Journal of Systems Engineering and Electronics (Indexed by Science Citation Index,Engineering Index) With the rapid development of mega-constellations,orbital gaming,on-orbit servicing and the advent of the AI era,spa...Journal of Systems Engineering and Electronics (Indexed by Science Citation Index,Engineering Index) With the rapid development of mega-constellations,orbital gaming,on-orbit servicing and the advent of the AI era,space intelligent sensing and manipulation has become a cutting-edge research hotspot in the space domain,attracting growing attention from scholars worldwide.展开更多
This research conducts a comprehensive experimental study of the entire filling system process at the Weishan Lake Rare Earth Mine(WSLREM)in Shandong Province,encompassing tailings thickening,feeding,slurry preparatio...This research conducts a comprehensive experimental study of the entire filling system process at the Weishan Lake Rare Earth Mine(WSLREM)in Shandong Province,encompassing tailings thickening,feeding,slurry preparation,and pipeline transportation.It proposes a complete and efficient upgrade solution for an intelligent paste filling system.The results show that the F1 flocculant was selected to prepare a flocculant solution with a solution concentration of 0.1%.The unit consumption is set to 25 g·t^(-1),and the flocculation and sedimentation effects are optimal when the mass concentration is 15%,with an underflow concentration of 62%.The selection experiment of cementitious material shows that the effect of using new cementitious material is better than that of traditional 32.5R Portland cement.At the same time,rheological experiments on the filling slurry were carried out,and the filling transportation pressure was studied by combining theoretical calculations with numerical simulations.The research results have guiding significance for the debugging of filling pumps and the selection of a filling pipeline.After the application of industrial transformation,the underflow concentration of the sand silo was 64%–66%,the slurry concentration was 68%–72%,the addition range of the cementing material was 1∶16–1∶4,and the filling capacity was 40–60 m^(3)·h^(-1).The intelligent upgrade and transformation of the filling system have yielded remarkable results,providing significant reference value for the intelligent filling transformation of similar mines.展开更多
Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and...Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.展开更多
Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belongi...Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.展开更多
Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belongi...Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.展开更多
《智能系统学报》(CAAI Transactions on Intelligent Systems)2006年创刊,双月刊,为中国人工智能学会会刊,由哈尔滨工程大学和中国人工智能学会联合主办,由中国人工智能学会名誉理事长李德毅院士担任名誉主编,中国人工智能学会理事长...《智能系统学报》(CAAI Transactions on Intelligent Systems)2006年创刊,双月刊,为中国人工智能学会会刊,由哈尔滨工程大学和中国人工智能学会联合主办,由中国人工智能学会名誉理事长李德毅院士担任名誉主编,中国人工智能学会理事长戴琼海院士担任主编。展开更多
Deep reinforcement learning algorithms are revolutionizing intelligent decision-making in air combat,drawing widespread attention and extensive research.However,air combat agents trained with these algorithms face sig...Deep reinforcement learning algorithms are revolutionizing intelligent decision-making in air combat,drawing widespread attention and extensive research.However,air combat agents trained with these algorithms face significant challenges,such as limited decision-making capacities due to adversarial training against relatively fixed and singular expert strategies,and a lack of interpretability and reliability in their decisionmaking processes.To tackle these issues,this paper proposes a self-play training mechanism based on policy switching and opponent selection,allowing air combat agents to refine their capabilities via engaging with previous versions of themselves.Additionally,an explainable decision tree model is developed to clarify the decision logic of these agents.Simulations and results demonstrate that the proposed self-play training approach significantly enhances the decision-making abilities of air combat agents,with late-stage agents showing a 38%improvement over early-stage agents in confrontations with an expert strategy.Moreover,the explainable decision tree model effectively elucidates the decision logic and achieves an 86%win rate against the expert strategy,comparable to the 88%win rate of the air combat agents.展开更多
Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When opera...Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.展开更多
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development...To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.展开更多
High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and ...High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and process optimization,characterized by low efficiency and high costs.The integration of Artificial Intelligence(AI)technologies has provided innovative solutions for HEAs research.This review presented a detailed overview of recent advancements in AI applications for structural modeling and mechanical property prediction of HEAs.Furthermore,it discussed the advantages of big data analytics in facilitating alloy composition design and screening,quality control,and defect prediction,as well as the construction and sharing of specialized material databases.The paper also addressed the existing challenges in current AI-driven HEAs research,including issues related to data quality,model interpretability,and cross-domain knowledge integration.Additionally,it proposed prospects for the synergistic development of AI-enhanced computational materials science and experimental validation systems.展开更多
