Search Results (24)

Airtight cabins with highly complex human–machine systems impose an excessive cognitive load on operators. However, the traditional cognitive load assessment methods often cannot fully extract physiological features such as electroencephalogram and electrocardiogram signals, relying heavily on artificial feature extraction. Therefore, this study proposes an evaluation method based on a one-dimensional convolutional neural network to evaluate the cognitive load of airtight cabin workers. This evaluation method preprocesses and intercepts raw physiological signals such as electroencephalogram and electrocardiogram signals and then inputs them into the model for evaluation. The experimental results demonstrate that the training accuracy rate of the one-dimensional convolutional neural network is 97.6%, and the test classification accuracy rate is 86.5%. Despite sample size limitations, the proposed method demonstrates valid effectiveness in this study. Finally, taking a manned submersible as an example, cognitive load in different difficult tasks is identified, evaluated, and classified. Full article

With the acceleration of urbanization and the increased frequency of extreme rainfall events, flooding has emerged as one of the most serious natural disaster problems, particularly affecting riparian cities. This study conducted a flooding risk assessment and an analysis of the driving factors behind flood disasters in the Songhua River Basin utilizing an improved Soil Conservation Service Curve Number (SCS-CN) model. First, the model was improved by slope adjustments and effective precipitation coefficient correction, with its performance evaluated using the Nash–Sutcliffe efficiency coefficient (NSE) and the Root Mean Square Error (RMSE). Second, flood risk mapping was performed based on the improved model, and the distribution characteristics of the flooding risk were analyzed. Additionally, the Geographical Detector (GD), a spatial statistical method for detecting factor interactions, was employed to explore the influence of natural, economic, and social factors on flooding risk using factor detection and interaction detection methods. The results demonstrated that the improvements to the SCS-CN model encompassed two key aspects: (1) the optimization of the CN value through slope correction, resulting in an optimized CN value of 50.13, and (2) the introduction of a new parameter, the effective precipitation coefficient, calculated based on rainfall intensity and the static infiltration rate, with a value of 0.67. Compared to the original model (NSE = 0.71, rRMSE = 19.96), the improved model exhibited a higher prediction accuracy (NSE = 0.82, rRMSE = 15.88). The flood risk was categorized into five levels based on submersion depth: waterlogged areas, low-risk areas, medium-risk areas, high-risk areas, and extreme-risk areas. In terms of land use, the proportions of high-risk and extreme-risk areas were ranked as follows: water > wetland > cropland > grassland > shrub > forests, with man-made surfaces exacerbating flood risks. Yilan (39.41%) and Fangzheng (31.12%) faced higher flood risks, whereas the A-cheng district (6.4%) and Shuangcheng city (9.4%) had lower flood risks. Factor detection results from the GD revealed that river networks (0.404) were the most significant driver of flooding, followed by the Digital Elevation Model (DEM) (0.35) and the Normalized Difference Vegetation Index (NDVI) (0.327). The explanatory power of natural factors was found to be greater than that of economic and social factors. Interaction detection indicated that interactions between factors had a more significant impact on flooding than individual factors alone, with the highest explanatory power for flood risk observed in the interaction between annual precipitation and DEM (q = 0.762). These findings provide critical insights for understanding the spatial drivers of flood disasters and offer valuable references for disaster prevention and mitigation strategies. Full article

Deep-sea biological detection is a pivotal technology for the exploration and conservation of marine resources. Nonetheless, the inherent complexities of the deep-sea environment, the scarcity of available deep-sea organism samples, and the significant refraction and scattering effects of underwater light collectively impose formidable challenges on the current detection algorithms. To address these issues, we propose an advanced deep-sea biometric identification framework based on an enhanced YOLOv8n architecture, termed PSVG-YOLOv8n. Specifically, our model integrates a highly efficient Partial Spatial Attention module immediately preceding the SPPF layer in the backbone, thereby facilitating the refined, localized feature extraction of deep-sea organisms. In the neck network, a Slim-Neck module (GSconv + VoVGSCSP) is incorporated to reduce the parameter count and model size while simultaneously augmenting the detection performance. Moreover, the introduction of a squeeze–excitation residual module (C2f_SENetV2), which leverages a multi-branch fully connected layer, further bolsters the network’s global representational capacity. Finally, an improved detection head synergistically fuses all the modules, yielding substantial enhancements in the overall accuracy. Experiments conducted on a dataset of deep-sea images acquired by the Jiaolong manned submersible indicate that the proposed PSVG-YOLOv8n model achieved a precision of 79.9%, an mAP50 of 67.2%, and an mAP50-95 of 50.9%. These performance metrics represent improvements of 1.2%, 2.3%, and 1.1%, respectively, over the baseline YOLOv8n model. The observed enhancements underscore the effectiveness of the proposed modifications in addressing the challenges associated with deep-sea organism detection, thereby providing a robust framework for accurate deep-sea biological identification. Full article

