Middle latency response correlates of single and double deviant stimuli in a multi-feature paradigm.

Science.gov (United States)

Althen, H; Huotilainen, M; Grimm, S; Escera, C

2016-01-01

This study aimed to test single and double deviance-related modulations of the middle latency response (MLR) and the applicability of the optimum-2 multi-feature paradigm. The MLR and the MMN to frequency, intensity and double-feature deviants of an optimum-2 multi-feature paradigm and the MMN to double-feature deviants of an oddball paradigm were recorded in young adults. Double deviants elicited significant enhancements of the Nb and Pb MLR waves compared with the waves elicited by standard stimuli. These enhancements equalled approximately the sum of the numerical amplitude differences elicited by the single deviants. In contrast, the MMN to double deviants did not show such additivity. MMNs elicited by double deviants of the multi-feature and the oddball paradigm showed no significant difference in amplitude or latency. The optimum-2 multi-feature paradigm is suitable for recording double deviance-related modulations of the MLR. Interspersed intensity and frequency deviants in the standard trace of the optimum-2 condition multi-feature paradigm did not weaken the double MMN. The optimum-2 multi-feature paradigm could be especially beneficial for clinical studies on early deviance-related modulations in the MLR, due to its optimized utilization of the recording time. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Systems contestability in electricity distribution networks. An analysis from the telecommunications models within the neoclassical economic paradigm

International Nuclear Information System (INIS)

Schweickardt, Gustavo Alejandro; Pistonesi, Hector

2008-01-01

The introduction of contestability conditions in the market of electricity distribution, following the dominant economic paradigm (Neoclassical or Marginalist) and solidary to the commercialization segment, it doesn't exhibit satisfactory solutions at the present time. This asseveration, of general character, have special incumbency for those countries of Latin America that, from regulatory schemes, try to define a deregulated market for certain kind of user (denominated eligible). A eligible user is characterized by to have demands equal or higher than a preset threshold value of electric power/ energy. In this work, considering the models implemented in the telecommunications networks, the problem of allocation of distribution costs, as the first step toward a contestable offer in the retail energy service, is discussed to establishing access prices in the distribution networks (non contestable markets). The analysis is focalized to definition of two market segments: one regulated and other competitive. Their methodological and instrumentation difficulties, are presented, concluding in the necessity of an alternative paradigm.

Bacteria Hunt: Evaluating multi-paradigm BCI interaction

NARCIS (Netherlands)

Mühl, C.; Gürkök, Hayrettin; Plass - Oude Bos, D.; Thurlings, Marieke E.; Scherffig, Lasse; Duvinage, Matthieu; Elbakyan, Alexandra A.; Kang, SungWook; Poel, Mannes; Heylen, Dirk K.J.

The multimodal, multi-paradigm brain-computer interfacing (BCI) game Bacteria Hunt was used to evaluate two aspects of BCI interaction in a gaming context. One goal was to examine the effect of feedback on the ability of the user to manipulate his mental state of relaxation. This was done by having

A multi-state reliability evaluation model for P2P networks

International Nuclear Information System (INIS)

Fan Hehong; Sun Xiaohan

2010-01-01

The appearance of new service types and the convergence tendency of the communication networks have endowed the networks more and more P2P (peer to peer) properties. These networks can be more robust and tolerant for a series of non-perfect operational states due to the non-deterministic server-client distributions. Thus a reliability model taking into account of the multi-state and non-deterministic server-client distribution properties is needed for appropriate evaluation of the networks. In this paper, two new performance measures are defined to quantify the overall and local states of the networks. A new time-evolving state-transition Monte Carlo (TEST-MC) simulation model is presented for the reliability analysis of P2P networks in multiple states. The results show that the model is not only valid for estimating the traditional binary-state network reliability parameters, but also adequate for acquiring the parameters in a series of non-perfect operational states, with good efficiencies, especially for highly reliable networks. Furthermore, the model is versatile for the reliability and maintainability analyses in that both the links and the nodes can be failure-prone with arbitrary life distributions, and various maintainability schemes can be applied.

Multi-Stratum Networks: toward a unified model of on-line identities

DEFF Research Database (Denmark)

Rossi, Luca; Magnani, Matteo

2012-01-01

One of the reasons behind the success of Social Network Analysis is its simple and general graph model made of nodes (representing individuals) and ties. However, when we focus on our daily on-line experience we must confront a more complex scenario: people inhabitate several on-line spaces...... interacting to several communities active on various technological infrastructures like Twitter, Facebook, YouTube or FourSquare and with distinct social objectives. This constitutes a complex network of interconnected networks where users' identities are spread and where information propagates navigating...... through different communities and social platforms. In this article we introduce a model for this layered scenario that we call multi-stratum network. Through a theoretical discussion and the analysis of real-world data we show how not only focusing on a single network may provide a very partial...

A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation.

Directory of Open Access Journals (Sweden)

Warren D Anderson

2017-07-01

Full Text Available Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension. We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

A multi-agent system architecture for sensor networks.

Science.gov (United States)

Fuentes-Fernández, Rubén; Guijarro, María; Pajares, Gonzalo

2009-01-01

The design of the control systems for sensor networks presents important challenges. Besides the traditional problems about how to process the sensor data to obtain the target information, engineers need to consider additional aspects such as the heterogeneity and high number of sensors, and the flexibility of these networks regarding topologies and the sensors in them. Although there are partial approaches for resolving these issues, their integration relies on ad hoc solutions requiring important development efforts. In order to provide an effective approach for this integration, this paper proposes an architecture based on the multi-agent system paradigm with a clear separation of concerns. The architecture considers sensors as devices used by an upper layer of manager agents. These agents are able to communicate and negotiate services to achieve the required functionality. Activities are organized according to roles related with the different aspects to integrate, mainly sensor management, data processing, communication and adaptation to changes in the available devices and their capabilities. This organization largely isolates and decouples the data management from the changing network, while encouraging reuse of solutions. The use of the architecture is facilitated by a specific modelling language developed through metamodelling. A case study concerning a generic distributed system for fire fighting illustrates the approach and the comparison with related work.

Modular Energy-Efficient and Robust Paradigms for a Disaster-Recovery Process over Wireless Sensor Networks.

Science.gov (United States)

Razaque, Abdul; Elleithy, Khaled

2015-07-06

Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN) applications. The lack of robust and efficient paradigms causes a reduction in the provision of quality of service (QoS) and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: (1) the operational medium access control (O-MAC) hybrid protocol and (2) the pheromone termite (PT) model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: (1) determining the packet generation rate to avoid congestion and (2) pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model; we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10) that monitors the activities of disaster recovery staff; hospital staff and disaster victims brought into the hospital. Moreover; the proposed paradigm can be used for general purpose applications. Finally; the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes.

Networks Within Cities and Among Cities: A Paradigm for Urban Development and Governance

OpenAIRE

Pompili, Tomaso

2006-01-01

Networks and networking have become fashionable concepts and terms in regional science, and in particular in regional and urban geography in the last decade: we speak about network firms, network society, network economy but also network cities, city-networks, reti urbane, reseaux de villes. Only catch-words for somebody; a true new scientific paradigm according to others. Our opinion is that in fact we are confronted with a new paradigm in spatial sciences, under some precise conditions: - t...

Optical network control plane for multi-domain networking

DEFF Research Database (Denmark)

Manolova, Anna Vasileva

This thesis focuses on multi-domain routing for traffice engineering and survivability support in optical transport networks under the Generalized Multi-Protocol Label Switching (GMPLS) control framework. First, different extensions to the Border Gateway Protocol for multi-domain Traffic...... process are not enough for efficient TE in mesh multi-domain networks. Enhancing the protocol with multi-path dissemination capability, combined with the employment of an end-to-end TE metric proves to be a highly efficient solution. Simulation results show good performance characteristics of the proposed...... is not as essential for improved network performance as the length of the provided paths. Second, the issue of multi-domain survivability support is analyzed. An AS-disjoint paths is beneficial not only for resilience support, but also for facilitating adequate network reactions to changes in the network, which...

Modular Energy-Efficient and Robust Paradigms for a Disaster-Recovery Process over Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Abdul Razaque

2015-07-01

Full Text Available Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN applications. The lack of robust and efficient paradigms causes a reduction in the provision of quality of service (QoS and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: (1 the operational medium access control (O-MAC hybrid protocol and (2 the pheromone termite (PT model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: (1 determining the packet generation rate to avoid congestion and (2 pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model; we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10 that monitors the activities of disaster recovery staff; hospital staff and disaster victims brought into the hospital. Moreover; the proposed paradigm can be used for general purpose applications. Finally; the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes.

Research on e-commerce transaction networks using multi-agent modelling and open application programming interface

Science.gov (United States)

Piao, Chunhui; Han, Xufang; Wu, Harris

2010-08-01

We provide a formal definition of an e-commerce transaction network. Agent-based modelling is used to simulate e-commerce transaction networks. For real-world analysis, we studied the open application programming interfaces (APIs) from eBay and Taobao e-commerce websites and captured real transaction data. Pajek is used to visualise the agent relationships in the transaction network. We derived one-mode networks from the transaction network and analysed them using degree and betweenness centrality. Integrating multi-agent modelling, open APIs and social network analysis, we propose a new way to study large-scale e-commerce systems.

Low Power Multi-Hop Networking Analysis in Intelligent Environments.

Science.gov (United States)

Etxaniz, Josu; Aranguren, Gerardo

2017-05-19

Intelligent systems are driven by the latest technological advances in many different areas such as sensing, embedded systems, wireless communications or context recognition. This paper focuses on some of those areas. Concretely, the paper deals with wireless communications issues in embedded systems. More precisely, the paper combines the multi-hop networking with Bluetooth technology and a quality of service (QoS) metric, the latency. Bluetooth is a radio license-free worldwide communication standard that makes low power multi-hop wireless networking available. It establishes piconets (point-to-point and point-to-multipoint links) and scatternets (multi-hop networks). As a result, many Bluetooth nodes can be interconnected to set up ambient intelligent networks. Then, this paper presents the results of the investigation on multi-hop latency with park and sniff Bluetooth low power modes conducted over the hardware test bench previously implemented. In addition, the empirical models to estimate the latency of multi-hop communications over Bluetooth Asynchronous Connectionless Links (ACL) in park and sniff mode are given. The designers of devices and networks for intelligent systems will benefit from the estimation of the latency in Bluetooth multi-hop communications that the models provide.

Resource allocation based uplink intercell interference model in multi-carrier networks

KAUST Repository

Tabassum, Hina; Yilmaz, Ferkan; Dawy, Zaher; Alouini, Mohamed-Slim

2013-01-01

Intercell interference (ICI) is a primary cause for performance limitation in emerging wireless cellular systems due to its highly indeterministic nature. In this paper, we derive an analytical statistical model for the uplink ICI in a multiuser multi-carrier cellular network considering the impact of various uncoordinated scheduling schemes on the locations and transmit powers of the interferers. The derived model applies to generic composite fading distributions and provides a useful computational tool to evaluate key performance metrics such as the network ergodic capacity. The derived model is extended to incorporate coordinated scheduling schemes. A study is then presented to quantify the potential performance gains of coordinated over uncoordinated scheduling schemes under various base station coordination scenarios. Numerical results demonstrate that different frequency allocation patterns significantly impact the network performance depending on the coordination among neighboring base stations. The accuracy of the derived analytical expressions is verified via Monte-Carlo simulations. © 2013 IEEE.

Resource allocation based uplink intercell interference model in multi-carrier networks

KAUST Repository

Tabassum, Hina

2013-06-01

Intercell interference (ICI) is a primary cause for performance limitation in emerging wireless cellular systems due to its highly indeterministic nature. In this paper, we derive an analytical statistical model for the uplink ICI in a multiuser multi-carrier cellular network considering the impact of various uncoordinated scheduling schemes on the locations and transmit powers of the interferers. The derived model applies to generic composite fading distributions and provides a useful computational tool to evaluate key performance metrics such as the network ergodic capacity. The derived model is extended to incorporate coordinated scheduling schemes. A study is then presented to quantify the potential performance gains of coordinated over uncoordinated scheduling schemes under various base station coordination scenarios. Numerical results demonstrate that different frequency allocation patterns significantly impact the network performance depending on the coordination among neighboring base stations. The accuracy of the derived analytical expressions is verified via Monte-Carlo simulations. © 2013 IEEE.

A Multi-Agent System Architecture for Sensor Networks

Directory of Open Access Journals (Sweden)

María Guijarro

2009-12-01

Full Text Available The design of the control systems for sensor networks presents important challenges. Besides the traditional problems about how to process the sensor data to obtain the target information, engineers need to consider additional aspects such as the heterogeneity and high number of sensors, and the flexibility of these networks regarding topologies and the sensors in them. Although there are partial approaches for resolving these issues, their integration relies on ad hoc solutions requiring important development efforts. In order to provide an effective approach for this integration, this paper proposes an architecture based on the multi-agent system paradigm with a clear separation of concerns. The architecture considers sensors as devices used by an upper layer of manager agents. These agents are able to communicate and negotiate services to achieve the required functionality. Activities are organized according to roles related with the different aspects to integrate, mainly sensor management, data processing, communication and adaptation to changes in the available devices and their capabilities. This organization largely isolates and decouples the data management from the changing network, while encouraging reuse of solutions. The use of the architecture is facilitated by a specific modelling language developed through metamodelling. A case study concerning a generic distributed system for fire fighting illustrates the approach and the comparison with related work.

Load-aware modeling for uplink cellular networks in a multi-channel environment

KAUST Repository

AlAmmouri, Ahmad

2014-09-01

We exploit tools from stochastic geometry to develop a tractable analytical approach for modeling uplink cellular networks. The developed model is load aware and accounts for per-user power control as well as the limited transmit power constraint for the users\\' equipment (UEs). The proposed analytical paradigm is based on a simple per-user power control scheme in which each user inverts his path-loss such that the signal is received at his serving base station (BS) with a certain power threshold ρ Due to the limited transmit power of the UEs, users that cannot invert their path-loss to their serving BSs are allowed to transmit with their maximum transmit power. We show that the proposed power control scheme not only provides a balanced cell center and cell edge user performance, it also facilitates the analysis when compared to the state-of-the-art approaches in the literature. To this end, we discuss how to manipulate the design variable ρ in response to the network parameters to optimize one or more of the performance metrics such as the outage probability, the network capacity, and the energy efficiency.

End-to-end Information Flow Security Model for Software-Defined Networks

Directory of Open Access Journals (Sweden)

D. Ju. Chaly

2015-01-01

Full Text Available Software-defined networks (SDN are a novel paradigm of networking which became an enabler technology for many modern applications such as network virtualization, policy-based access control and many others. Software can provide flexibility and fast-paced innovations in the networking; however, it has a complex nature. In this connection there is an increasing necessity of means for assuring its correctness and security. Abstract models for SDN can tackle these challenges. This paper addresses to confidentiality and some integrity properties of SDNs. These are critical properties for multi-tenant SDN environments, since the network management software must ensure that no confidential data of one tenant are leaked to other tenants in spite of using the same physical infrastructure. We define a notion of end-to-end security in context of software-defined networks and propose a semantic model where the reasoning is possible about confidentiality, and we can check that confidential information flows do not interfere with non-confidential ones. We show that the model can be extended in order to reason about networks with secure and insecure links which can arise, for example, in wireless environments.The article is published in the authors’ wording.

Social network analysis via multi-state reliability and conditional influence models

International Nuclear Information System (INIS)

Schneider, Kellie; Rainwater, Chase; Pohl, Ed; Hernandez, Ivan; Ramirez-Marquez, Jose Emmanuel

2013-01-01

This paper incorporates multi-state reliability measures into the assessment of a social network in which influence is treated as a multi-state commodity that flows through the network. The reliability of the network is defined as the probability that at least a certain level of influence reaches an intended target. We consider an individual's influence level as a function of the influence levels received from preceding actors in the network. We define several communication functions which describe the level of influence a particular actor will pass along to other actors within the network. Illustrative examples are presented, and the network reliability under the various communication influence levels is computed using exhaustive enumeration for a small example and Monte Carlo simulation for larger, more realistic sized examples.

Model and simulation of Krause model in dynamic open network

Science.gov (United States)

Zhu, Meixia; Xie, Guangqiang

2017-08-01

The construction of the concept of evolution is an effective way to reveal the formation of group consensus. This study is based on the modeling paradigm of the HK model (Hegsekmann-Krause). This paper analyzes the evolution of multi - agent opinion in dynamic open networks with member mobility. The results of the simulation show that when the number of agents is constant, the interval distribution of the initial distribution will affect the number of the final view, The greater the distribution of opinions, the more the number of views formed eventually; The trust threshold has a decisive effect on the number of views, and there is a negative correlation between the trust threshold and the number of opinions clusters. The higher the connectivity of the initial activity group, the more easily the subjective opinion in the evolution of opinion to achieve rapid convergence. The more open the network is more conducive to the unity of view, increase and reduce the number of agents will not affect the consistency of the group effect, but not conducive to stability.

Multi-agent modelling framework for water, energy and other resource networks

Science.gov (United States)

Knox, S.; Selby, P. D.; Meier, P.; Harou, J. J.; Yoon, J.; Lachaut, T.; Klassert, C. J. A.; Avisse, N.; Mohamed, K.; Tomlinson, J.; Khadem, M.; Tilmant, A.; Gorelick, S.

2015-12-01

Bespoke modelling tools are often needed when planning future engineered interventions in the context of various climate, socio-economic and geopolitical futures. Such tools can help improve system operating policies or assess infrastructure upgrades and their risks. A frequently used approach is to simulate and/or optimise the impact of interventions in engineered systems. Modelling complex infrastructure systems can involve incorporating multiple aspects into a single model, for example physical, economic and political. This presents the challenge of combining research from diverse areas into a single system effectively. We present the Pynsim 'Python Network Simulator' framework, a library for building simulation models capable of representing, the physical, institutional and economic aspects of an engineered resources system. Pynsim is an open source, object oriented code aiming to promote integration of different modelling processes through a single code library. We present two case studies that demonstrate important features of Pynsim's design. The first is a large interdisciplinary project of a national water system in the Middle East with modellers from fields including water resources, economics, hydrology and geography each considering different facets of a multi agent system. It includes: modelling water supply and demand for households and farms; a water tanker market with transfer of water between farms and households, and policy decisions made by government institutions at district, national and international level. This study demonstrates that a well-structured library of code can provide a hub for development and act as a catalyst for integrating models. The second focuses on optimising the location of new run-of-river hydropower plants. Using a multi-objective evolutionary algorithm, this study analyses different network configurations to identify the optimal placement of new power plants within a river network. This demonstrates that Pynsim can be

Nanotechnology convergence and modeling paradigm of sustainable energy system using polymer electrolyte membrane fuel cell as a benchmark example

International Nuclear Information System (INIS)

Chung, Pil Seung; So, Dae Sup; Biegler, Lorenz T.; Jhon, Myung S.

2012-01-01

Developments in nanotechnology have led to innovative progress and converging technologies in engineering and science. These demand novel methodologies that enable efficient communications from the nanoscale all the way to decision-making criteria for actual production systems. In this paper, we discuss the convergence of nanotechnology and novel multi-scale modeling paradigms by using the fuel cell system as a benchmark example. This approach includes complex multi-phenomena at different time and length scales along with the introduction of an optimization framework for application-driven nanotechnology research trends. The modeling paradigm introduced here covers the novel holistic integration from atomistic/molecular phenomena to meso/continuum scales. System optimization is also discussed with respect to the reduced order parameters for a coarse-graining procedure in multi-scale model integration as well as system design. The development of a hierarchical multi-scale paradigm consolidates the theoretical analysis and enables large-scale decision-making of process level design, based on first-principles, and therefore promotes the convergence of nanotechnology to sustainable energy technologies.

Nanotechnology convergence and modeling paradigm of sustainable energy system using polymer electrolyte membrane fuel cell as a benchmark example

Energy Technology Data Exchange (ETDEWEB)

Chung, Pil Seung; So, Dae Sup; Biegler, Lorenz T.; Jhon, Myung S., E-mail: mj3a@andrew.cmu.edu [Carnegie Mellon University, Department of Chemical Engineering (United States)

2012-08-15

Developments in nanotechnology have led to innovative progress and converging technologies in engineering and science. These demand novel methodologies that enable efficient communications from the nanoscale all the way to decision-making criteria for actual production systems. In this paper, we discuss the convergence of nanotechnology and novel multi-scale modeling paradigms by using the fuel cell system as a benchmark example. This approach includes complex multi-phenomena at different time and length scales along with the introduction of an optimization framework for application-driven nanotechnology research trends. The modeling paradigm introduced here covers the novel holistic integration from atomistic/molecular phenomena to meso/continuum scales. System optimization is also discussed with respect to the reduced order parameters for a coarse-graining procedure in multi-scale model integration as well as system design. The development of a hierarchical multi-scale paradigm consolidates the theoretical analysis and enables large-scale decision-making of process level design, based on first-principles, and therefore promotes the convergence of nanotechnology to sustainable energy technologies.

Controlling the dynamics of multi-state neural networks

International Nuclear Information System (INIS)

Jin, Tao; Zhao, Hong

2008-01-01

In this paper, we first analyze the distribution of local fields (DLF) which is induced by the memory patterns in the Q-Ising model. It is found that the structure of the DLF is closely correlated with the network dynamics and the system performance. However, the design rule adopted in the Q-Ising model, like the other rules adopted for multi-state neural networks with associative memories, cannot be applied to directly control the DLF for a given set of memory patterns, and thus cannot be applied to further study the relationships between the structure of the DLF and the dynamics of the network. We then extend a design rule, which was presented recently for designing binary-state neural networks, to make it suitable for designing general multi-state neural networks. This rule is able to control the structure of the DLF as expected. We show that controlling the DLF not only can affect the dynamic behaviors of the multi-state neural networks for a given set of memory patterns, but also can improve the storage capacity. With the change of the DLF, the network shows very rich dynamic behaviors, such as the 'chaos phase', the 'memory phase', and the 'mixture phase'. These dynamic behaviors are also observed in the binary-state neural networks; therefore, our results imply that they may be the universal behaviors of feedback neural networks

Modelling innovation performance of European regions using multi-output neural networks.

Science.gov (United States)

Hajek, Petr; Henriques, Roberto

2017-01-01

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

Modelling innovation performance of European regions using multi-output neural networks.

Directory of Open Access Journals (Sweden)

Petr Hajek

Full Text Available Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

Control theory of digitally networked dynamic systems

CERN Document Server

Lunze, Jan

2013-01-01

The book gives an introduction to networked control systems and describes new modeling paradigms, analysis methods for event-driven, digitally networked systems, and design methods for distributed estimation and control. Networked model predictive control is developed as a means to tolerate time delays and packet loss brought about by the communication network. In event-based control the traditional periodic sampling is replaced by state-dependent triggering schemes. Novel methods for multi-agent systems ensure complete or clustered synchrony of agents with identical or with individual dynamic

Towards Effective Intra-flow Network Coding in Software Defined Wireless Mesh Networks

OpenAIRE

Donghai Zhu; Xinyu Yang Yang; Peng Zhao; Wei Yu

2016-01-01

Wireless Mesh Networks (WMNs) have potential to provide convenient broadband wireless Internet access to mobile users.With the support of Software-Defined Networking (SDN) paradigm that separates control plane and data plane, WMNs can be easily deployed and managed. In addition, by exploiting the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks, intra-flow network coding has shown a greater benefit in comparison with traditional routing paradigm...

A new multi objective optimization model for designing a green supply chain network under uncertainty

Directory of Open Access Journals (Sweden)

Mohammad Mahdi Saffar

2015-01-01

Full Text Available Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with ε-constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE is applied.

Models and synchronization of time-delayed complex dynamical networks with multi-links based on adaptive control

International Nuclear Information System (INIS)

Peng Haipeng; Wei Nan; Li Lixiang; Xie Weisheng; Yang Yixian

2010-01-01

In this Letter, time-delay has been introduced in to split the networks, upon which a model of complex dynamical networks with multi-links has been constructed. Moreover, based on Lyapunov stability theory and some hypotheses, we achieve synchronization between two complex networks with different structures by designing effective controllers. The validity of the results was proved through numerical simulations of this Letter.

Multi-Paradigm and Multi-Lingual Information Extraction as Support for Medical Web Labelling Authorities

Directory of Open Access Journals (Sweden)

Martin Labsky

2010-10-01

Full Text Available Until recently, quality labelling of medical web content has been a pre-dominantly manual activity. However, the advances in automated text processing opened the way to computerised support of this activity. The core enabling technology is information extraction (IE. However, the heterogeneity of websites offering medical content imposes particular requirements on the IE techniques to be applied. In the paper we discuss these requirements and describe a multi-paradigm approach to IE addressing them. Experiments on multi-lingual data are reported. The research has been carried out within the EU MedIEQ project.

Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks.

Science.gov (United States)

Mahjoub, Reem K; Elleithy, Khaled

2017-04-14

The actor nodes are the spine of wireless sensor and actor networks (WSANs) that collaborate to perform a specific task in an unverified and uneven environment. Thus, there is a possibility of high failure rate in such unfriendly scenarios due to several factors such as power consumption of devices, electronic circuit failure, software errors in nodes or physical impairment of the actor nodes and inter-actor connectivity problem. Therefore, it is extremely important to discover the failure of a cut-vertex actor and network-disjoint in order to improve the Quality-of-Service (QoS). In this paper, we propose an Efficient Actor Recovery (EAR) paradigm to guarantee the contention-free traffic-forwarding capacity. The EAR paradigm consists of a Node Monitoring and Critical Node Detection (NMCND) algorithm that monitors the activities of the nodes to determine the critical node. In addition, it replaces the critical node with backup node prior to complete node-failure which helps balancing the network performance. The packets are handled using Network Integration and Message Forwarding (NIMF) algorithm that determines the source of forwarding the packets; either from actor or sensor. This decision-making capability of the algorithm controls the packet forwarding rate to maintain the network for a longer time. Furthermore, for handling the proper routing strategy, Priority-Based Routing for Node Failure Avoidance (PRNFA) algorithm is deployed to decide the priority of the packets to be forwarded based on the significance of information available in the packet. To validate the effectiveness of the proposed EAR paradigm, the proposed algorithms were tested using OMNET++ simulation.

Risk Evaluation of Railway Coal Transportation Network Based on Multi Level Grey Evaluation Model

Science.gov (United States)

Niu, Wei; Wang, Xifu

2018-01-01

The railway transport mode is currently the most important way of coal transportation, and now China’s railway coal transportation network has become increasingly perfect, but there is still insufficient capacity, some lines close to saturation and other issues. In this paper, the theory and method of risk assessment, analytic hierarchy process and multi-level gray evaluation model are applied to the risk evaluation of coal railway transportation network in China. Based on the example analysis of Shanxi railway coal transportation network, to improve the internal structure and the competitiveness of the market.

An Adaptive Data Gathering Scheme for Multi-Hop Wireless Sensor Networks Based on Compressed Sensing and Network Coding.

Science.gov (United States)

Yin, Jun; Yang, Yuwang; Wang, Lei

2016-04-01

Joint design of compressed sensing (CS) and network coding (NC) has been demonstrated to provide a new data gathering paradigm for multi-hop wireless sensor networks (WSNs). By exploiting the correlation of the network sensed data, a variety of data gathering schemes based on NC and CS (Compressed Data Gathering--CDG) have been proposed. However, these schemes assume that the sparsity of the network sensed data is constant and the value of the sparsity is known before starting each data gathering epoch, thus they ignore the variation of the data observed by the WSNs which are deployed in practical circumstances. In this paper, we present a complete design of the feedback CDG scheme where the sink node adaptively queries those interested nodes to acquire an appropriate number of measurements. The adaptive measurement-formation procedure and its termination rules are proposed and analyzed in detail. Moreover, in order to minimize the number of overall transmissions in the formation procedure of each measurement, we have developed a NP-complete model (Maximum Leaf Nodes Minimum Steiner Nodes--MLMS) and realized a scalable greedy algorithm to solve the problem. Experimental results show that the proposed measurement-formation method outperforms previous schemes, and experiments on both datasets from ocean temperature and practical network deployment also prove the effectiveness of our proposed feedback CDG scheme.

Multi-agent based modeling for electric vehicle integration in a distribution network operation

DEFF Research Database (Denmark)

Hu, Junjie; Morais, Hugo; Lind, Morten

2016-01-01

The purpose of this paper is to present a multi-agent based modeling technology for simulating and operating a hierarchical energy management of a power distribution system with focus on EVs integration. The proposed multi-agent system consists of four types of agents: i) Distribution system...... operator (DSO) technical agent and ii) DSO market agents that both belong to the top layer of the hierarchy and their roles are to manage the distribution network by avoiding grid congestions and using congestion prices to coordinate the energy scheduled; iii) Electric vehicle virtual power plant agents...

Multi-hop routing in wireless sensor networks an overview, taxonomy, and research challenges

CERN Document Server

Rani, Shalli

2016-01-01

This brief provides an overview of recent developments in multi-hop routing protocols for Wireless Sensor Networks (WSNs). It introduces the various classifications of routing protocols and lists the pros and cons of each category, going beyond the conceptual overview of routing classifications offered in other books. Recently many researchers have proposed numerous multi-hop routing protocols and thereby created a need for a book that provides its readers with an up-to-date road map of this research paradigm.   The authors present some of the most relevant results achieved by applying an algorithmic approach to the research on multi-hop routing protocols. The book covers measurements, experiences and lessons learned from the implementation of multi-hop communication prototypes. Furthermore, it describes future research challenges and as such serves as a useful guide for students and researchers alike.

Consensus of Multi-Agent Systems with Prestissimo Scale-Free Networks

International Nuclear Information System (INIS)

Yang Hongyong; Lu Lan; Cao Kecai; Zhang Siying

2010-01-01

In this paper, the relations of the network topology and the moving consensus of multi-agent systems are studied. A consensus-prestissimo scale-free network model with the static preferential-consensus attachment is presented on the rewired link of the regular network. The effects of the static preferential-consensus BA network on the algebraic connectivity of the topology graph are compared with the regular network. The robustness gain to delay is analyzed for variable network topology with the same scale. The time to reach the consensus is studied for the dynamic network with and without communication delays. By applying the computer simulations, it is validated that the speed of the convergence of multi-agent systems can be greatly improved in the preferential-consensus BA network model with different configuration. (interdisciplinary physics and related areas of science and technology)

a Multi Objective Model for Optimization of a Green Supply Chain Network

Science.gov (United States)

Paksoy, Turan; Özceylan, Eren; Weber, Gerhard-Wilhelm

2010-06-01

This study develops a model of a closed-loop supply chain (CLSC) network which starts with the suppliers and recycles with the decomposition centers. As a traditional network design, we consider minimizing the all transportation costs and the raw material purchasing costs. To pay attention for the green impacts, different transportation choices are presented between echelons according to their CO2 emissions. The plants can purchase different raw materials in respect of their recyclable ratios. The focuses of this paper are conducting the minimizing total CO2 emissions. Also we try to encourage the customers to use recyclable materials as an environmental performance viewpoint besides minimizing total costs. A multi objective linear programming model is developed via presenting a numerical example. We close the paper with recommendations for future researches.

Security for multi-hop wireless networks

CERN Document Server

Mahmoud, Mohamed M E A

2014-01-01

This Springer Brief discusses efficient security protocols and schemes for multi-hop wireless networks. It presents an overview of security requirements for these networks, explores challenges in securing networks and presents system models. The authors introduce mechanisms to reduce the overhead and identify malicious nodes that drop packets intentionally. Also included is a new, efficient cooperation incentive scheme to stimulate the selfish nodes to relay information packets and enforce fairness. Many examples are provided, along with predictions for future directions of the field. Security

A Model of Building Multi-campus Library Education Ｒesourses Network Based on MPLS VPN

Directory of Open Access Journals (Sweden)

WANG Guang-ze

2017-06-01

Full Text Available For the problems of most merged schools that it is difficulty to integrate the original library education resources network due to scattered campus，and it is inconvenient to use teaching resources for outwork teachers and students，this paper puts forward the technology of using multi － protocol label switching virtual private network ( MPLS VPN to integrate education resources in multi － campus library． Through setting up security tunnel between the library education subnet，MPLS VPN ensure the safety of data transmission，so as achieve the goal of resource integration，optimization and share，implements the exclusive use for the electronic teaching business of library． Experimental data show that this model has advantages compared with traditional VPN networking．

Effects of multi-state links in network community detection

International Nuclear Information System (INIS)

Rocco, Claudio M.; Moronta, José; Ramirez-Marquez, José E.; Barker, Kash

2017-01-01

A community is defined as a group of nodes of a network that are densely interconnected with each other but only sparsely connected with the rest of the network. The set of communities (i.e., the network partition) and their inter-community links could be derived using special algorithms account for the topology of the network and, in certain cases, the possible weights associated to the links. In general, the set of weights represents some characteristic as capacity, flow and reliability, among others. The effects of considering weights could be translated to obtain a different partition. In many real situations, particularly when modeling infrastructure systems, networks must be modeled as multi-state networks (e.g., electric power networks). In such networks, each link is characterized by a vector of known random capacities (i.e., the weight on each link could vary according to a known probability distribution). In this paper a simple Monte Carlo approach is proposed to evaluate the effects of multi-state links on community detection as well as on the performance of the network. The approach is illustrated with the topology of an electric power system. - Highlights: • Identify network communities when considering multi-state links. • Identified how effects of considering weights translate to different partition. • Identified importance of Inter-Community Links and changes with respect to community. • Preamble to performing a resilience assessment able to mimic the evolution of the state of each community.

Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network

Directory of Open Access Journals (Sweden)

Kai Lin

2016-07-01

Full Text Available With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC. The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods.

Queueing Models for Mobile Ad Hoc Networks

NARCIS (Netherlands)

de Haan, Roland

2009-01-01

This thesis presents models for the performance analysis of a recent communication paradigm: \\emph{mobile ad hoc networking}. The objective of mobile ad hoc networking is to provide wireless connectivity between stations in a highly dynamic environment. These dynamics are driven by the mobility of

A Jackson network model and threshold policy for joint optimization of energy and delay in multi-hop wireless networks

KAUST Repository

Xia, Li; Shihada, Basem

2014-01-01

This paper studies the joint optimization problem of energy and delay in a multi-hop wireless network. The optimization variables are the transmission rates, which are adjustable according to the packet queueing length in the buffer. The optimization goal is to minimize the energy consumption of energy-critical nodes and the packet transmission delay throughout the network. In this paper, we aim at understanding the well-known decentralized algorithms which are threshold based from a different research angle. By using a simplified network model, we show that we can adopt the semi-open Jackson network model and study this optimization problem in closed form. This simplified network model further allows us to establish some significant optimality properties. We prove that the system performance is monotonic with respect to (w.r.t.) the transmission rate. We also prove that the threshold-type policy is optimal, i.e., when the number of packets in the buffer is larger than a threshold, transmit with the maximal rate (power); otherwise, no transmission. With these optimality properties, we develop a heuristic algorithm to iteratively find the optimal threshold. Finally, we conduct some simulation experiments to demonstrate the main idea of this paper.

A Jackson network model and threshold policy for joint optimization of energy and delay in multi-hop wireless networks

KAUST Repository

Xia, Li

2014-11-20

This paper studies the joint optimization problem of energy and delay in a multi-hop wireless network. The optimization variables are the transmission rates, which are adjustable according to the packet queueing length in the buffer. The optimization goal is to minimize the energy consumption of energy-critical nodes and the packet transmission delay throughout the network. In this paper, we aim at understanding the well-known decentralized algorithms which are threshold based from a different research angle. By using a simplified network model, we show that we can adopt the semi-open Jackson network model and study this optimization problem in closed form. This simplified network model further allows us to establish some significant optimality properties. We prove that the system performance is monotonic with respect to (w.r.t.) the transmission rate. We also prove that the threshold-type policy is optimal, i.e., when the number of packets in the buffer is larger than a threshold, transmit with the maximal rate (power); otherwise, no transmission. With these optimality properties, we develop a heuristic algorithm to iteratively find the optimal threshold. Finally, we conduct some simulation experiments to demonstrate the main idea of this paper.

Modelling, Estimation and Control of Networked Complex Systems

CERN Document Server

Chiuso, Alessandro; Frasca, Mattia; Rizzo, Alessandro; Schenato, Luca; Zampieri, Sandro

2009-01-01

The paradigm of complexity is pervading both science and engineering, leading to the emergence of novel approaches oriented at the development of a systemic view of the phenomena under study; the definition of powerful tools for modelling, estimation, and control; and the cross-fertilization of different disciplines and approaches. This book is devoted to networked systems which are one of the most promising paradigms of complexity. It is demonstrated that complex, dynamical networks are powerful tools to model, estimate, and control many interesting phenomena, like agent coordination, synchronization, social and economics events, networks of critical infrastructures, resources allocation, information processing, or control over communication networks. Moreover, it is shown how the recent technological advances in wireless communication and decreasing in cost and size of electronic devices are promoting the appearance of large inexpensive interconnected systems, each with computational, sensing and mobile cap...

Multi-equilibrium property of metabolic networks: SSI module

Directory of Open Access Journals (Sweden)

Chen Luonan

2011-06-01

Full Text Available Abstract Background Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. Results In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. Conclusions In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module.

Network management paradigm in education as a component of information society

Directory of Open Access Journals (Sweden)

O. O. Kolesnic

2014-05-01

Full Text Available Network organization has always existed, but that today information technologies create the material basis for the penetration of this type of structure in all areas. This allows to interpret the formation of a network management paradigm as one of the innovative processes of information society . The effects on the development of the virtual environment in the form of a network society leads to the fact that traditional forms of management education change it its own characteristics and there are conditions for the effective use of new forms of governance, it is connected with the network nature of education as a space object management. In practice, the process of modernization of the existing network of educational institutions and organizations that generally means use of information technology and the significant decline in the share of traditional hierarchical forms of control by increasing the share of collective polyarchical forms. Today, the introduction of a network management paradigm of education is performed primarily as a stochastic process of entering educational institutions in the space of network society. The main methodological shortcomings of network management in education is the lack of understanding of its specificity in the context of providing social impact of educational activities. Promising area of implementation of network management in education is the use of targeted management practices that focus on the development of very specific forms of network co­operation in education.

Wireless Multi Hop Access Networks and Protocols

OpenAIRE

Nilsson Plymoth, Anders

2007-01-01

As more and more applications and services in our society now depend on the Internet, it is important that dynamically deployed wireless multi hop networks are able to gain access to the Internet and other infrastructure networks and services. This thesis proposes and evaluates solutions for providing multi hop Internet Access. It investigates how ad hoc networks can be combined with wireless and mesh networks in order to create wireless multi hop access networks. When several access points t...

Multi-Input Convolutional Neural Network for Flower Grading

Directory of Open Access Journals (Sweden)

Yu Sun

2017-01-01

Full Text Available Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.

SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.

Science.gov (United States)

Sripad, Athul; Sanchez, Giovanny; Zapata, Mireya; Pirrone, Vito; Dorta, Taho; Cambria, Salvatore; Marti, Albert; Krishnamourthy, Karthikeyan; Madrenas, Jordi

2018-01-01

Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN's connectivity, to compile the neuron-synapse model and to monitor SNN's activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.

Science.gov (United States)

Colclough, Giles L; Woolrich, Mark W; Harrison, Samuel J; Rojas López, Pedro A; Valdes-Sosa, Pedro A; Smith, Stephen M

2018-05-07

A Bayesian model for sparse, hierarchical inverse covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fmri, meg and eeg data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in meg beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity. Copyright © 2018. Published by Elsevier Inc.

Netpixl: Towards a New Paradigm for Networked Application Development

OpenAIRE

Diakopoulos, Dimitri; Kapur, Ajay

2013-01-01

Netpixl is a new micro-toolkit built to network devices within interactiveinstallations and environments. Using a familiar client-server model, Netpixlcentrally wraps an important aspect of ubiquitous computing: real-timemessaging. In the context of sound and music computing, the role of Netpixl isto fluidly integrate endpoints like OSC and MIDI within a larger multi-usersystem. This paper considers useful design principles that may be applied totoolkits like Netpixl while also emphasizing re...

Multi-port network and 3D finite-element models for accurate transformer calculations: Single-phase load-loss test

Energy Technology Data Exchange (ETDEWEB)

Escarela-Perez, R. [Departamento de Energia, Universidad Autonoma Metropolitana, Av. San Pablo 180, Col. Reynosa, C.P. 02200, Mexico D.F. (Mexico); Kulkarni, S.V. [Electrical Engineering Department, Indian Institute of Technology, Bombay (India); Melgoza, E. [Instituto Tecnologico de Morelia, Av. Tecnologico 1500, Morelia, Mich., C.P. 58120 (Mexico)

2008-11-15

A six-port impedance network for a three-phase transformer is obtained from a 3D time-harmonic finite-element (FE) model. The network model properly captures the eddy current effects of the transformer tank and frame. All theorems and tools of passive linear networks can be used with the multi-port model to simulate several important operating conditions without resorting anymore to computationally expensive 3D FE simulations. The results of the network model are of the same quality as those produced by the FE program. Although the passive network may seem limited by the assumption of linearity, many important transformer operating conditions imply unsaturated states. Single-phase load-loss measurements are employed to demonstrate the effectiveness of the network model and to understand phenomena that could not be explained with conventional equivalent circuits. In addition, formal deduction of novel closed-form formulae is presented for the calculation of the leakage impedance measured at the high and low voltage sides of the transformer. (author)

CNN a paradigm for complexity

CERN Document Server

Chua, Leon O

1998-01-01

Revolutionary and original, this treatise presents a new paradigm of EMERGENCE and COMPLEXITY, with applications drawn from numerous disciplines, including artificial life, biology, chemistry, computation, physics, image processing, information science, etc.CNN is an acronym for Cellular Neural Networks when used in the context of brain science, or Cellular Nonlinear Networks, when used in the context of emergence and complexity. A CNN is modeled by cells and interactions: cells are defined as dynamical systems and interactions are defined via coupling laws. The CNN paradigm is a universal Tur

Multi-agent: a technique to implement geo-visualization of networked virtual reality

Science.gov (United States)

Lin, Zhiyong; Li, Wenjing; Meng, Lingkui

2007-06-01

Networked Virtual Reality (NVR) is a system based on net connected and spatial information shared, whose demands cannot be fully meet by the existing architectures and application patterns of VR to some extent. In this paper, we propose a new architecture of NVR based on Multi-Agent framework. which includes the detailed definition of various agents and their functions and full description of the collaboration mechanism, Through the prototype system test with DEM Data and 3D Models Data, the advantages of Multi-Agent based Networked Virtual Reality System in terms of the data loading time, user response time and scene construction time etc. are verified. First, we introduce the characters of Networked Virtual Realty and the characters of Multi-Agent technique in Section 1. Then we give the architecture design of Networked Virtual Realty based on Multi-Agent in Section 2.The Section 2 content includes the rule of task division, the multi-agent architecture design to implement Networked Virtual Realty and the function of agents. Section 3 shows the prototype implementation according to the design. Finally, Section 4 discusses the benefits of using Multi-Agent to implement geovisualization of Networked Virtual Realty.

Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.

Science.gov (United States)

Chen, Chen; Tong, Hanghang; Xie, Lei; Ying, Lei; He, Qing

2017-08-01

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm Fascinate that can reveal unobserved dependencies with linear complexity. Moreover, we derive Fascinate-ZERO, an online variant of Fascinate that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.

Radio resource management scheme and outage analysis for network-assisted multi-hop D2D communications

Directory of Open Access Journals (Sweden)

Leila Melki

2016-11-01

Full Text Available In a cellular network it's very difficult to make spectrum resource more efficiently. Device-to-Device (D2D technology enables new service opportunities, and provides high throughput and reliable communication while reducing the base station load. For better total performance, short-range D2D links and cellular links share the same radio resource and the management of interference becomes a crucial task. Here we argue that single-hop D2D technology can be used to further improve cellular networks performance if the key D2D radio resource management algorithms are suitably extended to support multi-hop D2D communications. Aiming to establish a new paradigm for the analysis and design of multi-hop D2D communications, We propose a radio resource allocation for multi-hop D2D routes based on interference avoidance approach in LTE-A networks. On top of that, we investigate the outage probability of D2D communication. We first introduce a new definition of outage probability by considering the maximum distance to be allowable for single-hop transmission. Then we study and analyze the outage performance of a multi-hop D2D route. We derive the general closed form expression of outage probability of the multi-hop D2D routes. The results demonstrate that the D2D radio, sharing the same resources as the cellular network, provide higher capacity compared to pure cellular communication where all the data is transmitted through the base station. They also demonstrate that the new method of calculation of D2D multi hop outage probability has better performance than classical method defined in the literature.

Multi-scale computational model of three-dimensional hemodynamics within a deformable full-body arterial network

Energy Technology Data Exchange (ETDEWEB)

Xiao, Nan [Department of Bioengineering, Stanford University, Stanford, CA 94305 (United States); Department of Biomedical Engineering, King’s College London, London SE1 7EH (United Kingdom); Humphrey, Jay D. [Department of Biomedical Engineering, Yale University, New Haven, CT 06520 (United States); Figueroa, C. Alberto, E-mail: alberto.figueroa@kcl.ac.uk [Department of Biomedical Engineering, King’s College London, London SE1 7EH (United Kingdom)

2013-07-01

In this article, we present a computational multi-scale model of fully three-dimensional and unsteady hemodynamics within the primary large arteries in the human. Computed tomography image data from two different patients were used to reconstruct a nearly complete network of the major arteries from head to foot. A linearized coupled-momentum method for fluid–structure-interaction was used to describe vessel wall deformability and a multi-domain method for outflow boundary condition specification was used to account for the distal circulation. We demonstrated that physiologically realistic results can be obtained from the model by comparing simulated quantities such as regional blood flow, pressure and flow waveforms, and pulse wave velocities to known values in the literature. We also simulated the impact of age-related arterial stiffening on wave propagation phenomena by progressively increasing the stiffness of the central arteries and found that the predicted effects on pressure amplification and pulse wave velocity are in agreement with findings in the clinical literature. This work demonstrates the feasibility of three-dimensional techniques for simulating hemodynamics in a full-body compliant arterial network.

Network Coding on Heterogeneous Multi-Core Processors for Wireless Sensor Networks

Science.gov (United States)

Kim, Deokho; Park, Karam; Ro, Won W.

2011-01-01

While network coding is well known for its efficiency and usefulness in wireless sensor networks, the excessive costs associated with decoding computation and complexity still hinder its adoption into practical use. On the other hand, high-performance microprocessors with heterogeneous multi-cores would be used as processing nodes of the wireless sensor networks in the near future. To this end, this paper introduces an efficient network coding algorithm developed for the heterogenous multi-core processors. The proposed idea is fully tested on one of the currently available heterogeneous multi-core processors referred to as the Cell Broadband Engine. PMID:22164053

A multi-objective model for locating distribution centers in a supply chain network considering risk and inventory decisions

Directory of Open Access Journals (Sweden)

Sara Gharegozloo Hamedani

2013-04-01

Full Text Available This paper presents a multi-objective location problem in a three level supply chain network under uncertain environment considering inventory decisions. The proposed model of this paper considers uncertainty for different parameters including procurement, transportation costs, supply, demand and the capacity of various facilities. The proposed model presents a robust optimization model, which specifies locations of distribution centers to be opened, inventory control parameters (r, Q, and allocation of supply chain components, concurrently. The resulted mixed-integer nonlinear programming minimizes the expected total cost of such a supply chain network comprising location, procurement, transportation, holding, ordering, and shortage costs. The model also minimizes the variability of the total cost of relief chain and minimizes the financial risk or the probability of not meeting a certain budget. We use the ε-constraint method, which is a multi-objective technique with implicit trade-off information given, to solve the problem and using a couple of numerical instances, we examine the performance of the proposed approach.

Predictive modeling of coupled multi-physics systems: II. Illustrative application to reactor physics

International Nuclear Information System (INIS)

Cacuci, Dan Gabriel; Badea, Madalina Corina

2014-01-01

Highlights: • We applied the PMCMPS methodology to a paradigm neutron diffusion model. • We underscore the main steps in applying PMCMPS to treat very large coupled systems. • PMCMPS reduces the uncertainties in the optimally predicted responses and model parameters. • PMCMPS is for sequentially treating coupled systems that cannot be treated simultaneously. - Abstract: This work presents paradigm applications to reactor physics of the innovative mathematical methodology for “predictive modeling of coupled multi-physics systems (PMCMPS)” developed by Cacuci (2014). This methodology enables the assimilation of experimental and computational information and computes optimally predicted responses and model parameters with reduced predicted uncertainties, taking fully into account the coupling terms between the multi-physics systems, but using only the computational resources that would be needed to perform predictive modeling on each system separately. The paradigm examples presented in this work are based on a simple neutron diffusion model, chosen so as to enable closed-form solutions with clear physical interpretations. These paradigm examples also illustrate the computational efficiency of the PMCMPS, which enables the assimilation of additional experimental information, with a minimal increase in computational resources, to reduce the uncertainties in predicted responses and best-estimate values for uncertain model parameters, thus illustrating how very large systems can be treated without loss of information in a sequential rather than simultaneous manner

Designing multi-targeted agents: An emerging anticancer drug discovery paradigm.

Science.gov (United States)

Fu, Rong-Geng; Sun, Yuan; Sheng, Wen-Bing; Liao, Duan-Fang

2017-08-18

The dominant paradigm in drug discovery is to design ligands with maximum selectivity to act on individual drug targets. With the target-based approach, many new chemical entities have been discovered, developed, and further approved as drugs. However, there are a large number of complex diseases such as cancer that cannot be effectively treated or cured only with one medicine to modulate the biological function of a single target. As simultaneous intervention of two (or multiple) cancer progression relevant targets has shown improved therapeutic efficacy, the innovation of multi-targeted drugs has become a promising and prevailing research topic and numerous multi-targeted anticancer agents are currently at various developmental stages. However, most multi-pharmacophore scaffolds are usually discovered by serendipity or screening, while rational design by combining existing pharmacophore scaffolds remains an enormous challenge. In this review, four types of multi-pharmacophore modes are discussed, and the examples from literature will be used to introduce attractive lead compounds with the capability of simultaneously interfering with different enzyme or signaling pathway of cancer progression, which will reveal the trends and insights to help the design of the next generation multi-targeted anticancer agents. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow.

Science.gov (United States)

Zubek, Julian; Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems.

Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models

Science.gov (United States)

2015-03-03

based whole-cell models of E. coli [6]. Conversely , highly abstracted kinetic frameworks, such as the cybernetic framework, represented a paradigm shift...metabolic objective function has been the optimization of biomass formation [18], although other metabolic objectives have also been estimated [19...experimental data. Toward these questions, we explored five hypothetical cell-free networks. Each network shared the same enzymatic connectivity, but

Identifying the community structure of the food-trade international multi-network

Science.gov (United States)

Torreggiani, S.; Mangioni, G.; Puma, M. J.; Fagiolo, G.

2018-05-01

Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of international food trade networks are still poorly documented, especially from a multi-network perspective. In particular, nothing is known about the multi-network’s community structure. Here we find that the individual crop-specific layers of the multi-network have densely connected trading groups, a consistent characteristic over the period 2001–2011. Further, the multi-network is characterized by low variability over this period but with substantial heterogeneity across layers in each year. In particular, the layers are mostly assortative: more-intensively connected countries tend to import from and export to countries that are themselves more connected. We also fit econometric models to identify social, economic and geographic factors explaining the probability that any two countries are co-present in the same community. Our estimates indicate that the probability of country pairs belonging to the same food trade community depends more on geopolitical and economic factors—such as geographical proximity and trade-agreement co-membership—than on country economic size and/or income. These community-structure findings of the multi-network are especially valuable for efforts to understand past and emerging dynamics in the global food system, especially those that examine potential ‘shocks’ to global food trade.

Hazard interactions and interaction networks (cascades) within multi-hazard methodologies

Science.gov (United States)

Gill, Joel C.; Malamud, Bruce D.

2016-08-01

This paper combines research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between multi-layer single-hazard approaches and multi-hazard approaches that integrate such interactions. This synthesis suggests that ignoring interactions between important environmental and anthropogenic processes could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. In this paper we proceed to present an enhanced multi-hazard framework through the following steps: (i) description and definition of three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment, (ii) outlining of three types of interaction relationship (triggering, increased probability, and catalysis/impedance), and (iii) assessment of the importance of networks of interactions (cascades) through case study examples (based on the literature, field observations and semi-structured interviews). We further propose two visualisation frameworks to represent these networks of interactions: hazard interaction matrices and hazard/process flow diagrams. Our approach reinforces the importance of integrating interactions between different aspects of the Earth system, together with human activity, into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability

Multi-objective optimization in computer networks using metaheuristics

CERN Document Server

Donoso, Yezid

2007-01-01

Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...

9th KES Conference on Agent and Multi-Agent Systems : Technologies and Applications

CERN Document Server

Howlett, Robert; Jain, Lakhmi

2015-01-01

Agents and multi-agent systems are related to a modern software paradigm which has long been recognized as a promising technology for constructing autonomous, complex and intelligent systems. The topics covered in this volume include agent-oriented software engineering, agent co-operation, co-ordination, negotiation, organization and communication, distributed problem solving, specification of agent communication languages, agent privacy, safety and security, formalization of ontologies and conversational agents. The volume highlights new trends and challenges in agent and multi-agent research and includes 38 papers classified in the following specific topics: learning paradigms, agent-based modeling and simulation, business model innovation and disruptive technologies, anthropic-oriented computing, serious games and business intelligence, design and implementation of intelligent agents and multi-agent systems, digital economy, and advances in networked virtual enterprises. Published p...

Vehicular Networking Enhancement And Multi-Channel Routing Optimization, Based on Multi-Objective Metric and Minimum Spanning Tree

Directory of Open Access Journals (Sweden)

Peppino Fazio

2013-01-01

Full Text Available Vehicular Ad hoc NETworks (VANETs represent a particular mobile technology that permits the communication among vehicles, offering security and comfort. Nowadays, distributed mobile wireless computing is becoming a very important communications paradigm, due to its flexibility to adapt to different mobile applications. VANETs are a practical example of data exchanging among real mobile nodes. To enable communications within an ad-hoc network, characterized by continuous node movements, routing protocols are needed to react to frequent changes in network topology. In this paper, the attention is focused mainly on the network layer of VANETs, proposing a novel approach to reduce the interference level during mobile transmission, based on the multi-channel nature of IEEE 802.11p (1609.4 standard. In this work a new routing protocol based on Distance Vector algorithm is presented to reduce the delay end to end and to increase packet delivery ratio (PDR and throughput in VANETs. A new metric is also proposed, based on the maximization of the average Signal-to-Interference Ratio (SIR level and the link duration probability between two VANET nodes. In order to relieve the effects of the co-channel interference perceived by mobile nodes, transmission channels are switched on a basis of a periodical SIR evaluation. A Network Simulator has been used for implementing and testing the proposed idea.

Poster: A Software-Defined Multi-Camera Network

OpenAIRE

Chen, Po-Yen; Chen, Chien; Selvaraj, Parthiban; Claesen, Luc

2016-01-01

The widespread popularity of OpenFlow leads to a significant increase in the number of applications developed in SoftwareDefined Networking (SDN). In this work, we propose the architecture of a Software-Defined Multi-Camera Network consisting of small, flexible, economic, and programmable cameras which combine the functions of the processor, switch, and camera. A Software-Defined Multi-Camera Network can effectively reduce the overall network bandwidth and reduce a large amount of the Capex a...

Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms

International Nuclear Information System (INIS)

Atashkari, K.; Nariman-Zadeh, N.; Goelcue, M.; Khalkhali, A.; Jamali, A.

2007-01-01

The main reason for the efficiency decrease at part load conditions for four-stroke spark-ignition (SI) engines is the flow restriction at the cross-sectional area of the intake system. Traditionally, valve-timing has been designed to optimize operation at high engine-speed and wide open throttle conditions. Several investigations have demonstrated that improvements at part load conditions in engine performance can be accomplished if the valve-timing is variable. Controlling valve-timing can be used to improve the torque and power curve as well as to reduce fuel consumption and emissions. In this paper, a group method of data handling (GMDH) type neural network and evolutionary algorithms (EAs) are firstly used for modelling the effects of intake valve-timing (V t ) and engine speed (N) of a spark-ignition engine on both developed engine torque (T) and fuel consumption (Fc) using some experimentally obtained training and test data. Using such obtained polynomial neural network models, a multi-objective EA (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are secondly used for Pareto based optimization of the variable valve-timing engine considering two conflicting objectives such as torque (T) and fuel consumption (Fc). The comparison results demonstrate the superiority of the GMDH type models over feedforward neural network models in terms of the statistical measures in the training data, testing data and the number of hidden neurons. Further, it is shown that some interesting and important relationships, as useful optimal design principles, involved in the performance of the variable valve-timing four-stroke spark-ignition engine can be discovered by the Pareto based multi-objective optimization of the polynomial models. Such important optimal principles would not have been obtained without the use of both the GMDH type neural network modelling and the multi-objective Pareto optimization approach

Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

International Nuclear Information System (INIS)

Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

2015-01-01

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug–target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively

Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

Science.gov (United States)

Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

2015-11-01

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively

Cyber threat model for tactical radio networks

Science.gov (United States)

Kurdziel, Michael T.

2014-05-01

The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.

Emerging Paradigms in Machine Learning

CERN Document Server

Jain, Lakhmi; Howlett, Robert

2013-01-01

This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary ...

A Mathematical Model to Improve the Performance of Logistics Network

Directory of Open Access Journals (Sweden)

Muhammad Izman Herdiansyah

2012-01-01

Full Text Available The role of logistics nowadays is expanding from just providing transportation and warehousing to offering total integrated logistics. To remain competitive in the global market environment, business enterprises need to improve their logistics operations performance. The improvement will be achieved when we can provide a comprehensive analysis and optimize its network performances. In this paper, a mixed integer linier model for optimizing logistics network performance is developed. It provides a single-product multi-period multi-facilities model, as well as the multi-product concept. The problem is modeled in form of a network flow problem with the main objective to minimize total logistics cost. The problem can be solved using commercial linear programming package like CPLEX or LINDO. Even in small case, the solver in Excel may also be used to solve such model.Keywords: logistics network, integrated model, mathematical programming, network optimization

Multi-Resolution Multimedia QoE Models for IPTV Applications

Directory of Open Access Journals (Sweden)

Prasad Calyam

2012-01-01

Full Text Available Internet television (IPTV is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to proactively deliver optimum user quality of experience (QoE for IPTV, service providers need to identify network bottlenecks in real time. In this paper, we develop psycho-acoustic-visual models that can predict user QoE of multimedia applications in real time based on online network status measurements. Our models are neural network based and cater to multi-resolution IPTV applications that include QCIF, QVGA, SD, and HD resolutions encoded using popular audio and video codec combinations. On the network side, our models account for jitter and loss levels, as well as router queuing disciplines: packet-ordered and time-ordered FIFO. We evaluate the performance of our multi-resolution multimedia QoE models in terms of prediction characteristics, accuracy, speed, and consistency. Our evaluation results demonstrate that the models are pertinent for real-time QoE monitoring and resource adaptation in IPTV content delivery networks.

Stochastic geometry model for multi-channel fog radio access networks

KAUST Repository

Emara, Mostafa

2017-06-29

Cache-enabled base station (BS) densification, denoted as a fog radio access network (F-RAN), is foreseen as a key component of 5G cellular networks. F-RAN enables storing popular files at the network edge (i.e., BS caches), which empowers local communication and alleviates traffic congestions at the core/backhaul network. The hitting probability, which is the probability of successfully transmitting popular files request from the network edge, is a fundamental key performance indicator (KPI) for F-RAN. This paper develops a scheduling aware mathematical framework, based on stochastic geometry, to characterize the hitting probability of F-RAN in a multi-channel environment. To this end, we assess and compare the performance of two caching distribution schemes, namely, uniform caching and Zipf caching. The numerical results show that the commonly used single channel environment leads to pessimistic assessment for the hitting probability of F-RAN. Furthermore, the numerical results manifest the superiority of the Zipf caching scheme and quantify the hitting probability gains in terms of the number of channels and cache size.

Comparing the OpenMP, MPI, and Hybrid Programming Paradigm on an SMP Cluster

Science.gov (United States)

Jost, Gabriele; Jin, Hao-Qiang; anMey, Dieter; Hatay, Ferhat F.

2003-01-01

Clusters of SMP (Symmetric Multi-Processors) nodes provide support for a wide range of parallel programming paradigms. The shared address space within each node is suitable for OpenMP parallelization. Message passing can be employed within and across the nodes of a cluster. Multiple levels of parallelism can be achieved by combining message passing and OpenMP parallelization. Which programming paradigm is the best will depend on the nature of the given problem, the hardware components of the cluster, the network, and the available software. In this study we compare the performance of different implementations of the same CFD benchmark application, using the same numerical algorithm but employing different programming paradigms.

Global forward-predicting dynamic routing for traffic concurrency space stereo multi-layer scale-free network

International Nuclear Information System (INIS)

Xie Wei-Hao; Zhou Bin; Liu En-Xiao; Lu Wei-Dang; Zhou Ting

2015-01-01

Many real communication networks, such as oceanic monitoring network and land environment observation network, can be described as space stereo multi-layer structure, and the traffic in these networks is concurrent. Understanding how traffic dynamics depend on these real communication networks and finding an effective routing strategy that can fit the circumstance of traffic concurrency and enhance the network performance are necessary. In this light, we propose a traffic model for space stereo multi-layer complex network and introduce two kinds of global forward-predicting dynamic routing strategies, global forward-predicting hybrid minimum queue (HMQ) routing strategy and global forward-predicting hybrid minimum degree and queue (HMDQ) routing strategy, for traffic concurrency space stereo multi-layer scale-free networks. By applying forward-predicting strategy, the proposed routing strategies achieve better performances in traffic concurrency space stereo multi-layer scale-free networks. Compared with the efficient routing strategy and global dynamic routing strategy, HMDQ and HMQ routing strategies can optimize the traffic distribution, alleviate the number of congested packets effectively and reach much higher network capacity. (paper)

Critical evaluation of paradigms for modelling integrated supply chains

NARCIS (Netherlands)

Van Dam, K.H.; Adhitya, A.; Srinivasan, R.; Lukszo, Z.

2009-01-01

Contemporary problems in process systems engineering often require model-based decision support tool. Among the various modelling paradigms, equation-based models and agent-based models are widely used to develop dynamic models of systems. Which is the most appropriate modelling paradigm for a

Proof-of-Concept System for Opportunistic Spectrum Access in Multi-user Decentralized Networks

Directory of Open Access Journals (Sweden)

Sumit J. Darak

2016-09-01

Full Text Available Poor utilization of an electromagnetic spectrum and ever increasing demand for spectrum have led to surge of interests in opportunistic spectrum access (OSA based paradigms like cognitive radio and unlicensed LTE. In OSA for decentralized networks, frequency band selection from wideband spectrum is a challenging task since secondary users (SUs do not share any information with each other. In this paper, a new decision making policy (DMP has been proposed for OSA in the multi-user decentralized networks. First contribution is an accurate characterization of frequency bands using Bayes-UCB algorithm. Then, a novel SU orthogonization scheme using Bayes-UCB algorithm is proposed replacing randomization based scheme. At the end, USRP testbed has been developed for analyzing the performance of DMPs using real radio signals. Experimental results show that the proposed DMP offers significant improvement in spectrum utilization, fewer subband switching and collisions compared to other DMPs.

Computationally Efficient Transient Stability Modeling of multi-terminal VSC-HVDC

DEFF Research Database (Denmark)

van der Meer, Arjen A; Rueda-Torres, José; Silva, Filipe Miguel Faria da

2016-01-01

This paper studies the inclusion of averaged VSC-based grid interfaces and HVDC networks into stability type simulations, and compares the accuracy and speed of three multi-terminal DC dynamic models: 1) a state-space based model, 2) a multi-rate improved model, and 3) a reduced-order model...

Competitive dynamics of lexical innovations in multi-layer networks

Science.gov (United States)

Javarone, Marco Alberto

2014-04-01

We study the introduction of lexical innovations into a community of language users. Lexical innovations, i.e. new term added to people's vocabulary, plays an important role in the process of language evolution. Nowadays, information is spread through a variety of networks, including, among others, online and offline social networks and the World Wide Web. The entire system, comprising networks of different nature, can be represented as a multi-layer network. In this context, lexical innovations diffusion occurs in a peculiar fashion. In particular, a lexical innovation can undergo three different processes: its original meaning is accepted; its meaning can be changed or misunderstood (e.g. when not properly explained), hence more than one meaning can emerge in the population. Lastly, in the case of a loan word, it can be translated into the population language (i.e. defining a new lexical innovation or using a synonym) or into a dialect spoken by part of the population. Therefore, lexical innovations cannot be considered simply as information. We develop a model for analyzing this scenario using a multi-layer network comprising a social network and a media network. The latter represents the set of all information systems of a society, e.g. television, the World Wide Web and radio. Furthermore, we identify temporal directed edges between the nodes of these two networks. In particular, at each time-step, nodes of the media network can be connected to randomly chosen nodes of the social network and vice versa. In doing so, information spreads through the whole system and people can share a lexical innovation with their neighbors or, in the event they work as reporters, by using media nodes. Lastly, we use the concept of "linguistic sign" to model lexical innovations, showing its fundamental role in the study of these dynamics. Many numerical simulations have been performed to analyze the proposed model and its outcomes.

