Feasibility of using adaptive logic networks to predict compressor unit failure
Armstrong, W.W.; Chungying Chu; Thomas, M.M. [Dendronic Decisions Limited, Edmonton (Canada)] [and others
1995-12-31
In this feasibility study, an adaptive logic network (ALN) was trained to predict failures of turbine-driven compressor units using a large database of measurements. No expert knowledge about compressor systems was involved. The predictions used only the statistical properties of the measurements and the indications of failure types. A fuzzy set was used to model measurements typical of normal operation. It was constrained by a requirement imposed during ALN training, that it should have a shape similar to a Gaussian density, more precisely, that its logarithm should be convex-up. Initial results obtained using this approach to knowledge discovery in the database were encouraging.
Fuzzy-Logic Adaptive Queuing for a Heuristic TCP Performance in Mobile Wireless Networks
Ghaida A. AL-Suhail
2012-06-01
Full Text Available In this paper, we propose a new Fuzzy-Logic Adaptive Queuing controller (FLAQ based on a classical Random Early Detection (RED algorithm in wireless cellular network. The controller predicts dynamically the packet dropping rate and the corresponding average queue length. It relies on the average queue length at the base station router and the packet loss rate caused by the channel variations in mobile environment; assuming there is no buffer overflow due to the congestion. Using this model, a heuristic TCP performance can be estimated over a time-varying channel under different conditions of user’s mobility. The results show a significant improvement in TCP throughput performance when the user’s mobility is below 5 m/s; and becomes constant (i.e., close to i.i.d beyond this speed especially at 5% of predefined packet error rate.
Adaptive logic networks in rehabilitation of persons with incomplete spinal cord injury
Armstrong, W.W. [Univ. of Alberta, Edmonton (Canada)]|[Dendronic Decisions Limited, Edmonton (Canada)] [and others
1995-12-31
Persons with incomplete spinal cord injury are generally at least partially paralyzed and are often unable to walk. Manually-controlled electrical stimulation has been used to act upon nerves or muscles to cause leg movement so such persons can achieve functional walking. They use crutches or a mobile walker for support, and initiate each stimulus by pressing a button. Artificial intelligence and machine learning techniques are now making it possible to automate the process of stimulus-initiation. Supervised training of an automatic system can be based on samples of correct stimulation given by the patient or by a therapist, accompanied by data from sensors indicating the state of the person`s body and its relation to the ground during walking. A major issue is generalization, i.e. whether the result of training can be used for control at a later time or in somewhat different circumstances. As the possibilities grow for increasing the number and variety of sensors on a patient, and for easily implanting more numerous stimulation channels, the need is increasing for powerful learning systems which can automatically develop effective and safe control algorithms. This paper explains the foundations of adaptive logic networks, and illustrates how they have been used to develop an experimental walking prosthesis used in a laboratory setting. Successful generalization has been observed using parameters from training which took place minutes to days earlier.
Franceschet, Massimo
2010-01-01
Networks are pervasive in the real world. Nature, society, economy, and technology are supported by ostensibly different networks that in fact share an amazing number of interesting structural properties. Network thinking exploded in the last decade, boosted by the availability of large databases on the topology of various real networks, mainly the Web and biological networks, and converged to the new discipline of network analysis - the holistic analysis of complex systems through the study of the network that wires their components. Physicists mainly drove the investigation, studying the structure and function of networks using methods and tools of statistical mechanics. Here, we give an alternative perspective on network analysis, proposing a logic for specifying general properties of networks and a modular algorithm for checking these properties. The logic borrows from two intertwined computing fields: XML databases and model checking.
Bergenholtz, Carsten; Bjerregaard, Toke
The present study investigates how a high-tech-small-firm (HTSF) can carry out an inter-organizational search of actors located at universities. Responding to calls to study how firms navigate multiple institutional norms, this research examines the different strategies used by a HTSF to balance...... adopted academic norm-sets, commercial imperatives and formal regulations to support formation of networks and collaborations with universities. The findings show how the significance of weak and strong ties for the formation of collaborations and networks with universities is relative to the...... institutional embeddedness in different industries. The research thus illuminates how a HTSF effectively can combine different search and collaboration strategies depending on institutional contexts and logics governing R&D work. The concluding section outlines implications for future research and the...
Adaptation of the FPGA to Logic Failures
Tyurin S.F.; Grekov A.V.; Gromov O.A.
2013-01-01
The paper proposes the restoration of logic programmable logic integrated circuits such as FPGA (field-programmable gate array) for critical applications by adapting to failures of logic elements. The principle of adaptation FPGA is to switch to the remaining functionality of the LUT (Look Up Table), with the possibility of hardware and software they use in the event of hardware failure after massive failures. Asked to ensure the preservation of the basis in the sense of Post logic functions ...
Fuzzy logic and neural network technologies
Villarreal, James A.; Lea, Robert N.; Savely, Robert T.
1992-01-01
Applications of fuzzy logic technologies in NASA projects are reviewed to examine their advantages in the development of neural networks for aerospace and commercial expert systems and control. Examples of fuzzy-logic applications include a 6-DOF spacecraft controller, collision-avoidance systems, and reinforcement-learning techniques. The commercial applications examined include a fuzzy autofocusing system, an air conditioning system, and an automobile transmission application. The practical use of fuzzy logic is set in the theoretical context of artificial neural systems (ANSs) to give the background for an overview of ANS research programs at NASA. The research and application programs include the Network Execution and Training Simulator and faster training algorithms such as the Difference Optimized Training Scheme. The networks are well suited for pattern-recognition applications such as predicting sunspots, controlling posture maintenance, and conducting adaptive diagnoses.
Logical impossibilities in biological networks
Monendra Grover
2011-10-01
Full Text Available Biological networks are complex and involve several kinds of molecules. For proper biological function it is important for these biomolecules to act at an individual level and act at the level of interaction of these molecules. In this paper some of the logical impossibilities that may arise in the biological networks and their possible solutions are discussed. It may be important to understand these paradoxes and their possible solutions in order to develop a holistic view of biological function.
Logic reliability analysis of adaptive control strategies
An approach is developed for the evaluation of the reliability of logic of adaptive control strategies, taking into account logic structural complexity and potential failure of programming modules. Flaws in the control system algorithm may not be discovered during debugging or initial testing and may only affect the performance under abnormal situations although the system may appear reliable in normal operations. Considering an adaptive control system designed for use in control of equipment employed in nuclear power stations, logic reliability evaluation is demonstrated. The approach given is applicable to any other designs and may be used to compare different control system logic structures from the reliability viewpoint. Evaluation of the reliability of control systems is essential to automated operation of equipment used in nuclear power plants. (author)
Logical Modes of Attack in Argumentation Networks
Gabbay, Dov M.; Garcez, A. S. D. Avila
2009-01-01
This paper studies methodologically robust options for giving logical contents to nodes in abstract argumentation networks. It defines a variety of notions of attack in terms of the logical contents of the nodes in a network. General properties of logics are refined both in the object level and in the meta level to suit the needs of the application. The network-based system improves upon some of the attempts in the literature to define attacks in terms of defeasible proofs, the...
Neural logic networks a new class of neural networks
Heng, Teh Hoon
1995-01-01
This book is the first of a series of technical reports of a key research project of the Real-World Computing Program supported by the MITI of Japan.The main goal of the project is to model human intelligence by a special class of mathematical systems called neural logic networks.The book consists of three parts. Part 1 describes the general theory of neural logic networks and their potential applications. Part 2 discusses a new logic called Neural Logic which attempts to emulate more closely the logical thinking process of human. Part 3 studies the special features of neural logic networks wh
Reconfigurable logic in nanoelectronic switching networks
We demonstrate how to configure and reconfigure a nanoelectronic nonlinear network to a universal set of logic gates by applying sequences of voltage pulses to the edges of the network. The nanoelectronic device is designed to consist of a self-assembled network of nanoparticles connected by two-terminal linker elements with hysteretic behaviour, allowing voltage-controlled switching between a linear and nonlinear current-voltage characteristic (IVC), making reconfigurable logic possible
Anatomy Ontology Matching Using Markov Logic Networks
Li, Chunhua; Zhao, Pengpeng; Wu, Jian; Cui, Zhiming
2016-01-01
The anatomy of model species is described in ontologies, which are used to standardize the annotations of experimental data, such as gene expression patterns. To compare such data between species, we need to establish relationships between ontologies describing different species. Ontology matching is a kind of solutions to find semantic correspondences between entities of different ontologies. Markov logic networks which unify probabilistic graphical model and first-order logic provide an exc...
Fuzzy logic systems are equivalent to feedforward neural networks
无
2000-01-01
Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.
Logic Learning in Hopfield Networks
Sathasivam, Saratha
2008-01-01
Synaptic weights for neurons in logic programming can be calculated either by using Hebbian learning or by Wan Abdullah's method. In other words, Hebbian learning for governing events corresponding to some respective program clauses is equivalent with learning using Wan Abdullah's method for the same respective program clauses. In this paper we will evaluate experimentally the equivalence between these two types of learning through computer simulations.
Sorting Network for Reversible Logic Synthesis
Islam, Md Saiful; Mahmud, Abdullah Al; karim, Muhammad Rezaul
2010-01-01
In this paper, we have introduced an algorithm to implement a sorting network for reversible logic synthesis based on swapping bit strings. The algorithm first constructs a network in terms of n*n Toffoli gates read from left to right. The number of gates in the circuit produced by our algorithm is then reduced by template matching and removing useless gates from the network. We have also compared the efficiency of the proposed method with the existing ones.
Diffusion Adaptation over Networks
Sayed, Ali H
2012-01-01
Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with processing and learning abilities. The agents are linked together through a connection topology, and they cooperate with each other through local interactions to solve distributed inference problems in real-time. The continuous diffusion of information across the network enables agents to adapt their performance in relation to changing data and network conditions; it also results in improved adaptation and learning performance relative to non-cooperative networks. This article provides an overview of diffusion strategies for adaptation and learning over networks. The article is divided into several sections: 1. Motivation; 2. Mean-Square-Error Estimation; 3. Distributed Optimization via Diffusion Strategies; 4. Adaptive Diffusion Strategies; 5. Performance of Ste...
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
Serafini, Luciano; Garcez, Artur d'Avila
2016-01-01
We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning. A logic formalism called Real Logic is defined on a first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers. Logical constants are interpreted as feature vectors of real numbers. Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational mac...
Fuzzy logic, neural networks, and soft computing
Zadeh, Lofti A.
1994-01-01
The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial
Fuzzy logic and neural networks basic concepts & application
Alavala, Chennakesava R
2008-01-01
About the Book: The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required. Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank
Quantum logic networks for probabilistic teleportation
刘金明; 张永生; 等
2003-01-01
By eans of the primitive operations consisting of single-qubit gates.two-qubit controlled-not gates,Von Neuman measurement and classically controlled operations.,we construct efficient quantum logic networks for implementing probabilistic teleportation of a single qubit,a two-particle entangled state,and an N-particle entanglement.Based on the quantum networks,we show that after the partially entangled states are concentrated into maximal entanglement,the above three kinds of probabilistic teleportation are the same as the standard teleportation using the corresponding maximally entangled states as the quantum channels.
Quantum logic networks for probabilistic teleportation
刘金明; 张永生; 郭光灿
2003-01-01
By means of the primitive operations consisting of single-qubit gates, two-qubit controlled-not gates, Von Neuman measurement and classically controlled operations, we construct efficient quantum logic networks for implementing probabilistic teleportation of a single qubit, atwo-particle entangled state, and an N-particle entanglement. Based on the quantum networks, we show that after the partially entangled states are concentrated into maximal entanglement,the above three kinds of probabilistic teleportation are the same as the standard teleportation using the corresponding maximally entangled states as the quantum channels.
Dynamic regimes of random fuzzy logic networks
Random multistate networks, generalizations of the Boolean Kauffman networks, are generic models for complex systems of interacting agents. Depending on their mean connectivity, these networks exhibit ordered as well as chaotic behavior with a critical boundary separating both regimes. Typically, the nodes of these networks are assigned single discrete states. Here, we describe nodes by fuzzy numbers, i.e. vectors of degree-of-membership (DOM) functions specifying the degree to which the nodes are in each of their discrete states. This allows our models to deal with imprecision and uncertainties. Compatible update rules are constructed by expressing the update rules of the multistate network in terms of Boolean operators and generalizing them to fuzzy logic (FL) operators. The standard choice for these generalizations is the Goedel FL, where AND and OR are replaced by the minimum and maximum of two DOMs, respectively. In mean-field approximations we are able to analytically describe the percolation and asymptotic distribution of DOMs in random Goedel FL networks. This allows us to characterize the different dynamic regimes of random multistate networks in terms of FL. In a low-dimensional example, we provide explicit computations and validate our mean-field results by showing that they agree well with network simulations.
Dynamic regimes of random fuzzy logic networks
Wittmann, Dominik M.; Theis, Fabian J.
2011-01-01
Random multistate networks, generalizations of the Boolean Kauffman networks, are generic models for complex systems of interacting agents. Depending on their mean connectivity, these networks exhibit ordered as well as chaotic behavior with a critical boundary separating both regimes. Typically, the nodes of these networks are assigned single discrete states. Here, we describe nodes by fuzzy numbers, i.e. vectors of degree-of-membership (DOM) functions specifying the degree to which the nodes are in each of their discrete states. This allows our models to deal with imprecision and uncertainties. Compatible update rules are constructed by expressing the update rules of the multistate network in terms of Boolean operators and generalizing them to fuzzy logic (FL) operators. The standard choice for these generalizations is the Gödel FL, where AND and OR are replaced by the minimum and maximum of two DOMs, respectively. In mean-field approximations we are able to analytically describe the percolation and asymptotic distribution of DOMs in random Gödel FL networks. This allows us to characterize the different dynamic regimes of random multistate networks in terms of FL. In a low-dimensional example, we provide explicit computations and validate our mean-field results by showing that they agree well with network simulations.
Chaudhuri, Arijit
2014-01-01
Combining the two statistical techniques of network sampling and adaptive sampling, this book illustrates the advantages of using them in tandem to effectively capture sparsely located elements in unknown pockets. It shows how network sampling is a reliable guide in capturing inaccessible entities through linked auxiliaries. The text also explores how adaptive sampling is strengthened in information content through subsidiary sampling with devices to mitigate unmanageable expanding sample sizes. Empirical data illustrates the applicability of both methods.
Adaptive network countermeasures.
McClelland-Bane, Randy; Van Randwyk, Jamie A.; Carathimas, Anthony G.; Thomas, Eric D.
2003-10-01
This report describes the results of a two-year LDRD funded by the Differentiating Technologies investment area. The project investigated the use of countermeasures in protecting computer networks as well as how current countermeasures could be changed in order to adapt with both evolving networks and evolving attackers. The work involved collaboration between Sandia employees and students in the Sandia - California Center for Cyber Defenders (CCD) program. We include an explanation of the need for adaptive countermeasures, a description of the architecture we designed to provide adaptive countermeasures, and evaluations of the system.
Compensatory fuzzy logic for intelligent social network analysis
Maikel Y. Leyva-Vázquez; Rafael Bello-Lara; Rafael Alejandro Espín-Andrade
2014-01-01
Fuzzy graph theory has gained in visibility for social network analysis. In this work fuzzy logic and their role in modeling social relational networks is discussed. We present a proposal for extending the fuzzy logic framework to intelligent social network analysis using the good properties of robustness and interpretability of compensatory fuzzy logic. We apply this approach to the concept path importance taking into account the length and strength of the connection. Results obtained with o...
NETWORK INTRUSION DETECTION SYSTEM USING FUZZY LOGIC
R. Shanmugavadivu
2011-02-01
Full Text Available IDS which are increasingly a key part of system defense are used to identify abnormal activities in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, variousdata-mining and machine learning techniques have been used in the literature. In the proposed system, we have designed fuzzy logic-based system for effectively identifying the intrusion activities within a network. The proposed fuzzy logic-based system can be able to detect an intrusion behavior of the networks since the rule base contains a better set of rules. Here, we have used automated strategy for generation of fuzzy rules, which are obtained from the definite rules using frequent items. The experiments and evaluations of the proposed intrusion detection system are performed with the KDD Cup 99 intrusion detection dataset. The experimentalresults clearly show that the proposed system achieved higher precision in identifying whether the records are normal or attack one.
Probabilistic logic modeling of network reliability for hybrid network architectures
Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.
1996-10-01
Sandia National Laboratories has found that the reliability and failure modes of current-generation network technologies can be effectively modeled using fault tree-based probabilistic logic modeling (PLM) techniques. We have developed fault tree models that include various hierarchical networking technologies and classes of components interconnected in a wide variety of typical and atypical configurations. In this paper we discuss the types of results that can be obtained from PLMs and why these results are of great practical value to network designers and analysts. After providing some mathematical background, we describe the `plug-and-play` fault tree analysis methodology that we have developed for modeling connectivity and the provision of network services in several current- generation network architectures. Finally, we demonstrate the flexibility of the method by modeling the reliability of a hybrid example network that contains several interconnected ethernet, FDDI, and token ring segments. 11 refs., 3 figs., 1 tab.
FUZZY LOGIC BASED ENERGY EFFICIENT PROTOCOL IN WIRELESS SENSOR NETWORKS
Zhan Wei Siew
2012-12-01
Full Text Available Wireless sensor networks (WSNs have been vastly developed due to the advances in microelectromechanical systems (MEMS using WSN to study and monitor the environments towards climates changes. In environmental monitoring, sensors are randomly deployed over the interest area to periodically sense the physical environments for a few months or even a year. Therefore, to prolong the network lifetime with limited battery capacity becomes a challenging issue. Low energy adaptive cluster hierarchical (LEACH is the common clustering protocol that aim to reduce the energy consumption by rotating the heavy workload cluster heads (CHs. The CHs election in LEACH is based on probability model which will lead to inefficient in energy consumption due to least desired CHs location in the network. In WSNs, the CHs location can directly influence the network energy consumption and further affect the network lifetime. In this paper, factors which will affect the network lifetime will be presented and the demonstration of fuzzy logic based CH selection conducted in base station (BS will also be carried out. To select suitable CHs that will prolong the network first node dies (FND round and consistent throughput to the BS, energy level and distance to the BS are selected as fuzzy inputs.
Compensatory fuzzy logic for intelligent social network analysis
Maikel Y. Leyva-Vázquez
2014-10-01
Full Text Available Fuzzy graph theory has gained in visibility for social network analysis. In this work fuzzy logic and their role in modeling social relational networks is discussed. We present a proposal for extending the fuzzy logic framework to intelligent social network analysis using the good properties of robustness and interpretability of compensatory fuzzy logic. We apply this approach to the concept path importance taking into account the length and strength of the connection. Results obtained with our model are more consistent with the way human make decisions. Additionally a case study to illustrate the applicability of the proposal on a coauthorship network is developed. Our main outcome is a new model for social network analysis based on compensatory fuzzy logic that gives more robust results and allows compensation. Moreover this approach makes emphasis in using language for social network analysis.
Communication in quantum networks of logical bus topology
Brougham, T; Jex, I
2009-01-01
Perfect state transfer (PST) is discussed in the context of passive quantum networks with logical bus topology, where many logical nodes communicate using the same shared media, without any external control. The conditions under which, a number of point-to-point PST links may serve as building blocks for the design of such multi-node networks are investigated. The implications of our results are discussed in the context of various Hamiltonians that act on the entire network, and are capable of providing PST between the logical nodes of a prescribed set in a deterministic manner.
Adaptive Dynamic Bayesian Networks
Ng, B M
2007-10-26
A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.
Deep Space Network Antenna Logic Controller
Ahlstrom, Harlow; Morgan, Scott; Hames, Peter; Strain, Martha; Owen, Christopher; Shimizu, Kenneth; Wilson, Karen; Shaller, David; Doktomomtaz, Said; Leung, Patrick
2007-01-01
The Antenna Logic Controller (ALC) software controls and monitors the motion control equipment of the 4,000-metric-ton structure of the Deep Space Network 70-meter antenna. This program coordinates the control of 42 hydraulic pumps, while monitoring several interlocks for personnel and equipment safety. Remote operation of the ALC runs via the Antenna Monitor & Control (AMC) computer, which orchestrates the tracking functions of the entire antenna. This software provides a graphical user interface for local control, monitoring, and identification of faults as well as, at a high level, providing for the digital control of the axis brakes so that the servo of the AMC may control the motion of the antenna. Specific functions of the ALC also include routines for startup in cold weather, controlled shutdown for both normal and fault situations, and pump switching on failure. The increased monitoring, the ability to trend key performance characteristics, the improved fault detection and recovery, the centralization of all control at a single panel, and the simplification of the user interface have all reduced the required workforce to run 70-meter antennas. The ALC also increases the antenna availability by reducing the time required to start up the antenna, to diagnose faults, and by providing additional insight into the performance of key parameters that aid in preventive maintenance to avoid key element failure. The ALC User Display (AUD) is a graphical user interface with hierarchical display structure, which provides high-level status information to the operation of the ALC, as well as detailed information for virtually all aspects of the ALC via drill-down displays. The operational status of an item, be it a function or assembly, is shown in the higher-level display. By pressing the item on the display screen, a new screen opens to show more detail of the function/assembly. Navigation tools and the map button allow immediate access to all screens.
Adaptive process control using fuzzy logic and genetic algorithms
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Generalized Adaptive Artificial Neural Networks
Tawel, Raoul
1993-01-01
Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.
Hardwired Logic and Multithread Design in Network Processors
李旭东; 徐扬; 刘斌; 王小军
2004-01-01
High-performance network processors are expected to play an important role in future high-speed routers. This paper focuses on two representative techniques needed for high-performance network processors: hardwired logic design and multithread design. Using hardwired logic, this paper compares a single-thread design with a multithread design, and proposes general models and principles to analyze the clock frequency and the resource cost for these environments. Then, two IP header processing schemes, one in single-thread mode and the other in double-thread mode, are developed using these principles and the implementation results verified the theoretical calculation.
Stereo logical analysis of fracture networks
Fractures are always present in geological formations over a large range of scales and their characterization is drastically limited by the virtual impossibility of measuring them in-situ. Therefore, most analyses are based on 1 D and 2D measurements of fracture traces along boreholes or on exposed outcrops which necessitate extrapolation by stereo-logical techniques to 3D. Such extrapolations have already been made for specific fracture shapes by Warburton, Piggott, Berkowitz and Adler and Sisavath et al. The purpose of this communication is to survey our recent works in this field
Output-back fuzzy logic systems and equivalence with feedback neural networks
无
2000-01-01
A new idea, output-back fuzzy logic systems, is proposed. It is proved that output-back fuzzy logic systems must be equivalent to feedback neural networks. After the notion of generalized fuzzy logic systems is defined, which contains at least a typical fuzzy logic system and an output-back fuzzy logic system, one important conclusion is drawn that generalized fuzzy logic systems are almost equivalent to neural networks.
Adaptive Background subtraction in Dynamic Environments Using Fuzzy Logic
Sivabalakrishnan.M
2010-03-01
Full Text Available Extracting a background from an image is the enabling step for many high-level vision processing tasks, such as object tracking andactivity analysis. Although there are a number of object extraction algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background isrelatively simple and static. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varyingbackground model. A problem with our system is that by adapting the background model, objects moved are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the object. In this paper, we develop a fuzzy logic inference system to detach the moving object from the background. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.
Povilaitytė, Živilė
2014-01-01
Given the novelty of political campaigning on social networking sites in Lithuania and the critique, it has received from social media experts, the object of the master thesis encompasses mediatization of Lithuanian politics on Facebook, when social media logic becomes adapted in political campaigning and integrated into the political agenda. Accordingly, the MA thesis developed its aim to define in what ways mediatization of Lithuanian politics manifests on Facebook and if public-relatio...
Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks
Li, Jiwei; Ritter, Alan; Jurafsky, Dan
2014-01-01
We propose a framework for inferring the latent attitudes or preferences of users by performing probabilistic first-order logical reasoning over the social network graph. Our method answers questions about Twitter users like {\\em Does this user like sushi?} or {\\em Is this user a New York Knicks fan?} by building a probabilistic model that reasons over user attributes (the user's location or gender) and the social network (the user's friends and spouse), via inferences like homophily (I am mo...
A high-speed interconnect network using ternary logic
Madsen, Jens Kargaard; Long, S. I.
1995-01-01
This paper describes the design and implementation of a high-speed interconnect network (ICN) for a multiprocessor system using ternary logic. By using ternary logic and a fast point-to-point communication technique called STARI (Self-Timed At Receiver's Input), the communication between the...... of two LSI GaAs chips, Interface and Crossbar, which were implemented in a 0.8 μm MESFET process. In a 4×4 ICN, communication at 300 Mbit/s per wire was demonstrated, which is twice as fast as pure synchronous and four times faster than pure asynchronous communication in the specific test set-up...
Logical Graphics Design Technique for Drawing Distribution Networks
Al-A`Ali, Mansoor
Electricity distribution networks normally consist of tens of primary feeders, thousands of substations and switching stations spread over large geographical areas and thus require a complex system in order to manage them properly from within the distribution control centre. We show techniques for using Delphi Object Oriented components to automatically generate, display and manage graphically and logically the circuits of the network. The graphics components are dynamically interactive and thus the system allows switching operations as well as displays. The object oriented approach was developed to replace an older system, which used Microstation with MDL as the programming language and ORACLE as the DBMS. Before this, the circuits could only be displayed schematically, which has many inherent problems in speed and readability of large displays. Schematic graphics displays were cumbersome when adding or deleting stations; this problem is now resolved using our approach by logically generating the graphics from the database connectivity information. This paper demonstrates the method of designing these Object Oriented components and how they can be used in specially created algorithms to generate the necessary interactive graphics. Four different logical display algorithms were created and in this study we present samples of the four different outputs of these algorithms which prove that distribution engineers can work with logical display of the circuits which are aimed to speed up the switching operations and for better clarity of the display.
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.
Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente
2015-01-01
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412
Decoupled Adapt-then-Combine diffusion networks with adaptive combiners
Fernandez-Bes, Jesus; Arenas-García, Jerónimo; Silva, Magno T. M.; Azpicueta-Ruiz, Luis A.
2015-01-01
In this paper we analyze a novel diffusion strategy for adaptive networks called Decoupled Adapt-then-Combine, which keeps a fully local estimate of the solution for the adaptation step. Our strategy, which is specially convenient for heterogeneous networks, is compared with the standard Adapt-then-Combine scheme and theoretically analyzed using energy conservation arguments. Such comparison shows the need of implementing adaptive combiners for both schemes to obtain a good performance in cas...
Ramstroem, Erik [TPS Termiska Processer AB, Nykoeping (Sweden)
2002-04-01
Grate-control is a complex task in many ways. The relations between controlled variables and the values they depend on are mostly unknown. Research projects are going on to create grate models based on physical laws. Those models are too complex for control implementation. The evaluation time is to long for control use. Another fundamental difficulty is that the relationships are none linear. That is, for a specific change in control value, the change in controlled value depends on the original size of control value, process disturbances and controlled values. There are extensive theories for linear process control. Non-linear control theory is used in robotic applications, but not in process and combustion control. The aim of grate control is to use as much of the grate area as possible, without having unburned material in ash. The outlined strategy is: To keep the position of the final bum out zone constant and its extension controlled. The control variables should be primary airflow, distribution of primary air, and fuel flow. Disturbances that should be measured are the fuel moisture content, the temperature of primary air and the grate temperature under the fuel bed. Technologies used are, fuzzy-logic and neural networks. A combination of booth could be used as well as any of them separately. A Fuzzy-logic controller acts as a computerised operator. Rules are specified with 'if - then' thesis. An example of that is: - if temperature is low, then close the valve The boundaries between the rules are made fuzzy. That makes it possible for the temperature to be just a bit low, which makes the valve open a bit. A lot of rules are created so that the controller knows what to do in every situation. Neural networks are sort of multi dimensional curves, with arbitrary degrees of freedom. The nets are used to predict future process values from measured ones. The model is evaluated from collected data. Parameters are adjusted for best correspondence between
Development of standard logic network for PWR NSSS system design
The self-reliance of NSSS System Design is required not only the design capability to perform the system design but also the management capability to control the resource and time for the Project effectively. The purpose of this study is to develop the simplified standard Logic Network that is scheduled on the time and resource using the PERT/CPM method. That is mainly focused on Ulchin 3, 4 Project. We prepare the management tool of NSSS System Design project. And we can utilize it as a reference tool for the similar project which are complex and long term in a next project. (Author)
Fuzzy Logic Based Multi User Adaptive Test System
2014-01-01
The present proliferation of e-learning has been actively underway for the last 10 years. Current research in Adaptive Testing System focuses on the development of psychometric models with items selection strategies applicable to adaptive testing processes. The key aspect of proposed Adaptive Testing System is to develop an increasingly sophisticated latent trait model which can assist users in developing and enhancing their skills. Computerized Adaptive Test (CAT) System requires a lot of in...
Logical Sensor Network: An Abstraction of Sensor Data Processing over Multidomain Sensor Network
Naoya Namatame; Jin Nakazawa; Hideyuki Tokuda
2012-01-01
This paper focuses on a sensor network virtualization over multidomain sensor network and proposes an abstraction called “logical sensor network (LSN)” for sensor data processing. In the proposed abstraction, processing is a directed acyclic graph that consists of nodes and streams, which represents a small data processor and communication rules between them, respectively. We have added a notion of a trigger to this graph. A trigger represents a timing of the process execution. We have implem...
Uncovering transcriptional interactions via an adaptive fuzzy logic approach
Chen Chung-Ming
2009-12-01
Full Text Available Abstract Background To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM performed poorly in inferring transcriptional interactions (TIs, which represent physical interactions between transcription factors (TF and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy. Results AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF, and using 19 transcription factors (TFs, we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms. Conclusion AdaFuzzy successfully integrates multiple types of
An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream
Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.
2016-01-01
This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081
Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller
Sree Bash Chandra Debnath; Pintu Chandra Shill; Kazuyuki Murase
2013-01-01
This paper presents a new method for learning and tuning a fuzzy logic controller automatically by means of a particle swarm optimization (PSO). The proposed self-learning fuzzy logic control that uses the PSO with adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions and fitness value where the optimization only considers certain points of the membership functions. To exhibit the effectiveness of proposed algorithm, it is used to optim...
Sensor Network Self-Localization Using Fuzzy Logic
Arash Dana
2007-12-01
Full Text Available Location awareness is an important capability for a series of enhanced wireless businesses. sensor networks are dense wireless networks of small low cost sensors, which collect and disseminate environmental data, for monitoring, military application and so on. Localization is an unconstrained optimization problem. position estimation is based on various, distance / path measures, which include anchor and non-anchor nodes. Anchor positions, have been predetermined to help us localize other nodes. This study proposes using a combination of fuzzy techniques, and advanced APS method, to estimate unknown nodes. In a network with twenty hundred nodes of which twenty percent operates as anchors. These nodes localize the other one hundred and sixties. It is necessary to select the best four anchors for localizing. We suppose that the anchors neighbor to unknown nodes are the best. It is time-consuming to find the distance of unknown anchors in such a widespread network. Using the fuzzy logic, putting the limitation of distance, and selecting the nearest anchor to the unknown node, the nearest four anchoress can be selected. In this case the rate of localization error will be decreased due to selecting neighbor anchors. Therefore, we can localize nodes by using ad-hoc positioning system. Fuzzy rules help us to estimate position in less than 2.4 seconds with mean normal positioning deviation of z =0.4597.
An evidential path logic for multi-relational networks
Rodriguez, Marko A [Los Alamos National Laboratory; Geldart, Joe [UNIV OF DURHAM
2008-01-01
Multi-relational networks are used extensively to structure knowledge. Perhaps the most popular instance, due to the widespread adoption of the Semantic Web, is the Resource Description Framework (RDF). One of the primary purposes of a knowledge network is to reason; that is, to alter the topology of the network according to an algorithm that uses the existing topological structure as its input. There exist many such reasoning algorithms. With respect to the Semantic Web, the bivalent, axiomatic reasoners of the RDF Schema (RDFS) and the Web Ontology Language (OWL) are the most prevalent. However, nothing prevents other forms of reasoning from existing in the Semantic Web. This article presents a non-bivalent, non-axiomatic, evidential logic and reasoner that is an algebraic ring over a multi-relational network and two binary operations that can be composed to perform various forms of inference. Given its multi-relational grounding, it is possible to use the presented evidential framework as another method for structuring knowledge and reasoning in the Semantic Web. The benefits of this framework are that it works with arbitrary, partial, and contradictory knowledge while, at the same time, supporting a tractable approximate reasoning process.
Fluctuating epidemics on adaptive networks
Shaw, Leah B
2008-01-01
A model for epidemics on an adaptive network is considered. Nodes follow an SIRS (susceptible-infective-recovered-susceptible) pattern. Connections are rewired to break links from non-infected nodes to infected nodes and are reformed to connect to other non-infected nodes, as the nodes that are not infected try to avoid the infection. Monte Carlo simulation and numerical solution of a mean field model are employed. The introduction of rewiring affects both the network structure and the epidemic dynamics. Degree distributions are altered, and the average distance from a node to the nearest infective increases. The rewiring leads to regions of bistability where either an endemic or a disease-free steady state can exist. Fluctuations around the endemic state and the lifetime of the endemic state are considered. The fluctuations are found to exhibit power law behavior.
Adaptive Dynamics of Regulatory Networks: Size Matters
Martinetz Thomas
2009-01-01
Full Text Available To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter network size. We show that small regulatory networks adapt fast, but not as good as larger networks in the longer perspective. Selection leads to an optimal network size dependent on heterogeneity of the environment and time pressure of adaptation. Optimal mutation rates are higher for smaller networks. We put special emphasis on discussing our simulation results on the background of functional observations from experimental and evolutionary biology.
Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller
Sree Bash Chandra Debnath
2013-01-01
Full Text Available This paper presents a new method for learning and tuning a fuzzy logic controller automatically by means of a particle swarm optimization (PSO. The proposed self-learning fuzzy logic control that uses the PSO with adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions and fitness value where the optimization only considers certain points of the membership functions. To exhibit the effectiveness of proposed algorithm, it is used to optimize the Gaussian membership functions of the fuzzy model of a nonlinear problem. Moreover, in order to design an effective adaptive fuzzy logic controller, an on line adaptive PSO based mechanism is presented to determine the parameters of the fuzzy mechanisms. Simulation results on two nonlinear problems are derived to demonstrate the powerful PSO learning algorithm and the proposed method is able to find good controllers better than neural controller and conventional controller for the target problem, cart pole type inverted pendulum system.
Evolution of a designless nanoparticle network into reconfigurable Boolean logic
Bose, S. K.; Lawrence, C. P.; Liu, Z.; Makarenko, K. S.; van Damme, R. M. J.; Broersma, H. J.; van der Wiel, W. G.
2015-12-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.
Evolution of a designless nanoparticle network into reconfigurable Boolean logic.
Bose, S K; Lawrence, C P; Liu, Z; Makarenko, K S; van Damme, R M J; Broersma, H J; van der Wiel, W G
2015-12-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures. PMID:26389658
Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks
Xia, Feng; Sun, Youxian; Tian, Yu-Chu
2008-01-01
Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantag...
Fuzzy Optimized Metric for Adaptive Network Routing
Ahmad Khader Haboush
2012-04-01
Full Text Available Network routing algorithms used today calculate least cost (shortest paths between nodes. The cost of a path is the sum of the cost of all links on that path. The use of a single metric for adaptive routing is insufficient to reflect the actual state of the link. In general, there is a limitation on the accuracy of the link state information obtained by the routing protocol. Hence it becomes useful if two or more metrics can be associated to produce a single metric that can describe the state of the link more accurately. In this paper, a fuzzy inference rule base is implemented to generate the fuzzy cost of each candidate path to be used in routing the incoming calls. This fuzzy cost is based on the crisp values of the different metrics; a fuzzy membership function is defined. The parameters of these membership functions reflect dynamically the requirement of the incoming traffic service as well as the current state of the links in the path. And this paper investigates how three metrics, the mean link bandwidth, queue utilization and the mean link delay, can be related using a simple fuzzy logic algorithm to produce a optimized cost of the link for a certain interval that is more „precise‟ than either of the single metric, to solve routing problem .
Learning ground CP-logic theories by leveraging Bayesian network learning techniques
Meert, Wannes; Struyf, Jan; Blockeel, Hendrik
2008-01-01
Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such relations. This paper investigates the learning of CP-logic theories (CP-theories) from training data. Its ﬁrst contribution is SEM-CP-logic, an algorithm that learns CP-theories by leveraging Bayesian network (BN) learning techniques. SEM-CP-logic is based on a transformation between CP-theories and BNs. That is, the ...
The Adaptive Control of Nonlinear Systems Using the T-S-K Fuzzy Logic
Martin Kratmüller
2009-07-01
Full Text Available Fuzzy adaptive tracking controllers for a class of uncertain nonlinear dynamicalsystems are proposed and analyzed. The controller consists of adaptive and robustifyingcomponents whose role is to nullify the effect of uncertainties and achieve a desiredtracking performance. The interactions between the two components have beeninvestigated. We use the Takagi-Sugeno-Kang type of the fuzzy logic system to approximatethe controller. It is proved that the closed-loop system using this adaptive fuzzy controlleris globally stable in the sense that all signals involved are bounded. Finally, we apply themethod of direct adaptive fuzzy controllers to control an inverted pendulum and thesimulation results are included.
