scholarly journals A Evaluation Method of Small Sample Data

1980 ◽  
Vol 1980 (148) ◽  
pp. 148-152
Author(s):  
Yoshitaka Nakamura
Keyword(s):  
Evaluation Method ◽  
Small Sample ◽  
Sample Data

scholarly journals A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

2021 ◽  
Vol 13 (23) ◽  
pp. 4864
Author(s):  
Langfu Cui ◽  
Qingzhen Zhang ◽  
Liman Yang ◽  
Chenggang Bai
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Prediction Method ◽  
Sliding Window ◽  
Small Sample ◽  
Grey Theory ◽  
Sensing System ◽  
Performance Change ◽  
Sample Data ◽  
Platform System

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

scholarly journals Abnormal Detection to Big Data Using Deep Neural Networks

2021 ◽  
Vol 2068 (1) ◽  
pp. 012025
Author(s):  
Jian Zheng ◽  
Zhaoni Li ◽  
Jiang Li ◽  
Hongling Liu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Risk Model ◽  
Small Sample ◽  
Small Samples ◽  
Small Data ◽  
Data Segmentation ◽  
Sample Data ◽  
Segmentation Algorithms ◽  
The Common

Abstract It is difficult to detect the anomalies in big data using traditional methods due to big data has the characteristics of mass and disorder. For the common methods, they divide big data into several small samples, then analyze these divided small samples. However, this manner increases the complexity of segmentation algorithms, moreover, it is difficult to control the risk of data segmentation. To address this, here proposes a neural network approch based on Vapnik risk model. Firstly, the sample data is randomly divided into small data blocks. Then, a neural network learns these divided small sample data blocks. To reduce the risks in the process of data segmentation, the Vapnik risk model is used to supervise data segmentation. Finally, the proposed method is verify on the historical electricity price data of Mountain View, California. The results show that our method is effectiveness.

Variation analysis driven by small-sample data for compliant aero-structure assembly

2019 ◽  
Vol 39 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Biao Mei ◽  
Weidong Zhu ◽  
Yinglin Ke ◽  
Pengyu Zheng
Keyword(s):  
Probability Distributions ◽  
Small Sample ◽  
Accurate Estimation ◽  
Production Volume ◽  
Analysis Method ◽  
Variation Analysis ◽  
Content Type ◽  
Aircraft Manufacturing ◽  
Sample Data ◽  
Structure Assembly

Purpose Assembly variation analysis generally demands probability distributions of variation sources. However, due to small production volume in aircraft manufacturing, especially prototype manufacturing, the probability distributions are hard to obtain, and only the small-sample data of variation sources can be consulted. Thus, this paper aims to propose a variation analysis method driven by small-sample data for compliant aero-structure assembly. Design/methodology/approach First, a hybrid assembly variation model, integrating rigid effects with flexibility, is constructed based on the homogeneous transformation and elasticity mechanics. Then, the bootstrap approach is introduced to estimate a variation source based on small-sample data. The influences of bootstrap parameters on the estimation accuracy are analyzed to select suitable parameters for acceptable estimation performance. Finally, the process of assembly variation analysis driven by small-sample data is demonstrated. Findings A variation analysis method driven by small-sample data, considering both rigid effects and flexibility, is proposed for aero-structure assembly. The method provides a good complement to traditional variation analysis methods based on probability distributions of variation sources. Practical implications With the proposed method, even if probability distribution information of variation sources cannot be obtained, accurate estimation of the assembly variation could be achieved. The method is well suited for aircraft assembly, especially in the stage of prototype manufacturing. Originality/value A variation analysis method driven by small-sample data is proposed for aero-structure assembly, which can be extended to deal with other similar applications.

scholarly journals Hydrodynamic Resistance of Intracranial Flow-Diverter Stents: Measurement Description and Data Evaluation

2019 ◽  
Vol 11 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Benjamin Csippa ◽  
Dániel Gyürki ◽  
Gábor Závodszky ◽  
István Szikora ◽  
György Paál
Keyword(s):  
Flow Rate ◽  
Evaluation Method ◽  
Small Sample Size ◽  
Flow Diverter ◽  
Small Sample ◽  
Low Flow ◽  
Hydrodynamic Resistance ◽  
Metallic Surface ◽  
Stent Deployment ◽  
Geometrical Properties

Abstract Purpose Intracranial aneurysms are malformations forming bulges on the walls of brain arteries. A flow diverter device is a fine braided wire structure used for the endovascular treatment of brain aneurysms. This work presents a rig and a protocol for the measurement of the hydrodynamic resistance of flow diverter stents. Hydrodynamic resistance is interpreted here as the pressure loss versus volumetric flow rate function through the mesh structure. The difficulty of the measurement is the very low flow rate range and the extreme sensitivity to contamination and disturbances. Methods Rigorous attention was paid to reproducibility, hence a strict protocol was designed to ensure controlled circumstances and accuracy. Somewhat unusually, the history of the development of the rig, including the pitfalls was included in the paper. In addition to the hydrodynamic resistance measurements, the geometrical properties—metallic surface area, pore density, deployed and unconstrained length and diameter—of the stent deployment were measured. Results Based on our evaluation method a confidence band can be determined for a given deployment scenario. Collectively analysing the hydrodynamic resistance and the geometric indices, a deeper understanding of an implantation can be obtained. Our results suggest that to correctly interpret the hydrodynamic resistance of a scenario, the deployment length has to be considered. To demonstrate the applicability of the measurement, as a pilot study the results of four intracranial flow diverter stents of two types and sizes have been reported in this work. The results of these measurements even on this small sample size provide valuable information on differences between stent types and deployment scenarios.

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