[Objective]Under the combined impact of climate change and urbanization,urban rainstorm flood disasters occur frequently,seriously restricting urban safety and sustainable development.Relying on traditional grey infra...[Objective]Under the combined impact of climate change and urbanization,urban rainstorm flood disasters occur frequently,seriously restricting urban safety and sustainable development.Relying on traditional grey infrastructure such as pipe networks for urban stormwater management is not enough to deal with urban rainstorm flood disasters under extreme rainfall events.The integration of green,grey and blue systems(GGB-integrated system)is gradually gaining recognition in the field of global flood prevention.It is necessary to further clarify the connotation,technical and engineering implementation strategies of the GGB-integrated system,to provide support for the resilient city construction.[Methods]Through literature retrieval and analysis,the relevant research and progress related to the layout optimization and joint scheduling optimization of the GGBintegrated system were systematically reviewed.In response to existing limitations and future engineering application requirements,key supporting technologies including the utilization of overground emergency storage spaces,safety protection of underground important infrastructure and multi-departmental collaboration,were proposed.A layout optimization framework and a joint scheduling framework for the GGB-integrated system were also developed.[Results]Current research on layout optimization predominantly focuses on the integration of green system and grey system,with relatively fewer studies incorporating blue system infrastructure into the optimization process.Moreover,these studies tend to be on a smaller scale with simpler scenarios,which do not fully capture the complexity of real-world systems.Additionally,optimization objective tend to prioritize environmental and economic goals,while social and ecological factors are less frequently considered.Current research on joint scheduling optimization is often limited to small-scale plots,with insufficient attention paid to the entire system.There is a deficiency in method for real-time,automated determination of optimal control strategies for combinations of multiple system facilities based on actual rainfall-runoff processes.Additionally,the application of emergency facilities during extreme conditions is not sufficiently addressed.Furthermore,both layout optimization and joint scheduling optimization lack consideration of the mute feed effect of flood and waterlogging in urban,watershed and regional scales.[Conclusion]Future research needs to improve the theoretical framework for layout optimization and joint scheduling optimization of GGB-integrated system.Through the comprehensive application of the Internet of things,artificial intelligence,coupling model development,multi-scale analysis,multi-scenario simulation,and the establishment of multi-departmental collaboration mechanisms,it can enhance the flood resilience of urban areas in response to rainfall events of varying intensities,particularly extreme rainfall events.展开更多
目前新疆植棉区已实现棉花栽培全程机械化与半自动化。在此基础上,通过融合代表新质生产力的第五代移动通信技术(fifth generation of mobile communications technology,5G)、物联网、人工智能(artificial intelligence,AI)、大数据、...目前新疆植棉区已实现棉花栽培全程机械化与半自动化。在此基础上,通过融合代表新质生产力的第五代移动通信技术(fifth generation of mobile communications technology,5G)、物联网、人工智能(artificial intelligence,AI)、大数据、云计算等先进技术,提出棉花“AI栽培体系”构想。论述了“AI栽培体系”硬件与软件的构成及其在棉花栽培中的运行机制,实现棉花栽培全流程精准化、生产集约化、资源最优化、管理高效化、决策智能化和作业无人化的技术突破,为智慧农业装备研发注入创新思路,为新疆未来推广全域自动化智能化棉花栽培提供实践指引。展开更多
This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,an...This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.展开更多
Interior acoustics modulation through sound field reproduction represents a crucial component of vehicular intelligent cockpits.However,challenges regarding solution accuracy and control robustness in practical applic...Interior acoustics modulation through sound field reproduction represents a crucial component of vehicular intelligent cockpits.However,challenges regarding solution accuracy and control robustness in practical applications require further investigation.To address the impact of occupant postural changes on sound field reproduction performance,this paper presents a targeted modulation strategy based on mapping relationships between facial features and binaural positions.This approach aims to optimize reproductive performance by adjusting control parameters in response to occupant postural variations.Both simulation and experimental results demonstrate that,with the maindriving and co-driving positions designated as acoustic reproduction regions,the proposed control framework effectively enhances inter-regional acoustic contrast within the target frequency range through numerical computation of subject binaural positions.展开更多
The Infrared Hyperspectral Atmospheric SounderⅡ(HIRAS-Ⅱ)is the key equipment on FengYun-3E(FY-3E)satellite,which can realize vertical atmospheric detection,featuring hyper spectral,high sensitivity and high precisio...The Infrared Hyperspectral Atmospheric SounderⅡ(HIRAS-Ⅱ)is the key equipment on FengYun-3E(FY-3E)satellite,which can realize vertical atmospheric detection,featuring hyper spectral,high sensitivity and high precision.To ensure its accuracy of detection,it is necessary to correlate their thermal models to in-orbit da⁃ta.In this work,an investigation of intelligent correlation method named Intelligent Correlation Platform for Ther⁃mal Model(ICP-TM)was established,the advanced Kriging surrogate model and efficient adaptive region opti⁃mization algorithm were introduced.After the correlation with this method for FY-3E/HIRAS-Ⅱ,the results indi⁃cate that compared with the data in orbit,the error of the thermal model has decreased from 5 K to within±1 K in cold case(10℃).Then,the correlated model is validated in hot case(20℃),and the correlated model exhibits good universality.This correlation precision is also much superiors to the general ones like 3 K in other similar lit⁃erature.Furthermore,the process is finished in 8 days using ICP-TM,the efficiency is much better than 3 months based on manual.The results show that the proposed approach significantly enhances the accuracy and efficiency of thermal model,this contributes to the precise thermal control of subsequent infrared optical payloads.展开更多
Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from...Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from observing phenomena to uncovering underlying mechanisms,from regional-scale investigations to global perspectives,and from experience-based inference toward data-and model-enabled intelligent prediction.AlphaEarth Foundations(AEF)is a next-generation geospatial intelligence platform that addresses these changes by introducing a unified 64-dimensional shared embedding space,enabling-for the first time-standardized representation and seamless integration of 12 distinct types of Earth observation data,including optical,radar,and lidar.This framework significantly improves data assimilation efficiency and resolves the persistent problem of“data silos”in geoscience research.AEF is helping redefine research methodologies and fostering breakthroughs,particularly in quantitative Earth system science.This paper systematically examines how AEF’s innovative architecture-featuring multi-source data fusion,high-dimensional feature representation learning,and a scalable computational framework-facilitates intelligent,precise,and realtime data-driven geoscientific research.Using case studies from resource and environmental applications,we demonstrate AEF’s broad potential and identify emerging innovation needs.Our findings show that AEF not only enhances the efficiency of solving traditional geoscientific problems but also stimulates novel research directions and methodological approaches.展开更多
Journal of Future Foods(ISSN 2772-5669.Owner:Bejjing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on bchalf of KcAi Communications Co,Ltd.)is an intecrnational,pcer-reviewed open access journal bclongi...Journal of Future Foods(ISSN 2772-5669.Owner:Bejjing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on bchalf of KcAi Communications Co,Ltd.)is an intecrnational,pcer-reviewed open access journal bclonging to the disciplinc of food scicnce and technology.The aim of the journal is to report latcst rescarch results of high-tcch in food science.We welcome submissions that drive the ficld of food science towards whole food nutrition,intelligencc and high technology.展开更多
文摘Journal of Systems Engineering and Electronics (Indexed by Science Citation Index,Engineering Index) With the rapid development of mega-constellations,orbital gaming,on-orbit servicing and the advent of the AI era,space intelligent sensing and manipulation has become a cutting-edge research hotspot in the space domain,attracting growing attention from scholars worldwide.
基金Supported by the National Natural Science Foundation of China(52004152)Shandong Provincial Natural Science Foundation(ZR2024ME006,ZR2023QE133,ZR2020QE100)+2 种基金Small and Medium-sized Technology Enterprises in Shandong Province(2022TSGC2077)Shandong College Youth Science and Technology Support Program(2023KJ149)National Key Laboratory Open Project Open Fund(2023-JSKSSYS-06)。
文摘This research conducts a comprehensive experimental study of the entire filling system process at the Weishan Lake Rare Earth Mine(WSLREM)in Shandong Province,encompassing tailings thickening,feeding,slurry preparation,and pipeline transportation.It proposes a complete and efficient upgrade solution for an intelligent paste filling system.The results show that the F1 flocculant was selected to prepare a flocculant solution with a solution concentration of 0.1%.The unit consumption is set to 25 g·t^(-1),and the flocculation and sedimentation effects are optimal when the mass concentration is 15%,with an underflow concentration of 62%.The selection experiment of cementitious material shows that the effect of using new cementitious material is better than that of traditional 32.5R Portland cement.At the same time,rheological experiments on the filling slurry were carried out,and the filling transportation pressure was studied by combining theoretical calculations with numerical simulations.The research results have guiding significance for the debugging of filling pumps and the selection of a filling pipeline.After the application of industrial transformation,the underflow concentration of the sand silo was 64%–66%,the slurry concentration was 68%–72%,the addition range of the cementing material was 1∶16–1∶4,and the filling capacity was 40–60 m^(3)·h^(-1).The intelligent upgrade and transformation of the filling system have yielded remarkable results,providing significant reference value for the intelligent filling transformation of similar mines.
文摘Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.
文摘Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.
文摘Journal of Future Foods(ISSN 2772-5669.Owner:Beijing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on behalf of KeAi Communications Co.,Ltd.)is an international,peer-reviewed open access journal belonging to the discipline of food science and technology.The aim of the journal is to report latest research results of high-tech in food science.We welcome submissions that drive the field of food science towards whole food nutrition,intelligence and high technology.
基金supported by the Joint Funds of the National Natural Science Foundation of China(U2341216).
文摘Deep reinforcement learning algorithms are revolutionizing intelligent decision-making in air combat,drawing widespread attention and extensive research.However,air combat agents trained with these algorithms face significant challenges,such as limited decision-making capacities due to adversarial training against relatively fixed and singular expert strategies,and a lack of interpretability and reliability in their decisionmaking processes.To tackle these issues,this paper proposes a self-play training mechanism based on policy switching and opponent selection,allowing air combat agents to refine their capabilities via engaging with previous versions of themselves.Additionally,an explainable decision tree model is developed to clarify the decision logic of these agents.Simulations and results demonstrate that the proposed self-play training approach significantly enhances the decision-making abilities of air combat agents,with late-stage agents showing a 38%improvement over early-stage agents in confrontations with an expert strategy.Moreover,the explainable decision tree model effectively elucidates the decision logic and achieves an 86%win rate against the expert strategy,comparable to the 88%win rate of the air combat agents.
文摘Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
文摘To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.
文摘High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and process optimization,characterized by low efficiency and high costs.The integration of Artificial Intelligence(AI)technologies has provided innovative solutions for HEAs research.This review presented a detailed overview of recent advancements in AI applications for structural modeling and mechanical property prediction of HEAs.Furthermore,it discussed the advantages of big data analytics in facilitating alloy composition design and screening,quality control,and defect prediction,as well as the construction and sharing of specialized material databases.The paper also addressed the existing challenges in current AI-driven HEAs research,including issues related to data quality,model interpretability,and cross-domain knowledge integration.Additionally,it proposed prospects for the synergistic development of AI-enhanced computational materials science and experimental validation systems.
文摘[Objective]Under the combined impact of climate change and urbanization,urban rainstorm flood disasters occur frequently,seriously restricting urban safety and sustainable development.Relying on traditional grey infrastructure such as pipe networks for urban stormwater management is not enough to deal with urban rainstorm flood disasters under extreme rainfall events.The integration of green,grey and blue systems(GGB-integrated system)is gradually gaining recognition in the field of global flood prevention.It is necessary to further clarify the connotation,technical and engineering implementation strategies of the GGB-integrated system,to provide support for the resilient city construction.[Methods]Through literature retrieval and analysis,the relevant research and progress related to the layout optimization and joint scheduling optimization of the GGBintegrated system were systematically reviewed.In response to existing limitations and future engineering application requirements,key supporting technologies including the utilization of overground emergency storage spaces,safety protection of underground important infrastructure and multi-departmental collaboration,were proposed.A layout optimization framework and a joint scheduling framework for the GGB-integrated system were also developed.[Results]Current research on layout optimization predominantly focuses on the integration of green system and grey system,with relatively fewer studies incorporating blue system infrastructure into the optimization process.Moreover,these studies tend to be on a smaller scale with simpler scenarios,which do not fully capture the complexity of real-world systems.Additionally,optimization objective tend to prioritize environmental and economic goals,while social and ecological factors are less frequently considered.Current research on joint scheduling optimization is often limited to small-scale plots,with insufficient attention paid to the entire system.There is a deficiency in method for real-time,automated determination of optimal control strategies for combinations of multiple system facilities based on actual rainfall-runoff processes.Additionally,the application of emergency facilities during extreme conditions is not sufficiently addressed.Furthermore,both layout optimization and joint scheduling optimization lack consideration of the mute feed effect of flood and waterlogging in urban,watershed and regional scales.[Conclusion]Future research needs to improve the theoretical framework for layout optimization and joint scheduling optimization of GGB-integrated system.Through the comprehensive application of the Internet of things,artificial intelligence,coupling model development,multi-scale analysis,multi-scenario simulation,and the establishment of multi-departmental collaboration mechanisms,it can enhance the flood resilience of urban areas in response to rainfall events of varying intensities,particularly extreme rainfall events.
文摘目前新疆植棉区已实现棉花栽培全程机械化与半自动化。在此基础上,通过融合代表新质生产力的第五代移动通信技术(fifth generation of mobile communications technology,5G)、物联网、人工智能(artificial intelligence,AI)、大数据、云计算等先进技术,提出棉花“AI栽培体系”构想。论述了“AI栽培体系”硬件与软件的构成及其在棉花栽培中的运行机制,实现棉花栽培全流程精准化、生产集约化、资源最优化、管理高效化、决策智能化和作业无人化的技术突破,为智慧农业装备研发注入创新思路,为新疆未来推广全域自动化智能化棉花栽培提供实践指引。
基金Supported by the Fundamental Research Funds for the Central Universities(2024300443)the Natural Science Foundation of Jiangsu Province(BK20241224).
文摘This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.
文摘Interior acoustics modulation through sound field reproduction represents a crucial component of vehicular intelligent cockpits.However,challenges regarding solution accuracy and control robustness in practical applications require further investigation.To address the impact of occupant postural changes on sound field reproduction performance,this paper presents a targeted modulation strategy based on mapping relationships between facial features and binaural positions.This approach aims to optimize reproductive performance by adjusting control parameters in response to occupant postural variations.Both simulation and experimental results demonstrate that,with the maindriving and co-driving positions designated as acoustic reproduction regions,the proposed control framework effectively enhances inter-regional acoustic contrast within the target frequency range through numerical computation of subject binaural positions.
基金Supported by the National Key Research and Development Program of China(2022YFB3904803)。
文摘The Infrared Hyperspectral Atmospheric SounderⅡ(HIRAS-Ⅱ)is the key equipment on FengYun-3E(FY-3E)satellite,which can realize vertical atmospheric detection,featuring hyper spectral,high sensitivity and high precision.To ensure its accuracy of detection,it is necessary to correlate their thermal models to in-orbit da⁃ta.In this work,an investigation of intelligent correlation method named Intelligent Correlation Platform for Ther⁃mal Model(ICP-TM)was established,the advanced Kriging surrogate model and efficient adaptive region opti⁃mization algorithm were introduced.After the correlation with this method for FY-3E/HIRAS-Ⅱ,the results indi⁃cate that compared with the data in orbit,the error of the thermal model has decreased from 5 K to within±1 K in cold case(10℃).Then,the correlated model is validated in hot case(20℃),and the correlated model exhibits good universality.This correlation precision is also much superiors to the general ones like 3 K in other similar lit⁃erature.Furthermore,the process is finished in 8 days using ICP-TM,the efficiency is much better than 3 months based on manual.The results show that the proposed approach significantly enhances the accuracy and efficiency of thermal model,this contributes to the precise thermal control of subsequent infrared optical payloads.
基金National Natural Science Foundation of China Key Project(No.42050103)Higher Education Disciplinary Innovation Program(No.B25052)+2 种基金the Guangdong Pearl River Talent Program Innovative and Entrepreneurial Team Project(No.2021ZT09H399)the Ministry of Education’s Frontiers Science Center for Deep-Time Digital Earth(DDE)(No.2652023001)Geological Survey Project of China Geological Survey(DD20240206201)。
文摘Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from observing phenomena to uncovering underlying mechanisms,from regional-scale investigations to global perspectives,and from experience-based inference toward data-and model-enabled intelligent prediction.AlphaEarth Foundations(AEF)is a next-generation geospatial intelligence platform that addresses these changes by introducing a unified 64-dimensional shared embedding space,enabling-for the first time-standardized representation and seamless integration of 12 distinct types of Earth observation data,including optical,radar,and lidar.This framework significantly improves data assimilation efficiency and resolves the persistent problem of“data silos”in geoscience research.AEF is helping redefine research methodologies and fostering breakthroughs,particularly in quantitative Earth system science.This paper systematically examines how AEF’s innovative architecture-featuring multi-source data fusion,high-dimensional feature representation learning,and a scalable computational framework-facilitates intelligent,precise,and realtime data-driven geoscientific research.Using case studies from resource and environmental applications,we demonstrate AEF’s broad potential and identify emerging innovation needs.Our findings show that AEF not only enhances the efficiency of solving traditional geoscientific problems but also stimulates novel research directions and methodological approaches.
文摘Journal of Future Foods(ISSN 2772-5669.Owner:Bejjing Academy of Food Sciences.Production and Hosting:Elsevier B.V.on bchalf of KcAi Communications Co,Ltd.)is an intecrnational,pcer-reviewed open access journal bclonging to the disciplinc of food scicnce and technology.The aim of the journal is to report latcst rescarch results of high-tcch in food science.We welcome submissions that drive the ficld of food science towards whole food nutrition,intelligencc and high technology.
