Search Results (882)

An effective sensor network strategy is fundamental to structural health monitoring (SHM). Optimal sensor placement (OSP) for transmission towers remains insufficiently studied, primarily owing to the extensive number of candidate nodes and the complex structural responses of these structures under diverse environmental loads. Utilizing finite element analysis (FEA), this paper proposes a novel framework for the sensor placement of transmission towers. The maximum modal order of a Y-shaped transmission tower is determined using the Fisher Information Matrix (FIM), which characterizes its dynamic properties, while the Modal Assurance Criterion (MAC) is employed to identify the optimal number of sensors. The Marine Predators Algorithm (MPA) is then utilized to determine the optimal sensor configuration for the transmission tower based on four different fitness functions. The performance of these four fitness functions in sensor layout design is systematically compared. The results indicate that the MPA can efficiently generate optimal sensor configurations under a constraint on the maximum number of sensors. The choice of fitness function has a significant impact on the sensor placement results. The proposed MPA-based OSP method provides a reliable technical framework for the optimal design of SHM systems in power transmission engineering. Full article

The oceans and other marine ecosystems are indispensable to life, so the understanding and knowledge of their biodiversity is crucial to the use of their resources and exploration. These environments are complex and difficult to access, so different types of remote sensing technologies are used to study them. These intelligent sensors can collect a massive amount of data, which, once reviewed and analyzed, can help to draw conclusions and increase knowledge of these underwater environments. Manually reviewing and organizing through this large amount of information is both time-consuming and costly. Therefore, it is advisable to employ automated techniques from machine learning and deep learning fields. In recent years, these methods have proven to be efficient and have obtained very good results in solving different problems applied to the marine world: image enhancement, image classification, segmentation and object detection. This paper presents a systematic review, conducted in accordance with the PRISMA 2020 guidelines, aimed at summarizing the methods used to address underwater problems and their reported results. Full article

Against the backdrop of global energy structure decarbonization, distributed transformation, and the rapid development of low-power electronic devices and sensor networks, micro-energy supply and intelligent sensing have emerged as critical bottlenecks limiting their large-scale application. Triboelectric nanogenerators (TENGs), leveraging advantages such as compatibility with diverse materials and adaptability to flexible and miniaturized fabrication, can efficiently harvest widely available low-frequency, low-amplitude distributed mechanical energy in the environment. Additionally, they exhibit self-powered sensing characteristics, where output signals are directly correlated with external physical quantities, demonstrating unique strengths in the fields of micro-/nano-energy and intelligent monitoring. This article systematically reviews the research progress in TENGs; elucidates their working modes and power generation principles; summarizes material design, structural optimization, and performance enhancement strategies for efficient energy harvesting; and outlines the current state of self-powered sensing technologies. It highlights their engineering applications in intelligent monitoring scenarios such as drones, marine environments, infrastructure, and wearable devices. Addressing the existing technical bottlenecks and theoretical challenges in integrated energy harvesting–sensing–monitoring systems, the paper envisions future trends toward high performance, integration, and intelligence, providing valuable insights for fundamental research on and engineering applications of TENGs in micro-energy supply and intelligent monitoring. Full article

Against the backdrop of intensifying global climate change, reducing carbon emissions has become a shared global objective. Blue carbon, as a significant carbon sink type, still lacks a mature assessment framework. Monitoring carbon fluxes in marine salt marsh wetlands is a core technology for accurately evaluating blue carbon potential. In response, this study independently developed a spatiotemporally coupled carbon flux monitoring system for marine salt marsh wetlands. The system consists of real-time monitoring equipment, a cloud-based intelligent storage and visualization analysis platform, and a terminal assessment system. It enables the real-time monitoring of carbon fluxes across multiple spatial scales and integrates time-series patterns to assess carbon sequestration potential from multiple dimensions. To address the bottleneck of sensor accuracy, a multi-algorithm fusion technology was innovatively developed, significantly enhancing the accuracy of monitoring data. A modular integrated design was employed to construct a land–air integrated monitoring architecture, which is adaptable to the complex environments of salt marsh wetlands. This facilitates long-term automated monitoring while reducing the need for manual intervention. The terminal assessment system processes spatial-scale data using the DeNitrification-DeComposition model (DNDC 9.5) and integrates time-series carbon flux patterns, enabling precise quantification of marine carbon sink potential through spatiotemporal comprehensive analysis. The system first completed performance verification during the experimental phase, acquiring a total of 5760 sets of valid monitoring data, with a data qualification rate of 99.72%. The proposed multi-algorithm fusion method kept monitoring data fluctuations within 0.5%, and the relative error of the spatiotemporal integrated prediction was as low as 0.31%, thereby ensuring the stability and accuracy of long-term in situ monitoring. Based on this, a one-year field validation was conducted in a 100-hectare coastal salt marsh wetland in Dafeng, Yancheng. Using a spatiotemporal coupling assessment, the annual total carbon sequestration of this area was estimated at 1498.4 tons of carbon, with an assessment error of only 5.1%, achieving precise quantification of the blue carbon sink in the salt marsh wetland. This study provides reliable technical support for evaluating the carbon sequestration capacity of coastal salt marsh wetlands, contributing to the implementation of carbon emission reduction strategies. It also offers a scientific basis for global carbon cycle research and carbon sink management decision-making. Full article

