Non-degradable polymers cause serious environmental pollution problem,such as the widely-used while unrecyclable coatings which significantly affect the overall degradation performance of products.It is imperative and ...Non-degradable polymers cause serious environmental pollution problem,such as the widely-used while unrecyclable coatings which significantly affect the overall degradation performance of products.It is imperative and attractive to develop biodegradable functional coatings.Herein,we proposed a novel strategy to successfully prepare biodegradable,thermoplastic and hydrophobic coatings with high transparence and biosafety by weakening the interchain interactions between cellulose chain.The natural cellulose and cinnamic acid were as raw materials.Via reducing the degree of polymerization(DP)of cellulose and regulating the degree of substitution(DS)of cinnamate moiety,the obtained cellulose cinnamate(CC)exhibited not only the thermalflow behavior but also good biodegradability,which solves the conflict between the thermoplasticity and biodegradability in cellulose-based materials.The glass transition temperature(T_(g))and thermalflow temperature(T_(f))of the CC could be adjusted in a range of 150–200℃ and 180–210℃,respectively.The CC with DS<1.2 and DP≤100 degraded more than 60%after an enzyme treatment for 7 days,and degraded more than 80%after a composting treatment for 42 days.Furthermore,CC had no toxicity to human epidermal cells even at a high concentration(0.5 mg mL^(-1)).In addition,CC could be easily fabricated into multifunctional coating with high hydrophobicity,thermal adhesion and high transparence.Therefore,after combining with cellophane and paperboard,CC coating with low DP and DS could be used to prepare fully-biodegradable heat-sealing packaging,art paper,paper cups,paper straws and food packaging boxes.展开更多
Aiming at defects such as low contrast in infrared ship images,uneven distribution of ship size,and lack of texture details,which will lead to unmanned ship leakage misdetection and slow detection,this paper proposes ...Aiming at defects such as low contrast in infrared ship images,uneven distribution of ship size,and lack of texture details,which will lead to unmanned ship leakage misdetection and slow detection,this paper proposes an infrared ship detection model based on the improved YOLOv8 algorithm(R_YOLO).The algorithm incorporates the Efficient Multi-Scale Attention mechanism(EMA),the efficient Reparameterized Generalized-feature extraction module(CSPStage),the small target detection header,the Repulsion Loss function,and the context aggregation block(CABlock),which are designed to improve the model’s ability to detect targets at multiple scales and the speed of model inference.The algorithm is validated in detail on two vessel datasets.The comprehensive experimental results demonstrate that,in the infrared dataset,the YOLOv8s algorithm exhibits improvements in various performance metrics.Specifically,compared to the baseline algorithm,there is a 3.1%increase in mean average precision at a threshold of 0.5(mAP(0.5)),a 5.4%increase in recall rate,and a 2.2%increase in mAP(0.5:0.95).Simultaneously,while less than 5 times parameters,the mAP(0.5)and frames per second(FPS)exhibit an increase of 1.7%and more than 3 times,respectively,compared to the CAA_YOLO algorithm.Finally,the evaluation indexes on the visible light data set have shown an average improvement of 4.5%.展开更多
Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on i...Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.展开更多
Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi...Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.展开更多
The constant panel method within the framework of potential flow theory in the time domain is developed for solving the hydrodynamic interactions between two parallel ships with forward speed.When solving problems wit...The constant panel method within the framework of potential flow theory in the time domain is developed for solving the hydrodynamic interactions between two parallel ships with forward speed.When solving problems within a time domain framework,the free water surface needs to simultaneously satisfy both the kinematic and dynamic boundary conditions of the free water surface.This provides conditions for adding artificial damping layers.Using the Runge−Kutta method to solve equations related to time.An upwind differential scheme is used in the present method to deal with the convection terms on the free surface to prevent waves upstream.Through the comparison with the available experimental data and other numerical methods,the present method is proved to have good mesh convergence,and satisfactory results can be obtained.The constant panel method is applied to calculate the hydrodynamic interaction responses of two parallel ships advancing in head waves.Numerical simulations are conducted on the effects of forward speed,different longitudinal and lateral distances on the motion response of two modified Wigley ships in head waves.Then further investigations are conducted on the effects of different ship types on the motion response.展开更多