The egg-shaped pressure shell, an essential component of manned submersibles, has garnered significant attention from researchers. However, the fabrication of such shells, particularly the welding process used to connect petals or frustums into a shell blank, has raised several concerns. This study investigates the distribution of welding residual stresses in a multi-segment frustum-assembled egg-shaped shell using a thermal–elastic–plastic method under an instantaneous heat source. A numerical model for a 12-segment frustum-welded egg-shaped shell is developed, and welding simulations are performed. The model’s boundary conditions are defined by cyclic symmetry, with a mesh element size of 2 mm to enhance computational efficiency. The results are validated through experimental tests. The findings indicate that the residual stress around the weld is tensile, while compressive stress is present on both sides of the weld. The length of the generatrix and the relative inclination angle significantly affect the distribution and overlap of circumferential residual stress, whereas axial residual stress primarily influences its magnitude. Finally, a simplified numerical model of the egg-shaped shell is proposed, with its simulation results showing good agreement with the distribution of welding residual stresses on the shell surface. This study provides valuable insights for optimizing the welding process of egg-shaped pressure shells in manned submersibles. Full article

The high-precision magnetic survey system is crucial for ocean exploration. However, most existing systems face challenges such as high noise levels, low sensitivity, and inadequate magnetic compensation effects. To address these issues, we developed a high-precision magnetic survey system based on the manned submersible “Deep Sea Warrior” for deep-ocean magnetic exploration. This system incorporates a compact optically pumped cesium (Cs) magnetometer sensor to measure the total strength of the external magnetic field. Additionally, a magnetic compensation sensor is included at the front end to measure real-time attitude changes of the platform. The measured data are then transmitted to a magnetic signal processor, where an algorithm compensates for the platform’s magnetic interference. We also designed a deep pressure chamber to allow for a maximum working depth of 4500 m. Experiments conducted in both indoor and field environments verified the performance of the proposed magnetic survey system. The results showed that the system’s sensitivity is ≤0.5 nT, the noise level of the magnetometer sensor is ≤1 pT/√Hz at 1 Hz, and the sampling rate is 10 Hz. The proposed system has potential applications in ocean and geophysical exploration. Full article

This study proposes a receding horizon optimization-based docking control method to address the autonomy and safety challenge of underwater docking between manned submersibles and unmanned vehicles, facilitating the integration of docking trajectory generation and tracking control. A novel approach for optimizing and generating reference trajectory is proposed to construct a docking corridor that satisfies safe collision-free and visual guidance effective regions. It generates dynamically feasible and continuously smooth docking trajectories by rolling optimization. Subsequently, a docking trajectory tracking control method based on nonlinear model predictive control (NMPC) is designed, which is specifically tailored to address thruster saturation and system state constraints while ensuring the feasibility and stability of the control system. The control performance and robustness of underwater docking were validated through simulation experiments. The optimized trajectory generated is continuous, smooth, and complies with the docking constraints. The control system demonstrates superior tracking accuracy than backstepping control, even under conditions where the model has a 40% error and bounded disturbances from currents are present. The research findings presented in this study contribute significantly to enhancing safety and efficiency in deep-sea development. Full article

Large amounts of video images have been collected for decades by scientific and governmental organizations in deep (>1000 m) water using manned and unmanned submersibles and towed cameras. The collected images were analyzed individually or were mosaiced in small areas with great effort. Here, we provide a workflow for utilizing modern photogrammetry to construct virtual geological outcrops hundreds or thousands of meters in length from these archived video images. The photogrammetry further allows quantitative measurements of these outcrops, which were previously unavailable. Although photogrammetry had been carried out in recent years in the deep sea, it had been limited to small areas with pre-defined overlapping dive paths. Here, we propose a workflow for constructing virtual outcrops from archived exploration dives, which addresses the complicating factors posed by single non-linear and variable-speed vehicle paths. These factors include poor navigation, variable lighting, differential color attenuation due to variable distance from the seafloor, and variable camera orientation with respect to the vehicle. In particular, the lack of accurate navigation necessitates reliance on image quality and the establishment of pseudo-ground-control points to build the photogrammetry model. Our workflow offers an inexpensive method for analyzing deep-sea geological environments from existing video images, particularly when coupled with rock samples. Full article

