WorldWideScience

Sample records for hybrid social-information networks

  1. Optimising social information by game theory and ant colony method to enhance routing protocol in opportunistic networks

    Directory of Open Access Journals (Sweden)

    Chander Prabha

    2016-09-01

    Full Text Available The data loss and disconnection of nodes are frequent in the opportunistic networks. The social information plays an important role in reducing the data loss because it depends on the connectivity of nodes. The appropriate selection of next hop based on social information is critical for improving the performance of routing in opportunistic networks. The frequent disconnection problem is overcome by optimising the social information with Ant Colony Optimization method which depends on the topology of opportunistic network. The proposed protocol is examined thoroughly via analysis and simulation in order to assess their performance in comparison with other social based routing protocols in opportunistic network under various parameters settings.

  2. Hybrid Noncoherent Network Coding

    CERN Document Server

    Skachek, Vitaly; Nedic, Angelia

    2011-01-01

    We describe a novel extension of subspace codes for noncoherent networks, suitable for use when the network is viewed as a communication system that introduces both dimension and symbol errors. We show that when symbol erasures occur in a significantly large number of different basis vectors transmitted through the network and when the min-cut of the networks is much smaller then the length of the transmitted codewords, the new family of codes outperforms their subspace code counterparts. For the proposed coding scheme, termed hybrid network coding, we derive two upper bounds on the size of the codes. These bounds represent a variation of the Singleton and of the sphere-packing bound. We show that a simple concatenated scheme that represents a combination of subspace codes and Reed-Solomon codes is asymptotically optimal with respect to the Singleton bound. Finally, we describe two efficient decoding algorithms for concatenated subspace codes that in certain cases have smaller complexity than subspace decoder...

  3. Social information

    Directory of Open Access Journals (Sweden)

    Luiz Fernando de Barros Campos

    Full Text Available Based on Erving Goffman's work, the article aims to discuss a definition of information centered on the type conveyed by individuals in a multimodal way, encompassing language and body in situations of co-presence, where face-to-face interaction occurs, and influencing inter-subjective formation of the self. Six types of information are highlighted: material information, expressive information, ritualized information, meta-information, strategic information, and information displays. It is argued that the construction of this empirical object tends to dissolve the tension among material, cognitive and pragmatic aspects, constituting an example of the necessary integration among them. Some vulnerable characteristics of the theory are critically mentioned and it is suggested that the concept of information displays could provide a platform to approach the question of the interaction order in its relations with the institutional and social orders, and consequently, to reassess the scope of the notion of social information analyzed.

  4. Social-network complexity in humans is associated with the neural response to social information.

    Science.gov (United States)

    Dziura, Sarah L; Thompson, James C

    2014-11-01

    Humans have evolved to thrive in large and complex social groups, and it is likely that this increase in group complexity has come with a greater need to decode and respond to complex and uncertain communicatory signals. In this functional MRI study, we examined whether complexity of social networks in humans is related to the functioning of brain regions key to the perception of basic, nonverbal social stimuli. Greater activation to biological than to scrambled motion in the right posterior superior temporal sulcus (pSTS) and right amygdala were positively correlated with the diversity of social-network roles. In the pSTS, in particular, this association was not due to a relationship between network diversity and network size. These findings suggest that increased functioning of brain regions involved in decoding social signals might facilitate the detection and decoding of subtle signals encountered in varied social settings.

  5. Network structure underlying resolution of conflicting non-verbal and verbal social information.

    Science.gov (United States)

    Watanabe, Takamitsu; Yahata, Noriaki; Kawakubo, Yuki; Inoue, Hideyuki; Takano, Yosuke; Iwashiro, Norichika; Natsubori, Tatsunobu; Takao, Hidemasa; Sasaki, Hiroki; Gonoi, Wataru; Murakami, Mizuho; Katsura, Masaki; Kunimatsu, Akira; Abe, Osamu; Kasai, Kiyoto; Yamasue, Hidenori

    2014-06-01

    Social judgments often require resolution of incongruity in communication contents. Although previous studies revealed that such conflict resolution recruits brain regions including the medial prefrontal cortex (mPFC) and posterior inferior frontal gyrus (pIFG), functional relationships and networks among these regions remain unclear. In this functional magnetic resonance imaging study, we investigated the functional dissociation and networks by measuring human brain activity during resolving incongruity between verbal and non-verbal emotional contents. First, we found that the conflict resolutions biased by the non-verbal contents activated the posterior dorsal mPFC (post-dmPFC), bilateral anterior insula (AI) and right dorsal pIFG, whereas the resolutions biased by the verbal contents activated the bilateral ventral pIFG. In contrast, the anterior dmPFC (ant-dmPFC), bilateral superior temporal sulcus and fusiform gyrus were commonly involved in both of the resolutions. Second, we found that the post-dmPFC and right ventral pIFG were hub regions in networks underlying the non-verbal- and verbal-content-biased resolutions, respectively. Finally, we revealed that these resolution-type-specific networks were bridged by the ant-dmPFC, which was recruited for the conflict resolutions earlier than the two hub regions. These findings suggest that, in social conflict resolutions, the ant-dmPFC selectively recruits one of the resolution-type-specific networks through its interaction with resolution-type-specific hub regions.

  6. STEER: Exploring the dynamic relationship between social information and networked media through experimentation

    NARCIS (Netherlands)

    Dijkstra, S.S; Niamut, O.A; Efthymiopoulos, N.; Denazis, S.; Race, N.; Mu, M.; Taal, J.

    2015-01-01

    With the growing popularity of social networks, online video services and smart phones, the traditional content consumers are becoming the editors and broadcasters of their own stories. Within the EU FP7 project STEER, project partners have developed a novel system of new algorithms and tool sets th

  7. Inference in hybrid Bayesian networks

    DEFF Research Database (Denmark)

    Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael

    2009-01-01

    Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....

  8. Unified Hybrid Network Theoretical Model Trilogy

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    The first of the unified hybrid network theoretical model trilogy (UHNTF) is the harmonious unification hybrid preferential model (HUHPM), seen in the inner loop of Fig. 1, the unified hybrid ratio is defined.

  9. Hybrid-Source Impedance Networks

    DEFF Research Database (Denmark)

    Li, Ding; Gao, Feng; Loh, Poh Chiang;

    2010-01-01

    the cascaded networks would have a higher output voltage gain and other unique advantages that currently have not been investigated yet. It is anticipated that these advantages would help the formed inverters find applications in photovoltaic and other renewable systems, where a high voltage gain is usually......Hybrid-source impedance networks have attracted attention among researchers because of their flexibility in performing buck-boost energy conversion. To date, three distinct types of impedance networks can be summarized for implementing voltage-type inverters with another three types summarized...

  10. Genomic networks of hybrid sterility.

    Directory of Open Access Journals (Sweden)

    Leslie M Turner

    2014-02-01

    Full Text Available Hybrid dysfunction, a common feature of reproductive barriers between species, is often caused by negative epistasis between loci ("Dobzhansky-Muller incompatibilities". The nature and complexity of hybrid incompatibilities remain poorly understood because identifying interacting loci that affect complex phenotypes is difficult. With subspecies in the early stages of speciation, an array of genetic tools, and detailed knowledge of reproductive biology, house mice (Mus musculus provide a model system for dissecting hybrid incompatibilities. Male hybrids between M. musculus subspecies often show reduced fertility. Previous studies identified loci and several X chromosome-autosome interactions that contribute to sterility. To characterize the genetic basis of hybrid sterility in detail, we used a systems genetics approach, integrating mapping of gene expression traits with sterility phenotypes and QTL. We measured genome-wide testis expression in 305 male F2s from a cross between wild-derived inbred strains of M. musculus musculus and M. m. domesticus. We identified several thousand cis- and trans-acting QTL contributing to expression variation (eQTL. Many trans eQTL cluster into eleven 'hotspots,' seven of which co-localize with QTL for sterility phenotypes identified in the cross. The number and clustering of trans eQTL-but not cis eQTL-were substantially lower when mapping was restricted to a 'fertile' subset of mice, providing evidence that trans eQTL hotspots are related to sterility. Functional annotation of transcripts with eQTL provides insights into the biological processes disrupted by sterility loci and guides prioritization of candidate genes. Using a conditional mapping approach, we identified eQTL dependent on interactions between loci, revealing a complex system of epistasis. Our results illuminate established patterns, including the role of the X chromosome in hybrid sterility. The integrated mapping approach we employed is

  11. Genomic networks of hybrid sterility.

    Science.gov (United States)

    Turner, Leslie M; White, Michael A; Tautz, Diethard; Payseur, Bret A

    2014-02-01

    Hybrid dysfunction, a common feature of reproductive barriers between species, is often caused by negative epistasis between loci ("Dobzhansky-Muller incompatibilities"). The nature and complexity of hybrid incompatibilities remain poorly understood because identifying interacting loci that affect complex phenotypes is difficult. With subspecies in the early stages of speciation, an array of genetic tools, and detailed knowledge of reproductive biology, house mice (Mus musculus) provide a model system for dissecting hybrid incompatibilities. Male hybrids between M. musculus subspecies often show reduced fertility. Previous studies identified loci and several X chromosome-autosome interactions that contribute to sterility. To characterize the genetic basis of hybrid sterility in detail, we used a systems genetics approach, integrating mapping of gene expression traits with sterility phenotypes and QTL. We measured genome-wide testis expression in 305 male F2s from a cross between wild-derived inbred strains of M. musculus musculus and M. m. domesticus. We identified several thousand cis- and trans-acting QTL contributing to expression variation (eQTL). Many trans eQTL cluster into eleven 'hotspots,' seven of which co-localize with QTL for sterility phenotypes identified in the cross. The number and clustering of trans eQTL-but not cis eQTL-were substantially lower when mapping was restricted to a 'fertile' subset of mice, providing evidence that trans eQTL hotspots are related to sterility. Functional annotation of transcripts with eQTL provides insights into the biological processes disrupted by sterility loci and guides prioritization of candidate genes. Using a conditional mapping approach, we identified eQTL dependent on interactions between loci, revealing a complex system of epistasis. Our results illuminate established patterns, including the role of the X chromosome in hybrid sterility. The integrated mapping approach we employed is applicable in a broad

  12. Hybrid percolation transition in complex networks

    Science.gov (United States)

    Kahng, Byungnam

    Percolation has been one of the most applied statistical models. Percolation transition is one of the most robust continuous transitions known thus far. However, recent extensive researches reveal that it exhibits diverse types of phase transitions such as discontinuous and hybrid phase transitions. Here hybrid phase transition means the phase transition exhibiting natures of both continuous and discontinuous phase transitions simultaneously. Examples include k-core percolation, cascading failures in interdependent networks, synchronization, etc. Thus far, it is not manifest if the critical behavior of hybrid percolation transitions conforms to the conventional scaling laws of second-order phase transition. Here, we investigate the critical behaviors of hybrid percolation transitions in the cascading failure model in inter-dependent networks and the restricted Erdos-Renyi model. We find that the critical behaviors of the hybrid percolation transitions contain some features that cannot be described by the conventional theory of second-order percolation transitions.

