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Parallel Physics-Informed Neural Networks Method with Regularization Strategies for the Forward-Inverse Problems of the Variable Coefficient Modified KdV Equation

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Journal of Systems Science & Complexity 2024年第2期37卷 511-544页

作者： ZHOU Huijuan School of Science Shanghai Maritime UniversityShanghai 201306China

This paper mainly introduces the parallel physics-informed neural networks(PPINNs)method with regularization strategies to solve the data-driven forward-inverse problems of the variable coefficient modified Korteweg-de Vries(VC-MKdV)equation.For the forward problem of the VC-MKdV equation,the authors use the traditional PINN method to obtain satisfactory data-driven soliton solutions and provide a detailed analysis of the impact of network width and depth on solving accuracy and speed.Furthermore,the author finds that the traditional PINN method outperforms the one with locally adaptive activation functions in solving the data-driven forward problems of the VC-MKdV equation.As for the data-driven inverse problem of the VC-MKdV equation,the author introduces a parallel neural networks to separately train the solution function and coefficient function,successfully addressing the function discovery problem of the VC-MKdV equation.To further enhance the network’s generalization ability and noise robustness,the author incorporates two regularization strategies into the PPINNs.An amount of numerical experimental data in this paper demonstrates that the PPINNs method can effectively address the function discovery problem of the VC-MKdV equation,and the inclusion of appropriate regularization strategies in the PPINNs can improves its performance.

关键词： Data-driven forward-inverse problems parallel physics-informed neural networks regularization strategies variable coefficient modified KdV equation

Localized wave solutions and interactions of the (2+1)-dimensional Hirota-Satsuma-Ito equation

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Chinese Physics B 2024年第4期33卷 409-416页

作者：巩乾坤王惠王云虎 School of Science Shanghai Maritime UniversityShanghai 201306China

This paper studies the(2+1)-dimensional Hirota-Satsuma-Ito equation.Based on an associated Hirota bilinear form,lump-type solution,two types of interaction solutions,and breather wave solution of the(2+1)-dimensional Hirota-Satsuma-Ito equation are obtained,which are all related to the seed solution of the equation.It is interesting that the rogue wave is aroused by the interaction between one-lump soliton and a pair of resonance stripe solitons,and the fusion and fission phenomena are also found in the interaction between lump solitons and one-stripe soliton.Furthermore,the breather wave solution is also obtained by reducing the two-soliton solutions.The trajectory and period of the one-order breather wave are analyzed.The corresponding dynamical characteristics are demonstrated by the graphs.

关键词： lump solution rogue wave solution breather wave solution (2+1)-dimensional Hirota-Satsuma-Ito equation

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2705-2727页

作者： Xinci Zhou Jin Zhu shanghai maritime university Logistics Science and Engineering Research InstituteShanghai200000China

As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.

关键词： Automated terminals multi-AGV multi-agent path finding(MAPF) conflict based search(CBS) AGV path planning

Characteristics of Particulate Matter Emissions for Low-Sulfur Heavy Oil Used in Low-Speed Two-Stroke Diesel Engines of Ocean-Going Ships

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Journal of Thermal Science 2024年第2期33卷 739-750页

作者： WU Gang MA Qianli WEI Lijiang JIANG Guohe WANG Tengfei LI Tie Merchant Marine College Shanghai Maritime UniversityShanghai201306China State Key Laboratory of Ocean Engineering Shanghai Jiao Tong UniversityShanghai200240China

In this work,particulate matter(PM) emissions from a large two-stroke,low-speed marine diesel engine were investigated when the engine was operated with low-sulfur heavy fuel oil(HFO) at various loads.Particle samples were collected in situ from the engine exhaust to determine the detailed physical and chemical properties.The nanostructure and morphology of the nanoparticles were analyzed using transmission electron microscopy images(TEM).The results show that volatile organic carbon(OC) accounts for more than 80% in the HFO particles and leads to an increase in particle size.The thermodynamic conditions of a low-speed engine favor the behavior of capturing the soluble organic components.A large number of spherical char HFO particles with aerodynamic diameters of 0.2 μm-0.5 μm and a suspected inner metal core were detected.The two peak aerodynamic diameters of the HFO nanoparticles are 15 nm and 86 nm.The morphological differences among the HFO nanoparticles in varied engine conditions represent the formation process from primary nascent particles to mature graphitized particles caused by thermodynamics.The above study will be valuable for understanding the characteristics of PM emissions from low-sulfur HFO to achieve the ship PM emissions reduction target.

