A survey on bio-inspired networking

The developments in the communication and networking technologies have yielded many existing and envisioned information network architectures such as cognitive radio networks, sensor and actor networks, quantum communication networks, terrestrial next generation Internet, and InterPlaNetary Internet. However, there exist many common significant challenges to be addressed for the practical realization of these current and envisioned networking paradigms such as the increased complexity with large scale networks, their dynamic nature, resource constraints, heterogeneous architectures, absence or impracticality of centralized control and infrastructure, need for survivability, and unattended resolution of potential failures. These challenges have been successfully dealt with by Nature, which, as a result of millions of years of evolution, have yielded many biological systems and processes with intrinsic appealing characteristics such as adaptivity to varying environmental conditions, inherent resiliency to failures and damages, successful and collaborative operation on the basis of a limited set of rules and with global intelligence which is larger than superposition of individuals, self-organization, survivability, and evolvability. Inspired by these characteristics, many researchers are currently engaged in developing innovative design paradigms to address the networking challenges of existing and envisioned information systems. In this paper, the current state-of-the-art in bio-inspired networking is captured. The existing bio-inspired networking and communication protocols and algorithms devised by looking at biology as a source of inspiration, and by mimicking the laws and dynamics governing these systems are presented along with open research issues for the bio-inspired networking. Furthermore, the domain of bio-inspired networking is linked to the emerging research domain of nanonetworks, which bring a set of unique challenges. The objective of this survey is to provide better understanding of the potentials for bio-inspired networking which is currently far from being fully recognized, and to motivate the research community to further explore this timely and exciting topic.     

Information enhancement for interpreting competitive learning

In this paper, we propose a new method called information enhancement to interpret internal representations of competitive learning. We consider competitive learning as a process of mutual information maximisation on input patterns. Then, we examine to what extent this mutual information can be increased or decreased by focusing upon or enhancing some elements in a network. If this enhancement for the elements increases information on input patterns, these elements possess more information on input patterns. Thus, we only have to carefully examine those elements in a network. We applied the method to an artificial problem, the Iris problem and an air pollution problem. In all problems, we succeeded in extracting important features in patterns. In addition, final maps were better than those obtained by the conventional self-organising map. We can say that this is the first step towards the full understanding of internal representations in competitive learning.     

Patterns Approach to Product Information Systems Engineering

This paper deals with the application of the pattern approach to product information systems (PIS) engineering. Two kind of patterns are distinguished: business patterns used for specification and providing solutions for application field problems, and software patterns used for implementation and providing solutions for technical problems (software). Particular attention is given to identifying and specifying different business patterns. The main focus is on the activity of design for reuse, i.e. discovery of business patterns and their integration in a pattern catalogue. The first step consisted of a field analysis providing a common terminology and a semantic of the principal concepts managed in PIS and proposing various models to fix these concepts. It forms a basis for exploring the problems frequently occurring during PIS specification. A pattern catalogue is then proposed to solve the identified problems.     

仿生隔振器设计方法研究进展

基于仿生学的思想,通过模仿生物系统的结构几何构成特性,创新优化隔振模型是当今隔振领域的热门问题.本文基于仿生灵感来源的不同,对现有的仿生隔振器进行了系统的分类,并对不同设计原理的仿生隔振器恢复力本构与隔振性能做了详细的阐述和对比,旨在通过隔振器隔振频带的横向对比厘清几何构型与可调参数的影响,以此厘清隔振器几何设计和隔振性能的关系.并且,本文展望了未来仿生隔振器的发展前景和研究方向.     

Hybrid bio-inspired techniques for land cover feature extraction: A remote sensing perspective

