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Littérature scientifique sur le sujet « Bioinspired Computation »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 11 mars 2023

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Articles de revues sur le sujet "Bioinspired Computation"

Cui, Zhihua, Rajan Alex, Rajendra Akerkar, and Xin-She Yang. "Recent Advances on Bioinspired Computation." Scientific World Journal 2014 (2014): 1–3. http://dx.doi.org/10.1155/2014/934890.

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Hanif, Muhammad Kashif, Ramzan Talib, Muhammad Awais, Muhammad Yahya Saeed, and Umer Sarwar. "Comparison of Bioinspired Computation and Optimization Techniques." Current Science 115, no. 3 (2018): 450. http://dx.doi.org/10.18520/cs/v115/i3/450-453.

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Botella, Guillermo, Antonio Garcia, Manuel Rodriguez-Alvarez, Eduardo Ros, Uwe Meyer-Baese, and María C. Molina. "Robust Bioinspired Architecture for Optical-Flow Computation." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 18, no. 4 (2010): 616–29. http://dx.doi.org/10.1109/tvlsi.2009.2013957.

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Gunji, Yukio-Pegio, and Daisuke Uragami. "Breaking of the Trade-Off Principle between Computational Universality and Efficiency by Asynchronous Updating." Entropy 22, no. 9 (2020): 1049. http://dx.doi.org/10.3390/e22091049.

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Although natural and bioinspired computing has developed significantly, the relationship between the computational universality and efficiency beyond the Turing machine has not been studied in detail. Here, we investigate how asynchronous updating can contribute to the universal and efficient computation in cellular automata (CA). First, we define the computational universality and efficiency in CA and show that there is a trade-off relation between the universality and efficiency in CA implemented in synchronous updating. Second, we introduce asynchronous updating in CA and show that asynchronous updating can break the trade-off found in synchronous updating. Our finding spells out the significance of asynchronous updating or the timing of computation in robust and efficient computation.

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Chen, Tinggui, Jianjun Yang, Kai Huang, and Qiang Cheng. "Bioinspired Computation and Its Applications in Operation Management." Scientific World Journal 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/356571.

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Demidova, L. A., and A. V. Gorchakov. "Application of bioinspired global optimization algorithms to the improvement of the prediction accuracy of compact extreme learning machines." Russian Technological Journal 10, no. 2 (2022): 59–74. http://dx.doi.org/10.32362/2500-316x-2022-10-2-59-74.

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Objectives. Recent research in machine learning and artificial intelligence aimed at improving prediction accuracy and reducing computational complexity resulted in a novel neural network architecture referred to as an extreme learning machine (ELM). An ELM comprises a single-hidden-layer feedforward neural network in which the weights of connections among input-layer neurons and hidden-layer neurons are initialized randomly, while the weights of connections among hidden-layer neurons and output-layer neurons are computed using a generalized Moore– Penrose pseudoinverse operation. The replacement of the iterative learning process currently used in many neural network architectures with the random initialization of input weights and the explicit computation of output weights significantly increases the performance of this novel machine learning algorithm while preserving good generalization performance. However, since the random initialization of input weights does not necessarily guarantee optimal prediction accuracy, the purpose of the present work was to develop and study approaches to intelligent adjustment of input weights in ELMs using bioinspired algorithms in order to improve the prediction accuracy of this data analysis tool in regression problems.Methods. Methods of optimization theory, theory of evolutionary computation and swarm intelligence, probability theory, mathematical statistics and systems analysis were used.Results. Approaches to the intelligent adjustment of input weights in ELMs were developed and studied. These approaches are based on the genetic algorithm, the particle swarm algorithm, the fish school search algorithm, as well as the chaotic fish school search algorithm with exponential step decay proposed by the authors. By adjusting input weights with bioinspired optimization algorithms, it was shown that the prediction accuracy of ELMs in regression problems can be improved to reduce the number of hidden-layer neurons to reach a high prediction accuracy on learning and test datasets. In the considered problems, the best ELM configurations can be obtained using the chaotic fish school search algorithm with exponential step decay.Conclusions. The obtained results showed that the prediction accuracy of ELMs can be improved by using bioinspired algorithms for the intelligent adjustment of input weights. Additional calculations are required to adjust the weights; therefore, the use of ELMs in combination with bioinspired algorithms may be advisable where it is necessary to obtain the most accurate and most compact ELM configuration.

