Structural Damage Assessment using an Artificial Immune System

2015 ◽  
pp. 86-102
Author(s):  
Maribel Anaya Vejar ◽  
Diego Alexander Tibaduiza Burgos ◽  
Francesc Pozo
Keyword(s):  
Principal Component Analysis ◽  
Immune System ◽  
Damage Assessment ◽  
Structural Damage ◽  
Artificial Immune System ◽  
Principal Component ◽  
Second Step ◽  
Artificial Immune ◽  
Current State ◽  
Using Data

Structural damage assessment methodologies allow providing knowledge about the current state of the structure. This information is important because allows to avoid possible accidents and perform maintenance tasks in the structure. This chapter proposes the use of an artificial immune system to detect and classify damages in structures by using data from a multi-actuator piezoelectric system that is working in several actuation phases. In a first step of the methodology, principal component analysis (PCA) is used to build a baseline model by using the collected data. In a second step, the same experiments under similar conditions are performed with the structure in different states (damaged or not). These data are projected into the different baseline models for each actuator, in order to obtain the damages indices and build the antigens. The antigens are compared with the antibodies by using an affinity function and the result of this process allows detecting and classifying damages.

Detection of Network Attacks With Artificial Immune System

2020 ◽  
pp. 41-58
Author(s):  
Feyzan Saruhan-Ozdag ◽  
Derya Yiltas-Kaplan ◽  
Tolga Ensari
Keyword(s):  
Principal Component Analysis ◽  
Immune System ◽  
Intrusion Detection ◽  
Artificial Immune System ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Intrusion Detection Systems ◽  
Artificial Immune ◽  
Detection Systems

Intrusion detection systems are one of the most important tools used against the threats to network security in ever-evolving network structures. Along with evolving technology, it has become a necessity to design powerful intrusion detection systems and integrate them into network systems. The main purpose of this research is to develop a new method by using different techniques together to increase the attack detection rates. Negative selection algorithm, a type of artificial immune system algorithms, is used and improved at the stage of detector generation. In phase of the preparation of the data, information gain is used as feature selection and principal component analysis is used as dimensionality reduction method. The first method is the random detector generation and the other one is the method developed by combining the information gain, principal component analysis, and genetic algorithm. The methods were tested using the KDD CUP 99 data set. Different performance values are measured, and the results are compared with different machine learning algorithms.

scholarly journals Classification techniques based on Artificial immune system algorithms for Heart disease using Principal Component Analysis

2021 ◽  
pp. 150-160
Author(s):  
Kirti Bala Bahekar
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Heart Disease ◽  
Immune System ◽  
Artificial Immune System ◽  
Principal Component ◽  
Component Analysis ◽  
Artificial Immune ◽  
Potential Applications ◽  
Modern Era

The modern era is a period of machine learning, which helps in finding new facts for future predictions. Classification is a machine learning tool that helps in the discovery of knowledge in Big data and has various potential applications. Researchers nowadays are more inclined to the techniques which are inspired by nature. The artificial immune system (AIS) is such a method that is originated by the qualities of the humanoid immune system. In this paper, artificial immune stimulated classifiers as supervised learning methods are used for classifying Heart disease datasets. The performance of the classifiers strongly depends on the datasets used for learning. Here it is observed that, when the principal component analysis is performed on the standard dataset, then classifiers' accuracy and other facts show improvement in performance, which leads to a fall in errors.

scholarly journals Multiway principal component analysis contributions for structural damage localization

2017 ◽  
Vol 17 (5) ◽  
pp. 1151-1165 ◽  
Author(s):  
Magda Ruiz ◽  
Luis Eduardo Mujica ◽  
Julián Sierra ◽  
Francesc Pozo ◽  
José Rodellar
Keyword(s):  
Principal Component Analysis ◽  
Structural Damage ◽  
Multivariable Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Guided Wave ◽  
Damage Localization ◽  
Using Data ◽  
Q Statistic ◽  
Multiway Principal Component Analysis

In this article, a novel methodology for damage localization is introduced. The approach is based on a multiactuator system. This means that the system itself has the ability of both exciting the specimen and measuring its response at different points in a pitch-catch mode. Once one of its actuators excites the specimen, the damage affects the normal travel of the guided wave, and this change is mainly detected by sensors in the direct route to the excitation point. In previous works by the authors, it can be observed that the progression using data-driven statistical models (multivariable analysis based on principal component analysis) of all recorded signals to determine whether the damage is present. However, the main contribution of this article is the demonstration of the possibility of localizing damages by analyzing the contribution of each sensor to this index which have detected it ( T2-statistic and Q-statistic). The proposed methodology has been applied and validated on an aircraft turbine blade. The results indicate that the presented methodology is able to accurately locate damages, analyzing the record signals from all actuation phases and giving a unique and reliable region.

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