  13. Economia Informal em Rede: trocas económicas e complexidade social Informal economy network: economic exchanges and social complexity

    Directory of Open Access Journals (Sweden)

    Marzia Grassi

    2012-03-01

    Full Text Available Partindo das realidades empíricas de Cabo Verde e diásporas, este texto explora, até certo ponto, as limitações heurísticas de certas noções sugeridas pela mainstream do modelo neoliberal da economia sobre o «informal» em África. O texto debruça-se sobre diferentes dimensões, espaços e protagonistas de práticas de economia informal em rede. As repercussões identitárias das dinâmicas destas redes entre os cabo-verdianos, apreendidas através da observação de certas formas de sociabilidade dos actores sociais considerados, são igualmente exploradas.Based on the empirical realities of Cape Verde and some of its Diasporas, this article explores, to a certain extent, the heuristic limitations of certain notions suggested by the mainstream of the neoliberal economy model concerning the so called «informal economy» in Africa. The text deals with different dimensions, spaces and protagonists of practices of the informal economic networks. The identitarian repercussions of the dynamics of these networks among Capeverdians, apprehended through the observation of certain forms of socialibility of these social actors are also explored.

  14. Hybrid neural network models of transducers

    Science.gov (United States)

    Xie, Shilin; Zhang, Xinong; Chen, Shenglai; Zhu, Changchun

    2011-10-01

    A hybrid neural network (NN) approach is proposed and applied to modeling of transducers in the paper. The modeling procedures are also presented in detail. First, the simulated studies on the modeling of single input-single output and multi input-multi output transducers are conducted respectively by use of the developed hybrid NN scheme. Secondly, the hybrid NN modeling approach is utilized to characterize a six-axis force sensor prototype based on the measured data. The results show that the hybrid NN approach can significantly improve modeling precision in comparison with the conventional modeling method. In addition, the method is superior to NN black-box modeling because the former possesses smaller network scale, higher convergence speed, higher model precision and better generalization performance.

  15. Hybrid Dynamic Network Data Envelopment Analysis

    Directory of Open Access Journals (Sweden)

    Ling Li

    2015-01-01

    Full Text Available Conventional DEA models make no hypothesis concerning the internal operations in a static situation. To open the “black box” and work with dynamic assessment issues synchronously, we put forward a hybrid model for evaluating the relative efficiencies of a set of DMUs over an observed time period with a composite of network DEA and dynamic DEA. We vertically deal with intermediate products between divisions with assignable inputs in the network structure and, horizontally, we extend network structure by means of a dynamic pattern with unrelated activities between two succeeding periods. The hybrid dynamic network DEA model proposed in this paper enables us to (i pry into the internal operations of DEA by another network structure, (ii obtain dynamic change of period efficiency, and (iii gain the overall dynamic efficiency of DMUs over the entire observed periods. We finally illustrate the calculation procedure of the proposed approach by a numerical example.

  16. Influence of Deterministic Attachments for Large Unifying Hybrid Network Model

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    Large unifying hybrid network model (LUHPM) introduced the deterministic mixing ratio fd on the basis of the harmonious unification hybrid preferential model, to describe the influence of deterministic attachment to the network topology characteristics,

  17. Hybrid simulation models of production networks

    CERN Document Server

    Kouikoglou, Vassilis S

    2001-01-01

    This book is concerned with a most important area of industrial production, that of analysis and optimization of production lines and networks using discrete-event models and simulation. The book introduces a novel approach that combines analytic models and discrete-event simulation. Unlike conventional piece-by-piece simulation, this method observes a reduced number of events between which the evolution of the system is tracked analytically. Using this hybrid approach, several models are developed for the analysis of production lines and networks. The hybrid approach combines speed and accuracy for exceptional analysis of most practical situations. A number of optimization problems, involving buffer design, workforce planning, and production control, are solved through the use of hybrid models.

  18. Filtering in hybrid dynamic Bayesian networks

    DEFF Research Database (Denmark)

    Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin

    2004-01-01

    We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2-Time Slice DBN (2T-DBN) from (Koller & Lerner, 2000) to model fault detection in a watertank system. In (Koller & Lerner, 2000) a generic Particle Filter (PF) is used for infere...

  19. Filtering in hybrid dynamic Bayesian networks (left)

    DEFF Research Database (Denmark)

    Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin

    We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2-Time Slice DBN (2T-DBN) from (Koller & Lerner, 2000) to model fault detection in a watertank system. In (Koller & Lerner, 2000) a generic Particle Filter (PF) is used for infere...

  20. Filtering in hybrid dynamic Bayesian networks (center)

    DEFF Research Database (Denmark)

    Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin

    We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2-Time Slice DBN (2T-DBN) from (Koller & Lerner, 2000) to model fault detection in a watertank system. In (Koller & Lerner, 2000) a generic Particle Filter (PF) is used for infere...

  1. Hybrid architecture for building secure sensor networks

    Science.gov (United States)

    Owens, Ken R., Jr.; Watkins, Steve E.

    2012-04-01

    Sensor networks have various communication and security architectural concerns. Three approaches are defined to address these concerns for sensor networks. The first area is the utilization of new computing architectures that leverage embedded virtualization software on the sensor. Deploying a small, embedded virtualization operating system on the sensor nodes that is designed to communicate to low-cost cloud computing infrastructure in the network is the foundation to delivering low-cost, secure sensor networks. The second area focuses on securing the sensor. Sensor security components include developing an identification scheme, and leveraging authentication algorithms and protocols that address security assurance within the physical, communication network, and application layers. This function will primarily be accomplished through encrypting the communication channel and integrating sensor network firewall and intrusion detection/prevention components to the sensor network architecture. Hence, sensor networks will be able to maintain high levels of security. The third area addresses the real-time and high priority nature of the data that sensor networks collect. This function requires that a quality-of-service (QoS) definition and algorithm be developed for delivering the right data at the right time. A hybrid architecture is proposed that combines software and hardware features to handle network traffic with diverse QoS requirements.

  2. Multiuser Cooperation with Hybrid Network Coding in Wireless Networks

    Directory of Open Access Journals (Sweden)

    G. Wang

    2014-04-01

    Full Text Available In this paper a hybrid Network Coding Cooperation (hybrid-NCC system is proposed to achieve both reliable transmission and high throughput in wireless networks. To balance the transmission reliability with throughput, the users are divided into cooperative sub-networks based on the geographical information, and the cooperation is implemented in each sub-network. After receiving signals from the cooperative partners, each user encodes them by exploiting hybrid network coding and then forwards the recoded symbols via the Link-Adaptive Regenerative (LAR relaying. First, the Diversity-Multiplexing Tradeoff (DMT is analyzed to demonstrate that the proposed system is bandwidth-efficient. Second, the Symbol Error Probability (SEP is also derived, which shows that the proposed system achieves a higher reliability as compared to the traditional Complex Field Network Coding Cooperation (CFNCC. Moreover, because dedicated relays are not required, our proposed system can both reduce the costs and enhance the flexibility of the implementation. Finally, the analytical results are supported and validated by numerical simulations.

  3. Hybrid Mobile Communication Networks for Planetary Exploration

    Science.gov (United States)

    Alena, Richard; Lee, Charles; Walker, Edward; Osenfort, John; Stone, Thom

    2007-01-01

    A paper discusses the continuing work of the Mobile Exploration System Project, which has been performing studies toward the design of hybrid communication networks for future exploratory missions to remote planets. A typical network could include stationary radio transceivers on a remote planet, mobile radio transceivers carried by humans and robots on the planet, terrestrial units connected via the Internet to an interplanetary communication system, and radio relay transceivers aboard spacecraft in orbit about the planet. Prior studies have included tests on prototypes of these networks deployed in Arctic and desert regions chosen to approximate environmental conditions on Mars. Starting from the findings of the prior studies, the paper discusses methods of analysis, design, and testing of the hybrid communication networks. It identifies key radio-frequency (RF) and network engineering issues. Notable among these issues is the study of wireless LAN throughput loss due to repeater use, RF signal strength, and network latency variations. Another major issue is that of using RF-link analysis to ensure adequate link margin in the face of statistical variations in signal strengths.

  4. Semantics for Hybrid Networks Using the Network Description Language

    NARCIS (Netherlands)

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

    2006-01-01

    Hybrid networks offer end users a mix of traditional connections and new optical services in the form of dedicated lightpaths. These must be requested in advance and are currently configured on demand by the operators. Because lightpaths are circuit switched, the user must be aware of the topology a

  5. Analysis of Network Parameters Influencing Performance of Hybrid Multimedia Networks

    Directory of Open Access Journals (Sweden)

    Dominik Kovac

    2013-10-01

    Full Text Available Multimedia networks is an emerging subject that currently attracts the attention of research and industrial communities. This environment provides new entertainment services and business opportunities merged with all well-known network services like VoIP calls or file transfers. Such a heterogeneous system has to be able satisfy all network and end-user requirements which are increasing constantly. Therefore the simulation tools enabling deep analysis in order to find the key performance indicators and factors which influence the overall quality for specific network service the most are highly needed. This paper provides a study on the network parameters like communication technology, routing protocol, QoS mechanism, etc. and their effect on the performance of hybrid multimedia network. The analysis was performed in OPNET Modeler environment and the most interesting results are discussed at the end of this paper

  6. Hybrid Optical Switching for Data Center Networks

    Directory of Open Access Journals (Sweden)

    Matteo Fiorani

    2014-01-01

    Full Text Available Current data centers networks rely on electronic switching and point-to-point interconnects. When considering future data center requirements, these solutions will raise issues in terms of flexibility, scalability, performance, and energy consumption. For this reason several optical switched interconnects, which make use of optical switches and wavelength division multiplexing (WDM, have been recently proposed. However, the solutions proposed so far suffer from low flexibility and are not able to provide service differentiation. In this paper we introduce a novel data center network based on hybrid optical switching (HOS. HOS combines optical circuit, burst, and packet switching on the same network. In this way different data center applications can be mapped to the optical transport mechanism that best suits their traffic characteristics. Furthermore, the proposed HOS network achieves high transmission efficiency and reduced energy consumption by using two parallel optical switches. We consider the architectures of both a traditional data center network and the proposed HOS network and present a combined analytical and simulation approach for their performance and energy consumption evaluation. We demonstrate that the proposed HOS data center network achieves high performance and flexibility while considerably reducing the energy consumption of current solutions.

