scholarly journals Optical combustion sensor data interpretation using hybrid negative selection algorithm with artificial immune networks

2015 ◽  
Vol 2 (1) ◽  
pp. 58-70
Author(s):  
V. Lytvynenko ◽  
◽  
A. Smolarz ◽  
W. Wójcik ◽  
J. Ballester ◽  
...  
Keyword(s):  
Negative Selection ◽  
Data Interpretation ◽  
Sensor Data ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Negative Selection Algorithm

A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set

2010 ◽  
Vol 121-122 ◽  
pp. 486-489
Author(s):  
Wen Chen ◽  
Tao Li ◽  
Xiao Jie Liu ◽  
Yuan Quan Shi
Keyword(s):  
Negative Selection ◽  
The Self ◽  
Hierarchical Level ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Data Set ◽  
Cluster Radius ◽  
Negative Selection Algorithm ◽  
Value Range ◽  
Level Cluster

In this article, we proposed a negative selection algorithm which based on hierarchical level cluster of self dataset CB-RNSA. First the self data set is clustered by different cluster radius, and then the self data are substituted by cluster centers to compare with candidate detectors to reduce the number of distance counting. In the detector creating process, the value of each detector property was restricted to a given value range so as to decrease the redundancy of detectors. The stimulation result shows that CB-RNSA is an effective algorithm for the creation of artificial immune detectors.

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

2014 ◽  
Vol 472 ◽  
pp. 544-549 ◽  
Author(s):  
Fernando Parra dos Anjos Lima ◽  
Fábio Roberto Chavarette ◽  
Simone Silva Frutuoso de Souza ◽  
Adriano dos Santos e Souza ◽  
Mara Lúcia Martins Lopes
Keyword(s):  
Negative Selection ◽  
Structural Integrity ◽  
Artificial Immune Systems ◽  
Structural Failure ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Immune Systems ◽  
Negative Selection Algorithm ◽  
Identification And Characterization

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

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

scholarly journals DETECÇÃO DE FALHAS ESTRUTURAIS DE UM PRÉDIO DE DOIS ANDARES UTILIZANDO SISTEMA IMUNOLÓGICO ARTIFICIA

2019 ◽  
Vol 11 (4) ◽  
pp. 73-84
Author(s):  
Thiago Carreta Moro ◽  
Fábio Roberto Chavarette ◽  
Luiz Gustavo Pereira Roéfero ◽  
Roberto Outa
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Structural Integrity ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Intelligent Computing ◽  
Negative Selection Algorithm ◽  
Story Building

This work presents the innovative proposal for the development of SHMs with focus on physical cyber systems applied in a two-story building based on Intelligent Computing (CI) techniques, the negative selection algorithm from the Artificial Immune System, to perform the analysis and monitoring of structural integrity in a building.

NUMERICAL NEGATIVE SELECTION ALGORITHM

2010 ◽  
Vol 19 (05) ◽  
pp. 703-712
Author(s):  
TAO CAI ◽  
SHIGUANG JU ◽  
DEJIAO NIU
Keyword(s):  
Negative Selection ◽  
Detection System ◽  
Immune Algorithm ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Time And Space ◽  
Data Set ◽  
Matching Rule ◽  
Artificial Immune Algorithm ◽  
Negative Selection Algorithm

The artificial immune algorithm is the hot topic in much research such as the intrusion detection system, the information retrieval system and the data mining system. The negative selection algorithm is the typical artificial immune algorithm. A common representation of binary strings for antibody (detector) and antigen have been associated with inefficiencies when generating detector and inspecting antigen. We use a single integer to represent the detector and provide the basis of improving negative selection algorithm efficiency. In the detector generation algorithm, extracting sub-strings in self that its length is larger than threshold and converting them to single integer in numerical interval, then the rest integers in numerical interval are selected as numerical detectors. It can reduce the time and space overhead of detector generation and provide the facility to analyze the positive and negative errors when antigen inspection. The numerical matching rule is given. The B-tree is used to create index of numerical detector. Extracting sub-strings in antigen that its length is larger than threshold and converting them to some integers. If there is the same value as those integers in the index of numerical detector, then the antigen matches one numerical detector. It can improve the efficiency of antigen inspection. Finally the prototype of the numerical negative selection algorithm and negative selection algorithm are realized to test the overhead of the detector generation and antigen inspection using the live data set. The results show that the numerical negative selection algorithm can reduce the time and space overhead and avoid fluctuation of the overhead.

scholarly journals MONITORAMENTO DE FALHAS EM SISTEMAS DINÂMICOS UTILIZANDO UM MÉTODO DE ANÁLISE DE DADOS BASEADO NO ALGORITMO DE SELEÇÃO NEGATIVA

2021 ◽  
Vol 13 (2) ◽  
pp. 78-87
Author(s):  
Driely Candido Santos ◽  
Mara Lúcia Martins Lopes ◽  
Fábio Roberto Chavarette ◽  
Bruno Ferreira Rossanês
Keyword(s):  
Negative Selection ◽  
Intelligent System ◽  
Artificial Immune Systems ◽  
Base Line ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Data Set ◽  
Vibration Signals ◽  
Immune Systems ◽  
Negative Selection Algorithm

This work presents the application of a method for monitoring and diagnosing failures in mechanical structures based on the theory of vibration signals and on Artificial Immune Systems to assist in data processing. It uses the Negative Selection Algorithm as a tool to identify fault samples extracted from the laboratory simulated signals of a dynamic rotor. This methodology can help mechanical structure maintenance professionals, facilitating decision-making. The data set used in the processing of the intelligent system was generated through experiments. For normal (base-line) conditions, the signals of the rotor in free operation were used, that is, without the addition of unbalance mass, and for the fault conditions, unbalance masses were added to the system. The results are satisfactory, showing precision and robustness.

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