Ocean monitoring is essential for understanding climate change and marine ecosystem dynamics, yet achieving comprehensive global coverage remains a challenge in oceanography. Current technologies face limitations in cost, power, hardware, and depth capacity that restrict widespread monitoring capabilities. Here we show that biohybrid robotic jellyfish (Aurelia aurita) can serve as autonomous vertical ocean profilers by integrating microcontrollers with positively buoyant sensor payloads, achieving controlled vertical-profiling capabilities. Laboratory experiments demonstrated repeatable up–down trajectories, quantified force balance limits, and identified predictable, size-dependent descent swimming speeds. Field deployments in Massachusetts coastal waters and the open ocean off the Florida Keys demonstrated field operation to ocean depths >25 m with successful in situ temperature and depth measurements. To our knowledge, this represents the first biohybrid jellyfish platform to combine autonomous, pressure-triggered vertical profiling with onboard oceanographic sensing in natural marine environments. This approach leverages the global distribution and remarkable swimming efficiency of living jellyfish while eliminating propulsion power requirements by utilizing the animal’s natural swimming capabilities. While further development is required for long-term ocean deployment, this study lays the groundwork for a new class of biohybrid ocean-sensing platforms with advantages in cost, power, and mission flexibility, providing a pathway toward dense sensor networks and increased ocean monitoring observations. Full article

High-resolution vertical profile observations of ocean environmental parameters are essential for investigating mesoscale ocean dynamic phenomena, such as internal waves, mesoscale eddies, and oceanic fronts. At present, vertical profile measurement in marine surveys mainly relies on shipborne winches to deploy and recover marine sensors, which entails high labor costs and considerable energy consumption. Unmanned observation platforms integrated with winch systems enable automatic sensor deployment and recovery, offering a viable approach to cutting observation costs. Nevertheless, inadequate energy supply remains a critical bottleneck restricting the large-scale popularization and application of such equipment. Accordingly, the development of high-efficiency winch systems tailored for unmanned autonomous observation platforms is of great engineering significance for facilitating long-term, continuous, and low-energy marine profile observation. This paper proposes a novel energy-saving winch with an embedded three-stage parallel nested energy storage structure for unmanned marine observation platforms. During operation, the coil spring energy storage system is charged during cable payout, and the stored elastic potential energy is released to assist motor driving in the cable retraction process. This auxiliary driving mode reduces motor power demand and improves the overall energy utilization efficiency of the platform. Experimental results demonstrate that, neglecting ocean current resistance, the proposed winch reduces energy consumption by 5% during cable payout and 21% during cable retraction. The overall energy consumption is decreased by 13% throughout a complete vertical profile measurement cycle. Under constrained and fixed energy supply conditions, this technology substantially enhances the sampling capability of unmanned marine platforms for ocean environmental monitoring. It further improves operational efficiency and extends continuous service time, providing key technical support for revealing ocean dynamic evolution and clarifying the formation and driving mechanisms of marine environmental phenomena. Full article

The exhaust gas temperature (EGT) of the gas generator is a critical indicator for the health management system of a marine gas turbine engine. Therefore, EGT prediction can not only support predictive maintenance decision-making but also serves as a reliable virtual sensor for EGT measurement. However, the engine EGT exhibits strongly nonlinear coupling relationships with other gas path variables, which causes challenges for data-driven prediction. Graph neural networks (GNNs) are particularly effective in capturing the coupling relationships among gas path sensor variables. However, conventional static graph structures fail to characterize the varying coupling strengths under different operating conditions. In this study, a thermodynamic knowledge-driven graph attention network (TKD-GAT) method is proposed for accurate and robust EGT prediction. First, a physics-guided graph topology is constructed based on the gas turbine thermodynamic equations. Subsequently, a multi-head attention mechanism is introduced to generate edge weights that capture the varying thermodynamic coupling strengths under different operation conditions. The proposed model is evaluated on a real-world LM2500 gas turbine, which is widely used in modern propulsion systems of commercial and military ships. The ablation study confirms that the thermodynamic knowledge-driven graph topology and the attention mechanism-based edge weights are both necessary to enhance the EGT prediction performance. The TKD-GAT model shows the best performance with an RMSE of 0.446% and an R² of 0.971 compared with state-of-the-art models. The paired t-test and effect size measurement (Cohen’s d) statistically confirm the significance of performance improvements. The statistical results from multiple independent experiments prove the stability of the TKD-GAT model. Additionally, the model achieves a competitive computational cost despite the integration of a physics-guided graph topology and attention mechanisms. Crucially, an interpretability analysis confirms that the learned attention weights adhere to thermodynamic principles under different operation conditions. The proposed TKD-GAT model provides an effective solution for EGT prediction in health management systems. Full article

