Autonomous Water Sampler for Oil Spill Response

Gomez-Ibañez, Daniel; Kukulya, Amy; Belani, Abhimanyu; Conmy, Robyn N.; Sundaravadivelu, Devi; DiPinto, Lisa

doi:10.3390/jmse10040526

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…However, the battery life is short, the cost is high, the requirements for the environment are strict, and improper disposal may lead to environmental pollution. How to achieve a long-term stable power supply for oil spill positioners has become a grand challenge [5].…”

Section: Introductionmentioning

confidence: 99%

A wave energy driven high-performance self-powered oil spill positioner

Li,

Chen,

Zhang

et al. 2023

Nanotechnology

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The oil spill positioner is capable of real-time monitoring oil films on the sea surface. However, the lack of high-performance power supply methods greatly restricts the application of oil spill positioner. In this research, we design a high-performance self-powered oil spill positioner based on a soft-contact-triboelectric-nanogenerator (SC-TENG). This device achieves soft-contact by attaching rabbit fur to the rotor, which can effectively reduce frictional resistance, quickly transfer charge to the electrode, and improve the durability of the parts. First, we calculate the highest occupied molecular orbital and the lowest unoccupied molecular orbital of polytetrafluoroethylene (PTFE) and polyvinylidene fluoride (PVDF) molecules through first-principles simulations, and compared the ease of electron excitation between the two materials. The results show that the performance of SC-TENG with PVDF as dielectric material is significantly better than that of PTFE. At the same time, this phenomenon has been confirmed by experiments. On the basis of experimental and simulate research on two types of power management circuits, a bridge rectifier circuit with the function of converting alternating current to direct current is selected to realize the self-power supply of the oil spill positioner. Additionally, by optimizing the structure of the SC-TENG and employing a bridge rectifier circuit, the SC-TENG can achieve a maximum open-circuit voltage of 1400 V and a short-circuit current of 3.49 μA, which are enough to light up 200 light-emitting diodes and power the oil spill positioner. Finally, we simulate the open-circuit voltage and short-circuit current of the SC-TENG on a six-degree-of-freedom platform and test its durability under real-world ocean wave conditions, all of which show excellent performance. This work develops an efficient wave energy conversion mechanism and successfully realizes the high-performance self-powering of the oil spill positioner, making oil spill monitoring more flexible and reliable.

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Section: Introductionmentioning

confidence: 99%

A wave energy driven high-performance self-powered oil spill positioner

Li,

Chen,

Zhang

et al. 2023

Nanotechnology

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Improved lightweight federated learning network for fault feature extraction of reciprocating machinery

Zhang,

Duan,

et al. 2024

Meas. Sci. Technol.

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he working environment of reciprocating machinery is complex, characterized by nonlinear and non-stationary signals. Deep learning can solve the above problems, but it has its own problems such as complex model and large amount of parameters. Additionally, privacy considerations among enterprises prevent data sharing, leading to the emergence of "data islands" and inadequate training of deep learning models. Based on the above analysis, this paper proposes a reciprocating mechanical feature extraction method based on an improved federated lightweight network. A lightweight network SqueezeNet model is used to solve the problems such as long training time of deep learning. By establishing a federated learning framework, the reciprocating mechanical data can be collectively diagnosed across various enterprises, thereby addressing the problem of limited model training caused by insufficient data. Furthermore, to enhance the accuracy of network training and diagnosis, modifications are made to the SqueezeNet network to reduce the number of model parameters while increasing the number and variety of feature extractions. Experimental results demonstrate that when the number of 1×1 and 3×3 channels is 1 to 7, the fault diagnosis accuracy is the highest, about 97.96%, which enriches the categories of feature extraction. The number of parameters in In-SqueezeNet is 56% of that in SqueezeNet network model, and the training time is reduced by nearly 15%. The fault diagnosis accuracy is increased from 95.1% to 97.3%, and the diversity of extracted features is increased. Compared with other network models such as ResNet, the improved lightweight federated learning network has a fault diagnosis accuracy of 96.6%, an improvement of 10.6%. At the same time, the training time was reduced to 1982s, a reduction of about 41.5%. The validity of the proposed model is further verified.

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