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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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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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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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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：1
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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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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：1
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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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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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Image restoration of finger-vein networks based on encoder-decoder model 认领 引用 被引量：1
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作者 GUO Xiao-jing LI Dan +1 位作者 ZHANG Hai-gang YANG Jin-feng 《Optoelectronics Letters》 EI 2019年第6期463-467,共5页
Finger-vein recognition is widely applied on access control system due to the high user acceptance and convince. Improving the integrity of finger-vein is helpful for increasing the finger-vein recognition accuracy. D... Finger-vein recognition is widely applied on access control system due to the high user acceptance and convince. Improving the integrity of finger-vein is helpful for increasing the finger-vein recognition accuracy. During the process of finger-vein imaging, foreign objects may be attached on fingers, which directly affects the integrity of finger-vein images. In order to effectively extract finger-vein networks, the integrity of venous networks is still not ideal after preprocessing of finger vein images. In this paper, we propose a novel deep learning based image restoration method to improve the integrity of finger-vein networks. First, a region detecting method based on adaptive threshold is presented to locate the incomplete region. Next, an encoder-decoder model is used to restore the venous networks of the finger-vein images. Then we analyze the restoration results using several different methods. Experimental results show that the proposed method is effective to restore the venous networks of the finger-vein images. 展开更多
关键词 decoder networks incomplete
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Classification of Arrhythmia Based on Convolutional Neural Networks and Encoder-Decoder Model 认领 引用
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作者 Jian Liu Xiaodong Xia +2 位作者 Chunyang Han Jiao Hui Jim Feng 《Computers, Materials & Continua》 SCIE EI 2022年第10期265-278,共14页
As a common and high-risk type of disease,heart disease seriously threatens people’s health.At the same time,in the era of the Internet of Thing(IoT),smart medical device has strong practical significance for medical... As a common and high-risk type of disease,heart disease seriously threatens people’s health.At the same time,in the era of the Internet of Thing(IoT),smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases.Therefore,the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases.In this paper,we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network(CNN)and Encoder-Decoder model.The model uses Long Short-Term Memory(LSTM)to consider the influence of time series features on classification results.Simultaneously,it is trained and tested by the MIT-BIH arrhythmia database.Besides,Generative Adversarial Networks(GAN)is adopted as a method of data equalization for solving data imbalance problem.The simulation results show that for the inter-patient arrhythmia classification,the hybrid model combining CNN and Encoder-Decoder model has the best classification accuracy,of which the accuracy can reach 94.05%.Especially,it has a better advantage for the classification effect of supraventricular ectopic beats(class S)and fusion beats(class F). 展开更多
关键词 Electroencephalography convolutional neural network long short-term memory encoder-decoder model generative adversarial network
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Effects of Urbanization on Amphibian Predation Networks in Kunming 认领 引用
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作者 Qisheng LI Pili WU +3 位作者 Yingzhi YAN Zhongping XIONG Yunfei MA Jielong ZHOU 《Asian Herpetological Research》 SCIE CAS CSCD 2026年第1期53-61,共9页
Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requiremen... Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requirements are complex.The present study investigated the effects of urbanization on amphibian predation networks in suburban Kunming in Yunnan,China and aimed to understand how predation network structure and stability vary with urbanization level.We constructed predation networks by analyzing the stomach contents of amphibians from 12d istinct urbanization gradients.We used the bipartite package in R to evaluate network robustness metrics such as modularity,nestedness,connectivity,and average shortest path length(ASPL).We found that urbanization level is negatively correlated with predation network connectivity(R=−0.67,Ρ=0.02),but there were no significant correlations between urbanization level and nestedness,modularity,or ASPL.Removal of the keystone species destabilized the predation networks at certain locations.The present work highlighted that maintaining prey quantity and diversity preserves predation network connectivity and stabilizes the overall network in urbanizing landscapes.It also underscored the critical role that keystone species play in sustaining network robustness.The results of this research provided insights into the ecological consequences of urbanization.They also suggested that conservation measures should protect the key species and habitats of amphibian predation networks and mitigate the negative impact of urban development on them. 展开更多
关键词 amphibian network robustness predation network urbanization
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Networked Predictive Control:A Survey 认领 引用
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作者 Zhong-Hua Pang Tong Mu +3 位作者 Yi Yu Haibin Guo Guo-Ping Liu Qing-Long Han 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期3-20,共18页
Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induc... Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts. 展开更多
关键词 Communication constraints cyber attacks networked control systems networked multi-agent systems networked predictive control
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