  7. Routing of multimedia connections in hybrid networks

    Science.gov (United States)

    Koegel, John F.; Syta, Andrzej

    1993-02-01

    The prevailing vision for next generation multimedia communication systems is a digital one. However, we anticipate a transitional period in which hybrid networks involving both analog and digital technology will coexist. These analog facilities will include crossbar audio-video switches, CATV distribution systems, and dedicated lines. For some scale of use, these facilities may offer economies for connectivity to conventional analog video equipment. We are interested in connection routing that will be needed in such hybrid networks for services including video conferencing and broadcast results. The routing problem in such topologies resembles but is not identical to that found in telephone systems because of the presence of broadcast connections. We discuss representative topologies, review related work, and describe algorithms and simulation results. In addition we describe a hybrid system that we have implemented in our research lab which involves several A/V switches, CATV channels, digital video on a LAN, and a point-to-point link to an offsite conference area.

  8. Hybrid synchronization of two independent chaotic systems on complex network

    Indian Academy of Sciences (India)

    NIAN FUZHONG; LIU WEILONG

    2016-06-01

    The real network nodes are always interfered by other messages. So, how to realize the hybrid synchronization of two independent chaotic systems based on the complex network is very important. To solve this problem, two other problems should be considered. One is how the same network node of the complex network was affected by different information sources. Another is how to achieve hybrid synchronization on the network. In this paper, the theoretical analysis andnumerical simulation on various complex networks are implemented. The results indicate that the hybrid synchronization of two independent chaotic systems is feasible.

  9. Time Series Prediction based on Hybrid Neural Networks

    Directory of Open Access Journals (Sweden)

    S. A. Yarushev

    2016-01-01

    Full Text Available In this paper, we suggest to use hybrid approach to time series forecasting problem. In first part of paper, we create a literature review of time series forecasting methods based on hybrid neural networks and neuro-fuzzy approaches. Hybrid neural networks especially effective for specific types of applications such as forecasting or classification problem, in contrast to traditional monolithic neural networks. These classes of problems include problems with different characteristics in different modules. The main part of paper create a detailed overview of hybrid networks benefits, its architectures and performance under traditional neural networks. Hybrid neural networks models for time series forecasting are discussed in the paper. Experiments with modular neural networks are given.

  10. Filtering in Hybrid Dynamic Bayesian Networks

    Science.gov (United States)

    Andersen, Morten Nonboe; Andersen, Rasmus Orum; Wheeler, Kevin

    2000-01-01

    We implement a 2-time slice dynamic Bayesian network (2T-DBN) framework and make a 1-D state estimation simulation, an extension of the experiment in (v.d. Merwe et al., 2000) and compare different filtering techniques. Furthermore, we demonstrate experimentally that inference in a complex hybrid DBN is possible by simulating fault detection in a watertank system, an extension of the experiment in (Koller & Lerner, 2000) using a hybrid 2T-DBN. In both experiments, we perform approximate inference using standard filtering techniques, Monte Carlo methods and combinations of these. In the watertank simulation, we also demonstrate the use of 'non-strict' Rao-Blackwellisation. We show that the unscented Kalman filter (UKF) and UKF in a particle filtering framework outperform the generic particle filter, the extended Kalman filter (EKF) and EKF in a particle filtering framework with respect to accuracy in terms of estimation RMSE and sensitivity with respect to choice of network structure. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. Furthermore, we investigate the influence of data noise in the watertank simulation using UKF and PFUKD and show that the algorithms are more sensitive to changes in the measurement noise level that the process noise level. Theory and implementation is based on (v.d. Merwe et al., 2000).

  11. High availability of hybrid wireless networks

    Science.gov (United States)

    Leitgeb, Erich; Gebhart, Michael; Birnbacher, Ulla; Kogler, Wolfgang; Schrotter, Peter

    2004-09-01

    Free Space Optical (FSO) links offer high bandwidth and the flexibility of wireless communication links. However, the availability of FSO links is limited by weather patterns like fog and heavy snowfall. Microwave based communication links operating at high frequencies (40 - 43 GHz) have similar characteristics like high data rates and needed line-of-sight. Link availability for microwave systems is limited by heavy rain. Combining FSO links with microwave links within a hybrid FSO/microwave communication network has the advantage of added redundancy and higher link availability. Measurements over a period of one year show a combined availability of 99.93% for the climatic region of Graz, Austria) which proves that the combination of both technologies leads to a highly available wireless connection offering high bandwidth.

  12. Capacity analysis of inhomogeneous hybrid wireless networks using directional antennas

    Institute of Scientific and Technical Information of China (English)

    WU Feng; ZHU Jiang; TIAN Yi-long; ZOU Jian-bin

    2016-01-01

    Most of studies on network capacity are based on the assumption that all the nodes are uniformly distributed, which means that the networks are characterized by homogeneity. However, many realistic networks exhibit inhomogeneity due to natural and man-made reasons. In this work, the capacity of inhomogeneous hybrid networks with directional antennas for the first time is studied. By setting different node distribution probabilities, the whole network can be devided into dense cells and sparse cells. On this basis, an inhomogeneous hybrid network model is proposed. The network can exhibit significant inhomogeneity due to the coexistence of two types of cells. Then, we derive the network capacity and maximize the capacity under different channel allocation schemes. Finally, how the network parameters influence the network capacity is analyzed. It is found that if there are plenty of base stations, the per-node throughput can achieve constant order, and if the beamwidth of directional antenna is small enough, the network capacity can scale.

  13. Filtering in Hybrid Dynamic Bayesian Networks

    Science.gov (United States)

    Andersen, Morten Nonboe; Andersen, Rasmus Orum; Wheeler, Kevin

    2004-01-01

    We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2 - T i e Slice DBN (2T-DBN) from [Koller & Lerner, 20001 to model fault detection in a watertank system. In [Koller & Lerner, 20001 a generic Particle Filter (PF) is used for inference. We extend the experiment and perform approximate inference using The Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Furthermore, we combine these techniques in a 'non-strict' Rao-Blackwellisation framework and apply it to the watertank system. We show that UKF and UKF in a PF framework outperfom the generic PF, EKF and EKF in a PF framework with respect to accuracy and robustness in terms of estimation RMSE. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. We also show that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the water[ank simulation. Theory and implementation is based on the theory presented.

  14. Identification of hybrid node and link communities in complex networks

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-01

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  15. Hybrid Neural Network Architecture for On-Line Learning

    CERN Document Server

    Chen, Yuhua; Wang, Lei

    2008-01-01

    Approaches to machine intelligence based on brain models have stressed the use of neural networks for generalization. Here we propose the use of a hybrid neural network architecture that uses two kind of neural networks simultaneously: (i) a surface learning agent that quickly adapt to new modes of operation; and, (ii) a deep learning agent that is very accurate within a specific regime of operation. The two networks of the hybrid architecture perform complementary functions that improve the overall performance. The performance of the hybrid architecture has been compared with that of back-propagation perceptrons and the CC and FC networks for chaotic time-series prediction, the CATS benchmark test, and smooth function approximation. It has been shown that the hybrid architecture provides a superior performance based on the RMS error criterion.

  16. On hybrid cooperation in underlay cognitive radio networks

    KAUST Repository

    Mahmood, Nurul Huda

    2012-11-01

    In wireless systems where transmitters are subject to a strict received power constraint, such as in underlay cognitive radio networks, cooperative communication is a promising strategy to enhance network performance, as it helps to improve the coverage area and outage performance of a network. However, this comes at the expense of increased resource utilization. To balance the performance gain against the possible over-utilization of resources, we propose a hybrid-cooperation technique for underlay cognitive radio networks, where secondary users cooperate only when required. Various performance measures of the proposed hybrid-cooperation technique are analyzed in this paper, and are also further validated numerically. © 2012 IEEE.

  17. Software architecture for hybrid electrical/optical data center network

    DEFF Research Database (Denmark)

    Mehmeri, Victor; Vegas Olmos, Juan José; Tafur Monroy, Idelfonso

    2016-01-01

    This paper presents hardware and software architecture based on Software-Defined Networking (SDN) paradigm and OpenFlow/NETCONF protocols for enabling topology management of hybrid electrical/optical switching data center networks. In particular, a development on top of SDN open-source controller...... OpenDaylight is presented to control an optical switching matrix based on Micro-Electro-Mechanical System (MEMS) technology.......This paper presents hardware and software architecture based on Software-Defined Networking (SDN) paradigm and OpenFlow/NETCONF protocols for enabling topology management of hybrid electrical/optical switching data center networks. In particular, a development on top of SDN open-source controller...

  18. Hybrid Wavelength Routed and Optical Packet Switched Ring Networks for the Metropolitan Area Network

    DEFF Research Database (Denmark)

    Nord, Martin

    2005-01-01

    Increased data traffic in the metropolitan area network calls for new network architectures. This paper evaluates optical ring architectures based on optical packet switching, wavelength routing, and hybrid combinations of the two concepts. The evaluation includes overall throughput and fairness...

  19. ABOUT HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    In this paper, hybrid bidirectional associative memory neural networks with discrete delays is considered. By ingeniously importing real parameters di > 0(i = 1,2,···,n) which can be adjusted, we establish some new sufficient conditions for the dynamical characteristics of hybrid bidirectional associative memory neural networks with discrete delays by the method of variation of parameters and some analysis techniques. Our results generalize and improve the related results in [10,11]. Our work is significant...