Recent advances in the theoretical and practical implementations of biogeography have led to the exploration of new bio-inspired techniques which can prove to be the building blocks of hybrid bio-inspired techniques. This aspect was discovered while considering the exploration of bio-inspired intelligence for developing generic optimization algorithms that can be adapted for performing the given land cover feature extraction task at hand. Certain bio-inspired techniques when integrated with the existing optimization techniques can drastically improve their optimization capability hence leading to better feature extraction. In this paper, we propose a generic architectural framework of a hybrid biologically inspired technique that is characterized by its capability to adapt according to the database of expert knowledge for a more efficient, focused and refined feature extraction. Since our hybrid feature extractor possesses intelligence for selective cluster identification for application of either of the constituent techniques which is in turn based on an inefficiency analysis, we term our classifier as the hybrid bio-inspired pattern analysis based intelligent classifier. Our hybrid classifier combines the strengths of the modified BBO Technique for land cover feature extraction with the Hybrid ACO2/PSO Technique for a more refined land cover feature extraction. The algorithm has been tested for for the remote sensing application of land cover feature extraction where we have applied it to the 7-Band carto-set satellite image of size 472 × 546 of the Alwar area in Rajasthan and gives far better feature extraction results than the original biogeography based land cover feature extractor [20] and the other soft computing techniques such as ACO, Hybrid PSO-ACO2, Hybrid ACO-BBO Classifier, Fuzzy sets, Rough-Fuzzy Tie up etc.. The 7-band Alwar Image is a benchmark image for testing the performance of a bio-inspired classifier on multi-spectral satellite images since this image is a complete image in the sense that it contains all the land cover features that we need to extract and hence land cover feature extraction results are demonstrated and compared using this image as the standard image.     

Design of the speed controller for sensorless electric drives based on AI techniques: a comparative study

The paper investigates applicability of different artificial intelligence (AI) techniques in the design of a speed controller for electric drives. A speed-sensorless drive system is considered. A controller structure consisting of a load torque observer, a speed estimator and a speed predictor is developed. Next, different AI based approaches to speed controller design are investigated. The speed controllers based on (1) feed-forward neural network, (2) neuro-fuzzy network, and (3) self-organising Takagi–Sugeno (TS) rule based model are designed. A comparative analysis of the drive behaviour with these three types of AI based speed controllers is performed. In addition, a comparison is made with respect to the drive performance obtained with a conventional optimised PI controller. A detailed simulation study of a number of transients indicates that the best performance, in terms of accuracy and computational complexity, is offered by the self-organising Takagi–Sugeno controller. The controllers are developed and tested for a plant comprising a variable-speed separately excited DC motor.     

Self-organising swarm (SOSwarm)

This paper introduces a novel version of the particle swarm optimisation (PSO) algorithm which we call self-organising swarm SOSwarm. SOSwarm can be used for unsupervised learning. In the algorithm, input vectors are projected into a lower-dimensional map space producing a visual representation of the input data in a manner similar to a self-organising map (SOM). In SOSwarm, particles react to input data during the learning process by modifying their velocities using an adaptation of the PSO velocity update function. SOSwarm is successfully applied to ten benchmark problems drawn from the UCI Machine Learning repository. The paper also demonstrates how the canonical SOM can be explored within the PSO paradigm. Illustrating this linkage between the heretofore distinct literatures of SOM and PSO opens up several new avenues of research for the development of novel self-organising algorithms.     

Obstacles for cognitive analogy in analysis and synthesis in microsystems development

In most cases, product development is not inventing a product completely new and from scratch, it typically either adds a few newly developed sub-functions to mainly known sub-functions, or it generates invention based on new combinations of already existing and slightly modified sub-functions. This synthesis is ideally based on a preceding and comprehensive system analysis that is necessary to define promising directions for advancement in further product development. In analysis as well as in synthesis the design engineer needs creativity—either to be able to recognize which functions are realized in a given system that is subject to analysis or to be able to think up new ways how a desired function could be realized in a system. The main cognitive mechanism for creativity is analogy and this mechanism in turn is based on schemas and mental models. In microsystems development, micro specific obstacles accrue when using analogies. In this work, the authors derive the nature and source of these obstacles and their implications on microsystems development. To overcome these obstacles, a functions catalogue is proposed. This catalogue can directly be used to add an external source for analogies during system synthesis. By transferring a method called geometrical similarity search to microsystems technology and combining it with the functions catalogue, system analysis can be supported as well.     

Collaboration and co-ordination in mature eXtreme programming teams

Mature eXtreme programming (XP) teams are highly collaborative and self-organising. In previous studies, we have observed that these teams rely on two apparently simple mechanisms of co-ordination and collaboration: story cards and the Wall. Story cards capture and embody the user stories which form the basis of implementation, while the Wall is a physical space used to organise and display the cards being implemented during the current development cycle (called an iteration). In this paper, we analyse the structure and use of story cards and the Wall in three mature XP teams, using a distributed cognition approach. The teams work in different commercial organisations developing different systems, yet we find significant similarities between their use of these two artefacts. Although simple, teams use the cards and the Wall in sophisticated ways to represent and communicate information that is vital to support their activities. We discuss the significance of the physical medium for the story cards and the Wall in an XP team and discuss the considerations that need to be taken into account for the design of technology to support the teams.     

