With the rapid development of the Internet of Things (IoT), there is a growing demand for electric energy in intelligent sensing equipment. Current smart sensors are mostly powered by lithium batteries, and it is necessary to develop a new self-powered device that can replace traditional lithium batteries. This paper designs a maglev electromagnetic–triboelectric hybrid energy converter (METHEC) containing both a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG), which can effectively collect low-frequency vibrational energy. This paper presents a complete device design and optimization system based on theoretical calculation and simulation verification, and establishes a test platform capable of evaluating the dynamic characteristics of METHEC and thus conducts some experiments for validation. In the past, the TENG related to vertical vibration only possessed the test part, and the current was less than 1 μA, with the power still less than 1 mW even with a large triboelectric area. Additionally, there is no complete research system for devices that utilize vertical vibration. The traditional EMG has a large starting torque, a large volume and can only be applied to higher frequencies, which is not convenient for wide application, and the power rarely exceeds 100 mW. According to this study, excited by the vibrational motion at a frequency of 5.2 Hz, the open-circuit voltages of TENG and EMG are 7.5 V and 9 V, respectively; the short-circuit currents of TENG and EMG are 1.1 μA and 87.7 mA, respectively; and the maximum power density of TENG reaches 7.5 mW m⁻², while the peak power of EMG reaches 789.3 mW. This paper provides a complete set of innovative exploration systems combining theory, experiment and simulation optimization, which also includes the structural optimization of TENG. METHEC does not require a large starting torque and can also be adapted to low frequencies, and can thus be adapted to more environments. Compared with traditional EMG, METHEC makes it easier to realize the miniaturization of power generation equipment, and according to this research system, other models can be studied expansively.

Wave resonance in the gaps formed by a four-float array for various drafts and incident wave frequencies is investigated using a numerical wave tank based on OpenFOAM. In the gap perpendicular to the direction of wave propagation, the resonant wave height is higher than that between two side-by-side floats under the same draft, and the resonant frequency is also different. Significant variations in wave height distribution are observed along the gap parallel to the wave propagation direction under different incident wave frequencies. When the incident wave frequencies are higher than the resonant frequency, the lateral force amplitude on the front floats increases, while the force amplitude on the rear floats does not show this effect. Using the dynamic mode decomposition method, we discover that the irregular distribution of wave heights across different frequencies leads to an increase in the lateral force amplitude on the front floats at non-resonant frequencies.

Six-degree-of-freedom (6-DoF) pose estimation of marine targets is essential for maritime and underwater operations. To address the instability caused by complex monocular marine scenes, this paper proposes GeoBox-Pose, a 6-DoF pose estimation method based on geometrically constrained 3D bounding boxes. The method constructs a 3D bounding-box representation from target physical dimensions, reducing dependence on accurate 3D models. A shared visual encoder with a multi-branch regression network is designed to jointly predict 2D projected corners, 3D orientation, and spatial translation. A projection-consistency constraint based on the camera imaging model is further introduced to reproject the pose-driven 3D bounding box onto the image plane, enhancing consistency between image features and 3D geometry. The results show that GeoBox-Pose achieves mean rotation errors of 14.96° and 12.36° on the BONK-pose and ROV6D datasets, respectively, with corresponding mean projection errors of 100.84 px and 75.63 px. The RotAcc@20° values reach 85.74% and 91.76%, while the ProjAcc@100px values reach 81.95% and 84.05%, respectively. The model contains 11.67 M parameters and achieves an inference speed of nearly 300 FPS. The experiments further confirm that the projection-consistency constraint contributes to improved pose-estimation performance. Overall, GeoBox-Pose enables monocular 6-DoF pose estimation for different types of marine targets using a unified 3D bounding-box geometric representation. While maintaining favorable pose-estimation performance, it substantially reduces model size and computational cost and achieves high network inference efficiency, thereby providing a good overall balance between pose-estimation accuracy and network inference efficiency. The proposed method offers a lightweight geometry-constrained pose-estimation solution for marine engineering tasks such as ship attitude monitoring and underwater-robot localization.