MODELING METHODOLOGY BASED ON ARTIFICIAL IMMUNE SYSTEM ALGORITHM AND NEURAL NETWORKS APPLIED TO REMOVAL OF HEAVY METALS FROM RESIDUAL WATERS

2012 ◽  
Vol 11 (11) ◽  
pp. 1907-1914 ◽  
Author(s):  
Silvia Curteanu ◽  
Elena-Niculina Dragoi ◽  
Gabriel Dan Suditu
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune ◽  
Modeling Methodology ◽  
Removal Of Heavy Metals ◽  
Artificial Immune System Algorithm

Structural failures diagnosis using a hybrid artificial intelligence method / Diagnóstico de falhas estruturais utilizando um método híbrido de inteligência artificial

2021 ◽  
Vol 7 (7) ◽  
pp. 66873-66893
Author(s):  
Simone Silva Frutuoso de Souza ◽  
Mailon Bruno Pedri de Campos ◽  
Fábio Roberto Chavarette ◽  
Fernando Parra dos Anjos Lima
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Artificial Immune ◽  
Aircraft Structure ◽  
Inspection Process ◽  
Artificial Immune System Algorithm ◽  
Structural Failures ◽  
Aluminum Beam ◽  
Hybrid Artificial Intelligence

This paper presents a Wavelet-artificial immune system algorithm to diagnose failures in aeronautical structures. Basically, after obtaining the vibration signals in the structure, is used the wavelet module for transformed the signals into the wavelet domain. Afterward, a negative selection artificial immune system realizes the diagnosis, identifying and classifying the failures. The main application of this methodology is the auxiliary structures inspection process in order to identify and characterize the flaws, as well as perform the decisions aiming at avoiding accidents or disasters. In order to evaluate this methodology, we carried out the modeling and simulation of signals from a numerical model of an aluminum beam, representing an aircraft structure such as a wing. The results demonstrate the robustness and accuracy methodology.

Automatic Selector Between Cardiogenic Oscillation and Airway Secretion in Mechanical Ventilation Flow Signals Using Artificial Immune System

2018 ◽  
Vol 9 (4) ◽  
pp. 65-78
Author(s):  
Samuel Sobral dos Santos ◽  
Hatus Vianna Wanderley ◽  
Fernando Buarque de Lima Neto
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune ◽  
Flow Signal ◽  
Specificity And Sensitivity ◽  
Airway Secretion ◽  
Artificial Immune System Algorithm ◽  
Ventilation Modes ◽  
Cardiogenic Oscillation

The accumulation of secretions in the airways of ventilator-dependent patients is a common problem, and if not detected and treated in due time, it greatly increases the risk of infections and asynchrony. Unfortunately, cardiogenic oscillation modifies the flow signal shape that can confuse clinical staff and modern lung ventilators. In this article, the authors use an artificial immune system algorithm in a pre-processed flow signal. The authors' approach was able to automatically detect the presence or absence of airway secretions, even if the sample contains the influence of cardiogenic oscillation. The training and validation of the algorithm was carried out using a database containing flow signals of 457 respiratory cycles, obtained from three patients in different ventilation modes. The algorithm trained with 60% of the base cycles, was able to achieve specificity and sensitivity above 0.96 in the classification of the remaining cycles of the base.

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