As a key component of deep-sea manned submersibles, the observation window is usually constructed with polymethyl methacrylate (PMMA) material. During the design of the observation windows, the consideration of actual lifespan and its influential factors is insufficient. There are no clear provisions in the widely applied specifications. In this paper, based on the continuum damage mechanics model, combined with the viscoelastic relationship of PMMA material, a series of calculations were performed on the PMMA observation window. The parametric analysis of the fatigue crack-initiation life of the observation window at various thickness-to-diameter ratios (1.6, 1.4, 1.2, and 1.0), different friction coefficients (0.1, 0.2, and 0.3), and different transition arc radii (4000 mm and 6000 mm) was carried out. The calculated crack positions in the numerical mode used for validation closely align with those in the tested window. And simulation results show that the fatigue life of the observation window gradually decreases with the decrease in the thickness–diameter ratio and the increase in the friction coefficient. However, the increase in the transition arc radius will prolong the fatigue life of the observation window, which is higher than that of the original structure. Full article

Observation windows are core components of the submersible manned cabins. The strength and stiffness of the observation window during the loading and load-sustaining process are crucial to ensure the safety of the equipment and personnel inside the manned cabin. It is extremely important to accurately calculate the structural creep performance of the observation window under a long-period sustaining load in seawater. In the present study, finite element analyses based on a temperature-dependent time-hardening creep model are conducted to investigate the performance of the observation window. The mesh convergence is studied first and the parametric analysis is accordingly carried out, taking different combinations of temperatures from 2~30 °C, different loading rates of 2.3 MPa/min, 4.5 MPa/min, 6 MPa/min, and 8 MPa/min, and different friction coefficients of 0.05, 0.1, 0.15, 0.2, 0.25, and 0.3 into account. The results show that the displacement in the y-axis direction of the center point of the lower surface of the viewport window increases with the increasing temperature and loading rate. On the contrary, the axial displacement of the observation window gradually decreases with the increase of the friction coefficient, and the axial displacement is the largest when the lowest friction coefficient is applied. This study aims to offer a more unified analysis and design methodology for the creep deformation of PMMA structures in underwater facilities. Full article

The fully transparent cabin used in a manned submersible is typically made of the viscoelastic material polymethyl methacrylate (PMMA). The pressure-bearing capacity of a PMMA-manned cabin was investigated considering the effects of initial geometrical imperfections and large openings. Three types of cabins were studied within the failure mode of nonlinear buckling, including an intact spherical cabin, a spherical cabin with a single opening, and a spherical cabin with double openings. The initial geometrical imperfection ranges from 0.1% to 0.5% of the inner diameter. The ultimate strength decreasing tendency for the different types of cabins with increasing initial imperfection was obtained and the thickness of the hatch cover determined based on the principle of equivalence differed its effects on the strength of the cabin. The influence of the hatch cover stiffness was not linear and indicated the necessity of exploring the coordinated design between the PMMA shell and the metal hatch cover for the transparent cabin. Full article

At present, research on pressure hull safety is mainly focused on the constitutive model of material properties and the evaluation model of structural parameters aiming at fatigue life prediction. The damage identification and quantitative evaluation methods of pressure hulls have not been studied. In this study, an eddy current thermal imaging method is introduced to detect micro-cracks in a deep-sea spherical pressure hull. In the detection method, temperature is used as a parameter to identify and quantify cracks. The temperature distribution around the cracks is studied using theoretical analysis and finite element simulation. A theoretical model is established using electromagnetic theory and heat transfer theory. Moreover, the temperature difference between the cracked area and the non-cracked area can be obtained by solving the heat conduction equation. A pulsed eddy current thermal imaging testing system is established, and a defective titanium alloy specimen is tested. At the same time, the temperature around the cracks in the specimens is simulated. The specimens have the same material and welding as a deep-sea spherical pressure hull. This paper discusses the possibility of its use in a pressure hull, which will provide a reference for micro-crack damage identification and quantitative evaluation of a deep-sea spherical pressure hull. Full article

