In this paper,a novel method for investigating the particle-crushing behavior of breeding particles in a fusion blanket is proposed.The fractal theory and Weibull distribution are combined to establish a theoretical m...In this paper,a novel method for investigating the particle-crushing behavior of breeding particles in a fusion blanket is proposed.The fractal theory and Weibull distribution are combined to establish a theoretical model,and its validity was verified using a simple impact test.A crushable discrete element method(DEM)framework is built based on the previously established theoretical model.The tensile strength,which considers the fractal theory,size effect,and Weibull variation,was assigned to each generated particle.The assigned strength is then used for crush detection by comparing it with its maximum tensile stress.Mass conservation is ensured by inserting a series of sub-particles whose total mass was equal to the quality loss.Based on the crushable DEM framework,a numerical simulation of the crushing behavior of a pebble bed with hollow cylindrical geometry under a uniaxial compression test was performed.The results of this investigation showed that the particle withstands the external load by contact and sliding at the beginning of the compression process,and the results confirmed that crushing can be considered an important method of resisting the increasing external load.A relatively regular particle arrangement aids in resisting the load and reduces the occurrence of particle crushing.However,a limit exists to the promotion of resistance.When the strain increases beyond this limit,the distribution of the crushing position tends to be isotropic over the entire pebble bed.The theoretical model and crushable DEM framework provide a new method for exploring the pebble bed in a fusion reactor,considering particle crushing.展开更多
异常检测旨在识别偏离正常模式的对象,广泛应用于金融、安防、医疗和网络等关键领域。在电力物联网中,异常检测对于应对频谱感知数据篡改攻击尤为重要,有助于保障系统运行稳定,减少潜在经济损失。然而,由于异常样本稀缺、分布不均且模...异常检测旨在识别偏离正常模式的对象,广泛应用于金融、安防、医疗和网络等关键领域。在电力物联网中,异常检测对于应对频谱感知数据篡改攻击尤为重要,有助于保障系统运行稳定,减少潜在经济损失。然而,由于异常样本稀缺、分布不均且模式复杂,传统监督方法难以适应动态变化的实际环境,亟需一种高效且鲁棒的检测方案。为此,本文提出一种基于模糊信息粒球的多尺度异常检测方法(Fuzzy Information Granular-Ball based Outlier Detection,FBOD),通过融合粗粒度与细粒度特征综合评估数据的异常程度。具体工作包含:1)利用粒球模型自适应划分样本集,并结合密度和邻域信息计算粗粒度异常分数,以捕捉全局异常模式;2)构建粒球模糊相似距离矩阵,提升对局部异常的识别能力;3)引入自然邻域异常度量,增强复杂模式的识别效果。实验结果表明,FBOD在多个标准异常检测数据集上AUC均在0.79以上,在电力物联网SSDF攻击数据集上AUC超过0.88,显著优于对比方法。该方法在提升电力系统安全性方面展现出良好适应性与检测性能,同时为其他领域的异常检测任务提供了新思路和技术支持。展开更多
基金supported by Anhui Provincial Natural Science Foundation(2408085QA030)Natural Science Research Project of Anhui Educational Committee,China(2022AH050825)+3 种基金Medical Special Cultivation Project of Anhui University of Science and Technology(YZ2023H2C008)the Excellent Research and Innovation Team of Anhui Province,China(2022AH010052)the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology,China(2021yjrc51)Collaborative Innovation Program of Hefei Science Center,CAS,China(2019HSC-CIP006).
文摘In this paper,a novel method for investigating the particle-crushing behavior of breeding particles in a fusion blanket is proposed.The fractal theory and Weibull distribution are combined to establish a theoretical model,and its validity was verified using a simple impact test.A crushable discrete element method(DEM)framework is built based on the previously established theoretical model.The tensile strength,which considers the fractal theory,size effect,and Weibull variation,was assigned to each generated particle.The assigned strength is then used for crush detection by comparing it with its maximum tensile stress.Mass conservation is ensured by inserting a series of sub-particles whose total mass was equal to the quality loss.Based on the crushable DEM framework,a numerical simulation of the crushing behavior of a pebble bed with hollow cylindrical geometry under a uniaxial compression test was performed.The results of this investigation showed that the particle withstands the external load by contact and sliding at the beginning of the compression process,and the results confirmed that crushing can be considered an important method of resisting the increasing external load.A relatively regular particle arrangement aids in resisting the load and reduces the occurrence of particle crushing.However,a limit exists to the promotion of resistance.When the strain increases beyond this limit,the distribution of the crushing position tends to be isotropic over the entire pebble bed.The theoretical model and crushable DEM framework provide a new method for exploring the pebble bed in a fusion reactor,considering particle crushing.
文摘异常检测旨在识别偏离正常模式的对象,广泛应用于金融、安防、医疗和网络等关键领域。在电力物联网中,异常检测对于应对频谱感知数据篡改攻击尤为重要,有助于保障系统运行稳定,减少潜在经济损失。然而,由于异常样本稀缺、分布不均且模式复杂,传统监督方法难以适应动态变化的实际环境,亟需一种高效且鲁棒的检测方案。为此,本文提出一种基于模糊信息粒球的多尺度异常检测方法(Fuzzy Information Granular-Ball based Outlier Detection,FBOD),通过融合粗粒度与细粒度特征综合评估数据的异常程度。具体工作包含:1)利用粒球模型自适应划分样本集,并结合密度和邻域信息计算粗粒度异常分数,以捕捉全局异常模式;2)构建粒球模糊相似距离矩阵,提升对局部异常的识别能力;3)引入自然邻域异常度量,增强复杂模式的识别效果。实验结果表明,FBOD在多个标准异常检测数据集上AUC均在0.79以上,在电力物联网SSDF攻击数据集上AUC超过0.88,显著优于对比方法。该方法在提升电力系统安全性方面展现出良好适应性与检测性能,同时为其他领域的异常检测任务提供了新思路和技术支持。