Underwater acoustic monitoring is a critical technology for marine resource development and modern aquaculture. The performance of acoustic sensors directly determines the effectiveness of biological behavior tracking in complex marine environments. This paper presents the design, fabrication, and characterization of a custom hydrophone utilizing a polyvinylidene fluoride (PVDF) piezoelectric film configured in a biomimetic tympanic cavity structure. Operating on the direct piezoelectric effect, the device employs a pre-tensioned PVDF diaphragm integrated with a dedicated charge amplifier circuit to condition high-impedance signals. Laboratory calibrations demonstrate a stable frequency response (with a sensitivity variation within ±1 dB) in the low-frequency range (1–200 Hz) with an average acoustic pressure sensitivity of approximately −206 dB (re 1 V/μPa), providing a higher relative voltage gain compared to a commercial reference hydrophone with a nominal sensitivity of −210 dB (re 1 V/μPa). Furthermore, extensive field evaluations were conducted in a marine net pen to analyze acoustic data across multiple fish feeding scenarios (baseline, pre-feeding, active feeding, and post-feeding). The proposed custom hydrophone exhibited a superior dynamic range and successfully locked onto a 24.4 Hz Golden Pompano (Trachinotus blochii) bioacoustic signature, maintaining remarkable feature stability even after active feeding ceased. This study validates the efficacy of the biomimetic PVDF hydrophone for low-frequency acoustic detection, providing a robust hardware foundation for automated behavioral recognition systems in aquaculture. Full article

In complex marine environments, achieving low-cost, highly reliable, and continuous navigation is crucial for the intelligent and autonomous operation of unmanned surface vehicles (USVs). Currently, the integrated Global Navigation Satellite System and Strapdown Inertial Navigation System (GNSS/SINS) serves as the primary navigation architecture for USVs. While the cost of high-performance GNSS receivers has steadily decreased, high-precision SINS remains prohibitively expensive. Consequently, micro-electromechanical system (MEMS)-based SINS has emerged as a preferred alternative due to its favorable balance of cost, power consumption, and size. However, significant inertial sensor errors make it difficult to maintain high-precision positioning during GNSS outages. To address this limitation, the single-axis rotational inertial navigation system (SRINS) has been introduced. Nevertheless, constrained by the single-axis mechanical structure and complex sea state disturbances, the system still struggles to effectively modulate random errors and azimuth gyroscope drift, rendering it insufficient for highly demanding navigation tasks. To overcome these bottlenecks, this article systematically reviews four core technologies: (1) Comprehensive denoising and temperature drift compensation techniques for MEMS gyroscopes; (2) rapid moving-base initial alignment models under high sea state disturbances; (3) fast online calibration methods for azimuth gyroscope drift; and (4) adaptive and robust GNSS/SINS integration architectures capable of accommodating high-dynamic conditions and non-Gaussian interference. Finally, this article discusses the engineering conflict between deploying high-precision algorithms and the limited onboard computational capacity of USVs. It concludes by highlighting a highly promising navigation paradigm for future research: the integration of factor graph optimization with physics-informed deep learning. Full article

High-precision underwater perception and scene reconstruction are critical techniques for marine surveying and resource exploration. Multi-sensor data fusion is currently the dominant method in underwater sensing. In this paper, a new approach for underwater sensing based on an integration of a 3D rotating scanning imaging sonar, an RTK (Real-Time Kinematic), and an IMU (Inertial Measurement Unit) systems onboard an unmanned surface vehicle (USV) is raised. By employing multi-sensor data fusion and image correlation calibration, combined with multi-view acoustic image synthesis, the system achieves accurate reconstruction of both water column and seabed scenes. The new system offers high reconstruction accuracy, and provides a cost-effective solution for scene reconstruction with a low requirement of the precise motion control of the USV platform. High-precision seabed imaging results have been validated through lake bed imaging tests. Full article

