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A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network 认领 引用 被引量：31
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作者 Hao HE Shuyang WANG +2 位作者 Shicheng WANG Dongfang YANG Xing LIU 《Journal of Geodesy and Geoinformation Science》 2020年第2期16-25,共10页
According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are r... According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are rich in local details and simple in semantic features,an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information.Secondly,as the road area is a small proportion in remote sensing images,the cross-entropy loss function is improved,which solves the imbalance between positive and negative samples in the training process.Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%,precision 82.5%and F1-score 82.9%,which can extract the road targets in remote sensing images completely and accurately.The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation,so it has a good application prospect. 展开更多
关键词 remote sensing road extraction deep learning semantic segmentation Encoder-Decoder network
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Action-Aware Encoder-Decoder Network for Pedestrian Trajectory Prediction 认领 引用
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作者 傅家威 赵旭 《Journal of Shanghai Jiaotong university(Science)》 EI 2023年第1期20-27,共8页
Accurate pedestrian trajectory predictions are critical in self-driving systems,as they are fundamental to the response-and decision-making of ego vehicles.In this study,we focus on the problem of predicting the futur... Accurate pedestrian trajectory predictions are critical in self-driving systems,as they are fundamental to the response-and decision-making of ego vehicles.In this study,we focus on the problem of predicting the future trajectory of pedestrians from a first-person perspective.Most existing trajectory prediction methods from the first-person view copy the bird’s-eye view,neglecting the differences between the two.To this end,we clarify the differences between the two views and highlight the importance of action-aware trajectory prediction in the first-person view.We propose a new action-aware network based on an encoder-decoder framework with an action prediction and a goal estimation branch at the end of the encoder.In the decoder part,bidirectional long short-term memory(Bi-LSTM)blocks are adopted to generate the ultimate prediction of pedestrians’future trajectories.Our method was evaluated on a public dataset and achieved a competitive performance,compared with other approaches.An ablation study demonstrates the effectiveness of the action prediction branch. 展开更多
关键词 pedestrian trajectory prediction first-person view action prediction encoder-decoder bidirectional long short-term memory(Bi-LSTM)
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Deep convolutional encoder-decoder networks based on ensemble learning for semantic segmentation of high-resolution aerial imagery 认领 引用
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作者 Huming Zhu Chendi Liu +5 位作者 Qiuming Li Lingyun Zhang Libing Wang Sifan Li Licheng Jiao Biao Hou 《CCF Transactions on High Performance Computing》 EI 2024年第4期408-424,共17页
Due to the complexity of object information and optical conditions of high-resolution aerial imagery,it is difficult to obtain fine semantic segmentation performance.Although various deep neural network structures hav... Due to the complexity of object information and optical conditions of high-resolution aerial imagery,it is difficult to obtain fine semantic segmentation performance.Although various deep neural network structures have been proposed to improve segmentation accuracy,there is still room for improving accuracy by making full use of multiscale features and integrating these single weak classifiers into a strong classifier.In this paper,we use a reduced SegNet network to realize the end-to-end classification of high-resolution aerial images.In addition,to use multiscale information,we present the R-SegUnet which combines the feature information of each convolution block in the reduced SegNet encoding network with the feature information of the corresponding convolution block in the decoding network.Furthermore,considering that the surface features in high-resolution aerial images are very complex,we investigate a 6to2_Net that converts the six-classification model into six binary-classification models for the recognition effect on small objects.Finally,we ensemble the above three different models to get the segmentation results.Experiment results on ISPRS Potsdam benchmark dataset show that our algorithm is state-of-the-art method.We also analyze the inference performance of our models on a variety of parallel computing devices. 展开更多
关键词 Aerial imagery Semantic segmentation Encoder-decoder network Ensemble ISPRS FCN
ACSF-ED: Adaptive Cross-Scale Fusion Encoder-Decoder for Spatio-Temporal Action Detection 认领 引用
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作者 Wenju Wang Zehua Gu +2 位作者 Bang Tang Sen Wang Jianfei Hao 《Computers, Materials & Continua》 SCIE EI 2025年第2期2389-2414,共26页
Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decode... Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods. 展开更多
关键词 Spatio-temporal action detection encoder-decoder cross-scale fusion multi-constraint loss function
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Scalable,binder-free,ultrathin,and outdoor stable passive cooling paints engineered by cellulose-weaved topological scattering network 认领 引用 被引量：1
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作者 Ting Yang Siying Guo +5 位作者 Xin Zhao Bianjing Sun Ruey Shan Chen Sinyee Gan Jonathan Woon-Chung Wong Chenyang Cai 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第4期584-594,I0015,共11页
Paints with passive daytime radiative cooling capability hold significant promise for energy-efficient buildings owing to their ease of processing.However,conventional radiative cooling paints require substantial thic... Paints with passive daytime radiative cooling capability hold significant promise for energy-efficient buildings owing to their ease of processing.However,conventional radiative cooling paints require substantial thickness to achieve effective outdoor cooling and must be combined with binders to enhance adhesion to the substrate.Meanwhile,their long-term outdoor durability remains poor.In this work,we proposed a scattering network-enhanced ultrathin photonic cooling paint(thickness of 78μm)fabricated without traditional binders through a universal,scalable solution-assembly strategy under a low-carbon production process.Cellulose nanofiber and cellulose nanocrystal were employed to wrap and entangle TiO2,forming a topological scattering network that prevents near-field coupling.Together with hierarchical pores,this structure enables high solar reflectance(96.4%)and an infrared emissivity of 0.94.This novel paint achieves temperature reduction of~5.6 and 3.8℃ under low and high-humidity conditions of midday,respectively,while maintaining long-term outdoor stability.Importantly,the cellulose-weaved topological scattering network can also be engineered with alternative photonic cooling pigments(Al2O3,SiO2,BaSO4,and mica),demonstrating its universality.In addition,life cycle assessment reveals that the obtained cooling paint offers very low carbon emissions and minimal environmental impacts.This work provides an economically viable and environmentally sustainable alternative to existing passive cooling materials. 展开更多
关键词 Weaved network Radiative cooling Cellulose Ultrathin structure
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An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling 认领 引用 被引量：2
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作者 Lei Liu Wei Li +7 位作者 Jian Gao Da-Li Yue De-Gang Wu Wu-Rong Wang Jin Lin Zhi-Bo Li Qian Zhong Jia-Gen Hou 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1754-1772,共19页
Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we... Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we innovatively propose an interpretable attention-guided generative adversarial network framework with dual-domain learning,which achieves precise sedimentary facies modeling under the constraints of well facies and soft probability data.Specifically,we first effectively extract and preserve prior information of sedimentary facies models from both spatial and frequency domain perspectives.Then,during simulation,to enhance the capability of the network model for finely characterizing complex heterogeneous models,cross-spatial attention mechanisms are designed to effectively capture short-range and long-range dependencies between multi-scale pattern features.Additionally,through systematic feature map visualization analysis,we elucidate the processes of conditional fitting and complex sedimentary facies model reconstruction,intuitively demonstrating the functional mechanisms of each module.Finally,systematic experiments are conducted on multiple datasets to validate the effectiveness of the proposed method.The results demonstrate that the generated sedimentary facies models exhibit high consistency with training datasets in terms of visual realism and statistical indicators.Quantitative comparisons reveal remarkable performance of the method,achieving low Wasserstein distance(0.09),Kernel Inception Distance(0.0017)and Kernel Maximum Mean Discrepancy(0.21).These findings further confirm the high realism of the generated realizations regarding pattern features.This study offers a reliable and practical method for geological reservoir modeling,thereby advancing quantitative,precise geological research with broad application prospects. 展开更多
关键词 Sedimentary facies models Attention-guided generative adversarial network Interpretable framework Sedimentary patterns Multi-condition modeling
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Diversified rotation reduced N2O emissions by shaping N-cycling microbial communities and increasing their network complexity 认领 引用 被引量：1
