The Research on the Detection Methods of EMU Break Valves Features

2014 ◽  
Vol 687-691 ◽  
pp. 1034-1037
Author(s):  
Chun Ling Guan
Keyword(s):  
Neural Network ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Characteristic Parameters ◽  
Spatial Function ◽  
Network Algorithm ◽  
Detection Technology ◽  
Neural Network Algorithm ◽  
Multiple Units

This paper focuses on the detection technology for Electric Multiple Units (EMU) break valves features. Aiming at the issues of EMU break valves features detection, this paper propose a kind of EMU break valves feature detection technology based on neural network algorithm which does not overly dependent on break valve characteristic parameters. The spatial function neural network algorithm is used to predict the EMU break valves features. The experiments illustrate the proposed algorithm can increase the detection accuracy with satisfactory effects in EMU break valves features detection.

The Analysis of Faults Detection Software Based on Improved Neural Network Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 2044-2047
Author(s):  
Miao Yan ◽  
Zhi Bao Liu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Improved Method ◽  
Software Faults ◽  
Component Software ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Detection Software

The large-scale software is consisted of the components which are quite different. The detection accuracy of the traditional faults detection methods for the large-scale component software is not satisfactory. This paper proposes a large-scale software faults detection methods based on improved neural network combining the features of the large-scale software by computing the stable probability and building the neural network faults detection models. The proposed method can analyze the serial faults of the large-scale software to determine the positions of the faults. The experiment and simulation results show that the improved method for large-scale software fault detection can greatly improve the accuracy.

scholarly journals Metal Surface Defect Detection Using Modified YOLO

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 257
Author(s):  
Yiming Xu ◽  
Kai Zhang ◽  
Li Wang
Keyword(s):  
Neural Network ◽  
Metal Surface ◽  
Defect Detection ◽  
Surface Defect ◽  
Detection Methods ◽  
Cluster Center ◽  
Detection Accuracy ◽  
Detection Technology ◽  
Speed Advantage ◽  
Surface Defect Detection

Aiming at the problems of inefficient detection caused by traditional manual inspection and unclear features in metal surface defect detection, an improved metal surface defect detection technology based on the You Only Look Once (YOLO) model is presented. The shallow features of the 11th layer in the Darknet-53 are combined with the deep features of the neural network to generate a new scale feature layer using the basis of the network structure of YOLOv3. Its goal is to extract more features of small defects. Furthermore, then, K-Means++ is used to reduce the sensitivity to the initial cluster center when analyzing the size information of the anchor box. The optimal anchor box is selected to make the positioning more accurate. The performance of the modified metal surface defect detection technology is compared with other detection methods on the Tianchi dataset. The results show that the average detection accuracy of the modified YOLO model is 75.1%, which ia higher than that of YOLOv3. Furthermore, it also has a great detection speed advantage, compared with faster region-based convolutional neural network (Faster R-CNN) and other detection algorithms. The improved YOLO model can make the highly accurate location information of the small defect target and has strong real-time performance.

scholarly journals Neural Network Algorithm MRI Images for Analysis of Influencing Factors for Patellar Dislocation in Exercise

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuorong Gao ◽  
Guangsheng Wang ◽  
Zhi Zhang
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Patellar Dislocation ◽  
Image Resolution ◽  
Dice Similarity Coefficient ◽  
Detection Accuracy ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Mri Image

The study focused on segmentation effects of the improved algorithm of traditional neural network algorithm, small kernels two-path convolutional neural network (SK-TPCNN) combined with random forest (RF) algorithm on MRI images for patella, and the influencing factors of patellar dislocation during exercise. In this article, the MRI images for patellar dislocation patients were detected by virtue of the neural network algorithm, to establish the patella-related MRI image segmentation algorithm. In terms of dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity, the detection accuracy of MRI images for patella was evaluated, and the segmentation effect of MRI images for patella was assessed. 30 patients, who were diagnosed as patellar dislocation patients in hospital, were chosen as the research subjects. No matter whether the MRI images of the patients went through the processing of the neural network algorithm or not, all of them were analyzed. The results showed that, among the traditional neural network algorithm, SK-TPCNN algorithm, and SK-TPCNN + RF algorithm, the DSC values were 0.82, 0.71, and 0.79, respectively; the PPV values were 0.77, 0.59, and 0.85, respectively; and the sensitivity values were 0.79, 0.62, and 0.89, respectively. Obviously, the various parameters of the SK-TPCNN + RF algorithm were significantly greater than those of the SK-TPCNN algorithm, and the difference was statistically significant ( P < 0.05 ). It indicated that the segmentation ability of MRI images for patella of the NN algorithm was clearly improved, and the MRI image resolution was dramatically raised, which provided a referable basis for the MRI diagnosis of patients with patellar dislocation during exercise.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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