Lignin,lignosulfonate,and synthesized phosphorylated lignosulfonate were introduced as greenfillers in citric acid-sucrose adhesives for bonding particleboard fabricated from areca leaf sheath(ALS).The characteristics ...Lignin,lignosulfonate,and synthesized phosphorylated lignosulfonate were introduced as greenfillers in citric acid-sucrose adhesives for bonding particleboard fabricated from areca leaf sheath(ALS).The characteristics of particleboards were compared to that of ultralow emitting formaldehyde(ULEF-UF).Thefillers derived from Eucalyptus spp.kraft-lignin were added forflame retardancy enhancement.10%of each lignin and modified lig-nin was added into the ULEF-UF and citric acid-sucrose bonded particleboards.Analyses applied to particle-boards included thermal characteristics,X-ray diffraction analysis(XRD),morphological properties,Fourier transform infrared spectroscopy(FTIR),as well as physical,mechanical,andfire resistance characteristics of the laboratory-fabricated particleboards.Lignin and modified lignin resulted in improved thermal stability of the composites bonded with ULEF-UF while the improvement in the particleboard bonded with citric acid-sucrose was not significant.The introduction offiller exerted a higher influence on the UF-bonded particleboards compared to composites fabricated with citric acid-sucrose.Generally,the presence of lignin,lignosulfonate,and phosphorylated lignosulfonate enhanced the mechanical strength of the ULEF-bonded particleboards,although their dimensional stability has deteriorated.Markedly,the use of lignin and lignosulfonate enhanced thefire resis-tance of the particleboards produced with lower observed weight loss.All laboratory particleboards exhibited satisfactoryfire resistance,attaining a V-0 rating in according to the UL-94 standard.展开更多
This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,mul...This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker’s efficiency,then a mathematical model is established to minimize the completion time. The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered. Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency. In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth. The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality. Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.展开更多
“The sky is dark,and it is about to rain,”goes a lyric from China’s coastal Minnan(southern Fujian)region.“The king ship is leaving the bay,papa is going out to sea,and mama is sending the ship off.May it bring us...“The sky is dark,and it is about to rain,”goes a lyric from China’s coastal Minnan(southern Fujian)region.“The king ship is leaving the bay,papa is going out to sea,and mama is sending the ship off.May it bring us wealth,food,and the gods’protection.”The 600-year-old custom is called Ong Chun,Wangchuan,Wangkang,or“Sending the King Ship.”展开更多
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition...The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.展开更多
欧洲水域自主航运倡议项目(Autonomous Shipping Initiative for European Waters,AUTOSHIP项目)由意大利Ciaotech S.r.l.公司牵头,联合康士伯等其他欧洲国家合作伙伴共同完成。该项目通过在不同环境下运营的两艘不同类型船舶上安装和...欧洲水域自主航运倡议项目(Autonomous Shipping Initiative for European Waters,AUTOSHIP项目)由意大利Ciaotech S.r.l.公司牵头,联合康士伯等其他欧洲国家合作伙伴共同完成。该项目通过在不同环境下运营的两艘不同类型船舶上安装和测试自主航行设施,加速新一代自主航行船舶发展,并为欧盟实现船舶自主航行制定商业化路线图。展开更多
This study aims to investigate whether Corporate Social Responsibility(CSR)activities reduce supply chain disruptions by examining the impact of the Suez Canal obstruction on the Ever Given container ship in March 202...This study aims to investigate whether Corporate Social Responsibility(CSR)activities reduce supply chain disruptions by examining the impact of the Suez Canal obstruction on the Ever Given container ship in March 2021.This study conclude that the more responsible companies have higher returns and are less affected by this event than the less responsible companies;the less responsible companies have lower returns.The companies with better CSR have a lower impact on their supply chains when faced with disruptions in the supply chain.展开更多
In this paper,an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks,such as the irregular shapes and varying sizes of ships.The improved model replaces the ...In this paper,an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks,such as the irregular shapes and varying sizes of ships.The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset.This paper also introduces a novel multi-scale feature fusion module,which comprises Path Aggregation Network(PAN)modules,enabling the efficient capture of ship features across different scales.Furthermore,data preprocessing is enhanced through the application of data augmentation techniques,including random rotation,scaling,and cropping,which serve to bolster data diversity and robustness.The distribution of positive and negative samples in the dataset is balanced using random sampling,ensuring a more accurate representation of real-world scenarios.Comprehensive experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches in terms of both detection accuracy and robustness,highlighting the potential of the improved YOLOv7 model for practical applications in the maritime domain.展开更多
