Aplicaciones de los modelos de predicción bio-inspirados en la administración

Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.

A navigating rat animat

This paper presents a new rat animat, a rat-sized bio-inspired robot platform currently being developed for embodied cognition and neuroscience research. The rodent animat is 150mm x 80mm x 70mm and has a different drive, visual, proximity, and odometry sensors, x86 PC, and LCD interface. The rat animat has a bio-inspired rodent navigation and mapping system called RatSLAM which demonstrates the capabilities of the platform and framework. A case study is presented of the robot's ability to learn the spatial layout of a figure of eight laboratory environment, including its ability to close physical loops based on visual input and odometry. A firing field plot similar to rodent 'non-conjunctive grid cells' is shown by plotting the activity of an internal network. Having a rodent animat the size of a real rat allows exploration of embodiment issues such as how the robot's sensori-motor systems and cognitive abilities interact. The initial observations concern the limitations of the deisgn as well as its strengths. For example, the visual sensor has a narrower field of view and is located much closer to the ground than for other robots in the lab, which alters the salience of visual cues and the effectiveness of different visual filtering techniques. The small size of the robot relative to corridors and open areas impacts on the possible trajectories of the robot. These perspective and size issues affect the formation and use of the cognitive map, and hence the navigation abilities of the rat animat.

Autonomous multisensor calibration and closed-loop fusion for SLAM

The vast majority of current robot mapping and navigation systems require specific well-characterized sensors that may require human-supervised calibration and are applicable only in one type of environment. Furthermore, if a sensor degrades in performance, either through damage to itself or changes in environmental conditions, the effect on the mapping system is usually catastrophic. In contrast, the natural world presents robust, reasonably well-characterized solutions to these problems. Using simple movement behaviors and neural learning mechanisms, rats calibrate their sensors for mapping and navigation in an incredibly diverse range of environments and then go on to adapt to sensor damage and changes in the environment over the course of their lifetimes. In this paper, we introduce similar movement-based autonomous calibration techniques that calibrate place recognition and self-motion processes as well as methods for online multisensor weighting and fusion. We present calibration and mapping results from multiple robot platforms and multisensory configurations in an office building, university campus, and forest. With moderate assumptions and almost no prior knowledge of the robot, sensor suite, or environment, the methods enable the bio-inspired RatSLAM system to generate topologically correct maps in the majority of experiments.

Fly-eye inspired superhydrophobic anti-fogging inorganic nanostructures

Fly-eye bio-inspired inorganic nanostructures are synthesized via a two-step self-assembly approach, which have low contact angle hysteresis and excellent anti-fogging properties, and are promising candidates for anti-freezing/fogging materials to be applied in extreme and hazardous environments.

Computational algorithms inspired by biological processes and evolution

In recent times computational algorithms inspired by biological processes and evolution are gaining much popularity for solving science and engineering problems. These algorithms are broadly classified into evolutionary computation and swarm intelligence algorithms, which are derived based on the analogy of natural evolution and biological activities. These include genetic algorithms, genetic programming, differential evolution, particle swarm optimization, ant colony optimization, artificial neural networks, etc. The algorithms being random-search techniques, use some heuristics to guide the search towards optimal solution and speed-up the convergence to obtain the global optimal solutions. The bio-inspired methods have several attractive features and advantages compared to conventional optimization solvers. They also facilitate the advantage of simulation and optimization environment simultaneously to solve hard-to-define (in simple expressions), real-world problems. These biologically inspired methods have provided novel ways of problem-solving for practical problems in traffic routing, networking, games, industry, robotics, economics, mechanical, chemical, electrical, civil, water resources and others fields. This article discusses the key features and development of bio-inspired computational algorithms, and their scope for application in science and engineering fields.

Biomimetic Cloning of Quantum Observables

We propose a bio-inspired sequential quantum protocol for the cloning and preservation of the statistics associated to quantum observables of a given system. It combines the cloning of a set of commuting observables, permitted by the no-cloning and no-broadcasting theorems, with a controllable propagation of the initial state coherences to the subsequent generations. The protocol mimics the scenario in which an individual in an unknown quantum state copies and propagates its quantum information into an environment of blank qubits Finally, we propose a realistic experimental implementation of this protocol in trapped ions.

Agrupamento espacial em robótica de enxame.