Monitoring nuclear reactor systems using neural networks and fuzzy logic
A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ''virtual measurements'' the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab
Monitoring nuclear reactor systems using neural networks and fuzzy logic
A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such virtual measurements the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up-or performance can be determined. In the methodology presented the output of virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems
Fluid Intelligence and Psychosocial Outcome: From Logical Problem Solving to Social Adaptation
David Huepe; María Roca; Natalia Salas; Andrés Canales-Johnson; Álvaro A Rivera-Rei; Leandro Zamorano; Aimée Concepción; Facundo Manes; Agustín Ibañez
2011-01-01
BACKGROUND: While fluid intelligence has proved to be central to executive functioning, logical reasoning and other frontal functions, the role of this ability in psychosocial adaptation has not been well characterized. METHODOLOGY/PRINCIPAL FINDINGS: A random-probabilistic sample of 2370 secondary school students completed measures of fluid intelligence (Raven's Progressive Matrices, RPM) and several measures of psychological adaptation: bullying (Delaware Bullying Questionnaire), domestic a...
Computation emerges from adaptive synchronization of networking neurons.
Zanin, Massimiliano; Del Pozo, Francisco; Boccaletti, Stefano
2011-01-01
The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress) of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain. PMID:22073167
Computation emerges from adaptive synchronization of networking neurons.
Massimiliano Zanin
Full Text Available The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain.
Holistic Evaluation of Novel Adaptation Logics for DASH and SVC
Sieber, Christian
2013-01-01
Streaming of videos has become the major traffic generator in today's Internet and the video traffic share is still increasing. According to Cisco's annual Visual Networking Index report, in 2012, 60% of the global Internet IP traffic was generated by video streaming services. Furthermore, the study predicts further increase to 73% by 2017. At the same time, advances in the fields of mobile communications and embedded devices lead to a widespread adoption of Internet video enabled mobile and ...
Adaptive Networks: the Governance for Sustainable Development
S.G. Nooteboom (Sibout)
2006-01-01
textabstractIn this book, I reconstruct how policy makers, working together in what I term adaptive networks, have enabled a breakthrough in thinking about sustainable mobility in certain policy circles. I define the conduct of leading actors in these adaptive networks as sustainable change manag
Quantum logic networks for cloning a quantum state near a given state
Zhou Yan-Hui
2011-01-01
Two quantum logic networks are proposed to simulate a cloning machine that copies the states near a given one.Probabilistic cloning based on the first network is realized and the cloning probability of success based on the second network is 100%.Therefore,the second network is more motivative than the first one.
Quantum logic networks for cloning a quantum state near a given state
Two quantum logic networks are proposed to simulate a cloning machine that copies the states near a given one. Probabilistic cloning based on the first network is realized and the cloning probability of success based on the second network is 100%. Therefore, the second network is more motivative than the first one. (general)
Dynamical Adaptation in Terrorist Cells/Networks
Hussain, Dil Muhammad Akbar; Ahmed, Zaki
2010-01-01
followers etc. In this research we analyze and predict the most likely role a particular node can adapt once a member of the network is either killed or caught. The adaptation is based on computing Bayes posteriori probability of each node and the level of the said node in the network structure.......Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long...
Adaptive cluster synchronization in complex dynamical networks
Cluster synchronization is investigated in different complex dynamical networks. In this Letter, a novel adaptive strategy is proposed to make a complex dynamical network achieve cluster synchronization, where the adaptive strategy of one edge is adjusted only according to its local information. A sufficient condition about the global stability arbitrarily grouped of cluster synchronization is derived. Several numerical simulations show the effectiveness of the adaptive strategy.
Recruitment dynamics in adaptive social networks
Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.
2011-01-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the...
Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller
Sree Bash Chandra Debnath
2013-02-01
Full Text Available This paper presents a new method for learning and tuning a fuzzy logic controller automatically by meansof a particle swarm optimization (PSO. The proposed self-learning fuzzy logic control that uses the PSOwith adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions andfitness value where the optimization only considers certain points of the membership functions. To exhibitthe effectiveness of proposed algorithm, it is used to optimize the Gaussian membership functions of thefuzzy model of a nonlinear problem. Moreover, in order to design an effective adaptive fuzzy logiccontroller, an on line adaptive PSO based mechanism is presented to determine the parameters of the fuzzymechanisms. Simulation results on two nonlinear problems are derived to demonstrate the powerful PSOlearning algorithm and the proposed method is able to find good controllers better than neural controllerand conventional controller for the target problem, cart pole type inverted pendulum system.
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic
Ning Li; José-Fernán Martínez; Vicente Hernández Díaz
2015-01-01
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer paramete...
Zachariadis, S.; Mascolo, C.
2003-01-01
With the recent developments in wireless networks (Wavelan, Bluetooth) and the sales of mobile computers of any kind (such as laptopcomputers, Personal Digital Assistants (PDAs), mobile phones etc.) soaring, we are experiencing the availability of increasinglypowerful mobile computing environments that can roam across different types of networks. We have also recently witnessed theacceptance of Logical Mobility (LM) techniques, or the ability to ship part of an application or even a complete ...
Fuzzy Logic and Neural Networks - a Glimpse of the Future
Manley, Raymond
2015-01-01
In 1965 Lofti Zadeh published his paper on fuzzy set theory , putting it forward as a way of more closely realising the human thought process. Many systems developed to aid human activities have been based on definitive , yes/no, type decision making processes. An example is the way all computers are based on the binary logic system where only two possible and separate logic levels are allowed, a logic 1 or logic 0. However, we know from everyday experience that humans think in terms of vague...
Energy-efficient adaptive wireless network design
Havinga, Paul J. M.; Smit, Gerard J.M.; Bos, Martinus
2000-01-01
Energy efficiency is an important issue for mobile computers since they must rely on their batteries. We present an energy-efficient highly adaptive architecture of a network interface and novel data link layer protocol for wireless networks that provides quality of service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations are necessary to achieve energy efficiency and an acceptable quality of service. The paper provides a review of ideas and...
Adaptive Control Based On Neural Network
Wei, Sun; Lujin, Zhang; Jinhai, Zou; Siyi, Miao
2009-01-01
In this paper, the adaptive control based on neural network is studied. Firstly, a neural network based adaptive robust tracking control design is proposed for robotic systems under the existence of uncertainties. In this proposed control strategy, the NN is used to identify the modeling uncertainties, and then the disadvantageous effects caused by neural network approximating error and external disturbances in robotic system are counteracted by robust controller. Especially the proposed cont...
Radi Radi
2011-08-01
Full Text Available Constructive Back Propagation Neural Network (CBPNN is a kind of back propagation neural network trained with constructive algorithm. Training of CBPNN is mainly conducted by developing the network’s architecture which commonly done by adding a number of new neuron units on learning process. Training of the network usually implements fixed method to develop its structure gradually by adding new units constantly. Although this method is simple and able to create an adaptive network for data pattern complexity, but it is wasteful and inefficient for computing. New unit addition affects directly to the computational load of training, speed of convergence, and structure of the final neural network. While increases training load significantly, excessive addition of units also tends to generate a large size of final network. Moreover, addition pattern with small unit number tends to drop off the adaptability of the network and extends time of training. Therefore, there is important to design an adaptive structure development pattern for CBPNN in order to minimize computing load of training. This study proposes Fuzzy Logic (FL algorithm to manage and develop structure of CBPNN. FL method was implemented on two models of CBPNN, i.e. designed with one and two hidden layers, used to recognize aroma patterns on an electronic nose system. The results showed that this method is effective to be applied due to its capability to minimize time of training, to reduce load of computational learning, and generate small size of network.
Neural Network Adaptations to Hardware Implementations
Moerland, Perry,; Fiesler,Emile
1997-01-01
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential.However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and ...
Neural Network Adaptations to Hardware Implementations
Moerland, Perry,; Fiesler,Emile; Beale, R
1997-01-01
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential. However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and...
Learning ground CP-logic theories by means of Bayesian network techniques
Meert, Wannes; Struyf, Jan; Blockeel, Hendrik
2007-01-01
Causal relationships are present in many application domains. CP-logic is a probabilistic modeling language that is especially designed to express such relationships. This paper investigates the learning of CP-theories from examples, and focusses on structure learning. The proposed approach is based on a transformation between CP-logic theories and Bayesian networks, that is, the method applies Bayesian network learning techniques to learn a CP-theory in the form of an equivalent Bayesian net...
Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing
Siddique, Nazmul
2013-01-01
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect
Quantum Logic Network for Cloning a State Near a Given One Based on Cavity QED
ZHANG Da-Wei; SHAO Xiao-Qiang; ZHU Ai-Dong
2008-01-01
A quantum logic network is constructed to simulate a cloning machine which copies states near a given one. Meanwhile, a scheme for implementing this cloning network based on the technique of cavity quantum electrody-namics (QED) is presented. It is easy to implement this network of cloning machine in the framework of cavity QED and feasible in the experiment.
Quantum Logic Network for Cloning a State Near a Given One Based on Cavity QED
A quantum logic network is constructed to simulate a cloning machine which copies states near a given one. Meanwhile, a scheme for implementing this cloning network based on the technique of cavity quantum electrodynamics (QED) is presented. It is easy to implement this network of cloning machine in the framework of cavity QED and feasible in the experiment. (general)
Recruitment dynamics in adaptive social networks.
Shkarayev, Maxim S; Schwartz, Ira B; Shaw, Leah B
2013-01-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). PMID:25395989
Recruitment dynamics in adaptive social networks
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). (paper)
Guarded Second-Order Logic, Spanning Trees, and Network Flows
Blumensath, Achim
2009-01-01
According to a theorem of Courcelle monadic second-order logic and guarded second-order logic (where one can also quantify over sets of edges) have the same expressive power over the class of all countable $k$-sparse hypergraphs. In the first part of the present paper we extend this result to hypergraphs of arbitrary cardinality. In the second part, we present a generalisation dealing with methods to encode sets of vertices by single vertices.
Obstacle avoidance for kinematically redundant robots using an adaptive fuzzy logic algorithm
In this paper the Adaptive Fuzzy Logic approach for solving the inverse kinematics of redundant robots in an environment with obstacles is presented. The obstacles are modeled as convex bodies. A fuzzy rule base that is updated via an adaptive law is used to solve the inverse kinematic problem. Additional rules have been introduced to take care of the obstacles avoidance problem. The proposed method has advantages such as high accuracy, simplicity of computations and generality for all redundant robots. Simulation results illustrate much better tracking performance than the dynamic base solution for a given trajectory in cartesian space, while guaranteeing a collision-free trajectory and observation of a mechanical joint limit
A computer program for the generation of logic networks from task chart data
Herbert, H. E.
1980-01-01
The Network Generation Program (NETGEN), which creates logic networks from task chart data is presented. NETGEN is written in CDC FORTRAN IV (Extended) and runs in a batch mode on the CDC 6000 and CYBER 170 series computers. Data is input via a two-card format and contains information regarding the specific tasks in a project. From this data, NETGEN constructs a logic network of related activities with each activity having unique predecessor and successor nodes, activity duration, descriptions, etc. NETGEN then prepares this data on two files that can be used in the Project Planning Analysis and Reporting System Batch Network Scheduling program and the EZPERT graphics program.
Network measures for characterising team adaptation processes
Barth, S.K.; Schraagen, J.M.C.; Schmettow, M.
2015-01-01
The aim of this study was to advance the conceptualisation of team adaptation by applying social network analysis (SNA) measures in a field study of a paediatric cardiac surgical team adapting to changes in task complexity and ongoing dynamic complexity. Forty surgical procedures were observed by tr
Neural network with dynamically adaptable neurons
Tawel, Raoul (Inventor)
1994-01-01
This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.
Computing single step operators of logic programming in radial basis function neural networks
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Adaptive Networks Theory, Models and Applications
Gross, Thilo
2009-01-01
With adaptive, complex networks, the evolution of the network topology and the dynamical processes on the network are equally important and often fundamentally entangled. Recent research has shown that such networks can exhibit a plethora of new phenomena which are ultimately required to describe many real-world networks. Some of those phenomena include robust self-organization towards dynamical criticality, formation of complex global topologies based on simple, local rules, and the spontaneous division of "labor" in which an initially homogenous population of network nodes self-organizes into functionally distinct classes. These are just a few. This book is a state-of-the-art survey of those unique networks. In it, leading researchers set out to define the future scope and direction of some of the most advanced developments in the vast field of complex network science and its applications.
联合模糊逻辑和神经网络的网络选择算法%Joint Fuzzy Logic and Neural Network for Network Selection
李航宇; 刘伟; 郭伟
2013-01-01
Propose a combination of fuzzy logic and neural network adaptive network selection algorithm for network selection in heterogeneous network environment. This method has the mechanism of reinforcement learning, therefore, the parameters of the membership function of fuzzy neural network can be dynamically adjusted online according to the output error, allowing the user to select the optimal network. Finally, a comparison wad made between the network selection algorithm of joint fuzzy logic and neural network and the network selection algorithm based on fuzzy logic. Simulation results show that this method can effectively guarantee that user comfort tends to the expectation of the ideal value to achieve the optimal network access options and to reduce the number of ping - pong effect, and have the higher user comfort compared with the non - adaptive fuzzy logic algorithm.%在网络优化选择问题的研究中,针对异构网络环境下的网络选择的问题,由于网络性能存在差异,提出一种联合模糊逻辑和神经网络的自适应网络选择算法.由于新方法具有学习训练的能力,所以能够根据输出误差对模糊神经网络的隶属度函数的参数进行动态的在线调整,从而使用户选择最优的网络.最后将联合模糊逻辑和神经网络的网络选择算法与基于模糊逻辑的网络选择算法进行了比较.仿真结果表明,改进方法能有效的保证用户舒适度比率趋于期望的理想值,实现了最优的网络接入选择,减少了乒乓效应发生的次数,并且相较于不自适应调整的模糊逻辑算法有更高的用户舒适度比率.
The Distributed Logical Reasoning Language D—Tuili and Its Implementation on Microcomputer Network
高全泉; 陆汝钤; 等
1992-01-01
D－Tuili,having been implemented on microcompute network,is a distributed logical reasoning programming language.D-Tuili supports parallel programming on the language level,and couples loosely with the distributed database management system,so data in distributed databases can be used in the distributed logic programs.In this paper,we mainly introduce the components of D-Tuili used to design distributed logic programs.Furthermore,the main principles to implement D-Tuili and the main technologies adopted in the implemented system of D-Tuili are described.
Performance of Networked DC Motor with Fuzzy Logic Controller
B. Sharmila; N. Devarajan
2010-01-01
In the recent years the usage of data networks has been increased due to its cost effective and flexible applications. A shared data network can effectively reduce complicated wiring connections, installation and maintenance for connecting a complex control system with various sensors, actuators, and controllers as a networked control system. For the time-sensitive application with networked control system the remote dc motor actuation control has been chosen. Due to time-varying network traf...
In-Network Adaptation of Video Streams Using Network Processors
Mohammad Shorfuzzaman
2009-01-01
problem can be addressed, near the network edge, by applying dynamic, in-network adaptation (e.g., transcoding of video streams to meet available connection bandwidth, machine characteristics, and client preferences. In this paper, we extrapolate from earlier work of Shorfuzzaman et al. 2006 in which we implemented and assessed an MPEG-1 transcoding system on the Intel IXP1200 network processor to consider the feasibility of in-network transcoding for other video formats and network processor architectures. The use of “on-the-fly” video adaptation near the edge of the network offers the promise of simpler support for a wide range of end devices with different display, and so forth, characteristics that can be used in different types of environments.
Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)
Wade, Robert L.; Walker, Gregory W.
1996-05-01
The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged target drones. FLAC-H exploits fuzzy logic in its flight control system to provide a robust solution to the control of the helicopter's dynamic, nonlinear system. Straight forward, common sense fuzzy rules governing helicopter flight are processed instead of complex mathematical models. This has resulted in a simplified solution to the complexities of helicopter flight. Incorporation of fuzzy logic reduced the cost of development and should also reduce the cost of maintenance of the system. An adaptive algorithm allows the FLAC-H to 'learn' how to fly the helicopter, enabling the control system to adjust to varying helicopter configurations. The adaptive algorithm, based on genetic algorithms, alters the fuzzy rules and their related sets to improve the performance characteristics of the system. This learning allows FLAC-H to automatically be integrated into a new airframe, reducing the development costs associated with altering a control system for a new or heavily modified aircraft. Successful flight tests of the FLAC-H on a UH-1H target drone were completed in September 1994 at the White Sands Missile Range in New Mexico. This paper discuses the objective of the system, its design, and performance.
Genetic and logic networks with the signal-inhibitor-activator structure are dynamically robust
LI Fangting; TAN Ning
2006-01-01
The proteins, DNA and RNA interaction networks govern various biological functions in living cells, these networks should be dynamically robust in the intracellular and environmental fluctuations. Here, we use Boolean network to study the robust structure of both genetic and logic networks. First, SOS network in bacteria E. coli, which regulates cell survival and repair after DNA damage, is shown to be dynamically robust. Comparing with cell cycle network in budding yeast and flagella network in E. coli, we find the signal-inhibitor-activator (SIA) structure in transcription regulatory networks. Second, under the dynamical rule that inhibition is much stronger than activation, we have searched 3-node non-self-loop logical networks that are dynamically robust, and that if the attractive basin of a final attractor is as large as seven, and the final attractor has only one active node, then the active node acts as inhibitor, and the SIA and signal-inhibitor (SI) structures are fundamental architectures of robust networks. SIA and SI networks with dynamic robustness against environment uncertainties may be selected and maintained over the course of evolution, rather than blind trial-error testing and be ing an accidental consequence of particular evolutionary history. SIA network can perform a more complex process than SI network, andSIA might be used to design robust artificial genetic network. Our results provide dynamical support for why the inhibitors and SIA/SI structures are frequently employed in cellular regulatory networks.
Traffic Engineering and Quality of Experience in MPLS Network by Fuzzy Logic characterization
Satya Prakash Rout; Palash Ghosal
2015-01-01
This paper proposes a load balancing algorithm using fuzzy logic so that maximum Quality of Experience can be achieved. Avoidance of congestion is one of the major performance objectives of traffic engineering in MPLS networks. Load balancing can prevent the congestion caused due to inefficient allocation of network resources. Another aspect of the network performance is Quality of Experience (QoE). QoE in telecommunications terminology, it is a measurement used to determine how w...
Towards a logic-based method to infer provenance-aware molecular networks
Aslaoui-Errafi, Zahira; Cohen-Boulakia, Sarah; Froidevaux, Christine; Gloaguen, Pauline; Poupon, Anne; Rougny, Adrien; Yahiaoui, Meriem
2012-01-01
International audience Providing techniques to automatically infer molecular networks is particularly important to understand complex relationships between biological objects. We present a logic-based method to infer such networks and show how it allows inferring signalling networks from the design of a knowledge base. Provenance of inferred data has been carefully collected, allowing quality evaluation. More precisely, our method (i) takes into account various kinds of biological experime...
Secure Reprogramming of a Network Connected Device : Securing programmable logic controllers
Tesfaye, Mussie
2012-01-01
This is a master’s thesis project entitled “Secure reprogramming of network connected devices”. The thesis begins by providing some background information to enable the reader to understand the current vulnerabilities of network-connected devices, specifically with regard to cyber security and data integrity. Today supervisory control and data acquisition systems utilizing network connected programmable logic controllers are widely used in many industries and critical infrastructures. These n...
A FUZZY-LOGIC CONTROL ALGORITHM FOR ACTIVE QUEUE MANAGEMENT IN IP NETWORKS
无
2008-01-01
Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router's ability in network congestion control in IP network.
Fuzzy logic based Adaptive Modulation Using Non Data Aided SNR Estimation for OFDM system
K.SESHADRI SASTRY
2010-06-01
Full Text Available As demand for high quality transmission increases increase of spectrum efficiency and an improvement of error performance in wireless communication systems are important . One of the promising approaches to 4G is adaptive OFDM (AOFDM . Fixed modulation systems uses only one type of modulation scheme (or order, so that either performance or capacity should be compromised Adaptive modulated systems are superior to fixed modulated systems, since they change modulation order depending on present SNR. In an adaptive modulation system SNR estimation is important since performance of adaptive modulated system depends of estimated SNR. Non-data-Aided (NDA SNR estimation systems are gaining importance in recent days since they estimate SNR range and requires less data as input .In this paper we propose an adaptive modulated OFDM system which uses NDA(Non-data Aided SNR estimation using fuzzy logic interface.The proposed system is simulated in Matlab 7.4 and The results of computer simulation show the improvement in system capacity .
Fluid intelligence and psychosocial outcome: from logical problem solving to social adaptation.
David Huepe
Full Text Available BACKGROUND: While fluid intelligence has proved to be central to executive functioning, logical reasoning and other frontal functions, the role of this ability in psychosocial adaptation has not been well characterized. METHODOLOGY/PRINCIPAL FINDINGS: A random-probabilistic sample of 2370 secondary school students completed measures of fluid intelligence (Raven's Progressive Matrices, RPM and several measures of psychological adaptation: bullying (Delaware Bullying Questionnaire, domestic abuse of adolescents (Conflict Tactic Scale, drug intake (ONUDD, self-esteem (Rosenberg's Self Esteem Scale and the Perceived Mental Health Scale (Spanish adaptation. Lower fluid intelligence scores were associated with physical violence, both in the role of victim and victimizer. Drug intake, especially cannabis, cocaine and inhalants and lower self-esteem were also associated with lower fluid intelligence. Finally, scores on the perceived mental health assessment were better when fluid intelligence scores were higher. CONCLUSIONS/SIGNIFICANCE: Our results show evidence of a strong association between psychosocial adaptation and fluid intelligence, suggesting that the latter is not only central to executive functioning but also forms part of a more general capacity for adaptation to social contexts.
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-05-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.
Fuzzy Logic Control of Adaptive ARQ for Video Distribution over a Bluetooth Wireless Link
R. Razavi
2007-01-01
Full Text Available Bluetooth's default automatic repeat request (ARQ scheme is not suited to video distribution resulting in missed display and decoded deadlines. Adaptive ARQ with active discard of expired packets from the send buffer is an alternative approach. However, even with the addition of cross-layer adaptation to picture-type packet importance, ARQ is not ideal in conditions of a deteriorating RF channel. The paper presents fuzzy logic control of ARQ, based on send buffer fullness and the head-of-line packet's deadline. The advantage of the fuzzy logic approach, which also scales its output according to picture type importance, is that the impact of delay can be directly introduced to the model, causing retransmissions to be reduced compared to all other schemes. The scheme considers both the delay constraints of the video stream and at the same time avoids send buffer overflow. Tests explore a variety of Bluetooth send buffer sizes and channel conditions. For adverse channel conditions and buffer size, the tests show an improvement of at least 4 dB in video quality compared to nonfuzzy schemes. The scheme can be applied to any codec with I-, P-, and (possibly B-slices by inspection of packet headers without the need for encoder intervention.
Logical Design and Control of Network in Local Mine Air-Reversing System
无
2001-01-01
This paper sets up a mathematical model of switching network and switching function by utilizing graphtheory to describe the logical function of different paths. The function varies with open and closed states of air doorsin a complex mine air sub-network, and the computer program for solving the switching function of complex net-works are offered. It gives the method for discriminating a reversible branch in a complex network by means of theswitching function, and the method of counter-inverted logical control of airflow inversion by means of open andshort circuit conversion of key branches. The research has solved the problem of the stablization of air flow for nor-mal ventination and reversing ventination in a diagonal network.
Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization
Castillo, Oscar; Kacprzyk, Janusz
2015-01-01
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...
Satisfiability of logic programming based on radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Quantum Logic Networks for Probabilistic Teleportation of an Arbitrary Three-Particle State
QIAN Xue-Min; FANG Jian-Xing; ZHU Shi-Qun; XI Yong-Jun
2005-01-01
The scheme for probabilistic teleportation of an arbitrary three-particle state is proposed. By using single qubit gate and three two-qubit gates, efficient quantum logic networks for probabilistic teleportation of an arbitrary three-particle state are constructed.
Fuzzy-Logic Based Multi-Sensory Quality Evaluation via Communication Network
LI Li-xiong(李力雄); TAN Yue-mei(谭月梅); FEI Minrui(费敏锐); T.C.Yang
2004-01-01
In this paper, a multi-sensory quality evaluation using an array of instruments to measure different sensory qualities is established via communication network. The network is used to transmit quality data to evaluation computer. And the network-induced delays between instruments and computer may have negative influence on final evaluation results. The main goal of this paper is to analyze network delays' influence on evaluation results, and present a fuzzy-logic based solution to eliminate the impact and improve the precision of evaluation. And simulations are conducted to show the effectiveness of the proposed approach.
Adaptation by Plasticity of Genetic Regulatory Networks
Brenner, Naama
2007-03-01
Genetic regulatory networks have an essential role in adaptation and evolution of cell populations. This role is strongly related to their dynamic properties over intermediate-to-long time scales. We have used the budding yeast as a model Eukaryote to study the long-term dynamics of the genetic regulatory system and its significance in evolution. A continuous cell growth technique (chemostat) allows us to monitor these systems over long times under controlled condition, enabling a quantitative characterization of dynamics: steady states and their stability, transients and relaxation. First, we have demonstrated adaptive dynamics in the GAL system, a classic model for a Eukaryotic genetic switch, induced and repressed by different carbon sources in the environment. We found that both induction and repression are only transient responses; over several generations, the system converges to a single robust steady state, independent of external conditions. Second, we explored the functional significance of such plasticity of the genetic regulatory network in evolution. We used genetic engineering to mimic the natural process of gene recruitment, placing the gene HIS3 under the regulation of the GAL system. Such genetic rewiring events are important in the evolution of gene regulation, but little is known about the physiological processes supporting them and the dynamics of their assimilation in a cell population. We have shown that cells carrying the rewired genome adapted to a demanding change of environment and stabilized a population, maintaining the adaptive state for hundreds of generations. Using genome-wide expression arrays we showed that underlying the observed adaptation is a global transcriptional programming that allowed tuning expression of the recruited gene to demands. Our results suggest that non-specific properties reflecting the natural plasticity of the regulatory network support adaptation of cells to novel challenges and enhance their evolvability.
Benschop, Nico F
2009-01-01
""Associative Digital Network Theory"" is intended for researchers at industrial laboratories, teachers and students at technical universities, in electrical engineering, computer science and applied mathematics departments, interested in new developments of modeling and designing digital networks (DN: state machines, sequential and combinational logic) in general, as a combined math/engineering discipline. As background an undergraduate level of modern applied algebra (Birkhoff-Bartee: ""Modern Applied Algebra"" - 1970, and Hartmanis-Stearns: ""Algebraic Structure of Sequential Machines"" - 1
A survey of paraconsistent logics
Middelburg, C.A.
2011-01-01
A survey of paraconsistent logics that are prominent representatives of the different approaches that have been followed to develop paraconsistent logics is provided. The paraconsistent logics that will be discussed are an enrichment of Priest's logic LP, the logic RM3 from the school of relevance logic, da Costa's logics Cn, Jaskowski's logic D2, and Subrahmanian's logics Ptau. A deontic logic based on the first of these logics will be discussed as well. Moreover, some proposed adaptations o...
Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees
Disruptions remain one of the most hazardous events in the operation of a tokamak device, since they can cause damage to the vacuum vessel and surrounding structures. Their potential danger increases with the plasma volume and energy content and therefore they will constitute an even more serious issue for the next generation of machines. For these reasons, in the recent years a lot of attention has been devoted to devise predictors, capable of foreseeing the imminence of a disruption sufficiently in advance, to allow time for undertaking remedial actions. In this paper, the results of applying fuzzy logic and classification and regression trees (CART) to the problem of predicting disruptions at JET are reported. The conceptual tools of fuzzy logic, in addition to being well suited to accommodate the opinion of experts even if not formulated in mathematical but linguistic terms, are also fully transparent, since their governing rules are human defined. They can therefore help not only in forecasting disruptions but also in studying their behaviour. The analysis leading to the rules of the fuzzy predictor has been complemented with a systematic investigation of the correlation between the various experimental signals and the imminence of a disruption. This has been performed with an exhaustive, non-linear and unbiased method based on decision trees. This investigation has confirmed that the relative importance of various signals can change significantly depending on the plasma conditions. On the basis of the results provided by CART on the information content of the various quantities, the prototype of an adaptive fuzzy logic predictor was trained and tested on JET database. Its performance is significantly better than the previous static one, proving that more flexible prediction strategies, not uniform over the whole discharge but tuned to the operational region of the plasma at any given time, can be very competitive and should be investigated systematically
Logical Reduction of Biological Networks to Their Most Determinative Components.
Matache, Mihaela T; Matache, Valentin
2016-07-01
Boolean networks have been widely used as models for gene regulatory networks, signal transduction networks, or neural networks, among many others. One of the main difficulties in analyzing the dynamics of a Boolean network and its sensitivity to perturbations or mutations is the fact that it grows exponentially with the number of nodes. Therefore, various approaches for simplifying the computations and reducing the network to a subset of relevant nodes have been proposed in the past few years. We consider a recently introduced method for reducing a Boolean network to its most determinative nodes that yield the highest information gain. The determinative power of a node is obtained by a summation of all mutual information quantities over all nodes having the chosen node as a common input, thus representing a measure of information gain obtained by the knowledge of the node under consideration. The determinative power of nodes has been considered in the literature under the assumption that the inputs are independent in which case one can use the Bahadur orthonormal basis. In this article, we relax that assumption and use a standard orthonormal basis instead. We use techniques of Hilbert space operators and harmonic analysis to generate formulas for the sensitivity to perturbations of nodes, quantified by the notions of influence, average sensitivity, and strength. Since we work on finite-dimensional spaces, our formulas and estimates can be and are formulated in plain matrix algebra terminology. We analyze the determinative power of nodes for a Boolean model of a signal transduction network of a generic fibroblast cell. We also show the similarities and differences induced by the alternative complete orthonormal basis used. Among the similarities, we mention the fact that the knowledge of the states of the most determinative nodes reduces the entropy or uncertainty of the overall network significantly. In a special case, we obtain a stronger result than in previous
Novel Spectrum Handoff in Cognitive Radio Networks Using Fuzzy Logic
Nisar A. Lala
2013-10-01
Full Text Available Cognitive radio is a technology initiated by many research organizations and academic institutions to raise the spectrum utilization of underutilized channels in order to alleviate spectrum scarcity problem to a larger extent. Spectrum handoff is initiated due to appearance of primary user (PU on the channels occupied by the secondary user (SU at that time and location or interference to the PU exceeds the certain threshold. In this paper, we propose a novel spectrum handoff algorithm using fuzzy logic based approach that does two important functions: 1 adjusts transmission power of SU intelligently in order to avoid handoff by reducing harmful interference to PUs and 2 takes handoff decisions intelligently in the light of new parameter such as expected holding time (HT of the channel as one of its antecedent. Simulated results show impact analysis of selection of the channel in the light of HT information and the comparison with random selection algorithm demonstrates that there is considerable reduction in handoff rate of SU.
Fuzzy-Based Adaptive Hybrid Burst Assembly Technique for Optical Burst Switched Networks
Abubakar Muhammad Umaru; Muhammad Shafie Abd Latiff; Yahaya Coulibaly
2014-01-01
The optical burst switching (OBS) paradigm is perceived as an intermediate switching technology for future all-optical networks. Burst assembly that is the first process in OBS is the focus of this paper. In this paper, an intelligent hybrid burst assembly algorithm that is based on fuzzy logic is proposed. The new algorithm is evaluated against the traditional hybrid burst assembly algorithm and the fuzzy adaptive threshold (FAT) burst assembly algorithm via simulation. Simulation results sh...
Bayesian Network Models for Adaptive Testing
Plajner, Martin; Vomlel, Jiří
Achen: Sun SITE Central Europe, 2016 - (Agosta, J.; Carvalho, R.), s. 24-33. (CEUR Workshop Proceedings. Vol 1565). ISSN 1613-0073. [The Twelfth UAI Bayesian Modeling Applications Workshop (BMAW 2015). Amsterdam (NL), 16.07.2015] R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Bayesian networks * Computerized adaptive testing Subject RIV: JD - Computer Applications, Robotics http://library.utia.cas.cz/separaty/2016/MTR/plajner-0458062.pdf
Adaptive scheduling in cellular access, wireless mesh and IP networks
Nieminen, Johanna
2011-01-01
Networking scenarios in the future will be complex and will include fixed networks and hybrid Fourth Generation (4G) networks, consisting of both infrastructure-based and infrastructureless, wireless parts. In such scenarios, adaptive provisioning and management of network resources becomes of critical importance. Adaptive mechanisms are desirable since they enable a self-configurable network that is able to adjust itself to varying traffic and channel conditions. The operation of adaptive me...
A Neural Network for Generating Adaptive Lessons
Hassina Seridi-Bouchelaghem
2005-01-01
Full Text Available Traditional sequencing technology developed in the field of intelligent tutoring systems have not find an immediate place in large-scale Web-based education. This study investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment over the Web. An approach for adaptive pedagogical hypermedia document generation is proposed and implemented in a prototype called KnowledgeClass. This approach is based on a specialized artificial neural network model. The system allows automatic generation of individualised courses according to the learners goal and previous knowledge and can dynamically adapt the course according to the learners success in acquiring knowledge. Several experiments showed the effectiveness of the proposed method.
The Fuzzy Logic of Network Connectivity in Mouse Visual Thalamus.
Morgan, Josh Lyskowski; Berger, Daniel Raimund; Wetzel, Arthur Willis; Lichtman, Jeff William
2016-03-24
In an attempt to chart parallel sensory streams passing through the visual thalamus, we acquired a 100-trillion-voxel electron microscopy (EM) dataset and identified cohorts of retinal ganglion cell axons (RGCs) that innervated each of a diverse group of postsynaptic thalamocortical neurons (TCs). Tracing branches of these axons revealed the set of TCs innervated by each RGC cohort. Instead of finding separate sensory pathways, we found a single large network that could not be easily subdivided because individual RGCs innervated different kinds of TCs and different kinds of RGCs co-innervated individual TCs. We did find conspicuous network subdivisions organized on the basis of dendritic rather than neuronal properties. This work argues that, in the thalamus, neural circuits are not based on a canonical set of connections between intrinsically different neuronal types but, rather, may arise by experience-based mixing of different kinds of inputs onto individual postsynaptic cells. PMID:27015312
Abdul Kareem
2012-07-01
Full Text Available This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness of the proposed controller over the first order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on a DC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desired transient response without causing chattering and error under steady-state conditions. The proposed controller is able to give robust performance in terms of rejection to input voltage variations and load variations.
Microsphere-based immunoassay integrated with a microfluidic network to perform logic operations
Lab on a chip (LOC) intelligent diagnostics can be described by molecular logic-based circuits. We report on the development of an LOC approach with logic capability for screening combinations of antigen and antibody in the same sample. A microsphere-based immunoassay was integrated with a microfluidic network device to perform the logic operations AND and INHIBIT. Using the clinically relevant biomarkers TNF-α cytokine and anti-TNF-α antibody, we obtained a fluorescent output in the presence of both inputs. This results in an AND operation, while the presence of only one specific input results in a different fluorescent signal, thereby indicating the INHIBIT operation. This approach demonstrates the effective use of molecular logic computation for developing portable, point-of-care technologies for diagnostic purposes due to fast detection times, minimal reagent consumption and low costs. This model system may be further expanded to screening of multiple disease markers, combinatorial logic applications, and developing “smart” sensors and therapeutic technologies. (author)
Fuzzy logic and neural networks in artificial intelligence and pattern recognition
Sanchez, Elie
1991-10-01
With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.
On Capability-Related Adaptation in Networked Service Systems
Finn Arve Aagesen; Patcharee Thongtra
2012-01-01
Adaptability is a property related to engineering as well as to the execution of networked service systems. This publication considers issues of adaptability both within a general and a scoped view. The generalview considers issues of adaptation at two levels: 1) System of entities, functions and adaptability types, and 2) Architectures supporting adaptability. Adaptability types defined are capability-related, functionality-related and context-related adaptation. The scoped view of the publi...
GMAG Dissertation Award Talk: All Spin Logic -- Multimagnet Networks interacting via Spin currents
Srinivasan, Srikant
2012-02-01
Digital logic circuits have traditionally been based on storing information as charge on capacitors, and the stored information is transferred by controlling the flow of charge. However, electrons carry both charge and spin, the latter being responsible for magnetic phenomena. In the last few decades, there has been a significant improvement in our ability to control spins and their interaction with magnets. All Spin Logic (ASL) represents a new approach to information processing where spins and magnets now mirror the roles of charges and capacitors in conventional logic circuits. In this talk I first present a model [1] that couples non-collinear spin transport with magnet-dynamics to predict the switching behavior of the basic ASL device. This model is based on established physics and is benchmarked against available experimental data that demonstrate spin-torque switching in lateral structures. Next, the model is extended to simulate multi-magnet networks coupled with spin transport channels. The simulations suggest ASL devices have the essential characteristics for building logic circuits. In particular, (1) the example of an ASL ring oscillator [2, 3] is used to provide a clear signature of directed information transfer in cascaded ASL devices without the need for external control circuitry and (2) a simulated NAND [4] gate with fan-out of 2 suggests that ASL can implement universal logic and drive subsequent stages. Finally I will discuss how ASL based circuits could also have potential use in the design of neuromorphic circuits suitable for hybrid analog/digital information processing because of the natural mapping of ASL devices to neurons [4]. [4pt] [1] B. Behin-Aein, A. Sarkar, S. Srinivasan, and S. Datta, ``Switching Energy-Delay of All-Spin Logic devices,'' Appl. Phys. Lett., 98, 123510 (2011).[0pt] [2] S. Srinivasan, A. Sarkar, B. Behin-Aein, and S. Datta, ``All Spin Logic Device with Inbuilt Non-reciprocity,'' IEEE Trans. Magn., 47, 10 (2011).[0pt] [3
Hu, Junjie; Zecchino, Antonio; Marinelli, Mattia
2016-01-01
load and an electric vehicle, which has the vehicle-to-grid function. Three control logics of the OLTC transformer are described in the study. The three control logics are classified based on their control objectives and control inputs, which include network currents and voltages, and can be measured...
Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor
Ondrej Linda; Todd Vollmer; Jason Wright; Milos Manic
2011-04-01
Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.
Traffic Engineering and Quality of Experience in MPLS Network by Fuzzy Logic characterization
Satya Prakash Rout,
2015-08-01
Full Text Available This paper proposes a load balancing algorithm using fuzzy logic so that maximum Quality of Experience can be achieved. Avoidance of congestion is one of the major performance objectives of traffic engineering in MPLS networks. Load balancing can prevent the congestion caused due to inefficient allocation of network resources. Another aspect of the network performance is Quality of Experience (QoE. QoE in telecommunications terminology, it is a measurement used to determine how well that network is satisfying the end user's requirements. The Mean Opinion Score (MOS is an important factor in determining the QoE. MOS is a measurement of the quality delivered by the network based on human perception at the destination end. Specifically we can tell mean opinion score (MOS provides a numerical indication of the perceived quality of received media after compression and/or transmission.
PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller
MARABA, V. A.
2011-11-01
Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.
Quantum Logic Networks for Probabilistic and Controlled Teleportation of Unknown Quantum States
GAO Ting
2004-01-01
We present simplification schemes for probabilistic and controlled teleportation of the unknown quantum states of both one particle and two particles and construct efficient quantum logic networks for implementing the new schemes by means of the primitive operations consisting of single-qubit gates, two-qubit controlled-not gates, Von Neumann measurement, and classically controlled operations. In these schemes the teleportation are not always successful but with certain probability.
Quantum logic networks for controlled teleportation of a single particle via W state
Yuan Hong-Chun; Qi Kai-Guo
2005-01-01
We discuss the scheme for probabilistic and controlled teleportation of an unknown state of one particle using the general three-particle W state as the quantum channel. The feature of this scheme is that teleportation between two sides depends on the agreement of the third side (Charlie), who may participate the process of quantum teleportation as a supervisor. In addition, we also construct efficient quantum logic networks for implementing the new scheme by means of the primitive operations.
Statistical, Logic-Based, and Neural Networks Based Methods for Mining Rules from Data
Holeňa, Martin
Dordrecht: Kluwer Academic Publishers, 2002 - (Hyder, A.; Shahbazian, E.; Waltz, E.), s. 511-532. (NATO Science Series). ISBN 1-4020-0722-1. [NATO Advanced study Institute on MSDF. Pitlochry (GB), 25.06.2000-07.07.2000] R&D Projects: GA AV ČR IAB2030007 Institutional research plan: AV0Z1030915 Keywords : data mining * integrative framework * observational logic * statistical hypotheses testing * rule extraction with artificial neural networks Subject RIV: BA - General Mathematics
Class-Based Constraint-Based Routing with Implemented Fuzzy Logic in MPLS-TE Networks
Michal Pištek
2014-01-01
Full Text Available The paper deals with constraint-based routing (CBR in MPLS-TE networks and proposes a new CBR algorithm based on fuzzy logic called Fuzzy Class-Based Algorithm (FCBA. Multiprotocol label switching with traffic engineering (MPLS-TE networks represent a popular mechanism to effectively use resources of service providers’ core networks. The paths can be either built by administrators (explicit routing or built by using existing routing algorithms which mostly decide based on the shortest paths towards the destination which might not be sufficient in nowadays’ multimedia networks. To address this problem various CBR algorithms have emerged which take into consideration various aspects important to existing traffic like QoS parameters or administrative policies. FCBA makes routing decisions based on traffic classes and by using fuzzy logic we can assign normalized values to various constraints based on the traffic class’ preferences (e.g., low delay paths for voice traffic and network administrator’s preferences (e.g., avoiding congested links. The paper provides comparison of FCBA with existing CBR approaches based on their ability to provide QoS parameters loss. The simulations show that FCBA provides the best results for the highest priority traffic where it uses lower priority traffic to efficiently utilize the network.
Fuzzy-Logic Based Probabilistic Broadcasting Technique for Mobile Adhoc Networks
Tasneem Bano
2012-05-01
Full Text Available Broadcasting also called as flooding is one of the earliest research interests in mobile ad hoc networking. The main objective of broadcasting scheme is to avoid broadcast storm problem, provide good network performance and scalability. Existing probabilistic scheme consider only one parameter such as network density, node location or counter value in calculating probability value for broadcasting. These probability scheme cannot change their broadcasting approach even though the objectives of the system may change based on node characteristics or network conditions. There is no probabilistic broadcasting schemes which consider multiple parameters for determining the probability to broadcast. The parameters that greatly affect broadcasting is the network density, node location, etc. in this paper a new probabilistic broadcasting algorithm has been proposed that aims to utilize up to date local topological characteristics of intermediate nodes such as network density and node location. As we are using multiple values to calculate probability for broadcasting there is a need of optimization technique (such as Fuzzy Logic. With the use of fuzzy logic the proposed algorithm can keep good balance between rebroadcast efficiency, reachability and throughput.
Cooperative Media Streaming Using Adaptive Network Compression
Møller, Janus Heide; Sørensen, Jesper Hemming; Krigslund, Rasmus;
2008-01-01
an adaptive hybrid between LC and MDC. In order to facilitate the use of MDC-CC, a new overlay network approach is proposed, using tree of meshes. A control system for managing description distribution and compression in a small mesh is implemented in the discrete event simulator NS-2. The two...... media distribution using traditional approaches. In particular, the asymmetric relationship between the uplink and the downlink bandwidth makes the cooperative distribution difﬁcult. A promising concept, termed MDC with Conditional Compression (MDC-CC), has been proposed [11], which essentially acts as...
Adaptive-network models of swarm dynamics
Huepe, Cristian [614 N Paulina Street, Chicago, IL 60622-6062 (United States); Zschaler, Gerd; Do, Anne-Ly; Gross, Thilo, E-mail: cristian@northwestern.edu [Max-Planck-Institut fuer Physik komplexer Systeme, Noethnitzer Strasse 38, 01187 Dresden (Germany)
2011-07-15
We propose a simple adaptive-network model describing recent swarming experiments. Exploiting an analogy with human decision making, we capture the dynamics of the model using a low-dimensional system of equations permitting analytical investigation. We find that the model reproduces several characteristic features of swarms, including spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, and intermittency. Reproducing these experimental observations using a non-spatial model suggests that spatial geometry may have less of an impact on collective motion than previously thought.
Adaptive-network models of swarm dynamics
We propose a simple adaptive-network model describing recent swarming experiments. Exploiting an analogy with human decision making, we capture the dynamics of the model using a low-dimensional system of equations permitting analytical investigation. We find that the model reproduces several characteristic features of swarms, including spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, and intermittency. Reproducing these experimental observations using a non-spatial model suggests that spatial geometry may have less of an impact on collective motion than previously thought.
Quantitative Adaptive RED in Differentiated Service Networks
LONG KePing(隆克平); WANG Qian(王茜); CHENG ShiDuan(程时端); CHEN JunLiang(陈俊亮)
2003-01-01
This paper derives a quantitative model between RED (Random Early Detection)maxp and committed traffic rate for token-based marking schemes in DiffServ IP networks. Then,a DiffServ Quantitative RED (DQRED) is presented, which can adapt its dropping probabilityto marking probability of the edge router to reflect not only the sharing bandwidth but also therequirement of performance of these services. Hence, DQRED can cooperate with marking schemesto guarantee fairness between different DiffServ AF class services. A new marking probabilitymetering algorithm is also proposed to cooperate with DQRED. Simulation results verify thatDQRED mechanism can not only control congestion of DiffServ network very well, but also satisfydifferent quality requirements of AF class service. The performance of DQRED is better than thatof WRED.
Ondrej Linda; Todd Vollmer; Jim Alves-Foss; Milos Manic
2011-08-01
Resiliency and cyber security of modern critical infrastructures is becoming increasingly important with the growing number of threats in the cyber-environment. This paper proposes an extension to a previously developed fuzzy logic based anomaly detection network security cyber sensor via incorporating Type-2 Fuzzy Logic (T2 FL). In general, fuzzy logic provides a framework for system modeling in linguistic form capable of coping with imprecise and vague meanings of words. T2 FL is an extension of Type-1 FL which proved to be successful in modeling and minimizing the effects of various kinds of dynamic uncertainties. In this paper, T2 FL provides a basis for robust anomaly detection and cyber security state awareness. In addition, the proposed algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental cyber-security test-bed.
Adaptive bipartite consensus on coopetition networks
Hu, Jiangping; Zhu, Hong
2015-07-01
In this paper, a bipartite consensus tracking problem is considered for a group of autonomous agents on a coopetition network, on which the agents interact cooperatively and competitively simultaneously. The coopetition network involves positive and negative edges and is conveniently modeled by a signed graph. Additionally, the dynamics of all the agents are subjected to unknown disturbances, which are represented by linearly parameterized models. An adaptive estimation scheme is designed for each agent by virtue of the relative position measurements and the relative velocity measurements from its neighbors. Then a consensus tracking law is proposed for a new distributed system, which uses the relative measurements as the new state variables. The convergence of the consensus tracking error and the parameter estimation are analyzed even when the coopetition network is time-varying and no more global information about the bounds of the unknown disturbances is available to all the agents. Finally, some simulation results are provided to demonstrate the formation of the bipartite consensus on the coopetition network.
Probabilistic Adaptive Anonymous Authentication in Vehicular Networks
Yong Xi; Ke-Wei Sha; Wei-Song Shi; Loren Schwiebert; Tao Zhang
2008-01-01
Vehicular networks have attracted extensive attention in recent years for their promises in improving safety and enabling other value-added services. Most previous work focuses on designing the media access and physical layer protocols.Privacy issues in vehicular systems have not been well addressed. We argue that privacy is a user-specific concept, and a good privacy protection mechanism should allow users to select the levels of privacy they wish to have. To address this requirement, we propose an adaptive anonymous authentication mechanism that can trade off the anonymity level with computational and communication overheads (resource usage). This mechanism, to our knowledge, is the first effort on adaptive anonymous authentication. The resources used by our protocol are few. A high traffic volume of 2000 vehicles per hour consumes about 60kbps bandwidth, which is less than one percent of the bandwidth of DSRC (Dedicated Short Range Communications). By using adaptive anonymity, the protocol response time can further be improved 2～4 times with lessthan 20% bandwidth overheads.
Adaptive Fuzzy Logic Controllers for DC Drives: A Survey of the State of the art
E. E. El-kholy; A. M. Dabroom; Adel E. El-kholy
2006-01-01
Fuzzy Logic Control (FLC) has gained a great demand in process control applications. Fuzzy Logic (FL) technology enables the use of engineering experience and experimental results in designing an expert system capable of handling uncertain or fuzzy quantities. This paper presents a comprehensive review of FLC in the field of Direct Current (DC) motor drive systems. Firstly, the principles of fuzzy logic theory will be briefly presented. Secondly, the employment of the FL techniques in a contr...
Lu, Thomas; Pham, Timothy; Liao, Jason
2011-01-01
This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.
Zhao, Yongli; Li, Shikun; Song, Yinan; Sun, Ji; Zhang, Jie
2015-12-01
Hierarchical control architecture is designed for software-defined multidomain optical networks (SD-MDONs), and a unified service logic processing model (USLPM) is first proposed for various applications. USLPM-based virtual optical network (VON) provisioning process is designed, and two VON mapping algorithms are proposed: random node selection and per controller computation (RNS&PCC) and balanced node selection and hierarchical controller computation (BNS&HCC). Then an SD-MDON testbed is built with OpenFlow extension in order to support optical transport equipment. Finally, VON provisioning service is experimentally demonstrated on the testbed along with performance verification.
Mobile IPv6 (MIPv6) is one of the pioneer standards that support mobility in IPv6 environment. It has been designed to support different types of technologies for providing seamless communications in next generation network. However, MIPv6 and subsequent standards have some limitations due to its handoff latency. In this paper, a fuzzy logic based mechanism is proposed to reduce the handoff latency of MIPv6 for Layer 2 (L2) by scanning the Access Points (APs) while the Mobile Node (MN) is moving among different APs. Handoff latency occurs when the MN switches from one AP to another in L2. Heterogeneous network is considered in this research in order to reduce the delays in L2. Received Signal Strength Indicator (RSSI) and velocity of the MN are considered as the input of fuzzy logic technique. This technique helps the MN to measure optimum signal quality from APs for the speedy mobile node based on fuzzy logic input rules and makes a list of interfaces. A suitable interface from the list of available interfaces can be selected like WiFi, WiMAX or GSM. Simulation results show 55% handoff latency reduction and 50% packet loss improvement in L2 compared to standard to MIPv6
Anwar, Farhat; Masud, Mosharrof H.; Latif, Suhaimi A.
2013-12-01
Mobile IPv6 (MIPv6) is one of the pioneer standards that support mobility in IPv6 environment. It has been designed to support different types of technologies for providing seamless communications in next generation network. However, MIPv6 and subsequent standards have some limitations due to its handoff latency. In this paper, a fuzzy logic based mechanism is proposed to reduce the handoff latency of MIPv6 for Layer 2 (L2) by scanning the Access Points (APs) while the Mobile Node (MN) is moving among different APs. Handoff latency occurs when the MN switches from one AP to another in L2. Heterogeneous network is considered in this research in order to reduce the delays in L2. Received Signal Strength Indicator (RSSI) and velocity of the MN are considered as the input of fuzzy logic technique. This technique helps the MN to measure optimum signal quality from APs for the speedy mobile node based on fuzzy logic input rules and makes a list of interfaces. A suitable interface from the list of available interfaces can be selected like WiFi, WiMAX or GSM. Simulation results show 55% handoff latency reduction and 50% packet loss improvement in L2 compared to standard to MIPv6.
Designing Logical Topology for Wireless Sensor Networks: A Multi-Chain Oriented Approach
Quazi Mamun
2013-02-01
Full Text Available An optimal logical topology of a wireless sensor ne twork (WSN facilitates the deployed sensor nodes t o communicate with each other with little overheads, lowers energy consumption, lengthens lifetime of th e network, provides scalability, increases reliabilit y, and reduces latency. Designing an optimal logica l topology for a WSN thus needs to consider numerous factors. Chain oriented topologies have been found to offer a number of improvements in energy consump tions, lifetime, and load balancing than other topologies of WSNs. However, they usually suffer fr om latency, scalability, reliability and interferen ce problems. In this paper, we present a chain oriente d logical topology, which offers solutions to those problems. The proposed topology is designed such th at it retains the advantages of the chain oriented topologies, and at the same time, overcomes the pro blems of the chain oriented topology such as latenc y, scalability, and data reliability. The proposed top ology provides a communication abstraction, which c an be easily used to devise a range of application pro tocols. Moreover, the logical topology offers node management, resource management, and other services . The performance of the proposed topology is compared with other topologies in respect to total energy consumption and lifetime of the network.
Integrated Adaptive Analysis and Visualization of Satellite Network Data Project
National Aeronautics and Space Administration — We propose to develop a system that enables integrated and adaptive analysis and visualization of satellite network management data. Integrated analysis and...
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS
Niu, Feng; Doan, AnHai; Shavlik, Jude
2011-01-01
Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current implementations of MLNs do not scale to large real-world data sets, which is preventing their wide-spread adoption. We present Tuffy that achieves scalability via three novel contributions: (1) a bottom-up approach to grounding that allows us to leverage the full power of the relational optimizer, (2) a novel hybrid architecture that allows us to perform AI-style local search efficiently using an RDBMS, and (3) a theoretical insight that shows when one can (exponentially) improve the efficiency of stochastic local search. We leverage (3) to build novel partitioning, loading, and parallel algorithms. We show that our approach outperforms state-of-the-art implementations in both quality and speed on several publicly available datasets.
Opportunistic Adaptive Relaying in Cognitive Radio Networks
Jaafar, Wael; Haccoun, David
2012-01-01
Combining cognitive radio technology with user cooperation could be advantageous to both primary and secondary transmissions. In this paper, we propose a first relaying scheme for cognitive radio networks (called "Adaptive relaying scheme 1"), where one relay node can assist the primary or the secondary transmission with the objective of improving the outage probability of the secondary transmission with respect to a primary outage probability threshold. Upper bound expressions of the secondary outage probability using the proposed scheme are derived over Rayleigh fading channels. Numerical and simulation results show that the secondary outage probability using the proposed scheme is lower than that of other relaying schemes. Then, we extend the proposed scheme to the case where the relay node has the ability to decode both the primary and secondary signals and also can assist simultaneously both transmissions. Simulations show the performance improvement that can be obtained due to this extension in terms of...
Link-based formalism for time evolution of adaptive networks
Zhou, Jie; Chen, Guanrong
2013-01-01
Network topology and nodal dynamics are two fundamental stones of adaptive networks. Detailed and accurate knowledge of these two ingredients is crucial for understanding the evolution and mechanism of adaptive networks. In this paper, by adopting the framework of the adaptive SIS model proposed by Gross et al. [Phys. Rev. Lett. 96, 208701 (2006)] and carefully utilizing the information of degree correlation of the network, we propose a link-based formalism for describing the system dynamics with high accuracy and subtle details. Several specific degree correlation measures are introduced to reveal the coevolution of network topology and system dynamics.
Adaptive training of feedforward neural networks by Kalman filtering
Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)
Song, Mingzhou (Joe) [New Mexico State University, Las Cruces; Lewis, Chris K. [New Mexico State University, Las Cruces; Lance, Eric [New Mexico State University, Las Cruces; Chesler, Elissa J [ORNL; Kirova, Roumyana [Bristol-Myers Squibb Pharmaceutical Research & Development, NJ; Langston, Michael A [University of Tennessee, Knoxville (UTK); Bergeson, Susan [Texas Tech University, Lubbock
2009-01-01
The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from high-throughput transcriptomic data is addressed. A network reconstruction algorithm was developed that uses the statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. Using temporal gene expression data collected from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol, this algorithm identified genes from a major neuronal pathway as putative components of the alcohol response mechanism. Three of these genes have known associations with alcohol in the literature. Several other potentially relevant genes, highlighted and agreeing with independent results from literature mining, may play a role in the response to alcohol. Additional, previously-unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.
Speed Adaptation in Urban Road Network Management
Raiyn Jamal
2016-06-01
Full Text Available Various forecasting schemes have been proposed to manage traffic data, which is collected by videos cameras, sensors, and mobile phone services. However, these are not sufficient for collecting data because of their limited coverage and high costs for installation and maintenance. To overcome the limitations of these tools, we introduce a hybrid scheme based on intelligent transportation system (ITS and global navigation satellite system (GNSS. Applying the GNSS to calculate travel time has proven efficient in terms of accuracy. In this case, GNSS data is managed to reduce traffic congestion and road accidents. This paper introduces a short-time forecasting model based on real-time travel time for urban heterogeneous road networks. Travel time forecasting has been achieved by predicting travel speeds using an optimized exponential moving Average (EMA model. Furthermore for speed adaptation in heterogeneous road networks, it is necessary to introduce asuitable control strategy for longitude, based on the GNSS. GNSS products provide worldwide and real-time services using precise timing information and, positioning technologies.
Opinion dynamics on an adaptive random network
Benczik, I. J.; Benczik, S. Z.; Schmittmann, B.; Zia, R. K. P.
2009-04-01
We revisit the classical model for voter dynamics in a two-party system with two basic modifications. In contrast to the original voter model studied in regular lattices, we implement the opinion formation process in a random network of agents in which interactions are no longer restricted by geographical distance. In addition, we incorporate the rapidly changing nature of the interpersonal relations in the model. At each time step, agents can update their relationships. This update is determined by their own opinion, and by their preference to make connections with individuals sharing the same opinion, or rather with opponents. In this way, the network is built in an adaptive manner, in the sense that its structure is correlated and evolves with the dynamics of the agents. The simplicity of the model allows us to examine several issues analytically. We establish criteria to determine whether consensus or polarization will be the outcome of the dynamics and on what time scales these states will be reached. In finite systems consensus is typical, while in infinite systems a disordered metastable state can emerge and persist for infinitely long time before consensus is reached.
Brain network adaptability across task states.
Elizabeth N Davison
2015-01-01
Full Text Available Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific and across (task-general brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.
A fuzzy logic based clustering strategy for improving vehicular ad-hoc network performance
Ali Çalhan
2015-04-01
This paper aims to improve the clustering of vehicles by using fuzzy logic in Vehicular Ad-Hoc Networks (VANETs) for making the network more robust and scalable. High mobility and scalability are two vital topics to be considered while providing efficient and reliable communication in VANETs. Clustering is of crucial significance in order to cope with the dynamic features of the VANET topologies. Plenty of parameters related to user preferences, network conditions and application requirements such as speed of mobile nodes, distance to cluster head, data rate and signal strength must be evaluated in the cluster head selection process together with the direction parameter for highly dynamic VANET structures. The prominent parameters speed, acceleration, distance and direction information are taken into account as inputs of the proposed cluster head selection algorithm. The simulation results show that developed fuzzy logic (FL) based cluster head selection algorithm (CHSA) has stable performance in various scenarios in VANETs. This study has also shown that the developed CHSAFL satisfies well the highly demanding requirements of both low speed and high speed vehicles on two-way multilane highway
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
2000-01-01
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
Fuzzy logic and artificial neural networks for nuclear power plant applications
This paper discusses the feasibility of applying fuzzy logic and neural networks to plant-wide monitoring, diagnostics, and control problems. Different data sets are gathered from several sources including two commercial Pressurized Water Reactors (PWR), the Experimental Breeder Reactor-II (EBR-II), and the conceptual design of Modular Liquid-Metal Reactor (PRISM). These data sets are used to illustrate applications to operating processes, and to PRISM design. The results show that the artificial intelligence approach to a number of operational tasks can considerably improve the safety and availability of nuclear power generation
Lam, H K; Nguyen, Hung T
2012-01-01
This book focuses on computational intelligence techniques and their applications - fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications.Fundamental concepts and essential analysis of various computatio
Adaptive-impulsive synchronization of uncertain complex dynamical networks
This Letter studies adaptive-impulsive synchronization of uncertain complex dynamical networks. Based on the stability analysis of impulsive system, several network synchronization criteria for local and global adaptive-impulsive synchronization are established. Numerical example is also given to illustrate the results
A Survey of Paraconsistent Logics
Middelburg, C A
2011-01-01
A survey of paraconsistent logics that are prominent representatives of the different approaches that have been followed to develop paraconsistent logics is provided. The paraconsistent logics that will be discussed are an enrichment of Priest's logic LP, the logic RM3 from the school of relevance logic, da Costa's logics Cn, Jaskowski's logic D2, and Subrahmanian's logics Ptau. A deontic logic based on the first of these logics will be discussed as well. Moreover, some proposed adaptations of the AGM theory of belief revision to paraconsistent logics will be mentioned.
Lodowski Kerrie H
2009-01-01
Full Text Available Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.
Water level forecasting through fuzzy logic and artificial neural network approaches
S. Alvisi
2006-01-01
Full Text Available In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively. All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of the three models is performed by using the same input and output variables. However, in order to evaluate their capability to deal with different levels of information, two different input sets are considered. The former is characterized by significant spatial and time aggregated rainfall information, while the latter considers rainfall information more distributed in space and time. The analysis is made with great attention to the reliability and accuracy of each model, with reference to the Reno river at Casalecchio di Reno (Bologna, Italy. It is shown that the two models based on the fuzzy logic approaches perform better when the physical phenomena considered are synthesised by both a limited number of variables and IF-THEN logic statements, while the ANN approach increases its performance when more detailed information is used. As regards the reliability aspect, it is shown that the models based on the fuzzy logic approaches may fail unexpectedly to forecast the water levels, in the sense that in the testing phase, some input combinations are not recognised by the rule system and thus no forecasting is performed. This problem does not occur in the ANN approach.
Synchronization of general complex networks via adaptive control schemes
Ping He; Chun-Guo Jing; Chang-Zhong Chen; Tao Fan; Hassan Saberi Nik
2014-03-01
In this paper, the synchronization problem of general complex networks is investigated by using adaptive control schemes. Time-delay coupling, derivative coupling, nonlinear coupling etc. exist universally in real-world complex networks. The adaptive synchronization scheme is designed for the complex network with multiple class of coupling terms. A criterion guaranteeing synchronization of such complex networks is established by employing the Lyapunov stability theorem and adaptive control schemes. Finally, an illustrative example with numerical simulation is given to show the feasibility and efficiency of theoretical results.
Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network
LI Yan; FAN Xiao-ping
2008-01-01
An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network.The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level.The control level decides the signal tunings in an intersection with a fuzzy logic controller.The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one.Consequently the system performances are improved.A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections.So the AFC combined with the WCC can be applied in a road network for signal timings.Simulations of the AFC on a real traffic scenario have been conducted.Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.
Adaptive Synchronization of Complex Dynamical Networks with State Predictor
Yuntao Shi; Bo Liu; Xiao Han
2013-01-01
This paper addresses the adaptive synchronization of complex dynamical networks with nonlinear dynamics. Based on the Lyapunov method, it is shown that the network can synchronize to the synchronous state by introducing local adaptive strategy to the coupling strengths. Moreover, it is also proved that the convergence speed of complex dynamical networks can be increased via designing a state predictor. Finally, some numerical simulations are worked out to illustrate the analytical results.
Electrooptical adaptive switching network for the hypercube computer
Chow, E.; Peterson, J.
1988-01-01
An all-optical network design for the hyperswitch network using regular free-space interconnects between electronic processor nodes is presented. The adaptive routing model used is described, and an adaptive routing control example is presented. The design demonstrates that existing electrooptical techniques are sufficient for implementing efficient parallel architectures without the need for more complex means of implementing arbitrary interconnection schemes. The electrooptical hyperswitch network significantly improves the communication performance of the hypercube computer.
ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL
无
2002-01-01
An artificial neural network(ANN) and a self-adjusting fuzzy logic controller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.
Farnaz Pakdeland
2016-08-01
Full Text Available Wireless sensor network is comprised of several sensor nodes. The retaining factors influence the network operation. In the clustering structure the cluster head failure can cause loss of information.The aim of this paper is to increase tolerance error in the cluster head node. At first, paying attention to the producing balance in the density of the cluster cause to postpone the death time of the cluster head node and lessen the collision due to the lack of the energy balance in clusters. The innovation in this stage is formed by using two fuzzy logic systems. One in the phase of evaluation of the cluster head chance, and the other in the phase of producing balance and the nodes migration to the qualified clusters to increase balance, Then the focus is on recognizing and repairing the cluster head fault.
Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms
Siddique, Nazmul
2014-01-01
Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...
A hybrid neural network---fuzzy logic approach to nuclear power plant transient identification
A methodology is presented that couples pretrained artificial neural networks (ANNs) to rule-based fuzzy logic systems, for the purpose of distinguishing different transients in a Nuclear Power Plant (NPP). A model referenced approach is utilized in order to provide timely concise and task specific information about the status of the system under consideration. A rule based system integrated with a set of neural networks, that typify steady-state operation as well as different transients, diagnoses the state of the system and identifies the type of transient under development. ANNs produce their response in the form of membership functions which independently represent individual transients and the steady-state. Membership functions condense functionally relevant information in order for the overall system to successfully perform transient identification, in a time span faster or at least comparable to that of the transient development. To demonstrate the proposed methodology simulated accidents corresponding to a particular category of transients are used. The results obtained demonstrate the excellent noise tolerance of the ANNs and suggest a new approach for transient identification within the framework of fuzzy logic
Geographic Routing Using Logical Levels in Wireless Sensor Networks for Sensor Mobility
Yassine SABRI; Najib EL KAMOUN
2015-01-01
In this paper we propose an improvement to the GRPW algorithm for wireless sensor networks called GRPW-M , which collects data in a wireless sensor network (WSN) using a mobile nodes. Performance of GRPW algorithm algorithm depends heavily on the immobile sensor nodes . This prediction can be hard to do. For that reason, we propose a modified algorithm that is able to adapt to the current situation in the network in which the sensor node considered mobile. The goal of the proposed algorithm i...
LTE Adaptation for Mobile Broadband Satellite Networks
Bastia Francesco
2009-01-01
Full Text Available One of the key factors for the successful deployment of mobile satellite systems in 4G networks is the maximization of the technology commonalities with the terrestrial systems. An effective way of achieving this objective consists in considering the terrestrial radio interface as the baseline for the satellite radio interface. Since the 3GPP Long Term Evolution (LTE standard will be one of the main players in the 4G scenario, along with other emerging technologies, such as mobile WiMAX; this paper analyzes the possible applicability of the 3GPP LTE interface to satellite transmission, presenting several enabling techniques for this adaptation. In particular, we propose the introduction of an inter-TTI interleaving technique that exploits the existing H-ARQ facilities provided by the LTE physical layer, the use of PAPR reduction techniques to increase the resilience of the OFDM waveform to non linear distortion, and the design of the sequences for Random Access, taking into account the requirements deriving from the large round trip times. The outcomes of this analysis show that, with the required proposed enablers, it is possible to reuse the existing terrestrial air interface to transmit over the satellite link.
Temporal percolation of a susceptible adaptive network
Valdez, L D; Braunstein, L A
2013-01-01
In the last decades, due to the appearance of many diseases such as SARS and the H1N1 flu strain, many authors studied the impact of the disease spreading in the evolution of the infected individuals using the susceptible-infected-recovered model. However, few authors focused on the temporal behavior of the susceptible individuals. Recently it was found that in an epidemic spreading, the dynamic of the size of the biggest susceptible cluster can be explained by a temporal node void percolation [Valdez et al PLoS ONE 7, e44188 (2012)]. It was shown that the size of the biggest susceptible cluster is the order parameter of this temporal percolation where the control parameter can be related to the number of links between susceptible individuals at a given time. As a consequence, there is a critical time at which the biggest susceptible cluster is destroyed. In this paper, we study the susceptible-infected-recovered model in an adaptive network where an intermittent social distancing strategy is applied. In this...
Adaptive Mobile Positioning in WCDMA Networks
Dong B.
2005-01-01
Full Text Available We propose a new technique for mobile tracking in wideband code-division multiple-access (WCDMA systems employing multiple receive antennas. To achieve a high estimation accuracy, the algorithm utilizes the time difference of arrival (TDOA measurements in the forward link pilot channel, the angle of arrival (AOA measurements in the reverse-link pilot channel, as well as the received signal strength. The mobility dynamic is modelled by a first-order autoregressive (AR vector process with an additional discrete state variable as the motion offset, which evolves according to a discrete-time Markov chain. It is assumed that the parameters in this model are unknown and must be jointly estimated by the tracking algorithm. By viewing a nonlinear dynamic system such as a jump-Markov model, we develop an efficient auxiliary particle filtering algorithm to track both the discrete and continuous state variables of this system as well as the associated system parameters. Simulation results are provided to demonstrate the excellent performance of the proposed adaptive mobile positioning algorithm in WCDMA networks.
A fuzzy logic and neural network-based intelligent mine winder-motion control system. Part 1
Szklarski, L.; Fijalkowski, B. [Akademia Gorniczo-Hutnicza, Krakow (Poland)
1994-09-01
A fuzzy logic and neural network-based intelligent mine winder-motion control system would appear to be a useful contribution to mining automation where efforts are continually being made to reduce costs. This paper briefly describes macroelectronic and microelectronic mine winder propulsion and dispulsion spheres discussing their advantages and disadvantages. A fuzzy logic and neural network-based intelligent mine winder-motion control used in modern real-time expert hypersphere techniques is described. Amongst examples presented is an electromechanically-driven and fluidomechanically-braked intelligent mine winder. This incorporates a four-groove rope motorised pulley-fitted with an AC-AC, AC-DC-AC or DC-AC macrocommutator pulley-hub motor, and an artificial intelligence application-specific integrated circuit, which is fuzzy logic and neural network-based programmable, together with a learning PID microcontroller. 13 refs., 2 figs.
Dynamic multimedia stream adaptation and rate control for heterogeneous networks
SZWABE Andrzej; SCHORR Andreas; HAUCK Franz J.; KASSLER Andreas J.
2006-01-01
Dynamic adaptation of multimedia content is seen as an important feature of next generation networks and pervasive systems enabling terminals and applications to adapt to changes in e.g. context, access network, and available Quality-of-Service(QoS) due to mobility of users, devices or sessions. We present the architecture of a multimedia stream adaptation service which enables communication between terminals having heterogeneous hardware and software capabilities and served by heterogeneous networks. The service runs on special content adaptation nodes which can be placed at any location within the network. The flexible structure of our architecture allows using a variety of different adaptation engines. A generic transcoding engine is used to change the codec of streams. An MPEG-21 Digital Item Adaptation (DIA) based transformation engine allows adjusting the data rate of scalable media streams. An intelligent decision-taking engine implements adaptive flow control which takes into account current network QoS parameters and congestion information. Measurements demonstrate the quality gains achieved through adaptive congestion control mechanisms under conditions typical for a heterogeneous network.
Development of an Adaptive Routing Mechanism in Software-Deﬁned Networks
A. N. Noskov
2015-10-01
Full Text Available The purpose of this work is to develop a unitary mechanism of adaptive routing of diﬀerent kinds, basing on the current requirements on the quality of service. The software conﬁguration of a network is the technology of the future. The trend in communication systems constantly conﬁrms this fact. However, the application of this technology in its current form is justiﬁed only in large networks of technology giants and telecom operators. Today we have a large number of dynamic routing protocols to route big volume traﬃc in communication networks. Our task is to create the solution that can use the opportunities of each node to make a decision on the transmission of information by all possible means for each type of traﬃc. Achieving this goal is possible by solving the problem of the development of generalized metrics, which details the links between devices in the network, and the problem of establishing a framework of adaptive logical network topology (route management to ensure the quality of the whole network in order to meet the current requirements on the quality of a particular type service.
Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks
M. Bazazzadeh
2011-01-01
Full Text Available This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc. are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.
Adapting Bayes Network Structures to Non-stationary Domains
Nielsen, Søren Holbech; Nielsen, Thomas Dyhre
2008-01-01
When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN is gradu......When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN...
Information Theoretic Adaptive Tracking of Epidemics in Complex Networks
Harrington, Patrick L
2013-01-01
Adaptively monitoring the states of nodes in a large complex network is of interest in domains such as national security, public health, and energy grid management. Here, we present an information theoretic adaptive tracking and sampling framework that recursively selects measurements using the feedback from performing inference on a dynamic Bayesian Network. We also present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of hidden node states resulting from actively monitoring the network. Since traditional epidemic thresholds are derived using observation independent Markov chains, the threshold of the posterior should more accurately model the true phase transition of a network. The adaptive tracking framework and epidemic threshold should provide insight into modeling the dynamic response of the updated posterior to active intervention and control policies while monitoring modern complex networks.
Memristor-based programmable logic array (PLA) and analysis as Memristive networks.
Lee, Kwan-Hee; Lee, Sang-Jin; Kim, Seok-Man; Cho, Kyoungrok
2013-05-01
A Memristor theorized by Chua in 1971 has the potential to dramatically influence the way electronic circuits are designed. It is a two terminal device whose resistance state is based on the history of charge flow brought about as the result of the voltage being applied across its terminals and hence can be thought of as a special case of a reconfigurable resistor. Nanoscale devices using dense and regular fabrics such as Memristor cross-bar is promising new architecture for System-on-Chip (SoC) implementations in terms of not only the integration density that the technology can offer but also both improved performance and reduced power dissipation. Memristor has the capacity to switch between high and low resistance states in a cross-bar circuit configuration. The cross-bars are formed from an array of vertical conductive nano-wires cross a second array of horizontal conductive wires. Memristors are realized at the intersection of the two wires in the array through appropriate processing technology such that any particular wire in the vertical array can be connected to a wire in the horizontal array by switching the resistance of a particular intersection to a low state while other cross-points remain in a high resistance state. However the approach introduces a number of challenges. The lack of voltage gain prevents logic being cascaded and voltage level degradation affects robustness of the operation. Moreover the cross-bars introduce sneak current paths when two or more cross points are connected through the switched Memristor. In this paper, we propose Memristor-based programmable logic array (PLA) architecture and develop an analytical model to analyze the logic level on the memristive networks. The proposed PLA architecture has 12 inputs maximum and can be cascaded for more input variables with R(off)/R(on) ratio in the range from 55 to 160 of Memristors. PMID:23858841
Association Rule Mining Based Extraction of Semantic Relations Using Markov Logic Network
K.Karthikeyan
2014-10-01
Full Text Available Ontology may be a conceptualization of a website into a human understandable, however machine - readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the in tentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family - tree of a outstanding personalit y employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarch y extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first ord er logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the fre quent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state - of - art method
Adaptive cluster synchronization of directed complex networks with time delays.
Heng Liu
Full Text Available This paper studied the cluster synchronization of directed complex networks with time delays. It is different from undirected networks, the coupling configuration matrix of directed networks cannot be assumed as symmetric or irreducible. In order to achieve cluster synchronization, this paper uses an adaptive controller on each node and an adaptive feedback strategy on the nodes which in-degree is zero. Numerical example is provided to show the effectiveness of main theory. This method is also effective when the number of clusters is unknown. Thus, it can be used in the community recognizing of directed complex networks.
Robust adaptive neural network control with supervisory controller
张天平; 梅建东
2004-01-01
The problem of direct adaptive neural network control for a class of uncertain nonlinear systems with unknown constant control gain is studied in this paper. Based on the supervisory control strategy and the approximation capability of multilayer neural networks (MNNs), a novel design scheme of direct adaptive neural network controller is proposed.The adaptive law of the adjustable parameter vector and the matrix of weights in the neural networks and the gain of sliding mode control term to adaptively compensate for the residual and the approximation error of MNNs is determined by using a Lyapunov method. The approach does not require the optimal approximation error to be square-integrable or the supremum of the optimal approximation error to be known. By theoretical analysis, the closed-loop control system is proven to be globally stable in the sense that all signals involved are bounded, with tracking error converging to zero.Simulation results demonstrate the effectiveness of the approach.
Hybrid Method for the Navigation of Mobile Robot Using Fuzzy Logic and Spiking Neural Networks
Zineb LAOUICI
2014-11-01
Full Text Available the aim of this paper is to present a strategy describing a hybrid approach for the navigation of a mobile robot in a partially known environment. The main idea is to combine between fuzzy logic approach suitable for the navigation in an unknown environment and spiking neural networks approach for solving the problem of navigation in a known environment. In the literature, many approaches exist for the navigation purpose, for solving separately the problem in both situations. Our idea is based on the fact that we consider a mixed environment, and try to exploit the known environment parts for improving the path and time of navigation between the starting point and the target. The Simulation results, which are shown on two simulated scenarios, indicate that the hybridization improves the performance of robot navigation with regard to path length and the time of navigation.
A review on application of neural networks and fuzzy logic to solve hydrothermal scheduling problem
Electrical power system is highly complicated having hydro and thermal mix with large number of machines. To reduce power production cost, hydro and thermal resources are mixed. Hydrothermal scheduling is the optimal coordination of hydro and thermal plants to meet the system load demand at minimum possible operational cost while satisfying the system constraints. Hydrothermal scheduling is dynamic, large scale, non-linear and non-convex optimization problem. The classical techniques have failed in solving such problem. Artificial Intelligence Tools based techniques are used now a day to solve this complex optimization problem because of their no requirements on the nature of the problem. The aim of this research paper is to provide a comprehensive survey of literature related to both Artificial Neural Network (ANN) and Fuzzy Logic (FL) as effective optimization algorithms for the hydrothermal scheduling problem. The outcomes along with the merits and demerits of individual techniques are also discussed. (author)
On the Performance of Adaptive Modulation in Cognitive Radio Networks
Foukalas, F.; Karetsos, G. T.
2013-01-01
We study the performance of cognitive radio networks (CRNs) when incorporating adaptive modulation at the physical layer. Three types of CRNs are considered, namely opportunistic spectrum access (OSA), spectrum sharing (SS) and sensing-based SS. We obtain closed-form expressions for the average spectral efficiency achieved at the secondary network and the optimal power allocation for both continuous and discrete rate types of adaptive modulation assuming perfect channel state information. The...
黄刚; 杨华中; 罗嵘; 汪蕙
2002-01-01
In deep submicron (DSM) integrated circuits (IC), coupling capacitors between inter-connects become dominant over grounded capacitors. As a result, the dynamic power dissipationof one node is no longer only in relation to the signal on that node, and it also depends on signalson its neighbor nodes through coupling capacitors. Thus, for their limitation in dealing with ca-pacitively coupled nets, past jobs on power estimation are facing rigorous challenges and need tobe ameliorated. This paper proposes and proves a simple and fast approach to predicting dynamicpower dissipation of coupled interconnect networks: a coupling capacitor in dynamic CMOS logiccircuits is decoupled and mapped into an equivalent cell containing an XOR gate and a groundedcapacitor, and the whole circuit after mapping, consuming the same power as the original one,could be easily managed by generally-used gate-level power estimation tools. This paper also in-vestigates the correlation coefficient method (CCM). Given the signal probabilities and the correla-tion coefficients between signals, the dynamic power of interconnect networks can be calculatedby using CCM. It can be proved that the decoupling method and CCM draw identical results, that isto say, the decoupling method implicitly preserves correlation properties between signals and thereis no accuracy loss in the decoupling process. Moreover, it is addressed that the coupling capaci-tors in static CMOS circuits could be decoupled and mapped into an equivalent cell containing amore complicated logic block, and the power can be obtained by the probability method for dy-namic CMOS logic circuits.
K. Nattar Kannan
2014-11-01
Full Text Available Wireless Sensor Networks (WSNs is a emerging technology of real time embedded systems for a variety of applications. In general, WSNs has great challenges in the factor of limited computation, energy and memory resources. Clustering techniques play a vital role in WSNs to increase the network lifetime and also made energy efficiency. Existing clustering approaches like LEACH uses neighboring information of the nodes for selecting cluster heads and other nodes spent more energy for transmitting data to cluster head. It was not considered the expected residual energy for selecting a cluster head. In this study, Genetic Algorithm (GA is used to form optimal clusters based on fitness parameters including Cluster Distance (CD, Direct Distance to Base Station (DDBS and Energy of nodes. Also, fuzzy logic approach is applied to select optimal cluster head by using expected residual energy that increases the network lifetime. The aim of the study is providing a solution for unbalanced energy consumption problem in a WSN. The simulation results show that the proposed protocol performs well than other protocols like LEACH and LEACH_ERE.
Adaptive nonlinear control using input normalized neural networks
An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small
Traffic flow on realistic road networks with adaptive traffic lights
de Gier, Jan; Rojas, Omar
2010-01-01
We present a model of traffic flow on generic urban road networks based on cellular automata. We apply this model to an existing road network in the Australian city of Melbourne, using empirical data as input. For comparison, we also apply this model to a square-grid network using hypothetical input data. On both networks we compare the effects of non-adative vs adaptive traffic lights, in which instantaneous traffic state information feeds back into the traffic signal schedule. We observe that not only do adaptive traffic lights result in better averages of network observables, they also lead to significantly smaller fluctuations in these observables. We furthermore compare two different systems of adaptive traffic signals, one which is informed by the traffic state on both upstream and downstream links, and one which is informed by upstream links only. We find that, in general, the total travel time is smallest when using the joint upstream-downstream control strategy.
Stochastic analysis of epidemics on adaptive time varying networks
Kotnis, Bhushan; Kuri, Joy
2013-06-01
Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an “adaptive threshold,” i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.
Linking Individual and Collective Behavior in Adaptive Social Networks
Pinheiro, Flávio L.; Santos, Francisco C.; Pacheco, Jorge M.
2016-03-01
Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N -person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.
Implementation of an Adaptive Learning System Using a Bayesian Network
Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki
2015-01-01
An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…
Adaptation to synchronization in phase-oscillator networks
Arizmendi, Fernando; Zanette, Damian H.
2008-01-01
We introduce an adaptation algorithm by which an ensemble of coupled oscillators with attractive and repulsive interactions is induced to adopt a prescribed synchronized state. While the performance of adaptation is controlled by measuring a macroscopic quantity, which characterizes the achieved degree of synchronization, adaptive changes are introduced at the microscopic level of the interaction network, by modifying the configuration of repulsive interactions. This scheme emulates the disti...
Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.
2003-01-01
Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…
Collaborative Trust Networks in Engineering Design Adaptation
Atkinson, Simon Reay; Maier, Anja; Caldwell, Nicholas;
2011-01-01
); applying the Change Prediction Method (CPM) tool. It posits the idea of the ‘Networks-in-Being’ with varying individual and collective characteristics. [Social] networks are considered to facilitate information exchange between actors. At the same time, networks failing to provide trusted-information can...... hinder effective communication and collaboration. Different combinations of trust may therefore improve or impair the likelihood of information flow, transfer and subsequent action (cause and effect). This paper investigates how analysing different types of network-structures-in-being can support......Within organisations, decision makers have to rely on collaboration with other actors from different disciplines working within highly dynamic and distributed associated networks of varying size and scales. This paper develops control and influence networks within Design Structure Matrices (DSM...
Adaptive optimization and control using neural networks
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Meng, Fanyu; Zhu, Aidong
2008-10-01
A quantum logic network to implement quantum telecloning is presented in this paper. The network includes two parts: the first part is used to create the telecloning channel and the second part to teleport the state. It can be used not only to implement universal telecloning for a bipartite entangled state which is completely unknown, but also to implement the phase-covariant telecloning for one that is partially known. Furthermore, the network can also be used to construct a tele-triplicator. It can easily be implemented in experiment because only single- and two-qubit operations are used in the network.
Meng Fanyu; Zhu Aidong [Department of Physics, College of Science, Yanbian University, Yanji, Jilin 133002 (China)], E-mail: adzhu@ybu.edu.cn
2008-10-28
A quantum logic network to implement quantum telecloning is presented in this paper. The network includes two parts: the first part is used to create the telecloning channel and the second part to teleport the state. It can be used not only to implement universal telecloning for a bipartite entangled state which is completely unknown, but also to implement the phase-covariant telecloning for one that is partially known. Furthermore, the network can also be used to construct a tele-triplicator. It can easily be implemented in experiment because only single- and two-qubit operations are used in the network.
This paper presents a new robust adaptive synchronization method for a class of uncertain dynamical complex networks with network failures and coupling time-varying delays. Adaptive schemes are proposed to adjust controller parameters for the faulty network compensations, as well as to estimate the upper and lower bounds of delayed state errors and perturbations to compensate the effects of delay and perturbation on-line without assuming symmetry or irreducibility of networks. It is shown that, through Lyapunov stability theory, distributed adaptive controllers constructed by the adaptive schemes are successful in ensuring the achievement of asymptotic synchronization of networks in the present of faulty and delayed networks, and perturbation inputs. A Chua's circuit network example is finally given to show the effectiveness of the proposed synchronization criteria. (general)
Adaptive swarm-based routing in communication networks
吕勇; 赵光宙; 苏凡军; 历小润
2004-01-01
Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
Adaptive swarm-based routing in communication networks
吕勇; 赵光宙; 苏凡军; 历小润
2004-01-01
Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features,including adaptation,robustness and distributed,decentralized nature,which are well suited for routing in modern communication networks.This paper describes an adaptive swarm-based routing algorithm that increases convergence speed,reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
Model for cascading failures with adaptive defense in complex networks
This paper investigates cascading failures in networks by considering interplay between the flow dynamic and the network topology, where the fluxes exchanged between a pair of nodes can be adaptively adjusted depending on the changes of the shortest path lengths between them. The simulations on both an artificially created scale-free network and the real network structure of the power grid reveal that the adaptive adjustment of the fluxes can drastically enhance the robustness of complex networks against cascading failures. Particularly, there exists an optimal region where the propagation of the cascade is significantly suppressed and the fluxes supported by the network are maximal. With this understanding, a costless strategy of defense for preventing cascade breakdown is proposed. It is shown to be more effective for suppressing the propagation of the cascade than the recent proposed strategy of defense based on the intentional removal of nodes. (general)
Adaptive Neurons For Artificial Neural Networks
Tawel, Raoul
1990-01-01
Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.
Network-topology-adaptive quantum conference protocols
Zhang Sheng; Wang Jian; Tang Chao-Jing; Zhang Quan
2011-01-01
As an important application of the quantum network communication,quantum multiparty conference has made multiparty secret communication possible.Previous quantum multiparty conference schemes based on quantum data encryption are insensitive to network topology.However,the topology of the quantum network significantly affects the communication efficiency,e.g.,parallel transmission in a channel with limited bandwidth.We have proposed two distinctive protocols,which work in two basic network topologies with efficiency higher than the existing ones.We first present a protocol which works in the reticulate network using Greeberger-Horne-Zeilinger states and entanglement swapping.Another protocol,based on quantum multicasting with quantum data compression,which can improve the efficiency of the network,works in the star-like network.The security of our protocols is guaranteed by quantum key distribution and one-time-pad encryption.In general,the two protocols can be applied to any quantum network where the topology can be equivalently transformed to one of the two structures we propose in our protocols.
A Neural Network for Generating Adaptive Lessons
Hassina Seridi-Bouchelaghem; Toufik Sari; Mokhtar Sellami
2005-01-01
Traditional sequencing technology developed in the field of intelligent tutoring systems have not find an immediate place in large-scale Web-based education. This study investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment over the Web. An approach for adaptive pedagogical hypermedia document generation is proposed and implemented in a prototype called KnowledgeClass. This approach is based on a specialized art...
TCP-Adaptive in High Speed Long Distance Networks
Quan Liu
2014-02-01
Full Text Available With the development of high performance computing and increasing of network bandwidth, more and more applications require fast data transfer over high-speed long-distance networks. Research shows that the standard TCP Reno cannot fulfill the requirement of fast transfer of massive data due to its conservative congestion control mechanism. Some works have been proposed to improve the TCP throughput performance using more aggressive window increasing tactics and obtain substantial achievements. However, they cannot be strictly proved to be comprehensively suitable for high-speed complex network environments. In this paper, we propose TCP-Adaptive, an adaptive congestion control algorithm adjusting the increasing congestion window dynamically. The algorithm improves logarithmic detection procedure for available bandwidth in the flow path by distinguishing the first detection in congestion avoidance and retransmission timeout. On the other hand, an adaptive control algorithm is proposed to achieve better performance in high-speed long-distance networks. The algorithm uses round trip time (RTT variations to predict the congestion trends to update the increments of congestion window. Simulations verify the property of TCP-Adaptive and show satisfying performance in throughput, RTT fairness aspects over high-speed long-distance networks. Especially in sporadic loss environment, TCP-Adaptive shows a significant adaptability with the variations of link quality
Temporal and structural heterogeneities emerging in adaptive temporal networks
Aoki, Takaaki; Gross, Thilo
2015-01-01
We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay between node activity and dynamic exchange of information through links. We study the model by using a master equation approach. Starting from a homogeneous initial configuration, we show that temporal and structural heterogeneities, characteristic of real-world networks, spontaneously emerge. This theoretically tractable model thus contributes to the understanding of the dynamics of human activity and interaction networks.
Adaptive Energy-Aware Gathering Strategy for Wireless Sensor Networks
E M Saad; Awadalla, M. H.; R. R. Darwish
2009-01-01
Energy hole problem is considered one of the most severe threats in wireless sensor networks. In this paper the idea of exploiting sink mobility for the purpose of culling the energy hole problem in hierarchical large-scale wireless sensor networks based on bees algorithm is presented. In the proposed scheme, a mobile sink equipped with a powerful transceiver and battery, traverses the entire field, and periodically gathers data from network cluster heads. The mobile sink follows an adaptive ...
Temporal and structural heterogeneities emerging in adaptive temporal networks
Aoki, Takaaki; Rocha, Luis E. C.; Gross, Thilo
2016-04-01
We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay between node activity and dynamic exchange of information through links. We study the model by using a master equation approach. Starting from a homogeneous initial configuration, we show that temporal and structural heterogeneities, characteristic of real-world networks, spontaneously emerge. This theoretically tractable model thus contributes to the understanding of the dynamics of human activity and interaction networks.
An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks
Ali Safa Sadiq
2014-01-01
Full Text Available We propose an adaptive handover prediction (AHP scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.
Time scales in evolutionary game on adaptive networks
Most previous studies concerning spatial games have assumed strategy updating occurs with a fixed ratio relative to interactions. We here set up a coevolutionary model to investigate how different ratio affects the evolution of cooperation on adaptive networks. Simulation results demonstrate that cooperation can be significantly enhanced under our rewiring mechanism, especially with slower natural selection. Meanwhile, slower selection induces larger network heterogeneity. Strong selection contracts the parameter area where cooperation thrives. Therefore, cooperation prevails whenever individuals are offered enough chances to adapt to the environment. Robustness of the results has been checked under rewiring cost or varied networks.
Time scales in evolutionary game on adaptive networks
Cong, Rui, E-mail: congrui0000@126.com [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Wu, Te; Qiu, Yuan-Ying [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Wang, Long [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Center for Systems and Control, State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing (China)
2014-02-01
Most previous studies concerning spatial games have assumed strategy updating occurs with a fixed ratio relative to interactions. We here set up a coevolutionary model to investigate how different ratio affects the evolution of cooperation on adaptive networks. Simulation results demonstrate that cooperation can be significantly enhanced under our rewiring mechanism, especially with slower natural selection. Meanwhile, slower selection induces larger network heterogeneity. Strong selection contracts the parameter area where cooperation thrives. Therefore, cooperation prevails whenever individuals are offered enough chances to adapt to the environment. Robustness of the results has been checked under rewiring cost or varied networks.
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
Pais, Darren
2013-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcations with respect to investment cost explains the observed hysteretic effect associated with recovery of migration in fragmented environments. Further, we show a minimum connectivity threshold above which there is evolutionary branching into leader and follower populations. For small populations, we show how the topology of the underlying social interaction network influences the emergence and location of leaders in the adaptive system. Our model and analysis can describe other adaptive network dynamics involving collective...
Time-adaptive and history-adaptive multicriterion routing in stochastic, time-dependent networks
Pretolani, Daniele; Nielsen, Lars Relund; Andersen, Kim Allan;
2009-01-01
We compare two different models for multicriterion routing in stochastic time-dependent networks: the classic "time-adaptive'' model and the more flexible "history-adaptive'' one. We point out several properties of the sets of efficient solutions found under the two models. We also devise a method...
Network-based business process management: embedding business logic in communications networks
Pau, Louis-François; Vervest, Peter
2003-01-01
textabstractAdvanced Business Process Management (BPM) tools enable the decomposition of previously integrated and often ill-defined processes into re-usable process modules. These process modules can subsequently be distributed on the Internet over a variety of many different actors, each with their own specialization and economies-of-scale. The economic benefits of process specialization can be huge. However, how should such actors in a business network find, select, and control, the best p...
Adaptive intelligent power systems: Active distribution networks
Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems
Concurrent enhancement of percolation and synchronization in adaptive networks
Eom, Young-Ho; Boccaletti, Stefano; Caldarelli, Guido
2016-06-01
Co-evolutionary adaptive mechanisms are not only ubiquitous in nature, but also beneficial for the functioning of a variety of systems. We here consider an adaptive network of oscillators with a stochastic, fitness-based, rule of connectivity, and show that it self-organizes from fragmented and incoherent states to connected and synchronized ones. The synchronization and percolation are associated to abrupt transitions, and they are concurrently (and significantly) enhanced as compared to the non-adaptive case. Finally we provide evidence that only partial adaptation is sufficient to determine these enhancements. Our study, therefore, indicates that inclusion of simple adaptive mechanisms can efficiently describe some emergent features of networked systems’ collective behaviors, and suggests also self-organized ways to control synchronization and percolation in natural and social systems.
Controling contagious processes on temporal networks via adaptive rewiring
Belik, Vitaly; Hövel, Philipp
2015-01-01
We consider recurrent contagious processes on a time-varying network. As a control procedure to mitigate the epidemic, we propose an adaptive rewiring mechanism for temporary isolation of infected nodes upon their detection. As a case study, we investigate the network of pig trade in Germany. Based on extensive numerical simulations for a wide range of parameters, we demonstrate that the adaptation mechanism leads to a significant extension of the parameter range, for which most of the index nodes (origins of the epidemic) lead to vanishing epidemics. We find that diseases with detection times around a week and infectious periods up to 3 months can be effectively controlled. Furthermore the performance of adaptation is very heterogeneous with respect to the index node. We identify index nodes that are most responsive to the adaptation strategy and quantify the success of the proposed adaptation scheme in dependence on the infectious period and detection times.
Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.
2007-01-01
In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.
Adaptive Capacity Management in Bluetooth Networks
Son, L.T.
With the Internet and mobile wireless development, accelerated by high-speed and low cost VLSI device evolution, short range wireless communications have become more and more popular, especially Bluetooth. Bluetooth is a new short range radio technology that promises to be very convenient, low...... power, and low cost mobile ad hoc solution for the global interconnection of all mobile devices. To implement Bluetooth network as a true mobile ad hoc wireless network operating in short radio range, highly dynamic network environment, low power, and scarce resources, many new research challenges occur......, such as limited wireless bandwidth operation, routing, scheduling, network control, etc. Currently Bluetooth specification particularly does not describe in details about how to implement Quality of Service and Resource Management in Bluetooth protocol stacks. These issues become significant, when the number...
Adaptive computational resource allocation for sensor networks
WANG Dian-hong; FEI E; YAN Yu-jie
2008-01-01
To efficiently utilize the limited computational resource in real-time sensor networks, this paper focu-ses on the challenge of computational resource allocation in sensor networks and provides a solution with the method of economies. It designs a mieroeconomic system in which the applications distribute their computational resource consumption across sensor networks by virtue of mobile agent. Further, it proposes the market-based computational resource allocation policy named MCRA which satisfies the uniform consumption of computational energy in network and the optimal division of the single computational capacity for multiple tasks. The simula-tion in the scenario of target tracing demonstrates that MCRA realizes an efficient allocation of computational re-sources according to the priority of tasks, achieves the superior allocation performance and equilibrium perform-ance compared to traditional allocation policies, and ultimately prolongs the system lifetime.
Improved Adaptive Routing for Multihop IEEE 802.15.6 Wireless Body Area Networks
Shariar Imtiaz
2013-11-01
Full Text Available Wireless Body Area Network has the ability to collect and send data on body measurement to the server through PDA or other device. Nodes (sensors collect vital signs from the body or environmental factor and check them. In IEEE 802.15.6 routing is discussed as a part of the link layer where multihop is not fully considered. Improving network performance, reducing energy consumption, thus extending the network lifetime is the main challenge in BANs. Several studies mention that multihop for BANs helps for achieving network performance, reducing energy consumption and extending network lifetime. One work presents the Adaptive multihop tree-based Routing (AMR protocol that is extensively evaluated in a real testbed deployment. They use fuzzy logic to combine all metrics they use. Another limitation is that they have used Prim's algorithm which is not a realistic approach. So in this work we have improved their multihop tree-based Routing (AMR protocol using Kruskal's algorithm instead of Prim's algorithm. The time complexity of Kruskal's algorithm is way less than prims's algorithm. We have used network simulator 3 (NS3 to simulate and found that our algorithm is better than AMR if many of nodes.
Yuanjiang Huang
2014-01-01
Full Text Available The sensor nodes in the Wireless Sensor Networks (WSNs are prone to failures due to many reasons, for example, running out of battery or harsh environment deployment; therefore, the WSNs are expected to be able to maintain network connectivity and tolerate certain amount of node failures. By applying fuzzy-logic approach to control the network topology, this paper aims at improving the network connectivity and fault-tolerant capability in response to node failures, while taking into account that the control approach has to be localized and energy efficient. Two fuzzy controllers are proposed in this paper: one is Learning-based Fuzzy-logic Topology Control (LFTC, of which the fuzzy controller is learnt from a training data set; another one is Rules-based Fuzzy-logic Topology Control (RFTC, of which the fuzzy controller is obtained through designing if-then rules and membership functions. Both LFTC and RFTC do not rely on location information, and they are localized. Comparing them with other three representative algorithms (LTRT, List-based, and NONE through extensive simulations, our two proposed fuzzy controllers have been proved to be very energy efficient to achieve desired node degree and improve the network connectivity when sensor nodes run out of battery or are subject to random attacks.
Geographic Routing Using Logical Levels in Wireless Sensor Networks for Sensor Mobility
Yassine SABRI
2015-07-01
Full Text Available In this paper we propose an improvement to the GRPW algorithm for wireless sensor networks called GRPW-M , which collects data in a wireless sensor network (WSN using a mobile nodes. Performance of GRPW algorithm algorithm depends heavily on the immobile sensor nodes . This prediction can be hard to do. For that reason, we propose a modified algorithm that is able to adapt to the current situation in the network in which the sensor node considered mobile. The goal of the proposed algorithm is to decrease the reconstruction cost and increase the data delivery ratio. In comparing the GRPW-M protocol with GRPW protocol in simulation, this paper demonstrates that adjustment process executed by GRPW-M does in fact decrease the reconstruction cost and increase the data delivery ratio . Simulations were performed on GRPW as well as on the proposed Routing algorithm. The efficiency factors that were evaluated was total number of transmissions in the network and total delivery rate. And in general the proposed Routing algorithm may perform reasonable well for a large number network setups.
Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.
Rodriguez, Ana; Crespo, Isaac; Androsova, Ganna; del Sol, Antonio
2015-01-01
High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell's response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm
Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.
Ana Rodriguez
Full Text Available High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell's response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization, are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the
Bifurcation Analysis of Equilibria in Competitive Logistic Networks with Adaptation
Raimondi, A.; Tebaldi, C.
2008-04-01
A general n-node network is considered for which, in absence of interactions, each node is governed by a logistic equation. Interactions among the nodes take place in the form of competition, which also includes adaptive abilities through a (short term) memory effect. As a consequence the dynamics of the network is governed by a system of n2 nonlinear ordinary differential equations. As a first step, equilibria and their stability are investigated analytically for the general network in dependence of the relevant parameters, namely the strength of competition, the adaptation rate and the network size. The existence of classes of invariant subspaces, related to symmetries, allows the introduction of a reduced model, four dimensional, where n appears as a parameter, which give full account of existence and stability for the equilibria in the network.
Tri Kusnandi Fazarudin
2015-12-01
Full Text Available Multi-Depot Vehicle Routing Problem with Time Window (MDVRPTW is a problem of finding an optimal route for a supplier. The supplier needs to deliver goods to a number of customers using the vehicles located in a number of depots. Each delivery must be done within the service time specified by each customer The vehicles used have a maximum limit on the amount of goods that can be loaded and the maximum time the vehicle may be used. MDVRPTW is one of the variations of Vehicle Routing Problem (VRP. There are various algorithms that have been used to solve VRP problems. Some of them are Genetic Algorithm (GA, Tabu Search, and Adaptive GA with Artificial Bee Colony. GA can solve the problem within a shorter time, but it is vulnerable to get trapped in a local optimum. A strategy to reduce the probability of it is to make the GA adaptive. In this research, MDVRPTW is solved with GA. To reduce the probability of getting trapped in a local optimum, the GA parameters are made adaptive using Fuzzy Logic Controller (FLC. Based on the results of this research, using FLC on GA causes the average of the solution to be better than the solution produced using GA without FLC.
Scalable Lunar Surface Networks and Adaptive Orbit Access
Wang, Xudong
2015-01-01
Teranovi Technologies, Inc., has developed innovative network architecture, protocols, and algorithms for both lunar surface and orbit access networks. A key component of the overall architecture is a medium access control (MAC) protocol that includes a novel mechanism of overlaying time division multiple access (TDMA) and carrier sense multiple access with collision avoidance (CSMA/CA), ensuring scalable throughput and quality of service. The new MAC protocol is compatible with legacy Institute of Electrical and Electronics Engineers (IEEE) 802.11 networks. Advanced features include efficiency power management, adaptive channel width adjustment, and error control capability. A hybrid routing protocol combines the advantages of ad hoc on-demand distance vector (AODV) routing and disruption/delay-tolerant network (DTN) routing. Performance is significantly better than AODV or DTN and will be particularly effective for wireless networks with intermittent links, such as lunar and planetary surface networks and orbit access networks.
Explosive Synchronization and Emergence of Assortativity on Adaptive Networks
JIANG Hui-Jun; WU Hao; HOU Zhong-Huai
2011-01-01
@@ We report an explosive transition from incoherence to synchronization of coupled phase oscillators on adaptive networks,following an Achlioptas process based on dynamic clustering information.During each adaptive step of the network topology,a portion of the links is randomly removed and the same amount of new links is generated following the so-called product rules(PRs) applied to the dynamic clusters.Particularly,two types of PRs are considered,namely,the min-PR and max-PR.We demonstrate that the synchronization transition becomes explosive in both cases.Interestingly,we find that the min-PR rule can lead to disassortativity of the network topology,while the max-PR rule leads to assortativity.%We report an explosive transition from incoherence to synchronization of coupled phase oscillators on adaptive networks, following an Achlioptas process based on dynamic clustering information. During each adaptive step of the network topology, a portion of the links is randomly removed and the same amount of new links is generated following the so-called product rules (PRs) applied to the dynamic clusters. Particularly, two types of PRs are considered, namely, the min-PR and max-PR. We demonstrate that the synchronization transition becomes explosive in both cases. Interestingly, we find that the min-PR rule can lead to disassortativity of the network topology, while the max-PR rule leads to assortativity.
Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism
Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu;
2012-01-01
known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control......Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and...... model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the...
Analysis of adaptive algorithms for an integrated communication network
Reed, Daniel A.; Barr, Matthew; Chong-Kwon, Kim
1985-01-01
Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes.
A candidate multimodal functional genetic network for thermal adaptation
Katharina C. Wollenberg Valero
2014-09-01
Full Text Available Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1, affect genes with different cellular functions, namely (2 lipoprotein metabolism, (3 membrane channels, (4 stress response, (5 response to oxidative stress, (6 muscle contraction and relaxation, and (7 vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
Pais, Darren; Leonard, Naomi Ehrich
2013-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcatio...
Adaptive Media Access Control for Energy Harvesting - Wireless Sensor Networks
Fafoutis, Xenofon; Dragoni, Nicola
2012-01-01
ODMAC (On-Demand Media Access Control) is a recently proposed MAC protocol designed to support individual duty cycles for Energy Harvesting — Wireless Sensor Networks (EH-WSNs). Individual duty cycles are vital for EH-WSNs, because they allow nodes to adapt their energy consumption to the ever...... three key properties of EH-WSNs: adaptability of energy consumption, distributed energy-aware load balancing and support for different application-specific requirements....
Scalable Harmonization of Complex Networks With Local Adaptive Controllers
Kárný, Miroslav; Herzallah, R.
-, - (2016). ISSN 2168-2216 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Adaptive control * Adaptive estimation * Bayes methods * Complex networks * Decentralized control * Feedback * Feedforward systems * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.699, year: 2014 http://library.utia.cas.cz/separaty/2016/AS/karny-0457337.pdf
Radio propagation and adaptive antennas for wireless communication networks
Blaunstein, Nathan
2014-01-01
Explores novel wireless networks beyond 3G, and advanced 4G technologies, such as MIMO, via propagation phenomena and the fundamentals of adapted antenna usage.Explains how adaptive antennas can improve GoS and QoS for any wireless channel, with specific examples and applications in land, aircraft and satellite communications.Introduces new stochastic approach based on several multi-parametric models describing various terrestrial scenarios, which have been experimentally verified in different environmental conditionsNew chapters on fundamentals of wireless networks, cellular and non-cellular,
QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding
Mohammad Abdur Razzaque
2014-12-01
Full Text Available Wireless body sensor networks (WBSNs for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS, in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network’s QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.
Adaptive Data Transmission in Multimedia Networks
Manimegalai Parry
2005-01-01
Full Text Available This study suggests a method where the packet size of each source is adjusted according to the network bandwidth. A controller is used to trace the data transmission rate at the router. An algorithm is developed and coded in Tool Command Language. Simulation is performed on NS-2 using 4 different test cases and the results show that the proposed algorithm avoids router queue overflow.
Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks
Jorgensen, Charles C.
1997-01-01
A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.
Study on Adaptive Control with Neural Network Compensation
单剑锋; 黄忠华; 崔占忠
2004-01-01
A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify pareters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.
An Adaptive Complex Network Model for Brain Functional Networks
Gomez Portillo, Ignacio J.; Gleiser, Pablo M.
2009-01-01
Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show diffe...
Maximizing Quality and Performance of Network Through Adaptive Traffic Engineering
Sameera Pallavi; Ch.Sandeep; P.Pramod Kumar
2013-01-01
Network management systems are to handle traffic dynamics in order to ensure congestion free network with highest throughput. IP environments are able to provide simple facilities for forwarding and routing packets. However, in presence of dynamic traffic conditions efficient management of resources is yet to be achieved. Recently Ning Wang et al. proposed a traffic engineering system which can ynamically adapt to traffic conditions with the help of virtual routing topologies. It has two majo...
Adaptive projective synchronization with different scaling factors in networks
Guo Liu-Xiao; Xu Zhen-Yuan; Hu Man-Feng
2008-01-01
We study projective synchronization with different scaling factors (PSDF) in N coupled chaotic systems networks.By using the adaptive linear control,some sufficient criteria for the PSDF in symmetrical and asymmetrical coupled networks are separately given based on the Lyapunov function method and the left eigenvalue theory.Numerical simulations for a generalized chaotic unified system are illustrated to verify the theoretical results.
Optimization of an adaptive neural network to predict breathing
Murphy, Martin J; Pokhrel, Damodar
2008-01-01
Purpose: To determine the optimal configuration and performance of an adaptive feed forward neural network filter to predict breathing in respiratory motion compensation systems for external beam radiation therapy. Method and Materials: A two-layer feed forward neural network was trained to predict future breathing amplitudes for 27 recorded breathing histories. The prediction intervals ranged from 100 to 500 ms. The optimal sampling frequency, number of input samples, training rate, and numb...
Social Networking Adapted for Distributed Scientific Collaboration
Karimabadi, Homa
2012-01-01
Share is a social networking site with novel, specially designed feature sets to enable simultaneous remote collaboration and sharing of large data sets among scientists. The site will include not only the standard features found on popular consumer-oriented social networking sites such as Facebook and Myspace, but also a number of powerful tools to extend its functionality to a science collaboration site. A Virtual Observatory is a promising technology for making data accessible from various missions and instruments through a Web browser. Sci-Share augments services provided by Virtual Observatories by enabling distributed collaboration and sharing of downloaded and/or processed data among scientists. This will, in turn, increase science returns from NASA missions. Sci-Share also enables better utilization of NASA s high-performance computing resources by providing an easy and central mechanism to access and share large files on users space or those saved on mass storage. The most common means of remote scientific collaboration today remains the trio of e-mail for electronic communication, FTP for file sharing, and personalized Web sites for dissemination of papers and research results. Each of these tools has well-known limitations. Sci-Share transforms the social networking paradigm into a scientific collaboration environment by offering powerful tools for cooperative discourse and digital content sharing. Sci-Share differentiates itself by serving as an online repository for users digital content with the following unique features: a) Sharing of any file type, any size, from anywhere; b) Creation of projects and groups for controlled sharing; c) Module for sharing files on HPC (High Performance Computing) sites; d) Universal accessibility of staged files as embedded links on other sites (e.g. Facebook) and tools (e.g. e-mail); e) Drag-and-drop transfer of large files, replacing awkward e-mail attachments (and file size limitations); f) Enterprise-level data and
Adaptive synchronization in an array of asymmetric coupled neural networks
Gao Ming; Cui Bao-Tong
2009-01-01
This paper investigates the global synchronization in an array of linearly coupled neural networks with constant and delayed coupling. By a simple combination of adaptive control and linear feedback with the updated laws, some sufficient conditions are derived for global synchronization of the coupled neural networks. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the realistic network. It is shown that the approaches developed here extend and improve the earlier works. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.
An Adaptive Neural Network Model for Nonlinear Programming Problems
Xiang-sun Zhang; Xin-jian Zhuo; Zhu-jun Jing
2002-01-01
In this paper a canonical neural network with adaptively changing synaptic weights and activation function parameters is presented to solve general nonlinear programming problems. The basic part of the model is a sub-network used to find a solution of quadratic programming problems with simple upper and lower bounds. By sequentially activating the sub-network under the control of an external computer or a special analog or digital processor that adjusts the weights and parameters, one then solves general nonlinear programming problems. Convergence proof and numerical results are given.
High dynamic adaptive mobility network model and performance analysis
LIU Hui; ZHANG Jun
2008-01-01
Since mobile networks are not currently deployed on a large scale, research in this area is mostly by simulation. Among other simulation parameters, the mobility model plays a very important role in determining the protocol performance in MANET. Based on random direction mobility model, a high dynamic adaptive mo-bility network model is proposed in the paper. The algorithms and modeling are mainly studied and explained in detail. The technique keystone is that normal dis-tribution is combined with uniform distribution and inertial feedback control is combined with kinematics, through the adaptive control on nodes speed and pre-diction tracking on nodes routes, an adaptive model is designed, which can be used in simulations to produce realistic and dynamic network scenarios. It is the adaptability that nodes mobile parameters can be adjusted randomly in three-dimensional space. As a whole, colony mobility can show some rules. Such ran-dom movement processes as varied speed and dwells are simulated realistically. Such problems as sharp turns and urgent stops are smoothed well. The model can be adapted to not only common dynamic scenarios, but also high dynamic sce-narios. Finally, the mobility model performance is analyzed and validated based on random dynamic scenarios simulations.
Carlo Penco
2013-01-01
A discussion of The Vienna Circle and the Nordic Countries. Networks and Transformations of Logical Empiricism, edited by Juha Manninen and Friedrich Stadtler, Vienna Circle Institute Yearbook vol.14, Springer, 2010.
Carlo Penco
2013-03-01
Full Text Available A discussion of The Vienna Circle and the Nordic Countries. Networks and Transformations of Logical Empiricism, edited by Juha Manninen and Friedrich Stadtler, Vienna Circle Institute Yearbook vol.14, Springer, 2010.
In-network adaptation of SHVC video in software-defined networks
Awobuluyi, Olatunde; Nightingale, James; Wang, Qi; Alcaraz Calero, Jose Maria; Grecos, Christos
2016-04-01
Software Defined Networks (SDN), when combined with Network Function Virtualization (NFV) represents a paradigm shift in how future networks will behave and be managed. SDN's are expected to provide the underpinning technologies for future innovations such as 5G mobile networks and the Internet of Everything. The SDN architecture offers features that facilitate an abstracted and centralized global network view in which packet forwarding or dropping decisions are based on application flows. Software Defined Networks facilitate a wide range of network management tasks, including the adaptation of real-time video streams as they traverse the network. SHVC, the scalable extension to the recent H.265 standard is a new video encoding standard that supports ultra-high definition video streams with spatial resolutions of up to 7680×4320 and frame rates of 60fps or more. The massive increase in bandwidth required to deliver these U-HD video streams dwarfs the bandwidth requirements of current high definition (HD) video. Such large bandwidth increases pose very significant challenges for network operators. In this paper we go substantially beyond the limited number of existing implementations and proposals for video streaming in SDN's all of which have primarily focused on traffic engineering solutions such as load balancing. By implementing and empirically evaluating an SDN enabled Media Adaptation Network Entity (MANE) we provide a valuable empirical insight into the benefits and limitations of SDN enabled video adaptation for real time video applications. The SDN-MANE is the video adaptation component of our Video Quality Assurance Manager (VQAM) SDN control plane application, which also includes an SDN monitoring component to acquire network metrics and a decision making engine using algorithms to determine the optimum adaptation strategy for any real time video application flow given the current network conditions. Our proposed VQAM application has been implemented and
Genetic adaptation of the antibacterial human innate immunity network
Lazarus Ross
2011-07-01
Full Text Available Abstract Background Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. Results Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. Conclusions We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response.
Adaptive Control of Flexible Redundant Manipulators Using Neural Networks
SONG Yimin; LI Jianxin; WANG Shiyu; LIU Jianping
2006-01-01
An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted.The smart links of the manipulator were synthesized with the flexible links to which were attached piezoceramic actuators and strain gauge sensors.A nonlinear adaptive control strategy named neural networks based indirect adaptive control (NNIAC) was employed to improve the dynamic performance of the manipulator.The mathematical model of the 4-layered dynamic recurrent neural networks (DRNN) was introduced.The neuro-identifier and the neurocontroller featuring the DRNN topology were designed off line so as to enhance the initial robustness of the NNIAC.By adjusting the neuro-identifier and the neuro-controller alternatively,the manipulator was controlled on line for achieving the desired dynamic performance.Finally,a planar 3R redundant manipulator with one smart link was utilized as an illustrative example.The simulation results proved the validity of the control strategy.
Adaptive Multipath Key Reinforcement for Energy Harvesting Wireless Sensor Networks
Di Mauro, Alessio; Dragoni, Nicola
2015-01-01
Energy Harvesting - Wireless Sensor Networks (EH-WSNs) constitute systems of networked sensing nodes that are capable of extracting energy from the environment and that use the harvested energy to operate in a sustainable state. Sustainability, seen as design goal, has a significant impact...... on the design of the security protocols for such networks, as the nodes have to adapt and optimize their behaviour according to the available energy. Traditional key management schemes do not take energy into account, making them not suitable for EH-WSNs. In this paper we propose a new multipath key...... reinforcement scheme specifically designed for EH-WSNs. The proposed scheme allows each node to take into consideration and adapt to the amount of energy available in the system. In particular, we present two approaches, one static and one fully dynamic, and we discuss some experimental results....
Smart handover based on fuzzy logic trend in IEEE802.11 mobile IPv6 networks
Lim, Joanne Mun-Yee
2012-01-01
A properly designed handoff algorithm is essential in reducing the connection quality deterioration when a mobile node moves across the cell boundaries. Therefore, to improve communication quality, we identified three goals in our paper. The first goal is to minimize unnecessary handovers and increase communication quality by reducing misrepresentations of RSSI readings due to multipath and shadow effect with the use of additional parameters. The second goal is to control the handover decisions depending on the users' mobility by utilizing location factors as one of the input parameters in a fuzzy logic handover algorithm. The third goal is to minimize false handover alarms caused by sudden fluctuations of parameters by monitoring the trend of fuzzy logic outputs for a period of time before making handover decision. In this paper, we use RSSI, speed and distance as the input decision criteria of a handover trigger algorithm by means of fuzzy logic. The fuzzy logic output trend is monitored for a period of tim...
Reconfigurable logic via gate controlled domain wall trajectory in magnetic network structure
Murapaka, C.; Sethi, P.; Goolaup, S.; Lew, W. S.
2016-02-01
An all-magnetic logic scheme has the advantages of being non-volatile and energy efficient over the conventional transistor based logic devices. In this work, we present a reconfigurable magnetic logic device which is capable of performing all basic logic operations in a single device. The device exploits the deterministic trajectory of domain wall (DW) in ferromagnetic asymmetric branch structure for obtaining different output combinations. The programmability of the device is achieved by using a current-controlled magnetic gate, which generates a local Oersted field. The field generated at the magnetic gate influences the trajectory of the DW within the structure by exploiting its inherent transverse charge distribution. DW transformation from vortex to transverse configuration close to the output branch plays a pivotal role in governing the DW chirality and hence the output. By simply switching the current direction through the magnetic gate, two universal logic gate functionalities can be obtained in this device. Using magnetic force microscopy imaging and magnetoresistance measurements, all basic logic functionalities are demonstrated.
A self-adaptive full asynchronous bi-directional transmission channel for network-on-chips
To improve two shortcomings of conventional network-on-chips, i.e. low utilization rate in channels between routers and excessive interconnection lines, this paper proposes a full asynchronous self-adaptive bi-directional transmission channel. It can utilize interconnection lines and register resources with high efficiency, and dynamically detect the data transmission state between routers through a direction regulator, which controls the sequencer to automatically adjust the transmission direction of the bi-directional channel, so as to provide a flexible data transmission environment. Null convention logic units are used to make the circuit quasi-delay insensitive and highly robust. The proposed bi-directional transmission channel is implemented based on SMIC 0.18 μm standard CMOS technology. Post-layout simulation results demonstrate that this self-adaptive bi-directional channel has better performance on throughput, transmission flexibility and channel bandwidth utilization compared to a conventional single direction channel. Moreover, the proposed channel can save interconnection lines up to 30% and can provide twice the bandwidth resources of a single direction transmission channel. The proposed channel can apply to an on-chip network which has limited resources of registers and interconnection lines. (semiconductor integrated circuits)
Network Experiences Lead to the Adaption of a Firm’s Network Competence
Bianka Kühne
2011-12-01
Full Text Available Networks become increasingly important as external sources of innovation for firms. Through networks firms get incontact with different actors with whom they can exchange information and collaborate. A firm’s ability to be asuccessful network actor depends on its network competence. This term can be defined as having the necessaryknowledge, skills and qualifications for networking as well as using them effectively. In this paper we investigate thelink between a firm’s network competence and the benefits resulting from it in a two‐way direction. First, thenetwork competence of the firm facilitates the adoption of information from other network actors which may leadto innovation success. Second the perceived network benefits shall in their turn influence the network competenceof the firm. Consequently, firms will adapt their network strategy corresponding their experiences. The objective ofthis paper is to investigate the dynamics of networking and its influence on the firm’s network competence. For thisexploratory research 3 Belgian networks are examined. In‐depth interviews are used in combination with semistructuredinterview guides to conduct the research. Our results indicate that some firms perceive benefits fromtheir network efforts, for others it is more a burden. Furthermore, in some of our cases we found that positiveexperiences with clear benefits motivate the firm to enhance its network competence. This is illustrated by the factthat collaborations are more frequently initiated, trust is more easily build, firms are more open to communicateinformation and the confidentiality threshold is overcome.
Liu, Zhe Peng; Li, Qing
2013-04-01
Due to their two-way electromechanical coupling effect, piezoelectric transducers can be used to synthesize passive vibration control schemes, e.g., RLC circuit with the integration of inductance and resistance elements that is conceptually similar to damped vibration absorber. Meanwhile, the wide usage of wireless sensors has led to the recent enthusiasm of developing piezoelectric-based energy harvesting devices that can convert ambient vibratory energy into useful electrical energy. It can be shown that the integration of circuitry elements such as resistance and inductance can benefit the energy harvesting capability. Here we explore a dual-purpose circuit that can facilitate simultaneous vibration suppression and energy harvesting. It is worth noting that the goal of vibration suppression and the goal of energy harvesting may not always complement each other. That is, the maximization of vibration suppression doesn't necessarily lead to the maximization of energy harvesting, and vice versa. In this research, we develop a fuzzy-logic based algorithm to decide the proper selection of circuitry elements to balance between the two goals. As the circuitry elements can be online tuned, this research yields an adaptive circuitry concept for the effective manipulation of system energy and vibration suppression. Comprehensive analyses are carried out to demonstrate the concept and operation.
Spontaneous formation of dynamical groups in an adaptive networked system
Li Menghui; Guan Shuguang [Temasek Laboratories, National University of Singapore, Singapore 117508 (Singapore); Lai, C-H, E-mail: tsllm@nus.edu.s [Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Singapore), National University of Singapore, Kent Ridge, Singapore 119260 (Singapore)
2010-10-15
In this work, we investigate a model of an adaptive networked dynamical system, where the coupling strengths among phase oscillators coevolve with the phase states. It is shown that in this model the oscillators can spontaneously differentiate into two dynamical groups after a long time evolution. Within each group, the oscillators have similar phases, while oscillators in different groups have approximately opposite phases. The network gradually converts from the initial random structure with a uniform distribution of connection strengths into a modular structure that is characterized by strong intra-connections and weak inter-connections. Furthermore, the connection strengths follow a power-law distribution, which is a natural consequence of the coevolution of the network and the dynamics. Interestingly, it is found that if the inter-connections are weaker than a certain threshold, the two dynamical groups will almost decouple and evolve independently. These results are helpful in further understanding the empirical observations in many social and biological networks.
Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting
Tran, Tich Phuoc; Tran, Dat; Nguyen, Cuong Duc
2009-01-01
This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.
Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting
Tich Phuoc Tran
2009-10-01
Full Text Available This article applies Machine Learning techniques to solve Intrusion Detection problems withincomputer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN, which integrates an adaptive boosting technique and a semi-parametric neural network to obtain good trade-off between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD-99 intrusion benchmark indicate that our model outperforms other state-of-the-art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.
Reliable adaptive multicast protocol in wireless Ad hoc networks
Sun Baolin; Li Layuan
2006-01-01
In wireless ad hoc network environments, every link is wireless and every node is mobile. Those features make data lost easily as well as multicasting inefficient and unreliable. Moreover, Efficient and reliable multicast in wireless ad hoc network is a difficult issue. It is a major challenge to transmission delays and packet losses due to link changes of a multicast tree at the provision of high delivery ratio for each packet transmission in wireless ad hoc network environment.In this paper, we propose and evaluate Reliable Adaptive Multicast Protocol (RAMP) based on a relay node concept. Relay nodes are placed along the multicast tree. Data recovery is done between relay nodes. RAMP supports a reliable multicasting suitable for mobile ad hoc network by reducing the number of packet retransmissions. We compare RAMP with SRM (Scalable Reliable Multicast). Simulation results show that the RAMP has high delivery ratio and low end-to-end delay for packet transmission.
Self-Adaptive Networked Entities for Building Pervasive Computing Aschitectures
Daněk, Martin; Philippe, J.-M.; Bartosinski, Roman; Honzík, Petr; Gamrat, Ch.
Heidelberg: Springer, 2008 - (Hornby, G.; Sekanina, L.; Haddow, P.), s. 94-105 ISBN 978-3-540-85856-0. ISSN 0302-9743. [International Conference on Evolvable System s: From Biology to Harware, 8th International Conference, ICES 2008. Praha (CZ), 22.09.2008-24.09.2008] R&D Projects: GA MŠk(CZ) 1M0567 EU Projects: European Commission(XE) 027611 - AETHER Institutional research plan: CEZ:AV0Z10750506 Keywords : Self-adaptation * FPGA * Simulink Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2008/ZS/danek-self- adaptive networked entities for building pervasive computing aschitectures.pdf
Adaptive Immune Evolutionary Algorithms Based on Immune Network Regulatory Mechanism
HE Hong; QIAN Feng
2007-01-01
Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm (AIEA) is proposed to improve the performance of genetic algorithms (GA) in this paper. AIEA adopts novel selection operation according to the stimulation level of each antibody. A memory base for good antibodies is devised simultaneously to raise the convergent rapidity of the algorithm and adaptive adjusting strategy of antibody population is used for preventing the loss of the population adversity. The experiments show AIFA has better convergence performance than standard genetic algorithm and is capable of maintaining the adversity of the population and solving function optimization problems in an efficient and reliable way.
Adaptive self-organization in a realistic neural network model
Meisel, Christian; Gross, Thilo
2009-12-01
Information processing in complex systems is often found to be maximally efficient close to critical states associated with phase transitions. It is therefore conceivable that also neural information processing operates close to criticality. This is further supported by the observation of power-law distributions, which are a hallmark of phase transitions. An important open question is how neural networks could remain close to a critical point while undergoing a continual change in the course of development, adaptation, learning, and more. An influential contribution was made by Bornholdt and Rohlf, introducing a generic mechanism of robust self-organized criticality in adaptive networks. Here, we address the question whether this mechanism is relevant for real neural networks. We show in a realistic model that spike-time-dependent synaptic plasticity can self-organize neural networks robustly toward criticality. Our model reproduces several empirical observations and makes testable predictions on the distribution of synaptic strength, relating them to the critical state of the network. These results suggest that the interplay between dynamics and topology may be essential for neural information processing.
Adaptive Data Rates for Flexible Transceivers in Optical Networks
Brian Thomas Teipen
2012-05-01
Full Text Available Efforts towards commercializing higher-speed optical transmission have demonstrated the need for advanced modulation formats, several of which require similar transceiver hardware architecture. Adaptive transceivers can be built to have a number of possible operational configurations selected by software. Such software-defined transceiver configurations can create specific modulation formats to support sets of data rates, corresponding tolerances to system impairments, and sets of electronic digital signal processing schemes chosen to best function in a given network environment. In this paper, we discuss possibilities and advantages of reconfigurable, bit-rate flexible transceivers, and their potential applications in future optical networks.
Real-Time Adaptive Color Segmentation by Neural Networks
Duong, Tuan A.
2004-01-01
Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural
Rescue of endemic states in interconnected networks with adaptive coupling.
Vazquez, F; Serrano, M Ángeles; Miguel, M San
2016-01-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network -and therefore on the interconnected system- the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime. PMID:27380771
Dynamic data-driven sensor network adaptation for border control
Bein, Doina; Madan, Bharat B.; Phoha, Shashi; Rajtmajer, Sarah; Rish, Anna
2013-06-01
Given a specific scenario for the border control problem, we propose a dynamic data-driven adaptation of the associated sensor network via embedded software agents which make sensor network control, adaptation and collaboration decisions based on the contextual information value of competing data provided by different multi-modal sensors. We further propose the use of influence diagrams to guide data-driven decision making in selecting the appropriate action or course of actions which maximize a given utility function by designing a sensor embedded software agent that uses an influence diagram to make decisions about whether to engage or not engage higher level sensors for accurately detecting human presence in the region. The overarching goal of the sensor system is to increase the probability of target detection and classification and reduce the rate of false alarms. The proposed decision support software agent is validated experimentally on a laboratory testbed for multiple border control scenarios.
Fan, Yuanchao; Koukal, Tatjana; Weisberg, Peter J.
2014-10-01
Canopy shadowing mediated by topography is an important source of radiometric distortion on remote sensing images of rugged terrain. Topographic correction based on the sun-canopy-sensor (SCS) model significantly improved over those based on the sun-terrain-sensor (STS) model for surfaces with high forest canopy cover, because the SCS model considers and preserves the geotropic nature of trees. The SCS model accounts for sub-pixel canopy shadowing effects and normalizes the sunlit canopy area within a pixel. However, it does not account for mutual shadowing between neighboring pixels. Pixel-to-pixel shadowing is especially apparent for fine resolution satellite images in which individual tree crowns are resolved. This paper proposes a new topographic correction model: the sun-crown-sensor (SCnS) model based on high-resolution satellite imagery (IKONOS) and high-precision LiDAR digital elevation model. An improvement on the C-correction logic with a radiance partitioning method to address the effects of diffuse irradiance is also introduced (SCnS + C). In addition, we incorporate a weighting variable, based on pixel shadow fraction, on the direct and diffuse radiance portions to enhance the retrieval of at-sensor radiance and reflectance of highly shadowed tree pixels and form another variety of SCnS model (SCnS + W). Model evaluation with IKONOS test data showed that the new SCnS model outperformed the STS and SCS models in quantifying the correlation between terrain-regulated illumination factor and at-sensor radiance. Our adapted C-correction logic based on the sun-crown-sensor geometry and radiance partitioning better represented the general additive effects of diffuse radiation than C parameters derived from the STS or SCS models. The weighting factor Wt also significantly enhanced correction results by reducing within-class standard deviation and balancing the mean pixel radiance between sunlit and shaded slopes. We analyzed these improvements with model
Adaptive Medium Access Control Protocol for Wireless Body Area Networks
Javaid, N.; Ahmad, A.; A. Rahim; Z.A. Khan; M. Ishfaq; Qasim, U.
2014-01-01
Wireless Body Area Networks (WBANs) are widely used for applications such as modern health-care systems, where wireless sensors (nodes) monitor the parameter(s) of interest. Nodes are provided with limited battery power and battery power is dependent on radio activity. MAC protocols play a key role in controlling the radio activity. Therefore, we present Adaptive Medium Access Control (A-MAC) protocol for WBANs supported by linear programming models for the minimization of energy consumption ...
Supervised Learning in Adaptive DNA Strand Displacement Networks.
Lakin, Matthew R; Stefanovic, Darko
2016-08-19
The development of engineered biochemical circuits that exhibit adaptive behavior is a key goal of synthetic biology and molecular computing. Such circuits could be used for long-term monitoring and control of biochemical systems, for instance, to prevent disease or to enable the development of artificial life. In this article, we present a framework for developing adaptive molecular circuits using buffered DNA strand displacement networks, which extend existing DNA strand displacement circuit architectures to enable straightforward storage and modification of behavioral parameters. As a proof of concept, we use this framework to design and simulate a DNA circuit for supervised learning of a class of linear functions by stochastic gradient descent. This work highlights the potential of buffered DNA strand displacement as a powerful circuit architecture for implementing adaptive molecular systems. PMID:27111037
Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks
Kanevski, Mikhail
2015-04-01
The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press
Adaptive network dynamics and evolution of leadership in collective migration
Pais, Darren; Leonard, Naomi E.
2014-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcations with respect to investment cost explains the observed hysteretic effect associated with recovery of migration in fragmented environments. Further, we show a minimum connectivity threshold above which there is evolutionary branching into leader and follower populations. For small populations, we show how the topology of the underlying social interaction network influences the emergence and location of leaders in the adaptive system. Our model and analysis can be extended to study the dynamics of collective tracking or collective learning more generally. Thus, this work may inform the design of robotic networks where agents use decentralized strategies that balance direct environmental measurements with agent interactions.
Adaptive local routing strategy on a scale-free network
Due to the heterogeneity of the structure on a scale-free network, making the betweennesses of all nodes become homogeneous by reassigning the weights of nodes or edges is very difficult. In order to take advantage of the important effect of high degree nodes on the shortest path communication and preferentially deliver packets by them to increase the probability to destination, an adaptive local routing strategy on a scale-free network is proposed, in which the node adjusts the forwarding probability with the dynamical traffic load (packet queue length) and the degree distribution of neighbouring nodes. The critical queue length of a node is set to be proportional to its degree, and the node with high degree has a larger critical queue length to store and forward more packets. When the queue length of a high degree node is shorter than its critical queue length, it has a higher probability to forward packets. After higher degree nodes are saturated (whose queue lengths are longer than their critical queue lengths), more packets will be delivered by the lower degree nodes around them. The adaptive local routing strategy increases the probability of a packet finding its destination quickly, and improves the transmission capacity on the scale-free network by reducing routing hops. The simulation results show that the transmission capacity of the adaptive local routing strategy is larger than that of three previous local routing strategies. (general)
Adaptive Influence Maximization in Social Networks: Why Commit when You can Adapt?
Vaswani, Sharan; Lakshmanan, Laks V. S.
2016-01-01
Most previous work on influence maximization in social networks is limited to the non-adaptive setting in which the marketer is supposed to select all of the seed users, to give free samples or discounts to, up front. A disadvantage of this setting is that the marketer is forced to select all the seeds based solely on a diffusion model. If some of the selected seeds do not perform well, there is no opportunity to course-correct. A more practical setting is the adaptive setting in which the ma...
Adaptive comanagement of a marine protected area network in Fiji.
Weeks, Rebecca; Jupiter, Stacy D
2013-12-01
Adaptive management of natural resources is an iterative process of decision making whereby management strategies are progressively changed or adjusted in response to new information. Despite an increasing focus on the need for adaptive conservation strategies, there remain few applied examples. We describe the 9-year process of adaptive comanagement of a marine protected area network in Kubulau District, Fiji. In 2011, a review of protected area boundaries and management rules was motivated by the need to enhance management effectiveness and the desire to improve resilience to climate change. Through a series of consultations, with the Wildlife Conservation Society providing scientific input to community decision making, the network of marine protected areas was reconfigured so as to maximize resilience and compliance. Factors identified as contributing to this outcome include well-defined resource-access rights; community respect for a flexible system of customary governance; long-term commitment and presence of comanagement partners; supportive policy environment for comanagement; synthesis of traditional management approaches with systematic monitoring; and district-wide coordination, which provided a broader spatial context for adaptive-management decision making. Co-Manejo Adaptativo de una Red de Áreas Marinas Protegidas en Fiyi. PMID:24112643
Adaptive Congestion Control Protocol (ACCP for Wireless Sensor Networks
James DzisiGadze
2013-10-01
Full Text Available In Wireless Sensor Networks (WSN when an event is detected there is an increase in data traffic that mightlead to packets being transmitted through the network close to the packet handling capacity of the WSN.The WSN experiences a decrease in network performance due to packet loss, long delays, and reduction inthroughput. In this paper we developed an adaptive congestion control algorithm that monitors networkutilization and adjust traffic levels and/or increases network resources to improve throughput and conserveenergy. The traffic congestion control protocol DelStatic is developed by introducing backpressuremechanism into NOAH. We analyzed various routing protocols and established that DSR has a higherresource congestion control capability. The proposed protocol, ACCP uses a sink switching algorithm totrigger DelStatic or DSR feedback to a congested node based on its Node Rank. From the simulationresults, ACCP protocol does not only improve throughput but also conserves energy which is critical tosensor application survivability on the field. Our Adaptive Congestion control achieved reliability, highthroughput and energy efficiency.
LAMAN: Load Adaptable MAC for Ad Hoc Networks
Realp Marc
2005-01-01
Full Text Available In mobile ad hoc radio networks, mechanisms on how to access the radio channel are extremely important in order to improve network efficiency. In this paper, the load adaptable medium access control for ad hoc networks (LAMAN protocol is described. LAMAN is a novel decentralized multipacket MAC protocol designed following a cross-layer approach. Basically, this protocol is a hybrid CDMA-TDMA-based protocol that aims at throughput maximization in multipacket communication environments by efficiently combining contention and conflict-free protocol components. Such combination of components is used to adapt the nodes' access priority to changes on the traffic load while, at the same time, accounting for the multipacket reception (MPR capability of the receivers. A theoretical analysis of the system is developed presenting closed expressions of network throughput and packet delay. By simulations the validity of our analysis is shown and the performances of a LAMAN-based system and an Aloha-CDMA-based one are compared.
Nilsson, Jørgen Fischer
A Gentle introduction to logical languages, logical modeling, formal reasoning and computational logic for computer science and software engineering students......A Gentle introduction to logical languages, logical modeling, formal reasoning and computational logic for computer science and software engineering students...
Kenney, Michael; Horgan, John; Horne, Cale; Vining, Peter; Carley, Kathleen M; Bigrigg, Michael W; Bloom, Mia; Braddock, Kurt
2013-09-01
Social networks are said to facilitate learning and adaptation by providing the connections through which network nodes (or agents) share information and experience. Yet, our understanding of how this process unfolds in real-world networks remains underdeveloped. This paper explores this gap through a case study of al-Muhajiroun, an activist network that continues to call for the establishment of an Islamic state in Britain despite being formally outlawed by British authorities. Drawing on organisation theory and social network analysis, we formulate three hypotheses regarding the learning capacity and social network properties of al-Muhajiroun (AM) and its successor groups. We then test these hypotheses using mixed methods. Our methods combine quantitative analysis of three agent-based networks in AM measured for structural properties that facilitate learning, including connectedness, betweenness centrality and eigenvector centrality, with qualitative analysis of interviews with AM activists focusing organisational adaptation and learning. The results of these analyses confirm that al-Muhajiroun activists respond to government pressure by changing their operations, including creating new platforms under different names and adjusting leadership roles among movement veterans to accommodate their spiritual leader's unwelcome exodus to Lebanon. Simple as they are effective, these adaptations have allowed al-Muhajiroun and its successor groups to continue their activism in an increasingly hostile environment. PMID:22726907
Adaptive topology evolution in information-sharing social networks
Chen, Duanbing; Lu, Linyuan; Medo, Matus; Zhang, Yi-Cheng; Zhou, Tao
2011-01-01
The advent of Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. In order to get the picture it is important to address issues like who people do follow and how they search for better information sources. In this work we conduct an empirical analysis on different on-line social networking sites, and draw inspiration from its results to present different source selection strategies in an adaptive model for social recommendation. We show that local search rules which enhance the typical topological features of real social communities give rise to network configurations that are globally optimal. Hence these abstract rules help to create networks which are both effective in information diffusion and people friendly.
Adaptive Decision-Making Scheme for Cognitive Radio Networks
Alqerm, Ismail
2014-05-01
Radio resource management becomes an important aspect of the current wireless networks because of spectrum scarcity and applications heterogeneity. Cognitive radio is a potential candidate for resource management because of its capability to satisfy the growing wireless demand and improve network efficiency. Decision-making is the main function of the radio resources management process as it determines the radio parameters that control the use of these resources. In this paper, we propose an adaptive decision-making scheme (ADMS) for radio resources management of different types of network applications including: power consuming, emergency, multimedia, and spectrum sharing. ADMS exploits genetic algorithm (GA) as an optimization tool for decision-making. It consists of the several objective functions for the decision-making process such as minimizing power consumption, packet error rate (PER), delay, and interference. On the other hand, maximizing throughput and spectral efficiency. Simulation results and test bed evaluation demonstrate ADMS functionality and efficiency.
Network and adaptive system of systems modeling and analysis.
Lawton, Craig R.; Campbell, James E. Dr. (.; .); Anderson, Dennis James; Eddy, John P.
2007-05-01
This report documents the results of an LDRD program entitled ''Network and Adaptive System of Systems Modeling and Analysis'' that was conducted during FY 2005 and FY 2006. The purpose of this study was to determine and implement ways to incorporate network communications modeling into existing System of Systems (SoS) modeling capabilities. Current SoS modeling, particularly for the Future Combat Systems (FCS) program, is conducted under the assumption that communication between the various systems is always possible and occurs instantaneously. A more realistic representation of these communications allows for better, more accurate simulation results. The current approach to meeting this objective has been to use existing capabilities to model network hardware reliability and adding capabilities to use that information to model the impact on the sustainment supply chain and operational availability.
Neuroinformatics I: Fuzzy Neural Networks of More-Equal-Less Logic (Static
Dobilas KIRVELIS
2003-04-01
Full Text Available This article analyzes the possibilities of neural nets composed of neurons - the summators of continuously varied impulse frequencies characterized by non-linearity {N}, when informational operations of fuzzy logic are performed. According to the facts of neurobiological research the neurons are divided into stellate and pyramidal ones, and their functional-static characteristics are presented. The operations performed by stellate neurons are characterized as qualitative (not quantitative informational estimations ``more'', ``less'', ``equal'', i.e., they function according to ``more-equal-less'' (M-E-L logic. Pyramidal neurons with suppressing entries perform algebraic signal operations and as a result of them the output signals are controlled by means of universal logical function ``NON disjunction'' (Pierce arrow or Dagger function. It is demonstrated how stellate and pyramidal neurons can be used to synthesize the neural nets functioning in parallel and realizing all logical and elementary algebraic functions as well as to perform the conditional controlled operations of information processing. Such neural nets functioning by principles of M-E-L and suppression logic can perform signals' classification, filtration and other informational procedures by non-quantitative assessment, and their informational possibilities (the amount of qualitative states, depending on the number n of analyzing elements-neurons, are proportional to n! or even to (2^{n}* n!, i.e., much bigger than the possibilities of traditional informational automats functioning by binary principle. In summary it is stated that neural nets are informational subsystems of parallel functioning and analogical neurocomputers of hybrid action.
Transition Semantics - The Dynamics of Dependence Logic
Galliani, Pietro
2012-01-01
We examine the relationship between Dependence Logic and game logics. A variant of Dynamic Game Logic, called Transition Logic, is developed, and van Benthem's representation theorem for First-Order Logic and Dynamic Game Logic is adapted to the case of Dependence Logic and Transition Logic. This suggests a new perspective on the interpretation of Dependence Logic formulas, in terms of assertions about reachability in games of imperfect information against Nature. We then capitalize on this intuition by developing expressively equivalent variants of Dependence Logic in which this interpretation is taken to the foreground.
An Adaptive Power Efficient Packet Scheduling Algorithm for Wimax Networks
Prasad, R Murali
2010-01-01
Admission control schemes and scheduling algorithms are designed to offer QoS services in 802.16/802.16e networks and a number of studies have investigated these issues. But the channel condition and priority of traffic classes are very rarely considered in the existing scheduling algorithms. Although a number of energy saving mechanisms have been proposed for the IEEE 802.16e, to minimize the power consumption of IEEE 802.16e mobile stations with multiple real-time connections has not yet been investigated. Moreover, they mainly consider non real- time connections in IEEE 802.16e networks. In this paper, we propose to design an adaptive power efficient packet scheduling algorithm that provides a minimum fair allocation of the channel bandwidth for each packet flow and additionally minimizes the power consumption. In the adaptive scheduling algorithm, packets are transmitted as per allotted slots from different priority of traffic classes adaptively, depending on the channel condition. Suppose if the buffer s...
Adaptive neural network error control for generalized perturbation theory
This paper addresses the issue of adaptive error control within generalized perturbation theory (GPT). The strategy herein assessed considers an artificial neural network (ANN) error estimator. The underlying tool facilitating this research is the FORMOSA-P code, a pressurized water reactor (PWR) nuclear fuel management optimization package, which combines simulated annealing and nodal GPT. A number of applications exist where traditional GPT may be limited by the magnitude of perturbations, which it can accurately handle. In fact, other alternative such as supervariational techniques (i.e., n'th-order GPT) and/or multireference strategies (i.e., rodded adjoints) are being sought for boiling water reactor and rodded applications. A perhaps not-so-obvious alternative could be to employ a neural network for adaptive error control within GPT. This study presents the results of two ANN models. The first model constitutes an intensively well-trained ANN used to contrast its global core parameter (i.e., keff) prediction capability versus that of a GPT model. The second model is a similar ANN intended for adaptive GPT error correction. In other words, the latter ANN is trained on-the-fly within the scope of a standard FORMOSA-P calculation
Rescue of endemic states in interconnected networks with adaptive coupling
Vazquez, F; Miguel, M San
2015-01-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads only if the two layers are interconnected, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network, the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that finite-size effects are amplified by the rewiring, as the...
Multisource Adaptive Data Distribution and Routing in Wireless Sensor Networks
Mukherjee, Subhabrata; Naskar, Mrinal K; Mukherjee, Amitava
2012-01-01
The wireless sensor network is a collection of energy-constrained nodes. Their objective is to sense, collect and process information for some ad-hoc purpose. Typically the nodes are deployed in geographically inaccessible regions. Thus the most challenging task is to design a network with minimal power consumption. As the nodes have to collect and process data very fast, minimizing data delivery time is another objective. In addition to this, when multiple sources transmit data simultaneously, the network load gradually increases and it may lead to congestion. In this paper we have proposed an adaptive framework in which multiple sources transmit data simultaneously with minimal end-to-end data delivery time and minimal energy consumption besides ensuring that congestion remains at an optimum low so that minimal number of data packets are dropped. This paper presents an adaptive framework to achieve the above-mentioned objectives. This framework has been used over Mac 802.11 and extensive simulations have be...
Effects of adaptive dynamical linking in networked games
Yang, Zhihu; Li, Zhi; Wu, Te; Wang, Long
2013-10-01
The role of dynamical topologies in the evolution of cooperation has received considerable attention, as some studies have demonstrated that dynamical networks are much better than static networks in terms of boosting cooperation. Here we study a dynamical model of evolution of cooperation on stochastic dynamical networks in which there are no permanent partners to each agent. Whenever a new link is created, its duration is randomly assigned without any bias or preference. We allow the agent to adaptively adjust the duration of each link during the evolution in accordance with the feedback from game interactions. By Monte Carlo simulations, we find that cooperation can be remarkably promoted by this adaptive dynamical linking mechanism both for the game of pairwise interactions, such as the Prisoner's Dilemma game (PDG), and for the game of group interactions, illustrated by the public goods game (PGG). And the faster the adjusting rate, the more successful the evolution of cooperation. We also show that in this context weak selection favors cooperation much more than strong selection does. What is particularly meaningful is that the prosperity of cooperation in this study indicates that the rationality and selfishness of a single agent in adjusting social ties can lead to the progress of altruism of the whole population.
An adaptive blind watermarking scheme utilizing neural network for synchronization
WU Jian-zhen; XIE Jian-ying; YANG Yu-pu
2007-01-01
An important problem constraining the practical implementation of robust watermarking technology is the low robustness of existing algorithms against geometrical distortions. An adaptive blind watermarking scheme utilizing neural network for synchronization is proposed in this paper,which allows to recover watermark even if the image has been subjected to generalized geometrical transforms. Through classification of image's brightness, texture and contrast sensitivity utilizing fuzzy clustering theory and human visual system, more robust watermark is adaptively embedded in DWT domain. In order to register rotation, scaling and translation parameters, feedforward neural network is utilized to learn image geometric pattern represented by six combined low order image moments. The distortion can be inverted after determining the affine distortion applied to the image and watermark can be extracted in a standard way without original image. It only needs a trained neural network. Experimental results demonstrate its advantages over previous method in terms of computational effectiveness and parameter estimation accuracy. It can embed more robust watermark under certain visual distance, and effectively resist JPEG compression, noise and geometric attacks.
Neuroinformatics I: Fuzzy Neural Networks of More-Equal-Less Logic (Static)
Dobilas KIRVELIS; Dagyte, Girstaute
2003-01-01
This article analyzes the possibilities of neural nets composed of neurons - the summators of continuously varied impulse frequencies characterized by non-linearity {N}, when informational operations of fuzzy logic are performed. According to the facts of neurobiological research the neurons are divided into stellate and pyramidal ones, and their functional-static characteristics are presented. The operations performed by stellate neurons are characterized as qualitative (not quantitative) in...
Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks
M. Bazazzadeh; Badihi, H.; A Shahriari
2011-01-01
This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC) for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc.) are obtained. These parameters provide a precious database, which train a neural...
Özcan Dülger
2014-05-01
Full Text Available Predicting Mathematics 1 course success of students is very important to prepare them before the semester. It is difficult to obtain solution because of the non-linear property of data set. Fuzzy logic is one of the common methods for the problems which involve numeric values. In fuzzy logic, it is important to determine membership functions and their parameter's values correctly. This can be done by an expert or can be learned with a data set. In this study, we aimed to predict the Mathematics 1 course success of 434 students who enrolled to Engineering Faculty of Pamukkale University in 2007-2008 academic year by using their university exam data. For this, the adaptive-network-based fuzzy inference system (ANFIS which combines the important characteristics of artificial neural network and fuzzy logic was used. In training section, nine parameters which are selected from sixteen parameters in data set with different combinations were given to the ANFIS. When an ANFIS structure with nine input parameters has at least three membership functions for each input, it will have at least 3^9 fuzzy rules. Because of this, the training part is too slow and too much memory is needed. Instead of this inefficient structure, a hierarchical method was proposed. In this method, the ANFIS is partitioned to the sub-systems. Each sub-system performs some part of input parameters and sends their result to the final ANFIS structure to obtain the overall system output. After testing with one-third of data set, two best prediction results with ratio 77.77% and 78.47% are obtained. When these results are analyzed, it is seen that 64 successful students from 85 students and 48 unsuccessful students from 59 students in Mathematics 1 course were predicted truly in the result with ratio 77.77%. Similarly, 69 successful students from 85 students, and 44 unsuccessful students from 59 students were predicted truly in the result with ratio 78.47%.
Sensor Activation and Radius Adaptation (SARA) in Heterogeneous Sensor Networks
Bartolini, Novella; la Porta, Thomas; Petrioli, Chiara; Silvestri, Simone
2010-01-01
In this paper we address the problem of prolonging the lifetime of wireless sensor networks (WSNs) deployed to monitor an area of interest. In this scenario, a helpful approach is to reduce coverage redundancy and therefore the energy expenditure due to coverage. We introduce the first algorithm which reduces coverage redundancy by means of Sensor Activation and sensing Radius Adaptation (SARA)in a general applicative scenario with two classes of devices: sensors that can adapt their sensing range (adjustable sensors) and sensors that cannot (fixed sensors). In particular, SARA activates only a subset of all the available sensors and reduces the sensing range of the adjustable sensors that have been activated. In doing so, SARA also takes possible heterogeneous coverage capabilities of sensors belonging to the same class into account. It specifically addresses device heterogeneity by modeling the coverage problem in the Laguerre geometry through Voronoi-Laguerre diagrams. SARA executes quickly and is guarante...
Li, Xiao-Jian; Yang, Guang-Hong
2016-01-01
This paper is concerned with the problem of synchronization control of complex dynamical networks (CDN) subject to nonlinear couplings and uncertainties. An fuzzy logical system-based adaptive distributed controller is designed to achieve the synchronization. The asymptotic convergence of synchronization errors is analyzed by combining algebraic graph theory and Lyapunov theory. In contrast to the existing results, the proposed synchronization control method is applicable for the CDN with system uncertainties and unknown topology. Especially, the considered uncertainties are allowed to occur in the node local dynamics as well as in the interconnections of different nodes. In addition, it is shown that a unified controller design framework is derived for the CDN with or without coupling delays. Finally, simulations on a Chua's circuit network are provided to validate the effectiveness of the theoretical results. PMID:25720020
A two-bit arithmetic logic unit (ALU) was successfully fabricated on a GaAs-based regular nanowire network with hexagonal topology. This fundamental building block of central processing units can be implemented on a regular nanowire network structure with simple circuit architecture based on graphical representation of logic functions using a binary decision diagram and topology control of the graph. The four-instruction ALU was designed by integrating subgraphs representing each instruction, and the circuitry was implemented by transferring the logical graph structure to a GaAs-based nanowire network formed by electron beam lithography and wet chemical etching. A path switching function was implemented in nodes by Schottky wrap gate control of nanowires. The fabricated circuit integrating 32 node devices exhibits the correct output waveforms at room temperature allowing for threshold voltage variation.
An Adaptive Amplifier System for Wireless Sensor Network Applications
Carlos Marqués; Eduardo Romero; Mónica Lovay; Gabriela Peretti
2012-01-01
This paper presents an adaptive amplifier that is part of a sensor node in a wireless sensor network. The system presents a target gain that has to be maintained without direct human intervention despite the presence of faults. In addition, its bandwidth must be as large as possible. The system is composed of a software-based built-in self-test scheme implemented in the node that checks all the available gains in the amplifiers, a reconfigurable amplifier, and a genetic algorithm (GA) for rec...
Minimum-Risk Path Finding by an Adaptive Amoebal Network
Nakagaki, Toshiyuki; Iima, Makoto; Ueda, Tetsuo; Nishiura, Yasumasa; Saigusa, Tetsu; Tero, Atsushi; Kobayashi, Ryo; Showalter, Kenneth
2007-08-01
When two food sources are presented to the slime mold Physarum in the dark, a thick tube for absorbing nutrients is formed that connects the food sources through the shortest route. When the light-avoiding organism is partially illuminated, however, the tube connecting the food sources follows a different route. Defining risk as the experimentally measurable rate of light-avoiding movement, the minimum-risk path is exhibited by the organism, determined by integrating along the path. A model for an adaptive-tube network is presented that is in good agreement with the experimental observations.
Adaptive Framework for Data Distribution in Wireless Sensor Networks
Mukherjee, Subhabrata; Mukherjee, Amitava
2012-01-01
In recent years, the wireless sensor network (WSN) is playing a key role in sensing, collecting and disseminating information in various applications. An important feature associated with WSN is to develop an efficient data distribution and routing scheme to ensure better quality of service (QoS) that reduces the power consumption and the end-to-end data delivery time. In this work, we propose an adaptive framework to transmit data packets from a source to the sink in WSN across multiples paths with strategically distributed data packets so as to minimize the power consumption as well as the end-to-end data delivery time.
LOAD AWARE ADAPTIVE BACKBONE SYNTHESIS IN WIRELESS MESH NETWORKS
Yuan Yuan; Zheng Baoyu
2009-01-01
Wireless Mesh Networks (WMNs) are envisioned to support the wired backbone with a wireless Backbone Networks (BNet) for providing internet connectivity to large-scale areas.With a wide range of internet-oriented applications with different Quality of Service (QoS) requirement,the large-scale WMNs should have good scalability and large bandwidth.In this paper,a Load Aware Adaptive Backbone Synthesis (LAABS) algorithm is proposed to automatically balance the traffic flow in the WMNs.The BNet will dynamically split into smaller size or merge into bigger one according to statistic load information of Backbone Nodes (BNs).Simulation results show LAABS generates moderate BNet size and converges quickly,thus providing scalable and stable BNet to facilitate traffic flow.
Strategic tradeoffs in competitor dynamics on adaptive networks
Hébert-Dufresne, Laurent; Noël, Pierre-André; Young, Jean-Gabriel; Libby, Eric
2016-01-01
Non-linear competitor dynamics have been studied on several non-trivial but static network structures. We consider a general model on adaptive networks and interpret the resulting structure as a signature of competitor strategies. We combine the voter model with a directed stochastic block model to encode how a strategy targets competitors (i.e., an aggressive strategy) or its own type (i.e., a defensive strategy). We solve the dynamics in particular cases with tradeoffs between aggressiveness and defensiveness. These tradeoffs yield interesting behaviors such as long transient dynamics, sensitive dependence to initial conditions, and non-transitive dynamics. Not only are such results reminiscent of classic voting paradoxes but they also translate to a dynamical view of political campaign strategies. Finally, while in a two competitor system there exists an optimal strategy that balances aggressiveness and defensiveness, three competitor systems have no such solution. The introduction of extreme strategies ca...
Feedback Stabilization over Wireless Network Using Adaptive Coded Modulation
Li Yang; Xin-Ping Guan; Cheng-Nian Long; Xiao-Yuan Luo
2008-01-01
In this paper, we apply adaptive coded modulation (ACM) schemes to a wireless networked control system (WNCS)to improve the energy efficiency and increase the data rate over a fading channel. To capture the characteristics of varying rate,interference, and routing in wireless transmission channels, the concepts of equivalent delay (ED) and networked condition index (NCI)are introduced. Also, the analytic lower and upper bounds of EDs are obtained. Furthermore, we model the WNCS as a multicontroller switched system (MSS) under consideration of EDs and loss index in the wireless transmission. Sufficient stability condition of the closed-loop WNCS and corresponding dynamic state feedback controllers are derived in terms of linear matrix inequality (LMI).Numerical results show the validity and advantage of our proposed control strategies.
Adaptive model predictive process control using neural networks
Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.
1997-01-01
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.
Multiple cardiac arrhythmia recognition using adaptive wavelet network.
Lin, Chia-Hung; Chen, Pei-Jarn; Chen, Yung-Fu; Lee, You-Yun; Chen, Tainsong
2005-01-01
This paper proposes a method for electrocardiogram (ECG) heartbeat pattern recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting from feature extraction and conversion of QRS complexes, and then identifying cardiac arrhythmias based on the detected features. The discrimination method of ECG beats is a two-subnetwork architecture, consisting of a wavelet layer and a probabilistic neural network (PNN). Morlet wavelets are used to extract the features from each heartbeat, and then PNN is used to analyze the meaningful features and perform discrimination tasks. The AWN is suitable for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results obtained by testing the data of the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed method. PMID:17281539
ADAPTATIVE IMAGE WATERMARKING SCHEME BASED ON NEURAL NETWORK
BASSEL SOLAIMANE
2011-01-01
Full Text Available Digital image watermarking has been proposed as a method to enhance medical data security, confidentiality and integrity. Medical image watermarking requires extreme care when embedding additional data, given their importance to clinical diagnosis, treatment, and research. In this paper, a novel image watermarking approach based on the human visual system (HVS model and neural network technique is proposed. The watermark was inserted into the middle frequency coefficients of the cover image’s blocked DCT based transform domain. In order to make the watermark stronger and less susceptible to different types of attacks, it is essential to find the maximum amount of interested watermark before the watermark becomes visible. In this paper, neural networks are used to implement an automated system of creating maximum-strength watermarks. The experimental results show that such method can survive of common image processing operations and has good adaptability for automated watermark embedding.
Adaptive Fault Tolerant Routing In Interconnection Networks: A Review
B.V.Suresh Kumar
2011-05-01
Full Text Available A multi-processor / computer systems are connected by varieties of interconnection networks. To enable any nonfaulty component (Node / Link to communicate with any other non-faulty component in an injured interconnection network, the information on component failure is to be made available to non-faulty components, so as to route messages around the faulty components. In this paper we have reviewed to adaptive routing schemes proposed by Dally and Aloki , Glass and Ni ,and also the implementation details of reliable router. Moreover , it is proved that these schemes of routing messages via shortest paths with high probability and the expected length of routing path is very close to that of shortest path.
The emergence of complexity and restricted pleiotropy in adapting networks
Le Nagard Hervé
2011-11-01
Full Text Available Abstract Background The emergence of organismal complexity has been a difficult subject for researchers because it is not readily amenable to investigation by experimental approaches. Complexity has a myriad of untested definitions and our understanding of its evolution comes primarily from static snapshots gleaned from organisms ranked on an intuitive scale. Fisher's geometric model of adaptation, which defines complexity as the number of phenotypes an organism exposes to natural selection, provides a theoretical framework to study complexity. Yet investigations of this model reveal phenotypic complexity as costly and therefore unlikely to emerge. Results We have developed a computational approach to study the emergence of complexity by subjecting neural networks to adaptive evolution in environments exacting different levels of demands. We monitored complexity by a variety of metrics. Top down metrics derived from Fisher's geometric model correlated better with the environmental demands than bottom up ones such as network size. Phenotypic complexity was found to increase towards an environment-dependent level through the emergence of restricted pleiotropy. Such pleiotropy, which confined the action of mutations to only a subset of traits, better tuned phenotypes in challenging environments. However, restricted pleiotropy also came at a cost in the form of a higher genetic load, as it required the maintenance by natural selection of more independent traits. Consequently, networks of different sizes converged in complexity when facing similar environment. Conclusions Phenotypic complexity evolved as a function of the demands of the selective pressures, rather than the physical properties of the network architecture, such as functional size. Our results show that complexity may be more predictable, and understandable, if analyzed from the perspective of the integrated task the organism performs, rather than the physical architecture used to
Providing Adapted Contextual Information in an Overlay Vehicular Network
Andrés Muñoz
2010-01-01
Full Text Available Current vehicular networks are developed upon commercial solutions based on cellular networks (CNs or vehicular ad-hoc networks (VANETs, both present in numerous research proposals. Current approximations are not enough to cover the communication necessities of several applications at the same time, and they are not suitable for future vehicular pervasive services. The vehicular network presented in this paper fills the existent gap between solutions lacking in flexibility, mainly supported by an infrastructure deployment, and those highly local and distributed, such as sole-VANET approximations. In this manner, an overlay communication platform which can work over the CN basis has been designed and developed. This architecture is complemented by an additional support of an information system located at the infrastructure side. Moreover, since most of the information received from current notification services is not relevant for the driver, an additional subsystem has been devised to provide adapted information to users. This has been carried out by means of an ontology model which represents users' preferences and contextual information. Finally, using a whole prototype of the telematic platform, the performance of this interring process has been evaluated to point out its impact on the system operation.
Trajanovski, S.; Guo, D.; Van Mieghem, P.F.A.
2015-01-01
The continuous-time adaptive susceptible-infected-susceptible (ASIS) epidemic model and the adaptive information diffusion (AID) model are two adaptive spreading processes on networks, in which a link in the network changes depending on the infectious state of its end nodes, but in opposite ways: (i
Analysis of Fuzzy Logic Based Intrusion Detection Systems in Mobile Ad Hoc Networks
A. Chaudhary
2014-01-01
Full Text Available Due to the advancement in wireless technologies, many of new paradigms have opened for communications. Among these technologies, mobile ad hoc networks play a prominent role for providing communication in many areas because of its independent nature of predefined infrastructure. But in terms of security, these networks are more vulnerable than the conventional networks because firewall and gateway based security mechanisms cannot be applied on it. That’s why intrusion detection systems are used as keystone in these networks. Many number of intrusion detection systems have been discovered to handle the uncertain activity in mobile ad hoc networks. This paper emphasized on proposed fuzzy based intrusion detection systems in mobile ad hoc networks and presented their effectiveness to identify the intrusions. This paper also examines the drawbacks of fuzzy based intrusion detection systems and discussed the future directions in the field of intrusion detection for mobile ad hoc networks.
Adaptive control of call acceptance in WCDMA network
Milan Manojle Šunjevarić
2013-10-01
Full Text Available In this paper, an overview of the algorithms for access control in mobile wireless networks is presented. A review of adaptive control methods of accepting a call in WCDMA networks is discussed, based on the overview of the algorithms used for this purpose, and their comparison. Appropriate comments and conculsions in comparison with the basic characteristics of these algorithms are given. The OVSF codes are explained as well as how the allocation method influences the capacity and probability of blocking.. Introduction We are witnessing a steady increase in the number of demands placed upon modern wireless networks. New applications and an increasing number of users as well as user activities growth in recent years reinforce the need for an efficient use of the spectrum and its proper distribution among different applications and classes of services. Besides humans, the last few years saw different computers, machines, applications, and, in the future, many other devices, RFID applications, and finally networked objects, as a new kind of wireless networks "users". Because of the exceptional rise in the number of users, the demands placed upon modern wireless networks are becoming larger, and spectrum management plays an important role. For these reasons, choosing an appropriate call admission control algorithm is of great importance. Multiple access and resource management in wireless networks Radio resource management of mobile networks is a set of algorithms to manage the use of radio resources with the aim is to maximize the total capacity of wireless systems with equal distribution of resources to users. Management of radio resources in cellular networks is usually located in the base station controller, the base station and the mobile terminal, and is based on decisions made on appropriate measurement and feedback. It is often defined as the maximum volume of traffic load that the system can provide for some of the requirements for the
A National Climate Change Adaptation Network for Protecting Water Security
Weaver, A.; Sauchyn, D.; Byrne, J. M.
2009-12-01
Water security and resource-dependent community-survival are being increasingly challenged as a consequence of climate change, and it is urgent that we plan now for the security of our water supplies which support our lives and livelihoods. However, the range of impacts of climate change on water availability, and the consequent environmental and human adaptations that are required, is so complex and serious that it will take the combined work of natural, health and social scientists working with industries and communities to solve them. Networks are needed that will identify crucial water issues under climate change at a range of scales in order to provide regionally-sensitive, solutions-oriented research and adaptation. We suggest national and supra-national water availability and community sustainability issues must be addressed by multidisciplinary research and adaptation networks. The work must be driven by a bottom-up research paradigm — science in the service of community and governance. We suggest that interdisciplinary teams of researchers, in partnership with community decision makers and local industries, are the best means to develop solutions as communities attempt to address future water demands, protect their homes from infrastructure damage, and meet their food, drinking water, and other essential resource requirements. The intention is to cover: the impact of climate change on Canadian natural resources, both marine and terrestrial; issues of long-term sustainability and resilience in human communities and the environments in which they are embedded; the making and moving of knowledge, be that between members of Indigenous and non-Indigenous communities, researchers of different disciplines, communities, industry, policymakers and the academy and the crucial involvement of the various orders of government in the response to water problems, under conditions of heightened uncertainty. Such an adaptation network must include a national
Analysis of Fuzzy Logic Based Intrusion Detection Systems in Mobile Ad Hoc Networks
Chaudhary, A.; V. N. Tiwari; Kumar, A
2014-01-01
Due to the advancement in wireless technologies, many of new paradigms have opened for communications. Among these technologies, mobile ad hoc networks play a prominent role for providing communication in many areas because of its independent nature of predefined infrastructure. But in terms of security, these networks are more vulnerable than the conventional networks because firewall and gateway based security mechanisms cannot be applied on it. That’s why intrusion detection systems are us...
A DYNAMIC APPROACH FOR RATE ADAPTATION IN MOBILE ADHOC NETWORKS
Suganya Subramaniam
2013-01-01
Full Text Available A Mobile Ad hoc Network (MANET is a collection of mobile nodes with no fixed infrastructure. The absence of central authorization facility in dynamic and distributed environment affects the optimal utilization of resources like, throughput, power and bandwidth. Rate adaptation is the key technique to optimize the resource throughput. Some recently proposed rate adaptations use Request to Send/Clear to Send (RTS/CTS to suppress the collision effect by differentiating collisions from channel errors. This study presents a methodology to detect the misbehavior of nodes in MANET and proposed the new dynamic algorithm for rate adaptation which in turn can improve the throughput. The proposed approach is implemented in the distributed stipulating architecture with core and access routers. This method does not require additional probing overhead incurred by RTS/CTS exchanges and may be practically deployed without change in firmware. The collision and channel error occurrence will be detected by core router and intimated to the access router to choose alternate route and retain the current rate for transmission. The extensive simulation results demonstrate the effectiveness of proposed method by comparing with existing approaches.
A Logically Centralized Approach for Control and Management of Large Computer Networks
Iqbal, Hammad A.
2012-01-01
Management of large enterprise and Internet service provider networks is a complex, error-prone, and costly challenge. It is widely accepted that the key contributors to this complexity are the bundling of control and data forwarding in traditional routers and the use of fully distributed protocols for network control. To address these…
Institutional networks and adaptive water governance in the Klamath River Basin, USA.
Polycentric networks of formal organizations and informal stakeholder groups, as opposed to centralized institutional hierarchies, can be critically important for strengthening the capacity of governance systems to adapt to unexpected social and biophysical change. Adaptive gover...
José Raúl Castro
2016-02-01
Full Text Available This paper presents an efficient algorithm to solve the multi-objective (MO voltage control problem in distribution networks. The proposed algorithm minimizes the following three objectives: voltage variation on pilot buses, reactive power production ratio deviation, and generator voltage deviation. This work leverages two optimization techniques: fuzzy logic to find the optimum value of the reactive power of the distributed generation (DG and Pareto optimization to find the optimal value of the pilot bus voltage so that this produces lower losses under the constraints that the voltage remains within established limits. Variable loads and DGs are taken into account in this paper. The algorithm is tested on an IEEE 13-node test feeder and the results show the effectiveness of the proposed model.
Adaptive RBF Neural Network Control for Three-Phase Active Power Filter
Juntao Fei; Zhe Wang
2013-01-01
An adaptive radial basis function (RBF) neural network control system for three‐phase active power filter (APF) is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non‐linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achie...
Xueling Jiang
2014-01-01
Full Text Available The problem of adaptive asymptotical synchronization is discussed for the stochastic complex dynamical networks with time-delay and Markovian switching. By applying the stochastic analysis approach and the M-matrix method for stochastic complex networks, several sufficient conditions to ensure adaptive asymptotical synchronization for stochastic complex networks are derived. Through the adaptive feedback control techniques, some suitable parameters update laws are obtained. Simulation result is provided to substantiate the effectiveness and characteristics of the proposed approach.
Fuzzy logic in real time voltage/reactive power control in FARS regional electric network
Rahideh, Akbar; Rahideh, Abbas [School of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz (Iran); Gitizadeh, M. [Fars Regional Electric Company, Shiraz (Iran)
2006-07-15
In this paper, a new method based on fuzzy logic for voltage/reactive power control and simultaneously loss reduction in power systems is presented. The purpose is to provide a solution, which does both voltage improvement and loss reduction for every practical power systems. In this idea, the voltage violation level of buses and also controlling ability of controlling devices such as shunt capacitors/reactors and tap changing transformers are translated into fuzzy sets, but at first the controlling ability of these devices are calculated using sensitivity coefficient. A feasible solution set, which causes voltage improvement, is attained using the max-min operator of fuzzy sets and finally a solution for power loss reduction is taken into account. A standard IEEE 30-bus test system and Fars regional power system are used to validate the performance of the proposed method. The obtained results show that the method is efficient, practical and fast. (author)
DSTN (Distributed Sleep Transistor Network) for Low Power Programmable Logic array Design
Singla, Pradeep; Malik, Naveen Kr; 10.5120/7004-9563
2012-01-01
With the high demand of the portable electronic products, Low- power design of VLSI circuits & Power dissipation has been recognized as a challenging technology in the recent years. PLA (Programming logic array) is one of the important off shelf part in the industrial application. This paper describes the new design of PLA using power gating structure sleep transistor at circuit level implementation for the low power applications. The important part of the power gating design i.e. header and footer switch selection is also describes in the paper. The simulating results of the proposed architecture of the new PLA is shown and compared with the conventional PLA. This paper clearly shows the optimization in the reduction of power dissipation in the new design implementation of the PLA. The transient response of the power gates structure of PLA is also illustrate in the paper by using TINA-PRO software.
Gurudeo Anand Tularam; Siti Amri
2012-01-01
House price prediction continues to be important for government agencies insurance companies and real estate industry. This study investigates the performance of house sales price models based on linear and non-linear approaches to study the effects of selected variables. Linear stepwise Multivariate Regression (MR) and nonlinear models of Neural Network (NN) and Adaptive Neuro-Fuzzy (ANFIS) are developed and compared. The GIS methods are used to integrate the data for the study area (Bathurs...
Pretolani, Daniele; Nielsen, Lars Relund; Andersen, Kim Allan; Ehrgott, Matthias
We compare two different models for multicriterion routing in stochastic time-dependent networks: the classic "time-adaptive'' route choice and the more flexible "history-adaptive'' route choice. We point out some interesting properties of the sets of efficient solutions ("strategies'') found un...... under the two models. We also suggest possible directions for improving computational techniques....
Kamalakshi.N
2009-11-01
Full Text Available Today's wireless networks are highly heterogeneous, with mobile devices consisting of multiple wireless network interfaces (WNICs. Since battery lifetime is limited, power management of the interfaces has become essential with flexible and open architecture, capable of supporting various types of networks, terminals and applications. However how to integrate the protocols to meet the heterogeneous network environments becomes a significant challenge in the fourth generation wireless network. Adaptive protocols are proposed to solve heterogeneity problem in future wireless networks. This paper discusses two protocols R²CP, and RCP and feasibility of RCP protocols applied to the manage power efficiently and adaptive Congestion control on heterogeneous wireless network.
Fuzzy-Logic-Based Energy Optimized Routing for Wireless Sensor Networks
Haifeng Jiang; Yanjing Sun; Renke Sun; Hongli Xu
2013-01-01
Wireless sensor nodes are usually powered by batteries and deployed in unmanned outdoors or dangerous regions. So, constrained energy is a prominent feature for wireless sensor networks. Since the radio transceiver typically consumes more energies than any other hardware component on a sensor node, it is of great importance to design energy optimized routing algorithm to prolong network lifetime. In this work, based on analysis of energy consumption for data transceiver, single-hop forwarding...
ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster
Eva Volna; Martin Kotyrba; Hashim Habiballa
2015-01-01
The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experim...
Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET
Rodriguez, Ana; Crespo, Isaac; Androsova, Ganna; del Sol Mesa, Antonio
2015-01-01
High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. ...
Inference in Belief Network using Logic Sampling and Likelihood Weighing algorithms
Jasmine, K. S.; Gavani PRATHVIRAJ S.; P Ijantakar RAJASHEKAR; K. A. SUMITHRA DEVI
2013-01-01
Over the time in computational history, belief networks have become an increasingly popular mechanism for dealing with uncertainty in systems. It is known that identifying the probability values of belief network nodes given a set of evidence is not amenable in general. Many different simulation algorithms for approximating solution to this problem have been proposed and implemented. This paper details the implementation of such algorithms, in particular the two algorithms of the belief netwo...
Analysis of Social Network Based on Graph Theory and Fuzzy Logic
Dr.A.Kavitha*1, Dr. G.S.G.N.Anjaneyulu2
2014-01-01
A social network consists of a set of individuals connected by a binary relationship, which can be represented as friendship between them, together with the communities that they join and the information that they exchange through forums at a global level. Presently, there are more than 200 social networking websites. This paper will help researchers to implement search optimization. In this paper, we analyze the behavior of people in joining social communities and incentive o...
Using fuzzy logic in the evaluation of user perception of security risk on social networking sites
Afful-Dadzie, Eric
2012-01-01
Human nature often frowns on engaging or interacting with near strangers but on online social media networks, this is largely ignored. There is an open interaction among both known users and loosely-connected users on social media networks, and as a result, the normal social barriers against interacting with strangers are lowered. This rather careless openness has resulted in rampant increase in cybercrime and identity theft worldwide, awaiting a potential privacy disaster in the near future ...
Flexible and dynamic network coding for adaptive data transmission in DTNs
Radenkovic, Milena; Zakhary, Sameh
2012-01-01
Existing network coding approaches for Delay-Tolerant Networks (DTNs) do not detect and adapt to congestion in the network. In this paper we describe CafNC (Congestion aware forwarding with Network Coding) that combines adaptive network coding and adaptive forwarding in DTNs. In CafNC each node learns the status of its neighbours, and their egonetworks in order to detect coding opportunities, and codes as long as the recipients can decode. Our flexible design allows CafNC to efficiently supp...
Modeling and adaptive pinning synchronization control for a chaotic-motion motor in complex network
Zhu, Darui, E-mail: zdarui@163.com [State Key Laboratory of Electrical Insulation and Power Equipment, Xi' an 710049 (China); School of Electrical Engineering, Xi' an Jiaotong University, Xi' an 710049 (China); Liu, Chongxin; Yan, Bingnan [State Key Laboratory of Electrical Insulation and Power Equipment, Xi' an 710049 (China); School of Electrical Engineering, Xi' an Jiaotong University, Xi' an 710049 (China)
2014-01-24
We introduce a chaos model for a permanent-magnet synchronous motor and construct a coupled chaotic motor in a complex dynamic network using the Newman–Watts small-world network algorithm. We apply adaptive pinning control theory for complex networks to obtain suitable adaptive feedback gain and the number of nodes to be pinned. Nodes of low degree are pinned to realize global asymptotic synchronization in the complex network. The proposed adaptive pinning controller is added to the complex motor network for simulation and verification.
Modeling and adaptive pinning synchronization control for a chaotic-motion motor in complex network
We introduce a chaos model for a permanent-magnet synchronous motor and construct a coupled chaotic motor in a complex dynamic network using the Newman–Watts small-world network algorithm. We apply adaptive pinning control theory for complex networks to obtain suitable adaptive feedback gain and the number of nodes to be pinned. Nodes of low degree are pinned to realize global asymptotic synchronization in the complex network. The proposed adaptive pinning controller is added to the complex motor network for simulation and verification.
Adaptive hybrid simulations for multiscale stochastic reaction networks
The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest
Skeleton-supported stochastic networks of organic memristive devices: Adaptations and learning
Stochastic networks of memristive devices were fabricated using a sponge as a skeleton material. Cyclic voltage-current characteristics, measured on the network, revealed properties, similar to the organic memristive device with deterministic architecture. Application of the external training resulted in the adaptation of the network electrical properties. The system revealed an improved stability with respect to the networks, composed from polymer fibers
An Adaptive Channel Model for VBLAST in Vehicular Networks
Ghassan M. T. Abdalla
2009-01-01
Full Text Available The wireless transmission environment in vehicular ad hoc systems varies from line of sight with few surroundings to rich Rayleigh fading. An efficient communication system must adapt itself to these diverse conditions. Multiple antenna systems are known to provide superior performance compared to single antenna systems in terms of capacity and reliability. The correlation between the antennas has a great effect on the performance of MIMO systems. In this paper we introduce a novel adaptive channel model for MIMO-VBLAST systems in vehicular ad hoc networks. Using the proposed model, the correlation between the antennas was investigated. Although the line of sight is ideal for single antenna systems, it severely degrades the performance of VBLAST systems since it increases the correlation between the antennas. A channel update algorithm using single tap Kalman filters for VBLAST in flat fading channels has also been derived and evaluated. At 12 dB Es/N0, the new algorithm showed 50% reduction in the mean square error (MSE between the actual channel and the corresponding updated estimate compared to the MSE without update. The computational requirement of the proposed algorithm for a p×q VBLAST is 6p×q real multiplications and 4p×q real additions.
Scalable Lunar Surface Networks and Adaptive Orbit Access Project
National Aeronautics and Space Administration — Innovative network architecture, protocols, and algorithms are proposed for both lunar surface networks and orbit access networks. Firstly, an overlaying...
Chertkov, Michael [Los Alamos National Laboratory
2012-07-24
The goal of the DTRA project is to develop a mathematical framework that will provide the fundamental understanding of network survivability, algorithms for detecting/inferring pre-cursors of abnormal network behaviors, and methods for network adaptability and self-healing from cascading failures.
Haldar, Arabinda; Adeyeye, Adekunle Olusola
2016-01-26
Information processing based on nanomagnetic networks is an emerging area of spintronics, as the energy consumption and integration density of the current semiconductor technology are reaching their fundamental limits. Nanomagnet-based devices rely on manipulating the magnetic ground states for device operations. While the static behavior of nanomagnets has been explored, little information is available on their dynamic behavior. Here, we demonstrate an additional functionality based on their collective dynamic response and explore the concept utilizing networks of bistable rhomboid nanomagnets. The control of the magnetic ground states of the networks was achieved by the geometrical design of the nanomagnets instead of the conventional interelement dipolar coupling. Dynamic responses of both the ferromagnetic and antiferromagnetic ground states were monitored using broadband ferromagnetic resonance spectroscopy, the Brillouin light scattering technique, and direct magnetic force microscopy. Micromagnetic simulations and numerical calculations validate our experimental observations. This method would have potential implications for low-power magnonic devices based on reconfigurable microwave properties. PMID:26738567
AQM Algorithm with Adaptive Reference Queue Threshold for Communication Networks
Liming Chen
2012-09-01
Full Text Available Nowadays, congestion in communication networks has been more intractable than ever before due to the explosive growth of network scale and multimedia traffic. Active queue management (AQM algorithms had been proposed to alleviate congestion to improve quality of service (QoS, but existing algorithms often suffer from some flaws in one aspect or another. In this paper, a novel AQM algorithm with adaptive reference queue threshold (ARTAQM is proposed of which the main innovative contributions are recounted as follows. First, traffic is predicted to calculate the packet loss ratio (PLR and the traffic rate based on traffic prediction algorithm. Second, by means of periodical measurements, a weighted PLR is obtained to dynamically adjust packet dropping probability in ARTAQM algorithm. Third, ARTAQM algorithm runs in both coarse and fine granularities. In coarse granularity, the mismatch of the predicted traffic rate and link capacity can adjusts the reference queue length in every period, while in fine granularity, reference queue remains fixed and the instantaneous queue is adjusted packet by packet in one period. Simulation results indicate that ARTAQM algorithm not only maintains stable queue and fast response speed, but has lower PLR and higher link utilization as well.
Discrete rate and variable power adaptation for underlay cognitive networks
Abdallah, Mohamed M.
2010-01-01
We consider the problem of maximizing the average spectral efficiency of a secondary link in underlay cognitive networks. In particular, we consider the network setting whereby the secondary transmitter employs discrete rate and variable power adaptation under the constraints of maximum average transmit power and maximum average interference power allowed at the primary receiver due to the existence of an interference link between the secondary transmitter and the primary receiver. We first find the optimal discrete rates assuming a predetermined partitioning of the signal-to-noise ratio (SNR) of both the secondary and interference links. We then present an iterative algorithm for finding a suboptimal partitioning of the SNR of the interference link assuming a fixed partitioning of the SNR of secondary link selected for the case where no interference link exists. Our numerical results show that the average spectral efficiency attained by using the iterative algorithm is close to that achieved by the computationally extensive exhaustive search method for the case of Rayleigh fading channels. In addition, our simulations show that selecting the optimal partitioning of the SNR of the secondary link assuming no interference link exists still achieves the maximum average spectral efficiency for the case where the average interference constraint is considered. © 2010 IEEE.
Complex information extraction from images is a key skill of intelligent machines, with wide application in automated systems, robotic manipulation and human-computer interaction. However, solving this problem with traditional, geometric or analytical, strategies is extremely difficult. Therefore, an approach based on learning from examples seems to be more appropriate. This thesis addresses the problem of 3D orientation, aiming to estimate the angular coordinates of a known object from an image shot from any direction. We describe a system based on artificial neural networks to solve this problem in real time. The implementation is performed using a programmable logic device. The digital system described in this paper has the ability to estimate two rotational coordinates of a 3D known object, in ranges from -800 to 800. The operation speed allows a real time performance at video rate. The system accuracy can be successively increased by increasing the size of the artificial neural network and using a larger number of training examples
This paper describes the methodology and application of the computer model CLEAR (Calculates Logical Evacuation And Response) which estimates the time required for a specific population density and distribution to evacuate an area using a specific transportation network. The CLEAR model simulates vehicle departure and movement on a transportation network according to the conditions and consequences of traffic flow. These include handling vehicles at intersecting road segments, calculating the velocity of travel on a road segment as a function of its vehicle density, and accounting for the delay of vehicles in traffic queues. The program also models the distribution of times required by individuals to prepare for an evacuation. In order to test its accuracy, the CLEAR model was used to estimate evacuation times for the emergency planning zone surrounding the Beaver Valley Nuclear Power Plant. The Beaver Valley site was selected because evacuation time estimates had previously been prepared by the licensee, Duquesne Light, as well as by the Federal Emergency Management Agency and the Pennsylvania Emergency Management Agency. A lack of documentation prevented a detailed comparison of the estimates based on the CLEAR model and those obtained by Duquesne Light. However, the CLEAR model results compared favorably with the estimates prepared by the other two agencies. (author)
Embedding Logics into Product Logic
Baaz, M.; Hájek, Petr; Krajíček, Jan; Švejda, David
1998-01-01
Roč. 61, č. 1 (1998), s. 35-47. ISSN 0039-3215 R&D Projects: GA AV ČR IAA1030601 Grant ostatní: COST(XE) Action 15 Keywords : fuzzy logic * Lukasiewicz logic * Gödel logic * product logic * computational complexity * arithmetical hierarchy Subject RIV: BA - General Mathematics
Suppressing Halo-chaos for Intense Ion Beamby Neural Network Adaptation Control Strategy
FANGJin-qing; LUOXiao-shu; WENGJia-qiang; ZHULun-wu
2003-01-01
Neural network has some advantages of adaptation, learn-self, self-organization and suitable for high-dimension for various applications in many fields, especially among them the feed-forward back-propagating neural network self-adaptation method is suitable for control of nonlinear systems.
Neural and fuzzy computation techniques for playout delay adaptation in VoIP networks.
Ranganathan, Mohan Krishna; Kilmartin, Liam
2005-09-01
Playout delay adaptation algorithms are often used in real time voice communication over packet-switched networks to counteract the effects of network jitter at the receiver. Whilst the conventional algorithms developed for silence-suppressed speech transmission focused on preserving the relative temporal structure of speech frames/packets within a talkspurt (intertalkspurt adaptation), more recently developed algorithms strive to achieve better quality by allowing for playout delay adaptation within a talkspurt (intratalkspurt adaptation). The adaptation algorithms, both intertalkspurt and intratalkspurt based, rely on short term estimations of the characteristics of network delay that would be experienced by up-coming voice packets. The use of novel neural networks and fuzzy systems as estimators of network delay characteristics are presented in this paper. Their performance is analyzed in comparison with a number of traditional techniques for both inter and intratalkspurt adaptation paradigms. The design of a novel fuzzy trend analyzer system (FTAS) for network delay trend analysis and its usage in intratalkspurt playout delay adaptation are presented in greater detail. The performance of the proposed mechanism is analyzed based on measured Internet delays. Index Terms-Fuzzy delay trend analysis, intertalkspurt, intratalkspurt, multilayer perceptrons (MLPs), network delay estimation, playout buffering, playout delay adaptation, time delay neural networks (TDNNs), voice over Internet protocol (VoIP). PMID:16252825
Autonomous embedded fuzzy logic based wireless sensor network for indoor energy management
Feradyan, Anthony; Holmes, Violeta
2013-01-01
The 2008 Climate Change Act requires a reduction in carbon emissions by 80% on 1990 levels by 2050. With the building sector contributing to 40 - 45% of all CO2 emissions in the UK, it is vital to address home and building energy management by making them more energy efficient, and ensure that new buildings are designed and built with high levels of energy efficiency (Government 2013). Wireless Sensor Networks (WSN), due to their low cost and energy consumption, and robust communicat...
Smullyan, Raymond
2008-01-01
This book features a unique approach to the teaching of mathematical logic by putting it in the context of the puzzles and paradoxes of common language and rational thought. It serves as a bridge from the author's puzzle books to his technical writing in the fascinating field of mathematical logic. Using the logic of lying and truth-telling, the author introduces the readers to informal reasoning preparing them for the formal study of symbolic logic, from propositional logic to first-order logic, a subject that has many important applications to philosophy, mathematics, and computer science. T
Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Network
Priya Mohite
2015-01-01
The data fusion process has led to an evolution for emerging Wireless Sensor Networks (WSNs) and examines the impact of various factors on energy consumption. Significantly there has always been a constant effort to enhance network efficiency without decreasing the quality of information. Based on Adaptive Fusion Steiner Tree (AFST), this paper proposes a heuristic algorithm called Modified Adaptive Fusion Steiner Tree (M-AFST) for energy efficient routing which not only does adaptively adjus...
On adaptive control of mobile slotted aloha networks
Lim J.-T.
1995-01-01
Full Text Available An adaptive control scheme for mobile slotted ALOHA is presented and the effect of capture on the adaptive control scheme is investigated. It is shown that with the proper choice of adaptation parameters the adaptive control scheme can be made independent of the effect of capture.
Pinsky, Vladimir; Shapira, Avi
2016-05-01
The geographical area where a seismic event of magnitude M ≥ M t is detected by a seismic station network, for a defined probability is derived from a station probability of detection estimated as a function of epicentral distance. The latter is determined from both the bulletin data and the waveforms recorded by the station during the occurrence of the event with and without band-pass filtering. For simulating the real detection process, the waveforms are processed using the conventional Carl Johnson detection and association algorithm. The attempt is presented to account for the association time criterion in addition to the conventional approach adopted by the known PMC method.
A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks
Loureiro, Antonio A. F.; Carlos M. S. Figueiredo; Eduardo F. Nakamura
2009-01-01
Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts ...
Ali Amiri
2016-01-01
Efficient management of bandwidth in wireless networks is a critical factor for a successful communication system. Special features of wireless networks such user mobility and growth of wireless applications and their high bandwidth intensity create a major challenge to utilize bandwidth resources optimally. In this research, we propose a model for an adaptable network bandwidth management method that combines bandwidth reservation and bandwidth adaptation to reduce call blocking ...
Adaptive Synchronization of Fractional Neural Networks with Unknown Parameters and Time Delays
Weiyuan Ma
2014-12-01
Full Text Available In this paper, the parameters identification and synchronization problem of fractional-order neural networks with time delays are investigated. Based on some analytical techniques and an adaptive control method, a simple adaptive synchronization controller and parameter update laws are designed to synchronize two uncertain complex networks with time delays. Besides, the system parameters in the uncertain network can be identified in the process of synchronization. To demonstrate the validity of the proposed method, several illustrative examples are presented.
