This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key de...This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.展开更多
Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve ...Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.展开更多
Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function...Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.展开更多
The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the ...The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration （RCA）, which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.展开更多
含水层介质的非均质性使得有机污染场地普遍呈现污染源区复杂、污染物反向扩散及浓度反弹等问题。传统依赖稀疏钻孔取样的低分辨率调查难以精确刻画污染物迁移分布,已成为制约场地精准治理的关键瓶颈。场地精细刻画,也称为高分辨率场地...含水层介质的非均质性使得有机污染场地普遍呈现污染源区复杂、污染物反向扩散及浓度反弹等问题。传统依赖稀疏钻孔取样的低分辨率调查难以精确刻画污染物迁移分布,已成为制约场地精准治理的关键瓶颈。场地精细刻画,也称为高分辨率场地刻画(high-resolution site characterization,HRSC)已逐渐成为有机污染场地调查与修复实践的核心技术。本次系统梳理了HRSC的发展历程与研究进展。从关键非均质尺度与采样体积等角度界定了HRSC的基本原理,重点阐述了实时监测技术—动态采样策略—数据驱动决策的3大核心步骤,总结了HRSC在污染物相态精准识别、源区划定和优势通道辨识中的应用进展。现有研究表明,基于直推探测、原位传感与地球物理成像等实时技术,可在厘米到米级关键尺度上显著提升对残留非水相液体分布、污染羽范围以及低渗区反向扩散等的刻画能力,结合动态采样与多源证据可有效降低概念模型的不确定性并提高靶区修复决策的可靠性。HRSC正由“以数据获取为主”向“多源数据融合与智能化决策支撑”演进,以支撑风险管控导向的高效调查与精准修复决策。展开更多
新型电力系统建设背景下,新能源渗透率不断提高造成电力系统灵活性需求剧增。针对电力系统灵活性供需失衡问题,提出含碳捕集电厂的源荷储资源数据驱动鲁棒优化调度模型。首先,基于碳捕集电厂、抽水蓄能等源荷储灵活性资源运行特性,刻画...新型电力系统建设背景下,新能源渗透率不断提高造成电力系统灵活性需求剧增。针对电力系统灵活性供需失衡问题,提出含碳捕集电厂的源荷储资源数据驱动鲁棒优化调度模型。首先,基于碳捕集电厂、抽水蓄能等源荷储灵活性资源运行特性,刻画电力系统灵活性供给能力;其次,采用数据驱动方法构建椭球不确定性集以刻画风电、光伏波动区间,根据各时刻边界值量化灵活性需求;进而,结合灵活性供需关系,提出源荷储资源数据驱动鲁棒优化模型,并采用改进列与约束生成算法(column and constraint generation,C&CG)进行求解。算例仿真表明,通过协同调度源荷储灵活性资源有助于支撑电力系统功率平衡,提高灵活性裕度,数据驱动鲁棒优化方法能够剔除传统盒式不确定集中的不实际恶劣场景,进而改善传统鲁棒优化模型过于保守的问题,并显著提升计算效率。展开更多
基金supported by Poongsan-KAIST Future Research Center Projectthe fund support provided by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(Grant No.2023R1A2C2005661)。
文摘This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.
基金Supported by National Basic Research Program of China （973 Program） （2009CB320600）, National Natural Science Foundation of China （60828007, 60534010, 60821063）, the Leverhulme Trust （F/00. 120/BC） in the United Kingdom, and the 111 Project （B08015）
基金the National Natural Science Foundation of China (Grants No. 12072090 and No.12302056) to provide fund for conducting experiments。
文摘Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.
文摘Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.
基金Supported by State Key Program of National Natural Science Foundation of China （60834001） and National Natural Science Foundation of China （60774022）.Acknowledgement Authors would like to thank NSFC organizers and participants who shared their ideas and works with us during the NSFC workshop on data-based control, decision making, scheduling, and fault diagnosis. In particular, authors would like to thank Chai Tian-You, Sun You-Xian, Wang Hong, Yan Hong-Sheng, and Gao Fu-Rong for discussing the concept on design model shown in Fig. 12, the concept on temporal multi-scale shown in Fig. 8, the concept on fault diagnosis shown in Fig. 14, the concept on dynamic scheduling shown in Fig. 15, and the concept on interval model shown in Fig. 16, respectively.
基金Supported by National Basic Research Program of China（973 Program）（2013CB035500） National Natural Science Foundation of China（61233004,61221003,61074061）+1 种基金 International Cooperation Program of Shanghai Science and Technology Commission （12230709600） the Higher Education Research Fund for the Doctoral Program of China（20120073130006）
基金supported by the National Natural Science Foundation of China (61202078 61071139)the National High Technology Research and Development Program of China (863 Program)(SQ2011AA110101)
文摘The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration （RCA）, which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.
文摘含水层介质的非均质性使得有机污染场地普遍呈现污染源区复杂、污染物反向扩散及浓度反弹等问题。传统依赖稀疏钻孔取样的低分辨率调查难以精确刻画污染物迁移分布,已成为制约场地精准治理的关键瓶颈。场地精细刻画,也称为高分辨率场地刻画(high-resolution site characterization,HRSC)已逐渐成为有机污染场地调查与修复实践的核心技术。本次系统梳理了HRSC的发展历程与研究进展。从关键非均质尺度与采样体积等角度界定了HRSC的基本原理,重点阐述了实时监测技术—动态采样策略—数据驱动决策的3大核心步骤,总结了HRSC在污染物相态精准识别、源区划定和优势通道辨识中的应用进展。现有研究表明,基于直推探测、原位传感与地球物理成像等实时技术,可在厘米到米级关键尺度上显著提升对残留非水相液体分布、污染羽范围以及低渗区反向扩散等的刻画能力,结合动态采样与多源证据可有效降低概念模型的不确定性并提高靶区修复决策的可靠性。HRSC正由“以数据获取为主”向“多源数据融合与智能化决策支撑”演进,以支撑风险管控导向的高效调查与精准修复决策。
文摘新型电力系统建设背景下,新能源渗透率不断提高造成电力系统灵活性需求剧增。针对电力系统灵活性供需失衡问题,提出含碳捕集电厂的源荷储资源数据驱动鲁棒优化调度模型。首先,基于碳捕集电厂、抽水蓄能等源荷储灵活性资源运行特性,刻画电力系统灵活性供给能力;其次,采用数据驱动方法构建椭球不确定性集以刻画风电、光伏波动区间,根据各时刻边界值量化灵活性需求;进而,结合灵活性供需关系,提出源荷储资源数据驱动鲁棒优化模型,并采用改进列与约束生成算法(column and constraint generation,C&CG)进行求解。算例仿真表明,通过协同调度源荷储灵活性资源有助于支撑电力系统功率平衡,提高灵活性裕度,数据驱动鲁棒优化方法能够剔除传统盒式不确定集中的不实际恶劣场景,进而改善传统鲁棒优化模型过于保守的问题,并显著提升计算效率。
