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
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
The exploration of high-performance materials presents a fundamental challenge in materials science,particularly in predicting properties for materials beyond the known range of target property values(extrapolation).T...The exploration of high-performance materials presents a fundamental challenge in materials science,particularly in predicting properties for materials beyond the known range of target property values(extrapolation).This study formally investigated the interpolation-extrapolation trade-off phenomenon in the prediction capabilities of machine learning(ML)models.A new ML scheme was proposed,featuring a newly developed ML model and forward cross-validation-based hyperparameter optimization,which demonstrated superior extrapolation prediction across multiple materials datasets.Based on this ML scheme,multi-objective optimization was performed to systematically identify lightweight Mg-Zn-Al alloys with both high bulk modulus and high Debye temperature.Subsequently,the designed alloys were validated through density functional theory calculations.Furthermore,a three-category classification strategy was summarized through the dual-driven approach combining domain knowledge and data,emphasizing their synergistic potential for materials discovery.The practical framework developed in this study provides a novel research perspective for exploring high-performance materials.展开更多
双有源桥(dual active bridge,DAB)变换器理论物理模型与实际模型之间存在差异,而现有基于理论物理模型的调制优化方法未考虑该差异,故其所得理论最优路径在实际应用中难以达到理论最优效果。为此针对DAB变换器提出一种基于物理模型-数...双有源桥(dual active bridge,DAB)变换器理论物理模型与实际模型之间存在差异,而现有基于理论物理模型的调制优化方法未考虑该差异,故其所得理论最优路径在实际应用中难以达到理论最优效果。为此针对DAB变换器提出一种基于物理模型-数据混合驱动的扩展移相调制复合优化策略。首先,建立DAB变换器在扩展移相调制全工作模式下的理论物理模型,基于该理论模型训练神经网络(neural network,NN)数据驱动模型,并结合小样本实测数据对NN数据驱动模型进行迁移学习,从而得到高精度实际电路模型。然后,根据复合优化目标提出一种基于二重遍历的控制路径寻优算法,并设计基于三次样条插值的最优控制路径连续化方法,实现DAB变换器连续最优控制。最后,通过实验验证所提优化策略的有效性,结果表明与基于理论模型的优化策略相较,所提优化策略进一步提升了DAB变换器控制优化效果,减小了回流功率和电流应力,提高了功率传输效率,并且所提策略自动化执行程度高,能够取代传统复杂调制寻优分析过程,便于工业应用与数字化实现。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 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supported by National Natural Science Foundation of China(No.51671075 and 51971086)Natural Science Foundation of Heilongjiang Province of China(No.LH2022E081).
文摘The exploration of high-performance materials presents a fundamental challenge in materials science,particularly in predicting properties for materials beyond the known range of target property values(extrapolation).This study formally investigated the interpolation-extrapolation trade-off phenomenon in the prediction capabilities of machine learning(ML)models.A new ML scheme was proposed,featuring a newly developed ML model and forward cross-validation-based hyperparameter optimization,which demonstrated superior extrapolation prediction across multiple materials datasets.Based on this ML scheme,multi-objective optimization was performed to systematically identify lightweight Mg-Zn-Al alloys with both high bulk modulus and high Debye temperature.Subsequently,the designed alloys were validated through density functional theory calculations.Furthermore,a three-category classification strategy was summarized through the dual-driven approach combining domain knowledge and data,emphasizing their synergistic potential for materials discovery.The practical framework developed in this study provides a novel research perspective for exploring high-performance materials.
文摘双有源桥(dual active bridge,DAB)变换器理论物理模型与实际模型之间存在差异,而现有基于理论物理模型的调制优化方法未考虑该差异,故其所得理论最优路径在实际应用中难以达到理论最优效果。为此针对DAB变换器提出一种基于物理模型-数据混合驱动的扩展移相调制复合优化策略。首先,建立DAB变换器在扩展移相调制全工作模式下的理论物理模型,基于该理论模型训练神经网络(neural network,NN)数据驱动模型,并结合小样本实测数据对NN数据驱动模型进行迁移学习,从而得到高精度实际电路模型。然后,根据复合优化目标提出一种基于二重遍历的控制路径寻优算法,并设计基于三次样条插值的最优控制路径连续化方法,实现DAB变换器连续最优控制。最后,通过实验验证所提优化策略的有效性,结果表明与基于理论模型的优化策略相较,所提优化策略进一步提升了DAB变换器控制优化效果,减小了回流功率和电流应力,提高了功率传输效率,并且所提策略自动化执行程度高,能够取代传统复杂调制寻优分析过程,便于工业应用与数字化实现。