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Wang, Zhaocai, Xiaoguang Bao, and Tunhua Wu. "A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing." Computational Intelligence and Neuroscience 2021 (January 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/8814947.

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The Chinese postman problem is a classic resource allocation and scheduling problem, which has been widely used in practice. As a classical nondeterministic polynomial problem, finding its efficient algorithm has always been the research direction of scholars. In this paper, a new bioinspired algorithm is proposed to solve the Chinese postman problem based on molecular computation, which has the advantages of high computational efficiency, large storage capacity, and strong parallel computing ability. In the calculation, DNA chain is used to properly represent the vertex, edge, and corresponding weight, and then all possible path combinations are effectively generated through biochemical reactions. The feasible solution space is obtained by deleting the nonfeasible solution chains, and the optimal solution is solved by algorithm. Then the computational complexity and feasibility of the DNA algorithm are proved. By comparison, it is found that the computational complexity of the DNA algorithm is significantly better than that of previous algorithms. The correctness of the algorithm is verified by simulation experiments. With the maturity of biological operation technology, this algorithm has a broad application space in solving large-scale combinatorial optimization problems.

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Herrmann, Sven. "Review of bioinspired computation in combinatorial optimization by Frank Neumann and Carsten Witt." ACM SIGACT News 44, no. 2 (2013): 22–26. http://dx.doi.org/10.1145/2491533.2491540.

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Chen, Lei, Liyi Zhang, Yanju Guo, Yong Huang, and Jingyi Liang. "Blind Source Separation Based on Covariance Ratio and Artificial Bee Colony Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/484327.

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The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms.

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Ayton, Lorna J. "Bioinspired aerofoil adaptations: the next steps for theoretical models." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 377, no. 2159 (2019): 20190070. http://dx.doi.org/10.1098/rsta.2019.0070.

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The extended introduction in this paper reviews the theoretical modelling of leading- and trailing-edge noise, various bioinspired aerofoil adaptations to both the leading and trailing edges of blades, and how these adaptations aid in the reduction of aerofoil–turbulence interaction noise. Attention is given to the agreement between current theoretical predictions and experimental measurements, in particular, for turbulent interactions at the trailing edge of an aerofoil. Where there is a poor agreement between theoretical models and experimental data the features neglected from the theoretical models are discussed. Notably, it is known that theoretical predictions for porous trailing-edge adaptations do not agree well with experimental measurements. Previous works propose the reason for this: theoretical models do not account for surface roughness due to the porous material and thus omit a key noise source. The remainder of this paper, therefore, presents an analytical model, based upon the acoustic analogy, to predict the far-field noise due to a rough surface at the trailing edge of an aerofoil. Unlike previous roughness noise models which focus on roughness over an infinite wall, the model presented here includes diffraction by a sharp edge. The new results are seen to be in better agreement with experimental data than previous models which neglect diffraction by an edge. This new model could then be used to improve theoretical predictions for far-field noise generated by turbulent interactions with a (rough) porous trailing edge. This article is part of the theme issue ‘Frontiers of aeroacoustics research: theory, computation and experiment’.

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Thèses sur le sujet "Bioinspired Computation"

MANZONI, LUCA. "Dynamics of bioinspired computation." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/42373.