Adaptive approach to global synchronization of directed networks with fast switching topologies
Global synchronization of directed networks with switching topologies is investigated. It is found that if there exists at least one directed spanning tree in the network with the fixed time-average topology and the time-average topology is achieved sufficiently fast, the network will reach global synchronization for appreciate coupling strength. Furthermore, this appreciate coupling strength may be obtained by local adaptive approach. A sufficient condition about the global synchronization is given. Numerical simulations verify the effectiveness of the adaptive strategy.
An Adaptive Amplifier System for Wireless Sensor Network Applications
Mónica Lovay
2012-01-01
Full Text Available This paper presents an adaptive amplifier that is part of a sensor node in a wireless sensor network. The system presents a target gain that has to be maintained without direct human intervention despite the presence of faults. In addition, its bandwidth must be as large as possible. The system is composed of a software-based built-in self-test scheme implemented in the node that checks all the available gains in the amplifiers, a reconfigurable amplifier, and a genetic algorithm (GA for reconfiguring the node resources that runs on a host computer. We adopt a PSoC device from Cypress for the node implementation. The performance evaluation of the scheme presented is made by adopting four different types of fault models in the amplifier gains. The fault simulation results show that GA finds the target gain with low error, maintains the bandwidth above the minimum tolerable bandwidth, and presents a runtime lower than exhaustive search method.
ADAPTIVELY IMPROVING LONG DISTANCE NETWORK TRANSFERS WITH LOGISTICS
LaBissoniere, D.; Roche, K.
2007-01-01
Long distance data movement is an essential activity of modern computing. However, the congestion control mechanisms in the Internet’s Transmission Control Protocol (TCP) severely limit the bandwidth achieved by long distance data transfers. The throughput of such transfers can be improved by applying the logistical technique of breaking a single long distance transfer into multiple shorter transfers. This technique can result in signifi cantly improved throughput while still respecting the shared nature of the Internet by not attempting to circumvent the TCP congestion controls. This technique has been incorporated into an algorithm which attempts to dynamically schedule transfers for optimal throughput. The algorithm couples graph techniques with real-time latency and bandwidth measurements to discover the best path and adaptively respond to network dynamics. The algorithm shows improvements in speed and fl exibility over standard data transfer methods such as FTP. Specifi c transfers tests performed between Oak Ridge National Laboratory and a destination in Sunnyvale, CA show throughput increases by a factor of two.
Efficient community-based control strategies in adaptive networks
Most studies on adaptive networks concentrate on the properties of steady state, but neglect transient dynamics. In this study, we pay attention to the emergence of community structure in the transient process and the effects of community-based control strategies on epidemic spreading. First, by normalizing the modularity, we investigate the evolution of community structure during the transient process, and find that a strong community structure is induced by the rewiring mechanism in the early stage of epidemic dynamics, which, remarkably, delays the outbreak of disease. We then study the effects of control strategies started at different stages on the prevalence. Both immunization and quarantine strategies indicate that it is not ‘the earlier, the better’ for the implementation of control measures. And the optimal control effect is obtained if control measures can be efficiently implemented in the period of a strong community structure. For the immunization strategy, immunizing the susceptible nodes on susceptible–infected links and immunizing susceptible nodes randomly have similar control effects. However, for the quarantine strategy, quarantining the infected nodes on susceptible–infected links can yield a far better result than quarantining infected nodes randomly. More significantly, the community-based quarantine strategy performs better than the community-based immunization strategy. This study may shed new light on the forecast and the prevention of epidemics among humans. (paper)
Distributed reinforcement learning for adaptive and robust network intrusion response
Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel
2015-07-01
Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.
Adaptive autonomous Communications Routing Optimizer for Network Efficiency Management Project
National Aeronautics and Space Administration — Maximizing network efficiency for NASA's Space Networking resources is a large, complex, distributed problem, requiring substantial collaboration. We propose the...
Kleene, Stephen Cole
2002-01-01
Undergraduate students with no prior instruction in mathematical logic will benefit from this multi-part text. Part I offers an elementary but thorough overview of mathematical logic of 1st order. Part II introduces some of the newer ideas and the more profound results of logical research in the 20th century. 1967 edition.
Asoodeh Mojtaba
2015-06-01
Full Text Available Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR log. Despite conventional well logs (CWLs, NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS technique, then fuzzy logic (FL is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.
V. M. Varatharaju; Badrilal Mathur; Udhayakumar
2011-01-01
Problem statement: The tuning methodology for the parameters of adaptive speed controller causes a transient deviation of the response from the set reference following variation in load torque in a permanent-magnet brushless DC (BLDC) motor drive system. Approach: This study develops a mathematical model of the BLDC drive system, firstly. Secondly, discusses a design of the closed loop drive system employing the Adaptive-Network-based Fuzzy Interference System (ANFIS). The nonlinear simulatio...
Yanushkevich, Svetlana N
2008-01-01
Preface Design Process and Technology Theory of logic design Analysis and synthesis Implementation technologies Predictable technologies Contemporary CAD of logic networks Number Systems Positional numbers Counting in a positional number system Basic arithmetic operations in various number systems Binary arithmetic Radix-complement representations Techniques for conversion of numbers in various radices Overflow Residue arithmetic Other binary codes Redundancy and reliability Graphical Data Structures Graphs in discrete devices and systems design Basic definitions T
Design of artificial genetic regulatory networks with multiple delayed adaptive responses
Kaluza, Pablo
2016-01-01
Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways.
Acoustic leak detection at complicated geometrical structures using fuzzy logic and neural networks
An acoustic method based on pattern recognition is being developed. During the learning phase, the localization classifier is trained with sound patterns that are generated with simulated leaks at all locations endangered by leak. The patterns are extracted from the signals of an appropriate sensor array. After training unknown leak positions can be recognized through comparison with the training patterns. The experimental part is performed at an acoustic 1:3 model of the reactor vessel and head and at an original VVER-440 reactor in the former NPP Greifswald. The leaks were simulated at the vessel head using mobile sound sources driven either by compressed air, a piezoelectric transmitter or by a thin metal blade excited through a jet of compressed air. The sound patterns of the simulated leaks are simultaneously detected with an AE-sensor array and with high frequency microphones measuring structure-borne sound and airborne sound, respectively. Pattern classifiers based on Fuzzy Pattern Classification (FPC) and Artificial Neural Networks (ANN) are currently tested for validation of the acoustic emission-sensor array (FPC), leak localization via structure-borne sound (FPC) and the leak localization using microphones (ANN). The initial results show the used classifiers principally to be capable of detecting and locating leaks, but they also show that further investigations are necessary to develop a reliable method applicable at NPPs. (orig./HP)
Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min
2015-12-01
In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.
Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.
2015-04-01
The purpose of this study is to evaluate the uncertainty, using various methodologies, in a combined Artificial Neural Network (ANN) - Fuzzy logic - Kriging system, which can simulate spatially and temporally the hydraulic head in an aquifer. This system uses ANNs for the temporal prediction of hydraulic head in various locations, one ANN for every location with available data, and Kriging for the spatial interpolation of ANN's results. A fuzzy logic is used for the interconnection of these two methodologies. The full description of the initial system and its functionality can be found in Tapoglou et al. (2014). Two methodologies were used for the calculation of uncertainty for the implementation of the algorithm in a study area. First, the uncertainty of Kriging parameters was examined using a Bayesian bootstrap methodology. In this case the variogram is calculated first using the traditional methodology of Ordinary Kriging. Using the parameters derived and the covariance function of the model, the covariance matrix is constructed. A common method for testing a statistical model is the use of artificial data. Normal random numbers generation is the first step in this procedure and by multiplying them by the decomposed covariance matrix, correlated random numbers (sample set) can be calculated. These random values are then fitted into a variogram and the value in an unknown location is estimated using Kriging. The distribution of the simulated values using the Kriging of different correlated random values can be used in order to derive the prediction intervals of the process. In this study 500 variograms were constructed for every time step and prediction point, using the method described above, and their results are presented as the 95th and 5th percentile of the predictions. The second methodology involved the uncertainty of ANNs training. In this case, for all the data points 300 different trainings were implemented having different training datasets each time
Construction of a new adaptive wavelet network and its learning algorithm
无
2001-01-01
A new adaptive learning algorithm for constructing and training wavelet networks is proposed based on the time-frequency localization properties of wavelet frames and the adaptive projection algorithm. The exponential convergence of the adaptive projection algorithm in finite-dimensional Hilbert spaces is constructively proved, with exponential decay ratios given with high accuracy. The learning algorithm can sufficiently utilize the time-frequency information contained in the training data, iteratively determines the number of the hidden layer nodes and the weights of wavelet networks, and solves the problem of structure optimization of wavelet networks. The algorithm is simple and efficient, as illustrated by examples of signal representation and denoising.
Breast image feature learning with adaptive deconvolutional networks
Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.
2012-03-01
Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).
Adaptive robotic control driven by a versatile spiking cerebellar network.
Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Carrillo, Richard R; Luque, Niceto R; Ros, Eduardo; Pedrocchi, Alessandra; D'Angelo, Egidio
2014-01-01
The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions. PMID:25390365
Towards a Framework for Self-Adaptive Reliable Network Services in Highly-Uncertain Environments
Grønbæk, Lars Jesper; Schwefel, Hans-Peter; Ceccarelli, Andrea;
2010-01-01
In future inhomogeneous, pervasive and highly dynamic networks, end-nodes may often only rely on unreliable and uncertain observations to diagnose hidden network states and decide upon possible remediation actions. Inherent challenges exists to identify good and timely decision strategies...... to improve resilience of end-node services. In this paper we present a framework, called ODDR (Observation, Diagnosis, Decision, Remediation), for improving resilience of network based services through integration of self-adaptive monitoring services, network diagnosis, decision actions, and finally...
A study of task-based strategies for adaptively constructive neural networks
The authors investigated the strategies for optimizing neural networks under the unified frame based on task, focused for constructive neural networks on two typical and practical schemes, which are adaptively constructive neural networks by growing hidden or layers of hidden nodes and by growing sub net. With the Layer Multinet Model proposed by the research group, the authors investigated task-based algorithms for constructive neural networks, their perspective, strength and weakness
This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.
Adaptive routing in wireless communication networks using swarm intelligence
Arabshahi, P.; Gray, A.; Kassabalidis, I.; Das, A.; Narayanan, S.; Sharkawi, M. El; Marks, R. J.
2001-01-01
In this paper we focus on the network routing problem, and survey swarm intelligent approaches for its efficient solution, after a brief overview of power-aware routing schemes, which are important in the network examples outlined above.
Dynamic adaptable overlay networks for personalised service delivery
Mathieu, B.; Stiemerling, M.; Soveri, M.C.; Galis, A.; Jean, K.; Ocampo, R.; Lai, Z.; Kampmann, M.; Tariq, M.A.; Balos, K.; Ahmed, O.K.; Busropan, B.J.; Prins, M.J.
2007-01-01
Overlay Networks have been designed as a promising solution to deliver new services via the use of intermediate nodes, acting as proxies or relays. This concept enables to hide the heterogeneity and variability of the underlying networks. In the Ambient Networks (ANs) project, the objectives are to
Exploring Educational and Cultural Adaptation through Social Networking Sites
Ryan, Sherry D.; Magro, Michael J.; Sharp, Jason H.
2011-01-01
Social networking sites have seen tremendous growth and are widely used around the world. Nevertheless, the use of social networking sites in educational contexts is an under explored area. This paper uses a qualitative methodology, autoethnography, to investigate how social networking sites, specifically Facebook[TM], can help first semester…
Matthew Andrews; Spyridon Antonakopoulos; Steve Fortune; Andrea Francini; Lisa Zhang
2011-07-12
This Concept Definition Study focused on developing a scientific understanding of methods to reduce energy consumption in data networks using rate adaptation. Rate adaptation is a collection of techniques that reduce energy consumption when traffic is light, and only require full energy when traffic is at full provisioned capacity. Rate adaptation is a very promising technique for saving energy: modern data networks are typically operated at average rates well below capacity, but network equipment has not yet been designed to incorporate rate adaptation. The Study concerns packet-switching equipment, routers and switches; such equipment forms the backbone of the modern Internet. The focus of the study is on algorithms and protocols that can be implemented in software or firmware to exploit hardware power-control mechanisms. Hardware power-control mechanisms are widely used in the computer industry, and are beginning to be available for networking equipment as well. Network equipment has different performance requirements than computer equipment because of the very fast rate of packet arrival; hence novel power-control algorithms are required for networking. This study resulted in five published papers, one internal report, and two patent applications, documented below. The specific technical accomplishments are the following: • A model for the power consumption of switching equipment used in service-provider telecommunication networks as a function of operating state, and measured power-consumption values for typical current equipment. • An algorithm for use in a router that adapts packet processing rate and hence power consumption to traffic load while maintaining performance guarantees on delay and throughput. • An algorithm that performs network-wide traffic routing with the objective of minimizing energy consumption, assuming that routers have less-than-ideal rate adaptivity. • An estimate of the potential energy savings in service-provider networks
Highlights: • A colorimetric and multistage biological network has been developed. • This system was on the basis of the enzyme-regulated changes of pH values. • This enzyme-based system could assemble large biological circuit. • Two signal transducers (DNA/AuNPs and acid–base indicators) were used. • The compositions of samples could be detected through visual output signals. - Abstract: Based on enzymatic reactions-triggered changes of pH values and biocomputing, a novel and multistage interconnection biological network with multiple easy-detectable signal outputs has been developed. Compared with traditional chemical computing, the enzyme-based biological system could overcome the interference between reactions or the incompatibility of individual computing gates and offer a unique opportunity to assemble multicomponent/multifunctional logic circuitries. Our system included four enzyme inputs: β-galactosidase (β-gal), glucose oxidase (GOx), esterase (Est) and urease (Ur). With the assistance of two signal transducers (gold nanoparticles and acid–base indicators) or pH meter, the outputs of the biological network could be conveniently read by the naked eyes. In contrast to current methods, the approach present here could realize cost-effective, label-free and colorimetric logic operations without complicated instrument. By designing a series of Boolean logic operations, we could logically make judgment of the compositions of the samples on the basis of visual output signals. Our work offered a promising paradigm for future biological computing technology and might be highly useful in future intelligent diagnostics, prodrug activation, smart drug delivery, process control, and electronic applications
Huang, Yanyan; Ran, Xiang; Lin, Youhui [Laboratory of Chemical Biology, Division of Biological Inorganic Chemistry, State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022 (China); Graduate School of University of Chinese Academy of Sciences, Beijing 100039 (China); Ren, Jinsong, E-mail: jren@ciac.ac.cn [Laboratory of Chemical Biology, Division of Biological Inorganic Chemistry, State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022 (China); Qu, Xiaogang [Laboratory of Chemical Biology, Division of Biological Inorganic Chemistry, State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022 (China)
2015-04-22
Highlights: • A colorimetric and multistage biological network has been developed. • This system was on the basis of the enzyme-regulated changes of pH values. • This enzyme-based system could assemble large biological circuit. • Two signal transducers (DNA/AuNPs and acid–base indicators) were used. • The compositions of samples could be detected through visual output signals. - Abstract: Based on enzymatic reactions-triggered changes of pH values and biocomputing, a novel and multistage interconnection biological network with multiple easy-detectable signal outputs has been developed. Compared with traditional chemical computing, the enzyme-based biological system could overcome the interference between reactions or the incompatibility of individual computing gates and offer a unique opportunity to assemble multicomponent/multifunctional logic circuitries. Our system included four enzyme inputs: β-galactosidase (β-gal), glucose oxidase (GOx), esterase (Est) and urease (Ur). With the assistance of two signal transducers (gold nanoparticles and acid–base indicators) or pH meter, the outputs of the biological network could be conveniently read by the naked eyes. In contrast to current methods, the approach present here could realize cost-effective, label-free and colorimetric logic operations without complicated instrument. By designing a series of Boolean logic operations, we could logically make judgment of the compositions of the samples on the basis of visual output signals. Our work offered a promising paradigm for future biological computing technology and might be highly useful in future intelligent diagnostics, prodrug activation, smart drug delivery, process control, and electronic applications.
Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin;
2015-01-01
mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online...
Ma, Chuang; Zhang, Hai-Feng
2016-01-01
So far, many network-structure-based link prediction methods have been proposed. However, these traditional methods were proposed by highlighting one or two structural features of networks, and then use the methods to implement link prediction in different networks. In many cases, the performance is not ideal since each network has its unique underlying structural features. In this article, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same networks, their inner structural features are utterly different. Inspired by these facts, an \\emph{adaptive} link prediction method is proposed to incorporate multiple structural features from the perspective of combination optimization. In the model, the weight of each structural feature is \\emph{adaptively } determined by logistic regression but not be artificially given in advance. According to our experimental results, we find that the logistic regression based link ...
Le Balleur, J. C.
1988-01-01
The applicability of conventional mathematical analysis (based on the combination of two-valued logic and probability theory) to problems in which human judgment, perception, or emotions play significant roles is considered theoretically. It is shown that dispositional logic, a branch of fuzzy logic, has particular relevance to the common-sense reasoning typical of human decision-making. The concepts of dispositionality and usuality are defined analytically, and a dispositional conjunctive rule and dispositional modus ponens are derived.
Mapping DSP algorithms to a reconfigurable architecture Adaptive Wireless Networking (AWGN)
Rauwerda, Gerard
2003-01-01
This report will discuss the Adaptive Wireless Networking project. The vision of the Adaptive Wireless Networking project will be given. The strategy of the project will be the implementation of multiple communication systems in dynamically reconfigurable heterogeneous hardware. An overview of a wireless LAN communication system, namely HiperLAN/2, and a Bluetooth communication system will be given. Possible implementations of these systems in a dynamically reconfigurable architecture are dis...
Adaptivity Support for MPSoCs Based on Process Migration in Polyhedral Process Networks
Emanuele Cannella; Onur Derin; Paolo Meloni; Giuseppe Tuveri; Todor Stefanov
2012-01-01
System adaptivity is becoming an important feature of modern embedded multiprocessor systems. To achieve the goal of system adaptivity when executing Polyhedral Process Networks (PPNs) on a generic tiled Network-on-Chip (NoC) MPSoC platform, we propose an approach to enable the run-time migration of processes among the available platform resources. In our approach, process migration is allowed by a middleware layer which comprises two main components. The first component concerns the inter-ti...
An Adaptive WLAN Interference Mitigation Scheme for ZigBee Sensor Networks
Jo Woon Chong; Chae Ho Cho; Ho Young Hwang; Dan Keun Sung
2015-01-01
We propose an adaptive interference avoidance scheme that enhances the performance of ZigBee networks by adapting ZigBees' transmissions to measured wireless local area network (WLAN) interference. Our proposed algorithm is based on a stochastic analysis of ZigBee operation that is interfered with by WLAN transmission, given ZigBee and WLAN channels are overlaid in the industrial, scientific, and medical (ISM) band. We assume that WLAN devices have higher transmission power than ZigBee device...
Hundebøll, Martin; Pedersen, Morten Videbæk; Roetter, Daniel Enrique Lucani;
2014-01-01
on the TCP protocol for reliability in data delivery. TCP is known to drop its throughput performance by several fold in the presence of even 1% or 2% packet losses, which are common in wireless systems. This will force DASH to settle at a much lower video resolution, thus reducing the user's quality...... of experience. We show that the use of FRANC, an adaptive network coding protocol that provides both low delay and high throughput to upper layers, as a reliability mechanism for TCP can significantly increase video quality. As part of our analysis, we benchmark the performance of various TCP versions......, including CUBIC, Reno, Veno, Vegas, and Westwood+, under different packet loss rates in wireless systems using a real testbed with Raspberry Pi devices. Our goal was to choose the most promising TCP version in terms of delay performance, in this case TCP Reno, and make a fair comparison between TCP running...
Lubov Muhamedovna Mkrtchyan
2015-01-01
Full Text Available The development of a network communicative space causes the transformation of labor and actualizes the problem of employment as the main indicator of social security. In this regard, the article considers one of the mechanisms ensuring social security of the person as the cultural development of society and the ability to create and maintain a system of values and professional orientations of the person - effective professional adaptation, requirements to which are largely determined by the ability to manage information network technologies, professional and informational culture, the ability to self-education. In its turn, social security in modern conditions acquires the features of socio-network security as a stable state of human security and protection from the negative effects of network communications, which can modify the behavior of a person, his values, and professional orientations and mislead him in the information space. Therefore, this article analyzes the relationship of professional adaptation and professional culture which in the context of network communication is determined by specific activities of the network professional communities, propagating certain values and behaviors. The level of information culture requires from individuals not only systematic knowledge and skills in information technology, but also the ability to think critically, to provide appropriate selection of information, to oppose network risks and threats, to use the communication network as an adaptive resource in the process of self-education.
Nonlinear functional approximation with networks using adaptive neurons
Tawel, Raoul
1992-01-01
A novel mathematical framework for the rapid learning of nonlinear mappings and topological transformations is presented. It is based on allowing the neuron's parameters to adapt as a function of learning. This fully recurrent adaptive neuron model (ANM) has been successfully applied to complex nonlinear function approximation problems such as the highly degenerate inverse kinematics problem in robotics.
Yao, Wei; Fang, Jiakun; Zhao, Ping;
2013-01-01
In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have the...
Improved adaptive-threshold burst assembly in optical burst switching networks
Jiuru Yang; Gang Wang; Shilou Jia
2007-01-01
An improved adaptive-threshold burst assembly algorithm is proposed to alleviate the limitation of conventional assembly schemes on data loss and delay. The algorithm will adjust the values of assembly factors according to variant traffic regions. And the simulation results show that, by using the adaptive-factor adaptive assembly scheme, the performance of networks is extensively enhanced in terms of burst loss probability and average queuing delay.
FANG Jin-Qing; LUO Xiao-Shu; HUANG Guo-Xian
2006-01-01
Subject of the halo-chaos control in beam transport networks (channels) has become a key concerned issue for many important applications of high-current proton beam since 1990'. In this paper, the magnetic field adaptive control based on the neuralnetwork with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network.
Network Latency Adaptive Tempo in the Public Sound Objects System
Barbosa, Álvaro; Cardoso, Jorge; Geiger, Günter
2005-01-01
In recent years Computer Network-Music has increasingly captured the attention of the Computer Music Community. With the advent of Internet communication, geographical displacement amongst the participants of a computer mediated music performance achieved world wide extension. However, when established over long distance networks, this form of musical communication has a fundamental problem: network latency (or net-delay) is an impediment for real-time collaboration. From a recent study, carr...
Transparent Adaptable Network Access and Service Content Differentiation
Senneset, Thomas
2006-01-01
Today s most advanced mobile devices support communication through a variety of network technologies; GSM (including GPRS and EDGE), UMTS, WLAN, Bluetooth, and IR. This master thesis characterizes different network technologies and protocols available to mobile devices, phones in particular. In addition, service provisioning capabilities over different types of networks are identified. Internet access till now has been provided over GSM or UMTS, often via a WAP Gateway. With a WAP Gateway, ...
Donges, Jonathan; Lucht, Wolfgang; Wiedermann, Marc; Heitzig, Jobst; Kurths, Jürgen
2015-04-01
In the anthropocene, the rise of global social and economic networks with ever increasing connectivity and speed of interactions, e.g., the internet or global financial markets, is a key challenge for sustainable development. The spread of opinions, values or technologies on these networks, in conjunction with the coevolution of the network structures themselves, underlies nexuses of current concern such as anthropogenic climate change, biodiversity loss or global land use change. To isolate and quantitatively study the effects and implications of network dynamics for sustainable development, we propose an agent-based model of information flow on adaptive networks between myopic harvesters that exploit private renewable resources. In this conceptual model of a network of socio-ecological systems, information on management practices flows between agents via boundedly rational imitation depending on the state of the resource stocks involved in an interaction. Agents can also adapt the structure of their social network locally by preferentially connecting to culturally similar agents with identical management practices and, at the same time, disconnecting from culturally dissimilar agents. Investigating in detail the statistical mechanics of this model, we find that an increasing rate of information flow through faster imitation dynamics or growing density of network connectivity leads to a marked increase in the likelihood of environmental resource collapse. However, we show that an optimal rate of social network adaptation can mitigate this negative effect without loss of social cohesion through network fragmentation. Our results highlight that seemingly immaterial network dynamics of spreading opinions or values can be of large relevance for the sustainable management of socio-ecological systems and suggest smartly conservative network adaptation as a strategy for mitigating environmental collapse. Hence, facing the great acceleration, these network dynamics should
Adaptive Reference Control for Pressure Management in Water Networks
Kallesøe, Carsten; Jensen, Tom Nørgaard; Wisniewski, Rafal
2015-01-01
Water scarcity is an increasing problem worldwide and at the same time a huge amount of water is lost through leakages in the distribution network. It is well known that improved pressure control can lower the leakage problems. In this work water networks with a single pressure actuator and several...... consumers are considered. Under mild assumptions on the consumption pattern and hydraulic resistances of pipes we use properties of the network graph and Kirchhoffs node and mesh laws to show that simple relations exist between the actuator pressure and critical point pressures inside the network...
Modeling and simulating the adaptive electrical properties of stochastic polymeric 3D networks
Memristors are passive two-terminal circuit elements that combine resistance and memory. Although in theory memristors are a very promising approach to fabricate hardware with adaptive properties, there are only very few implementations able to show their basic properties. We recently developed stochastic polymeric matrices with a functionality that evidences the formation of self-assembled three-dimensional (3D) networks of memristors. We demonstrated that those networks show the typical hysteretic behavior observed in the ‘one input-one output’ memristive configuration. Interestingly, using different protocols to electrically stimulate the networks, we also observed that their adaptive properties are similar to those present in the nervous system. Here, we model and simulate the electrical properties of these self-assembled polymeric networks of memristors, the topology of which is defined stochastically. First, we show that the model recreates the hysteretic behavior observed in the real experiments. Second, we demonstrate that the networks modeled indeed have a 3D instead of a planar functionality. Finally, we show that the adaptive properties of the networks depend on their connectivity pattern. Our model was able to replicate fundamental qualitative behavior of the real organic 3D memristor networks; yet, through the simulations, we also explored other interesting properties, such as the relation between connectivity patterns and adaptive properties. Our model and simulations represent an interesting tool to understand the very complex behavior of self-assembled memristor networks, which can finally help to predict and formulate hypotheses for future experiments. (paper)
Towards adaptive security for convergent wireless sensor networks in beyond 3G environments
Mitseva, Anelia; Aivaloglou, Efthimia; Marchitti, Maria-Antonietta;
2010-01-01
The integration of wireless sensor networks with different network systems gives rise to many research challenges to ensure security, privacy and trust in the overall architecture. The main contribution of this paper is a generic security, privacy and trust framework providing context-aware adapt...
Adaptive pinning synchronization in fractional-order uncertain complex dynamical networks with delay
Liang, Song; Wu, Ranchao; Chen, Liping
2016-02-01
Based on the stability theory of fractional-order systems, synchronization of general fractional-order uncertain complex networks with delay is investigated in this paper. By the inequality of the fractional derivative and the comparison principle of the linear fractional equation with delay, synchronization of complex networks with delay is realized under adaptive control. Some sufficient criteria ensuring local asymptotical synchronization under adaptive control and global asymptotical synchronization under adaptive pinning control are derived, respectively. Finally, numerical simulations are presented to demonstrate the validity and feasibility of the proposed synchronization criteria.
Zu Yun-Xiao; Zhou Jie
2012-01-01
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed,and a fitness function is provided.Simulations are conducted using the adaptive niche immune genetic algorithm,the simulated annealing algorithm,the quantum genetic algorithm and the simple genetic algorithm,respectively.The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation,and has quick convergence speed and strong global searching capability,which effectively reduces the system power consumption and bit error rate.
Tat-Bao-Thien Nguyen
2014-01-01
Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.
Li, Ning; Cao, Jinde
2015-01-01
In this paper, we investigate synchronization for memristor-based neural networks with time-varying delay via an adaptive and feedback controller. Under the framework of Filippov's solution and differential inclusion theory, and by using the adaptive control technique and structuring a novel Lyapunov functional, an adaptive updated law was designed, and two synchronization criteria were derived for memristor-based neural networks with time-varying delay. By removing some of the basic literature assumptions, the derived synchronization criteria were found to be more general than those in existing literature. Finally, two simulation examples are provided to illustrate the effectiveness of the theoretical results. PMID:25299765
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate. (geophysics, astronomy, and astrophysics)
Adaptive Relay Activation in the Network Coding Protocols
Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Fitzek, Frank
2015-01-01
State-of-the-art Network coding based routing protocols exploit the link quality information to compute the transmission rate in the intermediate nodes. However, the link quality discovery protocols are usually inaccurate, and introduce overhead in wireless mesh networks. In this paper, we present...
Adaptive Epidemic Dynamics in Networks: Thresholds and Control
Xu, Shouhuai; Lu, Wenlian; Xu, Li; Zhan, Zhenxin
2013-01-01
Theoretical modeling of computer virus/worm epidemic dynamics is an important problem that has attracted many studies. However, most existing models are adapted from biological epidemic ones. Although biological epidemic models can certainly be adapted to capture some computer virus spreading scenarios (especially when the so-called homogeneity assumption holds), the problem of computer virus spreading is not well understood because it has many important perspectives that are not necessarily ...
Adaptive Synchronization between Two Different Complex Networks with Time-Varying Delay Coupling
A new general network model for two complex networks with time-varying delay coupling is presented. Then we investigate its synchronization phenomena. The two complex networks of the model differ in dynamic nodes, the number of nodes and the coupling connections. By using adaptive controllers, a synchronization criterion is derived. Numerical examples are given to demonstrate the effectiveness of the obtained synchronization criterion. This study may widen the application range of synchronization, such as in chaotic secure communication. (general)
Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network
Manjula Devi, R.; R. C. Suganthe; S. KUPPUSWAMI
2013-01-01
Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do ...
Adaptive Dvisible Load Scheduling Strategies for Workstation Clusters with Unknown Network Resources
Ghose, Debasish; Kim, Hyoung Joong; Kim, Tae Hoon
2005-01-01
Conventional divisible load scheduling algorithms attempt to achieve optimal partitioning of massive loads to be distributed among processors in a distributed computing system in the presence of communication delays in the network. However, these algorithms depend strongly upon the assumption of prior knowledge of network parameters and cannot handle variations or lack of information about these parameters. In this paper, we present an adaptive strategy that estimates network parameter values...
An Adaptive Failure Detector Based on Quality of Service in Peer-to-Peer Networks
Jian Dong; Xiao Ren; Decheng Zuo; Hongwei Liu
2014-01-01
The failure detector is one of the fundamental components that maintain high availability of Peer-to-Peer (P2P) networks. Under different network conditions, the adaptive failure detector based on quality of service (QoS) can achieve the detection time and accuracy required by upper applications with lower detection overhead. In P2P systems, complexity of network and high churn lead to high message loss rate. To reduce the impact on detection accuracy, baseline detection strategy based on ret...
Toward an Adaptive Learning System Framework: Using Bayesian Network to Manage Learner Model
Viet Anh Nguyen
2012-12-01
Full Text Available This paper represents a new approach to manage learner modeling in an adaptive learning system framework. It considers developing the basic components of an adaptive learning system such as the learner model, the course content model and the adaptation engine. We use the overlay model and Bayesian network to evaluate learners’ knowledge. In addition, we also propose a new content modeling method as well as adaptation engine to generate adaptive course based on learner’s knowledge. Based on this approach, we developed an adaptive learning system named is ACGS-II, that teaches students how to design an Entity Relationship model in a database system course. Empirical testing results for students who used the application indicate that our proposed model is very helpful as guidelines to develop adaptive learning system to meet learners’ demands.
Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks
Gorski, Piotr J; Holyst, Janusz A
2015-01-01
Random Boolean networks (RBNs) are frequently employed for modelling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system consisting of distinct adaptive RBNs - subnetworks - connected by a set of permanent interlinks. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. We investigate mean node information, mean edge information as well as a mean node degree as functions of model parameters and demonstrate HARBN's ability to describe complex hierarchical systems.
Structural self-assembly and avalanchelike dynamics in locally adaptive networks
Gräwer, Johannes; Modes, Carl D.; Magnasco, Marcelo O.; Katifori, Eleni
2015-07-01
Transport networks play a key role across four realms of eukaryotic life: slime molds, fungi, plants, and animals. In addition to the developmental algorithms that build them, many also employ adaptive strategies to respond to stimuli, damage, and other environmental changes. We model these adapting network architectures using a generic dynamical system on weighted graphs and find in simulation that these networks ultimately develop a hierarchical organization of the final weighted architecture accompanied by the formation of a system-spanning backbone. In addition, we find that the long term equilibration dynamics exhibit behavior reminiscent of glassy systems characterized by long periods of slow changes punctuated by bursts of reorganization events.
Lu LU; Fagui LIU; Weixiang SHI
2008-01-01
In this paper, a novel control law is presented, which uses neural-network techniques to approximate the affine class nonlinear system having unknown or uncertain dynamics and noise disturbances. It adopts an adaptive control law to adjust the network parameters online and adds another control component according to H-infinity control theory to attenuate the disturbance. This control law is applied to the position tracking control of pneumatic servo systems. Simulation and experimental results show that the tracking precision and convergence speed is obviously superior to the results by using the basic BP-network controller and self-tuning adaptive controller.
Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.
2007-01-01
To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.
Cluster synchronization in the adaptive complex dynamical networks via a novel approach
This Letter investigates cluster synchronization in the adaptive complex dynamical networks with nonidentical nodes by a local control method and a novel adaptive strategy for the coupling strengths of the networks. In this approach, the coupling strength of each node adjusts adaptively only based on the state information of its neighborhood. By means of the proposed scheme, the sufficient conditions for achieving cluster synchronization are derived analytically by utilizing Lyapunov stability theory. It is demonstrated that the synchronization performance is sensitively affected by the control gain, the inner-coupling matrix and the network topological structure. The numerical simulations are performed to verify the effectiveness of the theoretical results. - Highlights: → We present a more realistic adaptive complex network model with diverse nodes. → The local controllers are designed based the community structure of the network. → Each node's coupling strength adapts self only by the state of its neighborhood. → The synchronization effect is affected by the network structure and control gain. → The Cluster synchronization method is robust against noise perturbation.
An OCP Compliant Network Adapter for GALS-based SoC Design Using the MANGO Network-on-Chip
Bjerregaard, Tobias; Mahadevan, Shankar; Olsen, Rasmus Grøndahl;
2005-01-01
The demand for IP reuse and system level scalability in System-on-Chip (SoC) designs is growing. Network-onchip (NoC) constitutes a viable solution space to emerging SoC design challenges. In this paper we describe an OCP compliant network adapter (NA) architecture for the MANGO NoC. The NA...... decouples communication and computation, providing memory-mapped OCP transactions based on primitive message-passing services of the network. Also, it facilitates GALS-type systems, by adapting to the clockless network. This helps leverage a modular SoC design flow. We evaluate performance and cost of 0.......13 um CMOS standard cell instantiations of the architecture....
Adaptive Control of Networked Systems in the Presence of Bounded Disturbances
A. H. Tahoun
2007-01-01
Full Text Available The insertion of data network in the feedback adaptive control loops makes the analysis and design of networked control systems more complex than traditional control systems. This paper addresses the adaptive stabilization problem of linear time-invariant networked control systems when the measurements of the plant states are corrupted by bounded disturbances. The case of state feedback is treated in which only an upper bound on the norm of matrix A is needed. The problem is to find an upper bound on the transmission period h that guarantees the stability of the overall adaptive networked control system under an ideal transmission process, i.e. no transmission delay or packet dropout. Rigorous mathematical proofs are established, that relies heavily on Lyapunov's stability criterion and dead-zone Technique. Simulation results are given to illustrate the efficacy of our design approach.
Yang Fang
2014-01-01
Full Text Available This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally’s inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI approach. The sufficient conditions for robust adaptive exponential synchronization in mean square of uncoupled uncertain stochastic chaotic delayed neural networks are derived in terms of linear matrix inequalities (LMIs. The effect of nonidentical uncertain parameter uncertainties is suppressed by the designed robust adaptive feedback controller rapidly. A numerical example is provided to validate the effectiveness of the proposed method.
Multiple-model-and-neural-network-based nonlinear multivariable adaptive control
Yue FU; Tianyou CHAI
2007-01-01
A multivariable adaptive controller feasible for implementation on distributed computer systems (DCS) is presented for a class of uncertain nonlinear multivariable discrete time systems. The adaptive controller is composed of a linear adaptive controller, a neural network nonlinear adaptive controller and a switching mechanism. The linear controller can provide boundedness of the input and output signals, and the nonlinear controller can improve the performance of the system. The purpose of using the switching mechanism is to obtain the improved system performance and stability simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.
Adaptive Resource Allocation For MAI Minimization In Wireless Adhoc Network
Mohammed Abdul Waheed
2011-05-01
Full Text Available Coding-based solutions for MANETs have emerged as a basic solution to current high rate data accessing in adhoc network. This has become essential related to the absence of centralized control such as a monitoring station. A code assignment protocol is needed to assign distinct codes to different terminals. This problem is less effective in small networks, but becomes dominative in large networks where the numbers of code sequence are lesser than the number of terminals to code, demanding reuse of the codes. The issue of code allocation in communication is focused in this paper with the evaluation of MAI in wireless network. Unlike previously proposed protocols in this paper a focus for the multiple access interference (MAI, thereby addressing the limiting near-far problem that decreases the throughput performance in MANETs is made. The code assignment scheme is developed for the proper usage of users code under MANETs communication to minimize the MAI impact.
Adaptive artificial neural network for autonomous robot control
Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.
1992-01-01
The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.
Networked Adaptive Interactive Hybrid Systems (NAIHS) for multiplatform engagement capability
Kester, L.J.H.M.
2008-01-01
Advances in network technologies enable distributed systems, operating in complex physical environments, to coordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public
Method for designing networking adaptive interactive hybrid systems
Kester, L. J.H.M.