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作者 Taobing Yu Lang Cheng +5 位作者 Pin Wang Lei Yang Tengxiang Lian Huadong Zang Zhaohai Zeng Yadong Yang 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第4期593-602,共10页
Increased crop diversity can alter soil nitrogen(N)levels,soil properties,and functional microbial communities,leading to changes in potential nitrous oxide(N2O)emissions.However,our understanding on relationships ... Increased crop diversity can alter soil nitrogen(N)levels,soil properties,and functional microbial communities,leading to changes in potential nitrous oxide(N2O)emissions.However,our understanding on relationships between N2O emissions and related microbes in diversified rotation systems is still limited.Here,we established a long-term field experiment to investigate the response of N2O emissions regulated by five N-cycling genes in three rotation systems.Our results showed that N2O emissions in wheat and maize seasons in diversified rotations(spring maize→winter wheat–summer maize and spring peanut→winter wheat–summer maize)were 15.5%-51.1%and 15.9%-53.3%lower than that in winter wheat–summer maize rotation(P<0.05),respectively.Diversified rotations decreased abundance of ammonia-oxidizing archaea(AOA)amoA,AOB amoA,nirK and nirS genes in both wheat and maize seasons,while increased abundance of nosZ gene in maize season,leading to lower soil N2O emissions.Changes in these functional genes correlated significantly with soil moisture,nitrogen availability,and enzyme activity(L-leucine aminopeptidase and Urease).Besides,diversified rotations increased number of nodes,edges and degree of the co-occurring network and sub-network,while reduced average path length and betweeness.These microbial co-occurrence network complexity indicators were significantly correlated with N2O emissions.This indicates that increase in aboveground crop diversity drives the increase in complexity of belowground N-cycling related microbial interaction networks,which leads to lower N2O emissions.In summary,diversified rotations show promising potentials to lower N2O emissions in agricultural soils. 展开更多
关键词 Diversified rotation Nitrous oxide Ammonia oxidation Denitrification Microbial network complexity
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The Agentic-AI Core:An AI-Empowered,Mission-Oriented Core Network for Next-Generation Mobile Telecommunications 认领 引用 被引量：1
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作者 Xu Li Weisen Shi +3 位作者 Hang Zhang Chenghui Peng Shaoyun Wu Wen Tong 《Engineering》 SCIE EI CSCD 2026年第1期104-119,共16页
While the complexity of fifth-generation wireless networks is being widely commented upon,there is great anticipation for the arrival of the sixth generation(6G),with its enriched capabilities and features.It can easi... While the complexity of fifth-generation wireless networks is being widely commented upon,there is great anticipation for the arrival of the sixth generation(6G),with its enriched capabilities and features.It can easily be imagined that,without proper design,the enrichment of 6G will further increase system complexity.To address this issue,we propose the Agentic-AI Core(A-Core),an artificial intelligence(AI)-empowered,mission-oriented core network architecture for next-generation mobile telecommunications.In A-Core,network capabilities can be added and updated on the fly and further programmed into missions for enabling and offering diverse services to customers.These missions are created and executed by autonomous network agents according to the customer's intent,which may be expressed in natural language.The agents resolve intents from customers into workflows of network capabilities by leveraging a large-scale network AI model and follow the workflows to execute the mission.As an open,agile system architecture,A-Core holds promise for accelerating innovation and greatly reducing standard release times.The advantages of A-Core are demonstrated through two use cases. 展开更多
关键词 Sixth generation Core network Generative artificial intelligence Artificial intelligence agent
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Spatial morphology optimization for reconciling urban expansion with ecological integrity based on a multi-level ecological network framework 认领 引用 被引量：2
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作者 LU Jie JIAO Sheng CHEN Xingli 《Journal of Geographical Sciences》 SCIE CSCD 2026年第2期399-420,共22页
Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecol... Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecological metrics and rarely addressed the dynamic USM optimization across spatial scales.Here,we developed a multi-level ecological network(MEN)framework to resolve the tension between urban expansion and ecological integrity.By integrating the cost-weighted distance analysis with a hierarchical network transmission mechanism,we established a cross-scale spatial optimization system,which coordinated the regional ecological corridors and local habitat patches.Comparative experiments with conventional single-scale approaches and scenario simulations using the PLUS model show that the MEN framework had superior performance in three dimensions:(1)spatial governance:the primary-level network(peri-urban natural reserves)effectively contained urban sprawl,and the secondary-level network(intra-urban green corridors)mitigated habitat fragmentation and improved the built-environment;(2)scenario robustness:the model maintained an optimal compactness-loose balance in multiple development pathways;(3)landscape metrics:patch fragmentation decreased by 18.25%,and the internal landscape richness improved by 10.66%compared to the scenario without USM optimization.The findings provide new insight to establish a hierarchical ecological optimization framework as a nature-based spatial protocol to reconcile metropolitan growth with landscape sustainability. 展开更多