Design Factors for Summary Visualization in Visual Analytics

Data summarization allows analysts to explore datasets that may be too complex or too large to visualize in detail. Designers face a number of design and implementation choices when using summarization in visual analytics systems. While these choices influence the utility of the resulting system, there are no clear guidelines for the use of these summarization techniques. In this paper, we codify summarization use in existing systems to identify key factors in the design of summary visualizations. We use quantitative content analysis to systematically survey examples of visual analytics systems and enumerate the use of these design factors in data summarization. Through this analysis, we expose the relationship between design considerations, strategies for data summarization in visualization systems, and how different summarization methods influence the analyses supported by systems. We use these results to synthesize common patterns in real‐world use of summary visualizations and highlight open challenges and opportunities that these patterns offer for designing effective systems. This work provides a more principled understanding of design practices for summary visualization and offers insight into underutilized approaches.     

Computational intelligence approach to PID controller design using the universal model

Despite the popularity of PID (Proportional-Integral-Derivative) controllers, their tuning aspect continues to present challenges for researches and plant operators. Various control design methodologies have been proposed in the literature, such as auto-tuning, self-tuning, and pattern recognition. The main drawback of these methodologies in the industrial environment is the number of tuning parameters to be selected. In this paper, the design of a PID controller, based on the universal model of the plant, is derived, in which there is only one parameter to be tuned. This is an attractive feature from the viewpoint of plant operators. Fuzzy and neural approaches - bio-inspired methods in the field of computational intelligence - are used to design and assess the efficiency of the PID controller design based on differential evolution optimization in nonlinear plants. The numerical results presented herein indicate that the proposed bio-inspired design is effective for the nonlinear control of nonlinear plants.     

An adaptive locomotion controller for a hexapod robot: CPG,kinematics and force feedback

Insects can perform versatile locomotion behaviors such as multiple gaits, adapting to different terrains, fast escaping, etc. However, most of the existing bio-inspired legged robots do not possess such walking ability, especially when they walk on irregular terrains. To tackle this challenge, a central pattern generator （CPG）-based locomotion control methodology is proposed, integrated with a contact force feedback function. In this approach, multiple gaits are produced by the CFG module. After passing through a post-processing circuit and a delay-line, the control signal is fed into six trajectory generators to generate predefined feet trajectories for the six legs. Then, force feedback is employed to adjust these trajectories so as to adapt the robot to rough terrains. Finally the regulated trajectories are sent to inverse kinematics modules such that the position control instructions are generated to control the actuators. In both simulations and real robot experiments, we consistently show that the robot can perform sophisticated walking patterns. What is more, the robot can use the force feedback mechanism to deal with the irregularity in rough terrain. With this mechanism, the stability and adaptability of the robot are enhanced. In conclusion, the CPG-base control is an effective approach for legged robots and the force feedback approach is able to improve walking ability of the robots, especially when they walk on irregular terrains.     

Deployment of robust wireless sensor networks using gene regulatory networks: An isomorphism-based approach

The communication between nodes in a Wireless Sensor Network (WSN) may fail due to different factors, such as hardware malfunctions, energy depletion, temporal variations of the wireless channel and interference. To maximize efficiency, the sensor network deployment must be robust and resilient to such failures. One effective solution to this problem is to exploit a bio-inspired approach based on Gene Regulatory Networks (GRNs). Owing to million years of evolution, GRNs display intrinsic properties of adaptation and robustness, thus making them suitable for dynamic network environments. In this article, we exploit the genetic structure of real organisms to deploy bio-inspired WSNs that are isomorphic to certain GRN sub-networks. Exhaustive structural analysis, simulations and experimental results on a WSN testbed demonstrate that bio-inspired WSNs are resilient to node and link failures and offer better performance than existing solutions for robust WSNs.     