Zero-emission eco-friendly vehicles with partly or fully electric powertrains have exhibited rapidly increased demand for reducing the emissions of air pollutants and improving the energy efficiency. Advanced catalyti...Zero-emission eco-friendly vehicles with partly or fully electric powertrains have exhibited rapidly increased demand for reducing the emissions of air pollutants and improving the energy efficiency. Advanced catalytic and energy materials are essential as the significant portions in the key technologies of eco-friendly vehicles, such as the exhaust emission control system,power lithium ion battery and hydrogen fuel cell. Precise synthesis and surface modification of the functional materials and electrodes are required to satisfy the efficient surface and interface catalysis, as well as rapid electron/ion transport. Atomic layer deposition(ALD), an atomic and close-to-atomic scale manufacturing method, shows unique characteristics of precise thickness control, uniformity and conformality for film deposition, which has emerged as an important technique to design and engineer advanced catalytic and energy materials. This review has summarized recent process of ALD on the controllable preparation and modification of metal and oxide catalysts, as well as lithium ion battery and fuel cell electrodes. The enhanced catalytic and electrochemical performances are discussed with the unique nanostructures prepared by ALD. Recent works on ALD reactors for mass production are highlighted. The challenges involved in the research and development of ALD on the future practical applications are presented, including precursor and deposition process investigation, practical device performance evaluation, large-scale and efficient production, etc.展开更多
Synthetic Aperture Radar(SAR)image target detection has widespread applications in both military and civil domains.However,SAR images pose challenges due to strong scattering,indistinct edge contours,multi-scale repre...Synthetic Aperture Radar(SAR)image target detection has widespread applications in both military and civil domains.However,SAR images pose challenges due to strong scattering,indistinct edge contours,multi-scale representation,sparsity,and severe background interference,which make the existing target detection methods in low accuracy.To address this issue,this paper proposes a multi-scale fusion framework(Swin-PAFF)for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure(FPN).Firstly,to tackle the issue of inadequate perceptual image context information in SAR target detection,we propose an end-to-end SAR target detection network with the Transformer structure as the backbone.Furthermore,we enhance the ability of the Swin Transformer to acquire contextual features and cross-information by incorporating a Swin-CC backbone network model that combines the Spatial Depthwise Pooling(SDP)module and the self-attentive mechanism.Finally,we design a cross-layer fusion neck module(PAFF)that better handles multi-scale variations and complex situations(such as sparsity,background interference,etc.).Our devised approach yields a noteworthy AP@0.5:0.95 performance of 91.3%when assessed on the HRSID dataset.The application of our proposed technique has resulted in a noteworthy advancement of 8%in the AP@0.5:0.95 scores on the HRSID dataset.展开更多
Ship rolling in random waves is a complicated nonlinear motion that contributes substantially to ship instability and capsizing.The finite element method(FEM)is employed in this paper to solve the Fokker Planck(FP)equ...Ship rolling in random waves is a complicated nonlinear motion that contributes substantially to ship instability and capsizing.The finite element method(FEM)is employed in this paper to solve the Fokker Planck(FP)equations numerically for homoclinic and heteroclinic ship rolling under random waves described as periodic and Gaussian white noise excitations.The transient joint probability density functions(PDFs)and marginal PDFs of the rolling responses are also obtained.The effects of stimulation strength on ship rolling are further investigated from a probabilistic standpoint.The homoclinic ship rolling has two rolling states,the connection between the two peaks of the PDF is observed when the periodic excitation amplitude or the noise intensity is large,and the PDF is remarkably distributed in phase space.These phenomena increase the possibility of a random jump in ship motion states and the uncertainty of ship rolling,and the ship may lose stability due to unforeseeable facts or conditions.Meanwhile,only one rolling state is observed when the ship is in heteroclinic rolling.As the periodic excitation amplitude grows,the PDF concentration increases and drifts away from the beginning location,suggesting that the ship rolling substantially changes in a cycle and its stability is low.The PDF becomes increasingly uniform and covers a large region as the noise intensity increases,reducing the certainty of ship rolling and navigation safety.The current numerical solutions and analyses may be applied to evaluate the stability of a rolling ship in irregular waves and capsize mechanisms.展开更多
Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,du...Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,due to the high similarity between the man-made targets near shore and inshore ships,the classical methods are unable to achieve effective detection of inshore ships.To mitigate the influence of onshore ship-like objects,this paper proposes an inshore ship detection method in SAR images by using hybrid features.Firstly,the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images.Then,a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water.On this basis,the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left.Finally,to verify the effectiveness of the proposed method,numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.展开更多
Through systematical experiment design, the physical blowing agent(PBA) mass loss of bio-based polyurethane rigid foam(PURF)in the foaming process was measured and calculated in this study, and different eco-friendly ...Through systematical experiment design, the physical blowing agent(PBA) mass loss of bio-based polyurethane rigid foam(PURF)in the foaming process was measured and calculated in this study, and different eco-friendly PBA mass losses were measured quantitatively for the first time. The core of the proposed method is to add water to replace the difference, and this method has a high fault tolerance rate for different foaming forms of foams. The method was proved to be stable and reliable through the standard deviations σ1and σ2for R1(ratio of the PBA mass loss to the material total mass except the PBA) and R2(ratio of the PBA mass loss to the PBA mass in the material total mass) in parallel experiments. It can be used to measure and calculate the actual PBA mass loss in the foaming process of both bio-based and petroleumbased PURF. The results show that the PBA mass loss in PURF with different PBA systems is controlled by its initial mass content of PBA in PU materials ω. The main way for PBA to dissipate into the air is evaporation/escape along the upper surface of foam. This study further reveals the mechanism of PBA mass loss: the evaporation/escape of PBA along the upper surface of foam is a typical diffusion behavior. Its spread power comes from the difference between the chemical potential of PBA in the interface layer and that in the outside air. For a certain PURF system, R1has approximately linear relationship with the initial mass content of PBA in PU materials ω, which can be expressed by the functional relationship R1= kω, where k is a variable related to PBA’s own attributes.展开更多
基金supported by the National Natural Science Foundation of China(No.52173292)the National Key Research and Development Project of China(No.2020YFC1910303)the Youth Innovation Promotion Association CAS(No.2018040).
文摘Non-degradable polymers cause serious environmental pollution problem,such as the widely-used while unrecyclable coatings which significantly affect the overall degradation performance of products.It is imperative and attractive to develop biodegradable functional coatings.Herein,we proposed a novel strategy to successfully prepare biodegradable,thermoplastic and hydrophobic coatings with high transparence and biosafety by weakening the interchain interactions between cellulose chain.The natural cellulose and cinnamic acid were as raw materials.Via reducing the degree of polymerization(DP)of cellulose and regulating the degree of substitution(DS)of cinnamate moiety,the obtained cellulose cinnamate(CC)exhibited not only the thermalflow behavior but also good biodegradability,which solves the conflict between the thermoplasticity and biodegradability in cellulose-based materials.The glass transition temperature(T_(g))and thermalflow temperature(T_(f))of the CC could be adjusted in a range of 150–200℃ and 180–210℃,respectively.The CC with DS<1.2 and DP≤100 degraded more than 60%after an enzyme treatment for 7 days,and degraded more than 80%after a composting treatment for 42 days.Furthermore,CC had no toxicity to human epidermal cells even at a high concentration(0.5 mg mL^(-1)).In addition,CC could be easily fabricated into multifunctional coating with high hydrophobicity,thermal adhesion and high transparence.Therefore,after combining with cellophane and paperboard,CC coating with low DP and DS could be used to prepare fully-biodegradable heat-sealing packaging,art paper,paper cups,paper straws and food packaging boxes.
