A feasible strain-history extraction method using machine learning for the durability evaluation of automotive parts

2021 ◽  
Author(s):  
Dong-Won Jang ◽  
Jong-Su Kang ◽  
Jae-Yong Lim
Keyword(s):  
Machine Learning ◽  
Extraction Method ◽  
Strain History ◽  
Automotive Parts

scholarly journals Near-field Sensors with Machine Learning for Breast Tumor Detection

2018 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Maged A. Aldhaeebi ◽  
Thamer S. Almoneef ◽  
Omar M. Ramahi
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Breast Tumor ◽  
Extraction Method ◽  
Near Field ◽  
Tumor Detection ◽  
Electromagnetic Properties ◽  
Feature Extraction Method ◽  
The Difference ◽  
Breast Tissues

In this work, we propose the use of an electrically small novel antenna as a probe combined with a classification algorithm for nearfield microwave breast tumor detection. The resonant probe ishighly sensitive to the changes in the electromagnetic properties of the breast tissues such that the presence of the tumor is estimatedby determining the changes in the magnitude and phase responseof the reflection coefficient of the sensor. The Principle Component placed at the middle of the probe as shown in Fig. 1. The mainAnalysis (PCA) feature extraction method is applied to emphasize the difference in the probe responses for both the healthy and thetumourous cases . We show that when a numerical realistic breast with and without tumor cells is placed in the near field of the probe, the probe is capable of distinguishing between healthy and tumorous tissue. In addition, the probe is able to identify tumors with various sizes placed in single locations.

scholarly journals Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation

JAMIA Open ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 431-438
Author(s):  
Anobel Y Odisho ◽  
Briton Park ◽  
Nicholas Altieri ◽  
John DeNero ◽  
Matthew R Cooperberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Extraction Method ◽  
Calibration Error ◽  
Large Set ◽  
Diverse Range ◽  
Pathologic Features ◽  
Calibration Methods ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Pathology Reports

Abstract Objective Cancer is a leading cause of death, but much of the diagnostic information is stored as unstructured data in pathology reports. We aim to improve uncertainty estimates of machine learning-based pathology parsers and evaluate performance in low data settings. Materials and methods Our data comes from the Urologic Outcomes Database at UCSF which includes 3232 annotated prostate cancer pathology reports from 2001 to 2018. We approach 17 separate information extraction tasks, involving a wide range of pathologic features. To handle the diverse range of fields, we required 2 statistical models, a document classification method for pathologic features with a small set of possible values and a token extraction method for pathologic features with a large set of values. For each model, we used isotonic calibration to improve the model’s estimates of its likelihood of being correct. Results Our best document classifier method, a convolutional neural network, achieves a weighted F1 score of 0.97 averaged over 12 fields and our best extraction method achieves an accuracy of 0.93 averaged over 5 fields. The performance saturates as a function of dataset size with as few as 128 data points. Furthermore, while our document classifier methods have reliable uncertainty estimates, our extraction-based methods do not, but after isotonic calibration, expected calibration error drops to below 0.03 for all extraction fields. Conclusions We find that when applying machine learning to pathology parsing, large datasets may not always be needed, and that calibration methods can improve the reliability of uncertainty estimates.

scholarly journals The Surface Crack Extraction Method Based on Machine Learning of Image and Quantitative Feature Information Acquisition Method

2021 ◽  
Vol 13 (8) ◽  
pp. 1534
Author(s):  
Fan Zhang ◽  
Zhenqi Hu ◽  
Kun Yang ◽  
Yaokun Fu ◽  
Zewei Feng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Crack ◽  
Information Acquisition ◽  
Extraction Method ◽  
Accurate Method ◽  
Extraction Methods ◽  
Background Information ◽  
True Value ◽  
Feature Information ◽  
Uav Images

In order to effectively control the damage caused by surface cracks to a geological environment, we need to find a convenient, efficient, and accurate method to obtain crack information. The existing crack extraction methods based on unmanned air vehicle (UAV) images inevitably have some erroneous pixels because of the complexity of background information. At the same time, there are few researches on crack feature information. In view of this, this article proposes a surface crack extraction method based on machine learning of UAV images, the data preprocessing steps, and the content and calculation methods for crack feature information: length, width, direction, location, fractal dimension, number, crack rate, and dispersion rate. The results show that the method in this article can effectively avoid the interference by vegetation and soil crust. By introducing the concept of dispersion rate, the method combining crack rate and dispersion rate can describe the distribution characteristics of regional cracks more clearly. Compared to field survey data, the calculation result of the crack feature information in this article is close to the true value, which proves that this is a reliable method for obtaining quantitative crack feature information.

Research on computer network information security based on improved machine learning

2020 ◽  
pp. 1-12
Author(s):  
Yu Guangxu
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Information Security ◽  
Extraction Method ◽  
Situational Awareness ◽  
Rapid Development ◽  
Selection Method ◽  
Feature Extraction Method ◽  
Network Information ◽  
Awareness System

The 21st century is an era of rapid development of the Internet. Internet technology is widely used in various fields. With the rapid development of network, the importance of network information security is also highlighted. The traditional network information security technology has been difficult to ensure the security of network information. Therefore, we mainly study the application of machine learning feature extraction method in situational awareness system. A feature selection method based on machine learning is proposed to extract situational features.By analyzing whether the background of network information is safe or not, and according to the current research situation at home and abroad and the trend of Internet development, this paper tries out the practical application of machine learning feature extraction method in a certain perception system. Based on the above points, a selection method based on machine learning is proposed to extract situational features. The accuracy and timeliness of situational awareness system detection are seriously affected by the high dimension, noise and redundant features of massive network traffic data.Therefore, it is of great value to further study network intrusion detection technology on the basis of machine learning.

scholarly journals Electronic Tongue Recognition with Feature Specificity Enhancement

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 772 ◽  
Author(s):  
Tao Liu ◽  
Yanbing Chen ◽  
Dongqi Li ◽  
Tao Yang ◽  
Jianhua Cao
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Extraction Method ◽  
Sensor Array ◽  
Electronic Tongue ◽  
Learning Models ◽  
Learning Methods ◽  
Feature Extraction Method ◽  
Machine Learning Methods ◽  
Machine Learning Models

As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.

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