  20. Small-Firm Networks: hybrid arrangement or organizational form?

    OpenAIRE

    Verschoore,Jorge Renato; Balestrin,Alsones; Perucia,Alexandre

    2014-01-01

    In the field of organizations, one relevant question is whether or not to consider networks as organizational forms. On the one hand, Williamson (1985) says that networks are hybrid arrangements. On the other, authors like Powell (1990) argue that networks constitute themselves as organizational forms. Given this dilemma, the present article proposes the analysis of organizational characteristics of small-firm networks (SFN). To reach such objective, twelve SFNs in distinct stages of developm...

  1. On the Capacity of Hybrid Wireless Networks with Opportunistic Routing

    Directory of Open Access Journals (Sweden)

    Tan Le

    2010-01-01

    Full Text Available This paper studies the capacity of hybrid wireless networks with opportunistic routing (OR. We first extend the opportunistic routing algorithm to exploit high-speed data transmissions in infrastructure network through base stations. We then develop linear programming models to calculate the end-to-end throughput bounds from multiple source nodes to single as well as multiple destination nodes. The developed models are applied to study several hybrid wireless network examples. Through case studies, we investigate several factors that have significant impacts on the hybrid wireless network capacity under opportunistic routing, such as node transmission range, density and distribution pattern of base stations (BTs, and number of wireless channels on wireless nodes and base stations. Our numerical results demonstrate that opportunistic routing could achieve much higher throughput on both ad hoc and hybrid networks than traditional unicast routing (UR. Moreover, opportunistic routing can efficiently utilize base stations and achieve significantly higher throughput gains in hybrid wireless networks than in pure ad hoc networks especially with multiple-channel base stations.

  2. Fastest Distributed Consensus on Star-Mesh Hybrid Sensor Networks

    CERN Document Server

    Jafarizadeh, Saber

    2010-01-01

    Solving Fastest Distributed Consensus (FDC) averaging problem over sensor networks with different topologies has received some attention recently and one of the well known topologies in this issue is star-mesh hybrid topology. Here in this work we present analytical solution for the problem of FDC algorithm by means of stratification and semidefinite programming, for the Star-Mesh Hybrid network with K-partite core (SMHK) which has rich symmetric properties. Also the variations of asymptotic and per step convergence rate of SMHK network versus its topological parameters have been studied numerically.

  3. On Hybrid Cooperation in Underlay Cognitive Radio Networks

    DEFF Research Database (Denmark)

    Mahmood, Nurul Huda; Yilmaz, Ferkan; Øien, Geir E.;

    2013-01-01

    of opportunistic wireless systems such as cognitive radio networks. In order to balance the performance gains from cooperative communication against the possible over-utilization of resources, we propose and analyze an adaptive-cooperation technique for underlay cognitive radio networks, termed as hybrid......-cooperation. Under the proposed cooperation scheme, secondary users in a cognitive radio network cooperate adaptively to enhance the spectral efficiency and the error performance of the network. The bit error rate, the spectral efficiency and the outage performance of the network under the proposed hybrid......Cooperative communication is a promising strategy to enhance the performance of a communication network as it helps to improve the coverage area and the outage performance. However, such enhancement comes at the expense of increased resource utilization, which is undesirable; more so in the case...

  4. Hybrid evolving clique-networks and their communicability

    Science.gov (United States)

    Ding, Yimin; Zhou, Bin; Chen, Xiaosong

    2014-08-01

    Aiming to understand real-world hierarchical networks whose degree distributions are neither power law nor exponential, we construct a hybrid clique network that includes both homogeneous and inhomogeneous parts, and introduce an inhomogeneity parameter to tune the ratio between the homogeneous part and the inhomogeneous one. We perform Monte-Carlo simulations to study various properties of such a network, including the degree distribution, the average shortest-path-length, the clustering coefficient, the clustering spectrum, and the communicability.

  5. On Hybrid Cooperation in Underlay Cognitive Radio Networks

    DEFF Research Database (Denmark)

    Mahmood, Nurul Huda; Yilmaz, Ferkan; Øien, Geir E.

    2013-01-01

    of opportunistic wireless systems such as cognitive radio networks. In order to balance the performance gains from cooperative communication against the possible over-utilization of resources, we propose and analyze an adaptive-cooperation technique for underlay cognitive radio networks, termed as hybrid......-cooperation. Under the proposed cooperation scheme, secondary users in a cognitive radio network cooperate adaptively to enhance the spectral efficiency and the error performance of the network. The bit error rate, the spectral efficiency and the outage performance of the network under the proposed hybrid...... cooperation scheme with amplify-and-forward relaying are analyzed in this paper, and compared against conventional cooperation technique. Findings of the analytical performance analyses are further validated numerically through selected computer-based Monte-Carlo simulations. The proposed scheme is found...

  6. Design of Hybrid Mobile Communication Networks for Planetary Exploration

    Science.gov (United States)

    Alena, Richard L.; Ossenfort, John; Lee, Charles; Walker, Edward; Stone, Thom

    2004-01-01

    The Mobile Exploration System Project (MEX) at NASA Ames Research Center has been conducting studies into hybrid communication networks for future planetary missions. These networks consist of space-based communication assets connected to ground-based Internets and planetary surface-based mobile wireless networks. These hybrid mobile networks have been deployed in rugged field locations in the American desert and the Canadian arctic for support of science and simulation activities on at least six occasions. This work has been conducted over the past five years resulting in evolving architectural complexity, improved component characteristics and better analysis and test methods. A rich set of data and techniques have resulted from the development and field testing of the communication network during field expeditions such as the Haughton Mars Project and NASA Mobile Agents Project.

  7. Social Information Processing in Deaf Adolescents

    Science.gov (United States)

    Torres, Jesús; Saldaña, David; Rodríguez-Ortiz, Isabel R.

    2016-01-01

    The goal of this study was to compare the processing of social information in deaf and hearing adolescents. A task was developed to assess social information processing (SIP) skills of deaf adolescents based on Crick and Dodge's (1994; A review and reformulation of social information-processing mechanisms in children's social adjustment.…

  8. Existing PON Infrastructure Supported Hybrid Fiber-Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Yu, Xianbin; Zhao, Ying; Deng, Lei;

    2012-01-01

    We propose a hybrid fiber wireless sensor network based on the existing PON infrastructure. The feasibility of remote sensing and PON convergence is experimentally proven by transmitting direct-sequence spread-spectrum wireless sensing and 2.5Gbps GPON signals.......We propose a hybrid fiber wireless sensor network based on the existing PON infrastructure. The feasibility of remote sensing and PON convergence is experimentally proven by transmitting direct-sequence spread-spectrum wireless sensing and 2.5Gbps GPON signals....

  9. Hybrid positioning with lighting LEDs and Zigbee multihop wireless network

    Science.gov (United States)

    Lee, Y. U.; Baang, S.; Park, J.; Zhou, Z.; Kavehrad, M.

    2012-01-01

    A simple, accurate, secure, long-lasting, and portable hybrid positioning system is proposed and designed in this paper. It consists of a lighting LED that generates visible light data corresponding to position information of a target and a Zigbee wireless network communication module with low power, security, and service area expansion characteristics. Under an indoor environment where there is 23.62m distance between an observer and the target, the presented hybrid positioning system is tested and is verified with the functions of Zigbee three hop wireless networking and visible light communication (VLC) scheme. The test results are analyzed and discussed.

  10. The research of controller area network on hybrid electrical vehicle

    Institute of Scientific and Technical Information of China (English)

    Wu Hongxing; Song Liwei; Kou Baoquan; Cheng Shukang

    2006-01-01

    It is of increasing importance to design and implement vehicle networks for transferring information between electrical control units on Hybrid Electrical Vehicle (HEV). This paper presents a scheme of using Controller Area Network (CAN) technology to realize communication and datasharing between the electrical units on the HEV. The principle and communication protocol of Electrical Control Units (ECU) CAN node are introduced. By considering different sensitivity of the devices to the latency of data transportation, a new design procedure is proposed for the purpose of simplifying network codes and wiring harness, reducing assembly space and weight, improving assembly efficiency, and enhancing fault-diagnose in auto networks.

  11. Hybrid RRM Architecture for Future Wireless Networks

    DEFF Research Database (Denmark)

    Tragos, Elias; Mihovska, Albena D.; Mino, Emilio

    2007-01-01

    The concept of ubiquitous and scalable system is applied in the IST WINNER II [1] project to deliver optimum performance for different deployment scenarios from local area to wide area wireless networks. The integration of cellular and local area networks in a unique radio system will provide a g...

  12. Hybrid Distributed Iterative Capacity Allocation over Bluetooth Network

    DEFF Research Database (Denmark)

    Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun

    With the current development of mobile devices, short range wireless communications have become more and more popular, and many researches on short range wireless communications, such as Bluetooth, have gained special interests, in industry as well as in academy. This paper analyzes capacity...... allocation issues in Bluetooth network as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. The hybrid distributed capacity allocation scheme is proposed as an approximated solution of the formulated problem that satisfies quality...... of service requirements and constraints in Bluetooth network, such as limited capacity, decentralized, frequent changes of topology and of capacities assigned to nodes in the network. The simulation shows that the performance of Bluetooth could be improved by applying the hybrid distributed iterative...

  13. Hybrid Distributed Iterative Capacity Allocation over Bluetooth Network

    DEFF Research Database (Denmark)

    Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun

    2002-01-01

    With the current development of mobile devices, short range wireless communications have become more and more popular, and many researches on short range wireless communications, such as Bluetooth, have gained special interests, in industry as well as in academy. This paper analyzes capacity...... allocation issues in Bluetooth network as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. The hybrid distributed capacity allocation scheme is proposed as an approximated solution of the formulated problem that satisfies quality...... of service requirements and constraints in Bluetooth network, such as limited capacity, decentralized, frequent changes of topology and of capacities assigned to nodes in the network. The simulation shows that the performance of Bluetooth could be improved by applying the hybrid distributed iterative...

  14. Scalable and Hybrid Radio Resource Management for Future Wireless Networks

    DEFF Research Database (Denmark)

    Mino, E.; Luo, Jijun; Tragos, E.;

    2007-01-01

    The concept of ubiquitous and scalable system is applied in the IST WINNER II [1] project to deliver optimum performance for different deployment scenarios, from local area to wide area wireless networks. The integration in a unique radio system of a cellular and local area type networks supposes...... describes a proposal for scalable and hybrid radio resource management to efficiently integrate the different WINNER system modes. Index...