Cluster imaging of multi-brain networks (CIMBN: a general framework for hyperscanning and modeling a group of interacting brains

Directory of Open Access Journals (Sweden)

Lian eDuan

2015-07-01

Full Text Available Studying the neural basis of human social interactions is a key topic in the field of social neuroscience. Brain imaging studies in this field usually focus on the neural correlates of the social interactions between two participants. However, as the participant number further increases, even by a small amount, great difficulties raise. One challenge is how to concurrently scan all the interacting brains with high ecological validity, especially for a large number of participants. The other challenge is how to effectively model the complex group interaction behaviors emerging from the intricate neural information exchange among a group of socially organized people. Confronting these challenges, we propose a new approach called Cluster Imaging of Multi-brain Networks (CIMBN. CIMBN consists of two parts. The first part is a cluster imaging technique with high ecological validity based on multiple functional near-infrared spectroscopy (fNIRS systems. Using this technique, we can easily extend the simultaneous imaging capacity of social neuroscience studies up to dozens of participants. The second part of CIMBN is a multi-brain network (MBN modeling method based on graph theory. By taking each brain as a network node and the relationship between any two brains as a network edge, one can construct a network model for a group of interacting brains. The emergent group social behaviors can then be studied using the network’s properties, such as its topological structure and information exchange efficiency. Although there is still much work to do, as a general framework for hyperscanning and modeling a group of interacting brains, CIMBN can provide new insights into the neural correlates of group social interactions, and advance social neuroscience and social psychology.

Multi-year expansion planning of large transmission networks

Energy Technology Data Exchange (ETDEWEB)

Binato, S; Oliveira, G C [Centro de Pesquisas de Energia Eletrica (CEPEL), Rio de Janeiro, RJ (Brazil)

1994-12-31

This paper describes a model for multi-year transmission network expansion to be used in long-term system planning. The network is represented by a linearized (DC) power flow and, for each year, operation costs are evaluated by a linear programming (LP) based algorithm that provides sensitivity indices for circuit reinforcements. A Backward/Forward approaches is proposed to devise an expansion plan over the study period. A case study with the southeastern Brazilian system is presented and discussed. (author) 18 refs., 5 figs., 1 tab.

Energy-aware architecture for multi-rate ad hoc networks

Directory of Open Access Journals (Sweden)

Ahmed Yahya

2010-06-01

Full Text Available The backbone of ad hoc network design is energy performance and bandwidth resources limitations. Multi-rate adaptation architectures have been proposed to reduce the control overhead and to increase bandwidth utilization efficiency. In this paper, we propose a multi-rate protocol to provide the highest network performance under very low control overhead. The efficiency of the proposed auto multi-rate protocol is validated extensive simulations using QualNet network simulator. The simulation results demonstrate that our solution significantly improves the overall network performance.

Flexible Design for α-Duplex Communications in Multi-Tier Cellular Networks

KAUST Repository

Alammouri, Ahmad; Elsawy, Hesham; Alouini, Mohamed-Slim

2016-01-01

the foreseen FD gains. This paper presents flexible and tractable modeling framework for multi-tier cellular networks with FD BSs and FD/HD UEs. The presented model is based on stochastic geometry and accounts for the intrinsic vulnerability of uplink

Collaborative multi-layer network coding for cellular cognitive radio networks

KAUST Repository

Sorour, Sameh

2013-06-01

In this paper, we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in underlay cellular cognitive radio networks. This scheme allows the collocated primary and cognitive radio base-stations to collaborate with each other, in order to minimize their own and each other\\'s packet recovery overheads, and thus improve their throughput, without any coordination between them. This non-coordinated collaboration is done using a novel multi-layer instantly decodable network coding scheme, which guarantees that each network\\'s help to the other network does not result in any degradation in its own performance. It also does not cause any violation to the primary networks interference thresholds in the same and adjacent cells. Yet, our proposed scheme both guarantees the reduction of the recovery overhead in collocated primary and cognitive radio networks, and allows early recovery of their packets compared to non-collaborative schemes. Simulation results show that a recovery overhead reduction of 15% and 40% can be achieved by our proposed scheme in the primary and cognitive radio networks, respectively, compared to the corresponding non-collaborative scheme. © 2013 IEEE.

The juggling paradigm: a novel social neuroscience approach to identify neuropsychophysiological markers of team mental models.

Science.gov (United States)

Filho, Edson; Bertollo, Maurizio; Robazza, Claudio; Comani, Silvia

2015-01-01

Since the discovery of the mirror neuron system in the 1980s, little, if any, research has been devoted to the study of interactive motor tasks (Goldman, 2012). Scientists interested in the neuropsychophysiological markers of joint motor action have relied on observation paradigms and passive tasks rather than dynamic paradigms and interactive tasks (Konvalinka and Roepstorff, 2012). Within this research scenario, we introduce a novel research paradigm that uses cooperative juggling as a platform to capture peripheral (e.g., skin conductance, breathing and heart rates, electromyographic signals) and central neuropsychophysiological (e.g., functional connectivity within and between brains) markers underlying the notion of team mental models (TMM). We discuss the epistemological and theoretical grounds of a cooperative juggling paradigm, and propose testable hypotheses on neuropsychophysiological markers underlying TMM. Furthermore, we present key methodological concerns that may influence peripheral responses as well as single and hyperbrain network configurations during joint motor action. Preliminary findings of the paradigm are highlighted. We conclude by delineating avenues for future research.

Research on the model of home networking

Science.gov (United States)

Yun, Xiang; Feng, Xiancheng

2007-11-01

It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network

Unified Multi-Layer among Software Defined Multi-Domain Optical Networks (Invited

Directory of Open Access Journals (Sweden)

Hui Yang

2015-06-01

Full Text Available The software defined networking (SDN enabled by OpenFlow protocol has gained popularity which can enable the network to be programmable and accommodate both fixed and flexible bandwidth services. In this paper, we present a unified multi-layer (UML architecture with multiple controllers and a dynamic orchestra plane (DOP for software defined multi-domain optical networks. The proposed architecture can shield the differences among various optical devices from multi-vendors and the details of connecting heterogeneous networks. The cross-domain services with on-demand bandwidth can be deployed via unified interfaces provided by the dynamic orchestra plane. Additionally, the globalization strategy and practical capture of signal processing are presented based on the architecture. The overall feasibility and efficiency of the proposed architecture is experimentally verified on the control plane of our OpenFlow-based testbed. The performance of globalization strategy under heavy traffic load scenario is also quantitatively evaluated based on UML architecture compared with other strategies in terms of blocking probability, average hops, and average resource consumption.

Multi-Domain SDN Survivability for Agricultural Wireless Sensor Networks.

Science.gov (United States)

Huang, Tao; Yan, Siyu; Yang, Fan; Liu, Jiang

2016-11-06

Wireless sensor networks (WSNs) have been widely applied in agriculture field; meanwhile, the advent of multi-domain software-defined networks (SDNs) have improved the wireless resource utilization rate and strengthened network management. In recent times, multi-domain SDNs have been applied to agricultural sensor networks, namely multi-domain software-defined wireless sensor networks (SDWSNs). However, when the SDNs controlling agriculture networks suddenly become unavailable, whether intra-domain or inter-domain, sensor network communication is abnormal because of the loss of control. Moreover, there are controller and switch info-updating problems even if the controller becomes available again. To resolve these problems, this paper proposes a new approach based on an Open vSwitch extension for multi-domain SDWSNs, which can enhance agriculture network survivability and stability. We achieved this by designing a connection-state mechanism, a communication mechanism on both L2 and L3, and an info-updating mechanism based on Open vSwitch. The experimental results show that, whether it is agricultural inter-domain or intra-domain during the controller failure period, the sensor switches can enter failure recovery mode as soon as possible so that the sensor network keeps a stable throughput, a short failure recovery time below 300 ms, and low packet loss. Further, the domain can smoothly control the domain network again once the controller becomes available. This approach based on an Open vSwitch extension can enhance the survivability and stability of multi-domain SDWSNs in precision agriculture.

Multi-Domain SDN Survivability for Agricultural Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Tao Huang

2016-11-01

Full Text Available Wireless sensor networks (WSNs have been widely applied in agriculture field; meanwhile, the advent of multi-domain software-defined networks (SDNs have improved the wireless resource utilization rate and strengthened network management. In recent times, multi-domain SDNs have been applied to agricultural sensor networks, namely multi-domain software-defined wireless sensor networks (SDWSNs. However, when the SDNs controlling agriculture networks suddenly become unavailable, whether intra-domain or inter-domain, sensor network communication is abnormal because of the loss of control. Moreover, there are controller and switch info-updating problems even if the controller becomes available again. To resolve these problems, this paper proposes a new approach based on an Open vSwitch extension for multi-domain SDWSNs, which can enhance agriculture network survivability and stability. We achieved this by designing a connection-state mechanism, a communication mechanism on both L2 and L3, and an info-updating mechanism based on Open vSwitch. The experimental results show that, whether it is agricultural inter-domain or intra-domain during the controller failure period, the sensor switches can enter failure recovery mode as soon as possible so that the sensor network keeps a stable throughput, a short failure recovery time below 300 ms, and low packet loss. Further, the domain can smoothly control the domain network again once the controller becomes available. This approach based on an Open vSwitch extension can enhance the survivability and stability of multi-domain SDWSNs in precision agriculture.

Healthy full-term infants' brain responses to emotionally and linguistically relevant sounds using a multi-feature mismatch negativity (MMN) paradigm.

Science.gov (United States)

Kostilainen, Kaisamari; Wikström, Valtteri; Pakarinen, Satu; Videman, Mari; Karlsson, Linnea; Keskinen, Maria; Scheinin, Noora M; Karlsson, Hasse; Huotilainen, Minna

2018-03-23

We evaluated the feasibility of a multi-feature mismatch negativity (MMN) paradigm in studying auditory processing of healthy newborns. The aim was to examine the automatic change-detection and processing of semantic and emotional information in speech in newborns. Brain responses of 202 healthy newborns were recorded with a multi-feature paradigm including a Finnish bi-syllabic pseudo-word/ta-ta/as a standard stimulus, six linguistically relevant deviant stimuli and three emotionally relevant stimuli (happy, sad, angry). Clear responses to emotional sounds were found already at the early latency window 100-200 ms, whereas responses to linguistically relevant minor changes and emotional stimuli at the later latency window 300-500 ms did not reach significance. Moreover, significant interaction between gender and emotional stimuli was found in the early latency window. Further studies on using multi-feature paradigms with linguistic and emotional stimuli in newborns are needed, especially those containing of follow-ups, enabling the assessment of the predictive value of early variations between subjects. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Optimal Traffic Allocation for Multi-Stream Aggregation in Heterogeneous Networks

DEFF Research Database (Denmark)

Popovska Avramova, Andrijana; Iversen, Villy Bæk

2015-01-01

nature of radio access networks are considered as important factors for performance improvement by multi-stream aggregation. Therefore, in our model, the networks are represented by different queueing systems in order to indicate networks with opposite quality of service provisioning, capacity and delay...... variations. Furthermore, services with different traffic characteristics in terms of quality of service requirements are considered. The simulation results show the advantages of our scheme with respect to efficient increase in data rate and delay performance compared to traditional schemes....

Target-Centric Network Modeling

DEFF Research Database (Denmark)

Mitchell, Dr. William L.; Clark, Dr. Robert M.

In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence...... reporting formats, along with a tested process that facilitates the production of a wide range of analytical products for civilian, military, and hybrid intelligence environments. Readers will learn how to perform the specific actions of problem definition modeling, target network modeling......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.

Science.gov (United States)

Jang, Yeongjun; Yu, Namhee; Seo, Jihae; Kim, Sun; Lee, Sanghyuk

2016-03-18

Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .

Multi-Hop Link Capacity of Multi-Route Multi-Hop MRC Diversity for a Virtual Cellular Network

Science.gov (United States)

Daou, Imane; Kudoh, Eisuke; Adachi, Fumiyuki

In virtual cellular network (VCN), proposed for high-speed mobile communications, the signal transmitted from a mobile terminal is received by some wireless ports distributed in each virtual cell and relayed to the central port that acts as a gateway to the core network. In this paper, we apply the multi-route MHMRC diversity in order to decrease the transmit power and increase the multi-hop link capacity. The transmit power, the interference power and the link capacity are evaluated for DS-CDMA multi-hop VCN by computer simulation. The multi-route MHMRC diversity can be applied to not only DS-CDMA but also other access schemes (i. e. MC-CDMA, OFDM, etc.).

On stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control

KAUST Repository

Elsawy, Hesham; Hossain, Ekram

2014-01-01

Using stochastic geometry, we develop a tractable uplink modeling paradigm for outage probability and spectral efficiency in both single and multi-tier cellular wireless networks. The analysis accounts for per user equipment (UE) power control

Agent-based modeling of the energy network for hybrid cars

International Nuclear Information System (INIS)

Gonzalez de Durana, José María; Barambones, Oscar; Kremers, Enrique; Varga, Liz

2015-01-01

Highlights: • An approach to represent and calculate multicarrier energy networks has been developed. • It provides a modeling method based on agents, for multicarrier energy networks. • It allows the system representation on a single sheet. • Energy flows circulating in the system can be observed dynamically during simulation. • The method is technology independent. - Abstract: Studies in complex energy networks devoted to the modeling of electrical power grids, were extended in previous work, where a computational multi-layered ontology, implemented using agent-based methods, was adopted. This structure is compatible with recently introduced Multiplex Networks which using Multi-linear Algebra generalize some of classical results for single-layer networks, to multilayer networks in steady state. Static results do not assist overly in understanding dynamic networks in which the values of the variables in the nodes and edges can change suddenly, driven by events, and even where new nodes or edges may appear or disappear, also because of other events. To address this gap, a computational agent-based model is developed to extend the multi-layer and multiplex approaches. In order to demonstrate the benefits of a dynamical extension, a model of the energy network in a hybrid car is presented as a case study

Modeling GMPLS and Optical MPLS Networks

DEFF Research Database (Denmark)

Christiansen, Henrik Lehrmann; Wessing, Henrik

2003-01-01

. The MPLS concept is attractive because it can work as a unifying control structure. covering all technologies. This paper describes how a novel scheme for optical MPLS and circuit switched GMPLS based networks can incorporated in such multi-domain, MPLS-based scenarios and how it could be modeled. Network...

The emerging paradigm of network medicine in the study of human disease.

Science.gov (United States)

Chan, Stephen Y; Loscalzo, Joseph

2012-07-20

The molecular pathways that govern human disease consist of molecular circuits that coalesce into complex, overlapping networks. These network pathways are presumably regulated in a coordinated fashion, but such regulation has been difficult to decipher using only reductionistic principles. The emerging paradigm of "network medicine" proposes to utilize insights garnered from network topology (eg, the static position of molecules in relation to their neighbors) as well as network dynamics (eg, the unique flux of information through the network) to understand better the pathogenic behavior of complex molecular interconnections that traditional methods fail to recognize. As methodologies evolve, network medicine has the potential to capture the molecular complexity of human disease while offering computational methods to discern how such complexity controls disease manifestations, prognosis, and therapy. This review introduces the fundamental concepts of network medicine and explores the feasibility and potential impact of network-based methods for predicting individual manifestations of human disease and designing rational therapies. Wherever possible, we emphasize the application of these principles to cardiovascular disease.

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

KAUST Repository

Cannistraci, C.V.; Alanis-Lobato, G.; Ravasi, Timothy

2013-01-01

for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems

Computer-Supported Modelling of Multi modal Transportation Networks Rationalization

Directory of Open Access Journals (Sweden)

Ratko Zelenika

2007-09-01

Full Text Available This paper deals with issues of shaping and functioning ofcomputer programs in the modelling and solving of multimoda Itransportation network problems. A methodology of an integrateduse of a programming language for mathematical modellingis defined, as well as spreadsheets for the solving of complexmultimodal transportation network problems. The papercontains a comparison of the partial and integral methods ofsolving multimodal transportation networks. The basic hypothesisset forth in this paper is that the integral method results inbetter multimodal transportation network rationalization effects,whereas a multimodal transportation network modelbased on the integral method, once built, can be used as the basisfor all kinds of transportation problems within multimodaltransport. As opposed to linear transport problems, multimodaltransport network can assume very complex shapes. This papercontains a comparison of the partial and integral approach totransp01tation network solving. In the partial approach, astraightforward model of a transp01tation network, which canbe solved through the use of the Solver computer tool within theExcel spreadsheet inteiface, is quite sufficient. In the solving ofa multimodal transportation problem through the integralmethod, it is necessmy to apply sophisticated mathematicalmodelling programming languages which supp01t the use ofcomplex matrix functions and the processing of a vast amountof variables and limitations. The LINGO programming languageis more abstract than the Excel spreadsheet, and it requiresa certain programming knowledge. The definition andpresentation of a problem logic within Excel, in a manner whichis acceptable to computer software, is an ideal basis for modellingin the LINGO programming language, as well as a fasterand more effective implementation of the mathematical model.This paper provides proof for the fact that it is more rational tosolve the problem of multimodal transportation networks by

The juggling paradigm: a novel social neuroscience approach to identify neuropsychophysiological markers of team mental models

Directory of Open Access Journals (Sweden)

Edson eFilho

2015-06-01

Full Text Available Since the discovery of the mirror neuron system in the 1990s, little, if any, research has been devoted to the study of interactive motor tasks (Goldman, 2012. Scientists interested in the neuropsychophysiological markers of joint motor action have relied on observation paradigms and passive tasks rather than dynamic paradigms and interactive tasks (Konvalinka and Roepstorff, 2012. Within this research scenario, we introduce a novel research paradigm that uses cooperative juggling as a platform to capture peripheral (e.g., skin conductance, breathing and heart rates, electromyographic signals and central neuropsychophysiological (e.g., functional connectivity within and between brains markers underlying the conceptual notion of team mental models (TMM. We discuss the epistemological and theoretical grounds of a cooperative juggling paradigm, and propose testable hypotheses on neuropsychophysiological markers underlying TMM. Furthermore, we present key methodological concerns that may influence peripheral responses as well as single and hyperbrain network configurations during joint motor action. Preliminary findings of the paradigm are highlighted. We conclude by delineating avenues for future research.

Using a multi-feature paradigm to measure mismatch responses to minimal sound contrasts in children with cochlear implants and hearing aids.

Science.gov (United States)

Uhlén, Inger; Engström, Elisabet; Kallioinen, Petter; Nakeva von Mentzer, Cecilia; Lyxell, Björn; Sahlén, Birgitta; Lindgren, Magnus; Ors, Marianne

2017-10-01

Our aim was to explore whether a multi-feature paradigm (Optimum-1) for eliciting mismatch negativity (MMN) would objectively capture difficulties in perceiving small sound contrasts in children with hearing impairment (HI) listening through their hearing aids (HAs) and/or cochlear implants (CIs). Children aged 5-7 years with HAs, CIs and children with normal hearing (NH) were tested in a free-field setting using a multi-feature paradigm with deviations in pitch, intensity, gap, duration, and location. There were significant mismatch responses across all subjects that were positive (p-MMR) for the gap and pitch deviants (F(1,43) = 5.17, p = 0.028 and F(1,43) = 6.56, p = 0.014, respectively) and negative (MMN) for the duration deviant (F(1,43) = 4.74, p = 0.035). Only the intensity deviant showed a significant group interaction with MMN in the HA group and p-MMR in the CI group (F(2,43) = 3.40, p = 0.043). The p-MMR correlated negatively with age, with the strongest correlation in the NH subjects. In the CI group, the late discriminative negativity (LDN) was replaced by a late positivity with a significant group interaction for the location deviant. Children with severe HI can be assessed through their hearing device with a fast multi-feature paradigm. For further studies a multi-feature paradigm including more complex speech sounds may better capture variation in auditory processing in these children. © 2017 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Unified Stochastic Geometry Model for MIMO Cellular Networks with Retransmissions

KAUST Repository

Afify, Laila H.

2016-10-11

This paper presents a unified mathematical paradigm, based on stochastic geometry, for downlink cellular networks with multiple-input-multiple-output (MIMO) base stations (BSs). The developed paradigm accounts for signal retransmission upon decoding errors, in which the temporal correlation among the signal-to-interference-plus-noise-ratio (SINR) of the original and retransmitted signals is captured. In addition to modeling the effect of retransmission on the network performance, the developed mathematical model presents twofold analysis unification for MIMO cellular networks literature. First, it integrates the tangible decoding error probability and the abstracted (i.e., modulation scheme and receiver type agnostic) outage probability analysis, which are largely disjoint in the literature. Second, it unifies the analysis for different MIMO configurations. The unified MIMO analysis is achieved by abstracting unnecessary information conveyed within the interfering signals by Gaussian signaling approximation along with an equivalent SISO representation for the per-data stream SINR in MIMO cellular networks. We show that the proposed unification simplifies the analysis without sacrificing the model accuracy. To this end, we discuss the diversity-multiplexing tradeoff imposed by different MIMO schemes and shed light on the diversity loss due to the temporal correlation among the SINRs of the original and retransmitted signals. Finally, several design insights are highlighted.

Unified Stochastic Geometry Model for MIMO Cellular Networks with Retransmissions

KAUST Repository

Afify, Laila H.; Elsawy, Hesham; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2016-01-01

This paper presents a unified mathematical paradigm, based on stochastic geometry, for downlink cellular networks with multiple-input-multiple-output (MIMO) base stations (BSs). The developed paradigm accounts for signal retransmission upon decoding errors, in which the temporal correlation among the signal-to-interference-plus-noise-ratio (SINR) of the original and retransmitted signals is captured. In addition to modeling the effect of retransmission on the network performance, the developed mathematical model presents twofold analysis unification for MIMO cellular networks literature. First, it integrates the tangible decoding error probability and the abstracted (i.e., modulation scheme and receiver type agnostic) outage probability analysis, which are largely disjoint in the literature. Second, it unifies the analysis for different MIMO configurations. The unified MIMO analysis is achieved by abstracting unnecessary information conveyed within the interfering signals by Gaussian signaling approximation along with an equivalent SISO representation for the per-data stream SINR in MIMO cellular networks. We show that the proposed unification simplifies the analysis without sacrificing the model accuracy. To this end, we discuss the diversity-multiplexing tradeoff imposed by different MIMO schemes and shed light on the diversity loss due to the temporal correlation among the SINRs of the original and retransmitted signals. Finally, several design insights are highlighted.

Collaborative Multi-Layer Network Coding in Hybrid Cellular Cognitive Radio Networks

KAUST Repository

Moubayed, Abdallah J.; Sorour, Sameh; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2015-01-01

In this paper, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated

Collaborative Multi-Layer Network Coding For Hybrid Cellular Cognitive Radio Networks

KAUST Repository

Moubayed, Abdallah J.

2014-01-01

In this thesis, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated

Multi-focus and multi-level techniques for visualization and analysis of networks with thematic data

Science.gov (United States)

Cossalter, Michele; Mengshoel, Ole J.; Selker, Ted

2013-01-01

Information-rich data sets bring several challenges in the areas of visualization and analysis, even when associated with node-link network visualizations. This paper presents an integration of multi-focus and multi-level techniques that enable interactive, multi-step comparisons in node-link networks. We describe NetEx, a visualization tool that enables users to simultaneously explore different parts of a network and its thematic data, such as time series or conditional probability tables. NetEx, implemented as a Cytoscape plug-in, has been applied to the analysis of electrical power networks, Bayesian networks, and the Enron e-mail repository. In this paper we briefly discuss visualization and analysis of the Enron social network, but focus on data from an electrical power network. Specifically, we demonstrate how NetEx supports the analytical task of electrical power system fault diagnosis. Results from a user study with 25 subjects suggest that NetEx enables more accurate isolation of complex faults compared to an especially designed software tool.

Design considerations for energy efficient, resilient, multi-layer networks

DEFF Research Database (Denmark)

Fagertun, Anna Manolova; Hansen, Line Pyndt; Ruepp, Sarah Renée

2016-01-01

measures. In this complex problem, considerations such as client traffic granularity, applied grooming policies and multi-layer resiliency add even more complexity. A commercially available network planning tool is used to investigate the interplay between different methods for resilient capacity planning......This work investigates different network design considerations with respect to energy-efficiency, under green-field resilient multi-layer network deployment. The problem of energy efficient, reliable multi-layer network design is known to result in different trade-offs between key performance....... Switching off low-utilized transport links has been investigated via a pro-active re-routing applied during the network planning. Our analysis shows that design factors such as the applied survivability strategy and the applied planning method have higher impact on the key performance indicators compared...

Neural network tagging in a toy model

International Nuclear Information System (INIS)

Milek, Marko; Patel, Popat

1999-01-01

The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of 'events' was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed

Export policies for multi-domain WDM networks

DEFF Research Database (Denmark)

Manolova, Anna Vasileva; Ruepp, Sarah Renée

2010-01-01

We analyze the performance of six export policies for a multi-domain routing protocol in WDM networks. We show that providing many AS-disjoint paths for survivability and load-balancing does not necessarily guarantee the lowest connection blocking......We analyze the performance of six export policies for a multi-domain routing protocol in WDM networks. We show that providing many AS-disjoint paths for survivability and load-balancing does not necessarily guarantee the lowest connection blocking...

Neural networks within multi-core optic fibers.

Science.gov (United States)

Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

2016-07-07

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

Spatio-temporal model based optimization framework to design future hydrogen infrastructure networks

International Nuclear Information System (INIS)

Konda, N.V.S.; Shah, N.; Brandon, N.P.

2009-01-01

A mixed integer programming (MIP) spatio-temporal model was used to design hydrogen infrastructure networks for the Netherlands. The detailed economic analysis was conducted using a multi-echelon model of the entire hydrogen supply chain, including feed, production, storage, and transmission-distribution systems. The study considered various near-future and commercially available technologies. A multi-period model was used to design evolutionary hydrogen supply networks in coherence with growing demand. A scenario-based analysis was conducted in order to account for uncertainties in future demand. The study showed that competitive hydrogen networks can be designed for any conceivable scenario. It was concluded that the multi-period model presented significant advantages in relation to decision-making over long time-horizons

A mathematical model for optimization of an integrated network logistic design

Directory of Open Access Journals (Sweden)

Lida Tafaghodi

2011-10-01

Full Text Available In this study, the integrated forward/reverse logistics network is investigated, and a capacitated multi-stage, multi-product logistics network design is proposed by formulating a generalized logistics network problem into a mixed-integer nonlinear programming model (MINLP for minimizing the total cost of the closed-loop supply chain network. Moreover, the proposed model is solved by using optimization solver, which provides the decisions related to the facility location problem, optimum quantity of shipped product, and facility capacity. Numerical results show the power of the proposed MINLP model to avoid th sub-optimality caused by separate design of forward and reverse logistics networks and to handle various transportation modes and periodic demand.

Functional networks inference from rule-based machine learning models.

Science.gov (United States)

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

Science.gov (United States)

Li, Zhiqiang; Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-04-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.

Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor.

Science.gov (United States)

Pandey, Daya Shankar; Das, Saptarshi; Pan, Indranil; Leahy, James J; Kwapinski, Witold

2016-12-01

In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHV p ) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg-Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Stochastic Geometry Model for Multi-hop Highway Vehicular Communication

KAUST Repository

Farooq, Muhammad Junaid; Elsawy, Hesham; Alouini, Mohamed-Slim

2015-01-01

dissemination. This paper exploits stochastic geometry to develop a tractable and accurate modeling framework to characterize the multi-hop transmissions for vehicular networks in a multi-lane highway setup. In particular, we study the tradeoffs between per

Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs

KAUST Repository

Yasin Yazicioǧlu, A.; Egerstedt, Magnus; Shamma, Jeff S.

2015-01-01

Multi-Agent networks are often modeled as interaction graphs, where the nodes represent the agents and the edges denote some direct interactions. The robustness of a multi-Agent network to perturbations such as failures, noise, or malicious attacks largely depends on the corresponding graph. In many applications, networks are desired to have well-connected interaction graphs with relatively small number of links. One family of such graphs is the random regular graphs. In this paper, we present a decentralized scheme for transforming any connected interaction graph with a possibly non-integer average degree of k into a connected random m-regular graph for some m ϵ [k+k ] 2. Accordingly, the agents improve the robustness of the network while maintaining a similar number of links as the initial configuration by locally adding or removing some edges. © 2015 IEEE.

Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs

KAUST Repository

Yasin Yazicioǧlu, A.

2015-11-25

Multi-Agent networks are often modeled as interaction graphs, where the nodes represent the agents and the edges denote some direct interactions. The robustness of a multi-Agent network to perturbations such as failures, noise, or malicious attacks largely depends on the corresponding graph. In many applications, networks are desired to have well-connected interaction graphs with relatively small number of links. One family of such graphs is the random regular graphs. In this paper, we present a decentralized scheme for transforming any connected interaction graph with a possibly non-integer average degree of k into a connected random m-regular graph for some m ϵ [k+k ] 2. Accordingly, the agents improve the robustness of the network while maintaining a similar number of links as the initial configuration by locally adding or removing some edges. © 2015 IEEE.

Topology Control in Aerial Multi-Beam Directional Networks

Science.gov (United States)

2017-04-24

Topology Control in Aerial Multi-Beam Directional Networks Brian Proulx, Nathaniel M. Jones, Jennifer Madiedo, Greg Kuperman {brian.proulx, njones...significant interference. Topology control (i.e., selecting a subset of neighbors to communicate with) is vital to reduce the interference. Good topology ...underlying challenges to topology control in multi-beam direction networks. Two topology control algorithms are developed: a centralized algorithm

Handoff Rate and Coverage Analysis in Multi-tier Heterogeneous Networks

OpenAIRE

Sadr, Sanam; Adve, Raviraj S.

2015-01-01

This paper analyzes the impact of user mobility in multi-tier heterogeneous networks. We begin by obtaining the handoff rate for a mobile user in an irregular cellular network with the access point locations modeled as a homogeneous Poisson point process. The received signal-to-interference-ratio (SIR) distribution along with a chosen SIR threshold is then used to obtain the probability of coverage. To capture potential connection failures due to mobility, we assume that a fraction of handoff...

Multi-objective optimal power flow for active distribution network considering the stochastic characteristic of photovoltaic

Science.gov (United States)

Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming

2017-05-01

To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.

Network coding for multi-resolution multicast

DEFF Research Database (Denmark)

2013-01-01

A method, apparatus and computer program product for utilizing network coding for multi-resolution multicast is presented. A network source partitions source content into a base layer and one or more refinement layers. The network source receives a respective one or more push-back messages from one...... or more network destination receivers, the push-back messages identifying the one or more refinement layers suited for each one of the one or more network destination receivers. The network source computes a network code involving the base layer and the one or more refinement layers for at least one...... of the one or more network destination receivers, and transmits the network code to the one or more network destination receivers in accordance with the push-back messages....

Multi-Level Secure Local Area Network

OpenAIRE

Naval Postgraduate School (U.S.); Center for Information Systems Studies Security and Research (CISR)

2011-01-01

Multi-Level Secure Local Area Network is a cost effective, multi-level, easy to use office environment leveraging existing high assurance technology. The Department of Defense and U.S. Government have an identified need to securely share information classified at differing security levels. Because there exist no commercial solutions to this problem, NPS is developing a MLS LAN. The MLS LAN extends high assurance capabilities of an evaluated multi-level secure system to commercial personal com...

Multi-model ensemble hydrological simulation using a BP Neural Network for the upper Yalongjiang River Basin, China

Science.gov (United States)

Li, Zhanjie; Yu, Jingshan; Xu, Xinyi; Sun, Wenchao; Pang, Bo; Yue, Jiajia

2018-06-01

Hydrological models are important and effective tools for detecting complex hydrological processes. Different models have different strengths when capturing the various aspects of hydrological processes. Relying on a single model usually leads to simulation uncertainties. Ensemble approaches, based on multi-model hydrological simulations, can improve application performance over single models. In this study, the upper Yalongjiang River Basin was selected for a case study. Three commonly used hydrological models (SWAT, VIC, and BTOPMC) were selected and used for independent simulations with the same input and initial values. Then, the BP neural network method was employed to combine the results from the three models. The results show that the accuracy of BP ensemble simulation is better than that of the single models.

Adjusting Sensing Range to Maximize Throughput on Ad-Hoc Multi-Hop Wireless Networks

National Research Council Canada - National Science Library

Roberts, Christopher

2003-01-01

.... Such a network is referred to as a multi-hop ad-hoc network, or simply a multi-hop network. Most multi-hop network protocols use some form of carrier sensing to determine if the wireless channel is in use...