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Since its birth, Computer Science has been inspired by natural phenomena. Two main approaches where developed through the years. The first one is concerned with the study of the properties nature-inspired models that can also be used to model nature. Examples in this fields are Cellular Automata and Reaction Systems. The second one uses nature-inspired model to perform optimization tasks where exact algorithms are not applicable. Evolutionary algorithms, like Genetic Algorithms and Genetic Programming are some of the most prominent examples. The main aim of this thesis is the study of the dynamics of four different nature-inspired models with the goal of providing both an improvement in the single areas and a cross-pollination of methods and techniques. The four chosen models are: Genetic Algorithms. A traditional optimization techniques that is inspired by the Darwinian theory of evolution. Genetic Programming. A more recent technique, similar to classical Genetic Algorithms, that uses programs instead of fixed length binary strings as a representation method. Reaction Systems. A recently developed formalism inspired by chemical reactions. Cellular Automata. A extensively studied model made of a lattice of identical automata that can exchange information only locally. As for Genetic Algorithm, one of their main operators, the crossover, was studied. In particular, the minimum number of iteration of the crossover operator needed to produce certain individuals was investigated. As for Genetic Programming, two new measures to quantify the quality of the solution learned were developed. To better understand the dynamics of Genetic Programming a new benchmark, inspired by a similar benchmark in Genetic Algorithms, was introduced. Finally, a method to reduce the worst case space requirements of Semantic Genetic Programming from exponential to polynomial was devised. Some combinatorial properties of Reaction Systems were studied. Furthermore, an evolutionary version of Reaction Systems to be used for optimization was introduced. This new algorithm proved to have performances comparable to current state-of-the-art machine learning algorithms. The dynamical and computational properties of a variation of classical Cellular Automata, fully-Asynchronous Cellular Automata, have been studied. Furthermore, a first step in providing a more general framework to study asynchronicity in Cellular Automata has been made with the introduction of m-Asynchronous Cellular Automata. This thesis provides both improvements in these four different areas and a first step toward an exchange of methods and techniques between those areas.

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Benítez, César Manuel Vargas. "Contributions to the study of the protein folding problem using bioinspired computation and molecular dynamics." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/1211.

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O Problema de Dobramento de Proteínas (PDP) é considerado um dos desafios abertos mais importantes da Biologia e Bioinformática. Nesta tese, uma nova abordagem para simular os pathways de dobramento de proteínas é proposta onde, ao invés de utilizar a estrutura tridimensional da proteína, os estados de dobramento são representados por Mapas de Contatos (MC). Autômatos Celulares bidimensionais (2D-CA) são utilizados para simular o processo de dobramento, onde cada configuração representa um estado de dobramento e é obtida em relação ao seu estado predecessor e uma regra de transição. Determinar uma regra de transição para um dado comportamento dinâmico representa uma tarefa complexa. Portanto, é apresentada uma abordagem distribuida baseada em Programação de Expressão Gênica, chamada pGEP-CA. Funções de fitness específicas, baseadas em medidas de similaridade e simetria, são propostas. Também, um algoritmo heterogêneo paralelo Ecologicamente-inspirado é proposto. Este algoritmo, chamado pECO, é utilizado na reconstrução de estruturas a partir de MCs, usando o modelo 3D-AB off-lattice. De acordo com o nosso conhecimento, é apresentada a primeira aplicação de Dinâmica Molecular (DM) ao PFP, usando o mesmo modelo de proteínas. Experimentos foram realizados para verificar a adequabilidade das abordagens propostas. Além disto, uma breve análise sobre o balanceamento de carga de processamento das arquiteturas paralelas é apresentada. Os resultados mostram que as abordagens obtiveram resultados coerentes, sugerindo que são adequadas para o problema. As regras de transição induzidas pelo pGEP-CA são capazes de gerar 2D-CA que representam MCs corretamente. Sobre a abordagem pECO, os resultados demonstram que a combinação de abordagens evolucionárias concorrentes se beneficia do efeito da coevolução e das diferentes estratégias de busca. Além disto, pode ser observado que a abordagem de DM é capaz de levar a conformações que mimetizam propriedades biológicas, como a formação do núcleo hidrofóbico e os movimentos de respiração (breathing) das proteínas. Também foi observado que o processamento paralelo é essencial, permitindo a obtenção de resultados em tempos de processamento razoáveis. Finalmente, as conclusões e diversas direções de pesquisa são apresentadas. The Protein Folding Problem (PFP) is considered one of the most important open cha- llenges in Biology and Bioinformatics. In this thesis, a novel approach for simulating the protein folding pathways is proposed where, instead using the three-dimensional structure of the protein, the folding states are represented by Contact Maps (CM). A two-dimensional Cellular Automata (2D-CA) evolver is used to simulate the fol- ding process, where each configuration represents a folding state and it is obtained according to its predecessor and a transition rule. Since finding transition rules for simulating a dynamic behavior is a very difficult task, it is proposed a distributed Gene-Expression Programming (GEP)-based approach, called pGEP-CA. Specific fit- ness functions, based on similarity and symmetry measures, are proposed. Futhermore, a heterogeneous parallel Ecology-inspired algorithm is proposed. This algorithm, called pECO, is used for reconstructing the structures from the CMs, using the 3D-AB off-lattice model. Moreover, to the best of our knowledge, it is presented the first application of Molecular Dynamics (MD) to the PFP, using the same model of proteins. Experiments were done to evaluate the adequacy of the proposed approaches. Also, a brief analysis of the load balancing of the parallel architectures is presented. Results show that the approaches obtained coherent results, suggesting their adequacy for the problem. The induced transition rules by the pGEP-CA are able to generate 2D-CA that represent CMs correctly. Concerning the pECO approach, results show that the combination of concurrent evolutionary approaches took advantage of both the coevolution effect and the different search strategies. In addition, it can be observed that the MD approach is capable of displaying biological features such as the hydrophobic core formation and the protein breathing motion. Furthermore, it is observed that parallel processing was not only justified but also essential for obtaining results in reasonable processing time. Finally, concluding remarks and several research directions for future works are presented.