关键词： low-speed diesel engine maritime regulations heavy fuel oil TEM particles size and distribution

Integer multiple quantum image scaling based on NEQR and bicubic interpolation

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Chinese Physics B 2024年第4期33卷 259-273页

作者：蔡硕周日贵罗佳陈思哲 College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Research Center of Intelligent Information Processing and Quantum Intelligent Computing Shanghai 201306China College of Merchant Marine Shanghai Maritime UniversityShanghai 201306China School of Mathematics and Computational Science Shangrao Normal UniversityShangrao 334001China

As a branch of quantum image processing,quantum image scaling has been widely studied.However,most of the existing quantum image scaling algorithms are based on nearest-neighbor interpolation and bilinear interpolation,the quantum version of bicubic interpolation has not yet been studied.In this work,we present the first quantum image scaling scheme for bicubic interpolation based on the novel enhanced quantum representation(NEQR).Our scheme can realize synchronous enlargement and reduction of the image with the size of 2^(n)×2^(n) by integral multiple.Firstly,the image is represented by NEQR and the original image coordinates are obtained through multiple CNOT modules.Then,16 neighborhood pixels are obtained by quantum operation circuits,and the corresponding weights of these pixels are calculated by quantum arithmetic modules.Finally,a quantum matrix operation,instead of a classical convolution operation,is used to realize the sum of convolution of these pixels.Through simulation experiments and complexity analysis,we demonstrate that our scheme achieves exponential speedup over the classical bicubic interpolation algorithm,and has better effect than the quantum version of bilinear interpolation.

关键词： quantum image processing image scaling bicubic interpolation quantum circuit

Fault diagnosis of a marine power-generation diesel engine based on the Gramian angular field and a convolutional neural network

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Journal of Zhejiang university-Science A(Applied Physics & Engineering) 2024年第6期25卷 470-482页

作者： Congyue LI Yihuai HU Jiawei JIANG Dexin CUI Merchant Marine College Shanghai Maritime UniversityShanghai 201306China School of Mechanical and Energy Engineering Shanghai Technical Institute of Electronics&InformationShanghai 2014llChina

Marine power-generation diesel engines operate in harsh environments.Their vibration signals are highly complex and the feature information exhibits a non-linear distribution.It is difficult to extract effective feature information from the network model,resulting in low fault-diagnosis accuracy.To address this problem,we propose a fault-diagnosis method that combines the Gramian angular field(GAF)with a convolutional neural network(CNN).Firstly,the vibration signals are transformed into 2D images by taking advantage of the GAF,which preserves the temporal correlation.The raw signals can be mapped to 2D image features such as texture and color.To integrate the feature information,the images of the Gramian angular summation field(GASF)and Gramian angular difference field(GADF)are fused by the weighted average fusion method.Secondly,the channel attention mechanism and temporal attention mechanism are introduced in the CNN model to optimize the CNN learning mechanism.Introducing the concept of residuals in the attention mechanism improves the feasibility of optimization.Finally,the weighted average fused images are fed into the CNN for feature extraction and fault diagnosis.The validity of the proposed method is verified by experiments with abnormal valve clearance.The average diagnostic accuracy is 98.40%.When−20 dB≤signal-to-noise ratio(SNR)≤20 dB,the diagnostic accuracy of the proposed method is higher than 94.00%.The proposed method has superior diagnostic performance.Moreover,it has a certain anti-noise capability and variable-load adaptive capability.

关键词： Multi-attention mechanisms(MAM) Convolutional neural network(CNN) Gramian angular field(GAF) Image fusion Marine power-generation diesel engine Fault diagnosis

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1637-1661页

作者： Hongyan Xia Jin Zhu Institute of Logistics Science and Engineering Shanghai Maritime UniversityShanghai201306China

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

关键词： Automated container terminal buffer cooperative scheduling heuristic rules space-time network

An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

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Chinese Physics B 2024年第5期33卷 223-237页

作者：吴凯周日贵罗佳 College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Research Center of Intelligent Information Processing and Quantum Intelligent Computing Shanghai 201306China School of Mathematics and Computational Science Shangrao Normal UniversityShangrao 334001China

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

关键词： quantum color image processing anti-aliasing filtering algorithm quantum multiplier pyramid model

Computational model for perception of objects and motions

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Science China(Life Sciences) 2008年第6期51卷 526-536页

作者： YANG WenLu1,2, ZHANG LiQing1 & MA LiBo1 1 Department of Computer Science and Engineering, shanghai Jiao Tong university, shanghai 200240, China 2 Department of Electronic Engineering, shanghai maritime university, shanghai 200135, China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Electronic Engineering Shanghai Maritime University Shanghai China

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The compu- tational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.

关键词： visual perception visual cortex computational model receptive fields simple cells complex cells