Delay Bounded Multi-Source Multicast in Software-Defined Networking

Directory of Open Access Journals (Sweden)

Thabo Semong

2018-01-01

Full Text Available Software-Defined Networking (SDN is the next generation network architecture with exciting application prospects. The control function in SDN is decoupled from the data forwarding plane, hence it provides a new centralized architecture with flexible network resource management. Although SDN is attracting much attention from both industry and research, its advantage over the traditional networks has not been fully utilized. Multicast is designed to deliver content to multiple destinations. The current traffic engineering in SDN focuses mainly on unicast, however, multicast can effectively reduce network resource consumption by serving multiple clients. This paper studies a novel delay-bounded multi-source multicast SDN problem, in which among the set of potential sources, we select a source to build the multicast-tree, under the constraint that the transmission delay for every destination is bounded. This problem is more difficult than the traditional Steiner minimum tree (SMT problem, since it needs to find a source from the set of all potential sources. We model the problem as a mixed-integer linear programming (MILP and prove its NP-Hardness. To solve the problem, a delay bounded multi-source (DBMS scheme is proposed, which includes a DBMS algorithm to build a minimum delay cost DBMS-Forest. Through a MATLAB experiment, we demonstrate that DBMS is significantly more efficient and outperforms other existing algorithms in the literature.

Artificial neural network cardiopulmonary modeling and diagnosis

Science.gov (United States)

Kangas, Lars J.; Keller, Paul E.

1997-01-01

The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

Science.gov (United States)

Zhu, Aichun; Wang, Tian; Snoussi, Hichem

2018-03-01

This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

Directory of Open Access Journals (Sweden)

Aichun Zhu

2018-03-01

Full Text Available This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN. Firstly, a Relative Mixture Deformable Model (RMDM is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

Problems With Deployment of Multi-Domained, Multi-Homed Mobile Networks

Science.gov (United States)

Ivancic, William D.

2008-01-01

This document describes numerous problems associated with deployment of multi-homed mobile platforms consisting of multiple networks and traversing large geographical areas. The purpose of this document is to provide insight to real-world deployment issues and provide information to groups that are addressing many issues related to multi-homing, policy-base routing, route optimization and mobile security - particularly those groups within the Internet Engineering Task Force.

Agent based modeling of energy networks

International Nuclear Information System (INIS)

Gonzalez de Durana, José María; Barambones, Oscar; Kremers, Enrique; Varga, Liz

2014-01-01

Highlights: • A new approach for energy network modeling is designed and tested. • The agent-based approach is general and no technology dependent. • The models can be easily extended. • The range of applications encompasses from small to large energy infrastructures. - Abstract: Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed

Pinning adaptive synchronization of a class of uncertain complex dynamical networks with multi-link against network deterioration

International Nuclear Information System (INIS)

Li, Lixiang; Li, Weiwei; Kurths, Jürgen; Luo, Qun; Yang, Yixian; Li, Shudong

2015-01-01

For the reason that the uncertain complex dynamic network with multi-link is quite close to various practical networks, there is superiority in the fields of research and application. In this paper, we focus upon pinning adaptive synchronization for uncertain complex dynamic networks with multi-link against network deterioration. The pinning approach can be applied to adapt uncertain coupling factors of deteriorated networks which can compensate effects of uncertainty. Several new synchronization criterions for networks with multi-link are derived, which ensure the synchronized states to be local or global stable with uncertainty and deterioration. Results of simulation are shown to demonstrate the feasibility and usefulness of our method

Multi-Cluster Network on a Chip Reconfigurable Radiation Hardened Radio, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The objective of the Phase-I research is to architect, model and simulate a multi-cluster Network on a Chip (NoC) reconfigurable Radio in SystemC RTL, with...

Integrated topology optimisation of multi-energy networks

NARCIS (Netherlands)

Mazairac, L.A.J.; Salenbien, R.; Vanhoudt, D.; Desmedt, J.; Vries, de B.

2015-01-01

Multi-carrier hybrid energy distribution net- works provide flexibility in case of network malfunctions, energy shortages and price fluctuations through energy conversion and storage. Therefore hybrid networks can cope with large-scale integration of distributed and intermittent renewable energy

Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images.

Science.gov (United States)

Zou, Zhengxia; Shi, Zhenwei

2018-03-01

We propose a new paradigm for target detection in high resolution aerial remote sensing images under small target priors. Previous remote sensing target detection methods frame the detection as learning of detection model + inference of class-label and bounding-box coordinates. Instead, we formulate it from a Bayesian view that at inference stage, the detection model is adaptively updated to maximize its posterior that is determined by both training and observation. We call this paradigm "random access memories (RAM)." In this paradigm, "Memories" can be interpreted as any model distribution learned from training data and "random access" means accessing memories and randomly adjusting the model at detection phase to obtain better adaptivity to any unseen distribution of test data. By leveraging some latest detection techniques e.g., deep Convolutional Neural Networks and multi-scale anchors, experimental results on a public remote sensing target detection data set show our method outperforms several other state of the art methods. We also introduce a new data set "LEarning, VIsion and Remote sensing laboratory (LEVIR)", which is one order of magnitude larger than other data sets of this field. LEVIR consists of a large set of Google Earth images, with over 22 k images and 10 k independently labeled targets. RAM gives noticeable upgrade of accuracy (an mean average precision improvement of 1% ~ 4%) of our baseline detectors with acceptable computational overhead.

Effects of multi-stakeholder platforms on multi-stakeholder innovation networks: Implications for research for development interventions targeting innovations at scale

Science.gov (United States)

Schut, Marc; Hermans, Frans; van Asten, Piet; Leeuwis, Cees

2018-01-01

Multi-stakeholder platforms (MSPs) have been playing an increasing role in interventions aiming to generate and scale innovations in agricultural systems. However, the contribution of MSPs in achieving innovations and scaling has been varied, and many factors have been reported to be important for their performance. This paper aims to provide evidence on the contribution of MSPs to innovation and scaling by focusing on three developing country cases in Burundi, Democratic Republic of Congo, and Rwanda. Through social network analysis and logistic models, the paper studies the changes in the characteristics of multi-stakeholder innovation networks targeted by MSPs and identifies factors that play significant roles in triggering these changes. The results demonstrate that MSPs do not necessarily expand and decentralize innovation networks but can lead to contraction and centralization in the initial years of implementation. They show that some of the intended next users of interventions with MSPs–local-level actors–left the innovation networks, whereas the lead organization controlling resource allocation in the MSPs substantially increased its centrality. They also indicate that not all the factors of change in innovation networks are country specific. Initial conditions of innovation networks and funding provided by the MSPs are common factors explaining changes in innovation networks across countries and across different network functions. The study argues that investigating multi-stakeholder innovation network characteristics targeted by the MSP using a network approach in early implementation can contribute to better performance in generating and scaling innovations, and that funding can be an effective implementation tool in developing country contexts. PMID:29870559

Effects of multi-stakeholder platforms on multi-stakeholder innovation networks: Implications for research for development interventions targeting innovations at scale.

Science.gov (United States)

Sartas, Murat; Schut, Marc; Hermans, Frans; Asten, Piet van; Leeuwis, Cees

2018-01-01

Multi-stakeholder platforms (MSPs) have been playing an increasing role in interventions aiming to generate and scale innovations in agricultural systems. However, the contribution of MSPs in achieving innovations and scaling has been varied, and many factors have been reported to be important for their performance. This paper aims to provide evidence on the contribution of MSPs to innovation and scaling by focusing on three developing country cases in Burundi, Democratic Republic of Congo, and Rwanda. Through social network analysis and logistic models, the paper studies the changes in the characteristics of multi-stakeholder innovation networks targeted by MSPs and identifies factors that play significant roles in triggering these changes. The results demonstrate that MSPs do not necessarily expand and decentralize innovation networks but can lead to contraction and centralization in the initial years of implementation. They show that some of the intended next users of interventions with MSPs-local-level actors-left the innovation networks, whereas the lead organization controlling resource allocation in the MSPs substantially increased its centrality. They also indicate that not all the factors of change in innovation networks are country specific. Initial conditions of innovation networks and funding provided by the MSPs are common factors explaining changes in innovation networks across countries and across different network functions. The study argues that investigating multi-stakeholder innovation network characteristics targeted by the MSP using a network approach in early implementation can contribute to better performance in generating and scaling innovations, and that funding can be an effective implementation tool in developing country contexts.

Towards Effective Intra-flow Network Coding in Software Defined Wireless Mesh Networks

Directory of Open Access Journals (Sweden)

Donghai Zhu

2016-01-01

Full Text Available Wireless Mesh Networks (WMNs have potential to provide convenient broadband wireless Internet access to mobile users.With the support of Software-Defined Networking (SDN paradigm that separates control plane and data plane, WMNs can be easily deployed and managed. In addition, by exploiting the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks, intra-flow network coding has shown a greater benefit in comparison with traditional routing paradigms in data transmission for WMNs. In this paper, we develop a novel OpenCoding protocol, which combines the SDN technique with intra-flow network coding for WMNs. Our developed protocol can simplify the deployment and management of the network and improve network performance. In OpenCoding, a controller that works on the control plane makes routing decisions for mesh routers and the hop-by-hop forwarding function is replaced by network coding functions in data plane. We analyze the overhead of OpenCoding. Through a simulation study, we show the effectiveness of the OpenCoding protocol in comparison with existing schemes. Our data shows that OpenCoding outperforms both traditional routing and intra-flow network coding schemes.

A novel neural network for multi project programming with limited resources

International Nuclear Information System (INIS)

Liping, Z.; Jianhua, W.; Fenfang, Z.; Guojian, H.

1996-01-01

This paper discusses the theory of multi project programming and how to use Artificial Neural Network model to solve this problem. To obtain global optimum solution, the simulated annealing technology is used in our scheme. To improve the convergence property of argument matrix in the process of optimization for target function. Lagrange operator is replaced with the inverse of temperature in simulated annealing. Combining the Hopfield networks algorithm, this problem is solved speedily and satisfactorily. Experimental results show it is very effective to use Artificial Neural Network to solve the problem

Range based power control for multi-radio multi-channel wireless mesh networks

CSIR Research Space (South Africa)

Olwal, TO

2009-08-01

Full Text Available Multi-Radio Multi-Channel (MRMC) systems are key to power control problems in Wireless Mesh Networks (WMNs). In this paper, researchers present a range based dynamic power control for MRMC WMNs. First, WMN is represented as a set of disjoint Unified...

Multi-Valued Associative Memory Neural Network

Institute of Scientific and Technical Information of China (English)

修春波; 刘向东; 张宇河

2003-01-01

A novel learning method for multi-valued associative memory network is introduced, which is based on Hebb rule, but utilizes more information. According to the current probe vector, the connection weights matrix could be chosen dynamically. Double-valued and multi-valued associative memory are all realized in our simulation experiment. The experimental results show that the method could enhance the associative success rate.

Multi-hop localization algorithm based on grid-scanning for wireless sensor networks.

Science.gov (United States)

Wan, Jiangwen; Guo, Xiaolei; Yu, Ning; Wu, Yinfeng; Feng, Renjian

2011-01-01

For large-scale wireless sensor networks (WSNs) with a minority of anchor nodes, multi-hop localization is a popular scheme for determining the geographical positions of the normal nodes. However, in practice existing multi-hop localization methods suffer from various kinds of problems, such as poor adaptability to irregular topology, high computational complexity, low positioning accuracy, etc. To address these issues in this paper, we propose a novel Multi-hop Localization algorithm based on Grid-Scanning (MLGS). First, the factors that influence the multi-hop distance estimation are studied and a more realistic multi-hop localization model is constructed. Then, the feasible regions of the normal nodes are determined according to the intersection of bounding square rings. Finally, a verifiably good approximation scheme based on grid-scanning is developed to estimate the coordinates of the normal nodes. Additionally, the positioning accuracy of the normal nodes can be improved through neighbors' collaboration. Extensive simulations are performed in isotropic and anisotropic networks. The comparisons with some typical algorithms of node localization confirm the effectiveness and efficiency of our algorithm.

A Service-Oriented Approach for Dynamic Chaining of Virtual Network Functions over Multi-Provider Software-Defined Networks

Directory of Open Access Journals (Sweden)

Barbara Martini

2016-06-01

Full Text Available Emerging technologies such as Software-Defined Networks (SDN and Network Function Virtualization (NFV promise to address cost reduction and flexibility in network operation while enabling innovative network service delivery models. However, operational network service delivery solutions still need to be developed that actually exploit these technologies, especially at the multi-provider level. Indeed, the implementation of network functions as software running over a virtualized infrastructure and provisioned on a service basis let one envisage an ecosystem of network services that are dynamically and flexibly assembled by orchestrating Virtual Network Functions even across different provider domains, thereby coping with changeable user and service requirements and context conditions. In this paper we propose an approach that adopts Service-Oriented Architecture (SOA technology-agnostic architectural guidelines in the design of a solution for orchestrating and dynamically chaining Virtual Network Functions. We discuss how SOA, NFV, and SDN may complement each other in realizing dynamic network function chaining through service composition specification, service selection, service delivery, and placement tasks. Then, we describe the architecture of a SOA-inspired NFV orchestrator, which leverages SDN-based network control capabilities to address an effective delivery of elastic chains of Virtual Network Functions. Preliminary results of prototype implementation and testing activities are also presented. The benefits for Network Service Providers are also described that derive from the adaptive network service provisioning in a multi-provider environment through the orchestration of computing and networking services to provide end users with an enhanced service experience.

Balanced sensitivity functions for tuning multi-dimensional Bayesian network classifiers

NARCIS (Netherlands)

Bolt, J.H.; van der Gaag, L.C.

Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted topological structure, which are tailored to classifying data instances into multiple dimensions. Like more traditional classifiers, multi-dimensional classifiers are typically learned from data and may include

Software Defined Networking (SDN) controlled all optical switching networks with multi-dimensional switching architecture

Science.gov (United States)

Zhao, Yongli; Ji, Yuefeng; Zhang, Jie; Li, Hui; Xiong, Qianjin; Qiu, Shaofeng

2014-08-01

Ultrahigh throughout capacity requirement is challenging the current optical switching nodes with the fast development of data center networks. Pbit/s level all optical switching networks need to be deployed soon, which will cause the high complexity of node architecture. How to control the future network and node equipment together will become a new problem. An enhanced Software Defined Networking (eSDN) control architecture is proposed in the paper, which consists of Provider NOX (P-NOX) and Node NOX (N-NOX). With the cooperation of P-NOX and N-NOX, the flexible control of the entire network can be achieved. All optical switching network testbed has been experimentally demonstrated with efficient control of enhanced Software Defined Networking (eSDN). Pbit/s level all optical switching nodes in the testbed are implemented based on multi-dimensional switching architecture, i.e. multi-level and multi-planar. Due to the space and cost limitation, each optical switching node is only equipped with four input line boxes and four output line boxes respectively. Experimental results are given to verify the performance of our proposed control and switching architecture.

Dynamic supply chain network design with capacity planning and multi-period pricing

DEFF Research Database (Denmark)

Fattahi, Mohammad; Mahootchi, Masoud; Govindan, Kannan

2015-01-01

This paper addresses a new problem in designing and planning a multi-echelon and multi-product supply chain network over a multi-period horizon in which customer zones have price-sensitive demands. Based on price-demand relationships, a generic method is presented to obtain price levels...... for products and then, a mixed-integer linear programming model is developed. Due to the problem intractability, a simulated annealing algorithm that uses some developed linear relaxation-based heuristics for capacity planning and pricing is presented. Numerical results demonstrate the significance...

Multi-layer service function chaining scheduling based on auxiliary graph in IP over optical network

Science.gov (United States)

Li, Yixuan; Li, Hui; Liu, Yuze; Ji, Yuefeng

2017-10-01

Software Defined Optical Network (SDON) can be considered as extension of Software Defined Network (SDN) in optical networks. SDON offers a unified control plane and makes optical network an intelligent transport network with dynamic flexibility and service adaptability. For this reason, a comprehensive optical transmission service, able to achieve service differentiation all the way down to the optical transport layer, can be provided to service function chaining (SFC). IP over optical network, as a promising networking architecture to interconnect data centers, is the most widely used scenarios of SFC. In this paper, we offer a flexible and dynamic resource allocation method for diverse SFC service requests in the IP over optical network. To do so, we firstly propose the concept of optical service function (OSF) and a multi-layer SFC model. OSF represents the comprehensive optical transmission service (e.g., multicast, low latency, quality of service, etc.), which can be achieved in multi-layer SFC model. OSF can also be considered as a special SF. Secondly, we design a resource allocation algorithm, which we call OSF-oriented optical service scheduling algorithm. It is able to address multi-layer SFC optical service scheduling and provide comprehensive optical transmission service, while meeting multiple optical transmission requirements (e.g., bandwidth, latency, availability). Moreover, the algorithm exploits the concept of Auxiliary Graph. Finally, we compare our algorithm with the Baseline algorithm in simulation. And simulation results show that our algorithm achieves superior performance than Baseline algorithm in low traffic load condition.

Multi-state modeling of biomolecules.

Directory of Open Access Journals (Sweden)

Melanie I Stefan

2014-09-01

Full Text Available Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the "specification problem" and the problem of how to use a computer to simulate the progress of the system over time (the "computation problem". To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus, BioNetGen, the Allosteric Network Compiler, and others. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim, DYNSTOC, RuleMonkey, and the Network-Free Stochastic Simulator (NFSim, and spatial simulators, including Meredys, SRSim, and MCell. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.

Statistical inference to advance network models in epidemiology.

Science.gov (United States)

Welch, David; Bansal, Shweta; Hunter, David R

2011-03-01

Contact networks are playing an increasingly important role in the study of epidemiology. Most of the existing work in this area has focused on considering the effect of underlying network structure on epidemic dynamics by using tools from probability theory and computer simulation. This work has provided much insight on the role that heterogeneity in host contact patterns plays on infectious disease dynamics. Despite the important understanding afforded by the probability and simulation paradigm, this approach does not directly address important questions about the structure of contact networks such as what is the best network model for a particular mode of disease transmission, how parameter values of a given model should be estimated, or how precisely the data allow us to estimate these parameter values. We argue that these questions are best answered within a statistical framework and discuss the role of statistical inference in estimating contact networks from epidemiological data. Copyright © 2011 Elsevier B.V. All rights reserved.

Adaptive multi-resolution Modularity for detecting communities in networks

Science.gov (United States)

Chen, Shi; Wang, Zhi-Zhong; Bao, Mei-Hua; Tang, Liang; Zhou, Ji; Xiang, Ju; Li, Jian-Ming; Yi, Chen-He

2018-02-01

Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.

A New Era for Urban Modelling

OpenAIRE

Pumain , Denise

1994-01-01

International audience; In the last two decades, several interesting innovations have appeared in the field of urban research. New paradigms such as the dynamics of open systems, self-organization, synergetics, chaos, evolution, were recognized as conveying fruitful analogies for urban theory. New types of modeling were investigated, as sets of non-linear differential equations for spatial systems, cellular automata, multi-agents models, fractal growth, neural networks, evolutionary models… H...

Immune networks: multi-tasking capabilities at medium load

Science.gov (United States)

Agliari, E.; Annibale, A.; Barra, A.; Coolen, A. C. C.; Tantari, D.

2013-08-01

Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ˜ log N. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P ˜ Nδ with δ ∈ (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for δ frameworks are required to achieve effective retrieval.

Multi-subject Manifold Alignment of Functional Network Structures via Joint Diagonalization.

Science.gov (United States)

Nenning, Karl-Heinz; Kollndorfer, Kathrin; Schöpf, Veronika; Prayer, Daniela; Langs, Georg

2015-01-01

Functional magnetic resonance imaging group studies rely on the ability to establish correspondence across individuals. This enables location specific comparison of functional brain characteristics. Registration is often based on morphology and does not take variability of functional localization into account. This can lead to a loss of specificity, or confounds when studying diseases. In this paper we propose multi-subject functional registration by manifold alignment via coupled joint diagonalization. The functional network structure of each subject is encoded in a diffusion map, where functional relationships are decoupled from spatial position. Two-step manifold alignment estimates initial correspondences between functionally equivalent regions. Then, coupled joint diagonalization establishes common eigenbases across all individuals, and refines the functional correspondences. We evaluate our approach on fMRI data acquired during a language paradigm. Experiments demonstrate the benefits in matching accuracy achieved by coupled joint diagonalization compared to previously proposed functional alignment approaches, or alignment based on structural correspondences.

A path finding implementation for multi-layer networks

NARCIS (Netherlands)

Dijkstra, F.; van der Ham, J.; Grosso, P.; de Laat, C.

2009-01-01

The goal of the OptIPuter project is to tightly couple research applications with dynamically allocated paths. Since OptIPuter is a multi-disciplinary project, the paths through the network often span multiple network domains, and the applications are challenged to find valid network connections

A path finding implementation for multi-layer network

NARCIS (Netherlands)

Dijkstra, F.; Ham, J.J. van der; Grosso, P.; Laat, C. de

2009-01-01

The goal of the OptIPuter project is to tightly couple research applications with dynamically allocated paths. Since OptIPuter is a multi-disciplinary project, the paths through the network often span multiple network domains, and the applications are challenged to find valid network connections

A multi-paradigm language for reactive synthesis

Directory of Open Access Journals (Sweden)

Ioannis Filippidis

2016-02-01

Full Text Available This paper proposes a language for describing reactive synthesis problems that integrates imperative and declarative elements. The semantics is defined in terms of two-player turn-based infinite games with full information. Currently, synthesis tools accept linear temporal logic (LTL as input, but this description is less structured and does not facilitate the expression of sequential constraints. This motivates the use of a structured programming language to specify synthesis problems. Transition systems and guarded commands serve as imperative constructs, expressed in a syntax based on that of the modeling language Promela. The syntax allows defining which player controls data and control flow, and separating a program into assumptions and guarantees. These notions are necessary for input to game solvers. The integration of imperative and declarative paradigms allows using the paradigm that is most appropriate for expressing each requirement. The declarative part is expressed in the LTL fragment of generalized reactivity(1, which admits efficient synthesis algorithms, extended with past LTL. The implementation translates Promela to input for the Slugs synthesizer and is written in Python. The AMBA AHB bus case study is revisited and synthesized efficiently, identifying the need to reorder binary decision diagrams during strategy construction, in order to prevent the exponential blowup observed in previous work.

The Underlying Social Dynamics of Paradigm Shifts.

Science.gov (United States)

Rodriguez-Sickert, Carlos; Cosmelli, Diego; Claro, Francisco; Fuentes, Miguel Angel

2015-01-01

We develop here a multi-agent model of the creation of knowledge (scientific progress or technological evolution) within a community of researchers devoted to such endeavors. In the proposed model, agents learn in a physical-technological landscape, and weight is attached to both individual search and social influence. We find that the combination of these two forces together with random experimentation can account for both i) marginal change, that is, periods of normal science or refinements on the performance of a given technology (and in which the community stays in the neighborhood of the current paradigm); and ii) radical change, which takes the form of scientific paradigm shifts (or discontinuities in the structure of performance of a technology) that is observed as a swift migration of the knowledge community towards the new and superior paradigm. The efficiency of the search process is heavily dependent on the weight that agents posit on social influence. The occurrence of a paradigm shift becomes more likely when each member of the community attaches a small but positive weight to the experience of his/her peers. For this parameter region, nevertheless, a conservative force is exerted by the representatives of the current paradigm. However, social influence is not strong enough to seriously hamper individual discovery, and can act so as to empower successful individual pioneers who have conquered the new and superior paradigm.

The Underlying Social Dynamics of Paradigm Shifts.

Directory of Open Access Journals (Sweden)

Carlos Rodriguez-Sickert

Full Text Available We develop here a multi-agent model of the creation of knowledge (scientific progress or technological evolution within a community of researchers devoted to such endeavors. In the proposed model, agents learn in a physical-technological landscape, and weight is attached to both individual search and social influence. We find that the combination of these two forces together with random experimentation can account for both i marginal change, that is, periods of normal science or refinements on the performance of a given technology (and in which the community stays in the neighborhood of the current paradigm; and ii radical change, which takes the form of scientific paradigm shifts (or discontinuities in the structure of performance of a technology that is observed as a swift migration of the knowledge community towards the new and superior paradigm. The efficiency of the search process is heavily dependent on the weight that agents posit on social influence. The occurrence of a paradigm shift becomes more likely when each member of the community attaches a small but positive weight to the experience of his/her peers. For this parameter region, nevertheless, a conservative force is exerted by the representatives of the current paradigm. However, social influence is not strong enough to seriously hamper individual discovery, and can act so as to empower successful individual pioneers who have conquered the new and superior paradigm.

Algorithm for queueing networks with multi-rate traffic

DEFF Research Database (Denmark)

Iversen, Villy Bæk; Ko, King-Tim

2011-01-01

the nodes behave as independent nodes. For closed queueing networks with multiple servers in every node and multi-rate services we may apply multidimensional convolution algorithm to aggregate the nodes so that we end up with two nodes, the aggregated node and a single node, for which we can calculate......In this paper we present a new algorithm for evaluating queueing networks with multi-rate traffic. The detailed state space of a node is evaluated by explicit formulæ. We consider reversible nodes with multi-rate traffic and find the state probabilities by taking advantage of local balance. Theory...... of queueing networks in general, presumes that we have product form between the nodes. Otherwise, we have the state space explosion. Even so, the detailed state space of each node may become very large because there is no product form between chains inside a node. A prerequisite for product form...

Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm.

Science.gov (United States)

Al Ajmi, Eiman; Forghani, Behzad; Reinhold, Caroline; Bayat, Maryam; Forghani, Reza

2018-06-01

There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

3D multi-view convolutional neural networks for lung nodule classification

Science.gov (United States)

Kang, Guixia; Hou, Beibei; Zhang, Ningbo

2017-01-01

The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. We conduct a binary classification (benign and malignant) and a ternary classification (benign, primary malignant and metastatic malignant) on Computed Tomography (CT) images from Lung Image Database Consortium and Image Database Resource Initiative database (LIDC-IDRI). All results are obtained via 10-fold cross validation. As regards the MV-CNN with chain architecture, results show that the performance of 3D MV-CNN surpasses that of 2D MV-CNN by a significant margin. Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy. PMID:29145492

Large-signal modeling of multi-finger InP DHBT devices at millimeter-wave frequencies

DEFF Research Database (Denmark)

Johansen, Tom Keinicke; Midili, Virginio; Squartecchia, Michele

2017-01-01

A large-signal modeling approach has been developed for multi-finger devices fabricated in an Indium Phosphide (InP) Double Heterojunction Bipolar Transistor (DHBT) process. The approach utilizes unit-finger device models embedded in a multi-port parasitic network. The unit-finger model is based...... on an improved UCSD HBT model formulation avoiding an erroneous RciCbci transit-time contribution from the intrinsic collector region as found in other III-V based HBT models. The mutual heating between fingers is modeled by a thermal coupling network with parameters extracted from electro-thermal simulations...

Multiple-state based power control for multi-radio multi-channel wireless mesh networks

CSIR Research Space (South Africa)

Olwal, TO

2009-01-01

Full Text Available Multi-Radio Multi-Channel (MRMC) systems are key to power control problems in wireless mesh networks (WMNs). In this paper, we present asynchronous multiple-state based power control for MRMC WMNs. First, WMN is represented as a set of disjoint...

A Stochastic Geometry Model for Multi-hop Highway Vehicular Communication

KAUST Repository

Farooq, Muhammad Junaid

2015-11-19

Carrier sense multiple access (CSMA) protocol is standardized for vehicular communication to ensure a distributed and efficient communication between vehicles. However, several vehicular applications require efficient multi-hop information dissemination. This paper exploits stochastic geometry to develop a tractable and accurate modeling framework to characterize the multi-hop transmissions for vehicular networks in a multi-lane highway setup. In particular, we study the tradeoffs between per-hop packet forward progress, per-hop transmission success probability, and spatial frequency reuse (SFR) efficiency imposed by different packet forwarding schemes, namely, most forward with fixed radius (MFR), the nearest with forward progress (NFP), and the random with forward progress (RFP). We also define a new performance metric, denoted as the aggregate packet progress (APP), which is a dimensionless quantity that captures the aforementioned tradeoffs. To this end, the developed model reveals the interplay between the spectrum sensing threshold (th) of the CSMA protocol and the packet forwarding scheme. Our results show that, in contrary to ALOHA networks which always favor NFP, MFR may achieve the highest APP in CSMA networks if th is properly chosen.

Coordinated Multi-layer Multi-domain Optical Network (COMMON) for Large-Scale Science Applications (COMMON)

Energy Technology Data Exchange (ETDEWEB)

Vokkarane, Vinod [University of Massachusetts

2013-09-01

We intend to implement a Coordinated Multi-layer Multi-domain Optical Network (COMMON) Framework for Large-scale Science Applications. In the COMMON project, specific problems to be addressed include 1) anycast/multicast/manycast request provisioning, 2) deployable OSCARS enhancements, 3) multi-layer, multi-domain quality of service (QoS), and 4) multi-layer, multidomain path survivability. In what follows, we outline the progress in the above categories (Year 1, 2, and 3 deliverables).

Spatial-temporal data model and fractal analysis of transportation network in GIS environment

Science.gov (United States)

Feng, Yongjiu; Tong, Xiaohua; Li, Yangdong

2008-10-01

How to organize transportation data characterized by multi-time, multi-scale, multi-resolution and multi-source is one of the fundamental problems of GIS-T development. A spatial-temporal data model for GIS-T is proposed based on Spatial-temporal- Object Model. Transportation network data is systemically managed using dynamic segmentation technologies. And then a spatial-temporal database is built to integrally store geographical data of multi-time for transportation. Based on the spatial-temporal database, functions of spatial analysis of GIS-T are substantively extended. Fractal module is developed to improve the analyzing in intensity, density, structure and connectivity of transportation network based on the validation and evaluation of topologic relation. Integrated fractal with GIS-T strengthens the functions of spatial analysis and enriches the approaches of data mining and knowledge discovery of transportation network. Finally, the feasibility of the model and methods are tested thorough Guangdong Geographical Information Platform for Highway Project.

Automation of multi-agent control for complex dynamic systems in heterogeneous computational network

Science.gov (United States)

Oparin, Gennady; Feoktistov, Alexander; Bogdanova, Vera; Sidorov, Ivan

2017-01-01

The rapid progress of high-performance computing entails new challenges related to solving large scientific problems for various subject domains in a heterogeneous distributed computing environment (e.g., a network, Grid system, or Cloud infrastructure). The specialists in the field of parallel and distributed computing give the special attention to a scalability of applications for problem solving. An effective management of the scalable application in the heterogeneous distributed computing environment is still a non-trivial issue. Control systems that operate in networks, especially relate to this issue. We propose a new approach to the multi-agent management for the scalable applications in the heterogeneous computational network. The fundamentals of our approach are the integrated use of conceptual programming, simulation modeling, network monitoring, multi-agent management, and service-oriented programming. We developed a special framework for an automation of the problem solving. Advantages of the proposed approach are demonstrated on the parametric synthesis example of the static linear regulator for complex dynamic systems. Benefits of the scalable application for solving this problem include automation of the multi-agent control for the systems in a parallel mode with various degrees of its detailed elaboration.

The Multi-Layered Perceptrons Neural Networks for the Prediction of Daily Solar Radiation

OpenAIRE

Radouane Iqdour; Abdelouhab Zeroual

2007-01-01

The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in term of the statistical indicators, with a linear model most used in literature, is also perfo...

Artificial Neural Network Application for Power Transfer Capability and Voltage Calculations in Multi-Area Power System

Directory of Open Access Journals (Sweden)

Palukuru NAGENDRA

2010-12-01

Full Text Available In this study, the use of artificial neural network (ANN based model, multi-layer perceptron (MLP network, to compute the transfer capabilities in a multi-area power system was explored. The input for the ANN is load status and the outputs are the transfer capability among the system areas, voltage magnitudes and voltage angles at concerned buses of the areas under consideration. The repeated power flow (RPF method is used in this paper for calculating the power transfer capability, voltage magnitudes and voltage angles necessary for the generation of input-output patterns for training the proposed MLP neural network. Preliminary investigations on a three area 30-bus system reveal that the proposed model is computationally faster than the conventional method.

Algorithm for queueing networks with multi-rate traffic

DEFF Research Database (Denmark)

Iversen, Villy Bæk; King-Tim, Ko

2011-01-01

the nodes behave as independent nodes. For closed queueing networks with multiple servers in every node and multi-rate services we may apply multidimensional convolutions to aggregate the nodes so that we end up with two nodes, the aggregated node and a single node, for which we can calculate the detailed......In this paper we present a new algorithm for evaluating queueing networks with multi-rate traffic. The detailed state space of a node is evaluated by explicit formulæ. We consider reversible nodes with multi-rate traffic and find the state probabilities by taking advantage of local balance. Theory...... of queueing networks in general presumes that we have product form between the nodes. Other ways we have the state space explosion. Even so the detailed state space of each node may easily become very large because there is no product form between chains inside a node. A prerequisite for product form...

Single-process versus multiple-strategy models of decision making: evidence from an information intrusion paradigm.

Science.gov (United States)

Söllner, Anke; Bröder, Arndt; Glöckner, Andreas; Betsch, Tilmann

2014-02-01

When decision makers are confronted with different problems and situations, do they use a uniform mechanism as assumed by single-process models (SPMs) or do they choose adaptively from a set of available decision strategies as multiple-strategy models (MSMs) imply? Both frameworks of decision making have gathered a lot of support, but only rarely have they been contrasted with each other. Employing an information intrusion paradigm for multi-attribute decisions from givens, SPM and MSM predictions on information search, decision outcomes, attention, and confidence judgments were derived and tested against each other in two experiments. The results consistently support the SPM view: Participants seemingly using a "take-the-best" (TTB) strategy do not ignore TTB-irrelevant information as MSMs would predict, but adapt the amount of information searched, choose alternative choice options, and show varying confidence judgments contingent on the quality of the "irrelevant" information. The uniformity of these findings underlines the adequacy of the novel information intrusion paradigm and comprehensively promotes the notion of a uniform decision making mechanism as assumed by single-process models. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Restoration in multi-domain GMPLS-based networks

DEFF Research Database (Denmark)

Manolova, Anna; Ruepp, Sarah Renée; Dittmann, Lars

2011-01-01

In this paper, we evaluate the efficiency of using restoration mechanisms in a dynamic multi-domain GMPLS network. Major challenges and solutions are introduced and two well-known restoration schemes (End-to-End and Local-to-End) are evaluated. Additionally, new restoration mechanisms are introdu......In this paper, we evaluate the efficiency of using restoration mechanisms in a dynamic multi-domain GMPLS network. Major challenges and solutions are introduced and two well-known restoration schemes (End-to-End and Local-to-End) are evaluated. Additionally, new restoration mechanisms...... are introduced: one based on the position of a failed link, called Location-Based, and another based on minimizing the additional resources consumed during restoration, called Shortest-New. A complete set of simulations in different network scenarios show where each mechanism is more efficient in terms, such as...

An evidential path logic for multi-relational networks

Energy Technology Data Exchange (ETDEWEB)

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.

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

Science.gov (United States)

Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

2018-03-06

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

Mathematical Modelling Plant Signalling Networks

KAUST Repository

Muraro, D.

2013-01-01

During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.

Immune networks: multi-tasking capabilities at medium load

International Nuclear Information System (INIS)

Agliari, E; Annibale, A; Barra, A; Coolen, A C C; Tantari, D

2013-01-01

Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ∼ log N. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P ∼ N δ with δ ∈ (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for δ < 1 the existence of large regions in the phase diagram where the network can retrieve all stored patterns simultaneously. Finally, in the high-load regime δ = 1 we find that the system behaves as a spin-glass, suggesting that finite-connectivity frameworks are required to achieve effective retrieval. (paper)

Collaborative-Hybrid Multi-Layer Network Control for Emerging Cyber-Infrastructures

Energy Technology Data Exchange (ETDEWEB)

Lehman, Tom [USC; Ghani, Nasir [UNM; Boyd, Eric [UCAID

2010-08-31

At a high level, there were four basic task areas identified for the Hybrid-MLN project. They are: o Multi-Layer, Multi-Domain, Control Plane Architecture and Implementation, including OSCARS layer2 and InterDomain Adaptation, Integration of LambdaStation and Terapaths with Layer2 dynamic provisioning, Control plane software release, Scheduling, AAA, security architecture, Network Virtualization architecture, Multi-Layer Network Architecture Framework Definition; o Heterogeneous DataPlane Testing; o Simulation; o Project Publications, Reports, and Presentations.

A business case modelling framework for smart multi-energy districts

OpenAIRE

Good, Nicholas; Martinez Cesena, Eduardo Alejandro; Liu, Xuezhi; Mancarella, Pierluigi

2017-01-01

The potential energy, environmental, technical and economic benefits that might arise from multi-energy systems are increasing interest in smart districts. However, in a liberalised market, it is essential to develop a relevant attractive business case. This paper presents a holistic techno-economic framework that couples building/district, multi-network and business case assessment models for the development of robust business cases for smart multi-energy districts. The framework is demonstr...

Scalable and practical multi-objective distribution network expansion planning

NARCIS (Netherlands)

Luong, N.H.; Grond, M.O.W.; Poutré, La J.A.; Bosman, P.A.N.

2015-01-01

We formulate the distribution network expansion planning (DNEP) problem as a multi-objective optimization (MOO) problem with different objectives that distribution network operators (DNOs) would typically like to consider during decision making processes for expanding their networks. Objectives are

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Institute of Scientific and Technical Information of China (English)

Su YAN; Kaiquan CAI

2017-01-01

Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO),the network-wide 4D flight trajectories planning (N4DFTP) problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs) (3D position and time) for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategic level conflict management is developed in this paper.Specifically,a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm (MOMMA) that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the pro posed MOMMA is effective to solve the N4DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Directory of Open Access Journals (Sweden)

Su YAN

2017-06-01

Full Text Available Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO, the network-wide 4D flight trajectories planning (N4DFTP problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs (3D position and time for all the flights in the whole airway network. Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity, an efficient model for strategic-level conflict management is developed in this paper. Specifically, a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated. In consideration of the large-scale, high-complexity, and multi-objective characteristics of the N4DFTP problem, a multi-objective multi-memetic algorithm (MOMMA that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented. It is capable of rapidly and effectively allocating 4DTs via rerouting, target time controlling, and flight level changing. Additionally, to balance the ability of exploitation and exploration of the algorithm, a special hybridization scheme is adopted for the integration of local and global search. Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4DFTP problem. The solutions achieved are competitive for elaborate decision support under a TBO environment.

A framework for multi-object tracking over distributed wireless camera networks

Science.gov (United States)

Gau, Victor; Hwang, Jenq-Neng

2010-07-01

In this paper, we propose a unified framework targeting at two important issues in a distributed wireless camera network, i.e., object tracking and network communication, to achieve reliable multi-object tracking over distributed wireless camera networks. In the object tracking part, we propose a fully automated approach for tracking of multiple objects across multiple cameras with overlapping and non-overlapping field of views without initial training. To effectively exchange the tracking information among the distributed cameras, we proposed an idle probability based broadcasting method, iPro, which adaptively adjusts the broadcast probability to improve the broadcast effectiveness in a dense saturated camera network. Experimental results for the multi-object tracking demonstrate the promising performance of our approach on real video sequences for cameras with overlapping and non-overlapping views. The modeling and ns-2 simulation results show that iPro almost approaches the theoretical performance upper bound if cameras are within each other's transmission range. In more general scenarios, e.g., in case of hidden node problems, the simulation results show that iPro significantly outperforms standard IEEE 802.11, especially when the number of competing nodes increases.

Time-Varying, Multi-Scale Adaptive System Reliability Analysis of Lifeline Infrastructure Networks

Energy Technology Data Exchange (ETDEWEB)

Gearhart, Jared Lee [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kurtz, Nolan Scot [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2014-09-01

The majority of current societal and economic needs world-wide are met by the existing networked, civil infrastructure. Because the cost of managing such infrastructure is high and increases with time, risk-informed decision making is essential for those with management responsibilities for these systems. To address such concerns, a methodology that accounts for new information, deterioration, component models, component importance, group importance, network reliability, hierarchical structure organization, and efficiency concerns has been developed. This methodology analyzes the use of new information through the lens of adaptive Importance Sampling for structural reliability problems. Deterioration, multi-scale bridge models, and time-variant component importance are investigated for a specific network. Furthermore, both bridge and pipeline networks are studied for group and component importance, as well as for hierarchical structures in the context of specific networks. Efficiency is the primary driver throughout this study. With this risk-informed approach, those responsible for management can address deteriorating infrastructure networks in an organized manner.

Multi-dimensional discovery of biomarker and phenotype complexes

Directory of Open Access Journals (Sweden)

Huang Kun

2010-10-01

Full Text Available Abstract Background Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature. Results In response to the preceding gap in knowledge and practice, we report upon a prototypical series of experiments that utilize multi-modal approaches to network induction. These experiments are intended to elicit meaningful and significant biomarker-phenotype complexes spanning multiple levels of granularity. This work has been performed in the experimental context of a large-scale clinical and basic science data repository maintained by the National Cancer Institute (NCI funded Chronic Lymphocytic Leukemia Research Consortium. Conclusions Our results indicate that it is computationally tractable to link orthogonal networks of genes, clinical features, and conceptual knowledge to create multi-dimensional models of interrelated biomarkers and phenotypes. Further, our results indicate that such systems-level models contain interrelated bio-molecular and clinical markers capable of supporting hypothesis discovery and testing. Based on such findings, we propose a conceptual model intended to inform the cross-linkage of the results of such methods. This model has as its aim the identification of novel and knowledge-anchored biomarker-phenotype complexes.

Network-Oriented Modeling of Multi-Criteria Homophily and Opinion Dynamics in Social Media

NARCIS (Netherlands)

Kozyreva, Olga; Pechina, Anna; Treur, J.

2018-01-01

In this paper we model the opinion dynamics in social groups in combination with adaptation of the connections based on a multicriteria homophily principle. The adaptive network model has been designed according to a Network-Oriented Modeling approach based on temporal-causal networks. The model has

A novel network of chaotic elements and its application in multi-valued associative memory

International Nuclear Information System (INIS)

Xiu Chunbo; Liu Xiangdong; Tang Yunyu; Zhang Yuhe

2004-01-01

We give a novel chaotic element model whose activation function composed of Gauss and Sigmoid function. It is shown that the model may exhibit a complex dynamic behavior. The most significant bifurcation processes, leading to chaos, are investigated through the computation of the Lyapunov exponents. Based on this model, we propose a novel network of chaotic elements, which can be applied in associative memory, and then investigate its dynamic behavior. It is worth noting that multi-valued associative memory can also be realized by this network

Adding the 'heart' to hanging drop networks for microphysiological multi-tissue experiments.

Science.gov (United States)

Rismani Yazdi, Saeed; Shadmani, Amir; Bürgel, Sebastian C; Misun, Patrick M; Hierlemann, Andreas; Frey, Olivier

2015-11-07

Microfluidic hanging-drop networks enable culturing and analysis of 3D microtissue spheroids derived from different cell types under controlled perfusion and investigating inter-tissue communication in multi-tissue formats. In this paper we introduce a compact on-chip pumping approach for flow control in hanging-drop networks. The pump includes one pneumatic chamber located directly above one of the hanging drops and uses the surface tension at the liquid-air-interface for flow actuation. Control of the pneumatic protocol provides a wide range of unidirectional pulsatile and continuous flow profiles. With the proposed concept several independent hanging-drop networks can be operated in parallel with only one single pneumatic actuation line at high fidelity. Closed-loop medium circulation between different organ models for multi-tissue formats and multiple simultaneous assays in parallel are possible. Finally, we implemented a real-time feedback control-loop of the pump actuation based on the beating of a human iPS-derived cardiac microtissue cultured in the same system. This configuration allows for simulating physiological effects on the heart and their impact on flow circulation between the organ models on chip.

Modeling most likely pathways for smuggling radioactive and special nuclear materials on a worldwide multi-modal transportation network

Energy Technology Data Exchange (ETDEWEB)

Saeger, Kevin J [Los Alamos National Laboratory; Cuellar, Leticia [Los Alamos National Laboratory

2010-10-28

Nuclear weapons proliferation is an existing and growing worldwide problem. To help with devising strategies and supporting decisions to interdict the transport of nuclear material, we developed the Pathway Analysis, Threat Response and Interdiction Options Tool (PATRIOT) that provides an analytical approach for evaluating the probability that an adversary smuggling radioactive or special nuclear material will be detected during transit. We incorporate a global, multi-modal transportation network, explicit representation of designed and serendipitous detection opportunities, and multiple threat devices, material types, and shielding levels. This paper presents the general structure of PATRIOT, all focuses on the theoretical framework used to model the reliabilities of all network components that are used to predict the most likely pathways to the target.

Outsmarting neural networks: an alternative paradigm for machine learning

Energy Technology Data Exchange (ETDEWEB)

Protopopescu, V.; Rao, N.S.V.

1996-10-01

We address three problems in machine learning, namely: (i) function learning, (ii) regression estimation, and (iii) sensor fusion, in the Probably and Approximately Correct (PAC) framework. We show that, under certain conditions, one can reduce the three problems above to the regression estimation. The latter is usually tackled with artificial neural networks (ANNs) that satisfy the PAC criteria, but have high computational complexity. We propose several computationally efficient PAC alternatives to ANNs to solve the regression estimation. Thereby we also provide efficient PAC solutions to the function learning and sensor fusion problems. The approach is based on cross-fertilizing concepts and methods from statistical estimation, nonlinear algorithms, and the theory of computational complexity, and is designed as part of a new, coherent paradigm for machine learning.

Deep multi-scale convolutional neural network for hyperspectral image classification

Science.gov (United States)

Zhang, Feng-zhe; Yang, Xia

2018-04-01

In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

Identifying beneficial task relations for multi-task learning in deep neural networks

DEFF Research Database (Denmark)

Bingel, Joachim; Søgaard, Anders

2017-01-01

Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...

Multi-level policies and adaptive social networks – a conceptual modeling study for maintaining a polycentric governance system

Directory of Open Access Journals (Sweden)

Jean-Denis Mathias

2017-03-01

Full Text Available Information and collaboration patterns embedded in social networks play key roles in multilevel and polycentric modes of governance. However, modeling the dynamics of such social networks in multilevel settings has been seldom addressed in the literature. Here we use an adaptive social network model to elaborate the interplay between a central and a local government in order to maintain a polycentric governance. More specifically, our analysis explores in what ways specific policy choices made by a central agent affect the features of an emerging social network composed of local organizations and local users. Using two types of stylized policies, adaptive co-management and adaptive one-level management, we focus on the benefits of multi-level adaptive cooperation for network management. Our analysis uses viability theory to explore and to quantify the ability of these policies to achieve specific network properties. Viability theory gives the family of policies that enables maintaining the polycentric governance unlike optimal control that gives a unique blueprint. We found that the viability of the policies can change dramatically depending on the goals and features of the social network. For some social networks, we also found a very large difference between the viability of the adaptive one-level management and adaptive co-management policies. However, results also show that adaptive co-management doesn’t always provide benefits. Hence, we argue that applying viability theory to governance networks can help policy design by analyzing the trade-off between the costs of adaptive co-management and the benefits associated with its ability to maintain desirable social network properties in a polycentric governance framework.

Cyanobacterial Biofuels: Strategies and Developments on Network and Modeling.

Science.gov (United States)

Klanchui, Amornpan; Raethong, Nachon; Prommeenate, Peerada; Vongsangnak, Wanwipa; Meechai, Asawin

Cyanobacteria, the phototrophic microorganisms, have attracted much attention recently as a promising source for environmentally sustainable biofuels production. However, barriers for commercial markets of cyanobacteria-based biofuels concern the economic feasibility. Miscellaneous strategies for improving the production performance of cyanobacteria have thus been developed. Among these, the simple ad hoc strategies resulting in failure to optimize fully cell growth coupled with desired product yield are explored. With the advancement of genomics and systems biology, a new paradigm toward systems metabolic engineering has been recognized. In particular, a genome-scale metabolic network reconstruction and modeling is a crucial systems-based tool for whole-cell-wide investigation and prediction. In this review, the cyanobacterial genome-scale metabolic models, which offer a system-level understanding of cyanobacterial metabolism, are described. The main process of metabolic network reconstruction and modeling of cyanobacteria are summarized. Strategies and developments on genome-scale network and modeling through the systems metabolic engineering approach are advanced and employed for efficient cyanobacterial-based biofuels production.

Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

Science.gov (United States)

2009-03-01

A. Van Veldhuizen . Evolutionary Algorithms for Solving Multi-Objective Problems, chapter MOEA Parallelization. Springer, 2007. 37. Das, Subrata...Mike P. and Willem-Jan van den Heuvel. Service Oriented Architectures: Approaches, Technologies, and Research Issues. Technical report, Tilburg...Tanenbaum, Andrew S. and Maarten Van Steen. Distributed Systems: Principles and Paradigms. Prentice Hall, 2006. 128. Thomas, Tavaris J. Fire Ant: An

Multi-modular neural networks for the classification of e+e- hadronic events

International Nuclear Information System (INIS)

Proriol, J.

1994-01-01

Some multi-modular neural network methods of classifying e + e - hadronic events are presented. We compare the performances of the following neural networks: MLP (multilayer perceptron), MLP and LVQ (learning vector quantization) trained sequentially, and MLP and RBF (radial basis function) trained sequentially. We introduce a MLP-RBF cooperative neural network. Our last study is a multi-MLP neural network. (orig.)

Distributed Multi-Cell Resource Allocation with Price Based ICI Coordination in Downlink OFDMA Networks

Science.gov (United States)

Lv, Gangming; Zhu, Shihua; Hui, Hui

Multi-cell resource allocation under minimum rate request for each user in OFDMA networks is addressed in this paper. Based on Lagrange dual decomposition theory, the joint multi-cell resource allocation problem is decomposed and modeled as a limited-cooperative game, and a distributed multi-cell resource allocation algorithm is thus proposed. Analysis and simulation results show that, compared with non-cooperative iterative water-filling algorithm, the proposed algorithm can remarkably reduce the ICI level and improve overall system performances.

Multi-processor network implementations in Multibus II and VME

International Nuclear Information System (INIS)

Briegel, C.

1992-01-01

ACNET (Fermilab Accelerator Controls Network), a proprietary network protocol, is implemented in a multi-processor configuration for both Multibus II and VME. The implementations are contrasted by the bus protocol and software design goals. The Multibus II implementation provides for multiple processors running a duplicate set of tasks on each processor. For a network connected task, messages are distributed by a network round-robin scheduler. Further, messages can be stopped, continued, or re-routed for each task by user-callable commands. The VME implementation provides for multiple processors running one task across all processors. The process can either be fixed to a particular processor or dynamically allocated to an available processor depending on the scheduling algorithm of the multi-processing operating system. (author)

Keystone Business Models for Network Security Processors

OpenAIRE

Arthur Low; Steven Muegge

2013-01-01

Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor...

Investigation of Alien Wavelength Quality in Live Multi-Domain, Multi-Vendor Link Using Advanced Simulation Tool

DEFF Research Database (Denmark)

Petersen, Martin Nordal; Nuijts, Roeland; Bjorn, Lars Lange

2014-01-01

This article presents an advanced optical model for simulation of alien wavelengths in multi-domain and multi-vendor dense wavelength-division multiplexing networks. The model aids optical network planners with a better understanding of the non-linear effects present in dense wavelength-division ......This article presents an advanced optical model for simulation of alien wavelengths in multi-domain and multi-vendor dense wavelength-division multiplexing networks. The model aids optical network planners with a better understanding of the non-linear effects present in dense wavelength......-division multiplexing systems and better utilization of alien wavelengths in future applications. The limiting physical effects for alien wavelengths are investigated in relation to power levels, channel spacing, and other factors. The simulation results are verified through experimental setup in live multi...

A multi-layered network of the (Colombian) sovereign securities market

NARCIS (Netherlands)

Renneboog, Luc; Leon Rincon, Carlos; Pérez, Jhonatan; Alexandrova-Kabadjova, Bilana; Diehl, Martin; Heuver, Richard; Martinez-Jaramillo, Serafín

2015-01-01

We study the network of Colombian sovereign securities settlements. With data from the settlement market infrastructure we study financial institutions’ transactions from three different trading and registering individual networks that we combine into a multi-layer network. Examining this network of

A source-controlled data center network model.

Science.gov (United States)

Yu, Yang; Liang, Mangui; Wang, Zhe

2017-01-01

The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS.

A source-controlled data center network model

Science.gov (United States)

Yu, Yang; Liang, Mangui; Wang, Zhe

2017-01-01

The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS. PMID:28328925

Multi-terminal direct-current grids modeling, analysis, and control

CERN Document Server

Chaudhuri, Nilanjan; Majumder, Rajat; Yazdani, Amirnaser

2014-01-01

A comprehensive modeling, analysis, and control design framework for multi-terminal direct current (MTDC) grids is presented together with their interaction with the surrounding AC networks and the impact on overall stability. The first book of its kind on the topic of multi-terminal DC (MTDC) grids  Presents a comprehensive modeling framework for MTDC grids which is compatible with the standard AC system modeling for stability studies Includes modal analysis and study of the interactions between the MTDC grid and the surrounding AC systems Addresses the problems of autonomous power sharing an

Governance Paradigms of Public Universities: An International Comparative Study

Science.gov (United States)

Christopher, Joe

2012-01-01

This study aims to develop a conceptual model of the wider influencing forces impacting the governance paradigms of public universities. It draws on the multi-theoretical governance concept and seeks to identify these forces through the lens of chief audit executives using a qualitative research approach. The interview data supported by published…

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Science.gov (United States)

Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

2017-01-01

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

Throughput analysis of point-to-multi-point hybric FSO/RF network

KAUST Repository

Rakia, Tamer

2017-07-31

This paper presents and analyzes a point-to-multi-point (P2MP) network that uses a number of free-space optical (FSO) links for data transmission from the central node to the different remote nodes. A common backup radio-frequency (RF) link is used by the central node for data transmission to any remote node in case of the failure of any one of FSO links. We develop a cross-layer Markov chain model to study the throughput from central node to a tagged remote node. Numerical examples are presented to compare the performance of the proposed P2MP hybrid FSO/RF network with that of a P2MP FSO-only network and show that the P2MP Hybrid FSO/RF network achieves considerable performance improvement over the P2MP FSO-only network.

Energy Model of Networks-on-Chip and a Bus

NARCIS (Netherlands)

Wolkotte, P.T.; Smit, Gerardus Johannes Maria; Kavaldjiev, N.K.; Becker, Jens E.; Becker, Jürgen; Nurmi, J.; Takala, J.; Hamalainen, T.D.

2005-01-01

A Network-on-Chip (NoC) is an energy-efficient onchip communication architecture for Multi-Processor Systemon-Chip (MPSoC) architectures. In earlier papers we proposed two Network-on-Chip architectures based on packet-switching and circuit-switching. In this paper we derive an energy model for both

Industry 4.0 and the New Simulation Modelling Paradigm

Directory of Open Access Journals (Sweden)

Rodič Blaž

2017-08-01

Full Text Available Background and Purpose: The aim of this paper is to present the influence of Industry 4.0 on the development of the new simulation modelling paradigm, embodied by the Digital Twin concept, and examine the adoption of the new paradigm via a multiple case study involving real-life R&D cases involving academia and industry.

Dynamic clustering scheme based on the coordination of management and control in multi-layer and multi-region intelligent optical network

Science.gov (United States)

Niu, Xiaoliang; Yuan, Fen; Huang, Shanguo; Guo, Bingli; Gu, Wanyi

2011-12-01

A Dynamic clustering scheme based on coordination of management and control is proposed to reduce network congestion rate and improve the blocking performance of hierarchical routing in Multi-layer and Multi-region intelligent optical network. Its implement relies on mobile agent (MA) technology, which has the advantages of efficiency, flexibility, functional and scalability. The paper's major contribution is to adjust dynamically domain when the performance of working network isn't in ideal status. And the incorporation of centralized NMS and distributed MA control technology migrate computing process to control plane node which releases the burden of NMS and improves process efficiently. Experiments are conducted on Multi-layer and multi-region Simulation Platform for Optical Network (MSPON) to assess the performance of the scheme.

Application of neural computing paradigms for signal validation

International Nuclear Information System (INIS)

Upadhyaya, B.R.; Eryurek, E.; Mathai, G.

1989-01-01

Signal validation and process monitoring problems often require the prediction of one or more process variables in a system. The feasibility of applying neural network paradigms to relate one variable with a set of other related variables is studied. The backpropagation network (BPN) is applied to develop models of signals from both a commercial power plant and the EBR-II. Modification of the BPN algorithm is studied with emphasis on the speed of network training and the accuracy of prediction. The prediction of process variables in a Westinghouse PWR is presented in this paper

Survivable integrated grooming in multi-granularity optical networks

Science.gov (United States)

Wu, Jingjing; Guo, Lei; Wei, Xuetao; Liu, Yejun

2012-05-01

Survivability is an important issue to ensure the service continuity in optical network. At the same time, with the granularity of traffic demands ranging from sub-wavelength-level to wavelength-level, traffic demands need to be aggregated and carried over the network in order to utilize resources effectively. Therefore, multi-granularity grooming is proposed to save the cost and reduce the number of switching ports in Optical-Cross Connects (OXCs). However, current works mostly addressed the survivable wavelength or waveband grooming. Therefore, in this paper, we propose three heuristic algorithms called Multi-granularity Dedicated Protection Grooming (MDPG), Multi-granularity Shared Protection Grooming (MSPG) and Multi-granularity Mixed Protection Grooming (MMPG), respectively. All of them are performed based on the Survivable Multi-granularity Integrated Auxiliary Graph (SMIAG) that includes one Wavelength Integrated Auxiliary Graph (WIAG) for wavelength protection and one waveBand Integrated Auxiliary Graph (BIAG) for waveband protection. Numerical results show that MMPG has the lowest average port-cost, the best resource utilization ratio and the lowest blocking probability among these three algorithms. Compared with MDPG, MSPG has lower average port-cost, better resource utilization ratio and lower blocking probability.

Testing Paradigms of Ecosystem Change under Climate Warming in Antarctica

Science.gov (United States)

Melbourne-Thomas, Jessica; Constable, Andrew; Wotherspoon, Simon; Raymond, Ben

2013-01-01

Antarctic marine ecosystems have undergone significant changes as a result of human activities in the past and are now responding in varied and often complicated ways to climate change impacts. Recent years have seen the emergence of large-scale mechanistic explanations–or “paradigms of change”–that attempt to synthesize our understanding of past and current changes. In many cases, these paradigms are based on observations that are spatially and temporally patchy. The West Antarctic Peninsula (WAP), one of Earth’s most rapidly changing regions, has been an area of particular research focus. A recently proposed mechanistic explanation for observed changes in the WAP region relates changes in penguin populations to variability in krill biomass and regional warming. While this scheme is attractive for its simplicity and chronology, it may not account for complex spatio-temporal processes that drive ecosystem dynamics in the region. It might also be difficult to apply to other Antarctic regions that are experiencing some, though not all, of the changes documented for the WAP. We use qualitative network models of differing levels of complexity to test paradigms of change for the WAP ecosystem. Importantly, our approach captures the emergent effects of feedback processes in complex ecological networks and provides a means to identify and incorporate uncertain linkages between network elements. Our findings highlight key areas of uncertainty in the drivers of documented trends, and suggest that a greater level of model complexity is needed in devising explanations for ecosystem change in the Southern Ocean. We suggest that our network approach to evaluating a recent and widely cited paradigm of change for the Antarctic region could be broadly applied in hypothesis testing for other regions and research fields. PMID:23405116

Optimal Allocation of Generalized Power Sources in Distribution Network Based on Multi-Objective Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Li Ran

2017-01-01

Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.

Error performance analysis in K-tier uplink cellular networks using a stochastic geometric approach

KAUST Repository

Afify, Laila H.

2015-09-14

In this work, we develop an analytical paradigm to analyze the average symbol error probability (ASEP) performance of uplink traffic in a multi-tier cellular network. The analysis is based on the recently developed Equivalent-in-Distribution approach that utilizes stochastic geometric tools to account for the network geometry in the performance characterization. Different from the other stochastic geometry models adopted in the literature, the developed analysis accounts for important communication system parameters and goes beyond signal-to-interference-plus-noise ratio characterization. That is, the presented model accounts for the modulation scheme, constellation type, and signal recovery techniques to model the ASEP. To this end, we derive single integral expressions for the ASEP for different modulation schemes due to aggregate network interference. Finally, all theoretical findings of the paper are verified via Monte Carlo simulations.

Multi-modal Social Networks: A MRF Learning Approach

Science.gov (United States)

2016-06-20

Network forensics: random infection vs spreading epidemic , Proceedings of ACM Sigmetrics. 11-JUN-12, London, UK. : , TOTAL: 4 06/09/2016 Received Paper...Multi-modal Social Networks A MRF Learning Approach The work primarily focused on two lines of research. 1. We propose new greedy algorithms...Box 12211 Research Triangle Park, NC 27709-2211 social networks , learning and inference REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT

Multi-zone coupling productivity of horizontal well fracturing with complex fracture networks in shale gas reservoirs

Directory of Open Access Journals (Sweden)

Weiyao Zhu

2018-02-01

Full Text Available In this paper, a series of specific studies were carried out to investigate the complex form of fracture networks and figure out the multi-scale flowing laws of nano/micro pores–complex fracture networks–wellbore during the development of shale reservoirs by means of horizontal well fracturing. First, hydraulic fractures were induced by means of Brazilian splitting tests. Second, the forms of the hydraulic fractures inside the rock samples were observed by means of X-ray CT scanning to measure the opening of hydraulic fractures. Third, based on the multi-scale unified flowing model, morphological description of fractures and gas flowing mechanism in the matrix–complex fracture network–wellbore, the productivity equation of single-stage horizontal well fracturing which includes diffusion, slipping and desorption was established. And fourthly, a productivity prediction model of horizontal well multi-stage fracturing in the shale reservoir was established considering the interference between the multi-stage fracturing zones and the pressure drop in the horizontal wellbore. The following results were obtained. First, hydraulic fractures are in the form of a complex network. Second, the measured opening of hydraulic fractures is in the range of 4.25–453 μm, averaging 112 μm. Third, shale gas flowing in different shapes of fracture networks follows different nonlinear flowing laws. Forth, as the fracture density in the strongly stimulated zones rises and the distribution range of the hydraulic fractures in strongly/weakly stimulated zones enlarges, gas production increases gradually. As the interference occurs in the flowing zones of fracture networks between fractured sections, the increasing amplitude of gas production rates decreases. Fifth, when the length of a simulated horizontal well is 1500 m and the half length of a fracture network in the strongly stimulated zone is 100 m, the productivity effect of stage 10 fracturing is the

Energy efficiency optimisation for distillation column using artificial neural network models

International Nuclear Information System (INIS)

Osuolale, Funmilayo N.; Zhang, Jie

2016-01-01

This paper presents a neural network based strategy for the modelling and optimisation of energy efficiency in distillation columns incorporating the second law of thermodynamics. Real-time optimisation of distillation columns based on mechanistic models is often infeasible due to the effort in model development and the large computation effort associated with mechanistic model computation. This issue can be addressed by using neural network models which can be quickly developed from process operation data. The computation time in neural network model evaluation is very short making them ideal for real-time optimisation. Bootstrap aggregated neural networks are used in this study for enhanced model accuracy and reliability. Aspen HYSYS is used for the simulation of the distillation systems. Neural network models for exergy efficiency and product compositions are developed from simulated process operation data and are used to maximise exergy efficiency while satisfying products qualities constraints. Applications to binary systems of methanol-water and benzene-toluene separations culminate in a reduction of utility consumption of 8.2% and 28.2% respectively. Application to multi-component separation columns also demonstrate the effectiveness of the proposed method with a 32.4% improvement in the exergy efficiency. - Highlights: • Neural networks can accurately model exergy efficiency in distillation columns. • Bootstrap aggregated neural network offers improved model prediction accuracy. • Improved exergy efficiency is obtained through model based optimisation. • Reductions of utility consumption by 8.2% and 28.2% were achieved for binary systems. • The exergy efficiency for multi-component distillation is increased by 32.4%.

Reference Architecture for Multi-Layer Software Defined Optical Data Center Networks

Directory of Open Access Journals (Sweden)

Casimer DeCusatis

2015-09-01

Full Text Available As cloud computing data centers grow larger and networking devices proliferate; many complex issues arise in the network management architecture. We propose a framework for multi-layer; multi-vendor optical network management using open standards-based software defined networking (SDN. Experimental results are demonstrated in a test bed consisting of three data centers interconnected by a 125 km metropolitan area network; running OpenStack with KVM and VMW are components. Use cases include inter-data center connectivity via a packet-optical metropolitan area network; intra-data center connectivity using an optical mesh network; and SDN coordination of networking equipment within and between multiple data centers. We create and demonstrate original software to implement virtual network slicing and affinity policy-as-a-service offerings. Enhancements to synchronous storage backup; cloud exchanges; and Fibre Channel over Ethernet topologies are also discussed.

Adding the ‘heart’ to hanging drop networks for microphysiological multi-tissue experiments†

Science.gov (United States)

Yazdi, Saeed Rismani; Shadmani, Amir; Bürgel, Sebastian C.; Misun, Patrick M.; Hierlemann, Andreas; Frey, Olivier

2017-01-01

Microfluidic hanging-drop networks enable culturing and analysis of 3D microtissue spheroids derived from different cell types under controlled perfusion and investigating inter-tissue communication in multi-tissue formats. In this paper we introduce a compact on-chip pumping approach for flow control in hanging-drop networks. The pump includes one pneumatic chamber located directly above one of the hanging drops and uses the surface tension at the liquid–air-interface for flow actuation. Control of the pneumatic protocol provides a wide range of unidirectional pulsatile and continuous flow profiles. With the proposed concept several independent hanging-drop networks can be operated in parallel with only one single pneumatic actuation line at high fidelity. Closed-loop medium circulation between different organ models for multi-tissue formats and multiple simultaneous assays in parallel are possible. Finally, we implemented a real-time feedback control-loop of the pump actuation based on the beating of a human iPS-derived cardiac microtissue cultured in the same system. This configuration allows for simulating physiological effects on the heart and their impact on flow circulation between the organ models on chip. PMID:26401602

High-precision multi-node clock network distribution.

Science.gov (United States)

Chen, Xing; Cui, Yifan; Lu, Xing; Ci, Cheng; Zhang, Xuesong; Liu, Bo; Wu, Hong; Tang, Tingsong; Shi, Kebin; Zhang, Zhigang

2017-10-01

A high precision multi-node clock network for multiple users was built following the precise frequency transmission and time synchronization of 120 km fiber. The network topology adopts a simple star-shaped network structure. The clock signal of a hydrogen maser (synchronized with UTC) was recovered from a 120 km telecommunication fiber link and then was distributed to 4 sub-stations. The fractional frequency instability of all substations is in the level of 10 -15 in a second and the clock offset instability is in sub-ps in root-mean-square average.

Interference-aware power control for multi-radio multi-channel wireless mesh networks

CSIR Research Space (South Africa)

Olwal, TO

2009-09-01

Full Text Available are well known in [9], [6]. The operation of multi-radio multi-channel (MRMC) WMNs generally requires sustainable energy supply. Substantial deployments of WMNs have recently been witnessed in rural and remote communities [4]. In such applications...]. Power resources are dynamically adjusted by each NIC using intra and inter-subsystem (channel) states. Due to the decentralized nature, each MP assumes imperfect knowledge about the global network. Further we assume that there exists...

Multi-level Control Framework for Enhanced Flexibility of Active Distribution Network

DEFF Research Database (Denmark)

Nainar, Karthikeyan; Pokhrel, Basanta Raj; Pillai, Jayakrishnan Radhakrishna

2017-01-01

In this paper, the control objectives of future active distribution networks with high penetration of renewables and flexible loads are analyzed and reviewed. From a state of the art review, the important control objectives seen from the perspective of a distribution system operator are identifie......-ordination and management of the network assets at different voltage levels and geographical locations. The paper finally shows the applicability of the multi-level control architecture to some of the key challenges in the distribution system operation by relevant scenarios....... to be hosting capacity improvement, high reliable operation and cost effective network management. Based on this review and a state of the art review concerning future distribution network control methods, a multi-level control architecture is constructed for an active distribution network, which satisfies...... the selected control objectives and provides enhanced flexibility. The control architecture is supported by generation/load forecasting and distribution state estimation techniques to improve the controllability of the network. The multi-level control architecture consists of three levels of hierarchical...

Simulation and Modeling of a New Medium Access Control Scheme for Multi-Beam Directional Networking

Science.gov (United States)

2017-03-03

Multi-beam directional systems are a novel approach to networking which leverage recent advances in physical layer technology, allowing formation of...for a programmatic method for setting up emulation experiments. Rather than hard code all of the underlying pieces for EMANE (such as the over-the-air

Multi-Level Marketing as a business model

Directory of Open Access Journals (Sweden)

Bogdan Gregor

2013-03-01

Full Text Available Multi Level Marketing is a very popular business model in the Western countries. It is a kind of hybrid of the method of distribution of goods and the method of building a sales network. It is one of the safest (carries a very low risk ways of conducting a business activity. The knowledge about functioning of this business model, both among theoreticians (scanty literature on the subject and practitioners, is still insufficient in Poland. Thus, the presented paper has been prepared as — in the Authors' opinion — it, at least infinitesimally, bridges the gap in the recognition of Multi Level Marketing issues. The aim of the study was, first of all, to describe Multi Level Marketing, to indicate practical benefits of this business model as well as to present basic systems of calculating a commission, which are used in marketing plans of companies. The discussion was based on the study of literature and the knowledge gained in the course of free-form interviews with the leaders of the sector.

A new cut-based algorithm for the multi-state flow network reliability problem

International Nuclear Information System (INIS)

Yeh, Wei-Chang; Bae, Changseok; Huang, Chia-Ling

2015-01-01

Many real-world systems can be modeled as multi-state network systems in which reliability can be derived in terms of the lower bound points of level d, called d-minimal cuts (d-MCs). This study proposes a new method to find and verify obtained d-MCs with simple and useful found properties for the multi-state flow network reliability problem. The proposed algorithm runs in O(mσp) time, which represents a significant improvement over the previous O(mp 2 σ) time bound based on max-flow/min-cut, where p, σ and m denote the number of MCs, d-MC candidates and edges, respectively. The proposed algorithm also conquers the weakness of some existing methods, which failed to remove duplicate d-MCs in special cases. A step-by-step example is given to demonstrate how the proposed algorithm locates and verifies all d-MC candidates. As evidence of the utility of the proposed approach, we present extensive computational results on 20 benchmark networks in another example. The computational results compare favorably with a previously developed algorithm in the literature. - Highlights: • A new method is proposed to find all d-MCs for the multi-state flow networks. • The proposed method can prevent the generation of d-MC duplicates. • The proposed method is simpler and more efficient than the best-known algorithms

Multi-channel distributed coordinated function over single radio in wireless sensor networks.

Science.gov (United States)

Campbell, Carlene E-A; Loo, Kok-Keong Jonathan; Gemikonakli, Orhan; Khan, Shafiullah; Singh, Dhananjay

2011-01-01

Multi-channel assignments are becoming the solution of choice to improve performance in single radio for wireless networks. Multi-channel allows wireless networks to assign different channels to different nodes in real-time transmission. In this paper, we propose a new approach, Multi-channel Distributed Coordinated Function (MC-DCF) which takes advantage of multi-channel assignment. The backoff algorithm of the IEEE 802.11 distributed coordination function (DCF) was modified to invoke channel switching, based on threshold criteria in order to improve the overall throughput for wireless sensor networks (WSNs) over 802.11 networks. We presented simulation experiments in order to investigate the characteristics of multi-channel communication in wireless sensor networks using an NS2 platform. Nodes only use a single radio and perform channel switching only after specified threshold is reached. Single radio can only work on one channel at any given time. All nodes initiate constant bit rate streams towards the receiving nodes. In this work, we studied the impact of non-overlapping channels in the 2.4 frequency band on: constant bit rate (CBR) streams, node density, source nodes sending data directly to sink and signal strength by varying distances between the sensor nodes and operating frequencies of the radios with different data rates. We showed that multi-channel enhancement using our proposed algorithm provides significant improvement in terms of throughput, packet delivery ratio and delay. This technique can be considered for WSNs future use in 802.11 networks especially when the IEEE 802.11n becomes popular thereby may prevent the 802.15.4 network from operating effectively in the 2.4 GHz frequency band.

Multi-view and 3D deformable part models.

Science.gov (United States)

Pepik, Bojan; Stark, Michael; Gehler, Peter; Schiele, Bernt

2015-11-01

As objects are inherently 3D, they have been modeled in 3D in the early days of computer vision. Due to the ambiguities arising from mapping 2D features to 3D models, 3D object representations have been neglected and 2D feature-based models are the predominant paradigm in object detection nowadays. While such models have achieved outstanding bounding box detection performance, they come with limited expressiveness, as they are clearly limited in their capability of reasoning about 3D shape or viewpoints. In this work, we bring the worlds of 3D and 2D object representations closer, by building an object detector which leverages the expressive power of 3D object representations while at the same time can be robustly matched to image evidence. To that end, we gradually extend the successful deformable part model [1] to include viewpoint information and part-level 3D geometry information, resulting in several different models with different level of expressiveness. We end up with a 3D object model, consisting of multiple object parts represented in 3D and a continuous appearance model. We experimentally verify that our models, while providing richer object hypotheses than the 2D object models, provide consistently better joint object localization and viewpoint estimation than the state-of-the-art multi-view and 3D object detectors on various benchmarks (KITTI [2] , 3D object classes [3] , Pascal3D+ [4] , Pascal VOC 2007 [5] , EPFL multi-view cars[6] ).

Multi-Objective Design Of Optimal Greenhouse Gas Observation Networks

Science.gov (United States)

Lucas, D. D.; Bergmann, D. J.; Cameron-Smith, P. J.; Gard, E.; Guilderson, T. P.; Rotman, D.; Stolaroff, J. K.

2010-12-01

One of the primary scientific functions of a Greenhouse Gas Information System (GHGIS) is to infer GHG source emission rates and their uncertainties by combining measurements from an observational network with atmospheric transport modeling. Certain features of the observational networks that serve as inputs to a GHGIS --for example, sampling location and frequency-- can greatly impact the accuracy of the retrieved GHG emissions. Observation System Simulation Experiments (OSSEs) provide a framework to characterize emission uncertainties associated with a given network configuration. By minimizing these uncertainties, OSSEs can be used to determine optimal sampling strategies. Designing a real-world GHGIS observing network, however, will involve multiple, conflicting objectives; there will be trade-offs between sampling density, coverage and measurement costs. To address these issues, we have added multi-objective optimization capabilities to OSSEs. We demonstrate these capabilities by quantifying the trade-offs between retrieval error and measurement costs for a prototype GHGIS, and deriving GHG observing networks that are Pareto optimal. [LLNL-ABS-452333: This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

A Framework for Joint Optical-Wireless Resource Management in Multi-RAT, Heterogeneous Mobile Networks

DEFF Research Database (Denmark)

Zakrzewska, Anna; Popovska Avramova, Andrijana; Christiansen, Henrik Lehrmann

2013-01-01

Mobile networks are constantly evolving: new Radio Access Technologies (RATs) are being introduced, and backhaul architectures like Cloud-RAN (C-RAN) and distributed base stations are being proposed. Furthermore, small cells are being deployed to enhance network capacity. The end-users wish...... to be always connected to a high-quality service (high bit rates, low latency), thus causing a very complex network control task from an operator’s point of view. We thus propose a framework allowing joint overall network resource management. This scheme covers different types of network heterogeneity (multi......-RAT, multi-layer, multi-architecture) by introducing a novel, hierarchical approach to network resource management. Self-Organizing Networks (SON) and cognitive network behaviors are covered as well as more traditional mobile network features. The framework is applicable to all phases of network operation...

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Directory of Open Access Journals (Sweden)

Marko Budinich

Full Text Available Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA and multi-objective flux variability analysis (MO-FVA. Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity that take place at the ecosystem scale.

Multi-agents Based Modelling for Distribution Network Operation with Electric Vehicle Integration

DEFF Research Database (Denmark)

Hu, Junjie; Morais, Hugo; Zong, Yi

2014-01-01

Electric vehicles (EV) can become integral part of a smart grid because instead of just consuming power they are capable of providing valuable services to power systems. To integrate EVs smoothly into the power systems, a multi-agents system (MAS) with hierarchical organization structure...... and its role is to manage the distribution network safely by avoiding grid congestions and using congestion prices to coordinate the energy schedule of VPPs. VPP agents belong to the middle level and their roles are to manage the charge periods of the EVs. EV agents sit in the bottom level...

Hybrid digital signal processing and neural networks applications in PWRs

International Nuclear Information System (INIS)

Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

1991-01-01

Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications

Two-Language, Two-Paradigm Introductory Computing Curriculum Model and its Implementation

OpenAIRE

Zanev, Vladimir; Radenski, Atanas

2011-01-01

This paper analyzes difficulties with the introduction of object-oriented concepts in introductory computing education and then proposes a two-language, two-paradigm curriculum model that alleviates such difficulties. Our two-language, two-paradigm curriculum model begins with teaching imperative programming using Python programming language, continues with teaching object-oriented computing using Java, and concludes with teaching object-oriented data structures with Java.

OPNET Modeler simulations of performance for multi nodes wireless systems

Directory of Open Access Journals (Sweden)

Krupanek Beata

2016-01-01

Full Text Available Paper presents a study under the Quality of Service in modern wireless sensor networks. Such a networks are characterized by small amount of data transmitted in fixed periods. Very often this data must by transmitted in real time so data transmission delays should be well known. This article shows multimode network simulated in packet OPNET Modeler. Also nowadays the quality of services is very important especially in multi-nodes systems such a home automation or measurement systems.

Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

ZHENG Zhuo

2018-05-01

Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

Research of negotiation in network trade system based on multi-agent

Science.gov (United States)

Cai, Jun; Wang, Guozheng; Wu, Haiyan

2009-07-01

A construction and implementation technology of network trade based on multi-agent is described in this paper. First, we researched the technology of multi-agent, then we discussed the consumer's behaviors and the negotiation between purchaser and bargainer which emerges in the traditional business mode and analysed the key technology to implement the network trade system. Finally, we implement the system.

Quantitative analysis of distributed control paradigms of robot swarms

DEFF Research Database (Denmark)

Ngo, Trung Dung

2010-01-01

describe the physical and simulated robots, experiment scenario, and experiment setup. Third, we present our robot controllers based on behaviour based and neural network based paradigms. Fourth, we graphically show their experiment results and quantitatively analyse the results in comparison of the two......Given a task of designing controller for mobile robots in swarms, one might wonder which distributed control paradigms should be selected. Until now, paradigms of robot controllers have been within either behaviour based control or neural network based control, which have been recognized as two...... mainstreams of controller design for mobile robots. However, in swarm robotics, it is not clear how to determine control paradigms. In this paper we study the two control paradigms with various experiments of swarm aggregation. First, we introduce the two control paradigms for mobile robots. Second, we...

Limitations of demand- and pressure-driven modeling for large deficient networks

Science.gov (United States)

Braun, Mathias; Piller, Olivier; Deuerlein, Jochen; Mortazavi, Iraj

2017-10-01

The calculation of hydraulic state variables for a network is an important task in managing the distribution of potable water. Over the years the mathematical modeling process has been improved by numerous researchers for utilization in new computer applications and the more realistic modeling of water distribution networks. But, in spite of these continuous advances, there are still a number of physical phenomena that may not be tackled correctly by current models. This paper will take a closer look at the two modeling paradigms given by demand- and pressure-driven modeling. The basic equations are introduced and parallels are drawn with the optimization formulations from electrical engineering. These formulations guarantee the existence and uniqueness of the solution. One of the central questions of the French and German research project ResiWater is the investigation of the network resilience in the case of extreme events or disasters. Under such extraordinary conditions where models are pushed beyond their limits, we talk about deficient network models. Examples of deficient networks are given by highly regulated flow, leakage or pipe bursts and cases where pressure falls below the vapor pressure of water. These examples will be presented and analyzed on the solvability and physical correctness of the solution with respect to demand- and pressure-driven models.

Is This the Paradigm Shift We Need?

Science.gov (United States)

Mirvis, Jonathan

2012-01-01

Dr. Woocher's essay, states Mirvis, is seminal in the field of Jewish education. It proposes a new paradigm for Jewish education in North America. This proposed paradigm is supported by a comprehensive multi-disciplinary research drawing on literature from education, philosophy, history, sociology, psychology, and economics. The essay reflects a…

Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

Science.gov (United States)

Lee, Jongpil; Nam, Juhan

2017-08-01

Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management.

Science.gov (United States)

Cruz-Piris, Luis; Rivera, Diego; Fernandez, Susel; Marsa-Maestre, Ivan

2018-02-02

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

A Distributed Multi-dimensional SOLAP Model of Remote Sensing Data and Its Application in Drought Analysis

Directory of Open Access Journals (Sweden)

LI Jiyuan

2014-06-01

Full Text Available SOLAP (Spatial On-Line Analytical Processing has been applied to multi-dimensional analysis of remote sensing data recently. However, its computation performance faces a considerable challenge from the large-scale dataset. A geo-raster cube model extended by Map-Reduce is proposed, which refers to the application of Map-Reduce (a data-intensive computing paradigm in the OLAP field. In this model, the existing methods are modified to adapt to distributed environment based on the multi-level raster tiles. Then the multi-dimensional map algebra is introduced to decompose the SOLAP computation into multiple distributed parallel map algebra functions on tiles under the support of Map-Reduce. The drought monitoring by remote sensing data is employed as a case study to illustrate the model construction and application. The prototype is also implemented, and the performance testing shows the efficiency and scalability of this model.

A cyber-anima-based model of material conscious information network

Directory of Open Access Journals (Sweden)

Jianping Shen

2017-03-01

Full Text Available Purpose – This paper aims to study the node modeling, multi-agent architecture and addressing method for the material conscious information network (MCIN, which is a large-scaled, open-styled, self-organized and ecological intelligent network of supply–demand relationships. Design/methodology/approach – This study models the MCIN by node model definition, multi-agent architecture design and addressing method presentation. Findings – The prototype of novel E-commerce platform based on the MCIN shows the effectiveness and soundness of the MCIN modeling. By comparing to current internet, the authors also find that the MCIN has the advantages of socialization, information integration, collective intelligence, traceability, high robustness, unification of producing and consuming, high scalability and decentralization. Research limitations/implications – Leveraging the dimensions of structure, character, knowledge and experience, a modeling approach of the basic information can fit all kinds of the MCIN nodes. With the double chain structure for both basic and supply–demand information, the MCIN nodes can be modeled comprehensively. The anima-desire-intention-based multi-agent architecture makes the federated agents of the MCIN nodes self-organized and intelligent. The MCIN nodes can be efficiently addressed by the supply–demand-oriented method. However, the implementation of the MCIN is still in process. Practical implications – This paper lays the theoretical foundation for the future networked system of supply–demand relationship and the novel E-commerce platform. Originality/value – The authors believe that the MCIN, first proposed in this paper, is a transformational innovation which facilitates the infrastructure of the future networked system of supply–demand relationship.

Routing protocol extension for resilient GMPLS multi-domain networks

DEFF Research Database (Denmark)

Manolova, Anna Vasileva; Ruepp, Sarah Renée; Romeral, Ricardo

2010-01-01

This paper evaluates the performance of multi-domain networks under the Generalized Multi-Protocol Label Switching control framework in case of a single inter-domain link failure. We propose and evaluate a routing protocol extension for the Border Gateway Protocol, which allows domains to obtain...... two Autonomous System disjoint paths and use them efficiently under failure conditions. Three main applications for the protocol extension are illustrated: reducing traffic loss on existing connections by xploiting pre-selected backup paths derived with our proposal, applying multi-domain restoration...... as survivability mechanism in case of single link failure, and employing proper failure notification mechanisms for routing of future connection requests under routing protocol re-convergence. Via simulations we illustrate the benefits of utilizing the proposed routing protocol extension for networks employing...

Temporal neural networks and transient analysis of complex engineering systems

Science.gov (United States)

Uluyol, Onder

A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

Directory of Open Access Journals (Sweden)

Syed Ali Abbas Kazmi

2017-02-01

Full Text Available Distribution networks (DNWs are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN needs to consider choices, primarily distributed generation (DG, network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs based on mult-objective optimizations (MOOs in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at

Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

Science.gov (United States)

Krajíček, Jiří

This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

Design and implementation of space physics multi-model application integration based on web

Science.gov (United States)

Jiang, Wenping; Zou, Ziming

With the development of research on space environment and space science, how to develop network online computing environment of space weather, space environment and space physics models for Chinese scientific community is becoming more and more important in recent years. Currently, There are two software modes on space physics multi-model application integrated system (SPMAIS) such as C/S and B/S. the C/S mode which is traditional and stand-alone, demands a team or workshop from many disciplines and specialties to build their own multi-model application integrated system, that requires the client must be deployed in different physical regions when user visits the integrated system. Thus, this requirement brings two shortcomings: reducing the efficiency of researchers who use the models to compute; inconvenience of accessing the data. Therefore, it is necessary to create a shared network resource access environment which could help users to visit the computing resources of space physics models through the terminal quickly for conducting space science research and forecasting spatial environment. The SPMAIS develops high-performance, first-principles in B/S mode based on computational models of the space environment and uses these models to predict "Space Weather", to understand space mission data and to further our understanding of the solar system. the main goal of space physics multi-model application integration system (SPMAIS) is to provide an easily and convenient user-driven online models operating environment. up to now, the SPMAIS have contained dozens of space environment models , including international AP8/AE8 IGRF T96 models and solar proton prediction model geomagnetic transmission model etc. which are developed by Chinese scientists. another function of SPMAIS is to integrate space observation data sets which offers input data for models online high-speed computing. In this paper, service-oriented architecture (SOA) concept that divides system into

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

Science.gov (United States)

Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

2017-12-20

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

Multi-perspective workflow modeling for online surgical situation models.

Science.gov (United States)

Franke, Stefan; Meixensberger, Jürgen; Neumuth, Thomas

2015-04-01

Surgical workflow management is expected to enable situation-aware adaptation and intelligent systems behavior in an integrated operating room (OR). The overall aim is to unburden the surgeon and OR staff from both manual maintenance and information seeking tasks. A major step toward intelligent systems behavior is a stable classification of the surgical situation from multiple perspectives based on performed low-level tasks. The present work proposes a method for the classification of surgical situations based on multi-perspective workflow modeling. A model network that interconnects different types of surgical process models is described. Various aspects of a surgical situation description were considered: low-level tasks, high-level tasks, patient status, and the use of medical devices. A study with sixty neurosurgical interventions was conducted to evaluate the performance of our approach and its robustness against incomplete workflow recognition input. A correct classification rate of over 90% was measured for high-level tasks and patient status. The device usage models for navigation and neurophysiology classified over 95% of the situations correctly, whereas the ultrasound usage was more difficult to predict. Overall, the classification rate decreased with an increasing level of input distortion. Autonomous adaptation of medical devices and intelligent systems behavior do not currently depend solely on low-level tasks. Instead, they require a more general type of understanding of the surgical condition. The integration of various surgical process models in a network provided a comprehensive representation of the interventions and allowed for the generation of extensive situation descriptions. Multi-perspective surgical workflow modeling and online situation models will be a significant pre-requisite for reliable and intelligent systems behavior. Hence, they will contribute to a cooperative OR environment. Copyright © 2015 Elsevier Inc. All rights reserved.

Predictive analytics of environmental adaptability in multi-omic network models.

Science.gov (United States)

Angione, Claudio; Lió, Pietro

2015-10-20

Bacterial phenotypic traits and lifestyles in response to diverse environmental conditions depend on changes in the internal molecular environment. However, predicting bacterial adaptability is still difficult outside of laboratory controlled conditions. Many molecular levels can contribute to the adaptation to a changing environment: pathway structure, codon usage, metabolism. To measure adaptability to changing environmental conditions and over time, we develop a multi-omic model of Escherichia coli that accounts for metabolism, gene expression and codon usage at both transcription and translation levels. After the integration of multiple omics into the model, we propose a multiobjective optimization algorithm to find the allowable and optimal metabolic phenotypes through concurrent maximization or minimization of multiple metabolic markers. In the condition space, we propose Pareto hypervolume and spectral analysis as estimators of short term multi-omic (transcriptomic and metabolic) evolution, thus enabling comparative analysis of metabolic conditions. We therefore compare, evaluate and cluster different experimental conditions, models and bacterial strains according to their metabolic response in a multidimensional objective space, rather than in the original space of microarray data. We finally validate our methods on a phenomics dataset of growth conditions. Our framework, named METRADE, is freely available as a MATLAB toolbox.

Outage and ser performance of an opportunistic multi-user underlay cognitive network

KAUST Repository

Khan, Fahd Ahmed

2012-10-01

Consider a multi-user underlay cognitive network where multiple cognitive users concurrently share the spectrum with a primary network and a single secondary user is selected for transmission. The channel is assumed to have independent but not identical Nakagami-m fading. Closed form expressions for the outage performance and the symbol-error-rate performance of the opportunistic multi-user secondary network are derived when a peak interference power constraint is imposed on the secondary network in addition to the limited peak transmit power of each secondary user. © 2012 IEEE.

Research priorities for a multi-center child abuse pediatrics network - CAPNET.

Science.gov (United States)

Lindberg, Daniel M; Wood, Joanne N; Campbell, Kristine A; Scribano, Philip V; Laskey, Antoinette; Leventhal, John M; Pierce, Mary Clyde; Runyan, Desmond K

2017-03-01

Although child maltreatment medical research has benefited from several multi-center studies, the new specialty of child abuse pediatrics has not had a sustainable network capable of pursuing multiple, prospective, clinically-oriented studies. The Child Abuse Pediatrics Network (CAPNET) is a new multi-center research network dedicated to child maltreatment medical research. In order to establish a relevant, practical research agenda, we conducted a modified Delphi process to determine the topic areas with highest priority for such a network. Research questions were solicited from members of the Ray E. Helfer Society and study authors and were sorted into topic areas. These topic areas were rated for priority using iterative rounds of ratings and in-person meetings. The topics rated with the highest priority were missed diagnosis and selected/indicated prevention. This agenda can be used to target future multi-center child maltreatment medical research. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimal brain network synchrony visualization: application in an alcoholism paradigm.

Science.gov (United States)

Sakkalis, Vangelis; Tsiaras, Vassilis; Zervakis, Michalis; Tollis, Ioannis

2007-01-01

Although Electroencephalographic (EEG) signal synchronization studies have been a topic of increasing interest lately, there is no similar effort in the visualization of such measures. In this direction a graph-theoretic approach devised to study and stress the coupling dynamics of task-performing dynamical networks is proposed. Both linear and nonlinear interdependence measures are investigated in an alcoholism paradigm during mental rehearsal of pictures, which is known to reflect synchronization impairment. More specifically, the widely used magnitude squared coherence; phase synchronization and a robust nonlinear state-space generalized synchronization assessment method are investigated. This paper mostly focuses on a signal-based technique of selecting the optimal visualization threshold using surrogate datasets to correctly identify the most significant correlation patterns. Furthermore, a graph statistical parameter attempts to capture and quantify collective motifs present in the functional brain network. The results are in accordance with previous psychophysiology studies suggesting that an alcoholic subject has impaired synchronization of brain activity and loss of lateralization during the rehearsal process, most prominently in alpha (8-12 Hz) band, as compared to a control subject. Lower beta (13-30 Hz) synchronization was also evident in the alcoholic subject.

Collaborative Multi-Layer Network Coding in Hybrid Cellular Cognitive Radio Networks

KAUST Repository

Moubayed, Abdallah J.

2015-05-01

In this paper, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated collaboration between the collocated primary and cognitive radio base-stations in order to minimize their own as well as each other\\'s packet recovery overheads, thus by improving their throughput. The proposed scheme ensures that each network\\'s performance is not degraded by its help to the other network. Moreover, it guarantees that the primary network\\'s interference threshold is not violated in the same and adjacent cells. Yet, the scheme allows the reduction of the recovery overhead in the collocated primary and cognitive radio networks. The reduction in the cognitive radio network is further amplified due to the perfect detection of spectrum holes which allows the cognitive radio base station to transmit at higher power without fear of violating the interference threshold of the primary network. For the secondary network, simulation results show reductions of 20% and 34% in the packet recovery overhead, compared to the non-collaborative scheme, for low and high probabilities of primary packet arrivals, respectively. For the primary network, this reduction was found to be 12%. © 2015 IEEE.

Extraction of network topology from multi-electrode recordings: Is there a small-world effect?

Directory of Open Access Journals (Sweden)

Felipe eGerhard

2011-02-01

Full Text Available The simultaneous recording of the activity of many neurons poses challenges for multivariate data analysis. Here, we propose a general scheme of reconstruction of the functional network from spike train recordings. Effective, causal interactions are estimated by fitting Generalized Linear Models (GLMs on the neural responses, incorporating effects of the neurons' self-history, of input from other neurons in the recorded network and of modulation by an external stimulus. The coupling terms arising from synaptic input can be transformed by thresholding into a binary connectivity matrix which is directed. Each link between two neurons represents a causal influence from one neuron to the other, given the observation of all other neurons from the population. The resulting graph is analyzed with respect to small-world and scale-free properties using quantitative measures for directed networks. Such graph-theoretic analyses have been performed on many complex dynamic networks, including the connectivity structure between different brain areas. Only few studies have attempted to look at the structure of cortical neural networks on the level of individual neurons. Here, using multi-electrode recordings from the visual system of the awake monkey, we find that cortical networks lack scale-free behavior, but show a small, but significant small-world structure. Assuming a simple distance-dependent probabilistic wiring between neurons, we find that this connectivity structure can account for all of the networks' observed small-world-ness. Moreover, for multi-electrode recordings the sampling of neurons is not uniform across the population. We show that the small-world-ness obtained by such a localized sub-sampling overestimates the strength of the true small-world-structure of the network. This bias is likely to be present in all previous experiments based on multi-electrode recordings.

Wireless multi-hop networks with stealing : large buffer asymptotics

NARCIS (Netherlands)

Guillemin, F.; Knessl, C.; Leeuwaarden, van J.S.H.

2010-01-01

Wireless networks equipped with CSMA are scheduled in a fully distributed manner. A disadvantage of such distributed control in multi-hop networks is the hidden node problem that causes the effect of stealing, in which a downstream node steals the channel from an upstream node with probability p.

Modeling and optimization of cloud-ready and content-oriented networks

CERN Document Server

Walkowiak, Krzysztof

2016-01-01

This book focuses on modeling and optimization of cloud-ready and content-oriented networks in the context of different layers and accounts for specific constraints following from protocols and technologies used in a particular layer. It addresses a wide range of additional constraints important in contemporary networks, including various types of network flows, survivability issues, multi-layer networking, and resource location. The book presents recent existing and new results in a comprehensive and cohesive way. The contents of the book are organized in five chapters, which are mostly self-contained. Chapter 1 briefly presents information on cloud computing and content-oriented services, and introduces basic notions and concepts of network modeling and optimization. Chapter 2 covers various optimization problems that arise in the context of connection-oriented networks. Chapter 3 focuses on modeling and optimization of Elastic Optical Networks. Chapter 4 is devoted to overlay networks. The book concludes w...

Maximizing hosting capacity of renewable energy sources in distribution networks: A multi-objective and scenario-based approach

International Nuclear Information System (INIS)

Rabiee, Abbas; Mohseni-Bonab, Seyed Masoud

2017-01-01

Due to the development of renewable energy sources (RESs), maximization of hosting capacity (HC) of RESs has gained significant interest in the existing and future power systems. HC maximization should be performed considering various technical constraints like power flow equations, limits on the distribution feeders' voltages and currents, as well as economic constraints such as the cost of energy procurement from the upstream network and power generation by RESs. RESs are volatile and uncertain in nature. Thus, it is necessary to handle their inherent uncertainties in the HC maximization problem. Wind power is now the fastest growing RESs around the world. Hence, in this paper a stochastic multi-objective optimization model is proposed to maximize the distribution network's HC for wind power and minimize the energy procurement costs in a wind integrated power system. The following objective functions are considered: 1) Cost of the purchased energy from upstream network (to be minimized) and 2) Operation and maintenance cost of wind farms. The proposed model is examined on a standard radial 69 bus distribution feeder and a practical 152 bus distribution system. The numerical results substantiate that the proposed model is an effective tool for distribution network operators (DNOs) to consider both technical and economic aspects of distribution network's HC for RESs. - Highlights: • Hosting capacity of wind power is improved in distribution feeders. • A stochastic multi-objective optimization model is proposed. • Wind power and load uncertainties are modeled by scenario based approach. • Purchased energy cost from upstream network and O&M cost of wind farms are used.

Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-Ⅱ

Institute of Scientific and Technical Information of China (English)

Xi JIN; Jie ZHANG; Jin-liang GAO; Wen-yan WU

2008-01-01

Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Aigorithm-Ⅱ (NSGA-Ⅱ) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-Ⅱ into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by introduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated; this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.

Feature-based comparison and selection of software defined networking (SDN) controllers

OpenAIRE

Khondoker, Rahamatullah; Zaalouk, Adel; Marx, Ronald; Bayarou, Kpatcha

2014-01-01

Software Defined Networking (SDN) is seen as one way to solve some problems of the Internet including security, managing complexity, multi-casting, load balancing, and energy efficiency. SDN is an architectural paradigm that separates the control plane of a networking device (e.g., a switch / router) from its data plane, making it feasible to control, monitor, and manage a network from a centralized node (the SDN controller). However, today there exists many SDN controllers including POX, Flo...

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

DEFF Research Database (Denmark)

Hagen, Espen; Dahmen, David; Stavrinou, Maria L

2016-01-01

on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network......With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical...... and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely...

Probabilistic Optimal Power Dispatch in Multi-Carrier Networked Microgrids under Uncertainties

Directory of Open Access Journals (Sweden)

Vahid Amir

2017-11-01

Full Text Available A microgrid (MG is a small-scale version of the power system which makes possible the integration of renewable resources as well as achieving maximum demand side management (DSM utilization. The future power system will be faced with severe uncertainties owing to penetration of renewable resources. Consequently, the uncertainty assessment of system performance is essential. The conventional energy scheduling in an MG may not be suitable for active distribution networks. Hence, this study focuses on the probabilistic analysis of optimal power dispatch considering economic aspects in a multi-carrier networked microgrid. The aim is to study the impact of uncertain behavior of loads, renewable resources, and electricity market on the optimal management of a multi-carrier networked microgrid. Furthermore, a novel time-based demand side management is proposed in order to reshape the load curve, as well as preventing the excessive use of energy in peak hours. The optimization model is formulated as a mixed integer nonlinear program (MINLP and is solved using MATLAB and GAMS software. Results show that the energy sharing capability between MCMGs and MCMGs and the main grids as well as utilization of demand side management can decrease operating costs for smart distribution grids.

Multilayer Brain Networks

Science.gov (United States)

Vaiana, Michael; Muldoon, Sarah Feldt

2018-01-01

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.

From Single Target to Multitarget/Network Therapeutics in Alzheimer’s Therapy

Directory of Open Access Journals (Sweden)

Hailin Zheng

2014-01-01

Full Text Available Brain network dysfunction in Alzheimer’s disease (AD involves many proteins (enzymes, processes and pathways, which overlap and influence one another in AD pathogenesis. This complexity challenges the dominant paradigm in drug discovery or a single-target drug for a single mechanism. Although this paradigm has achieved considerable success in some particular diseases, it has failed to provide effective approaches to AD therapy. Network medicines may offer alternative hope for effective treatment of AD and other complex diseases. In contrast to the single-target drug approach, network medicines employ a holistic approach to restore network dysfunction by simultaneously targeting key components in disease networks. In this paper, we explore several drugs either in the clinic or under development for AD therapy in term of their design strategies, diverse mechanisms of action and disease-modifying potential. These drugs act as multi-target ligands and may serve as leads for further development as network medicines.

Computational Hydrodynamics: How Portable and Scalable Are Heterogeneous Programming Paradigms?

DEFF Research Database (Denmark)

Pawlak, Wojciech; Glimberg, Stefan Lemvig; Engsig-Karup, Allan Peter

New many-core era applications at the interface of mathematics and computer science adopt modern parallel programming paradigms and expose parallelism through proper algorithms. We present new performance results for a novel massively parallel free surface wave model suitable for advanced......-device system sizes from desktops to large HPC systems such as superclusters and in the cloud utilizing heterogeneous devices like multi-core CPUs, GPUs, and Xeon Phi coprocessors. The numerical efficiency is evaluated on heterogeneous devices like multi-core CPUs, GPUs and Xeon Phi coprocessors to test...

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks.

Science.gov (United States)

Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X

2015-12-26

Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Liu Yang

2015-12-01

Full Text Available Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs in the network act as routers to transmit data to base station (BS cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

Network connectivity paradigm for the large data produced by weather radar systems

Science.gov (United States)

Guenzi, Diego; Bechini, Renzo; Boraso, Rodolfo; Cremonini, Roberto; Fratianni, Simona

2014-05-01

The traffic over Internet is constantly increasing; this is due in particular to social networks activities but also to the enormous exchange of data caused especially by the so-called "Internet of Things". With this term we refer to every device that has the capability of exchanging information with other devices on the web. In geoscience (and, in particular, in meteorology and climatology) there is a constantly increasing number of sensors that are used to obtain data from different sources (like weather radars, digital rain gauges, etc.). This information-gathering activity, frequently, must be followed by a complex data analysis phase, especially when we have large data sets that can be very difficult to analyze (very long historical series of large data sets, for example), like the so called big data. These activities are particularly intensive in resource consumption and they lead to new computational models (like cloud computing) and new methods for storing data (like object store, linked open data, NOSQL or NewSQL). The weather radar systems can be seen as one of the sensors mentioned above: it transmit a large amount of raw data over the network (up to 40 megabytes every five minutes), with 24h/24h continuity and in any weather condition. Weather radar are often located in peaks and in wild areas where connectivity is poor. For this reason radar measurements are sometimes processed partially on site and reduced in size to adapt them to the limited bandwidth currently available by data transmission systems. With the aim to preserve the maximum flow of information, an innovative network connectivity paradigm for the large data produced by weather radar system is here presented. The study is focused on the Monte Settepani operational weather radar system, located over a wild peak summit in north-western Italy.

Multi-Node Thermal System Model for Lithium-Ion Battery Packs: Preprint

Energy Technology Data Exchange (ETDEWEB)

Shi, Ying; Smith, Kandler; Wood, Eric; Pesaran, Ahmad

2015-09-14

Temperature is one of the main factors that controls the degradation in lithium ion batteries. Accurate knowledge and control of cell temperatures in a pack helps the battery management system (BMS) to maximize cell utilization and ensure pack safety and service life. In a pack with arrays of cells, a cells temperature is not only affected by its own thermal characteristics but also by its neighbors, the cooling system and pack configuration, which increase the noise level and the complexity of cell temperatures prediction. This work proposes to model lithium ion packs thermal behavior using a multi-node thermal network model, which predicts the cell temperatures by zones. The model was parametrized and validated using commercial lithium-ion battery packs. neighbors, the cooling system and pack configuration, which increase the noise level and the complexity of cell temperatures prediction. This work proposes to model lithium ion packs thermal behavior using a multi-node thermal network model, which predicts the cell temperatures by zones. The model was parametrized and validated using commercial lithium-ion battery packs.

Hyper Hospital on the satellite multi-media network.

Science.gov (United States)

Yamaguchi, T

1997-01-01

We have been developing the Hyper Hospital, a network based VR mediated medical care system. The Hyper Hospital is composed of two seamlessly integrated environments, that is, the virtual and the real worlds. Of them, its virtual environment expands the conventional medical care system using the virtual reality technology as a principal human interface and a collaboration tool, in the present study, an attempt to extend the Hyper Hospital system to various modalities of communication network is reported. A satellite communication based multi-media network using Internet protocols with the WWW interface is used. Data transmission rate and other performances were measured under various conditions and the satellite network was shown to be suitable to the Hyper Hospital network.

Power and delay optimisation in multi-hop wireless networks

KAUST Repository

Xia, Li

2014-02-05

In this paper, we study the optimisation problem of transmission power and delay in a multi-hop wireless network consisting of multiple nodes. The goal is to determine the optimal policy of transmission rates at various buffer and channel states in order to minimise the power consumption and the queueing delay of the whole network. With the assumptions of interference-free links and independently and identically distributed (i.i.d.) channel states, we formulate this problem using a semi-open Jackson network model for data transmission and a Markov model for channel states transition. We derive a difference equation of the system performance under any two different policies. The necessary and sufficient condition of optimal policy is obtained. We also prove that the system performance is monotonic with respect to (w.r.t.) the transmission rate and the optimal transmission rate can be either maximal or minimal. That is, the ‘bang-bang’ control is an optimal control. This optimality structure greatly reduces the problem complexity. Furthermore, we develop an iterative algorithm to find the optimal solution. Finally, we conduct the simulation experiments to demonstrate the effectiveness of our approach. We hope our work can shed some insights on solving this complicated optimisation problem.

Cities, Europeanization and Multi-level Governance: Governing Climate Change through Transnational Municipal Networks

NARCIS (Netherlands)

Kern, K.; Bulkeley, H.

2009-01-01

This article focuses on a variant of multi-level governance and Europeanization, i.e. the transnational networking of local authorities. Focusing on local climate change policy, the article examines how transnational municipal networks (TMNs) govern in the context of multi-level European governance.

Linear and Non-linear Multi-Input Multi-Output Model Predictive Control of Continuous Stirred Tank Reactor

Directory of Open Access Journals (Sweden)

Muayad Al-Qaisy

2015-02-01

Full Text Available In this article, multi-input multi-output (MIMO linear model predictive controller (LMPC based on state space model and nonlinear model predictive controller based on neural network (NNMPC are applied on a continuous stirred tank reactor (CSTR. The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA and reactor temperature (T. NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.

Intercontinental Multi-Domain Monitoring for the LHC Optical Private Network

CERN Multimedia

CERN. Geneva

2012-01-01

The Large Hadron Collider (LHC) is currently running at CERN in Geneva, Switzerland. Physicists are using LHC to recreate the conditions just after the Big Bang, by colliding two beams of particles and heavy ions head-on at very high energy. The project is expected to generate 27 TB of raw data per day, plus 10 TB of "event summary data". This data is sent out from CERN to eleven Tier 1 academic institutions in Europe, Asia, and North America using a multi-gigabits Optical Private Network (OPN), the LHCOPN. Network monitoring on such complex network architecture to ensure robust and reliable operation is of crucial importance. The chosen approach for monitoring the OPN is based on the perfSONAR MDM framework (http://perfsonar.geant.net), which is designed for multi-domain monitoring environments. perfSONAR (www.perfsonar.net) is an infrastructure for performance monitoring data exchange between networks, making it easier to solve performance problems occurring between network measurement points interconne...

Analysis of a multi-server queueing model of ABR

NARCIS (Netherlands)

R. Núñez Queija (Rudesindo); O.J. Boxma (Onno)

1996-01-01

textabstractIn this paper we present a queueing model for the performance a-na-ly-sis of ABR traffic in ATM networks. We consider a multi-channel service station with two types of customers, the first having preemptive priority over the second. The arrivals occur according to two independent Poisson

MIMO wireless networks channels, techniques and standards for multi-antenna, multi-user and multi-cell systems

CERN Document Server

Clerckx, Bruno

2013-01-01

This book is unique in presenting channels, techniques and standards for the next generation of MIMO wireless networks. Through a unified framework, it emphasizes how propagation mechanisms impact the system performance under realistic power constraints. Combining a solid mathematical analysis with a physical and intuitive approach to space-time signal processing, the book progressively derives innovative designs for space-time coding and precoding as well as multi-user and multi-cell techniques, taking into consideration that MIMO channels are often far from ideal. Reflecting developments

A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

Science.gov (United States)

Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

2015-06-01

The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

Multi-Agent Market Modeling of Foreign Exchange Rates

Science.gov (United States)

Zimmermann, Georg; Neuneier, Ralph; Grothmann, Ralph

A market mechanism is basically driven by a superposition of decisions of many agents optimizing their profit. The oeconomic price dynamic is a consequence of the cumulated excess demand/supply created on this micro level. The behavior analysis of a small number of agents is well understood through the game theory. In case of a large number of agents one may use the limiting case that an individual agent does not have an influence on the market, which allows the aggregation of agents by statistic methods. In contrast to this restriction, we can omit the assumption of an atomic market structure, if we model the market through a multi-agent approach. The contribution of the mathematical theory of neural networks to the market price formation is mostly seen on the econometric side: neural networks allow the fitting of high dimensional nonlinear dynamic models. Furthermore, in our opinion, there is a close relationship between economics and the modeling ability of neural networks because a neuron can be interpreted as a simple model of decision making. With this in mind, a neural network models the interaction of many decisions and, hence, can be interpreted as the price formation mechanism of a market.

Steady state analysis of Boolean molecular network models via model reduction and computational algebra.

Science.gov (United States)

Veliz-Cuba, Alan; Aguilar, Boris; Hinkelmann, Franziska; Laubenbacher, Reinhard

2014-06-26

A key problem in the analysis of mathematical models of molecular networks is the determination of their steady states. The present paper addresses this problem for Boolean network models, an increasingly popular modeling paradigm for networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large networks is still unsolved in general. This paper presents an algorithm that consists of two main parts. The first is a graph theoretic reduction of the wiring diagram of the network, while preserving all information about steady states. The second part formulates the determination of all steady states of a Boolean network as a problem of finding all solutions to a system of polynomial equations over the finite number system with two elements. This problem can be solved with existing computer algebra software. This algorithm compares favorably with several existing algorithms for steady state determination. One advantage is that it is not heuristic or reliant on sampling, but rather determines algorithmically and exactly all steady states of a Boolean network. The code for the algorithm, as well as the test suite of benchmark networks, is available upon request from the corresponding author. The algorithm presented in this paper reliably determines all steady states of sparse Boolean networks with up to 1000 nodes. The algorithm is effective at analyzing virtually all published models even those of moderate connectivity. The problem for

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

Directory of Open Access Journals (Sweden)

Luis Cruz-Piris

2018-02-01

Full Text Available One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

A New Globalization Paradigm: World Unity or Alternatives for Development?

Directory of Open Access Journals (Sweden)

Oleg Shvydanenko

2005-02-01

Full Text Available This article deals with the conceptual foundations of the modern global economic system of development. It reflects the cyclical nature of changes to and the details of global integration processes. The creation of a global economy from a multi-paradigmatic angle is briefly outlined, taking into account the modern paradigms of globalization and the predominance of alternatives to the future development of a global economic space. The article investigates the development of a new type of world economy, a multi-system with a proven role for linkages and a more consolidated world economy. The article reveals the initial conditions for and main qualitative changes related to the integrated development of a complex network of interdependent national societies and macro-regional geo-economic structures. The article also reveals changes in the configuration of those factors that provide competitiveness for these societies and geo-economic formations.

Performance Analysis of RF-FSO Multi-Hop Networks

KAUST Repository

Makki, Behrooz

2017-05-12

We study the performance of multi-hop networks composed of millimeter wave (MMW)-based radio frequency (RF) and free-space optical (FSO) links. The results are obtained in the cases with and without hybrid automatic repeat request (HARQ). Taking the MMW characteristics of the RF links into account, we derive closed-form expressions for the network outage probability. We also evaluate the effect of various parameters such as power amplifiers efficiency, number of antennas as well as different coherence times of the RF and the FSO links on the system performance. Finally, we present mappings between the performance of RF- FSO multi-hop networks and the ones using only the RF- or the FSO-based communication, in the sense that with appropriate parameter settings the same outage probability is achieved in these setups. The results show the efficiency of the RF-FSO setups in different conditions. Moreover, the HARQ can effectively improve the outage probability/energy efficiency, and compensate the effect of hardware impairments in RF-FSO networks. For common parameter settings of the RF-FSO dual- hop networks, outage probability 10^{-4} and code rate 3 nats-per-channel-use, the implementation of HARQ with a maximum of 2 and 3 retransmissions reduces the required power, compared to the cases with no HARQ, by 13 and 17 dB, respectively.

Routing protocol for wireless quantum multi-hop mesh backbone network based on partially entangled GHZ state

Science.gov (United States)

Xiong, Pei-Ying; Yu, Xu-Tao; Zhang, Zai-Chen; Zhan, Hai-Tao; Hua, Jing-Yu

2017-08-01

Quantum multi-hop teleportation is important in the field of quantum communication. In this study, we propose a quantum multi-hop communication model and a quantum routing protocol with multihop teleportation for wireless mesh backbone networks. Based on an analysis of quantum multi-hop protocols, a partially entangled Greenberger-Horne-Zeilinger (GHZ) state is selected as the quantum channel for the proposed protocol. Both quantum and classical wireless channels exist between two neighboring nodes along the route. With the proposed routing protocol, quantum information can be transmitted hop by hop from the source node to the destination node. Based on multi-hop teleportation based on the partially entangled GHZ state, a quantum route established with the minimum number of hops. The difference between our routing protocol and the classical one is that in the former, the processes used to find a quantum route and establish quantum channel entanglement occur simultaneously. The Bell state measurement results of each hop are piggybacked to quantum route finding information. This method reduces the total number of packets and the magnitude of air interface delay. The deduction of the establishment of a quantum channel between source and destination is also presented here. The final success probability of quantum multi-hop teleportation in wireless mesh backbone networks was simulated and analyzed. Our research shows that quantum multi-hop teleportation in wireless mesh backbone networks through a partially entangled GHZ state is feasible.

Multi-granularity immunization strategy based on SIRS model in scale-free network

Science.gov (United States)

Nian, Fuzhong; Wang, Ke

2015-04-01

In this paper, a new immunization strategy was established to prevent the epidemic spreading based on the principle of "Multi-granularity" and "Pre-warning Mechanism", which send different pre-warning signal with the risk rank of the susceptible node to be infected. The pre-warning means there is a higher risk that the susceptible node is more likely to be infected. The multi-granularity means the susceptible node is linked with multi-infected nodes. In our model, the effect of the different situation of the multi-granularity immunizations is compared and different spreading rates are adopted to describe the epidemic behavior of nodes. In addition the threshold value of epidemic outbreak is investigated, which makes the result more convincing. The theoretical analysis and the simulations indicate that the proposed immunization strategy is effective and it is also economic and feasible.

Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression

Science.gov (United States)

Song, Lu-Kai; Wen, Jie; Fei, Cheng-Wei; Bai, Guang-Chen

2018-05-01

To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributed collaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid-structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design.

Decentralized formation of random regular graphs for robust multi-agent networks

KAUST Repository

Yazicioglu, A. Yasin

2014-12-15

Multi-agent networks are often modeled via interaction graphs, where the nodes represent the agents and the edges denote direct interactions between the corresponding agents. Interaction graphs have significant impact on the robustness of networked systems. One family of robust graphs is the random regular graphs. In this paper, we present a locally applicable reconfiguration scheme to build random regular graphs through self-organization. For any connected initial graph, the proposed scheme maintains connectivity and the average degree while minimizing the degree differences and randomizing the links. As such, if the average degree of the initial graph is an integer, then connected regular graphs are realized uniformly at random as time goes to infinity.

Modeling fiber Bragg grating device networks in photomechanical polymer optical fibers

Science.gov (United States)

Lanska, Joseph T.; Kuzyk, Mark G.; Sullivan, Dennis M.

2015-09-01

We report on the modeling of fiber Bragg grating (FBG) networks in poly(methyl methacrylate) (PMMA) polymer fibers doped with azo dyes. Our target is the development of Photomechanical Optical Devices (PODs), comprised of two FBGs in series, separated by a Fabry-Perot cavity of photomechanical material. PODs exhibit photomechanical multi-stability, with the capacity to access multiple length states for a fixed input intensity when a mechanical shock is applied. Using finite-difference time-domain (FDTD) numerical methods, we modeled the photomechanical response of both Fabry-Perot and Bragg-type PODs in a single polymer optical fiber. The polymer fiber was modeled as an instantaneous Kerr-type nonlinear χ(3) material. Our model correctly predicts the essential optical features of FBGs as well as the photomechanical multi-stability of nonlinear Fabry-Perot cavity-based PODs. Networks of PODs may provide a framework for smart shape-shifting materials and fast optical computation where the decision process is distributed over the entire network. In addition, a POD can act as memory, and its response can depend on input history. Our models inform and will accelerate targeted development of novel Bragg grating-based polymer fiber device networks for a variety of applications in optical computing and smart materials.

Design of Compressed Sensing Algorithm for Coal Mine IoT Moving Measurement Data Based on a Multi-Hop Network and Total Variation

Directory of Open Access Journals (Sweden)

Gang Wang

2018-05-01

Full Text Available As the application of a coal mine Internet of Things (IoT, mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently.

Design of Compressed Sensing Algorithm for Coal Mine IoT Moving Measurement Data Based on a Multi-Hop Network and Total Variation.

Science.gov (United States)

Wang, Gang; Zhao, Zhikai; Ning, Yongjie

2018-05-28

As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH) is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently.

Programmable multi-node quantum network design and simulation

Science.gov (United States)

Dasari, Venkat R.; Sadlier, Ronald J.; Prout, Ryan; Williams, Brian P.; Humble, Travis S.

2016-05-01

Software-defined networking offers a device-agnostic programmable framework to encode new network functions. Externally centralized control plane intelligence allows programmers to write network applications and to build functional network designs. OpenFlow is a key protocol widely adopted to build programmable networks because of its programmability, flexibility and ability to interconnect heterogeneous network devices. We simulate the functional topology of a multi-node quantum network that uses programmable network principles to manage quantum metadata for protocols such as teleportation, superdense coding, and quantum key distribution. We first show how the OpenFlow protocol can manage the quantum metadata needed to control the quantum channel. We then use numerical simulation to demonstrate robust programmability of a quantum switch via the OpenFlow network controller while executing an application of superdense coding. We describe the software framework implemented to carry out these simulations and we discuss near-term efforts to realize these applications.

First field trial of Virtual Network Operator oriented network on demand (NoD) service provisioning over software defined multi-vendor OTN networks

Science.gov (United States)

Li, Yajie; Zhao, Yongli; Zhang, Jie; Yu, Xiaosong; Chen, Haoran; Zhu, Ruijie; Zhou, Quanwei; Yu, Chenbei; Cui, Rui

2017-01-01

A Virtual Network Operator (VNO) is a provider and reseller of network services from other telecommunications suppliers. These network providers are categorized as virtual because they do not own the underlying telecommunication infrastructure. In terms of business operation, VNO can provide customers with personalized services by leasing network infrastructure from traditional network providers. The unique business modes of VNO lead to the emergence of network on demand (NoD) services. The conventional network provisioning involves a series of manual operation and configuration, which leads to high cost in time. Considering the advantages of Software Defined Networking (SDN), this paper proposes a novel NoD service provisioning solution to satisfy the private network need of VNOs. The solution is first verified in the real software defined multi-domain optical networks with multi-vendor OTN equipment. With the proposed solution, NoD service can be deployed via online web portals in near-real time. It reinvents the customer experience and redefines how network services are delivered to customers via an online self-service portal. Ultimately, this means a customer will be able to simply go online, click a few buttons and have new services almost instantaneously.

Optimal multicasting in a multi-line-rate ethernet-over-WDM network

Science.gov (United States)

Harve, Shruthi; Batayneh, Marwan; Mukherjee, Biswanath

2009-11-01

Ethernet is the dominant transport technology for Local Area Networks. Efforts are now under way to use carrier-grade Ethernet in backbone networks of different service providers. With the advent of applications such as IPTV and Videoon- Demand, there is need for techniques to route multicast traffic over the Ethernet backbone networks. Here, we address the problem of Routing and Wavelength Assignment (RWA) of a set of multicast requests in a Multi-Line-Rate Ethernet backbone network with the objective of minimizing the cost of setting up the network, in terms of the Service Provider's Capital Expenditure (CAPEX). We present an Auxiliary Graph based heuristic algorithm that routes each multicast request on a light-tree structure, and assigns minimum cost wavelengths along the route. We compare the properties of the algorithm to the optimal solution given by a mathematical model formulated as an Integer Linear Program (ILP), and show that they compare very well. We also find that the algorithm is most cost-effective when the incoming requests are processed in descending order of their bandwidth requirements.

Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model

Science.gov (United States)

Vahdani, Behnam; Tavakkoli-Moghaddam, Reza; Jolai, Fariborz; Baboli, Arman

2013-06-01

This article seeks to offer a systematic approach to establishing a reliable network of facilities in closed loop supply chains (CLSCs) under uncertainties. Facilities that are located in this article concurrently satisfy both traditional objective functions and reliability considerations in CLSC network designs. To attack this problem, a novel mathematical model is developed that integrates the network design decisions in both forward and reverse supply chain networks. The model also utilizes an effective reliability approach to find a robust network design. In order to make the results of this article more realistic, a CLSC for a case study in the iron and steel industry has been explored. The considered CLSC is multi-echelon, multi-facility, multi-product and multi-supplier. Furthermore, multiple facilities exist in the reverse logistics network leading to high complexities. Since the collection centres play an important role in this network, the reliability concept of these facilities is taken into consideration. To solve the proposed model, a novel interactive hybrid solution methodology is developed by combining a number of efficient solution approaches from the recent literature. The proposed solution methodology is a bi-objective interval fuzzy possibilistic chance-constraint mixed integer linear programming (BOIFPCCMILP). Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model in a supply chain environment and to help decision makers facilitate their analyses.

Multi-state supernetworks: recent progress and prospects

Directory of Open Access Journals (Sweden)

Feixiong Liao

2014-02-01

Full Text Available Supernetworks have long been adopted to address multi-dimensional choice problems, which are thorny to solve for classic singular networks. Originated from combining transport mode and route choice into a multi-modal network, supernetworks have been extended into multi-state networks to include activity-travel scheduling, centered around activity-based models of travel demand. A key feature of the network extensions is that multiple choice facets pertaining to conducting a full activity program can be modeled in a consistent and integrative fashion. Thus, interdependencies and constraints between related choice facets can be readily captured. Given this advantage of integrity, the modeling of supernetwork has become an emerging topic in transportation research. This paper summarizes the recent progress in modeling multi-state supernetworks and discusses future prospects.

A new fuzzy mathematical model for green supply chain network design

Directory of Open Access Journals (Sweden)

Mohsen Sadegh Amalnick

2017-01-01

Full Text Available The environmental changes caused by industrial activities have spurred a significant interest in designing supply chain networks by considering environmental issues such as CO2 emission. The pivotal role of taking uncertainty and risk into account in closed-loop supply chain networks has induced numerous researchers and practitioners to develop appropriate decision making tools to cope with these issues in such networks. To design a supply chain regarding environmental impacts under uncertainty of the input data and to cope with the operational risks, this paper proposes a multi objective possibilistic optimization model. The proposed model minimizes traditional costs such as cost of products shipment, purchasing machines and so on, as well as minimizing the environmental impact, and as a results strikes a balance between the two objective functions. Furthermore, in order to solve the proposed multi objective fuzzy mathematical programming model, an interactive fuzzy solution approach is applied. Numerical experiments are used to prove the applicability and feasibility of the developed possibilistic programming model and the usefulness of the applied hybrid solution approach.

AN INITIATIVE FOR CONSTRUCTION OF NEW-GENERATION LUNAR GLOBAL CONTROL NETWORK USING MULTI-MISSION DATA

Directory of Open Access Journals (Sweden)

K. Di

2017-07-01

Full Text Available A lunar global control network provides geodetic datum and control points for mapping of the lunar surface. The widely used Unified Lunar Control Network 2005 (ULCN2005 was built based on a combined photogrammetric solution of Clementine images acquired in 1994 and earlier photographic data. In this research, we propose an initiative for construction of a new-generation lunar global control network using multi-mission data newly acquired in the 21st century, which have much better resolution and precision than the old data acquired in the last century. The new control network will be based on a combined photogrammetric solution of an extended global image and laser altimetry network. The five lunar laser ranging retro-reflectors, which can be identified in LROC NAC images and have cm level 3D position accuracy, will be used as absolute control points in the least squares photogrammetric adjustment. Recently, a new radio total phase ranging method has been developed and used for high-precision positioning of Chang’e-3 lander; this shall offer a new absolute control point. Systematic methods and key techniques will be developed or enhanced, including rigorous and generic geometric modeling of orbital images, multi-scale feature extraction and matching among heterogeneous multi-mission remote sensing data, optimal selection of images at areas of multiple image coverages, and large-scale adjustment computation, etc. Based on the high-resolution new datasets and developed new techniques, the new generation of global control network is expected to have much higher accuracy and point density than the ULCN2005.

Modeling Remote I/O versus Staging Tradeoff in Multi-Data Center Computing

International Nuclear Information System (INIS)

Suslu, Ibrahim H

2014-01-01

In multi-data center computing, data to be processed is not always local to the computation. This is a major challenge especially for data-intensive Cloud computing applications, since large amount of data would need to be either moved the local sites (staging) or accessed remotely over the network (remote I/O). Cloud application developers generally chose between staging and remote I/O intuitively without making any scientific comparison specific to their application data access patterns since there is no generic model available that they can use. In this paper, we propose a generic model for the Cloud application developers which would help them to choose the most appropriate data access mechanism for their specific application workloads. We define the parameters that potentially affect the end-to-end performance of the multi-data center Cloud applications which need to access large datasets over the network. To test and validate our models, we implemented a series of synthetic benchmark applications to simulate the most common data access patterns encountered in Cloud applications. We show that our model provides promising results in different settings with different parameters, such as network bandwidth, server and client capabilities, and data access ratio

High throughput route selection in multi-rate wireless mesh networks

Institute of Scientific and Technical Information of China (English)

WEI Yi-fei; GUO Xiang-li; SONG Mei; SONG Jun-de

2008-01-01

Most existing Ad-hoc routing protocols use the shortest path algorithm with a hop count metric to select paths. It is appropriate in single-rate wireless networks, but has a tendency to select paths containing long-distance links that have low data rates and reduced reliability in multi-rate networks. This article introduces a high throughput routing algorithm utilizing the multi-rate capability and some mesh characteristics in wireless fidelity (WiFi) mesh networks. It uses the medium access control (MAC) transmission time as the routing metric, which is estimated by the information passed up from the physical layer. When the proposed algorithm is adopted, the Ad-hoc on-demand distance vector (AODV) routing can be improved as high throughput AODV (HT-AODV). Simulation results show that HT-AODV is capable of establishing a route that has high data-rate, short end-to-end delay and great network throughput.

A Quantum Implementation Model for Artificial Neural Networks

OpenAIRE

Daskin, Ammar

2016-01-01

The learning process for multi layered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow-Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, this iterative formulas result in terms formed by the principal components of the weight matrix: i.e., the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the phase...

Linear and Nonlinear Career Models: Metaphors, Paradigms, and Ideologies.

Science.gov (United States)

Buzzanell, Patrice M.; Goldzwig, Steven R.

1991-01-01

Examines the linear or bureaucratic career models (dominant in career research, metaphors, paradigms, and ideologies) which maintain career myths of flexibility and individualized routes to success in organizations incapable of offering such versatility. Describes nonlinear career models which offer suggestive metaphors for re-visioning careers…

A multi-scale network method for two-phase flow in porous media

Energy Technology Data Exchange (ETDEWEB)

Khayrat, Karim, E-mail: khayratk@ifd.mavt.ethz.ch; Jenny, Patrick

2017-08-01

Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces within each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.

A multi-scale network method for two-phase flow in porous media

International Nuclear Information System (INIS)

Khayrat, Karim; Jenny, Patrick

2017-01-01

Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces within each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.

An Integer Programming Model for Multi-Echelon Supply Chain Decision Problem Considering Inventories

Science.gov (United States)

Harahap, Amin; Mawengkang, Herman; Siswadi; Effendi, Syahril

2018-01-01

In this paper we address a problem that is of significance to the industry, namely the optimal decision of a multi-echelon supply chain and the associated inventory systems. By using the guaranteed service approach to model the multi-echelon inventory system, we develop a mixed integer; programming model to simultaneously optimize the transportation, inventory and network structure of a multi-echelon supply chain. To solve the model we develop a direct search approach using a strategy of releasing nonbasic variables from their bounds, combined with the “active constraint” method. This strategy is used to force the appropriate non-integer basic variables to move to their neighbourhood integer points.

Multi-digit handwritten sindhi numerals recognition using som neural network

International Nuclear Information System (INIS)

Chandio, A.A.; Jalbani, A.H.; Awan, S.A.

2017-01-01

In this research paper a multi-digit Sindhi handwritten numerals recognition system using SOM Neural Network is presented. Handwritten digits recognition is one of the challenging tasks and a lot of research is being carried out since many years. A remarkable work has been done for recognition of isolated handwritten characters as well as digits in many languages like English, Arabic, Devanagari, Chinese, Urdu and Pashto. However, the literature reviewed does not show any remarkable work done for Sindhi numerals recognition. The recognition of Sindhi digits is a difficult task due to the various writing styles and different font sizes. Therefore, SOM (Self-Organizing Map), a NN (Neural Network) method is used which can recognize digits with various writing styles and different font sizes. Only one sample is required to train the network for each pair of multi-digit numerals. A database consisting of 4000 samples of multi-digits consisting only two digits from 10-50 and other matching numerals have been collected by 50 users and the experimental results of proposed method show that an accuracy of 86.89% is achieved. (author)

Multi-Channel Multi-Radio Using 802.11 Based Media Access for Sink Nodes in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Kok-Keong Loo

2011-05-01

Full Text Available The next generation surveillance and multimedia systems will become increasingly deployed as wireless sensor networks in order to monitor parks, public places and for business usage. The convergence of data and telecommunication over IP-based networks has paved the way for wireless networks. Functions are becoming more intertwined by the compelling force of innovation and technology. For example, many closed-circuit TV premises surveillance systems now rely on transmitting their images and data over IP networks instead of standalone video circuits. These systems will increase their reliability in the future on wireless networks and on IEEE 802.11 networks. However, due to limited non-overlapping channels, delay, and congestion there will be problems at sink nodes. In this paper we provide necessary conditions to verify the feasibility of round robin technique in these networks at the sink nodes by using a technique to regulate multi-radio multichannel assignment. We demonstrate through simulations that dynamic channel assignment scheme using multi-radio, and multichannel configuration at a single sink node can perform close to optimal on the average while multiple sink node assignment also performs well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives.

Multi-channel multi-radio using 802.11 based media access for sink nodes in wireless sensor networks.

Science.gov (United States)

Campbell, Carlene E-A; Khan, Shafiullah; Singh, Dhananjay; Loo, Kok-Keong

2011-01-01

The next generation surveillance and multimedia systems will become increasingly deployed as wireless sensor networks in order to monitor parks, public places and for business usage. The convergence of data and telecommunication over IP-based networks has paved the way for wireless networks. Functions are becoming more intertwined by the compelling force of innovation and technology. For example, many closed-circuit TV premises surveillance systems now rely on transmitting their images and data over IP networks instead of standalone video circuits. These systems will increase their reliability in the future on wireless networks and on IEEE 802.11 networks. However, due to limited non-overlapping channels, delay, and congestion there will be problems at sink nodes. In this paper we provide necessary conditions to verify the feasibility of round robin technique in these networks at the sink nodes by using a technique to regulate multi-radio multichannel assignment. We demonstrate through simulations that dynamic channel assignment scheme using multi-radio, and multichannel configuration at a single sink node can perform close to optimal on the average while multiple sink node assignment also performs well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives.

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

Science.gov (United States)

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-03-29

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

Dynamic Task Allocation in Multi-Hop Multimedia Wireless Sensor Networks with Low Mobility

Directory of Open Access Journals (Sweden)

Klaus Moessner

2013-10-01

Full Text Available This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines.

Cross-layer optimization of wireless multi-hop networks

OpenAIRE

Soldati, Pablo

2007-01-01

The interest in wireless communications has grown constantly for the past decades, leading to an enormous number of applications and services embraced by billions of users. In order to meet the increasing demand for mobile Internet access, several high data-rate radio networking technologies have been proposed to offer wide area high-speed wireless communications, eventually replacing fixed (wired) networks for many applications. This thesis considers cross-layer optimization of multi-hop rad...

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

DEFF Research Database (Denmark)

Zhang, Chunyu; Ding, Yi; Wu, Qiuwei

2013-01-01

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO...... algorithm was proposed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified...

Network modelling of fluid retention behaviour in unsaturated soils

Directory of Open Access Journals (Sweden)

Athanasiadis Ignatios

2016-01-01

Full Text Available The paper describes discrete modelling of the retention behaviour of unsaturated porous materials. A network approach is used within a statistical volume element (SVE, suitable for subsequent use in hydro-mechanical analysis and incorporation within multi-scale numerical modelling. The soil pore structure is modelled by a network of cylindrical pipes connecting spheres, with the spheres representing soil voids and the pipes representing inter-connecting throats. The locations of pipes and spheres are determined by a Voronoi tessellation of the domain. Original aspects of the modelling include a form of periodic boundary condition implementation applied for the first time to this type of network, a new pore volume scaling technique to provide more realistic modelling and a new procedure for initiating drying or wetting paths in a network model employing periodic boundary conditions. Model simulations, employing two linear cumulative probability distributions to represent the distributions of sphere and pipe radii, are presented for the retention behaviour reported from a mercury porosimetry test on a sandstone.

Embarked electrical network robust control based on singular perturbation model.

Science.gov (United States)

Abdeljalil Belhaj, Lamya; Ait-Ahmed, Mourad; Benkhoris, Mohamed Fouad

2014-07-01

This paper deals with an approach of modelling in view of control for embarked networks which can be described as strongly coupled multi-sources, multi-loads systems with nonlinear and badly known characteristics. This model has to be representative of the system behaviour and easy to handle for easy regulators synthesis. As a first step, each alternator is modelled and linearized around an operating point and then it is subdivided into two lower order systems according to the singular perturbation theory. RST regulators are designed for each subsystem and tested by means of a software test-bench which allows predicting network behaviour in both steady and transient states. Finally, the designed controllers are implanted on an experimental benchmark constituted by two alternators supplying loads in order to test the dynamic performances in realistic conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

multi scale analysis of a function by neural networks elementary derivatives functions

International Nuclear Information System (INIS)

Chikhi, A.; Gougam, A.; Chafa, F.

2006-01-01

Recently, the wavelet network has been introduced as a special neural network supported by the wavelet theory . Such networks constitute a tool for function approximation problems as it has been already proved in reference . Our present work deals with this model, treating a multi scale analysis of a function. We have then used a linear expansion of a given function in wavelets, neglecting the usual translation parameters. We investigate two training operations. The first one consists on an optimization of the output synaptic layer, the second one, optimizing the output function with respect to scale parameters. We notice a temporary merging of the scale parameters leading to some interesting results : new elementary derivatives units emerge, representing a new elementary task, which is the derivative of the output task

Network based multi-channel digital flash X-ray imaging system

International Nuclear Information System (INIS)

Wang Jingjin; Yuan Jie; Liu Yaqiang; Lin Yong; Song Zheng; Liu Keyin; Zhang Qi; Zheng Futang

2000-01-01

A network based multi-channel digital flash X-ray imaging system has been developed. It can be used to acquire and digitize orthogonal flash X-ray images in multi-interval, and to distribute the images on the network. There is no need of films and chemical process, no anxiety of waiting and no trouble of film archiving. This system is useful for testing ballistics, jet, explode, armour-piercing and fast running machines. The system composing and acquired images are presented. The software for object separating, mass calculating, 3D positioning, speed determining and cavity reconstruction are described

Network based multi-channel digital flash X-ray imaging system

International Nuclear Information System (INIS)

Wang Jingjin; Yuan Jie; Liu Yaqiang; Lin Yong; Song Zheng; Liu Keyin

2003-01-01

A network based multi-channel digital flash X-ray imaging system has been developed. It can be used to acquire and digitize orthogonal flash X-ray images in multi-interval, and to distribute the images on the network. There is no need of films and chemical process, no anxiety of waiting and no trouble of film archiving. This system is useful for testing ballistics, jet, explode, armour-piercing and fast running machines. The system composing and acquired images of terminal ballistics are presented. The software for object separating, profile calculating and 3D cavity reconstruction are described

A Participatory Model for Multi-Document Health Information Summarisation

Directory of Open Access Journals (Sweden)

Dinithi Nallaperuma

2017-03-01

Full Text Available Increasing availability and access to health information has been a paradigm shift in healthcare provision as it empowers both patients and practitioners alike. Besides awareness, significant time savings and process efficiencies can be achieved through effective summarisation of healthcare information. Relevance and accuracy are key concerns when generating summaries for such documents. Despite advances in automated summarisation approaches, the role of participation has not been explored. In this paper, we propose a new model for multi-document health information summarisation that takes into account the role of participation. The updated IS user participation theory was extended to explicate these roles. The proposed model integrates both extractive and abstractive summarisation processes with continuous participatory inputs to each phase. The model was implemented as a client-server application and evaluated by both domain experts and health information consumers. Results from the evaluation phase indicates the model is successful in generating relevant and accurate summaries for diverse audiences.

Cloud Computing and Multi Agent System to improve Learning Object Paradigm

Directory of Open Access Journals (Sweden)

Ana B. Gil

2015-05-01

Full Text Available The paradigm of Learning Object provides Educators and Learners with the ability to access an extensive number of learning resources. To do so, this paradigm provides different technologies and tools, such as federated search platforms and storage repositories, in order to obtain information ubiquitously and on demand. However, the vast amount and variety of educational content, which is distributed among several repositories, and the existence of various and incompatible standards, technologies and interoperability layers among repositories, constitutes a real problem for the expansion of this paradigm. This study presents an agent-based architecture that uses the advantages provided by Cloud Computing platforms to deal with the open issues on the Learning Object paradigm.

Performance Analysis of RF-FSO Multi-Hop Networks

KAUST Repository

Makki, Behrooz; Svensson, Tommy; Brandt-Pearce, Maite; Alouini, Mohamed-Slim

2017-01-01

We study the performance of multi-hop networks composed of millimeter wave (MMW)-based radio frequency (RF) and free-space optical (FSO) links. The results are obtained in the cases with and without hybrid automatic repeat request (HARQ). Taking

An Ontology-Based Context Model for Wireless Sensor Network (WSN Management in the Internet of Things

Directory of Open Access Journals (Sweden)

Adnan Al-Anbuky

2013-09-01

Full Text Available Wireless sensor networks (WSNs are an enabling technology of context-aware systems. The Internet of Things (IoT, which has attracted much attention in recent years, is an emerging paradigm where everyday objects and spaces are made context-aware and interconnected through heterogeneous networks on a global scale. However, the IoT system can suffer from poor performances when its underlying networks are not optimized. In this paper, an ontology model for representing and facilitating context sharing between network entities in WSNs is proposed for the first time. The context model aims to enable optimal context-aware management of WSNs in IoT, which will also harness the rich context knowledge of IoT systems.

Degrees of Freedom of Asymmetrical Multi-Way Relay Networks

DEFF Research Database (Denmark)

Sun, Fan; De Carvalho, Elisabeth

2011-01-01

In this paper, we introduce an asymmetrical multi-way relay channel where a base station conveys independent symbols to K different users while receiving independent symbols from the users. In this network, user i Shas Mi antennas (i ∈ [1, ..., K]), the base station has Σi=1K Mi antennas and the ......In this paper, we introduce an asymmetrical multi-way relay channel where a base station conveys independent symbols to K different users while receiving independent symbols from the users. In this network, user i Shas Mi antennas (i ∈ [1, ..., K]), the base station has Σi=1K Mi antennas...... and the relay is equipped with NR antennas. To study the capacity of this network, the degree of freedom (DOF) is characterized to be 2 Σi=1K Mi if NR ≥ Σi=1K Mi. The DOF implies that the number of symbols any transmitter can deliver and receive is equivalent to its number of antennas. The DOF is achievable...

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

Science.gov (United States)

Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

Multi-scale analysis of the European airspace using network community detection.

Directory of Open Access Journals (Sweden)

Gérald Gurtner

Full Text Available We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspace and improve it by guiding the design of new ones. Specifically, we compare the performance of several community detection algorithms, both with fixed and variable resolution, and also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace.

Reconfigurable microfluidic hanging drop network for multi-tissue interaction and analysis.

Science.gov (United States)

Frey, Olivier; Misun, Patrick M; Fluri, David A; Hengstler, Jan G; Hierlemann, Andreas

2014-06-30

Integration of multiple three-dimensional microtissues into microfluidic networks enables new insights in how different organs or tissues of an organism interact. Here, we present a platform that extends the hanging-drop technology, used for multi-cellular spheroid formation, to multifunctional complex microfluidic networks. Engineered as completely open, 'hanging' microfluidic system at the bottom of a substrate, the platform features high flexibility in microtissue arrangements and interconnections, while fabrication is simple and operation robust. Multiple spheroids of different cell types are formed in parallel on the same platform; the different tissues are then connected in physiological order for multi-tissue experiments through reconfiguration of the fluidic network. Liquid flow is precisely controlled through the hanging drops, which enable nutrient supply, substance dosage and inter-organ metabolic communication. The possibility to perform parallelized microtissue formation on the same chip that is subsequently used for complex multi-tissue experiments renders the developed platform a promising technology for 'body-on-a-chip'-related research.

MULTI-SENSOR NETWORK FOR LANDSLIDES SIMULATION AND HAZARD MONITORING - DESIGN AND DEPLOYMENT

Directory of Open Access Journals (Sweden)

H. Wu

2012-08-01

Full Text Available This paper describes a newly developed multi-sensor network system for landslide and hazard monitoring. Landslide hazard is one of the most destructive natural disasters, which has severely affected human safety, properties and infrastructures. We report the results of designing and deploying the multi-sensor network, based on the simulated landslide model, to monitor typical landslide areas and with a goal to predict landslide hazard and mitigate damages. The integration and deployment of the prototype sensor network were carried out in an experiment area at Tongji University in Shanghai. In order to simulate a real landslide, a contractible landslide body is constructed in the experiment area by 7m*1.5m. Then, some different kind of sensors, such as camera, GPS, crackmeter, accelerometer, laser scanning system, inclinometer, etc., are installed near or in the landslide body. After the sensors are powered, continuous sampling data will be generated. With the help of communication method, such as GPRS, and certain transport devices, such as iMesh and 3G router, all the sensor data will be transported to the server and stored in Oracle. These are the current results of an ongoing project of the center. Further research results will be updated and presented in the near future.

Optimizing Intermodal Train Schedules with a Design Balanced Network Design Model

DEFF Research Database (Denmark)

Pedersen, Michael Berliner; Crainic, Teodor Gabriel

We present a modeling approach for optimizing intermodal trains schedules based on an infrastructure divided into time-dependent train paths. The formulation can be generalized to a capacitated multi commodity network design model with additional design balance constraints. We present a Tabu Search...

Analysing the Correlation between Social Network Analysis Measures and Performance of Students in Social Network-Based Engineering Education

Science.gov (United States)

Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav

2016-01-01

Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…

Dynamics of blood flow and thrombus formation in a multi-bypass microfluidic ladder network.

Science.gov (United States)

Zilberman-Rudenko, Jevgenia; Sylman, Joanna L; Lakshmanan, Hari H S; McCarty, Owen J T; Maddala, Jeevan

2017-02-01

The reaction dynamics of a complex mixture of cells and proteins, such as blood, in branched circulatory networks within the human microvasculature or extravascular therapeutic devices such as extracorporeal oxygenation machine (ECMO) remains ill-defined. In this report we utilize a multi-bypass microfluidics ladder network design with dimensions mimicking venules to study patterns of blood platelet aggregation and fibrin formation under complex shear. Complex blood fluid dynamics within multi-bypass networks under flow were modeled using COMSOL. Red blood cells and platelets were assumed to be non-interacting spherical particles transported by the bulk fluid flow, and convection of the activated coagulation factor II, thrombin, was assumed to be governed by mass transfer. This model served as the basis for predicting formation of local shear rate gradients, stagnation points and recirculation zones as dictated by the bypass geometry. Based on the insights from these models, we were able to predict the patterns of blood clot formation at specific locations in the device. Our experimental data was then used to adjust the model to account for the dynamical presence of thrombus formation in the biorheology of blood flow. The model predictions were then compared to results from experiments using recalcified whole human blood. Microfluidic devices were coated with the extracellular matrix protein, fibrillar collagen, and the initiator of the extrinsic pathway of coagulation, tissue factor. Blood was perfused through the devices at a flow rate of 2 µL/min, translating to physiologically relevant initial shear rates of 300 and 700 s -1 for main channels and bypasses, respectively. Using fluorescent and light microscopy, we observed distinct flow and thrombus formation patterns near channel intersections at bypass points, within recirculation zones and at stagnation points. Findings from this proof-of-principle ladder network model suggest a specific correlation between

Multiscale paradigms in integrated computational materials science and engineering materials theory, modeling, and simulation for predictive design

CERN Document Server

Runge, Keith; Muralidharan, Krishna

2016-01-01

This book presents cutting-edge concepts, paradigms, and research highlights in the field of computational materials science and engineering, and provides a fresh, up-to-date perspective on solving present and future materials challenges. The chapters are written by not only pioneers in the fields of computational materials chemistry and materials science, but also experts in multi-scale modeling and simulation as applied to materials engineering. Pedagogical introductions to the different topics and continuity between the chapters are provided to ensure the appeal to a broad audience and to address the applicability of integrated computational materials science and engineering for solving real-world problems.

Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty

Directory of Open Access Journals (Sweden)

Sonia Irshad Mari

2016-10-01

Full Text Available Researchers and practitioners are taking more interest in developing sustainable garment supply chains in recent times. On the other hand, the supply chain manager drops sustainability objectives while coping with unexpected natural and man-made disruption risks. Hence, supply chain managers are now trying to develop sustainable supply chains that are simultaneously resilient enough to cope with disruption risks. Owing to the importance of the considered issue, this study proposed a network optimization model for a sustainable and resilient supply chain network by considering sustainability via embodied carbon footprints and carbon emissions and resilience by considering resilience index. In this paper, initially, a possibilistic fuzzy multi-objective sustainable and resilient supply chain network model is developed for the garment industry considering economic, sustainable, and resilience objectives. Secondly, a possibilistic fuzzy linguistic weight-based interactive solution method is proposed. Finally, a numerical case example is presented to show the applicability of the proposed model and solution methodology.

Architecture of the Multi-Modal Organizational Research and Production Heterogeneous Network (MORPHnet)

Energy Technology Data Exchange (ETDEWEB)

Aiken, R.J.; Carlson, R.A.; Foster, I.T. [and others

1997-01-01

The research and education (R&E) community requires persistent and scaleable network infrastructure to concurrently support production and research applications as well as network research. In the past, the R&E community has relied on supporting parallel network and end-node infrastructures, which can be very expensive and inefficient for network service managers and application programmers. The grand challenge in networking is to provide support for multiple, concurrent, multi-layer views of the network for the applications and the network researchers, and to satisfy the sometimes conflicting requirements of both while ensuring one type of traffic does not adversely affect the other. Internet and telecommunications service providers will also benefit from a multi-modal infrastructure, which can provide smoother transitions to new technologies and allow for testing of these technologies with real user traffic while they are still in the pre-production mode. The authors proposed approach requires the use of as much of the same network and end system infrastructure as possible to reduce the costs needed to support both classes of activities (i.e., production and research). Breaking the infrastructure into segments and objects (e.g., routers, switches, multiplexors, circuits, paths, etc.) gives the capability to dynamically construct and configure the virtual active networks to address these requirements. These capabilities must be supported at the campus, regional, and wide-area network levels to allow for collaboration by geographically dispersed groups. The Multi-Modal Organizational Research and Production Heterogeneous Network (MORPHnet) described in this report is an initial architecture and framework designed to identify and support the capabilities needed for the proposed combined infrastructure and to address related research issues.

Daily global solar radiation modelling using multi-layer perceptron neural networks in semi-arid region

Directory of Open Access Journals (Sweden)

Mawloud GUERMOUI

2016-07-01

Full Text Available Accurate estimation of Daily Global Solar Radiation (DGSR has been a major goal for solar energy application. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly of the search for relationships between weather variables, such as temperature, humidity, sunshine duration, etc. In this respect, the present study focuses on the development of artificial neural network (ANN model for estimation of daily global solar radiation on horizontal surface in Ghardaia city (South Algeria. In this analysis back-propagation algorithm is applied. Daily mean air temperature, relative humidity and sunshine duration was used as climatic inputs parameters, while the daily global solar radiation (DGSR was the only output of the ANN. We have evaluated Multi-Layer Perceptron (MLP models to estimate DGSR using three year of measurement (2005-2008. It was found that MLP-model based on sunshine duration and mean air temperature give accurate results in term of Mean Absolute Bias Error, Root Mean Square Error, Relative Square Error and Correlation Coefficient. The obtained values of these indicators are 0.67 MJ/m², 1.28 MJ/m², 6.12%and 98.18%, respectively which shows that MLP is highly qualified for DGSR estimation in semi-arid climates.

Collaborative Multi-Layer Network Coding For Hybrid Cellular Cognitive Radio Networks

KAUST Repository

Moubayed, Abdallah J.

2014-05-01

In this thesis, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated collaboration between the collocated primary and cognitive radio base-stations in order to minimize their own as well as each other’s packet recovery overheads, thus by improving their throughput. The proposed scheme ensures that each network’s performance is not degraded by its help to the other network. Moreover, it guarantees that the primary network’s interference threshold is not violated in the same and adjacent cells. Yet, the scheme allows the reduction of the recovery overhead in the collocated primary and cognitive radio networks. The reduction in the cognitive radio network is further amplified due to the perfect detection of spectrum holes which allows the cognitive radio base station to transmit at higher power without fear of violating the interference threshold of the primary network. For the secondary network, simulation results show reductions of 20% and 34% in the packet recovery overhead, compared to the non-collaborative scheme, for low and high probabilities of primary packet arrivals, respectively. For the primary network, this reduction was found to be 12%. Furthermore, with the use of fractional cooperation, the average recovery overhead is further reduced by around 5% for the primary network and around 10% for the secondary network when a high fractional cooperation probability is used.

A DTM MULTI-RESOLUTION COMPRESSED MODEL FOR EFFICIENT DATA STORAGE AND NETWORK TRANSFER

Directory of Open Access Journals (Sweden)

L. Biagi

2012-08-01

Full Text Available In recent years the technological evolution of terrestrial, aerial and satellite surveying, has considerably increased the measurement accuracy and, consequently, the quality of the derived information. At the same time, the smaller and smaller limitations on data storage devices, in terms of capacity and cost, has allowed the storage and the elaboration of a bigger number of instrumental observations. A significant example is the terrain height surveyed by LIDAR (LIght Detection And Ranging technology where several height measurements for each square meter of land can be obtained. The availability of such a large quantity of observations is an essential requisite for an in-depth knowledge of the phenomena under study. But, at the same time, the most common Geographical Information Systems (GISs show latency in visualizing and analyzing these kind of data. This problem becomes more evident in case of Internet GIS. These systems are based on the very frequent flow of geographical information over the internet and, for this reason, the band-width of the network and the size of the data to be transmitted are two fundamental factors to be considered in order to guarantee the actual usability of these technologies. In this paper we focus our attention on digital terrain models (DTM's and we briefly analyse the problems about the definition of the minimal necessary information to store and transmit DTM's over network, with a fixed tolerance, starting from a huge number of observations. Then we propose an innovative compression approach for sparse observations by means of multi-resolution spline functions approximation. The method is able to provide metrical accuracy at least comparable to that provided by the most common deterministic interpolation algorithms (inverse distance weighting, local polynomial, radial basis functions. At the same time it dramatically reduces the number of information required for storing or for transmitting and rebuilding a

Content-based organization of the information space in multi-database networks

NARCIS (Netherlands)

Papazoglou, M.; Milliner, S.

1998-01-01

Abstract. Rapid growth in the volume of network-available data, complexity, diversity and terminological fluctuations, at different data sources, render network-accessible information increasingly difficult to achieve. The situation is particularly cumbersome for users of multi-database systems who

A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

International Nuclear Information System (INIS)

Tang, Ling; Yu, Lean; Wang, Shuai; Li, Jianping; Wang, Shouyang

2012-01-01

Highlights: ► A hybrid ensemble learning paradigm integrating EEMD and LSSVR is proposed. ► The hybrid ensemble method is useful to predict time series with high volatility. ► The ensemble method can be used for both one-step and multi-step ahead forecasting. - Abstract: In this paper, a novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting, based on the principle of “decomposition and ensemble”. This hybrid ensemble learning paradigm is formulated specifically to address difficulties in modeling nuclear energy consumption, which has inherently high volatility, complexity and irregularity. In the proposed hybrid ensemble learning paradigm, EEMD, as a competitive decomposition method, is first applied to decompose original data of nuclear energy consumption (i.e. a difficult task) into a number of independent intrinsic mode functions (IMFs) of original data (i.e. some relatively easy subtasks). Then LSSVR, as a powerful forecasting tool, is implemented to predict all extracted IMFs independently. Finally, these predicted IMFs are aggregated into an ensemble result as final prediction, using another LSSVR. For illustration and verification purposes, the proposed learning paradigm is used to predict nuclear energy consumption in China. Empirical results demonstrate that the novel hybrid ensemble learning paradigm can outperform some other popular forecasting models in both level prediction and directional forecasting, indicating that it is a promising tool to predict complex time series with high volatility and irregularity.

Multi-stability and almost periodic solutions of a class of recurrent neural networks

International Nuclear Information System (INIS)

Liu Yiguang; You Zhisheng

2007-01-01

This paper studies multi-stability, existence of almost periodic solutions of a class of recurrent neural networks with bounded activation functions. After introducing a sufficient condition insuring multi-stability, many criteria guaranteeing existence of almost periodic solutions are derived using Mawhin's coincidence degree theory. All the criteria are constructed without assuming the activation functions are smooth, monotonic or Lipschitz continuous, and that the networks contains periodic variables (such as periodic coefficients, periodic inputs or periodic activation functions), so all criteria can be easily extended to fit many concrete forms of neural networks such as Hopfield neural networks, or cellular neural networks, etc. Finally, all kinds of simulations are employed to illustrate the criteria

Networked Airborne Communications Using Adaptive Multi Beam Directional Links

Science.gov (United States)

2016-03-05

Networked Airborne Communications Using Adaptive Multi-Beam Directional Links R. Bruce MacLeod Member, IEEE, and Adam Margetts Member, IEEE MIT...provide new techniques for increasing throughput in airborne adaptive directional net- works. By adaptive directional linking, we mean systems that can...techniques can dramatically increase the capacity in airborne networks. Advances in digital array technology are beginning to put these gains within reach

Probabilistic, multi-variate flood damage modelling using random forests and Bayesian networks

Science.gov (United States)

Kreibich, Heidi; Schröter, Kai

2015-04-01

Decisions on flood risk management and adaptation are increasingly based on risk analyses. Such analyses are associated with considerable uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention recently, they are hardly applied in flood damage assessments. Most of the damage models usually applied in standard practice have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. This presentation will show approaches for probabilistic, multi-variate flood damage modelling on the micro- and meso-scale and discuss their potential and limitations. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., Merz, B. (2014): How useful are complex flood damage models? - Water Resources Research, 50, 4, p. 3378-3395.

Hop-by-HopWorm Propagation with Carryover Epidemic Model in Mobile Sensor Networks

Directory of Open Access Journals (Sweden)

Jun-Won Ho

2015-10-01

Full Text Available In the internet, a worm is usually propagated in a random multi-hop contact manner. However, the attacker will not likely select this random multi-hop propagation approach in a mobile sensor network. This is because multi-hop worm route paths to random vulnerable targets can be often breached due to node mobility, leading to failure of fast worm spread under this strategy. Therefore, an appropriate propagation strategy is needed for mobile sensor worms. To meet this need, we discuss a hop-by-hop worm propagation model in mobile sensor networks. In a hop-by-hop worm propagation model, benign nodes are infected by worm in neighbor-to-neighbor spread manner. Since worm infection occurs in hop-by-hop contact, it is not substantially affected by a route breach incurred by node mobility. We also propose the carryover epidemic model to deal with the worm infection quota deficiency that might occur when employing an epidemic model in a mobile sensor network. We analyze worm infection capability under the carryover epidemic model. Moreover, we simulate hop-by-hop worm propagation with carryover epidemic model by using an ns-2 simulator. The simulation results demonstrate that infection quota carryovers are seldom observed where a node’s maximum speed is no less than 20 m/s.

Water distribution network segmentation based on group multi-criteria decision approach

Directory of Open Access Journals (Sweden)

Marcele Elisa Fontana

Full Text Available Abstract A correct Network Segmentation (NS is necessary to perform proper maintenance activities in water distribution networks (WDN. For this, usually, isolation valves are allocating near the ends of pipes, blocking the flow of water. However, the allocation of valves increases costs substantially for the water supply companies. Additionally, other criteria should be taking account to analyze the benefits of the valves allocation. Thus, the problem is to define an alternative of NS which shows a good compromise in these different criteria. Moreover, usually, in this type of decision, there is more than one decision-maker involved, who can have different viewpoints. Therefore, this paper presents a model to support group decision-making, based on a multi-criteria method, in order to support the decision making procedure in the NS problem. As result, the model is able to find a solution that shows the best compromise regarding the benefits, costs, and the decision makers' preferences.

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

KAUST Repository

Cannistraci, C.V.

2013-04-08

Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.

Team Cooperation in a Network of Multi-Vehicle Unmanned Systems Synthesis of Consensus Algorithms

CERN Document Server

Semsar-Kazerooni, Elham

2013-01-01

Team Cooperation in a Network of Multi-Vehicle Unmanned Systems develops a framework for modeling and control of a network of multi-agent unmanned systems in a cooperative manner and with consideration of non-ideal and practical considerations. The main focus of this book is the development of “synthesis-based” algorithms rather than on conventional “analysis-based” approaches to the team cooperation, specifically the team consensus problems. The authors provide a set of modified “design-based” consensus algorithms whose optimality is verified through introduction of performance indices. This book also: Provides synthesis-based methodology for team cooperation Introduces a consensus-protocol optimized performance index  Offers comparisons for use of proper indices in measuring team performance Analyzes and predicts  performance of  previously designed consensus algorithms Analyses and predicts team behavior in the presence of non-ideal considerations such as actuator anomalies and faults as wel...

Automated road network extraction from high spatial resolution multi-spectral imagery

Science.gov (United States)

Zhang, Qiaoping

For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a

Distributed Multi-Commodity Network Flow Algorithm for Energy Optimal Routing in Wireless Sensor Networks.

Directory of Open Access Journals (Sweden)

J. Trdlicka

2010-12-01

Full Text Available This work proposes a distributed algorithm for energy optimal routing in a wireless sensor network. The routing problem is described as a mathematical problem by the minimum-cost multi-commodity network flow problem. Due to the separability of the problem, we use the duality theorem to derive the distributed algorithm. The algorithm computes the energy optimal routing in the network without any central node or knowledge of the whole network structure. Each node only needs to know the flow which is supposed to send or receive and the costs and capacities of the neighboring links. An evaluation of the presented algorithm on benchmarks for the energy optimal data flow routing in sensor networks with up to 100 nodes is presented.

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

Science.gov (United States)

Fan, Jianqing; Tong, Xin; Zeng, Yao

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning , to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

Estimation of effective connectivity using multi-layer perceptron artificial neural network.

Science.gov (United States)

Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

2018-02-01

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.

Network module detection: Affinity search technique with the multi-node topological overlap measure.

Science.gov (United States)

Li, Ai; Horvath, Steve

2009-07-20

Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/

Network neighborhood analysis with the multi-node topological overlap measure.

Science.gov (United States)

Li, Ai; Horvath, Steve

2007-01-15

The goal of neighborhood analysis is to find a set of genes (the neighborhood) that is similar to an initial 'seed' set of genes. Neighborhood analysis methods for network data are important in systems biology. If individual network connections are susceptible to noise, it can be advantageous to define neighborhoods on the basis of a robust interconnectedness measure, e.g. the topological overlap measure. Since the use of multiple nodes in the seed set may lead to more informative neighborhoods, it can be advantageous to define multi-node similarity measures. The pairwise topological overlap measure is generalized to multiple network nodes and subsequently used in a recursive neighborhood construction method. A local permutation scheme is used to determine the neighborhood size. Using four network applications and a simulated example, we provide empirical evidence that the resulting neighborhoods are biologically meaningful, e.g. we use neighborhood analysis to identify brain cancer related genes. An executable Windows program and tutorial for multi-node topological overlap measure (MTOM) based analysis can be downloaded from the webpage (http://www.genetics.ucla.edu/labs/horvath/MTOM/).

Regulatory networks, legal federalism, and multi-level regulatory systems

OpenAIRE

Kerber, Wolfgang; Wendel, Julia

2016-01-01

Transnational regulatory networks play important roles in multi-level regulatory regimes, as e.g, the European Union. In this paper we analyze the role of regulatory networks from the perspective of the economic theory of legal federalism. Often sophisticated intermediate institutional solutions between pure centralisation and pure decentralisation can help to solve complex tradeoff problems between the benefits and problems of centralised and decentralised solutions. Drawing upon the insight...

A big-data model for multi-modal public transportation with application to macroscopic control and optimisation

Science.gov (United States)

Faizrahnemoon, Mahsa; Schlote, Arieh; Maggi, Lorenzo; Crisostomi, Emanuele; Shorten, Robert

2015-11-01

This paper describes a Markov-chain-based approach to modelling multi-modal transportation networks. An advantage of the model is the ability to accommodate complex dynamics and handle huge amounts of data. The transition matrix of the Markov chain is built and the model is validated using the data extracted from a traffic simulator. A realistic test-case using multi-modal data from the city of London is given to further support the ability of the proposed methodology to handle big quantities of data. Then, we use the Markov chain as a control tool to improve the overall efficiency of a transportation network, and some practical examples are described to illustrate the potentials of the approach.

Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks

Directory of Open Access Journals (Sweden)

Andreas Koulouris

2013-01-01

Full Text Available This article investigates the Multiple Equilibria Regulation (MER model, i.e., an agent-based simulation model, to represent opinion dynamics in social networks. It relies on a small set of micro-prerequisites (intra-individual balance and confidence bound, leading to emergence of (nonstationary macro-outcomes. These outcomes may refer to consensus, polarization or fragmentation of opinions about taxation (e.g., congestion pricing or other policy measures, according to the way communication is structured. In contrast with other models of opinion dynamics, it allows for the impact of both the regulation of intra-personal discrepancy and the interpersonal variability of opinions on social learning and network dynamics. Several simulation experiments are presented to demonstrate, through the MER model, the role of different network structures (complete, star, cellular automata, small-world and random graphs on opinion formation dynamics and the overall evolution of the system. The findings can help to identify specific topological characteristics, such as density, number of neighbourhoods and critical nodes-agents, that affect the stability and system dynamics. This knowledge can be used to better organize the information diffusion and learning in the community, enhance the predictability of outcomes and manage possible conflicts. It is shown that a small-world organization, which depicts more realistic aspects of real-life and virtual social systems, provides increased predictability and stability towards a less fragmented and more manageable grouping of opinions, compared to random networks. Such macro-level organizations may be enhanced with use of web-based technologies to increase the density of communication and public acceptability of policy measures.

Global dynamics of a novel multi-group model for computer worms

International Nuclear Information System (INIS)

Gong Yong-Wang; Song Yu-Rong; Jiang Guo-Ping

2013-01-01

In this paper, we study worm dynamics in computer networks composed of many autonomous systems. A novel multi-group SIQR (susceptible-infected-quarantined-removed) model is proposed for computer worms by explicitly considering anti-virus measures and the network infrastructure. Then, the basic reproduction number of worm R 0 is derived and the global dynamics of the model are established. It is shown that if R 0 is less than or equal to 1, the disease-free equilibrium is globally asymptotically stable and the worm dies out eventually, whereas, if R 0 is greater than 1, one unique endemic equilibrium exists and it is globally asymptotically stable, thus the worm persists in the network. Finally, numerical simulations are given to illustrate the theoretical results. (general)