  15. Leading school networks, hybrid leadership in action?

    OpenAIRE

    Townsend, Andrew

    2015-01-01

    A range of different constructs are used to describe and define the way that leadership operates in education settings. This range can be presented as binary categories of leadership, in which either one, or the other form of leadership is preferred, but not both. An example of this is the contrast made between solo and distributed leadership. A more sophisticated alternative has been proposed, which is to consider leadership as a hybrid activity, one which entails a range of approaches inspi...

  16. Final Technical Report for Terabit-scale hybrid networking project.

    Energy Technology Data Exchange (ETDEWEB)

    Veeraraghavan, Malathi [Univ. of Virginia, Charlottesville, VA (United States)

    2015-12-12

    This report describes our accomplishments and activities for the project titled Terabit-Scale Hybrid Networking. The key accomplishment is that we developed, tested and deployed an Alpha Flow Characterization System (AFCS) in ESnet. It is being run in production mode since Sept. 2015. Also, a new QoS class was added to ESnet5 to support alpha flows.

  17. Reverse engineering cellular decisions for hybrid reconfigurable network modeling

    Science.gov (United States)

    Blair, Howard A.; Saranak, Jureepan; Foster, Kenneth W.

    2011-06-01

    Cells as microorganisms and within multicellular organisms make robust decisions. Knowing how these complex cells make decisions is essential to explain, predict or mimic their behavior. The discovery of multi-layer multiple feedback loops in the signaling pathways of these modular hybrid systems suggests their decision making is sophisticated. Hybrid systems coordinate and integrate signals of various kinds: discrete on/off signals, continuous sensory signals, and stochastic and continuous fluctuations to regulate chemical concentrations. Such signaling networks can form reconfigurable networks of attractors and repellors giving them an extra level of organization that has resilient decision making built in. Work on generic attractor and repellor networks and on the already identified feedback networks and dynamic reconfigurable regulatory topologies in biological cells suggests that biological systems probably exploit such dynamic capabilities. We present a simple behavior of the swimming unicellular alga Chlamydomonas that involves interdependent discrete and continuous signals in feedback loops. We show how to rigorously verify a hybrid dynamical model of a biological system with respect to a declarative description of a cell's behavior. The hybrid dynamical systems we use are based on a unification of discrete structures and continuous topologies developed in prior work on convergence spaces. They involve variables of discrete and continuous types, in the sense of type theory in mathematical logic. A unification such as afforded by convergence spaces is necessary if one wants to take account of the affect of the structural relationships within each type on the dynamics of the system.

  18. Self-Healing Hybrid Protection Architecture for Passive Optical Networks

    Directory of Open Access Journals (Sweden)

    Waqas A. Imtiaz

    2015-08-01

    Full Text Available Expanding size of passive optical networks (PONs along with high availability expectation makes the reliability performance a crucial need. Most protection architectures utilize redundant network components to enhance network survivability, which is not economical. This paper proposes new self-healing protection architecture for passive optical networks (PONs, with a single ring topology and star-ring topology at feeder and distribution level respectively. The proposed architecture provides desirable protection to the network by avoiding fiber duplication at both feeder and distribution level. Moreover, medium access control (MAC controlled switching is utilized to provide efficient detection, and restoration of faults or cuts throughout the network. Analytical analysis reveals that the proposed self-healing hybrid protection architecture ensures survivability of the affected traffic along with desirable connection availability of 99.9994 % at minimum deployment cost, through simple architecture and simultaneous protection against failures.

  19. Nafion–clay hybrids with a network structure

    KAUST Repository

    Burgaz, Engin

    2009-05-01

    Nafion-clay hybrid membranes with a unique microstructure were synthesized using a fundamentally new approach. The new approach is based on depletion aggregation of suspended particles - a well-known phenomenon in colloids. For certain concentrations of clay and polymer, addition of Nafion solution to clay suspensions in water leads to a gel. Using Cryo-TEM we show that the clay particles in the hybrid gels form a network structure with an average cell size in the order of 500 nm. The hybrid gels are subsequently cast to produce hybrid Nafion-clay membranes. Compared to pure Nafion the swelling of the hybrid membranes in water and methanol is dramatically reduced while their selectivity (ratio of conductivity over permeability) increases. The small decrease of ionic conductivity for the hybrid membranes is more than compensated by the large decrease in methanol permeability. Lastly the hybrid membranes are much stiffer and can withstand higher temperatures compared to pure Nafion. Both of these characteristics are highly desirable for use in fuel cell applications, since a) they will allow the use of a thinner membrane circumventing problems associated with the membrane resistance and b) enable high temperature applications. © 2009 Elsevier Ltd. All rights reserved.

  20. Novel Hybrid Intrusion Detection System For Clustered Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Hichem Sedjelmaci

    2011-08-01

    Full Text Available Wireless sensor network (WSN is regularly deployed in unattended and hostile environments. The WSN isvulnerable to security threats and susceptible to physical capture. Thus, it is necessary to use effective mechanisms to protect the network. It is widely known, that the intrusion detection is one of the mostefficient security mechanisms to protect the network against malicious attacks or unauthorized access. In this paper, we propose a hybrid intrusion detection system for clustered WSN. Our intrusion framework uses a combination between the Anomaly Detection based on support vector machine (SVM and the Misuse Detection. Experiments results show that most of routing attacks can be detected with low falsealarm.

  1. Hybrid pre training algorithm of Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Drokin I. S.

    2016-01-01

    Full Text Available This paper proposes a hybrid algorithm of pre training deep networks, using both marked and unmarked data. The algorithm combines and extends the ideas of Self-Taught learning and pre training of neural networks approaches on the one hand, as well as supervised learning and transfer learning on the other. Thus, the algorithm tries to integrate in itself the advantages of each approach. The article gives some examples of applying of the algorithm, as well as its comparison with the classical approach to pre training of neural networks. These examples show the effectiveness of the proposed algorithm.

  2. SINET3: advanced optical and IP hybrid network

    Science.gov (United States)

    Urushidani, Shigeo

    2007-11-01

    This paper introduces the new Japanese academic backbone network called SINET3, which has been in full-scale operation since June 2007. SINET3 provides a wide variety of network services, such as multi-layer transfer, enriched VPN, enhanced QoS, and layer-1 bandwidth on demand (BoD) services to create an innovative and prolific science infrastructure for more than 700 universities and research institutions. The network applies an advanced hybrid network architecture composed of 75 layer-1 switches and 12 high-performance IP routers to accommodate such diversified services in a single network platform, and provides sufficient bandwidth using Japan's first STM256 (40 Gbps) lines. The network adopts lots of the latest networking technologies, such as next-generation SDH (VCAT/GFP/LCAS), GMPLS, advanced MPLS, and logical-router technologies, for high network convergence, flexible resource assignment, and high service availability. This paper covers the network services, network design, and networking technologies of SINET3.

  3. Electroencephalography epilepsy classifications using hybrid cuckoo search and neural network

    Science.gov (United States)

    Pratiwi, A. B.; Damayanti, A.; Miswanto

    2017-07-01

    Epilepsy is a condition that affects the brain and causes repeated seizures. This seizure is episodes that can vary and nearly undetectable to long periods of vigorous shaking or brain contractions. Epilepsy often can be confirmed with an electrocephalography (EEG). Neural Networks has been used in biomedic signal analysis, it has successfully classified the biomedic signal, such as EEG signal. In this paper, a hybrid cuckoo search and neural network are used to recognize EEG signal for epilepsy classifications. The weight of the multilayer perceptron is optimized by the cuckoo search algorithm based on its error. The aim of this methods is making the network faster to obtained the local or global optimal then the process of classification become more accurate. Based on the comparison results with the traditional multilayer perceptron, the hybrid cuckoo search and multilayer perceptron provides better performance in term of error convergence and accuracy. The purpose methods give MSE 0.001 and accuracy 90.0 %.

  4. Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wan, Can; Song, Yonghua; Xu, Zhao

    2016-01-01

    probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties...... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....

  5. Intelligent Hybrid Cluster Based Classification Algorithm for Social Network Analysis

    Directory of Open Access Journals (Sweden)

    S. Muthurajkumar

    2014-05-01

    Full Text Available In this paper, we propose an hybrid clustering based classification algorithm based on mean approach to effectively classify to mine the ordered sequences (paths from weblog data in order to perform social network analysis. In the system proposed in this work for social pattern analysis, the sequences of human activities are typically analyzed by switching behaviors, which are likely to produce overlapping clusters. In this proposed system, a robust Modified Boosting algorithm is proposed to hybrid clustering based classification for clustering the data. This work is useful to provide connection between the aggregated features from the network data and traditional indices used in social network analysis. Experimental results show that the proposed algorithm improves the decision results from data clustering when combined with the proposed classification algorithm and hence it is proved that of provides better classification accuracy when tested with Weblog dataset. In addition, this algorithm improves the predictive performance especially for multiclass datasets which can increases the accuracy.

  6. Image Cytometry Data From Breast Lesions Analyzed using Hybrid Networks.

    Science.gov (United States)

    Mat Sakim, H A; Mat Isa, N A; G Naguib, Raouf; Sherbet, Gajanan

    2005-01-01

    The treatment and therapy to be administered on breast cancer patients are dependent on the stage of the disease at time of diagnosis. It is therefore crucial to determine the stage at the earliest time possible. Tumor dissemination to axillary lymph nodes has been regarded as an indication of tumor aggression, thus the stage of the disease. Neural networks have been employed in many applications including breast cancer prognosis. The performance of the networks have often been quoted based on accuracy and mean squared error. In this paper, the performance of hybrid networks based on Multilayer Perceptron and Radial Basis Function networks to predict axillary lymph node involvement have been investigated. A measurement of how confident the networks are with respect to the results produced is also proposed. The input layer of the networks include four image cytometry features extracted from fine needle aspiration of breast lesions. The highest accuracy achieved by the hybrid networks was 69% only. However, most of the correctly predicted cases had a high confidence level.