Visualizing Design Patterns in Their Applications and Compositions

Design patterns are generic design solutions that can be applied and composed in different applications where pattern-related information is generally implicit in the Unified Modeling Language (UML) diagrams of the applications. It is unclear in which pattern instances each modeling element, such as class, attribute, and operation, participates. It is hard for a designer to find the design patterns used in an application design. Consequently, the benefits of design patterns are compromised because designers cannot communicate with each other in terms of the design patterns they used and their design decisions and trade-offs. In this paper, we present a UML profile that defines new stereotypes, tagged values, and constraints for tracing design patterns in UML diagrams. These new stereotypes and tagged values are attached to a modeling element to explicitly represent the role the modeling element plays in a design pattern so that the user can identify the pattern in a UML diagram. Based on this profile, we also develop a Web service (tool) for explicitly visualizing design patterns in UML diagrams. With this service, users are able to visualize design patterns in their applications and compositions because pattern-related information can be dynamically displayed. A real-world case study and a comparative experiment with existing approaches are conducted to evaluate our approach.     

Fast bio-inspired computation using a GPU-based systemic computer

Biology is inherently parallel. Models of biological systems and bio-inspired algorithms also share this parallelism, although most are simulated on serial computers. Previous work created the systemic computer – a new model of computation designed to exploit many natural properties observed in biological systems, including parallelism. The approach has been proven through two existing implementations and many biological models and visualizations. However to date the systemic computer implementations have all been sequential simulations that do not exploit the true potential of the model. In this paper the first ever parallel implementation of systemic computation is introduced. The GPU Systemic Computation Architecture is the first implementation that enables parallel systemic computation by exploiting the multiple cores available in graphics processors. Comparisons with the serial implementation when running two programs at different scales show that as the number of systems increases, the parallel architecture is several hundred times faster than the existing implementations, making it feasible to investigate systemic models of more complex biological systems.     

Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modelling approaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organising modelling methods for the daily prediction of the exchange rate market. We also propose a combined approach where the parametric and nonparametric self-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchange rates: the American Dollar and the Deutche Mark against the British Pound.     

A linguistic cellular automata simulation approach for sustainable land development in a fast growing region

Rapid rural-urban land conversion as a consequence of economic growth has raised serious concerns over sustainable development. There is an urgent need to understand what possible urban scenarios can result from different policies towards land conversions. In many ways, the question resembles the exploration of a self-organising phenomenon which generates macroscopic patterns upon microscopic and local decision-making processes. In this paper a linguistic simulation approach has been developed. As a prototype, the study integrates cellular automata (CA) with heuristically-defined transition rules to simulate land use conversions in the rural-urban fringe of a fast growing metropolis. Fuzzy set theory has been applied to capture uncoordinated land development process. An innovative feature of the integrated approach lies in its definition of transition rules through a “natural language interface”, thus being more realistic and transparent. The model can simulate development scenarios in a gaming style. By providing a series of scenarios, it reveals risks inherent in certain development strategies which may jeopardize sustainable development of the city.     

Performance comparison of bio-inspired optimization algorithms for Sierpinski gasket fractal antenna design

The Sierpinski gasket fractal antenna is most popular structure in the domain of fractal antennas. This fractal antenna has multi-band performance, and hence, the design of this antenna for the desired frequencies is a challenging problem. The artificial intelligence tools like artificial neural networks, fuzzy logic systems, bio-inspired optimization techniques are appropriate to provide accurate design solution in such cases. In this paper, three most popular bio-inspired optimization algorithms: genetic algorithms, particle swarm optimization (PSO), and bacterial foraging optimization, have been proposed to solve the design issues of Sierpinski gasket pre-fractal antenna. Their performances are analyzed and are compared with the experimental results. A simplified expression for calculation of resonant frequency of Sierpinski gasket pre-fractal antenna is proposed and is used as the objective function. Finally, the effectiveness is compared on the basis of three different measures: mean absolute percentage error, the average time taken by the models to evaluate the results, and the coefficient of correlation. The results indicate that the PSO algorithm is most suitable for this type of antenna.     

An improved nonlinear control design for series DC motors

A series DC motor must be represented by a nonlinear model when nonlinearities such as magnetic saturation are considered. To provide effective control, nonlinearities and uncertainties in the model must be taken into account in the control design. In this paper, the recursive design method is applied to generate nonlinear control, nonlinear PI control, and robust control, and these controls are shown to be efficient and robust in the simulation study compared to existing results.