Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation

A novel approach to hardware fault tolerance is demonstrated that takes inspiration from the human immune system as a method of fault detection. The human immune system is a remarkable system of interacting cells and organs that protect the body from invasion and maintains reliable operation even in the presence of invading bacteria or viruses. This paper seeks to address the field of electronic hardware fault tolerance from an immunological perspective with the aim of showing how novel methods based upon the operation of the immune system can both complement and create new approaches to the development of fault detection mechanisms for reliable hardware systems. In particular, it is shown that by use of partial matching, as prevalent in biological systems, high fault coverage can be achieved with the added advantage of reducing memory requirements. The development of a generic finite-state-machine immunization procedure is discussed that allows any system that can be represented in such a manner to be "immunized" against the occurrence of faulty operation. This is demonstrated by the creation of an immunized decade counter that can detect the presence of faults in real tim

Wavelet artificial immune system algorithm applied to the faults aeronautical structural monitoring / Algoritmo do sistema imune artificial Wavelet aplicado a falhas monitoramento estrutural aeronáutico

This paper presents a Wavelet-artificial immune system algorithm to diagnose failures. Basically, after obtaining the vibration signals, 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. 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. The results demonstrate the robustness and accuracy methodology

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

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

DETECÇÃO DE FALHAS ESTRUTURAIS EM UM PORTICO METÁLICO UTILIZANDO A COMPUTAÇÃO INTELIGENTE

The Metal Gantries is one of the main structural compositions of gas stations, bridges and sky spiders. However, such structures are vulnerable to environmental, temporal and anthropological demands, generating wear and tear that can cause these structures to collapse. With the technological advances of the Fourth Industrial Revolution, there was a transformation of the relationship between physical space and man, called the Cyber-Physic model. This technological evolution surpassed the walls of Industries 4.0, and was also established in the civil branch, solving the problems of structural insecurity, reciprocal to the metallic portico through the Structural Health Monitoring Therefore, this research work presents an innovative proposal for the development of a Structural Health Monitoring applied to Metal Gantries with decision making based on Intelligent Computing. With this, this work seeks not only to implement the Structural Health Monitoring to guarantee safety in metallic frames, but also to optimize its operation with decision making based on the Artificial Immune System, through the Negative Selection Algorithm. Observing the results, this work proved to be efficient, robust and economically viable, having a high performance, representing the perfect Cyber-Physic measure in the monitoring of Metal Gantries and resolution of its structural problems.O pórtico metálico, é uma das principais composições estruturais de postos de combustíveis, pontes e aranha céus. Todavia, tais estruturas apresentam vulnerabilidade as solicitações ambientais, temporais e antropológicas, gerando desgastes que podem levar essas estruturas ao colapso. Com os avanços tecnológicos da Quarta revolução industrial, houve a transformação da relação do espaço físico e o homem, denominado modelo Ciber Físico Essa evolução tecnológica superou as paredes das Industrias 4.0, e se instaurou também no Ramo civil resolvendo os problemas de insegurança estrutural, reciproco ao pórtico metálico através do Sistema de Monitoramento de Integridade Estrutural Por isso, este trabalho de pesquisa apresenta uma proposta inovadora para o desenvolvimento de um Sistema de Monitoramento de Integridade Estrutural aplicado para Pórticos Metálicas com a tomada de decisões baseado na Computação Inteligente. Com isto, este trabalho busca não só implementar o Sistema de Monitoramento da Integridade Estrutural para garantir a segurança em pórticos metálicos, mas também otimizar seu funcionamento com tomada de decisões baseada no Sistema Imunológico Artificial, por intermédio do Algoritmo de Seleção Negativa. Observando os resultados, este trabalho provou ser eficiente, robusto e economicamente viável, tendo um alto desempenho, representando a medida perfeita Ciber Fisica no monitoramento Pórticos Metálicos e resolução dos seus problemas estruturais

Clinical diagnosis of breast cancer samples using artificial immune systems with negative selection / Diagnóstico clínico de amostras de cancro da mama utilizando sistemas imunitários artificiais com selecção negativa

This paper uses an artificial immune system applied for diagnosing breast cancer samples. Taking as basis an immunological process, the negative selection algorithm was used to discriminate the samples, attaining a classification for benign or malignant cases. The main application of the method is to assist professionals in the breast cancer diagnostic process, thereby providing decision-making agility, efficient treatment planning, reliability and the necessary intervention to save lives. To evaluate this method, the wisconsin breast cancer diagnosis database was used. This is an actual breast cancer database. The results obtained using the method, when compared with the specialized literature, show accuracy, robustness and efficiency in the breast cancer diagnostic process

POEtic Tissue: An Integrated Architecture for Bio-inspired Hardware

It is clear to all, after a moments thought, that nature has much wemight be inspired by when designing our systems, for example: robustness, adaptability and complexity, to name a few. The implementation of bio-inspired systems in hardware has however been limited, and more often than not been more a matter of artistry than engineering. The reasons for this are many, but one of the main problems has always been the lack of a universal platform, and of a proper methodology for the implementation of such systems. The ideas presented in this paper are early results of a new research project, "Reconfigurable POEtic Tissue". The goal of the project is the development of a hardware platform capable of implementing systems inspired by all three major axes (phylogenesis, ontogenesis, and epigenesis) of bio-inspiration, in digital hardware

