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
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
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 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).