文摘Aiming at defects such as low contrast in infrared ship images,uneven distribution of ship size,and lack of texture details,which will lead to unmanned ship leakage misdetection and slow detection,this paper proposes an infrared ship detection model based on the improved YOLOv8 algorithm(R_YOLO).The algorithm incorporates the Efficient Multi-Scale Attention mechanism(EMA),the efficient Reparameterized Generalized-feature extraction module(CSPStage),the small target detection header,the Repulsion Loss function,and the context aggregation block(CABlock),which are designed to improve the model’s ability to detect targets at multiple scales and the speed of model inference.The algorithm is validated in detail on two vessel datasets.The comprehensive experimental results demonstrate that,in the infrared dataset,the YOLOv8s algorithm exhibits improvements in various performance metrics.Specifically,compared to the baseline algorithm,there is a 3.1%increase in mean average precision at a threshold of 0.5(mAP(0.5)),a 5.4%increase in recall rate,and a 2.2%increase in mAP(0.5:0.95).Simultaneously,while less than 5 times parameters,the mAP(0.5)and frames per second(FPS)exhibit an increase of 1.7%and more than 3 times,respectively,compared to the CAA_YOLO algorithm.Finally,the evaluation indexes on the visible light data set have shown an average improvement of 4.5%.
基金This work was supported by the Outstanding Youth Science and Technology Innovation Team Project of Colleges and Universities in Hubei Province(Grant No.T201923)Key Science and Technology Project of Jingmen(Grant Nos.2021ZDYF024,2022ZDYF019)+2 种基金LIAS Pioneering Partnerships Award,UK(Grant No.P202ED10)Data Science Enhancement Fund,UK(Grant No.P202RE237)Cultivation Project of Jingchu University of Technology(Grant No.PY201904).
文摘Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.
文摘Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.52271278 and 52111530137)the Natural Science Found of Jiangsu Province(Grant No.BK20221389)the Newton Advanced Fellowships(Grant No.NAF\R1\180304)by the Royal Society.
文摘The constant panel method within the framework of potential flow theory in the time domain is developed for solving the hydrodynamic interactions between two parallel ships with forward speed.When solving problems within a time domain framework,the free water surface needs to simultaneously satisfy both the kinematic and dynamic boundary conditions of the free water surface.This provides conditions for adding artificial damping layers.Using the Runge−Kutta method to solve equations related to time.An upwind differential scheme is used in the present method to deal with the convection terms on the free surface to prevent waves upstream.Through the comparison with the available experimental data and other numerical methods,the present method is proved to have good mesh convergence,and satisfactory results can be obtained.The constant panel method is applied to calculate the hydrodynamic interaction responses of two parallel ships advancing in head waves.Numerical simulations are conducted on the effects of forward speed,different longitudinal and lateral distances on the motion response of two modified Wigley ships in head waves.Then further investigations are conducted on the effects of different ship types on the motion response.
基金funded by the Equity Project Universitas Sumatera Utara(Number:10/UN5.2.3.1/PPM/KPEP/2023),which is entitled Pengembangan Papan Partikel Tahan Api Rendah Emisi Berbahan Limbah Tanaman Mangrove dan Limbah Tanaman Pertanian Melalui Penambahan Lignin Terfosforilasi Sebagai Filler.PT Greenei Alam Indonesia(PT GAI)contributed to providing the areca leaf sheath through the implementation of a collaboration agreement with the Research Center for Biomass and Bioproducts BRIN FY 2023-2025.
文摘Lignin,lignosulfonate,and synthesized phosphorylated lignosulfonate were introduced as greenfillers in citric acid-sucrose adhesives for bonding particleboard fabricated from areca leaf sheath(ALS).The characteristics of particleboards were compared to that of ultralow emitting formaldehyde(ULEF-UF).Thefillers derived from Eucalyptus spp.kraft-lignin were added forflame retardancy enhancement.10%of each lignin and modified lig-nin was added into the ULEF-UF and citric acid-sucrose bonded particleboards.Analyses applied to particle-boards included thermal characteristics,X-ray diffraction analysis(XRD),morphological properties,Fourier transform infrared spectroscopy(FTIR),as well as physical,mechanical,andfire resistance characteristics of the laboratory-fabricated particleboards.Lignin and modified lignin resulted in improved thermal stability of the composites bonded with ULEF-UF while the improvement in the particleboard bonded with citric acid-sucrose was not significant.The introduction offiller exerted a higher influence on the UF-bonded particleboards compared to composites fabricated with citric acid-sucrose.Generally,the presence of lignin,lignosulfonate,and phosphorylated lignosulfonate enhanced the mechanical strength of the ULEF-bonded particleboards,although their dimensional stability has deteriorated.Markedly,the use of lignin and lignosulfonate enhanced thefire resis-tance of the particleboards produced with lower observed weight loss.All laboratory particleboards exhibited satisfactoryfire resistance,attaining a V-0 rating in according to the UL-94 standard.