2010-01-01
Advances in network technologies enable distributed systems, operating in complex physical environments, to co-ordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public
The Logic of Parametric Probability
Norman, Joseph W
2012-01-01
The computational method of parametric probability analysis is introduced. It is demonstrated how to embed logical formulas from the propositional calculus into parametric probability networks, thereby enabling sound reasoning about the probabilities of logical propositions. An alternative direct probability encoding scheme is presented, which allows statements of implication and quantification to be modeled directly as constraints on conditional probabilities. Several example problems are solved, from Johnson-Laird's aces to Smullyan's zombies. Many apparently challenging problems in logic turn out to be simple problems in algebra and computer science; often just systems of polynomial equations or linear optimization problems. This work extends the mathematical logic and parametric probability methods invented by George Boole.
IMPLEMENTATION OF ADAPTIVE ZONE ROUTING PROTOCOL FOR WIRELESS NETWORKS
T. RAVI NAYAK; SAKE. POTHALAIAH; Dr. K ASHOK BABU
2010-01-01
Mobile Ad hoc wireless Networks (MANETs) that do not need any fixed infrastructure. They are characterized by dynamic topology due to node mobility, limited channel bandwidth, and limited battery power of nodes. The key challenge in the design of ad hoc networks is the development of dynamic routing protocols that can efficiently findroutes between two communicating nodes. Thus, many ad hoc routing protocols have been proposed in recent years. All these routing protocols attempt to provide a ...
Adaptive network traffic management for multi user virtual environments
Oliver, Iain Angus
2011-01-01
Multi User Virtual Environments (MUVE) are a new class of Internet application with a significant user base. This thesis adds to our understanding of how MUVE network traffic fits into the mix of Internet traffic, and how this relates to the application's needs. MUVEs differ from established Internet traffic types in their requirements from the network. They differ from traditional data traffic in that they have soft real-time constraints, from game traffic in that their bandwidth requi...
Smart Grid Adaptive Volt-VAR Optimization in Distribution Networks
Manbachi, Moein
2015-01-01
The electrical distribution networks across the world are witnessing a steady infusion of smart grid technologies into every aspect of their infrastructure and operations. Technologies such as Energy Management Systems (EMS), Distribution Management Systems (DMS) and Advanced Metering Infrastructure (AMI) have partially addressed the needs of the distribution networks for automation, control, monitoring and optimization. Many utilities intend to explore the capabilities of advanced AMI system...
Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting
Tich Phuoc Tran; Longbing Cao; Dat Tran; Cuong Duc Nguyen
2009-01-01
This article applies Machine Learning techniques to solve Intrusion Detection problems withincomputer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspac...
A Security Adaptation Reference Monitor for Wireless Sensor Network
El-Maliki, Tewfiq; Seigneur, Jean-Marc
2012-01-01
Security in Wireless Sensor Network has become a hot research topic due to their wide deployment and the increasing new runtime attacks they are facing. We observe that traditional security protocols address conventional security problems and cannot deal with dynamic attacks such as sinkhole dynamic behavior. Moreover, they use resources, and limit the efficient use of sensor resources and inevitably the overall network efficiency is not guaranteed. Therefore, the requirements of new security...
Epidemic Dynamics On Information-Driven Adaptive Networks
Zhan, Xiu-Xiu; Sun, Gui-Quan; Zhang, Zi-Ke
2015-01-01
can evolve simultaneously. For the information-driven adaptive process, susceptible (infected) individuals who have abilities to recognize the disease would break the links of their infected (susceptible) neighbors to prevent the epidemic from further spreading. Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. In addition, the disease spreading and information diffusion pattern on the lattice give a visual representation about how the disease is trapped into an isolated field with the information-driven adaptive process. Furthermore, we perform the local bifurcation analysis on four types of dynamical regions, including healthy, oscillatory, bistable and endemic, to understand the evolution of the observed dynamical behaviors. This work may shed some lights on understanding how information affects h...
Mariluce Paes-de-Souza; Fabiana Rodrigues Riva; Tania Nunes Silva; Diego Cristovão A. Souza Paes
2013-01-01
The present paper has the objective to expose a proposition of organization within a chain and network logic, aiming to potentiate the extraction of the Native Açaí Berry at the Western Brazilian Amazon rainforest. This exploratory study involves the municipalities of Porto Velho, Guajará-Mirim and Machadinho D’Oeste, at the Brazilian state of Rondônia, with primary data originating mostly from conservation areas at the lower Madeira River region. As a result, it was possible to infer that fr...
An Optimized Technique of Increasing the Performance of Network Adapter on EML Layer
Prashanth L
2012-08-01
Full Text Available Simple Network Adapter initially which acts as an interface between the Transaction server and Network Elements communicates over the channel through tcppdu. Presently the disadvantage being involved in tcppdu is to maintain the channel contention, reservation of channel bandwidth. The disadvantage being involved is certain features, version of network elements communicates by receiving the xml over the socket. So, it’s not possible to change the entire framework, but by updating the framework an XML Over Socket(XOS formation should be supported. The XOS implementation is being performed using Java language through mainly in JVM. Such that by this deployment machines would become easier and form a good communication gap between them. This simple network adapter being developed should support operations of the North bounded server and gives an established authorized, secured, reliable portal. The interface being developed should provide a good performance in meeting the network demands and operated conversions of respective objects
Adaptive Security Architecture based on EC-MQV Algorithm in Personal Network (PN)
Mihovska, Albena D.; Prasad, Neeli R.
2007-01-01
Abstract — Personal Networks (PNs) have been focused on in order to support the user’s business and private activities without jeopardizing privacy and security of the users and their data. In such a network, it is necessary to produce a proper key agreement method according to the feature...... of the network. One of the features of the network is that the personal devices have deferent capabilities such as computational ability, memory size, transmission power, processing speed and implementation cost. Therefore an adaptive security mechanism should be contrived for such a network of various device...... combinations based on user’s location and device’s capability. The paper proposes new adaptive security architecture with three levels of asymmetric key agreement scheme by using context-aware security manager (CASM) based on elliptic curve cryptosystem (EC-MQV)....
Analysis of utility-theoretic heuristics for intelligent adaptive network routing
Mikler, A.R.; Honavar, V.; Wong, J.S.K. [Iowa State Univ., Ames, IA (United States)
1996-12-31
Utility theory offers an elegant and powerful theoretical framework for design and analysis of autonomous adaptive communication networks. Routing of messages in such networks presents a real-time instance of a multi-criterion optimization problem in a dynamic and uncertain environment. In this paper, we incrementally develop a set of heuristic decision functions that can be used to guide messages along a near-optimal (e.g., minimum delay) path in a large network. We present an analysis of properties of such heuristics under a set of simplifying assumptions about the network topology and load dynamics and identify the conditions under which they are guaranteed to route messages along an optimal path. The paper concludes with a discussion of the relevance of the theoretical results presented in the paper to the design of intelligent autonomous adaptive communication networks and an outline of some directions of future research.
CLASSIFICATIONS OF EEG SIGNALS FOR MENTAL TASKS USING ADAPTIVE RBF NETWORK
薛建中; 郑崇勋; 闫相国
2004-01-01
Objective This paper presents classifications of mental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) network with optimal centers and widths for the Brain-Computer Interface (BCI) schemes. Methods Initial centers and widths of the network are selected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during training phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three task pairs over four subjects achieves 87.0%. Moreover, this network runs fast due to the fewer hidden layer neurons. Conclusion The adaptive RBF network with optimal centers and widths has high recognition rate and runs fast. It may be a promising classifier for on-line BCI scheme.
ADAPTIVE FLIGHT CONTROL SYSTEM OF ARMED HELICOPTER USING WAVELET NEURAL NETWORK METHOD
ZHURong-gang; JIANGChangsheng; FENGBin
2004-01-01
A discussion is devoted to the design of an adaptive flight control system of the armed helicopter using wavelet neural network method. Firstly, the control loop of the attitude angle is designed with a dynamic inversion scheme in a quick loop and a slow loop. respectively. Then, in order to compensate the error caused by dynamic inversion, the adaptive flight control system of the armed helicopter using wavelet neural network method is put forward, so the BP wavelet neural network and the Lyapunov stable wavelet neural network are used to design the helicopter flight control system. Finally, the typical maneuver flight is simulated to demonstrate its validity and effectiveness. Result proves that the wavelet neural network has an engineering practical value and the effect of WNN is good.
Using social network analysis to evaluate health-related adaptation decision-making in Cambodia.
Bowen, Kathryn J; Alexander, Damon; Miller, Fiona; Dany, Va
2014-02-01
Climate change adaptation in the health sector requires decisions across sectors, levels of government, and organisations. The networks that link these different institutions, and the relationships among people within these networks, are therefore critical influences on the nature of adaptive responses to climate change in the health sector. This study uses social network research to identify key organisational players engaged in developing health-related adaptation activities in Cambodia. It finds that strong partnerships are reported as developing across sectors and different types of organisations in relation to the health risks from climate change. Government ministries are influential organisations, whereas donors, development banks and non-government organisations do not appear to be as influential in the development of adaptation policy in the health sector. Finally, the study highlights the importance of informal partnerships (or 'shadow networks') in the context of climate change adaptation policy and activities. The health governance 'map' in relation to health and climate change adaptation that is developed in this paper is a novel way of identifying organisations that are perceived as key agents in the decision-making process, and it holds substantial benefits for both understanding and intervening in a broad range of climate change-related policy problems where collaboration is paramount for successful outcomes. PMID:24487452
From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks
Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming
2016-03-01
The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.
An Adaptive Failure Detector Based on Quality of Service in Peer-to-Peer Networks
Jian Dong
2014-09-01
Full Text Available The failure detector is one of the fundamental components that maintain high availability of Peer-to-Peer (P2P networks. Under different network conditions, the adaptive failure detector based on quality of service (QoS can achieve the detection time and accuracy required by upper applications with lower detection overhead. In P2P systems, complexity of network and high churn lead to high message loss rate. To reduce the impact on detection accuracy, baseline detection strategy based on retransmission mechanism has been employed widely in many P2P applications; however, Chen’s classic adaptive model cannot describe this kind of detection strategy. In order to provide an efficient service of failure detection in P2P systems, this paper establishes a novel QoS evaluation model for the baseline detection strategy. The relationship between the detection period and the QoS is discussed and on this basis, an adaptive failure detector (B-AFD is proposed, which can meet the quantitative QoS metrics under changing network environment. Meanwhile, it is observed from the experimental analysis that B-AFD achieves better detection accuracy and time with lower detection overhead compared to the traditional baseline strategy and the adaptive detectors based on Chen’s model. Moreover, B-AFD has better adaptability to P2P network.
Modelling Self-Adaptive Networked Entities in Matlab/Simulink
Bartosinski, Roman; Daněk, Martin; Honzík, Petr; Kadlec, Jiří
Praha: Humusoft, 2007, s. 1-8. ISBN 978-80-7080-658-6. [Technical Computing Prague 2007. Praha (CZ), 14.11.2007-14.11.2007] R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : SANE * self- adaptive system * FPGA * simulation Subject RIV: JC - Computer Hardware ; Software
Cherenkov ring recognition using a non-adaptable network
We introduce a very simple and efficient technique to recognize one or several circles in a given pattern. The algorithm can (and in practical application should) be implemented as a massively parallel architecture, in which connections between units are not adaptable. We show examples of recognition of several (eventually overlapping) circles and a practical case of particle identification. (orig.)
Kirstie Cadger
2016-07-01
Full Text Available Social ties play an important role in agricultural knowledge exchange, particularly in developing countries with high exposure to agriculture development interventions. Institutions often facilitate agricultural training projects, with a focus on agroecological practices, such as agroforestry and agrobiodiversity. The structural characteristics of social networks amongst land managers influences decision-making to adopt such adaptive agroecoloigcal practice; however, the extent of knowledge transfer beyond direct project participants is often unknown. Using a social network approach, we chart the structure of agrarian knowledge networks (n = 131 in six communities, which have been differentially exposed to agriculture development interventions in Ghana. Farmer network size, density and composition were distinctly variable; development project-affiliated farmers were embedded in larger networks, had non-affiliated farmers within their networks, were engaged in more diverse agricultural production and reported adopting and adapting agroecological practice more frequently. Such bridging ties that link across distinctive groups in a network can expose network members to new and innovative agroecological practices, such as increasing agrobiodiversity, thus, contributing to livelihood strategies that mitigate environmental and market risk. Furthermore, we show that these knowledge networks were crop-specific where network size varied given the type of crop produced. Such factors, which may influence the rate and extent of agroecological knowledge diffusion, are critical for the effectiveness of land management practices as well as the persistence of agriculture development interventions.
Evolution of regulatory networks towards adaptability and stability in a changing environment.
Lee, Deok-Sun
2014-11-01
Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments. PMID:25493848
Utilizing Network QoS for Dependability of Adaptive Smart Grid Control
Madsen, Jacob Theilgaard; Kristensen, Thomas le Fevre; Olsen, Rasmus Løvenstein;
2014-01-01
A smart grid is a complex system consisting of a wide range of electric grid components, entities controlling power distribution, generation and consumption, and a communication network supporting data exchange. This paper focuses on the influence of imperfect network conditions on smart grid con......- trollers, and how this can be counteracted by utilizing Quality of Service (QoS) information from the communication network. Such an interface between grid controller and network QoS is particularly relevant for smart grid scenarios that use third party communication network infrastructure, where...... modification of networking and lower layer protocols are impossible. This paper defines a middleware solution for adaptation of smart grid control, which uses network QoS information and interacts with the smart grid controller to increase dependability. In order to verify the methodology, an example scenario...
Evolution of regulatory networks towards adaptability and stability in a changing environment
Lee, Deok-Sun
2014-11-01
Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.
Delay-induced diversity of firing behavior and ordered chaotic firing in adaptive neuronal networks
In this paper, we study the effect of time delay on the firing behavior and temporal coherence and synchronization in Newman–Watts thermosensitive neuron networks with adaptive coupling. At beginning, the firing exhibit disordered spiking in absence of time delay. As time delay is increased, the neurons exhibit diversity of firing behaviors including bursting with multiple spikes in a burst, spiking, bursting with four, three and two spikes, firing death, and bursting with increasing amplitude. The spiking is the most ordered, exhibiting coherence resonance (CR)-like behavior, and the firing synchronization becomes enhanced with the increase of time delay. As growth rate of coupling strength or network randomness increases, CR-like behavior shifts to smaller time delay and the synchronization of firing increases. These results show that time delay can induce diversity of firing behaviors in adaptive neuronal networks, and can order the chaotic firing by enhancing and optimizing the temporal coherence and enhancing the synchronization of firing. However, the phenomenon of firing death shows that time delay may inhibit the firing of adaptive neuronal networks. These findings provide new insight into the role of time delay in the firing activity of adaptive neuronal networks, and can help to better understand the complex firing phenomena in neural networks.
Baader, Franz
Description Logics (DLs) are a well-investigated family of logic-based knowledge representation formalisms, which can be used to represent the conceptual knowledge of an application domain in a structured and formally well-understood way. They are employed in various application domains, such as natural language processing, configuration, and databases, but their most notable success so far is the adoption of the DL-based language OWL as standard ontology language for the semantic web.
Glover, Richard
2013-01-01
1. Logical Harmonies 2 3:50 Philip Thomas (piano) 2. Beatings in a Linear Process 6:02 Ensemble Portmanto 3. Contracting Triads in Temperaments from 12-24 5:17 Bob Gilmore (keyboard) 4. Cello with Clarinet and Piano 6:50 Seth Woods (cello) Jonathan Sage (clarinet) Philip Thomas (piano) 5. Imperfect Harmony 10:38 Dominic Lash (double bass) 6. Gradual Music 9:00 musikFabrik 7. Logical Harmonies 1 ...
Copies of classical logic in intuitionistic logic
Gaspar, Jaime
2012-01-01
Classical logic (the logic of non-constructive mathematics) is stronger than intuitionistic logic (the logic of constructive mathematics). Despite this, there are copies of classical logic in intuitionistic logic. All copies usually found in the literature are the same. This raises the question: is the copy unique? We answer negatively by presenting three different copies.
Adaptive and Decentralized Operator Placement for In-Network Query Processing
Bonfils, Boris; Bonnet, Philippe
2003-01-01
In-network query processing is critical for reducing network traffic when accessing and manipulating sensor data. It requires placing a tree of query operators such as filters and aggregations but also correlations onto sensor nodes in order to minimize the amount of data transmitted in the network....... In this paper, we show that this problem is a variant of the task assignment problem for which polynomial algorithms have been developed. These algorithms are however centralized and cannot be used in a sensor network. We describe an adaptive and decentralized algorithm that progressively refines the...
Sam Pearsall
2005-12-01
Full Text Available Adaptive ecosystem management (AEM requires building and managing an interorganizational network of stakeholders to conserve ecosystem integrity while sustaining ecosystem services. This paper demonstrates the usefulness of applying the concepts of interorganizational networks and learning organizations to AEM. A case study of the lower Roanoke River in North Carolina illustrates how an AEM network can evolve to guide stakeholders in creating a shared framework for generative learning, consensus building through collaboration, and decision making. Environmental professionals can use this framework to guide institutional arrangements and to coordinate the systematic development of cohesive interorganizational AEM networks.
Study on the Robot Robust Adaptive Control Based on Neural Networks
温淑焕; 王洪瑞; 吴丽艳
2003-01-01
Force control based on neural networks is presented. Under the framework of hybrid control, an RBF neural network is used to compensate for all the uncertainties from robot dynamics and unknown environment first. The technique will improve the adaptability to environment stiffness when the end-effector is in contact with the environment, and does not require any a priori knowledge on the upper bound of syste uncertainties. Moreover, it need not compute the inverse of inertia matrix. Learning algorithms for neural networks to minimize the force error directly are designed. Simulation results have shown a better force/position tracking when neural network is used.
Disruption prediction with adaptive neural networks for ASDEX Upgrade
In this paper, an adaptive neural system has been built to predict the risk of disruption at ASDEX Upgrade. The system contains a Self Organizing Map, which determines the 'novelty' of the input of a Multi Layer Perceptron predictor module. The answer of the MLP predictor will be inhibited whenever a novel sample is detected. Furthermore, it is possible that the predictor produces a wrong answer although it is fed with known samples. In this case, a retraining procedure will be performed to update the MLP predictor in an incremental fashion using data coming from both the novelty detection, and from wrong predictions. In particular, a new update is performed whenever a missed alarm is triggered by the predictor. The performance of the adaptive predictor during the more recent experimental campaigns until November 2009 has been evaluated.
Adaptive feature annotation for large video sensor networks
Cai, Yang; Bunn, Andrew; Liang, Peter; Yang, Bing
2013-10-01
We present an adaptive feature extraction and annotation algorithm for articulating traffic events from surveillance cameras. We use approximate median filter for moving object detection, motion energy image and convex hull for lane detection, and adaptive proportion models for vehicle classification. It is found that our approach outperforms three-dimensional modeling and scale-independent feature transformation algorithms in terms of robustness. The multiresolution-based video codec algorithm enables a quality-of-service-aware video streaming according to the data traffic. Furthermore, our empirical data shows that it is feasible to use the metadata to facilitate the real-time communication between an infrastructure and a vehicle for safer and more efficient traffic control.
Disruption prediction with adaptive neural networks for ASDEX Upgrade
Cannas, B.; Fanni, A. [Electrical and Electronic Engineering Dept., University of Cagliari, Piazza D' Armi, 09123 Cagliari (Italy); Pautasso, G. [Max-Planck-Institut fuer Plasmaphysik, EURATOM Association, Garching (Germany); Sias, G., E-mail: giuliana.sias@diee.unica.it [Electrical and Electronic Engineering Dept., University of Cagliari, Piazza D' Armi, 09123 Cagliari (Italy)
2011-10-15
In this paper, an adaptive neural system has been built to predict the risk of disruption at ASDEX Upgrade. The system contains a Self Organizing Map, which determines the 'novelty' of the input of a Multi Layer Perceptron predictor module. The answer of the MLP predictor will be inhibited whenever a novel sample is detected. Furthermore, it is possible that the predictor produces a wrong answer although it is fed with known samples. In this case, a retraining procedure will be performed to update the MLP predictor in an incremental fashion using data coming from both the novelty detection, and from wrong predictions. In particular, a new update is performed whenever a missed alarm is triggered by the predictor. The performance of the adaptive predictor during the more recent experimental campaigns until November 2009 has been evaluated.
Pliable Cognitive MAC for Heterogeneous Adaptive Cognitive Radio Sensor Networks
Ali, Borhanuddin Mohd; Sali, Aduwati
2016-01-01
The rapid expansion of wireless monitoring and surveillance applications in several domains reinforces the trend of exploiting emerging technologies such as the cognitive radio. However, these technologies have to adjust their working concepts to consider the common characteristics of conventional wireless sensor networks (WSNs). The cognitive radio sensor network (CRSN), still an immature technology, has to deal with new networks that might have different types of data, traffic patterns, or quality of service (QoS) requirements. In this paper, we design and model a new cognitive radio-based medium access control (MAC) algorithm dealing with the heterogeneous nature of the developed networks in terms of either the traffic pattern or the required QoS for the node applications. The proposed algorithm decreases the consumed power on several fronts, provides satisfactory levels of latency and spectrum utilization with efficient scheduling, and manages the radio resources for various traffic conditions. An intensive performance evaluation is conducted to study the impact of key parameters such as the channel idle time length, node density, and the number of available channels. The performance evaluation of the proposed algorithm shows a better performance than the comparable protocols. Moreover, the results manifest that the proposed algorithm is suitable for real time monitoring applications. PMID:27257964
Effects of Implementing Adaptable Channelization in Wi-Fi Networks
Abid Hussain
2016-01-01
Full Text Available The unprecedented increase of wireless devices is now facing a serious threat of spectrum scarcity. The situation becomes even worse due to inefficient frequency distribution protocols, deployed in trivial Wi-Fi networks. The primary source of this inefficiency is static channelization used in wireless networks. In this work, we investigate the use of dynamic and flexible channelization, for optimal spectrum utilization in Wi-Fi networks. We propose optimal spectrum sharing algorithm (OSSA and analyze its effect on exhaustive list of essential network performance measuring parameters. The elementary concept of the proposed algorithm lies in the fact that frequency spectrum should be assigned to any access point (AP based on its current requirement. The OSSA algorithm assigns channels with high granularity, thus maximizing spectrum utilization by more than 20% as compared to static width channel allocation. This optimum spectrum utilization, in turn, increases throughput by almost 30% in many deployment scenarios. The achieved results depict considerable decrease in interference, while simultaneously increasing range. Similarly signal strength values at relatively longer distances improve significantly at narrower channel widths while simultaneously decreasing bit error rates. We found that almost 25% reduction in interference is possible in certain scenarios through proposed algorithm.
Pliable Cognitive MAC for Heterogeneous Adaptive Cognitive Radio Sensor Networks.
Al-Medhwahi, Mohammed; Hashim, Fazirulhisyam; Ali, Borhanuddin Mohd; Sali, Aduwati
2016-01-01
The rapid expansion of wireless monitoring and surveillance applications in several domains reinforces the trend of exploiting emerging technologies such as the cognitive radio. However, these technologies have to adjust their working concepts to consider the common characteristics of conventional wireless sensor networks (WSNs). The cognitive radio sensor network (CRSN), still an immature technology, has to deal with new networks that might have different types of data, traffic patterns, or quality of service (QoS) requirements. In this paper, we design and model a new cognitive radio-based medium access control (MAC) algorithm dealing with the heterogeneous nature of the developed networks in terms of either the traffic pattern or the required QoS for the node applications. The proposed algorithm decreases the consumed power on several fronts, provides satisfactory levels of latency and spectrum utilization with efficient scheduling, and manages the radio resources for various traffic conditions. An intensive performance evaluation is conducted to study the impact of key parameters such as the channel idle time length, node density, and the number of available channels. The performance evaluation of the proposed algorithm shows a better performance than the comparable protocols. Moreover, the results manifest that the proposed algorithm is suitable for real time monitoring applications. PMID:27257964
Social Networks-Based Adaptive Pairing Strategy for Cooperative Learning
Chuang, Po-Jen; Chiang, Ming-Chao; Yang, Chu-Sing; Tsai, Chun-Wei
2012-01-01
In this paper, we propose a grouping strategy to enhance the learning and testing results of students, called Pairing Strategy (PS). The proposed method stems from the need of interactivity and the desire of cooperation in cooperative learning. Based on the social networks of students, PS provides members of the groups to learn from or mimic…
Zeng, Yuanyuan; Sreenan, Cormac J; Sitanayah, Lanny; Xiong, Naixue; Park, Jong Hyuk; Zheng, Guilin
2011-01-01
Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work. PMID:22163774
Guilin Zheng
2011-03-01
Full Text Available Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.
Yuanyuan Zeng
2010-06-01
Full Text Available Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.
Robust adaptive synchronization of general dynamical networks with multiple delays and uncertainties
LU YIMING; HE PING; MA SHU-HUA; LI GUO-ZHI; MOBAYBEN SALEH
2016-06-01
In this article, a general complex dynamical network which contains multiple delays and uncertainties is introduced, which contains time-varying coupling delays, time-varying node delay, and uncertainties of both the inner- and outer-coupling matrices. A robust adaptive synchronization scheme for these general complex networks with multiple delays and uncertainties is established and raised by employing the robust adaptive control principle and the Lyapunov stability theory. We choose some suitable adaptive synchronization controllers to ensure the robust synchronization of this dynamical network. The numerical simulations of the time-delay Lorenz chaotic system as local dynamical node are provided to observe and verify the viability and productivity of the theoretical research in this paper. Compared to the achievement of previous research, theresearch in this paper seems quite comprehensive and universal.
Enabling Adaptive Rate and Relay Selection for 802.11 Mobile Ad Hoc Networks
Mehta, Neil; Wang, Wenye
2011-01-01
Mobile ad hoc networks (MANETs) are self-configuring wireless networks that lack permanent infrastructure and are formed among mobile nodes on demand. Rapid node mobility results in dramatic channel variation, or fading, that degrades MANET performance. Employing channel state information (CSI) at the transmitter can improve the throughput of routing and medium access control (MAC) protocols for mobile ad hoc networks. Several routing algorithms in the literature explicitly incorporate the fading signal strength into the routing metric, thus selecting the routes with strong channel conditions. While these studies show that adaptation to the time-variant channel gain is beneficial in MANETs, they do not address the effect of the outdated fading CSI at the transmitter. For realistic mobile node speeds, the channel gain is rapidly varying, and becomes quickly outdated due the feedback delay. We analyze the link throughput of joint rate adaptation and adaptive relay selection in the presence of imperfect CSI. Mor...
Adaptive learning with guaranteed stability for discrete-time recurrent neural networks
无
2007-01-01
To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15X15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.
Nonlinear adaptive control systems design of BTT missile based on fully tuned RBF neural networks
Hu, Yunan; Jin, Yuqiang; Li, Jing
2003-09-01
Based on fully tuned RBF neural networks and backstepping control techniques, a novel nonlinear adaptive control scheme is proposed for missile control systems with a general set of uncertainties. The effect of the uncertainties is synthesized one term in the design procedure. Then RBF neural networks are used to eliminate its effect. The nonlinear adaptive controller is designed using backstepping control techniques. The control problem is resolved while the control coefficient matrix is unknown. The adaptive tuning rules for updating all of the parameters of the fully tuned RBF neural networks are firstly derived by the Lyapunov stability theorem. Finally, nonlinear 6-DOF numerical simulation results for a BTT missile model are presented to demonstrate the effectiveness of the proposed method.
Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network
Yundi Chu
2015-01-01
Full Text Available An adaptive global sliding mode control (AGSMC using RBF neural network (RBFNN is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.
Decentralized direct adaptive neural network control for a class of interconnected systems
Zhang Tianping; Mei Jiandong
2006-01-01
The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconnections is studied in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized direct adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by adjusted the weights and sliding mode gains on-line for each subsystem using only local information. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.
Adaptive Home System Using Wireless Sensor Network And Multi Agent System
Jayarani Kamble
2014-03-01
Full Text Available Smart Home is an emerging technology growing continuously which includes number of new technologies which helps to improve human’s quality of living. This paper proposes an adaptive home system for optimum utilization of power, through Artificial Intelligence and Wireless Sensor network. Artificial Intelligence is a technology for developing adaptive system that can perceive the enviornmrnt, learn from the environment and can make decision using Rule based system.Zigbee is a wireless sensor network used to efficiently deliver solution for an energy management and efficiency for adaptive home. An algorithm used in adaptive home system is based on software agent approach that reduce the energy consumption at home by considering the user’s occupancy, temperature and user’s preferences as input to the system.
A novel adaptive modulation and coding strategy based on partial feedback for enhanced MBMS network
SHENG Yu; PENG Mu-gen; WANG Wen-bo
2008-01-01
The difference in link condition of broadcast/multicast users and the limitation of uplink resource, make itdifficult to utilize adaptive modulation and coding (AMC) in theenhanced multimedia broadcast and multicast service (E-MBMS)network. To obtain the improvement of system throughput, thisstudy proposes an adaptive modulation and coding schemebased on partial feedback, by which only partial users whosechannel qualities are lower than the system threshold need tomake a response to the modulation coding scheme (MCS)adaptation procedure. By this investigation, an adaptive schemecan be introduced in the E-MBMS network. Both the theoreticalanalysis and simulation results demonstrate the efficiency of theproposed strategy, in which the performance is close to the idealone and has a significant throughput improvement whencompared with that of the fixed MCS transmission scheme.
Predicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference Systems
Samira Chabaa
2009-01-01
Full Text Available Problem statement: The statistical modeling for predicting network traffic has now become a major tool used for network and is of significant interest in many domains: Adaptive application, congestion and admission control, wireless, network management and network anomalies. To comprehend the properties of IP-network traffic and system conditions, many kinds of reports based on measured network traffic data have been reported by several researchers. The goal of the present contribution was to complement these previous researches by predicting network traffic data. Approach: The Adaptive Neuro-Fuzzy Inference System (ANFIS was realized by an appropriate combination of fuzzy systems and neural networks. It was applied in different applications which have been increased in recent years and have multidisciplinary in several domains with a high accuracy. For this reason, we used a set of input and output data of packet transmission over Internet Protocol (IP networks as input and output of ANFIS to develop a model for predicting data. Results: ANFIS was compared with some existing model based on Volterra system with Laguerre functions. The obtained results demonstrate that the sequences of generated values have the same statistical characteristics as those really observed. Furthermore, the relative error using ANFIS model was better than this obtained by Volterra system model. Conclusion: The developed model fits well real data and can be used for predicting purpose with a high accuracy.
RESEARCH ON ADAPTIVE COMPRESSION CODING FOR NETWORK CODING IN WIRELESS SENSOR NETWORK
Liu Ying; Yang Zhen; Mei Zhonghui; Kong Yuanyuan
2012-01-01
Based on the sequence entropy of Shannon information theory,we work on the network coding technology in Wireless Sensor Network (WSN).In this paper,we take into account the similarity of the transmission sequences at the network coding node in the multi-sources and multi-receivers network in order to compress the data redundancy.Theoretical analysis and computer simulation results show that this proposed scheme not only further improves the efficiency of network transmission and enhances the throughput of the network,but also reduces the energy consumption of sensor nodes and extends the network life cycle.
A QoS-Driven Self-Adaptive Architecture For Wireless Sensor Networks
Jemal, Ahmed; Ben Halima, Riadh
2013-01-01
6 pages International audience Recently, Wireless Sensor Networks (WSN) have become increasingly used to perform distributed sensing and convey useful information. These kinds of environments are complex, heterogeneous and often affected by unpredictable behavior and poor management. This fostered considerable research on designs and techniques that enhance these systems with an adaptation behavior. In this paper, we focus on the self-adaptation branch of the research and give an overvi...
A Flow-Level Performance Model for Mobile Networks Carrying Adaptive Streaming Traffic
Bonald, Thomas; Elayoubi, Salah-Eddine; Lin, Yu-Ting
2015-01-01
International audience This paper proposes a performance model for mobile networks carrying adaptive streaming traffic. The proposed model takes into account the flow dynamics in addition to the main parameters influencing the performance of adaptive streaming, such as the playout buffer and the video bit rates. We show how to compute several performance metrics like the average video bit rate, the deficit rate, defined as the probability of having an instantaneous throughput lower than th...
Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks
Bin Xu; Jin Qi; Chunxia Zhou; Xiaoxuan Hu; Bianjia Xu; Yanfei Sun
2015-01-01
The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA) based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. ...
Braüner, Torben
2011-01-01
Intuitionistic hybrid logic is hybrid modal logic over an intuitionistic logic basis instead of a classical logical basis. In this short paper we introduce intuitionistic hybrid logic and we give a survey of work in the area.......Intuitionistic hybrid logic is hybrid modal logic over an intuitionistic logic basis instead of a classical logical basis. In this short paper we introduce intuitionistic hybrid logic and we give a survey of work in the area....
Profile-based adaptive anomaly detection for network security.
Zhang, Pengchu C. (Sandia National Laboratories, Albuquerque, NM); Durgin, Nancy Ann
2005-11-01
As information systems become increasingly complex and pervasive, they become inextricably intertwined with the critical infrastructure of national, public, and private organizations. The problem of recognizing and evaluating threats against these complex, heterogeneous networks of cyber and physical components is a difficult one, yet a solution is vital to ensuring security. In this paper we investigate profile-based anomaly detection techniques that can be used to address this problem. We focus primarily on the area of network anomaly detection, but the approach could be extended to other problem domains. We investigate using several data analysis techniques to create profiles of network hosts and perform anomaly detection using those profiles. The ''profiles'' reduce multi-dimensional vectors representing ''normal behavior'' into fewer dimensions, thus allowing pattern and cluster discovery. New events are compared against the profiles, producing a quantitative measure of how ''anomalous'' the event is. Most network intrusion detection systems (IDSs) detect malicious behavior by searching for known patterns in the network traffic. This approach suffers from several weaknesses, including a lack of generalizability, an inability to detect stealthy or novel attacks, and lack of flexibility regarding alarm thresholds. Our research focuses on enhancing current IDS capabilities by addressing some of these shortcomings. We identify and evaluate promising techniques for data mining and machine-learning. The algorithms are ''trained'' by providing them with a series of data-points from ''normal'' network traffic. A successful algorithm can be trained automatically and efficiently, will have a low error rate (low false alarm and miss rates), and will be able to identify anomalies in ''pseudo real-time'' (i.e., while the intrusion is still in progress
Jinhai Liu
2012-01-01
Full Text Available A novel adaptive fuzzy min-max neural network classifier called AFMN is proposed in this paper. Combined with principle component analysis and adaptive genetic algorithm, this integrated system can serve as a supervised and real-time classification technique. Considering the loophole in the expansion-contraction process of FMNN and GFMN and the overcomplex network architecture of FMCN, AFMN maintains the simple architecture of FMNN for fast learning and testing while rewriting the membership function, the expansion and contraction rules for hyperbox generation to solve the confusion problems in the hyperbox overlap region. Meanwhile, principle component analysis is adopted to finish dataset dimensionality reduction for increasing learning efficiency. After training, the confidence coefficient of each hyperbox is calculated based on the distribution of samples. During classifying procedure, utilizing adaptive genetic algorithm to complete parameter optimization for AFMN can also fasten the entire procedure than traversal method. For conditions where training samples are insufficient, data core weight updating is indispensible to enhance the robustness of classifier and the modified membership function can adjust itself according to the input varieties. The paper demonstrates the performance of AFMN through substantial examples in terms of classification accuracy and operating speed by comparing it with FMNN, GFMN, and FMCN.
Mariluce Paes-de-Souza
2013-05-01
Full Text Available The present paper has the objective to expose a proposition of organization within a chain and network logic, aiming to potentiate the extraction of the Native Açaí Berry at the Western Brazilian Amazon rainforest. This exploratory study involves the municipalities of Porto Velho, Guajará-Mirim and Machadinho D’Oeste, at the Brazilian state of Rondônia, with primary data originating mostly from conservation areas at the lower Madeira River region. As a result, it was possible to infer that from the native Açai Berry, derives food, pharmaceuticals and cosmetics, for both local consumption and international markets. It was found that beyond Açai Berry plantations availability, the lower Madeira River provides better transport logistic, consumer market and greater possibility of interaction with middleman than most Açai production areas. As a conclusion, it is made a proposition of an organizational arrangement to strengthen the extrativist productive chain of the Native Açaí Berry, based on the network and chain logic, oriented towards an organization based upon social organizations, manufacturing regularization and marketing.
Design of artificial genetic regulatory networks with multiple delayed adaptive responses*
Kaluza, Pablo; Inoue, Masayo
2016-06-01
Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways. Supplementary material in the form of one nets file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2016-70172-9
Diana Göhringer; Lukas Meder; Stephan Werner; Oliver Oey; Jürgen Becker; Michael Hübner
2012-01-01
This paper presents the hardware architecture and the software abstraction layer of an adaptive multiclient Network-on-Chip (NoC) memory core. The memory core supports the flexibility of a heterogeneous FPGA-based runtime adaptive multiprocessor system called RAMPSoC. The processing elements, also called clients, can access the memory core via the Network-on-Chip (NoC). The memory core supports a dynamic mapping of an address space for the different clients as well as different data transfer ...
Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teachers' College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Sun Wen [School of Mathematics and Information, Yangtze University, Hubei Jingzhou 434023 (China)
2010-04-05
This Letter investigates the synchronization of a general complex dynamical network with non-derivative and derivative coupling. Based on LaSalle's invariance principle, adaptive synchronization criteria are obtained. Analytical result shows that under the designed adaptive controllers, a general complex dynamical network with non-derivative and derivative coupling can asymptotically synchronize to a given trajectory, and several useful criteria for synchronization are given. What is more, the coupling matrix is not assumed to be symmetric or irreducible. Finally, simulations results show the method is effective.