A new approach experimental to diagnosis of the failures in mechanical structures using the artificial immune algorithm with negative selection / Uma nova abordagem experimental para o diagnóstico das falhas nas estruturas mecânicas utilizando o algoritmo de imunidade artificial com seleção negativa

In this paper we present a new experimental approach to diagnose failures in mechanical structures using as decision tool an artificial immune algorithm with negative selection. This method is divided into two modules, and the acquisition and data processing module and analysis, detecting and characterizing flaws module. The module for data acquisition and processing of the experimental apparatus is constituted as sensors and actuators, so as to capture the signals in the structure and store it in the computer. From the signal acquisition executed if the negative selection algorithm to identify and characterize flaws in the structure. The main application of this methodology is to assist in the inspection process of mechanical structures in order to identify and characterize the flaws, as well as perform the decisions in order to avoid accidents. To evaluate the proposed methodology, experiments were performed in the laboratory where a real signs database was captured in a structure of the beam type, made of aluminum. The results obtained in the tests show robustness and efficiency when compared to literature

Diagnóstico de falhas estruturais em um edifício utilizando inteligência artificial / Diagnosis of structural failure in a building using artificial intelligence

Este artigo apresenta um algoritmo de inteligência artificial baseado nos sistemas imunológicos artificiais para análise da integridade estrutural de um edifício. Inspirando-se em um processo biológico, utiliza-se o algoritmo de seleção negativa para realizar a identificação e caracterização das falhas estruturais. Esta ferramenta auxiliará profissionais, na inspeção de estruturas, de modo a identificar e caracterizar falhas, a fim de realizar manutenção preventiva, assegurar a integridade da estrutura e auxiliar a tomada de decisões. Para validar a metodologia foi utilizado um modelo matemático de um edifício, e a partir deste, foram geradas diversas situações (condição normal e condições em falhas), obtendo-se uma base de dados de sinais, que foram analisados pelo método proposto. Os resultados obtidos pelo algoritmo de seleção negativa apresentam eficiência e robustez. Vale ressaltar que a combinação de inteligência artificial com processamento de sinais permite uma maior qualidade no diagnóstico. Assim este artigo contribui com as linhas de pesquisa em monitoramento de saúde estrutural e inteligência artificial apresentando uma metodologia muito eficiente

ASN-MAMA: aplicativo para diagnóstico clínico de câncer de mama utilizando sistemas imunológicos artificiais / ASN-MAMA: application for clinical breast cancer diagnosis using artificial immune systems

Neste artigo apresenta-se um aplicativo para diagnóstico clínico de amostras de câncer de mama, utilizando uma abordagem baseada nos sistemas imunológicos artificiais. Tomando-se como base um processo imunológico, utiliza-se o Algoritmo de Seleção Negativa para discriminar as amostras, obtendo uma classificação em casos benignos ou malignos. A principal aplicação deste sistema é auxiliar profissionais no processo de diagnóstico de câncer de mama em ambiente hospitalar, proporcionando rapidez na tomada de decisão, eficiência no planejamento de tratamentos, confiabilidade e a assistência necessária para salvar vidas. O aplicativo também pode ser utilizado para treinamento de novos profissionais. Para calibrar e validar este aplicativo utilizou-se a base de dados Wisconsin Breast Cancer Diagnosis, trata-se de uma base de dados real de câncer de mama. O Aplicativo foi desenvolvido na linguagem C++, em modo visual, apresentando uma interface prática e de fácil utilização. Foram realizados testes com o sistema, e os resultados foram comparados com a literatura especializada, apresentando bom desempenho, precisão, robustez e eficiência no processo de diagnóstico de câncer de mama

Immune systems inspired multi-robot cooperative shepherding

Certain tasks require multiple robots to cooperate in order to solve them. The main problem with multi-robot systems is that they are inherently complex and usually situated in a dynamic environment. Now, biological immune systems possess a natural distributed control and exhibit real-time adaptivity, properties that are required to solve problems in multi-robot systems. In this thesis, biological immune systems and their response to external elements to maintain an organism's health state are researched. The objective of this research is to propose immune-inspired approaches to cooperation, to establish an adaptive cooperation algorithm, and to determine the refinements that can be applied in relation to cooperation. Two immune-inspired models that are based on the immune network theory are proposed, namely the Immune Network T-cell-regulated---with Memory (INT-M) and the Immune Network T-cell-regulated---Cross-Reactive (INT-X) models. The INT-M model is further studied where the results have suggested that the model is feasible and suitable to be used, especially in the multi-robot cooperative shepherding domain. The Collecting task in the RoboShepherd scenario and the application of the INT-M algorithm for multi-robot cooperation are discussed. This scenario provides a highly dynamic and complex situation that has wide applicability in real-world problems. The underlying 'mechanism of cooperation' in the immune inspired model (INT-M) is verified to be adaptive in this chosen scenario. Several multi-robot cooperative shepherding factors are studied and refinements proposed, notably methods used for Shepherds' Approach, Shepherds' Formation and Steering Points' Distance. This study also recognises the importance of flock identification in relation to cooperative shepherding, and the Connected Components Labelling method to overcome the related problem is presented. Further work is suggested on the proposed INT-X model that was not implemented in this study, since it builds on top of the INT-M algorithm and its refinements. This study can also be extended to include other shepherding behaviours, further investigation of other useful features of biological immune systems, and the application of the proposed models to other cooperative tasks