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Centi, Alessia. "Computational modelling and design of bioinspired silica materials." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27951.

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The use of bio-inspired methods for production of mesoporous silicas could lead to significant improvements in the synthetic conditions at which these materials are traditionally produced, removing the need for strong pH as well as high temperatures and pressures, and opening the way for milder treatments for template removal. However, due to the complexity of these systems, with many processes occurring simultaneously and high dependence on the specific synthetic conditions,obtaining a detailed description of their mechanism of formation based only on experimental methods is often very difficult. To overcome this difficulty, simulations methods, particularly molecular dynamics, have been developed and used to shed some light into this complex but fascinating problem. In the present thesis, the processes underlying the synthesis of bio-inspired silica materials are investigated at computational level by means of a multi-scale approach. This methodology has two major advantages: it enables to explore longer time and length scales, beyond the current limit of atomistic simulations, while allowing to maintain realism at the lower resolution levels, which are calibrated to match properties obtained at higher levels of theory. The work can be divided into two main parts. The first part aims to provide more insight into the synthesis of two early examples of bio-inspired materials(HMS and MSU-V), by means of a combination of atomistic and coarse-grained simulations. HMS and MSU-V materials share some common characteristics: they are both synthesised using amine surfactants as templates and a neutral templating route has been proposed to explain their formation. By simulating their synthesis at different pH values, it was possible to show that charged species are necessary to promote mesophase formation (disordered packing of rod-like micelles for HMS materials and lamellar structures for HMS). In both systems, in fact, neutral species produced phase separation of the templating materials into an unstructured and non-porous phase, and the lack of interactions with silicates indicates that these conditions cannot lead to structural organisation. Hence, molecular dynamics simulations reveal that, similarly to other mesoporous silicas and contrary to what has been previously hypothesised, charge matching interactions rather than hydrogen bonds are responsible for the self-assemble this class of materials. This knowledge is fundamental to provide further control over the properties of these solids and target their design for specific applications. In the second part, atomistic simulations are used to help elucidate the mechanism of template removal from a bio-inspired silica material by solvent extraction. This revealed that mild post-synthetic acid treatments allow to remove the templating additive by reducing, and eventually switching off, its interaction with the silica material. In agreement with experimental findings, which show that the majority of the additive is removed between pH 5 and 4, simulations indicate that at pH below 4.2 thermal fluctuations are sufficient to cause widespread release of the template. This result suggests that molecular simulations can be used as a simple and inexpensive tool for choosing appropriate solvents and experimental conditions in the material purification processes.

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Warren, Kristin M. "Passive Mechanical Lysis of Bioinspired Systems: Computational Modeling and Microfluidic Experiments." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/840.

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Many developed nations depend on oil for the production of gasoline, diesel, and natural gas. Meanwhile, oil shortages progress and bottlenecks in oil productions continue to materialize. These and other factors result in an energy crisis, which cause detrimental social and economic effects. Because of the impending energy crisis, various potential energy sources have developed including solar, wind, hydroelectric, nuclear, and biomass. Within the biomass sector for renewable energy sources, algae-based biofuels have become one of the most exciting, new feedstocks. Of the potential plant biofuel feedstocks, microalgae is attractive in comparison to other crops because it is versatile and doesn’t pose a threat to food sources. Despite its many advantages, the process to convert the microalgae into a biofuel is very complex and inefficient. All steps within the algae to biofuel production line must be optimized for microalgal biofuel to be sustainable. The production of biofuels from algae begins with selecting and cultivating an algae strain and giving it all the necessities to grow. The algae is then harvested and processed for specific uses. It is the harvesting or lysing step, which includes the extraction of the algal lipids, which is the biggest hindrance of algae being used as a cost effective energy source. The lysing step within the microalgal biofuel processing is of particular interest and will be the focus of this work. This work discusses the optimization of the biofuel production from microalgae biomass through computational and experimental approaches. With atomic force microscopy (AFM), a key mechanical property that would aid in the computational modeling of mechanical lysis in the in-house computational fluid dynamics (CFD) code, Particle-Surface Analysis Code (P-STAC), was determined. In P-STAC, various flow patterns were modeled that would most effectively lyse microalgal cells based on the shear stresses placed on the cells, which will be compared against microfluidic experiments using lipid specific dyes. These results would be influential in developing an energy-efficient method of processing microalgae for biofuel.

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David, Marcio Frayze. "Um algoritmo bioinspirado para agrupamento de dados." Universidade Presbiteriana Mackenzie, 2010. http://tede.mackenzie.br/jspui/handle/tede/1519.

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Made available in DSpace on 2016-03-15T19:38:16Z (GMT). No. of bitstreams: 1 Marcio Frayze David.pdf: 699315 bytes, checksum: 587538708d29252e3c3a8f5c46cbaa53 (MD5) Previous issue date: 2010-05-03 Fundo Mackenzie de Pesquisa This dissertation discusses the use of bio-inspired algorithms for data clustering, with emphasis on a model of emergent collective behavior of agents and a new clustering algorithm called cBoids is presented. The cBoids algorithm is a variation of the classic Boids model. In this new algorithm, each Boid represents an object from the data base and the three original rules from the Boids model were modified so that the objects of the database have influence on the behaviour of the Boids. Two new rules have also been proposed, responsible for the creation and destruction of centroids, which represent the formed clusters. In the experiments conducted in this work the algorithm was successfully tested on four databases. Esta dissertação aborda o uso de algoritmos bioinspirados para a tarefa de agrupamento de dados , com ênfase nos modelos de comportamentos emergentes coletivos de agentes e um novo algoritmo de agrupamento de dados chamado cBoids é apresentado. O algoritmo cBoids é uma variação do clássico modelo Boids. Neste novo algoritmo, cada Boid representa um objeto da base de dados e as três regras originais do modelo Boids foram alteradas para que os objetos da base de dados influenciem o comportamento dos Boids. Duas novas regras também foram propostas, responsáveis pela criação e destruição de centróides, que representam os clusters formados. Nos experimentos realizados nesta dissertação o algoritmo foi testado com sucesso em quatro bases de dados.

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Naldi, Murilo Coelho. "Agrupamento híbrido de dados utilizando algoritmos genéticos." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07112006-080351/.

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Técnicas de Agrupamento vêm obtendo bons resultados quando utilizados em diversos problemas de análise de dados, como, por exemplo, a análise de dados de expressão gênica. Porém, uma mesma técnica de agrupamento utilizada em um mesmo conjunto de dados pode resultar em diferentes formas de agrupar esses dados, devido aos possíveis agrupamentos iniciais ou à utilização de diferentes valores para seus parâmetros livres. Assim, a obtenção de um bom agrupamento pode ser visto como um processo de otimização. Esse processo procura escolher bons agrupamentos iniciais e encontrar o melhor conjunto de valores para os parâmetros livres. Por serem métodos de busca global, Algoritmos Genéticos podem ser utilizados durante esse processo de otimização. O objetivo desse projeto de pesquisa é investigar a utilização de Técnicas de Agrupamento em conjunto com Algoritmos Genéticos para aprimorar a qualidade dos grupos encontrados por algoritmos de agrupamento, principalmente o k-médias. Esta investigação será realizada utilizando como aplicação a análise de dados de expressão gênica. Essa dissertação de mestrado apresenta uma revisão bibliográfica sobre os temas abordados no projeto, a descrição da metodologia utilizada, seu desenvolvimento e uma análise dos resultados obtidos. Clustering techniques have been obtaining good results when used in several data analysis problems, like, for example, gene expression data analysis. However, the same clustering technique used for the same data set can result in different ways of clustering the data, due to the possible initial clustering or the use of different values for the free parameters. Thus, the obtainment of a good clustering can be seen as an optimization process. This process tries to obtain good clustering by selecting the best values for the free parameters. For being global search methods, Genetic Algorithms have been successfully used during the optimization process. The goal of this research project is to investigate the use of clustering techniques together with Genetic Algorithms to improve the quality of the clusters found by clustering algorithms, mainly the k-means. This investigation was carried out using as application the analysis of gene expression data, a Bioinformatics problem. This dissertation presents a bibliographic review of the issues covered in the project, the description of the methodology followed, its development and an analysis of the results obtained.

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Vargas, Patricia Amancio. "Sistemas computacionais bio-inspirados : sintese e aplicação em inteligencia computacional e homeostase artificial." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260780.

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Orientadores: Fernando Jose Von Zuben, Leandro Nunes de Castro Silva Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e Computação Made available in DSpace on 2018-08-06T14:08:06Z (GMT). No. of bitstreams: 1 Vargas_PatriciaAmancio_D.pdf: 4626705 bytes, checksum: b203a58e3f5f1c613db0787b3e396196 (MD5) Previous issue date: 2005 Resumo: Este trabalho propõe uma classificação circunstancial para sistemas complexos, incluindo uma estrutura unificada de descrição a ser empregada na análise e síntese de sistemas computacionais bio-inspirados. Como um ramo dos sistemas complexos organizados, os sistemas computacionais bio-inspirados admitem uma sub-divisão em sistemas de inteligência computacional e sistemas homeostáticos artificiais. Com base neste formalismo, duas abordagens híbridas são concebidas e aplicadas em problemas de navegação autônoma de robôs. A primeira abordagem envolve sistemas classificadores com aprendizado e sistemas imunológicos artificiais, visando explorar conjuntamente conceitos intrínsecos a sistemas complexos, como auto-organização, evolução e cognição dinâmica. Fundamentada nas interações neuro-imuno-endócrinas do corpo humano, a segunda abordagem propõe um novo modelo de sistema homeostático artificial, explorando mudanças de contexto e efeitos do meio sobre o comportamento autônomo de um robô móvel. Embora preliminares, os resultados obtidos envolvem simulação computacional em ambientes virtuais e alguns experimentos com robôs reais, permitindo extrair conclusões relevantes acerca do potencial das abordagens propostas e abrindo perspectivas para a síntese de sistemas complexos adaptativos de interesse prático Abstract: This work proposes a circumstantial classification for complex systems, including a unified description structure to be employed in the analysis and synthesis of biologically inspired computing metaphors. Considered as a branch of organized complex systems, these bio-inspired computing frameworks may be subdivided into computation intelligence systems and artificial homeostatic systems. Developed under this formalism, two novel hybrid systems are conceived and applied to robot autonomous navigation problems. The first approach involves learning classifier systems and artificial immune systems, in an attempt to investigate intrinsic concepts of complex systems as self-organization, evolution, and dynamic cognition. Drawn on the principles of the human nervous, immune and endocrine systems, the second approach envisages a new model of an artificial homeostatic system to explore context changes and environmental effects on the behaviour of an autonomous robotic agent. Though preliminary, the obtained results encompass computer simulation on virtual environments in addition to a number of real robot¿s experiments. Relevant conclusions can be invoked, mainly related to the potentiality of the proposed frameworks, thus opening attractive prospects for the synthesis of complex adaptive systems of practical interest Doutorado Engenharia de Computação Doutor em Engenharia Elétrica

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Grosberg, Anna. "A Bioinspired Computational Model of Cardiac Mechanics: Pathology and Development." Thesis, 2008. https://thesis.library.caltech.edu/2263/13/Ch2.pdf.

