Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The arti

Large eddy simulation of supercritical heat transfer to hydrocarbon fuel

Accepted for publication in a forthcoming issue of International Journal of Heat and Mass Transfer.In this article, a large eddy simulation (LES) method for the heat transfer of the hydrocarbon fuel flowing through the uniformly heated miniature round pipe at supercritical pressure has been formulated and validated. The four species surrogate model was used to simulate the real thermophysical properties of the fuel. Validation of the developed LES model was carried out through comparisons of the wall temperature and pressure drop with available experimental data and other turbulence model results. Results show that the LES gave the best prediction. Further calculations based on the proposed LES for three cases including subcritical, transcritical and supercritical temperature ranges were numerically investigated in a systematic manner. It was found that the entrance effect occurred among the subcritical, transcritical and supercritical temperature cases that caused by the developing thermal boundary layer. The significant variation of the thermophysical properties near the pseudo-critical temperature would weaken the heat transfer in the transcritical case where the velocity fluctuation affected more on turbulent heat transfer than the temperature fluctuation did.Peer reviewe

Enhancing Graph Neural Networks with Structure-Based Prompt

Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train, prompt" has shown promising results in adapting GNNs to various tasks with less supervised data. The success of such paradigm can be attributed to the more consistent objectives of pre-training and task-oriented prompt tuning, where the pre-trained knowledge can be effectively transferred to downstream tasks. However, an overlooked issue of existing studies is that the structure information of graph is usually exploited during pre-training for learning node representations, while neglected in the prompt tuning stage for learning task-specific parameters. To bridge this gap, we propose a novel structure-based prompting method for GNNs, namely SAP, which consistently exploits structure information in both pre-training and prompt tuning stages. In particular, SAP 1) employs a dual-view contrastive learning to align the latent semantic spaces of node attributes and graph structure, and 2) incorporates structure information in prompted graph to elicit more pre-trained knowledge in prompt tuning. We conduct extensive experiments on node classification and graph classification tasks to show the effectiveness of SAP. Moreover, we show that SAP can lead to better performance in more challenging few-shot scenarios on both homophilous and heterophilous graphs

Multi-Scale Object Detection Model for Autonomous Ship Navigation in Maritime Environment

Accurate detection of sea-surface objects is vital for the safe navigation of autonomous ships. With the continuous development of artificial intelligence, electro-optical (EO) sensors such as video cameras are used to supplement marine radar to improve the detection of objects that produce weak radar signals and small sizes. In this study, we propose an enhanced convolutional neural network (CNN) named VarifocalNet * that improves object detection in harsh maritime environments. Specifically, the feature representation and learning ability of the VarifocalNet model are improved by using a deformable convolution module, redesigning the loss function, introducing a soft non-maximum suppression algorithm, and incorporating multi-scale prediction methods. These strategies improve the accuracy and reliability of our CNN-based detection results under complex sea conditions, such as in turbulent waves, sea fog, and water reflection. Experimental results under different maritime conditions show that our method significantly outperforms similar methods (such as SSD, YOLOv3, RetinaNet, Faster R-CNN, Cascade R-CNN) in terms of the detection accuracy and robustness for small objects. The maritime obstacle detection results were obtained under harsh imaging conditions to demonstrate the performance of our network model

Diameter effect on the heat transfer of supercritical hydrocarbon fuel in horizontal tubes under turbulent conditions

This document is the Accepted Manuscript version of the following article: Zeyuan Cheng, Zhi Tao, Jianqin Zhu, and Hongwei Wu, ‘Diameter effect on the heat transfer of supercritical hydrocarbon fuel in horizontal tubes under turbulent conditions’, Applied Thermal Engineering, Vol. 134: 39-53, April 2018. Under embargo until 31 January 2019. The final, definitive version is available online at: https://doi.org/10.1016/j.applthermaleng.2018.01.105This article presented a numerical investigation of supercritical heat transfer of the hydrocarbon fuel in a series of horizontal tubes with different diameters. The Reynolds averaging equations of mass, momentum and energy with the LS low-Reynolds number turbulence model have been solved using the pressure-based segregated solver based on the finite volume method. For the purpose of comparison, a four-species surrogate model and a ten-species surrogate model of the aviation kerosene RP-3 (Rocket Propellant 3) were tested against the published experimental data. In the current study, the tube diameter varied from 2 mm to 10 mm and the pressure was 3 MPa with heat flux to mass flux ratios ranging from 0.25 to 0.71 kJ/kg. It was found that the buoyancy has significant effect on the wall temperature non-uniformity in the horizontal tube. With the increase of the diameter, the buoyancy effect enhances and the thermal-induced acceleration effect reduces. The buoyancy effect makes wall temperature at the top and bottom generatrices of the horizontal tube increase and decrease, respectively. Due to the coupled effect of the buoyancy and thermal-induced acceleration caused by the significant change of the properties, as the diameter increases, the heat transfer deteriorates dramatically at the top generatrix but remains almost unchanged at the bottom generatrix at high heat flux to mass flux ratio. Heat transfer enhancement is observed at low heat flux to mass flux ratio when the tube diameter is less than 6 mm. Moreover, the safety analysis has been performed in order to optimally design the supercritical cooling system.Peer reviewe

Upregulation of MIAT Regulates LOXL2 Expression by Competitively Binding MiR-29c in Clear Cell Renal Cell Carcinoma

Background/Aims: MIAT is a long noncoding RNA (lncRNA) involved in cell proliferation and the development of tumor. However, the exact effects and molecular mechanisms of MIAT in clear cell renal cell carcinoma (ccRCC) progression are still unknown. Methods: We screened the lncRNAs’ profile of ccRCC in The Cancer Genome Atlas database, and then examined the expression levels of lncRNA MIAT in 45 paired ccRCC tissue specimens and in cell lines by q-RT-PCR. MTS, colony formation, EdU, and Transwell assays were performed to examine the effect of MIAT on proliferation and metastasis of ccRCC. Western blot and luciferase assays were performed to determine whether MIAT can regulate Loxl2 expression by competitively binding miR-29c in ccRCC. Results: MIAT was up-regulated in ccRCC tissues and cell lines. High MIAT expression correlated with worse clinicopathological features and shorter survival rate. Functional assays showed that knockdown of MIAT inhibited renal cancer cell proliferation and metastasis in vitro and in vivo. Luciferase and western blot assays further confirmed that miR-29c binds with MIAT. Additionally, the correlation of miR-29c with MIAT and Loxl2 was further verified in patients' samples. Conclusion: Our data indicated that MIAT might be an oncogenic lncRNA that promoted proliferation and metastasis of ccRCC, and could be a potential therapeutic target in human ccRCC

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve