Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes.The number of structural planes and rock blocks were reduced in previous studies.This impair...Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes.The number of structural planes and rock blocks were reduced in previous studies.This impairs the ability to characterize complex rock slopes accurately and inhibits the identification of key blocks.In this paper,a knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes is proposed.Our basic idea is to integrate key block theory into data-driven models based on finely characterizing structural features to identify key blocks in complex rock slopes accurately.The proposed novel paradigm consists of(1)representing rock slopes as graph-structured data based on complex systems theory,(2)identifying key nodes in the graph-structured data using graph deep learning,and(3)mapping the key nodes of graph-structured data to corresponding key blocks in the rock slope.Verification experiments and real-case applications are conducted by the proposed method.The verification results demonstrate excellent model performance,strong generalization capability,and effective classification results.Moreover,the real case application is conducted on the northern slope of the Yanqianshan Iron Mine.The results show that the proposed method can accurately identify key blocks in complex rock slopes,which can provide a decision-making basis and rational recommendations for effective support and instability prevention of rock slopes,thereby ensuring the stability of rock engineering and the safety of life and property.展开更多
Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The ...Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.展开更多
Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional ...Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.展开更多
The dense extracellular matrix and high interstitial pressure within tumors hinder nanoparticle penetration,reducing therapeutic efficacy.To address this,we engineered a dual-driven nanomotor based on a diselenide met...The dense extracellular matrix and high interstitial pressure within tumors hinder nanoparticle penetration,reducing therapeutic efficacy.To address this,we engineered a dual-driven nanomotor based on a diselenide metal-organic framework(MOF)using a layer-by-layer assembly process for multimodal synergistic tumor therapy.Diselenide-containing imidazole derivatives coordinated with Zn2+form the MOF,sequentially encapsulating near-infrared-Ⅱ(NIR-Ⅱ)photothermal-responsive gold nanorods(AuRods),Mn_(2)CO_(10)(MnCO),and glucose oxidase(GOD).The nanoparticle surface was functionalized with 4T1 cancer cell membranes(DSACGM NPs),guiding it to drive toward the tumor site.The photothermal effect of AuRods and CO release drives nanomotor propulsion,enhancing tumor tissue penetration.GOD catalyzes glucose(Glu)oxidation,inducing tumor starvation,while the resulting H_(2)O_(2)triggers CO release,suppressing heat shock protein(HSP)expression and enhancing mild photothermal therapy(PTT).The release of CO and the Mn^(2+)-triggered Fenton-like reaction from MnCO increased intracellular ROS levels,while diselenide depletion of glutathione(GSH)amplified chemodynamic therapy(CDT).In vitro and in vivo experiments show that DSACGM NPs induce cancer cell apoptosis under NIR-Ⅱirradiation and efficiently ablate tumors in mice at sub-hyperthermic temperatures(<45℃)with excellent biocompatibility.This study provides valuable insights into nanomedicine design and its potential in advanced tumor therapies.展开更多
As the demand for wide-speed-range and long-endurance aircraft continues to grow,variable cycle engines have become a research hotspot due to their excellent multitask adaptability.However,traditional overall performa...As the demand for wide-speed-range and long-endurance aircraft continues to grow,variable cycle engines have become a research hotspot due to their excellent multitask adaptability.However,traditional overall performance simulation techniques face challenges when dealing with complex engine configurations,as they require solving largerscale and higher-dimensional computational problems.This results in decreased simulation efficiency and poorer convergence,making it difficult to meet the demands for rapid performance evaluation and optimization.Although existing overall performance surrogate models for engines offer notable computational advantages,they still suffer from high training costs,low prediction accuracy,and limited application scenarios.To address these issues,this paper proposes an engine overall performance surrogate model driven by both knowledge and data.This model innovatively incorporates fundamental physical laws and domain knowledge of the engine during training and application,transforming the traditional black-box surrogate model into a gray-box model with certain interpretability.This significantly enhances prediction accuracy and application flexibility.Numerical verification results using the adaptive cycle engine(one of the most complex variable cycle configurations)as the application object show that the proposed surrogate model not only effectively predicts engine performance with prediction errors controlled within 0.5%,but also significantly improves the convergence and computational efficiency of engine performance simulation models.When applied to engine performance optimization,it achieves a nearly 60-fold increase in computational speed compared to traditional optimization methods,with an optimization error of only 0.15%.This approach can be widely applied to various types of engines and supports more complex and diverse engineering needs,offering broad application prospects.展开更多
Landslide susceptibility mapping is a crucial tool for analyzing geohazards in a region.Recent publications have popularized data-driven models,particularly machine learning-based methods,owing to their strong capabil...Landslide susceptibility mapping is a crucial tool for analyzing geohazards in a region.Recent publications have popularized data-driven models,particularly machine learning-based methods,owing to their strong capability in dealing with complex nonlinear problems.However,a significant proportion of these models have neglected qualitative aspects during analysis,resulting in a lack of interpretability throughout the process and causing inaccuracies in the negative sample extraction.In this study,Scoops 3D was employed as a physics-informed tool to qualitatively assess slope stability in the study area(the Hubei Province section of the Three Gorges Reservoir Area).The non-landslide samples were extracted based on the calculated factor of safety(FS).Subsequently,the random forest algorithm was employed for data-driven landslide susceptibility analysis,with the area under the receiver operating characteristic curve(AUC)serving as the model evaluation index.Compared to the benchmark model(i.e.,the standard method of utilizing the pure random forest algorithm),the proposed method’s AUC value improved by 20.1%,validating the effectiveness of the dual-driven method(physics-informed data-driven).展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42277161,42230709).
文摘Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes.The number of structural planes and rock blocks were reduced in previous studies.This impairs the ability to characterize complex rock slopes accurately and inhibits the identification of key blocks.In this paper,a knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes is proposed.Our basic idea is to integrate key block theory into data-driven models based on finely characterizing structural features to identify key blocks in complex rock slopes accurately.The proposed novel paradigm consists of(1)representing rock slopes as graph-structured data based on complex systems theory,(2)identifying key nodes in the graph-structured data using graph deep learning,and(3)mapping the key nodes of graph-structured data to corresponding key blocks in the rock slope.Verification experiments and real-case applications are conducted by the proposed method.The verification results demonstrate excellent model performance,strong generalization capability,and effective classification results.Moreover,the real case application is conducted on the northern slope of the Yanqianshan Iron Mine.The results show that the proposed method can accurately identify key blocks in complex rock slopes,which can provide a decision-making basis and rational recommendations for effective support and instability prevention of rock slopes,thereby ensuring the stability of rock engineering and the safety of life and property.
文摘Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.
基金supported by the National Natural Science Foundation of China under(Grant No.52175531)in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant(Grant Nos.KJQN202000605 and KJZD-M202000602)。
文摘Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.
基金supported by the National Key R&D Program of China(2020YFA0908500)the National Natural Science Foundation of China(22161142015,22201058,and 22275046)+1 种基金the Interdisciplinary Research Project of Hangzhou Normal University(2024JCXK01)the Hangzhou Leading Innovation and Entrepreneurship Team Project of Hangzhou Science and Technology Bureau(TD2022001)。
文摘The dense extracellular matrix and high interstitial pressure within tumors hinder nanoparticle penetration,reducing therapeutic efficacy.To address this,we engineered a dual-driven nanomotor based on a diselenide metal-organic framework(MOF)using a layer-by-layer assembly process for multimodal synergistic tumor therapy.Diselenide-containing imidazole derivatives coordinated with Zn2+form the MOF,sequentially encapsulating near-infrared-Ⅱ(NIR-Ⅱ)photothermal-responsive gold nanorods(AuRods),Mn_(2)CO_(10)(MnCO),and glucose oxidase(GOD).The nanoparticle surface was functionalized with 4T1 cancer cell membranes(DSACGM NPs),guiding it to drive toward the tumor site.The photothermal effect of AuRods and CO release drives nanomotor propulsion,enhancing tumor tissue penetration.GOD catalyzes glucose(Glu)oxidation,inducing tumor starvation,while the resulting H_(2)O_(2)triggers CO release,suppressing heat shock protein(HSP)expression and enhancing mild photothermal therapy(PTT).The release of CO and the Mn^(2+)-triggered Fenton-like reaction from MnCO increased intracellular ROS levels,while diselenide depletion of glutathione(GSH)amplified chemodynamic therapy(CDT).In vitro and in vivo experiments show that DSACGM NPs induce cancer cell apoptosis under NIR-Ⅱirradiation and efficiently ablate tumors in mice at sub-hyperthermic temperatures(<45℃)with excellent biocompatibility.This study provides valuable insights into nanomedicine design and its potential in advanced tumor therapies.
基金funded by National Natural Science Foundation of China under Grants 52406006,Fund Project 127000020241460012024-CXPT-GF-JJ-88-0001Supported by National Key Laboratory of Science and Technology on Aero-Engine Aero-thermodynamics and the Outstanding Research Project of Shen Yuan Honors College,BUAA(230123208).
文摘As the demand for wide-speed-range and long-endurance aircraft continues to grow,variable cycle engines have become a research hotspot due to their excellent multitask adaptability.However,traditional overall performance simulation techniques face challenges when dealing with complex engine configurations,as they require solving largerscale and higher-dimensional computational problems.This results in decreased simulation efficiency and poorer convergence,making it difficult to meet the demands for rapid performance evaluation and optimization.Although existing overall performance surrogate models for engines offer notable computational advantages,they still suffer from high training costs,low prediction accuracy,and limited application scenarios.To address these issues,this paper proposes an engine overall performance surrogate model driven by both knowledge and data.This model innovatively incorporates fundamental physical laws and domain knowledge of the engine during training and application,transforming the traditional black-box surrogate model into a gray-box model with certain interpretability.This significantly enhances prediction accuracy and application flexibility.Numerical verification results using the adaptive cycle engine(one of the most complex variable cycle configurations)as the application object show that the proposed surrogate model not only effectively predicts engine performance with prediction errors controlled within 0.5%,but also significantly improves the convergence and computational efficiency of engine performance simulation models.When applied to engine performance optimization,it achieves a nearly 60-fold increase in computational speed compared to traditional optimization methods,with an optimization error of only 0.15%.This approach can be widely applied to various types of engines and supports more complex and diverse engineering needs,offering broad application prospects.
基金funded by the National Key R&D Program of China(Project No.2019YFC1509605)High-end Foreign Expert Introduction program(No.G20200022005 and DL2021165001L)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.HZ2021001)。
文摘Landslide susceptibility mapping is a crucial tool for analyzing geohazards in a region.Recent publications have popularized data-driven models,particularly machine learning-based methods,owing to their strong capability in dealing with complex nonlinear problems.However,a significant proportion of these models have neglected qualitative aspects during analysis,resulting in a lack of interpretability throughout the process and causing inaccuracies in the negative sample extraction.In this study,Scoops 3D was employed as a physics-informed tool to qualitatively assess slope stability in the study area(the Hubei Province section of the Three Gorges Reservoir Area).The non-landslide samples were extracted based on the calculated factor of safety(FS).Subsequently,the random forest algorithm was employed for data-driven landslide susceptibility analysis,with the area under the receiver operating characteristic curve(AUC)serving as the model evaluation index.Compared to the benchmark model(i.e.,the standard method of utilizing the pure random forest algorithm),the proposed method’s AUC value improved by 20.1%,validating the effectiveness of the dual-driven method(physics-informed data-driven).