Structural health monitoring (SHM) plays an increasingly important role in managing aging, safety-critical infrastructure under growing environmental and operational demands. In recent years, fiber-optic sensors (FOSs) have attracted significant attention for SHM applications due to their immunity to electromagnetic interference, durability in harsh environments, multiplexing capability, and suitability for both localized and fully distributed measurements. In parallel, advances in machine learning (ML) have enabled new approaches for extracting actionable insights from large, high-dimensional sensing datasets. This paper presents a systematic review of FOS-based SHM systems integrated with ML across civil, transportation, energy, marine, and aerospace infrastructures. Following PRISMA 2020 guidelines, peer-reviewed studies were identified and synthesized to examine sensing principles, deployment configurations, data characteristics, and learning-based analytical strategies. Fiber optic technologies are categorized into point-based, quasi-distributed, and fully distributed systems, and their capabilities for capturing strain, temperature, and spatiotemporal structural responses are critically evaluated. ML approaches are examined from a task-oriented perspective, including damage detection, localization, severity assessment, environmental compensation, and prognosis, with emphasis on the alignment between sensing configurations and appropriate learning paradigms. Key challenges remain, particularly regarding large data volumes, environmental variability, limited labeled damage datasets, model generalization, and system-level integration. Emerging directions such as physics-informed and hybrid learning, transfer learning, uncertainty-aware modeling, and integration with digital twins are discussed as pathways toward more robust and scalable SHM systems. By jointly addressing sensing physics and data-driven intelligence, this review provides a structured reference and practical roadmap for advancing intelligent FOS-based SHM in next-generation infrastructure. Full article

Over the past two decades, Citizen Science (CS) has experienced rapid growth, driven by technological advancements and the rise of digital platforms. This work examines the necessity for standardization in Citizen Science data management and discusses how existing data standards can enhance the impact of citizen-generated data. CS standardization ensures data quality, comparability, reusability, and interoperability, making data suitable for contributing to the Marine Strategy Framework Directive (MSFD) and the United Nations Sustainable Development Goals (SDGs). This paper examined 130 Citizen Science publications and found that most collected data referred to the MSFD Descriptor 1 (Biodiversity—44.96%) and Descriptor 10 (Marine Litter—20.93%), followed by the alien species distribution (D2—11.63%), hydrography (D7—6.20%), eutrophication (D5—6.20%), and marine pollution (D8—3.10%). Analysis of 108 publications on SDG alignment revealed that the majority (35.58%) focused on reducing marine pollution. This paper reviews the best practices for effective Citizen Science data management, including standards for data structures, content, values, and exchange. Based on this review, Darwin Core, Ecological Metadata Language (EML), and the OGC SensorThings API appear to be the most suitable standards for MSFD-relevant CS data. Therefore, policymakers could enable the formal integration of standardized CS datasets into MSFD monitoring workflows. Full article

Seafloor hydrothermal systems at mid-ocean ridges are focal points for heat and matter exchange between the seawater and lithosphere. While seafloor seismographs (OBS) and pressure recorders (BPR) are standard for regional monitoring, achieving high-precision, vertical sub-surface data in complex hydrothermal terrains remains a significant technical objective. This study presents a novel in situ penetration probe designed for multi-parameter monitoring of marine hydrothermal vent areas. A key innovation of this work is its operational versatility and engineering efficiency: the probe is specifically designed for post-drilling deployment in boreholes, effectively utilizing existing coring sites to achieve direct coupling with the deep-seated crust, or for targeted placement via Remotely Operated Vehicles (ROVs). The device integrates a titanium-alloy conical tip and cylindrical chamber, housing tri-axial accelerometers and dual temperature-pressure sensors. Numerical simulations using the SST k-ω turbulence model and finite element analysis optimized the cone aperture and assessed fluid–structure stability under deep-sea conditions. Laboratory vibration tests and shallow-water sea trials validated the probe’s basic dynamic response, electromechanical integrity, and capability to acquire coupled environmental parameters. This compact, modular design provides a scalable and cost-effective framework for precise three-dimensional observation of sub-surface hydrothermal processes and deep-sea resource exploration. Full article

Accurate measurement of ocean wave parameters is paramount for offshore engineering design and marine environmental monitoring. Distributed pressure sensing technology provides a robust data foundation for analyzing the spatio-temporal characteristics of wave fields through synchronized observations at multiple stations. However, multi-sensor data exhibit high-dimensional spatio-temporal coupling, posing significant challenges for traditional single-point signal processing methods in capturing the topological associations between measurement sites. To address these limitations, this study develops a framework for spatio-temporal feature modeling and wave height reconstruction based on Graph Neural Networks (GNNs). The proposed framework integrates the spatial configuration of sensor arrays with graph-theoretic topological representations. By fusing geometric distances and signal correlations, an adaptive adjacency matrix is constructed to establish a dynamically adjustable graph structure. On the feature extraction level, a spatio-temporal fusion method combining multi-scale graph convolutions and gated temporal modeling is proposed. The experimental results obtained on the Blancs Sablons Bay multi-sensor dataset demonstrate that the proposed method significantly outperforms traditional approaches, achieving lower prediction errors and validating the effectiveness of graph-structured modeling in distributed wave sensing. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article