The Monitoring, Detection, Isolation and Assessment of Information Warfare Attacks Through Multi-Level, Multi-Scale System Modeling and Model Based Technology

2004 ◽  
Author(s):  
Nong Ye
Keyword(s):  
System Modeling ◽  
Information Warfare ◽  
Multi Scale ◽  
Model Based ◽  
Multi Level

scholarly journals Design of HIL for Multi-Level Inverter Using Zynq-7000 Platform – Part 1

2017 ◽  
Vol 61 (3) ◽  
pp. 264 ◽  
Author(s):  
Balázs Farkas ◽  
Károly Veszprémi
Keyword(s):  
Rapid Prototyping ◽  
Software Design ◽  
System Modeling ◽  
Electronic Devices ◽  
Electronic System ◽  
Power Electronic ◽  
Hardware In The Loop ◽  
Model Based ◽  
New Approaches ◽  
Multi Level

Development of power electronic devices requires multi -disciplined engineering activities. These cover the thermal, electrical and software design. Due to this design complexity rapid prototyping methods and model-based design are becoming more and more important in the R&D projects in this field. In case of the multi-level inverter based drives the strict reliability requirements make the aforementioned new approaches more attractive. This article is the first part of the series which introduces the application of the model based design and Hardware-in-the-Loop (HIL) tools through the modeling of a Cellular H-Bridge inverter (CHB). This article focuses on the power electronic system modeling and verification. The model of the CHB is implemented and verified in Matlab.

BC-BLPM: A multi-level security access control model based on blockchain technology

2021 ◽  
Vol 18 (2) ◽  
pp. 110-135
Author(s):  
Xiang Yu ◽  
Zhangxiang Shu ◽  
Qiang Li ◽  
Jun Huang
Keyword(s):  
Access Control ◽  
Control Model ◽  
Access Control Model ◽  
Model Based ◽  
Blockchain Technology ◽  
Multi Level

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

scholarly journals Correction to: Multi-level, multi-scale habitat selection by a wide-ranging, federally threatened snake

2018 ◽  
Vol 33 (9) ◽  
pp. 1645-1646
Author(s):  
Javan M. Bauder ◽  
David R. Breininger ◽  
M. Rebecca Bolt ◽  
Michael L. Legare ◽  
Christopher L. Jenkins ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Multi Scale ◽  
Multi Level ◽  
Federally Threatened

A new multi-scale model based on CFD and macroscopic calculation for corrugated structured packing column

AIChE Journal ◽  
2013 ◽  
Vol 59 (8) ◽  
pp. 3119-3130 ◽  
Author(s):  
B. Sun ◽  
L. He ◽  
B.T. Liu ◽  
F. Gu ◽  
C.J. Liu
Keyword(s):  
Scale Model ◽  
Structured Packing ◽  
Multi Scale ◽  
Model Based ◽  
Packing Column

scholarly journals Optimal scale combination selection for inconsistent multi-scale decision tables

2021 ◽  
Author(s):  
Yingjie Zhu ◽  
Bin Yang
Keyword(s):  
Lattice Model ◽  
Selection Model ◽  
Decision Table ◽  
Scale Selection ◽  
Multi Scale ◽  
Optimal Scale ◽  
Model Based ◽  
Positive Region ◽  
Decision Tables ◽  
Combination Selection

Abstract Hierarchical structured data are very common for data mining and other tasks in real-life world. How to select the optimal scale combination from a multi-scale decision table is critical for subsequent tasks. At present, the models for calculating the optimal scale combination mainly include lattice model, complement model and stepwise optimal scale selection model, which are mainly based on consistent multi-scale decision tables. The optimal scale selection model for inconsistent multi-scale decision tables has not been given. Based on this, firstly, this paper introduces the concept of complement and lattice model proposed by Li and Hu. Secondly, based on the concept of positive region consistency of inconsistent multi-scale decision tables, the paper proposes complement model and lattice model based on positive region consistent and gives the algorithm. Finally, some numerical experiments are employed to verify that the model has the same properties in processing inconsistent multi-scale decision tables as the complement model and lattice model in processing consistent multi-scale decision tables. And for the consistent multi-scale decision table, the same results can be obtained by using the model based on positive region consistent. However, the lattice model based on positive region consistent is more time-consuming and costly. The model proposed in this paper provides a new theoretical method for the optimal scale combination selection of the inconsistent multi-scale decision table.

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