Safety of the observation window is one of the core concerns for manned submersibles. When subjected to underwater static pressure, extrusion and creep deformation always occur in the observation window, which can pose a threat to both safety and optical performance. To assess the deformation, real-time and non-contact monitoring methods are necessary. In this study, a conceptual setup based on the waveplate rotation and dual-DoFP (division of focal-plane polarimeter) polarization camera is built for the observation window’s creep monitoring by measuring the Mueller matrix images of the samples under different pressures and durations. Then, a series of characteristic parameters, such as t₁, R, r, R′, are extracted from the Muller matrix images by Mueller matrix transformation (MMT), Mueller matrix polar decomposition (MMPD), correlation analysis and phase unwrapping method. The results demonstrate that these parameters can effectively describe the observation window’s creep at different pressure levels which are simulated by finite element analysis. Additionally, more characterization parameters, such as ψ, A and D, are given from the Mueller matrix images and discussed to illustrate the method’s potential for further applications and investigations. Ultimately, future devices based on this method could serve as a valuable tool for real-time and non-contact creep monitoring of the submersible observation windows. Full article

In order to realize the intelligence of underwater robots, this exploration proposes a submersible vision system based on neurorobotics to obtain the target information in underwater camera data. This exploration innovatively proposes a method based on the convolutional neural network (CNN) to mine the target information in underwater camera data. First, the underwater functions of the manned submersible are analyzed and mined to obtain the specific objects and features of the underwater camera information. Next, the dataset of the specific underwater target image is further constructed. The acquisition system of underwater camera information of manned submersibles is designed through the Single Shot-MultiBox Detector algorithm of deep learning. Furthermore, CNN is adopted to classify the underwater target images, which realizes the intelligent detection and classification of underwater targets. Finally, the model’s performance is tested through experiments, and the following conclusions are obtained. The model can recognize underwater organisms’ local, global, and visual features. Different recognition methods have certain advantages in accuracy, speed, and other aspects. The design here integrates deep learning technology and computer vision technology and applies it to the underwater field, realizing the association of the identified biological information with the geographic information and marine information. This is of great significance to realize the multi-information fusion of manned submersibles and the intelligent field of outdoor education. The contribution of this exploration is to provide a reasonable direction for the intelligent development of outdoor diving education. Full article

Hadal seafloor organisms live under ultra-high pressure, in low temperatures, and other environments for a long time, which puts higher requirements on the structural design of deep-sea biological samplers. In this paper, we present a full-ocean-depth hydraulic suction macroorganism pressure-retaining sampling method, which actively samples seafloor macroorganisms by pumping and stabilizing the pressure inside the sampler using a pressure compensator. Firstly, the structure and working principle of the hydraulic suction macroorganism pressure-retaining sampler (HSMPS) were introduced. Then the flow field of the HSMPS sampling process was analyzed, and the velocity and pressure distribution of the flow field at different locations of the HSMPS were obtained. In response to the problem of the low viability of samples collected by deep-sea biological samplers, the changes in radial velocity and pressure at different positions of the sampler under different pumping flows were analyzed. Finally, the appropriate suction flow rate was selected based on the analysis results, and HSMPS suction tests and simulated sampling tests, under a 110 MPa high-pressure environment, were carried out using the developed HSMPS engineering prototype. The test results verify the feasibility of the HSMPS design, which will provide strong support for the deep abyssal seafloor sampling operation of the full-ocean-depth manned submersible. Full article

Residual stress inevitably occurs at the weld in the process of manufacturing thick pressure hulls for manned submersibles, which affects the bearing capacity of the hull. In this study, an electron-beam-welded 32 mm-thick Ti-6Al-4V plate specimen is first tested, then the measured data of residual stress distribution is applied to validate the accuracy of the simulation method. Accordingly, three-dimensional numerical analysis on the equator welding by electron beam method of a 32 mm-thick Ti-6Al-4V spherical pressure hull is conducted to obtain the variation tendency of residual stress during the welding process. The results indicate that both compressive and tensile stresses exist along the weld path on the outer surface of the hull comparing to total tensile stresses on the inner surface. The maximum tensile stress that occurs on the inner surface approximates to 850 MPa, which is almost equivalent to the yield stress of the material. Based on the acceptance criterion that the peak value of residual stress due to weld technique is restricted to be less than 40% of the material yield strength in room temperature, post-weld heat treatment must be performed. Simulation on post-weld heat treatment for optimizing process parameters can be done by taking the results of welding simulation in the present study as input. Full article