Os Sistemas Multi-Robôs proporcionam vantagens sobre um robô individual, quando da realização de uma tarefa com maiores velocidade, precisão e tolerância a falhas. Os estudos dos comportamentos sociais na natureza têm permitido desenvolver algoritmos bio-inspirados úteis na área da robótica de enxame. Seguindo instruções simples e repetitivas, grupos de robôs, fisicamente limitados, conseguem solucionar problemas complexos. Quando existem duas ou mais tarefas a serem realizadas e o conjunto de robôs é heterogêneo, é possível agrupá-los de acordo com as funcionalidades neles disponíveis. No caso em que o conjunto de robôs é homogêneo, o agrupamento pode ser realizado considerando a posição relativa do robô em relação a uma tarefa ou acrescentando alguma característica distintiva. Nesta dissertação, é proposta uma técnica de clusterização espacial baseada simplesmente na comunicação local de robôs. Por meio de troca de mensagens entre os robôs vizinhos, esta técnica permite formar grupos de robôs espacialmente próximos sem precisar movimentar os robôs. Baseando-se nos métodos de clusterização de fichas, a técnica proposta emprega a noção de fichas virtuais, que são chamadas de cargas, sendo que uma carga pode ser estática ou dinâmica. Se uma carga é estática permite determinar a classe à qual um robô pertence. Dependendo da quantidade e do peso das cargas disponíveis no sistema, os robôs intercambiam informações até alcançar uma disposição homogênea de cargas. Quando as cargas se tornam estacionárias, é calculada uma densidade que permite guiar aquelas que estão ainda em movimento. Durante as experiências, foi observado visualmente que as cargas com maior peso acabam se agrupando primeiro enquanto aquelas com menor peso continuam se deslocando no enxame, até que estas cargas formem faixas de densidades diferenciadas para cada classe, alcançando assim o objetivo final que é a clusterização dos robôs.

Swimming direction reversal of flagella through ciliary motion of mastigonemes.

Bio-inspired designs can provide an answer to engineering problems such as swimming strategies at the micron or nano-scale. Scientists are now designing artificial micro-swimmers that can mimic flagella-powered swimming of micro-organisms. In an application such as lab-on-a-chip in which micro-object manipulation in small flow geometries could be achieved by micro-swimmers, control of the swimming direction becomes an important aspect for retrieval and control of the micro-swimmer. A bio-inspired approach for swimming direction reversal (a flagellum bearing mastigonemes) can be used to design such a system and is being explored in the present work. We analyze the system using a computational framework in which the equations of solid mechanics and fluid dynamics are solved simultaneously. The fluid dynamics of Stokes flow is represented by a 2D Stokeslets approach while the solid mechanics behavior is realized using Euler-Bernoulli beam elements. The working principle of a flagellum bearing mastigonemes can be broken up into two parts: (1) the contribution of the base flagellum and (2) the contribution of mastigonemes, which act like cilia. These contributions are counteractive, and the net motion (velocity and direction) is a superposition of the two. In the present work, we also perform a dimensional analysis to understand the underlying physics associated with the system parameters such as the height of the mastigonemes, the number of mastigonemes, the flagellar wave length and amplitude, the flagellum length, and mastigonemes rigidity. Our results provide fundamental physical insight on the swimming of a flagellum with mastigonemes, and it provides guidelines for the design of artificial flagellar systems.

Templates and anchors for antenna-based wall following in cockroaches and robots

The interplay between robotics and neuromechanics facilitates discoveries in both fields: nature provides roboticists with design ideas, while robotics research elucidates critical features that confer performance advantages to biological systems. Here, we explore a system particularly well suited to exploit the synergies between biology and robotics: high-speed antenna-based wall following of the American cockroach (Periplaneta americana). Our approach integrates mathematical and hardware modeling with behavioral and neurophysiological experiments. Specifically, we corroborate a prediction from a previously reported wall-following template - the simplest model that captures a behavior - that a cockroach antenna-based controller requires the rate of approach to a wall in addition to distance, e.g., in the form of a proportional-derivative (PD) controller. Neurophysiological experiments reveal that important features of the wall-following controller emerge at the earliest stages of sensory processing, namely in the antennal nerve. Furthermore, we embed the template in a robotic platform outfitted with a bio-inspired antenna. Using this system, we successfully test specific PD gains (up to a scale) fitted to the cockroach behavioral data in a "real-world" setting, lending further credence to the surprisingly simple notion that a cockroach might implement a PD controller for wall following. Finally, we embed the template in a simulated lateral-leg-spring (LLS) model using the center of pressure as the control input. Importantly, the same PD gains fitted to cockroach behavior also stabilize wall following for the LLS model. © 2008 IEEE.

Self-organization, embodiment, and biologically inspired robotics.

Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility.

Treatment of estrogens and androgens in dairy wastewater by a constructed wetland system.

Constructed wetland systems (CWS) have been used as a low cost bio-filtration system to treat farm wastewater. While studies have shown that CWS are efficient in removing organic compounds and pathogens, there is limited data on the presence of hormones in this type of treatment system. The objective of this study was to evaluate the ability of the CWS to reduce estrogenic and androgenic hormone concentration in dairy wastewater. This was achieved through a year long study on dairy wastewater samples obtained froma surface flow CWS. Analysis of hormonal levels was performed using a solid phase extraction (SPE) sample clean-up method, combined with reporter gene assays (RGAs) which incorporate relevant receptors capable of measuring total estrogenic or androgenic concentrations as low as 0.24 ng L1 and 6.9 ng L1 respectively. Monthly analysis showed a mean removal efficiency for estrogens of 95.2%, corresponding to an average residual concentration of 3.2 ng L1 17b-estradiol equivalent (EEQ), below the proposed lowest observable effect concentration (LOEC) of 10 ng L1. However, for one month a peak EEQ concentration of 115 ng L1 was only reduced to 18.8 ng L1. The mean androgenic activity peaked at 360 ng L1 and a removal efficiency of 92.1% left an average residual concentration of 32.3 ng L1 testosterone equivalent (TEQ). The results obtained demonstrate that this type of CWS is an efficient system for the treatment of hormones in dairy wastewater. However, additional design improvements may be required to further enhance removal efficiency of peak hormone concentrations.

Robot and insect navigation by polarized skylight

A study of a large number of published experiments on the behaviour of insects navigating by skylight has led to the design of a system for navigation in lightly clouded skies, suitable for a robot or drone. The design is based on the measurement of the directions in the sky at which the polarization angle, i.e. the angle χ between the polarized E-vector and the meridian, equals ±π/4 or ±(π/4 + π/3) or ±(π/4 - π/3). For any one of these three options, at any given elevation, there are usually 4 such directions and these directions can give the azimuth of the sun accurately in a few short steps, as an insect can do. A simulation shows that this compass is accurate as well as simple and well suited for an insect or robot. A major advantage of this design is that it is close to being invariant to variable cloud cover. Also if at least two of these 12 directions are observed the solar azimuth can still be found by a robot, and possibly by an insect.

Collective intelligence on dynamic manufacturing scheduling optimization

Swarm Intelligence (SI) is a growing research field of Artificial Intelligence (AI). SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviours of insects and of other animals. This paper presents hybridization and combination of different AI approaches, like Bio-Inspired Techniques (BIT), Multi-Agent systems (MAS) and Machine Learning Techniques (ML T). The resulting system is applied to the problem of jobs scheduling to machines on dynamic manufacturing environments.

Sels d’imidazolium avec des anions catalytiques : vers le développement de nouveaux catalyseurs bio-hybrides actifs en milieu liquide ionique

Les liquides ioniques connaissent depuis quelques décennies un essor particulier en raison de leurs nombreuses propriétés physico-chimiques intéressantes, telles qu’une faible pression de vapeur saturante, une viscosité limitée, une faible miscibilité avec la plupart des solvants communs, ou encore des propriétés d’agencement supramoléculaire, qui en font des outils puissants dans de nombreux domaines de la chimie. Les sels d’imidazolium représentent la plus grande famille de liquides ioniques à ce jour. Leur modulabilité leur permet d’être dérivés pour de nombreuses applications spécifiques, notamment en synthèse organique, où ils sont utilisés majoritairement comme solvants, et plus récemment comme catalyseurs. Les travaux présentés dans cette thèse se concentrent sur leur utilisation en synthèse organique, à la fois comme solvants et principalement comme catalyseurs chiraux, catalyseurs pour lesquels l’anion du sel est l’espèce catalytique, permettant d’ajouter de la flexibilité et de la mobilité au système. En tirant parti de la tolérance des liquides ioniques envers la majorité des macromolécules naturelles, l’objectif principal des travaux présentés dans cette thèse est le développement d’un nouveau type de catalyseur bio-hybride reposant sur l’encapsulation d’un sel d’imidazolium dans une protéine. Par le biais de la technologie biotine-avidine, l’inclusion supramoléculaire de sels d’imidazolium biotinylés portant des contre-anions catalytiques dans l’avidine a été réalisée et exploitée en catalyse. Dans un premier temps, le développement et l’étude de deux sels de 1-butyl-3-méthylimidazolium possédant des anions chiraux dérivés de la trans-4-hydroxy-L-proline sont rapportés, ainsi que leur comportement dans des réactions énantiosélectives d’aldol et d’addition de Michael. Ces types de composés se sont révélés actifs et performants en milieu liquide ionique. Dans un second temps, la préparation de sels d’imidazolium dont le cation est biotinylé et portant un contre-anion achiral, a été réalisée. Le comportement de l’avidine en milieu liquide ionique et son apport en termes de chiralité sur le système bio-hybride ont été étudiés. Les résultats montrent le rôle crucial des liquides ioniques sur la conformation de la protéine et l’efficacité du catalyseur pour des réactions d’aldol. Dans un dernier temps, l’influence de la structure du cation et de l’anion sur le système a été étudiée. Différents espaceurs ont été introduits successivement dans les squelettes cationiques et anioniques des sels d’imidazolium biotinylés. Dans le cas du cation, les résultats ne révèlent aucune influence majeure sur l’efficacité du catalyseur. La structure de l’anion se montre cependant beaucoup plus importante : la préparation de différents catalyseurs bio-hybrides possédant des anions aux propriétés physico-chimiques différentes a permis d’obtenir de plus amples informations sur le mode de fonctionnement du système bio-hybride et de la coopérativité entre l’avidine et l’anion du sel d’imidazolium.La nature ionique de la liaison cation-anion offrant une liberté de mouvement accrue à l’anion dans la protéine, la tolérance à différents substrats a également été abordée après optimisation du système.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.