关键词 urban spatial morphology ecological network multi-level coupling scenarios simulation urban expansion
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Experimental data-driven deep neural network modeling and prediction for the streaks of turbulent separated shear flow 认领 引用 被引量：1
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作者 Xingyu Ma Jiateng Pan +1 位作者 Yihong Liu Nan Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期52-58,共7页
In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a... In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a plane backward-facing step,spanwise-aligned high-and low-speed streaks were generated within the separated shear layer behind the step.The velocity profiles of the shear flow were measured by single-probe hot-wire anemometer in both the streamwise-vertical and the streamwise-spanwise planes in the wind tunnel.Deep neural network models are trained and verified based on the experimental datasets.The input parameter sets include the vortex generator height,spanwise spacing,and the spatial coordinates within the measurement domain,while the output parameter sets are mean and root-mean-square velocities of the shear flow.Mean squared errors between the model-predicted and experimentally measured data are used for quality evaluation of different deep neural network model designs,among which the minimum error of the optimal design descends less than 1%.For other vortex generator parameters,which are not measured in the wind tunnel or used in the training,the model prediction provides reasonable mean velocity contours with streak structures.Thus,we find that the experimental data-driven modeling approach shows reliable robustness for nonlinear fitting of complex datasets as well as considerable generalization for turbulent coherent structures. 展开更多
关键词 Streak Experimental data-driven Deep neural network Vortex generator Backward-facing step
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Collision risk assessment for constellation satellites based on a space debris environment topological network model 认领 引用 被引量：1
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作者 Yurun YUAN Jingrui ZHANG +2 位作者 Keying YANG Lincheng LI Hao WU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期472-484,共13页
In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This s... In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites. 展开更多
关键词 Collision probability Computing resource Constellation Space debris Topological network model
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Underwater Acoustic Signal Noise Reduction Based on a Fully Convolutional Encoder-Decoder Neural Network 认领 引用
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作者 SONG Yongqiang CHU Qian +2 位作者 LIU Feng WANG Tao SHEN Tongsheng 《Journal of Ocean University of China》 SCIE CAS CSCD 2023年第6期1487-1496,共10页
Noise reduction analysis of signals is essential for modern underwater acoustic detection systems.The traditional noise reduction techniques gradually lose efficacy because the target signal is masked by biological an... Noise reduction analysis of signals is essential for modern underwater acoustic detection systems.The traditional noise reduction techniques gradually lose efficacy because the target signal is masked by biological and natural noise in the marine environ-ment.The feature extraction method combining time-frequency spectrograms and deep learning can effectively achieve the separation of noise and target signals.A fully convolutional encoder-decoder neural network(FCEDN)is proposed to address the issue of noise reduc-tion in underwater acoustic signals.The time-domain waveform map of underwater acoustic signals is converted into a wavelet low-frequency analysis recording spectrogram during the denoising process to preserve as many underwater acoustic signal characteristics as possible.The FCEDN is built to learn the spectrogram mapping between noise and target signals that can be learned at each time level.The transposed convolution transforms are introduced,which can transform the spectrogram features of the signals into listenable audio files.After evaluating the systems on the ShipsEar Dataset,the proposed method can increase SNR and SI-SNR by 10.02 and 9.5dB,re-spectively. 展开更多
关键词 deep learning convolutional encoder-decoder neural network wavelet low-frequency analysis recording spectrogram