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In this work we study the function and development of the myocardium by creating models that have been stripped down to essentials. The model for the adult myocardium is based on the double helical band formation of the heart muscle fibers, observed in both histological studies and advanced DTMRI images. The muscle fibers in the embryonic myocardium are modeled as a helical band wound around a tubular chamber. We model the myocardium as an elastic body, utilizing the finite element method for the computations. We show that when the spiral band architecture is combined with spatial wave excitations the structure is twisted, thus driving the development of the embryonic heart into an adult heart. The double helical band model of the adult heart allows us to gain insight into the long standing paradox between the modest, by only 15 %, ability of muscle fibers to contract, and the large left ventricular volume ejection fraction of 60 %. We show that the double helical band structure is the essential factor behind such efficiency. Additionally, when the double helical band model is excited following the path of the Purkinje nerve network, physiological twist behavior is reproduced. As an additional validation, we show that when the stripped down double helical band is placed inside a sack of soft collagen-like tissue it is capable of producing physiologically high pressures. We further develop the model to understand the different factors behind the loss of efficiency in heart with a common pathology such as dilated cardiomyopathy. Using the stripped down model we are able to show that the change to fiber angle is the much more important factor to heart function than the change in gross geometry. This finding has the potential to greatly impact the strategy used in certain surgical procedures.

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Huang, Tzu-Hsuan, and 黃子軒. "Computational Phase-Field Modeling for Microstructural Evolution in Bioinspired Material from Freeze-Casting Process." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/10558826010888428956.

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碩士 國立臺灣大學 土木工程學研究所 104 In this research, a numerical model for microstructure evolution in the freeze-casting process is established. The theoretical mechanism behind the ceramic colloidal suspension solidification process is revealed; also, the relationship between the critical factors and the porous structures is quantitatively described. The model is benchmarked with experimental results and found to be in good agreement. In recent decades, freeze-casting, with an excellent flexibility in microstructure control, has attracted great attention as a potential manufacture method of bioinspired materials. Solidification of ice crystal in ceramic colloidal suspension is found as an important role in freeze-casting dynamical process. The formation of microstructure in solidification results in a dendritic pattern within the ice-template crystallization, determining the macroscopic properties of the materials. In this dissertation, a phase-field model is proposed to describe the crystallization of the ice-template and the particle evolution during the solidification. The ceramic particle is regarded as a mass flow, namely a concentration field. Following the 1D freeze-casting model by Peppin and a general phase-field model for binary alloy casting, a sharp interface model is built up and transformed into a continuous boundary value problem by the phase-field method. The adaptive finite element technique is employed to decrease the computational cost; furthermore, the algorithm reconstructs the details of microstructure, and the influence of the anisotropy may be exhibited. Finally, the numerical results are compared with the experimental data, which demonstrate a good agreement. Both results identify several essential physical parameters controlling the ice-template morphology and the formation of microstructure, such as front velocity, temperature gradient, and particle concentration. The first numerical model to simulate the structural detail in freeze-casting is constructed in the study; moreover, significant perspectives on designing the bioinspired material is presented.

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Livres sur le sujet "Bioinspired Computation"

Neumann, Frank, and Carsten Witt. Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3.

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Ferrández Vicente, José Manuel, José Ramón Álvarez-Sánchez, Félix de la Paz López, Fco Javier Toledo-Moreo, and Hojjat Adeli, eds. Bioinspired Computation in Artificial Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18833-1.

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Neumann, Frank. Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity. Springer-Verlag Berlin Heidelberg, 2010.

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Mira, José, José Manuel Ferrández, José R. Álvarez, Félix de la Paz, and F. Javier Toledo, eds. Bioinspired Applications in Artificial and Natural Computation. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02267-8.

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David, Hutchison. Bioinspired Applications in Artificial and Natural Computation: Third International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2009, Santiago de Compostela, Spain, June 22-26, 2009, Proceedings, Part II. Springer Berlin Heidelberg, 2009.

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Cabestany, Joan, Alberto Prieto, and Francisco Sandoval, eds. Computational Intelligence and Bioinspired Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b136983.

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Silhavy, Radek, ed. Artificial Intelligence and Bioinspired Computational Methods. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51971-1.

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Ferrández, José Manuel. New Challenges on Bioinspired Applications: 4th International Work-conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, La Palma, Canary Islands, Spain, May 30 - June 3, 2011. Proceedings, Part II. Springer Berlin Heidelberg, 2011.

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Vega, Francisco Fernández de. Parallel Architectures and Bioinspired Algorithms. Springer Berlin Heidelberg, 2012.

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Joan, Cabestany, Prieto A. 1945-, and Sandoval Francisco, eds. Computational intelligence and bioinspired systems: 8th International Work-Conference on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltru, Barcelona, Spain, June 8-10, 2005 ; proceedings. Springer, 2005.

