An Optimal BP Neural Network Track Prediction Method Based on a GA–ACO Hybrid Algorithm

Ship position prediction is the key to inland river and sea navigation warning. Maritime traffic control centers, according to ship position monitoring, ship position prediction and early warning, can effectively avoid collisions. However, the prediction accuracy and computational efficiency of the ship’s future position are the key problems to be solved. In this paper, a path prediction model (GA–ACO–BP) combining a genetic algorithm, an ant colony algorithm and a BP neural network is proposed. The model is first used to perform deep pretreatment of raw AIS data, with the main body of the BP neural network as a prediction model, focused on the complementarity between genetic and ant colony algorithms, to determine the ant colony initialization pheromone concentration by the genetic algorithm, design the hybrid genetic–ant colony algorithm, and optimize this to the optimal weight and threshold of the BP neural network, in order to improve the convergence speed and effect of the traditional BP neural network. The test results show that the model greatly improves the fitness of track prediction, with higher accuracy and within a shorter time, and has a certain real-time and extensibility for track prediction of different river segments

Study on the Frost Resistance of Composite Limestone Powder Concrete against Coupling Effects of Sulfate Freeze–Thaw

Concrete in saline or coastal settings exposed to freezing temperatures is frequently affected by coupling actions of sulfate assault and freeze–thaw degradation, reducing the service life of concrete structures significantly. This study conducted an accelerated freeze–thaw cycle test in pure water and Na2SO4 solution with a mass proportion of 5% to examine the coupling impact of sulfate freeze–thaw on the frost resistance of composite limestone powder (CLP) concrete. Combined with SEM and XRD methods, the performance degradation mechanisms of composite limestone powder (CLP) concrete in coupling sulfate freeze–thaw conditions were analyzed with a microscopic point of view. The findings demonstrated that limestone powder has a filling effect but the activity is low. When the content is 10~20%, the chemical response is higher than the physical response. The pozzolanic effect of fly ash and slag can improve the pore structure and improve the compactness of concrete. The “superposition effect” of limestone powder, fly ash, and slag can improve the frost resistance of CLP concrete. The scenario of salt freezing cycles has negative effects that are worse than those of water freezing cycles on the antifreeze performance of CLP concrete, including apparent morphology, mass loss, relative dynamic modulus of elasticity, and compressive strength. Sulfate’s activation effect boosts slag’s activity effect, which significantly promotes the antifreeze performance of concrete subjected to salt frozen cycles over water frozen cycles. The freeze–thaw damage model of CLP concrete under coupling sulfate freeze–thaw is established through theorem analysis and experiment statistics, laying a theoretical framework for the popularization and use of this concrete

Discrete-vortex analysis of high Reynolds number flow past a rotating cylinder

Flow past a rotating cylinder is investigated using a two-dimensional discrete vortex simulation method in this study. The simplified Navier-Stokes equation is solved based on the relationship between the surface pressure gradient and the generated surface vortex strength. The Reynolds number based on the cylinder diameter and flow velocity is 105. The non-dimensional rotation rate, α (the ratio of the cylinder surface velocity and flow velocity), is varied between 0 and 19, and four different wake formations (vortex shedding, weak vortex shedding, wake, and rotating wake formations) have been derived by the imposed rotation. The relationship between the hydrodynamics and wake formation is illustrated. Under vortex shedding and weak vortex shedding formations, periodical hydrodynamics is induced. Under wake formation, no gap between the positive-vorticity and negative-vorticity layers results in the steady hydrodynamics. The separation of the rotating wake induces the huge fluctuation of hydrodynamics under rotating wake formation. These are significant for a flow control technique and for the design of ocean and civil engineering structures. With the increasing rotation rate, the variation of mean hydrodynamics has been discussed and the maximum mean hydrodynamics is considered to be decided by the rotation rate. According to these wake formations, the vortex shedding, weak vortex shedding, wake, and rotating wake areas are identified. Combining the initial, increasing, and equivalent areas for mean hydrodynamics, two different area-divisions have been conducted for mean hydrodynamics and the relationship between the two area-divisions has been illustrated. Finally, the disappearance of vortex shedding and variation of the Strouhal number have been discussed in detail. The critical value for the disappearance of vortex shedding is α ≈ 3.5, and the Strouhal number remains steady initially and then decreases

Prediction of viscosity of biodiesel blends using various artificial model and comparison with empirical correlations

International audienc

Sine-SSA-BP Ship Trajectory Prediction Based on Chaotic Mapping Improved Sparrow Search Algorithm

Objective: In this paper, we propose a Sine chaos mapping-based improved sparrow search algorithm (SSA) to optimize the BP neural network for trajectory prediction of inland river vessels because of the problems of poor accuracy and easy trapping in local optimum in BP neural networks. Method: First, a standard BP model is constructed based on the AIS data of ships in the Yangtze River section. A Sine-BP model is built using Sine chaos mapping to assign neural network weights and thresholds. Finally, a Sine-SSA-BP model is built using the sparrow search algorithm (SSA) to solve the optimal solutions of the neural network weights and thresholds. Result: The Sine-SSA-BP model effectively improves the initialized population of uniform distribution, and reduces the problem that population intelligence algorithms tend to be premature. Conclusions: The test results show that the Sine-SSA-BP neural network has higher prediction accuracy and better stability than conventional LSTM and SVM, especially in the prediction of corners, which is in good agreement with the real ship navigation trajectory