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A comprehensive survey of artificial intelligence applications in UAV-enabled wireless networks 认领 引用 被引量：2
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作者 Li Zhou Hao Yin +3 位作者 Haitao Zhao Jibo Wei Dewen Hu Victor C.M.Leung 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期561-583,共23页
This comprehensive survey paper examines the applications of Artificial Intelligence(AI)in Unmanned Aerial Vehicle(UAV)-enabled wireless networks.With the increasing demand for efficient and adaptive communication sys... This comprehensive survey paper examines the applications of Artificial Intelligence(AI)in Unmanned Aerial Vehicle(UAV)-enabled wireless networks.With the increasing demand for efficient and adaptive communication systems,the integration of AI with UAV networks promises to revolutionize various aspects of wireless communication.The paper first outlines the background and motivation behind AI integration,highlighting the potential for enhanced network performance,autonomy,and adaptability.It then delves into the key AI applications across different network layers,including data sensing and collection,placement and trajectory optimization,radio resource management,routing and topology control,edge computing and caching,as well as security and privacy enhancement.For each application,the paper discusses relevant AI techniques,main findings,optimization objects,and the potential benefits and challenges.The survey also identifies open issues,such as the practical implementation gap,standardization issues,and real-world application barriers,and proposes future directions to address these challenges and further advance the field.In conclusion,the integration of AI with UAV-enabled Wireless Networks(UWNs)holds tremendous potential for transforming wireless communication,enabling new applications and services with unprecedented capabilities. 展开更多
关键词 Artificial intelligence(AI) Machine learning(ML) Unmanned aerial vehicle(UAV) Wireless network
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Effect of dominant fractures on triaxial behavior of 3D-printed rock analogs with internal fracture networks 认领 引用 被引量：2
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作者 Lishuai Jiang Pimao Li +3 位作者 Xin He Yang Zhao Quansen Wu Ye Zhao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1390-1412,共23页
Internal structural defects in engineering rock masses vary in size,exhibit complex shapes,and are unevenly distributed.Dominant fractures within a rock mass often play a critical to its mechanical behavior,directly a... Internal structural defects in engineering rock masses vary in size,exhibit complex shapes,and are unevenly distributed.Dominant fractures within a rock mass often play a critical to its mechanical behavior,directly affecting the macromechanical properties and failure modes.These fractures affect the instability and failure of the surrounding rock,significantlyimpacting the overall stability of engineering structures.Herein,sand-powder three-dimensional(3D)printing technology was used to prepare rock-like specimens with internal fracture networks.Triaxial compression testing,post-failure fracture mapping,and fractal dimension analysis of the fracture surfaces were conducted to investigate the effects of dominant fracture angles on the strength and deformation of rocks with internal fracture networks under triaxial stress.The results indicate that the dominant fracture angle has a pronounced effect on the mechanical behavior of rock.With increasing angle,both compressive strength and elastic modulus exhibit an initial decline followed by an increase.Moreover,higher confiningpressure significantlyimproves the compressive strength of fractured rock.This enhancement weakens as the confiningpressure further increases.Moreover,with increasing confiningpressure,the differences between the maximum and minimum values of elastic moduli and lateral strain ratios in fractured rock gradually decrease.Thus,the impact of the dominant fracture angle on rock mass deformation decreases with increasing confiningpressure.This research elucidates the effects of dominant fracture angles on the mechanical and failure properties of complex fractured rock masses and the influenceof the confiningpressure on these relationships.It provides valuable theoretical insights and practical guidance for stability analyses in engineering rock masses. 展开更多
关键词 Sand powder three-dimensional(3D) printing Internal fracture networks Triaxial compression Rock mechanics Fractal dimension
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Combined Fault Tree Analysis and Bayesian Network for Reliability Assessment of Marine Internal Combustion Engine 认领 引用 被引量：1
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作者 Ivana Jovanović Çağlar Karatuğ +1 位作者 Maja Perčić Nikola Vladimir 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第1期239-258,共20页
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ... This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels. 展开更多
