approaches could be further categorized into computer vision-based approaches, where camera-captured images are analyzed by machine learning algorithms to detect instances of drowning, and sensing-based approaches, where sensing instruments are attached to swimmers to monitor their physical parameters. We explore the advantages and limitations of each approach. Additionally, we highlight technical challenges and unresolved issues related to this domain, such as data imbalance, accuracy, privacy concerns, and integration with rescue systems. We also identify future research opportunities, emphasizing the need for more advanced AI models, uniform datasets, and better integration of detection with autonomous rescue mechanisms. This study aims to provide a critical resource for researchers and practitioners, facilitating the development of more effective systems to enhance water safety and minimize drowning incidents. Full article

With the intensification of global warming and sea level rise, extreme weather and climate events occur frequently, increasing the probability and destructive power of marine disasters. The purpose of this paper is to propose the specific application of artificial intelligence (AI) in marine disaster risk reduction. First, this paper uses computer vision to assess the vulnerability of the target and then uses CNN-LSTM to forecast tropical cyclones. Second, this paper proposes a social media communication mechanism based on deep learning and a psychological crisis intervention mechanism based on AIGC. In addition, the rescue response system based on an intelligent unmanned platform is also the focus of this research. Third, this paper also attempts to discuss disaster loss assessment and reconstruction based on machine learning and smart city concepts. After proposing specific application measures, this paper proposes three policy recommendations. The first one is improving legislation to break the technological trap of AI. The second one is promoting scientific and technological innovation to break through key technologies of AI. The third one is strengthening coordination and cooperation to build a disaster reduction system that integrates man and machine. The purpose of this paper is to reduce the risk of marine disasters by applying AI. Furthermore, we hope to provide scientific references for sustainability and human adaptation to climate change. Full article

Floods are the most destructive, widespread, and frequent natural hazards. The extent of flood events is accelerating in the context of climate change, where flood management and disaster mitigation remain important long-term issues. Different studies have been utilizing data and images from various types of sensors for mapping, assessment, forecasting, early warning, rescue, and other disaster prevention and mitigation activities before, during, and after floods, including flash floods, coastal floods, and urban floods. These monitoring processes evolved from early ground-based observations relying on in situ sensors to high-precision, high-resolution, and high-coverage monitoring by airborne and remote sensing sensors. In this study, we have analyzed the different kinds of sensors from the literature review, case studies, and other methods to explore the development history of flood sensors and the driving role of floods in different countries. It is found that there is a trend towards the integration of flood sensors with artificial intelligence, and their state-of-the-art determines the effectiveness of local flood management to a large extent. This study helps to improve the efficiency of flood monitoring advancement and flood responses as it explores the different types of sensors and their effectiveness. Full article

A novel detection model, MS-YOLO, is developed in this paper to improve the efficiency of drowning rescue operations. The model is lightweight, high in precision, and applicable for intelligent hardware platforms. Firstly, the MD-C2F structure is built to capture the subtle movements and posture changes in various aquatic environments, with a light weight achieved by introducing dynamic convolution (DcConv). To make the model perform better in small object detection, the EMA mechanism is incorporated into the MD-C2F. Secondly, the MSI-SPPF module is constructed to improve the performance in identifying the features of different scales and the understanding of complex backgrounds. Finally, the ConCat single-channel fusion is replaced by BiFPN weighted channel fusion to retain more feature information and remove the irrelevant information in drowning features. Relative to the Faster R-CNN, SSD, YOLOv6, YOLOv9, and YOLOv10, the MS-YOLO achieves an average accuracy of 86.4% in detection on a self-built dataset at an ultra-low computational cost of 7.3 GFLOPs. Full article

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future. Full article

Aiming to improve the efficiency of the online process in path planning, a novel searching method is proposed based on environmental information analysis. Firstly, a search and rescue (SAR) environmental model and an unmanned ground vehicle (UGV) motion model are established according to the characteristics of a mining environment. Secondly, an online search area path-planning method is proposed based on the gray system theory and the reinforcement learning theory to handle multiple constraints. By adopting the multi-attribute intelligent (MAI) gray decision process, the action selection decision can be dynamically adjusted based on the current environment, ensuring the stable convergence of the model. Finally, experimental verification is conducted in different small-scale mine SAR simulation scenarios. The experimental results show that the proposed search planning method can capture the target in the search area with a smoother convergence effect and a shorter path length than other path-planning algorithms. Full article

With the rapid development of digital intelligence, drones can provide many conveniences for people’s lives, especially in executing rescue missions in special areas. When executing rescue missions in remote areas, communication cannot be fully covered. Therefore, to improve the online adaptability of the task chain link in task planning with a complex system structure as the background, a distributed source-task-capability allocation (DSTCA) problem was constructed. The first task chain coordination mechanism scheme was proposed, and a DSTCA architecture based on the task chain coordination mechanism was constructed to achieve the online adaptability of the swarm. At the same time, the existing algorithms cannot achieve this idea, and the DSTCA-CBBA algorithm based on CNP is proposed. The efficiency change, agent score, and time three indicators are evaluated through specific cases. In response to sudden changes in nodes in the task chain link, the maximum spanning tree algorithm is used to reconstruct the task chain link in a short time, thereby completing the mission task assigned to the drone entity. Meanwhile, the experimental results also prove the effectiveness of the proposed algorithm. Full article

As the nonlinear and coupling characteristics of autonomous underwater vehicles (AUVs) are the challenges for motion modeling, the nonparametric identification method is proposed based on dung beetle optimization (DBO) and deep temporal convolutional networks (DTCNs). First, the improved wavelet threshold is utilized to select the optimal threshold and wavelet basis functions, and the raw model test data are denoising. Second, the bidirectional temporal convolutional networks, the bidirectional gated recurrent unit, and the attention mechanism are used to achieve the nonlinear nonparametric model of the AUV motion. And the hyperparameters are optimized by the DBO. Finally, the lazy-search-based path planning and the line-of-sight-based path following control are used for the proposed AUV model. The simulation shows that the prediction accuracy of the DBO-DTCN is better than other artificial intelligence methods and mechanical models, and the path following of AUV is feasible. The methods proposed in this paper can provide an effective strategy for AUV modeling, searching, and rescue cruising. Full article