Recognition and Depth Estimation of Ships Based on Binocular Stereo Vision

To improve the navigation safety of inland river ships and enrich the methods of environmental perception, this paper studies the recognition and depth estimation of inland river ships based on binocular stereo vision (BSV). In the stage of ship recognition, considering the computational pressure brought by the huge network parameters of the classic YOLOv4 model, the MobileNetV1 network was proposed as the feature extraction module of the YOLOv4 model. The results indicate that the mAP value of the MobileNetV1-YOLOv4 model reaches 89.25%, the weight size of the backbone network was only 47.6 M, which greatly reduced the amount of computation while ensuring the recognition accuracy. In the stage of depth estimation, this paper proposes a feature point detection and matching algorithm based on the ORB algorithm at sub-pixel level, that is, firstly, the FSRCNN algorithm was used to perform super-resolution reconstruction of the original image, to further increase the density of image feature points and detection accuracy, which was more conducive to the calculation of the image parallax value. The ships’ depth estimation results indicate that when the distance to the target is about 300 m, the depth estimation error is less than 3%, which meets the depth estimation needs of inland ships. The ship target recognition and depth estimation technology based on BSV proposed in this paper makes up for the shortcomings of the existing environmental perception methods, improves the navigation safety of ships to a certain extent, and greatly promotes the development of intelligent ships in the future

Small seawater desalination system based on loop heat pipe principle

The small seawater desalination system based on loop heat pipe principle by using plate capillary pump technology, a new type of radiator spoiler evaporator and soaking plate finned condenser to realize desktop-class solar seawater desalination system. The Venturi tube principle is used to reduce the internal pressure and energy consumption, and solar photoelectric board with electric heating board function is used to solve problems for areas where there is a shortage of electricity and fresh water resources. Full automatic control system is used to realize the full automatic operation of the equipment.Desktop-class light and small seawater desalination equipment enjoys a broad market prospect. It can not only be used in islands, fishing boats, offshore operating platforms and other complex working scenarios, but also can be used as a large ship freshwater resources emergency equipment

Hybrid Structures of Sisal Fiber Derived Interconnected Carbon Nanosheets/MoS2/Polyaniline as Advanced Electrode Materials in Lithium-Ion Batteries

In this work, we designed and successfully synthesized an interconnected carbon nanosheet/MoS2/polyaniline hybrid (ICN/MoS2/PANI) by combining the hydrothermal method and in situ chemical oxidative polymerization. The as-synthesized ICNs/MoS2/PANI hybrid showed a “caramel treat-like” architecture in which the sisal fiber derived ICNs were used as hosts to grow “follower-like” MoS2 nanostructures, and the PANI film was controllably grown on the surface of ICNs and MoS2. As a LIBs anode material, the ICN/MoS2/PANI electrode possesses excellent cycling performance, superior rate capability, and high reversible capacity. The reversible capacity retains 583 mA h/g after 400 cycles at a high current density of 2 A/g. The standout electrochemical performance of the ICN/MoS2/PANI electrode can be attributed to the synergistic effects of ICNs, MoS2 nanostructures, and PANI. The ICN framework can buffer the volume change of MoS2, facilitate electron transfer, and supply more lithium inset sites. The MoS2 nanostructures provide superior rate capability and reversible capacity, and the PANI coating can further buffer the volume change and facilitate electron transfer

Recognition and Depth Estimation of Ships Based on Binocular Stereo Vision

To improve the navigation safety of inland river ships and enrich the methods of environmental perception, this paper studies the recognition and depth estimation of inland river ships based on binocular stereo vision (BSV). In the stage of ship recognition, considering the computational pressure brought by the huge network parameters of the classic YOLOv4 model, the MobileNetV1 network was proposed as the feature extraction module of the YOLOv4 model. The results indicate that the mAP value of the MobileNetV1-YOLOv4 model reaches 89.25%, the weight size of the backbone network was only 47.6 M, which greatly reduced the amount of computation while ensuring the recognition accuracy. In the stage of depth estimation, this paper proposes a feature point detection and matching algorithm based on the ORB algorithm at sub-pixel level, that is, firstly, the FSRCNN algorithm was used to perform super-resolution reconstruction of the original image, to further increase the density of image feature points and detection accuracy, which was more conducive to the calculation of the image parallax value. The ships’ depth estimation results indicate that when the distance to the target is about 300 m, the depth estimation error is less than 3%, which meets the depth estimation needs of inland ships. The ship target recognition and depth estimation technology based on BSV proposed in this paper makes up for the shortcomings of the existing environmental perception methods, improves the navigation safety of ships to a certain extent, and greatly promotes the development of intelligent ships in the future

A lightweight ship target detection model based on improved YOLOv5s algorithm.

Real-time and accurate detection of ships plays a vital role in ensuring navigation safety and ship supervision. Aiming at the problems of large parameters, large computation quantity, poor real-time performance, and high requirements for memory and computing power of the current ship detection model, this paper proposes a ship target detection algorithm MC-YOLOv5s based on YOLOv5s. First, the MobileNetV3-Small lightweight network is used to replace the original feature extraction backbone network of YOLOv5s to improve the detection speed of the algorithm. And then, a more efficient CNeB is designed based on the ConvNeXt-Block module of the ConvNeXt network to replace the original feature fusion module of YOLOv5s, which improves the spatial interaction ability of feature information and further reduces the complexity of the model. The experimental results obtained from the training and verification of the MC-YOLOv5s algorithm show that, compared with the original YOLOv5s algorithm, MC-YOLOv5s reduces the number of parameters by 6.98 MB and increases the mAP by about 3.4%. Even compared with other lightweight detection models, the improved model proposed in this paper still has better detection performance. The MC-YOLOv5s has been verified in the ship visual inspection and has great application potential. The code and models are publicly available at https://github.com/sakura994479727/datas