关键词 Fault tree analysis Bayesian network Reliability Redundancy Internal combustion engine
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The regulatory network composed of phytohormones,transcription factors and non-coding RNAs is involved in the flavonoids biosynthesis of fruits 认领 引用 被引量：1
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作者 Xiaoyuan Zheng Xuejiao Zhang Fankui Zeng 《Horticultural Plant Journal》 SCIE CAS CSCD 2026年第3期497-508,共12页
Flavonoids,abundant in the fruits,are pivotal to their growth,development,and storage.In addition,they have significant beneficial effects on human health.Consequently,research is increasingly concentrating on the reg... Flavonoids,abundant in the fruits,are pivotal to their growth,development,and storage.In addition,they have significant beneficial effects on human health.Consequently,research is increasingly concentrating on the regulatory mechanisms governing flavonoid biosynthesis in fruits.Phytohormones are involved in the regulation of flavonoid biosynthesis.The abscisic acid,ethylene,jasmonic acid,cytokinins,and brassinosteroids promote flavonoid biosynthesis,while auxin negatively regulates flavonoid biosynthesis.Subsequently,transcription factors from the MYB,bHLH,WRKY,NAC,and bZIP families are pivotal in regulating flavonoid biosynthesis.In addition,non-coding RNAs(microRNA and lncRNA)also participate in the regulation of flavonoids biosynthesis.MicroRNAs are generally believed to negatively regulate flavonoid metabolism in fruits,while lncRNAs have the opposite effect.Furthermore,the interactions between plant hormones,transcription factors,and non-coding RNAs in fruit flavonoid biosynthesis were analyzed.Ultimately,a foundational regulatory network for fruit flavonoid biosynthesis was hereby established. 展开更多
关键词 Flavonoids biosynthesis Phytohormone Transcription factor Non-coding RNAs Regulation network Fruit
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Physics-informed neural networks for hidden boundary detection and flow field reconstruction 认领 引用 被引量：1
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作者 Yongzheng Zhu Weizheng Chen +1 位作者 Jian Deng Xin Bian 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期131-149,共19页
Simultaneously detecting hidden solid boundaries and reconstructing flow fields from sparse observations poses a significant inverse challenge in fluid mechanics.This study presents a physics-informed neural network f... Simultaneously detecting hidden solid boundaries and reconstructing flow fields from sparse observations poses a significant inverse challenge in fluid mechanics.This study presents a physics-informed neural network framework designed to infer the presence,shape,and motion of static or moving solid boundaries within a flow field.By integrating a body fraction parameter into the governing equations,the model enforces no-slipo-penetration boundary conditions in solid regions while preserving conservation laws of fluid dynamics.Using partial flow field data,the method simultaneously reconstructs the unknown flow field and infers the body fraction distribution,thereby revealing solid boundaries.The framework is validated across diverse scenarios,including incompressible Navier-Stokes and compressible Euler flows,such as steady flow past a fixed cylinder,an inline oscillating cylinder,and subsonic flow over an airfoil.The results demonstrate accurate detection of hidden boundaries,reconstruction of missing flow data,and estimation of trajectories and velocities of a moving body.Further analysis examines the effects of data sparsity,velocity-only measurements,and noise on inference accuracy.The proposed method exhibits robustness and versatility,highlighting its potential for applications when only limited experimental or numerical data are available. 展开更多
关键词 Hidden moving boundary Flow reconstruction Inverse problem Physics-informed neural networks
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Ship Magnetic Field Modeling and Extrapolation Based on a Convolutional Neural Network 认领 引用 被引量：1
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作者 Ao Zhou Yadong Zhang +3 位作者 Wentie Yang Zuoshuai Wang Jianxun Wang Zhiwei Chen 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期536-549,共14页
Accurate modeling of ship magnetic fields is important for predicting their spatial distribution to improve the magnetic stealth effect of ships.This study proposes an extrapolation model for ship magnetic fields base... Accurate modeling of ship magnetic fields is important for predicting their spatial distribution to improve the magnetic stealth effect of ships.This study proposes an extrapolation model for ship magnetic fields based on genetic algorithms and convolutional neural networks(CNNs).The magnetic probe position matrix of the traditional equivalent source is utilized as input,and the three-directional components of the magnetic field measured by the probes are employed as output.The extrapolation model for ship magnetic fields is obtained through iterative training and fitting with CNNs.Variables such as the number of magnetic dipoles,the