In this paper, for the unbalanced Feistel network which employs diffusion matrices in a switching way, we study the fixed number of its differential active S-boxes. Firstly we obtain some lower bounds of the different...In this paper, for the unbalanced Feistel network which employs diffusion matrices in a switching way, we study the fixed number of its differential active S-boxes. Firstly we obtain some lower bounds of the differential active S-boxes for m, 2m and 3m rounds of Feistel structure, respectively. By concatenating these rounds, a fixed number of differential active S-boxes for arbitrary round number is derived. Our results imply that the unbalanced Feistel network using DSM is more secure than the traditional structure.展开更多
Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-eng...Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.展开更多
肾脏黏液性小管状和梭形细胞癌(mucinous tubular and spindlecell carcinoma,MTSCCa)是新确定一种罕见的低度恶性肾上皮性肿瘤。多见于青年女性,临床上无明显症状。以往常被诊断为梭形细胞(肉瘤样)肾细胞癌或不能分类的肾细胞癌。大体...肾脏黏液性小管状和梭形细胞癌(mucinous tubular and spindlecell carcinoma,MTSCCa)是新确定一种罕见的低度恶性肾上皮性肿瘤。多见于青年女性,临床上无明显症状。以往常被诊断为梭形细胞(肉瘤样)肾细胞癌或不能分类的肾细胞癌。大体肿物界限清楚,切面实性、灰白色。组织学特点是肿瘤细胞排列成管状和实性梁索状漂浮于黏液性基质中,Alcianblue染色阳性。免疫组化显示复合性免疫表型。临床预后好,可复发并具有潜在远处转移的可能,应重视与其他肾脏良恶性肿瘤(后肾腺瘤、肉瘤样癌和集合管癌等)相鉴别。展开更多
基金Supported by the National Natural Science Foundation of China(11204379)Innovation Scientists and Technicians Troop Construction Projects of Henan Province(104100510025)
文摘In this paper, for the unbalanced Feistel network which employs diffusion matrices in a switching way, we study the fixed number of its differential active S-boxes. Firstly we obtain some lower bounds of the differential active S-boxes for m, 2m and 3m rounds of Feistel structure, respectively. By concatenating these rounds, a fixed number of differential active S-boxes for arbitrary round number is derived. Our results imply that the unbalanced Feistel network using DSM is more secure than the traditional structure.
基金supported in part by the National Science and Technology Major Project of China(No.2019-I-0019-0018)the National Natural Science Foundation of China(Nos.61890920,61890921,12302065 and 12172073).
文摘Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.
文摘肾脏黏液性小管状和梭形细胞癌(mucinous tubular and spindlecell carcinoma,MTSCCa)是新确定一种罕见的低度恶性肾上皮性肿瘤。多见于青年女性,临床上无明显症状。以往常被诊断为梭形细胞(肉瘤样)肾细胞癌或不能分类的肾细胞癌。大体肿物界限清楚,切面实性、灰白色。组织学特点是肿瘤细胞排列成管状和实性梁索状漂浮于黏液性基质中,Alcianblue染色阳性。免疫组化显示复合性免疫表型。临床预后好,可复发并具有潜在远处转移的可能,应重视与其他肾脏良恶性肿瘤(后肾腺瘤、肉瘤样癌和集合管癌等)相鉴别。