  7. Energy efficiency in hybrid mobile and wireless networks

    Energy Technology Data Exchange (ETDEWEB)

    Ziaul Haq Abbas

    2012-07-01

    Wireless Internet access is almost pervasive nowadays, and many types of wireless networks can be used to access the Internet. However, along with this growth, there is an even greater concern about the energy consumption and efficiency of mobile devices as well as of the supporting networks, triggering the appearance of the concept of green communication. While some efforts have been made towards this direction, challenges still exist and need to be tackled from diverse perspectives. Cellular networks, WLANs, and ad hoc networks in the form of wireless mesh networks are the most popular technologies for wireless Internet access. The availability of such a variety of access networks has also paved the way to explore synergistic approaches for Internet access, leading to the concept of hybrid networks and relay communications. In addition, many mobile devices are being equipped with multiple interfaces, enabling them to operate in hybrid networks. In contrast, the improvements in the battery technology itself have not matched the pace of the emerging mobile applications. The situation becomes more sophisticated when a mobile device functions also as a relay node to forward other station's data. In the literature, energy efficiency of mobile devices has been addressed from various perspectives such as protocol-level efforts, battery management efforts, etc. However, there is little work on energy efficiency in hybrid mobile and wireless networks and devices with heterogeneous connections. For example, when there are multiple networks available to a mobile device, how to achieve optimum long-term energy consumption of such a device is an open question. Furthermore, in today's cellular networks, micro-, pico-, and femto-cells are the most popular network topologies in order to support high data rate services and high user density. With the growth of such small-cell solutions, the energy consumption of these networks is also becoming an important concern for operators

  8. Mobility-aware Hybrid Synchronization for Wireless Sensor Network

    DEFF Research Database (Denmark)

    Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

    2015-01-01

    Random mobility of node causes the frequent changes in the network dynamics causing the increased cost in terms of energy and bandwidth. It needs the additional efforts to synchronize the activities of nodes during data collection and transmission in Wireless Sensor Networks (WSNs). A key challenge...... in maintaining the effective data collection and transmission is to schedule and synchronize the activities of the nodes with the global clock. This paper proposes the Mobility-aware Hybrid Synchronization Algorithm (MHS) which works on the formation of cluster based on spanning tree mechanism (SPT). Nodes used...... for formation of the network have random mobility and heterogeneous in terms of energy with static sink. The nodes in the cluster and cluster heads in the network are synchronized with the notion of global time scale. In the initial stage, the algorithm establishes the hierarchical structure of the network...

  9. Bandwidth Efficient Hybrid Synchronization for Wireless Sensor Network

    DEFF Research Database (Denmark)

    Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

    2015-01-01

    Data collection and transmission are the fundamental operations of Wireless Sensor Networks (WSNs). A key challenge in effective data collection and transmission is to schedule and synchronize the activities of the nodes with the global clock. This paper proposes the Bandwidth Efficient Hybrid...... Synchronization Data Aggregation Algorithm (BESDA) using spanning tree mechanism (SPT). It uses static sink and mobile nodes in the network. BESDA considers the synchronization of a local clock of node with global clock of the network. In the initial stage algorithm established the hierarchical structure...... in the network and then perform the pair-wise synchronization. With the mobility of node, the structure frequently changes causing an increase in energy consumption. To mitigate the problem BESDA aggregate data with the notion of a global timescale throughout the network and schedule based time-division multiple...

  10. Hybrid recommendation methods in complex networks

    CERN Document Server

    Fiasconaro, A; Nicosia, V; Latora, V; Mantegna, R N

    2014-01-01

    We propose here two new recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three relevant data sets, and we compare their performance with several recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow to attain an improvement of performances of up to 20\\% with respect to existing non-parametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we studied how an increasing presence of random links in the network affects the recommendation scores, and we found that one of the two recommendation algorithms introduced here can systematically outpe...

  11. Hybrid recommendation methods in complex networks

    Science.gov (United States)

    Fiasconaro, A.; Tumminello, M.; Nicosia, V.; Latora, V.; Mantegna, R. N.

    2015-07-01

    We propose two recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three data sets, and we compare the performance of our methods to other recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow us to attain an improvement of performances of up to 20% with respect to existing nonparametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we study how an increasing presence of random links in the network affects the recommendation scores, finding that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets.

  12. Hybrid recommendation methods in complex networks.

    Science.gov (United States)

    Fiasconaro, A; Tumminello, M; Nicosia, V; Latora, V; Mantegna, R N

    2015-07-01

    We propose two recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three data sets, and we compare the performance of our methods to other recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow us to attain an improvement of performances of up to 20% with respect to existing nonparametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we study how an increasing presence of random links in the network affects the recommendation scores, finding that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets.

  13. A hybrid neural network model for consciousness

    Institute of Scientific and Technical Information of China (English)

    蔺杰; 金小刚; 杨建刚

    2004-01-01

    A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers,physical mnemonic layer and abstract thinking layer,which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness:(1)the reception process whereby cerebral subsystems group distributed signals into coherent object patterns;(2)the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and(3)the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework,various sorts of human actions can be explained,leading to a general approach for analyzing brain functions.

  14. A hybrid neural network model for consciousness

    Institute of Scientific and Technical Information of China (English)

    蔺杰; 金小刚; 杨建刚

    2004-01-01

    A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers, physical mnemonic layer and abstract thinking layer, which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness: (l) the reception process whereby cerebral subsystems group distributed signals into coherent object patterns; (2) the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and (3) the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework, various sorts of human actions can be explained, leading to a general approach for analyzing brain functions.

  15. On hybrid cooperation in underlay cognitive radio networks

    KAUST Repository

    Mahmood, Nurul Huda

    2013-09-01

    Cooperative communication is a promising strategy to enhance the performance of a communication network as it helps to improve the coverage area and the outage performance. However, such enhancement comes at the expense of increased resource utilization, which is undesirable; more so in the case of opportunistic wireless systems such as cognitive radio networks. In order to balance the performance gains from cooperative communication against the possible over-utilization of resources, we propose and analyze an adaptive-cooperation technique for underlay cognitive radio networks, termed as hybrid-cooperation. Under the proposed cooperation scheme, secondary users in a cognitive radio network cooperate adaptively to enhance the spectral efficiency and the error performance of the network. The bit error rate, the spectral efficiency and the outage performance of the network under the proposed hybrid cooperation scheme with amplify-and-forward relaying are analyzed in this paper, and compared against conventional cooperation technique. Findings of the analytical performance analyses are further validated numerically through selected computer-based Monte-Carlo simulations. The proposed scheme is found to achieve significantly better performance in terms of the spectral efficiency and the bit error rate, compared to the conventional amplify-and-forward cooperation scheme. © 2013 IEEE.

  16. Hybrid Network Defense Model Based on Fuzzy Evaluation

    Directory of Open Access Journals (Sweden)

    Ying-Chiang Cho

    2014-01-01

    Full Text Available With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network’s existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter’s inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.

  17. PV-Diesel Hybrid SCADA Experiment Network Design

    Science.gov (United States)

    Kalu, Alex; Durand, S.; Emrich, Carol; Ventre, G.; Wilson, W.; Acosta, R.

    1999-01-01

    The essential features of an experimental network for renewable power system satellite based supervisory, control and data acquisition (SCADA) are communication links, controllers, diagnostic equipment and a hybrid power system. Required components for implementing the network consist of two satellite ground stations, to satellite modems, two 486 PCs, two telephone receivers, two telephone modems, two analog telephone lines, one digital telephone line, a hybrid-power system equipped with controller and a satellite spacecraft. In the technology verification experiment (TVE) conducted by Savannah State University and Florida Solar Energy Center, the renewable energy hybrid system is the Apex-1000 Mini-Hybrid which is equipped with NGC3188 for user interface and remote control and the NGC2010 for monitoring and basic control tasks. This power system is connected to a satellite modem via a smart interface, RS232. Commands are sent to the power system control unit through a control PC designed as PC1. PC1 is thus connected to a satellite model through RS232. A second PC, designated PC2, the diagnostic PC is connected to both satellite modems via separate analog telephone lines for checking modems'health. PC2 is also connected to PC1 via a telephone line. Due to the unavailability of a second ground station for the ACTS, one ground station is used to serve both the sending and receiving functions in this experiment. Signal is sent from the control PC to the Hybrid system at a frequency f(sub 1), different from f(sub 2), the signal from the hybrid system to the control PC. f(sub l) and f(sub 2) are sufficiently separated to avoid interference.

  18. Personality predicts the use of social information

    NARCIS (Netherlands)

    Kurvers, R.H.J.M.; Oers, van K.; Nolet, B.A.; Jonker, R.M.; Wieren, van S.E.; Prins, H.H.T.; Ydenberg, R.C.

    2010-01-01

    The use of social information is known to affect various important aspects of an individual's ecology, such as foraging, dispersal and space use and is generally assumed to be entirely flexible and context dependent. However, the potential link between personality differences and social information

  19. Personality predicts the use of social information

    NARCIS (Netherlands)

    Kurvers, R.H.J.M.; Van Oers, K.; Nolet, B.A.; Jonker, R.M.; van Wieren, S.E.; Prins, H.H.T.; Ydenberg, R.C.

    2010-01-01

    The use of social information is known to affect various important aspects of an individual’s ecology, such as foraging, dispersal and space use and is generally assumed to be entirely flexible and context dependent. However, the potential link between personality differences and social information

  20. Hybrid Networks and Risk Management in a System Perspective

    DEFF Research Database (Denmark)

    Nørgaard, Katrine

    new possibilities and new types of risk, as well as legal and ethical concerns. At the same time, the rapid acceleration and hybridization of the battlespace challenges the classical military bureaucracies and its legal-rational decision-making processes. This paper will address some of the legal...... intelligent, autonomous systems and human operators in multi-domain Battle Management Networks, (i.e Command, Control, Communications, Computer and Intelligence (C4I) networks/sensor grids). However, the incorporation of intelligent and autonomous weapon systems in complex military operations introduces both...

  1. Hybrid-source impedance network and its generalized cascading concepts

    DEFF Research Database (Denmark)

    Li, Ding; Gao, Feng; Loh, Poh Chiang;

    2009-01-01

    . It is anticipated that these concepts and their formed inverters can find applications in photovoltaic and other renewable systems, which in turn motivate the investigation initiated here on two-level and three-level generalized cascading concepts. In addition to their theoretical performance merits, practical......Hybrid-source impedance networks have attracted attention among researchers because of their flexibility in performing buck-boost energy conversion. To date, three distinct types of impedance networks can be summarized for implementing voltage-type inverters, with another three types summarized...