基金Sponsored by the Ministry of Industry and Information Technology of China(Grant No.MIIT[2019]359)。
文摘This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker’s efficiency,then a mathematical model is established to minimize the completion time. The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered. Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency. In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth. The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality. Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.
文摘“The sky is dark,and it is about to rain,”goes a lyric from China’s coastal Minnan(southern Fujian)region.“The king ship is leaving the bay,papa is going out to sea,and mama is sending the ship off.May it bring us wealth,food,and the gods’protection.”The 600-year-old custom is called Ong Chun,Wangchuan,Wangkang,or“Sending the King Ship.”
基金supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(62325104).
文摘The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.
文摘欧洲水域自主航运倡议项目(Autonomous Shipping Initiative for European Waters,AUTOSHIP项目)由意大利Ciaotech S.r.l.公司牵头,联合康士伯等其他欧洲国家合作伙伴共同完成。该项目通过在不同环境下运营的两艘不同类型船舶上安装和测试自主航行设施,加速新一代自主航行船舶发展,并为欧盟实现船舶自主航行制定商业化路线图。
文摘This study aims to investigate whether Corporate Social Responsibility(CSR)activities reduce supply chain disruptions by examining the impact of the Suez Canal obstruction on the Ever Given container ship in March 2021.This study conclude that the more responsible companies have higher returns and are less affected by this event than the less responsible companies;the less responsible companies have lower returns.The companies with better CSR have a lower impact on their supply chains when faced with disruptions in the supply chain.
基金supported by the Key R&D Project of Hainan Province(Grant No.ZDYF2022GXJS348,ZDYF2022SHFZ039).
文摘In this paper,an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks,such as the irregular shapes and varying sizes of ships.The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset.This paper also introduces a novel multi-scale feature fusion module,which comprises Path Aggregation Network(PAN)modules,enabling the efficient capture of ship features across different scales.Furthermore,data preprocessing is enhanced through the application of data augmentation techniques,including random rotation,scaling,and cropping,which serve to bolster data diversity and robustness.The distribution of positive and negative samples in the dataset is balanced using random sampling,ensuring a more accurate representation of real-world scenarios.Comprehensive experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches in terms of both detection accuracy and robustness,highlighting the potential of the improved YOLOv7 model for practical applications in the maritime domain.
基金supported by the National Key R&D Program of China (2020YFB2010401 and 2022YFF1500400)National Natural Science Foundation of China (51835005and 52271216)+2 种基金Hubei Province Natural Science Foundation for Innovative Research Group (2020CFA030)Fundamental Research Funds for the Central Universities,HUST(2020kfy XJJS100)Tencent Foundation。
文摘Zero-emission eco-friendly vehicles with partly or fully electric powertrains have exhibited rapidly increased demand for reducing the emissions of air pollutants and improving the energy efficiency. Advanced catalytic and energy materials are essential as the significant portions in the key technologies of eco-friendly vehicles, such as the exhaust emission control system,power lithium ion battery and hydrogen fuel cell. Precise synthesis and surface modification of the functional materials and electrodes are required to satisfy the efficient surface and interface catalysis, as well as rapid electron/ion transport. Atomic layer deposition(ALD), an atomic and close-to-atomic scale manufacturing method, shows unique characteristics of precise thickness control, uniformity and conformality for film deposition, which has emerged as an important technique to design and engineer advanced catalytic and energy materials. This review has summarized recent process of ALD on the controllable preparation and modification of metal and oxide catalysts, as well as lithium ion battery and fuel cell electrodes. The enhanced catalytic and electrochemical performances are discussed with the unique nanostructures prepared by ALD. Recent works on ALD reactors for mass production are highlighted. The challenges involved in the research and development of ALD on the future practical applications are presented, including precursor and deposition process investigation, practical device performance evaluation, large-scale and efficient production, etc.
基金supported by the National Key Research and Development Program of China under Grant 2021YFC2801001the Natural Science Foundation of Shanghai under Grant 21ZR1426500the 2022 Graduate Top Innovative Talents Training Program at Shanghai Maritime University under Grant 2022YBR004.