distance between magnetic dipoles,the size and quantity of convolutional kernels,batch size,learning rate,and L2 regularization coefficient are optimized to boost the accuracy of the extrapolation model for magnetic fields.The fitting accuracy of the extrapolation model for ship magnetic fields is used as the optimization objective.Based on a finite element simulation model of ship magnetic fields,the accuracy and robustness of the CNN algorithm under different magnetic field conditions are validated using the known standard depth plane,the unknown depth at 1.125 times the standard depth plane,and the unknown depth at 1.25 times the standard depth plane.Results show that,after optimization,the fitting error for the magnetic field extrapolation model based on CNN is 1.50%for the standard depth plane,1.63%for the unknown depth at 1.125 times the standard depth plane,and 2.36%for the unknown depth at 1.25 times the standard depth plane.The error remains below 5%under varying magnetic field conditions.When a random measurement error of 0%-5%is introduced for the magnetic probes,the prediction error at 1.25 times the standard depth plane is 2.30%;with a random error of 0%-10%,the prediction error is 4.95%.This approach significantly improves the accuracy and robustness of magnetic field extrapolation,which makes it an effective and feasible method for ship magnetic field modeling. 展开更多
关键词 Shipboard magnetic field Convolutional neural network Genetic algorithm Equivalent source method Magnetic field extrapolation
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Social Network and Value Chain Integration:Unraveling the Formation and Evolution of Meizhou Pomelo Industry Cluster in China 认领 引用 被引量：1
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作者 YANG Ren LIN Yuancheng ZHANG Xin 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第2期239-255,共17页
The shift toward specialized and large-scale agricultural production has spurred the emergence of agricultural clusters as key forces of rural vitalization and sustainable development.This paper explored the formation... The shift toward specialized and large-scale agricultural production has spurred the emergence of agricultural clusters as key forces of rural vitalization and sustainable development.This paper explored the formation and evolution of Meizhou pomelo industry cluster in China,focusing on its role in restructuring rural socio-economic systems and integrating the whole value chains.Based on a case study employing qualitative methods such as in-depth interviews and participatory observation,the agricultural cluster evolution of Meizhou pomelo was categorized into three key phases of initial decentralization,self-organized scaling,and reorganized clustering.Geographical proximity and industrial agglomeration constitute the physical foundation,while vertical/horizontal linkages,technologic-al innovation,and policy support enhance competitiveness.Special mechanisms emerge through localized social networks,farmer co-operatives’activation,and cross-regional market expansion.The cluster’s impact is manifested in the shift from extensive to standard-ized and modernized production,diversified and flexible livelihood of farmers,and the integration of agriculture with industry and ser-vices.The development of the whole value chain based on agricultural cluster represents a critical pathway for achieving agricultural modernization,encompassing both internal and external value chain optimization.Through quality assurance systems,product diversi-fication strategies,operational efficiency improvements,and brand enhancement,these clusters amplify product value propositions and market competitiveness.This systemic approach facilitates supply-demand coordination,enables resource synergies,and optimizes eco-nomic returns across the horizontal and vertical value chain.This paper argues that agricultural clusters serve as strategic catalysts for sustainable rural development by reconstructing local production systems,fostering innovation ecosystems,and aligning agricultural modernization.It contributes to debates on rural vitalization by demonstrating how agricultural clustering can reconfigure rural areas as hubs of ecological modernization,rather than mere urban peripheries. 展开更多
关键词 agricultural cluster sustainable rural development agricultural systems social network whole value chain China
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4D printing of reprocessable thiocyanate covalent adaptable networks with reconfigurable shape memory ability 认领 引用 被引量：1
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作者 Ting Xu Kexiang Chen +7 位作者 Zhiyuan He Chuanzhen Zhang Xiaoyu Li Ziyan Zhang Wenbo Fan Zhishen Ge Chenhui Cui Yanfeng Zhang 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第2期505-511,共7页
Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability... Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue. 展开更多
关键词 4D Printing Dynamic thiocyanate ester bonds Covalent adaptable networks Cyanate ester resin Shape memory
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