  2. A Hybrid Satellite-Terrestrial Approach to Aeronautical Communication Networks

    Science.gov (United States)

    Kerczewski, Robert J.; Chomos, Gerald J.; Griner, James H.; Mainger, Steven W.; Martzaklis, Konstantinos S.; Kachmar, Brian A.

    2000-01-01

    Rapid growth in air travel has been projected to continue for the foreseeable future. To maintain a safe and efficient national and global aviation system, significant advances in communications systems supporting aviation are required. Satellites will increasingly play a critical role in the aeronautical communications network. At the same time, current ground-based communications links, primarily very high frequency (VHF), will continue to be employed due to cost advantages and legacy issues. Hence a hybrid satellite-terrestrial network, or group of networks, will emerge. The increased complexity of future aeronautical communications networks dictates that system-level modeling be employed to obtain an optimal system fulfilling a majority of user needs. The NASA Glenn Research Center is investigating the current and potential future state of aeronautical communications, and is developing a simulation and modeling program to research future communications architectures for national and global aeronautical needs. This paper describes the primary requirements, the current infrastructure, and emerging trends of aeronautical communications, including a growing role for satellite communications. The need for a hybrid communications system architecture approach including both satellite and ground-based communications links is explained. Future aeronautical communication network topologies and key issues in simulation and modeling of future aeronautical communications systems are described.

  3. Dynamic Resource Allocation in Hybrid Access Femtocell Network

    Directory of Open Access Journals (Sweden)

    Afaz Uddin Ahmed

    2014-01-01

    Full Text Available Intercell interference is one of the most challenging issues in femtocell deployment under the coverage of existing macrocell. Allocation of resources between femtocell and macrocell is essential to counter the effects of interference in dense femtocell networks. Advances in resource management strategies have improved the control mechanism for interference reduction at lower node density, but most of them are ineffective at higher node density. In this paper, a dynamic resource allocation management algorithm (DRAMA for spectrum shared hybrid access OFDMA femtocell network is proposed. To reduce the macro-femto-tier interference and to improve the quality of service, the proposed algorithm features a dynamic resource allocation scheme by controlling them both centrally and locally. The proposed scheme focuses on Femtocell Access Point (FAP owners’ satisfaction and allows maximum utilization of available resources based on congestion in the network. A simulation environment is developed to study the quantitative performance of DRAMA in hybrid access-control femtocell network and compare it to closed and open access mechanisms. The performance analysis shows that higher number of random users gets connected to the FAP without compromising FAP owners’ satisfaction allowing the macrocell to offload a large number of users in a dense heterogeneous network.

  4. 一种面向大规模社会信息网络的多层社区发现算法%A Multilevel Community Detection Algorithm for Large-Scale Social Information Networks

    Institute of Scientific and Technical Information of China (English)

    康颖; 古晓艳; 于博; 林政; 王伟平; 孟丹

    2016-01-01

    社区发现旨在挖掘社会信息网络的社区结构,是社会计算及其相关研究的基础.随着交互式社会信息网络规模的快速增长,传统的社区发现算法难以满足大规模网络的可扩展分析需求.多层社区发现算法如 PMetis、Graclus 等虽然可以分析包含数百万节点规模的网络,但是小于2的粗化缩减比率以及社会信息网络的幂律分布特性极大地制约着该类算法的性能优势.该文提出了一种基于三角形内点同一社区性粗化策略的多层社区发现算法 TMLCD.TMLCD 不仅以大于2的粗化缩减比率加快了大规模社会信息网络的粗化过程,而且从基本拓扑结构上保持了初始网络的社区效应,提高了社区发现精度.基于 YouTube、Orkut 等真实网络的实验结果表明:TMLCD在计算精度、内存占用以及运行时间方面的性能均优于目前典型的多层社区发现算法,适用于富含三角形的社会信息网络分析.%Community detection aims at mining the community structures of Social Information Networks (SINs),which is the foundation of other related researches on social computing.Due to the rapid inflation of interactive SINs,traditional community detection algorithms encounter obstacles in analyzing large-scale networks with scalability.Although multilevel community detection algorithms such as PMetis,Graclus etc.have the capability to analyze networks containing millions of nodes,the coarsening shrink rate is less than 2 and the SINs follow the power-law distribution,which constrain these algorithms’performance enormously.This paper proposes a multilevel community detection algorithm TMLCD,based on the coarsening policy of triangle’s inner nodes belonging to the same community.TMLCD accelerates the coarsening process of large-scale SINs at a coarsening shrink rate of greater than 2,and preserves the community effect of initial network from the point of basic topological

  5. SIMULATION OF WIRELESS SENSOR NETWORK WITH HYBRID TOPOLOGY

    Directory of Open Access Journals (Sweden)

    J. Jaslin Deva Gifty

    2016-03-01

    Full Text Available The design of low rate Wireless Personal Area Network (WPAN by IEEE 802.15.4 standard has been developed to support lower data rates and low power consuming application. Zigbee Wireless Sensor Network (WSN works on the network and application layer in IEEE 802.15.4. Zigbee network can be configured in star, tree or mesh topology. The performance varies from topology to topology. The performance parameters such as network lifetime, energy consumption, throughput, delay in data delivery and sensor field coverage area varies depending on the network topology. In this paper, designing of hybrid topology by using two possible combinations such as star-tree and star-mesh is simulated to verify the communication reliability. This approach is to combine all the benefits of two network model. The parameters such as jitter, delay and throughput are measured for these scenarios. Further, MAC parameters impact such as beacon order (BO and super frame order (SO for low power consumption and high channel utilization, has been analysed for star, tree and mesh topology in beacon disable mode and beacon enable mode by varying CBR traffic loads.

  6. Doubly Optimal Secure Multicasting: Hierarchical Hybrid Communication Network : Disaster Relief

    CERN Document Server

    Garimella, Rama Murthy; Singhal, Deepti

    2011-01-01

    Recently, the world has witnessed the increasing occurrence of disasters, some of natural origin and others caused by man. The intensity of the phenomenon that cause such disasters, the frequency in which they occur, the number of people affected and the material damage caused by them have been growing substantially. Disasters are defined as natural, technological, and human-initiated events that disrupt the normal functioning of the economy and society on a large scale. Areas where disasters have occurred bring many dangers to rescue teams and the communication network infrastructure is usually destroyed. To manage these hazards, different wireless technologies can be launched in the area of disaster. This paper discusses the innovative wireless technologies for Disaster Management. Specifically, issues related to the design of Hierarchical Hybrid Communication Network (arising in the communication network for disaster relief) are discussed.

  7. BETTER SCALABLE ROUTING PROTOCOL FOR HYBRID WIRELESS MESH NETWORK

    Directory of Open Access Journals (Sweden)

    Debraj Modak

    2013-02-01

    Full Text Available There are many routing approaches have been borrowed from mobile ad hoc network to achieve routing solutions in wireless mesh network. WMN was developed for reliable data communication and load balancing. AODV provides loop-free routes even while repairing broken links. This paper have been proposed an improved hierarchical AODV routing protocol (IH-AODV, which exhibits better scalability and performance in the network. This IH-AODV protocol has been proposed for improvement in the scaling potential of AODV. MAODV allows each node in the network to send out multicast data packets, used for multicast traffic. The wireless mesh network architecture provides reduction in installation cost, large scale deployment, reliability and self management. It is mainly focused on implementing military or specialized civilian applications. Two protocols MAODV and IH-AODV were simulated using NS2 package. Simulation results will demonstrate that, IH-AODV scales well for large network and other metrics are also better than or comparable to MAODV in hybrid WMNs.

  8. Hybrid optical CDMA-FSO communications network under spatially correlated gamma-gamma scintillation

    DEFF Research Database (Denmark)

    Jurado-Navas, Antonio; Raddo, Thiago R.; Garrido-Balsells, José María

    2016-01-01

    In this paper, we propose a new hybrid network solution based on asynchronous optical code-division multiple-access (OCDMA) and free-space optical (FSO) technologies for last-mile access networks, where fiber deployment is impractical. The architecture of the proposed hybrid OCDMA-FSO network is ...

  9. 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.

  10. RH+: A Hybrid Localization Algorithm for Wireless Sensor Networks

    Science.gov (United States)

    Basaran, Can; Baydere, Sebnem; Kucuk, Gurhan

    Today, localization of nodes in Wireless Sensor Networks (WSNs) is a challenging problem. Especially, it is almost impossible to guarantee that one algorithm giving optimal results for one topology will give optimal results for any other random topology. In this study, we propose a centralized, range- and anchor-based, hybrid algorithm called RH+ that aims to combine the powerful features of two orthogonal techniques: Classical Multi-Dimensional Scaling (CMDS) and Particle Spring Optimization (PSO). As a result, we find that our hybrid approach gives a fast-converging solution which is resilient to range-errors and very robust to topology changes. Across all topologies we studied, the average estimation error is less than 0.5m. when the average node density is 10 and only 2.5% of the nodes are beacons.

  11. Hybrid Multicast Transmission for Public Safety Network in 5G

    Directory of Open Access Journals (Sweden)

    Fei Qi

    2016-01-01

    Full Text Available We investigate the application of wireless multicast technology in public safety network (PSN in future wireless communication system. The hybrid unicast/multicast transmission system is proposed and analyzed in 3D massive multi-input multioutput (MIMO channel. The mutual coupling channel model is adopted under the different antenna array configuration scenarios. The proposed hybrid system adopts multicast beamforming in the multicast groups as well as multiuser-MIMO (MU-MIMO linear precoding in the unicast group to increase system throughput. The null space method based interference cancellation is further performed between each group to eliminate signal leakage generated from each group. Comparisons between two types of antenna array configurations, different channel models, linear precoding as well as multicast beamforming, and user grouping strategies for multicast services are presented and analyzed by simulation.

  12. A hybrid neural network model for noisy data regression.

    Science.gov (United States)

    Lee, Eric W M; Lim, Chee Peng; Yuen, Richard K K; Lo, S M

    2004-04-01

    A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.

