scholarly journals A Fast Small-Sample Modeling Method for Precision Inertial Systems Fault Prediction and Quantitative Anomaly Measurement

2022 ◽  
Vol 130 (1) ◽  
pp. 187-203
Author(s):  
Hongqiao Wang ◽  
Yanning Cai
Keyword(s):  
Small Sample ◽  
Modeling Method ◽  
Fault Prediction ◽  
Inertial Systems

scholarly journals Aircraft System State Recognition and Fault Prediction Based on a Test Diagnostic Model

2021 ◽  
Vol 1207 (1) ◽  
pp. 012025
Author(s):  
Yan Su ◽  
Hongcai Chen ◽  
Chenxuan Gu ◽  
Xiangyu Xing ◽  
Xuerui Liang
Keyword(s):  
Dynamic Testing ◽  
Complex Function ◽  
Modeling Method ◽  
Fault Prediction ◽  
Diagnostic Model ◽  
Prognostic Information ◽  
Skeleton Model ◽  
State Recognition ◽  
Aircraft Systems ◽  
Aircraft System

Abstract The existing testability models for fault prognosis of aircraft systems limit the implementation of prognosis and health management systems. This paper develops a test diagnosis modeling method and relevant algorithms to support dynamic testing and to evaluate fault prognostic ability during aircraft system design. According to the system principles and the complex function structure of aircraft systems, a test diagnostic model is established by integrating testing and prognostic information with a test diagnostic skeleton model using multi-signal flow. New test indexes are identified to assess the testability and prognostic ability of aircraft systems. Relevant state recognition and fault prediction algorithms are established by fusing the improved particle swarm optimization algorithm and Hidden Semi-Markov Model. The feasibility and validity of the test diagnostic modeling method and relevant algorithms are verified in an aircraft’s engine bleed air system. Training and test show that the model can support analysis and estimation, and the algorithms can ensure accurate results after training the HSMM using improved PSO algorithm.

scholarly journals 3D Reservoir Geological Modeling Algorithm Based on a Deep Feedforward Neural Network: A Case Study of the Delta Reservoir of Upper Urho Formation in the X Area of Karamay, Xinjiang, China

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6699
Author(s):  
Jianpeng Yao ◽  
Qingbin Liu ◽  
Wenling Liu ◽  
Yuyang Liu ◽  
Xiaodong Chen ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Oil And Gas ◽  
Reservoir Characterization ◽  
Optimization Methods ◽  
Small Sample ◽  
Modeling Method ◽  
Sequential Gaussian Simulation ◽  
Geological Modeling ◽  
Control Effect ◽  
Reservoir Properties

Three-dimensional (3D) reservoir geological modeling is an advanced reservoir characterization method, which runs through the exploration and the development process of oil and gas fields. Reservoir geological modeling is playing an increasingly significant role in determining the distribution, internal configuration, and quality of a reservoir as well. Conventional variogram-based methods such as statistical interpolation and reservoir geological modeling have difficulty characterizing complex reservoir geometries and heterogeneous reservoir properties. Taking advantage of deep feedforward neural networks (DFNNs) in nonlinear fitting, this paper compares the reservoir geological modeling results of different methods on the basis of an existing lithofacies model and seismic data from the X area of Karamay, Xinjiang, China. Adopted reservoir geological modeling methods include conventional sequential Gaussian simulation and DFNN-based reservoir geological modeling method. The constrained data in the experiment mainly include logging data, seismic attribute data, and lithofacies model. Then, based on the facies-controlled well-seismic combined reservoir geological modeling method, this paper explores the application of multioutput DFNN and transfer learning in reservoir geological modeling. The results show that the DFNN-based reservoir geological modeling results are closer to the actual model. In DFNN-based reservoir geological modeling, the facies control effect is obvious, and the simulation results have a higher coincidence rate in a test well experiment. The feasibility of applying multioutput DFNN and transfer learning in reservoir geological modeling provides solutions for further optimization methods, such as solving small-sample problems and improving the modeling efficiency.

Small-sample modeling method for nonstationary reactor noise analysis

1985 ◽  
Vol 12 (8) ◽  
pp. 399-407 ◽  
Author(s):  
M. Kitamura ◽  
T. Washio ◽  
K. Kotajima ◽  
K. Sugiyama
Keyword(s):  
Noise Analysis ◽  
Small Sample ◽  
Modeling Method ◽  
Reactor Noise

Probing the ultrastructure of the mitotic and meiotic spindle in eggs: An expeditious approach based on semi-thin (0.25 μm) serial sections

1983 ◽  
Vol 41 ◽  
pp. 552-555
Author(s):  
Conly L. Rieder ◽  
S. Bowser ◽  
R. Nowogrodzki ◽  
K. Ross ◽  
G. Sluder
Keyword(s):  
Small Sample Size ◽  
Small Sample ◽  
Meiotic Spindle ◽  
Serial Sections ◽  
Mitotic Apparatus ◽  
Thin Sections ◽  
Cell Cycles ◽  
Ultrastructural Studies

Eggs have long been a favorite material for studying the mechanism of karyokinesis in-vivo and in-vitro. They can be obtained in great numbers and, when fertilized, divide synchronously over many cell cycles. However, they are not considered to be a practical system for ultrastructural studies on the mitotic apparatus (MA) for several reasons, the most obvious of which is that sectioning them is a formidable task: over 1000 ultra-thin sections need to be cut from a single 80-100 μm diameter egg and of these sections only a small percentage will contain the area or structure of interest. Thus it is difficult and time consuming to obtain reliable ultrastructural data concerning the MA of eggs; and when it is obtained it is necessarily based on a small sample size.We have recently developed a procedure which will facilitate many studies concerned with the ultrastructure of the MA in eggs. It is based on the availability of biological HVEM's and on the observation that 0.25 μm thick serial sections can be screened at high resolution for content (after mounting on slot grids and staining with uranyl and lead) by phase contrast light microscopy (LM; Figs 1-2).

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