文摘Synthetic Aperture Radar(SAR)image target detection has widespread applications in both military and civil domains.However,SAR images pose challenges due to strong scattering,indistinct edge contours,multi-scale representation,sparsity,and severe background interference,which make the existing target detection methods in low accuracy.To address this issue,this paper proposes a multi-scale fusion framework(Swin-PAFF)for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure(FPN).Firstly,to tackle the issue of inadequate perceptual image context information in SAR target detection,we propose an end-to-end SAR target detection network with the Transformer structure as the backbone.Furthermore,we enhance the ability of the Swin Transformer to acquire contextual features and cross-information by incorporating a Swin-CC backbone network model that combines the Spatial Depthwise Pooling(SDP)module and the self-attentive mechanism.Finally,we design a cross-layer fusion neck module(PAFF)that better handles multi-scale variations and complex situations(such as sparsity,background interference,etc.).Our devised approach yields a noteworthy AP@0.5:0.95 performance of 91.3%when assessed on the HRSID dataset.The application of our proposed technique has resulted in a noteworthy advancement of 8%in the AP@0.5:0.95 scores on the HRSID dataset.
基金the National Natural Science Foundation of China(Nos.52088102,51875540)。
文摘Ship rolling in random waves is a complicated nonlinear motion that contributes substantially to ship instability and capsizing.The finite element method(FEM)is employed in this paper to solve the Fokker Planck(FP)equations numerically for homoclinic and heteroclinic ship rolling under random waves described as periodic and Gaussian white noise excitations.The transient joint probability density functions(PDFs)and marginal PDFs of the rolling responses are also obtained.The effects of stimulation strength on ship rolling are further investigated from a probabilistic standpoint.The homoclinic ship rolling has two rolling states,the connection between the two peaks of the PDF is observed when the periodic excitation amplitude or the noise intensity is large,and the PDF is remarkably distributed in phase space.These phenomena increase the possibility of a random jump in ship motion states and the uncertainty of ship rolling,and the ship may lose stability due to unforeseeable facts or conditions.Meanwhile,only one rolling state is observed when the ship is in heteroclinic rolling.As the periodic excitation amplitude grows,the PDF concentration increases and drifts away from the beginning location,suggesting that the ship rolling substantially changes in a cycle and its stability is low.The PDF becomes increasingly uniform and covers a large region as the noise intensity increases,reducing the certainty of ship rolling and navigation safety.The current numerical solutions and analyses may be applied to evaluate the stability of a rolling ship in irregular waves and capsize mechanisms.
基金Aeronautical Science Foundation of China(No.2018ZC51022)。
文摘Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,due to the high similarity between the man-made targets near shore and inshore ships,the classical methods are unable to achieve effective detection of inshore ships.To mitigate the influence of onshore ship-like objects,this paper proposes an inshore ship detection method in SAR images by using hybrid features.Firstly,the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images.Then,a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water.On this basis,the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left.Finally,to verify the effectiveness of the proposed method,numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.
文摘Through systematical experiment design, the physical blowing agent(PBA) mass loss of bio-based polyurethane rigid foam(PURF)in the foaming process was measured and calculated in this study, and different eco-friendly PBA mass losses were measured quantitatively for the first time. The core of the proposed method is to add water to replace the difference, and this method has a high fault tolerance rate for different foaming forms of foams. The method was proved to be stable and reliable through the standard deviations σ1and σ2for R1(ratio of the PBA mass loss to the material total mass except the PBA) and R2(ratio of the PBA mass loss to the PBA mass in the material total mass) in parallel experiments. It can be used to measure and calculate the actual PBA mass loss in the foaming process of both bio-based and petroleumbased PURF. The results show that the PBA mass loss in PURF with different PBA systems is controlled by its initial mass content of PBA in PU materials ω. The main way for PBA to dissipate into the air is evaporation/escape along the upper surface of foam. This study further reveals the mechanism of PBA mass loss: the evaporation/escape of PBA along the upper surface of foam is a typical diffusion behavior. Its spread power comes from the difference between the chemical potential of PBA in the interface layer and that in the outside air. For a certain PURF system, R1has approximately linear relationship with the initial mass content of PBA in PU materials ω, which can be expressed by the functional relationship R1= kω, where k is a variable related to PBA’s own attributes.