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Chapitres de livres sur le sujet "Bioinspired Computation"

Neumann, Frank, and Carsten Witt. "Combinatorial Optimization and Computational Complexity." In Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3_2.

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Neumann, Frank, and Carsten Witt. "Shortest Paths." In Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3_8.

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Neumann, Frank, and Carsten Witt. "Eulerian Cycles." In Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3_9.

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Neumann, Frank, and Carsten Witt. "Introduction." In Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3_1.

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Neumann, Frank, and Carsten Witt. "Multi-objective Minimum Spanning Trees." In Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3_10.

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Neumann, Frank, and Carsten Witt. "Minimum Spanning Trees Made Easier." In Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3_11.

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Neumann, Frank, and Carsten Witt. "Covering Problems." In Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3_12.

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Neumann, Frank, and Carsten Witt. "Cutting Problems." In Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3_13.

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Neumann, Frank, and Carsten Witt. "Stochastic Search Algorithms." In Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3_3.

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Neumann, Frank, and Carsten Witt. "Analyzing Stochastic Search Algorithms." In Bioinspired Computation in Combinatorial Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16544-3_4.

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Actes de conférences sur le sujet "Bioinspired Computation"

Neumann, Frank, and Carsten Witt. "Bioinspired computation in combinatorial optimization." In Proceeding of the fifteenth annual conference companion. ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2466738.

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Witt, Carsten. "Bioinspired computation in combinatorial optimization." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2605353.

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Neumann, Frank, and Carsten Witt. "Bioinspired computation in combinatorial optimization." In the fourteenth international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330928.

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da Silveira, Lucas A., Jose L. Soncco-Alvarez, Thaynara A. de Lima, and Mauricio Ayala-Rincon. "Behavior of Bioinspired Algorithms in Parallel Island Models." In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185732.

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Cotta, Carlos, Antonio J. Fernández-Leiva, Francisco Fernández de Vega, et al. "Ephemeral Computing and Bioinspired Optimization - Challenges and Opportunities." In 7th International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005628903190324.

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Fu, Qinbing, Jigen Peng, and Shigang Yue. "Bioinspired Contrast Vision Computation for Robust Motion Estimation Against Natural Signals." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533680.

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Hauptvogel, Michael, Jordi Madrenas, and J. Manuel Moreno. "SpiNDeK: An Integrated design tool for the multiprocessor emulation of complex bioinspired spiking neural networks." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4982941.

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Kim, Yujin. "Bioinspired Modularity in Evolutionary Computation and a Rule-Based Logic Design Solutions for Shared Office Space." In 37 Education and Research in Computer Aided Architectural Design in Europe and XXIII Iberoamerican Society of Digital Graphics, Joint Conference (N. 1). Editora Blucher, 2019. http://dx.doi.org/10.5151/proceedings-ecaadesigradi2019_138.

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Sharp, Nicholas, Virginia Hagen-Gates, Evan Hemingway, et al. "Computational Analysis of Undulatory Batoid Motion for Underwater Robotic Propulsion." In ASME 2014 4th Joint US-European Fluids Engineering Division Summer Meeting collocated with the ASME 2014 12th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fedsm2014-22077.

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Underwater fish of the class Batoidea, commonly known as rays and skates, use large cartilaginous wings to propel themselves through the water. This motion is of great interest in bioinspired robotics as an alternative propulsion mechanism. Prior research has focused primarily on the oscillating kinematics used by some species which resembles flapping; this study investigates undulatory motion induced by propagating sinusoidal waves along the fin. An analytical model of undulatory kinematics is presented and correlated with biological literature, and the model is then simulated via unsteady computational fluid dynamics and multiparticle collision dynamics. A bioinspired robot, Batoid Underwater Robotics Testbed (BURT), was developed to test the kinematics of the undulating propulsion system proposed. Finally, BURT was utilized as a platform to investigate engineering challenges in undulating Batoid robotics.

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Jara, Jessica, Danilo Chavez, and Oscar Camacho. "Linear System Identifier Computational Tool Based on Emerging Bioinspired Systems." In 2019 International Conference on Information Systems and Computer Science (INCISCOS). IEEE, 2019. http://dx.doi.org/10.1109/inciscos49368.2019.00039.

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