scholarly journals Analysis and Prognosis of Failures in Intelligent Hybrid Systems Using Bioengineering: Gear Coupling

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Roberto Outa ◽  
Fabio Roberto Chavarette ◽  
Vishnu Narayan Mishra ◽  
Aparecido Carlos Gonçalves ◽  
Adriana Garcia ◽  
...  
Keyword(s):  
Immune System ◽  
Hybrid Systems ◽  
Acoustic Signal ◽  
Signal Analysis ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Development Process ◽  
Wiener Filter ◽  
Artificial Immune ◽  
Acoustic Signal Analysis

This work is of multidisciplinary concept, whose development is difficult to perform. Considering also that, in one of the steps, the similarity between the FRF of the vibration and acoustic signal is demonstrated. The objective of this work is the analysis and prognosis of the progression of failures of a pair of gears using the artificial immune system (AIS) of negative selection. In order to have this condition met, during the development of this work, the Wiener filter technique, the vibration and acoustic signal analysis (FRF), the application of negative selection AIS techniques for classification and grouping of signals were applied. The final result successfully demonstrates the effectiveness of the development process of this work and the robustness of the negative selection AIS 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.

Attack-Resistant aiCAPTCHA Using a Negative Selection Artificial Immune System

2017 ◽  
Author(s):  
Brian M. Powell ◽  
Ekampreet Kalsy ◽  
Gaurav Goswami ◽  
Mayank Vatsa ◽  
Richa Singh ◽  
...  
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Artificial Immune

An advanced immune based strategy to obtain an optimal feasible assembly sequence

2016 ◽  
Vol 36 (2) ◽  
pp. 127-137 ◽  
Author(s):  
M.V.A. Raju Bahubalendruni ◽  
B.B.V.L. Deepak ◽  
Bibhuti Bhusan Biswal
Keyword(s):  
Bone Marrow ◽  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Problem Formulation ◽  
Computational Time ◽  
Assembly Sequence ◽  
Artificial Immune ◽  
Content Type ◽  
Assembly Sequences

Purpose The purpose of this study is to develop an intelligent methodology to find out an optimal feasible assembly sequence while considering the assembly predicates. Design/methodology/approach This proposed study is carried out by using two artificial immune system-based models, namely, Bone Marrow Model and Negative Selection Algorithms, to achieve the following objectives: to obtain the possible number of assembly sequences; to obtain the feasible assembly sequences while considering different assembly predicates; and to obtain an optimal feasible assembly sequence. Findings Proposed bone-marrow model determines the possible assembly sequences to ease the intricacy of the problem formulation. Further evaluation has been carried out through negative-selection censoring and monitoring models. These developed models reduce the overall computational time to determine the optimal feasible assembly sequence. Originality/value In this paper, the novel and efficient strategies based on artificial immune system have been developed and proposed to obtain all valid assembly sequences and optimized assembly sequence for a given assembled product using assembly attributes. The introduced methodology has proven its effectiveness in achieving optimal assembly sequence with less computational time.

scholarly journals The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immune System

2017 ◽  
Vol 7 (4) ◽  
pp. 2071
Author(s):  
Pongsarun Boonyopakorn ◽  
Phayung Meesad
Keyword(s):  
Genetic Algorithm ◽  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Optimization Methods ◽  
The Novel ◽  
Artificial Immune ◽  
Input Variables ◽  
Modified Algorithm ◽  
Better Than

This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multiobjective testing functions were then used. The result also illustrated that the modified approach still had the best performance.

scholarly journals An Immune Based Patient Anomaly Detection using RFID Technology

2013 ◽  
Vol 2 (1) ◽  
pp. 121-142 ◽  
Author(s):  
Sri Listia Rosa ◽  
Siti Mariyam Shamsuddin ◽  
Evizal Evizal
Keyword(s):  
Immune System ◽  
Monitoring System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Intelligent System ◽  
Patient Data ◽  
Rfid Technology ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Negative Selection Algorithm

Detecting of anomalies patients data are important to gives early alert to hospital, in this paper will explore on anomalies patient data detecting and processing using artificial computer intelligent system. Artificial Immune System (AIS) is an intelligent computational technique refers to human immunology system and has been used in many areas such as computer system, pattern recognition, stock market trading, etc. In this case, real value negative selection algorithm (RNSA) of artificial immune system used for detecting anomalies patient body parameters such as temperature. Patient data from monitoring system or database classified into real valued, real negative selection algorithm results is real values deduction by RNSA distance, the algorithm used is minimum distance and the value of detector generated for the algorithm. The real valued compared with the distance of data, if the distance is less than a RNSA detector distance then data classified into abnormal. To develop real time detecting and monitoring system, Radio Frequency Identification (RFID) technology has been used in this system. Keywords: AIS, RNSA, RFID, AbnormalDOI: 10.18495/comengapp.21.121142

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