  13. Robot Positioning and Navigation Based on Hybrid Wireless Sensor Network

    Institute of Scientific and Technical Information of China (English)

    Shun-cai YAO; Jin-dong TAN; Hong-xia PAN

    2010-01-01

    Traditional sensor network and robot navigation are based an the map of detecting the fields available in advance.The optimal algorithms are developed to solve the energy saving,the shortest path problems,etc.However,in the practical encironment,there are many fields,whose map is difficult to get,and needs to be detected.In this paper a kind of ad-hoc navigation algorithm is explored,which is based on the hybrid sensor network without the prior map in advance.The navigation system is composed of static nodes and dynamic nodes.The static nodes monitor the occurrances of the events and broadcast them.In the system,a kind of algorithm is to locate the robot,which is based on cluster broadcasting.The dynamic nodes detect the adversary or dangerous fields and broadcast warning messages.The robot gets the message and follows ad-hoc routine to arrive where the events occur.In the whole process,energy saving has been taken into account.The algorithms,which are based on the hybrid sensor network,are given in this paper.The simulation and practical results are also available.

  14. Reduction of electromagnetic exposure using hybrid (DVB-H/UMTS) networks

    Science.gov (United States)

    Schack, M.; Unger, P.; Kürner, T.

    2007-06-01

    The hybrid mobile communication network described in this paper consists of a point-to-point network (UMTS) and a point-to-multipoint network (DVB-H). Using an additional DVB-H network increases the downlink capacity of the communications system. Another benefit of combining these two networks is an optimised transfer of data by collecting several user requests for a single response via the broadcast network DVB-H. It is analysed how the hybrid network structure influences the electromagnetic exposure. Therefore, realistic scenarios have been developed consisting of different user behaviour and different network structures. These scenarios provide building data for investigations of indoor coverage and realistic propagation of signals. In order to evaluate the grade of exposure, criteria have been defined. These criteria have been used for comparing a hybrid network with a single UMTS network in terms of electromagnetic exposure. The simulation results of the scenarios are shown for different network structures and network configurations.

  15. Hybrid multiobjective evolutionary design for artificial neural networks.

    Science.gov (United States)

    Goh, Chi-Keong; Teoh, Eu-Jin; Tan, Kay Chen

    2008-09-01

    Evolutionary algorithms are a class of stochastic search methods that attempts to emulate the biological process of evolution, incorporating concepts of selection, reproduction, and mutation. In recent years, there has been an increase in the use of evolutionary approaches in the training of artificial neural networks (ANNs). While evolutionary techniques for neural networks have shown to provide superior performance over conventional training approaches, the simultaneous optimization of network performance and architecture will almost always result in a slow training process due to the added algorithmic complexity. In this paper, we present a geometrical measure based on the singular value decomposition (SVD) to estimate the necessary number of neurons to be used in training a single-hidden-layer feedforward neural network (SLFN). In addition, we develop a new hybrid multiobjective evolutionary approach that includes the features of a variable length representation that allow for easy adaptation of neural networks structures, an architectural recombination procedure based on the geometrical measure that adapts the number of necessary hidden neurons and facilitates the exchange of neuronal information between candidate designs, and a microhybrid genetic algorithm ( microHGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, the performances of well-known algorithms as well as the effectiveness and contributions of the proposed approach are analyzed and validated through a variety of data set types.

  16. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  17. A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

    Directory of Open Access Journals (Sweden)

    Nenad Kojić

    2012-06-01

    Full Text Available The networking infrastructure of wireless mesh networks (WMNs is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs. This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission. The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

  18. A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

    Science.gov (United States)

    Kojić, Nenad; Reljin, Irini; Reljin, Branimir

    2012-01-01

    The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

  19. A neural networks-based hybrid routing protocol for wireless mesh networks.

    Science.gov (United States)

    Kojić, Nenad; Reljin, Irini; Reljin, Branimir

    2012-01-01

    The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

  20. Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks

    Science.gov (United States)

    Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay

    2013-01-01

    The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more

  1. Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

    Directory of Open Access Journals (Sweden)

    Natalie Berestovsky

    Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them

  2. Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

    Directory of Open Access Journals (Sweden)

    Natalie Berestovsky

    Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them

  3. Networked Operations of Hybrid Radio Optical Communications Satellites

    Science.gov (United States)

    Hylton, Alan; Raible, Daniel

    2014-01-01

    In order to address the increasing communications needs of modern equipment in space, and to address the increasing number of objects in space, NASA is demonstrating the potential capability of optical communications for both deep space and near-Earth applications. The Integrated Radio Optical Communications (iROC) is a hybrid communications system that capitalizes on the best of both the optical and RF domains while using each technology to compensate for the other's shortcomings. Specifically, the data rates of the optical links can be higher than their RF counterparts, whereas the RF links have greater link availability. The focus of this paper is twofold: to consider the operations of one or more iROC nodes from a networking point of view, and to suggest specific areas of research to further the field. We consider the utility of Disruption Tolerant Networking (DTN) and the Virtual Mission Operation Center (VMOC) model.

  4. ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM

    Directory of Open Access Journals (Sweden)

    D. Amutha Guka

    2012-01-01

    Full Text Available Especially in today’s network scenario, when computers are interconnected through internet, security of an information system is very important issue. Because no system can be absolutely secure, the timely and accurate detection of anomalies is necessary. The main aim of this research paper is to improve the anomaly detection by using Hybrid Artificial Immune Algorithm (HAIA which is based on Artificial Immune Systems (AIS and Genetic Algorithm (GA. In this research work, HAIA approach is used to develop Network Anomaly Detection System (NADS. The detector set is generated by using GA and the anomalies are identified using Negative Selection Algorithm (NSA which is based on AIS. The HAIA algorithm is tested with KDD Cup 99 benchmark dataset. The detection rate is used to measure the effectiveness of the NADS. The results and consistency of the HAIA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.

  5. 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.

  6. A Hybrid Structure for Data Aggregation in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Hedieh Sajedi

    2014-01-01

    Full Text Available In recent years, wireless sensor networks have been used for various applications such as environmental monitoring, military and medical applications. A wireless sensor network uses a large number of sensor nodes that continuously collect and send data from a specific region to a base station. Data from sensors are collected from the study area in the common scenario of sensor networks. Afterward, sensed data is sent to the base station. However, neighboring sensors often lead to redundancy of data. Transmission of redundant data to the base station consumes energy and produces traffic, because process is run in a large network. Data aggregation was proposed in order to reduce redundancy in data transformation and traffic. The most popular communication protocol in this field is cluster based data aggregation. Clustering causes energy balance, but sometimes energy consumption is not efficient due to the long distance between cluster heads and base station. In another communication protocol, which is based on a tree construction, because of the short distance between the sensors, energy consumption is low. In this data aggregation approach, since each sensor node is considered as one of the vertices of a tree, the depth of tree is usually high. In this paper, an efficient hierarchical hybrid approach for data aggregation is presented. It reduces energy consumption based on clustering and minimum spanning tree. The benefit of combining clustering and tree structure is reducing the disadvantages of previous structures. The proposed method firstly employs clustering algorithm and then a minimum spanning tree is constructed based on cluster heads. Our proposed method was compared to LEACH which is a well-known data aggregation method in terms of energy consumption and the amount of energy remaining in each sensor network lifetime. Simulation results indicate that our proposed method is more efficient than LEACH algorithm considering energy

  7. Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

    Science.gov (United States)

    Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

    2012-01-01

    Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers

  8. Social Information Transmission in Animals: Lessons from Studies of Diffusion

    Science.gov (United States)

    Duboscq, Julie; Romano, Valéria; MacIntosh, Andrew; Sueur, Cédric

    2016-01-01

    The capacity to use information provided by others to guide behavior is a widespread phenomenon in animal societies. A standard paradigm to test if and/or how animals use and transfer social information is through social diffusion experiments, by which researchers observe how information spreads within a group, sometimes by seeding new behavior in the population. In this article, we review the context, methodology and products of such social diffusion experiments. Our major focus is the transmission of information from an individual (or group thereof) to another, and the factors that can enhance or, more interestingly, inhibit it. We therefore also discuss reasons why social transmission sometimes does not occur despite being expected to. We span a full range of mechanisms and processes, from the nature of social information itself and the cognitive abilities of various species, to the idea of social competency and the constraints imposed by the social networks in which animals are embedded. We ultimately aim at a broad reflection on practical and theoretical issues arising when studying how social information spreads within animal groups. PMID:27540368

  9. Hybrid Algorithm for the Optimization of Training Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Hayder M. Albeahdili

    2015-10-01

    Full Text Available The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN. Although stochastic gradient descend (SGD is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has vast limitations. In this paper, it is endeavor to diminish and tackle drawbacks inherited from SGD by proposing an alternate algorithm for CNN training optimization. A hybrid of genetic algorithm (GA and particle swarm optimization (PSO is deployed in this work. In addition to SGD, PSO and genetic algorithm (PSO-GA are also incorporated as a combined and efficient mechanism in achieving non trivial solutions. The proposed unified method achieves state-of-the-art classification results on the different challenge benchmark datasets such as MNIST, CIFAR-10, and SVHN. Experimental results showed that the results outperform and achieve superior results to most contemporary approaches.

  10. Wireless Network Control with Privacy Using Hybrid ARQ

    CERN Document Server

    Sarikaya, Yunus; Koksal, Emre C

    2012-01-01

    We consider the problem of resource allocation in a wireless cellular network, in which nodes have both open and private information to be transmitted to the base station over block fading uplink channels. We develop a cross-layer solution, based on hybrid ARQ transmission with incremental redundancy. We provide a scheme that combines power control, flow control, and scheduling in order to maximize a global utility function, subject to the stability of the data queues, an average power constraint, and a constraint on the privacy outage probability. Our scheme is based on the assumption that each node has an estimate of its uplink channel gain at each block, while only the distribution of the cross channel gains is available. We prove that our scheme achieves a utility, arbitrarily close to the maximum achievable utility given the available channel state information.

  11. Resonant UPS topologies for the emerging hybrid fiber coaxial networks

    Energy Technology Data Exchange (ETDEWEB)

    Pinheiro, H.

    1999-07-01

    Uninterruptible power systems (UPS)are essential to the operation of critical equipment such as life-support systems, computers and telecommunications systems. Ideally, UPS topologies, especially for the emerging hybrid fiber-coaxial networks, must be characterized by relatively small size, high input power factor and trapezoidal waveforms. None of the existing topologies meet all these requirements. Consequently, the objective of this study is to design and analyse UPS topologies that meet these requirements. To meet this objective novel UPS topologies and control techniques are proposed to allow operation of high switching frequencies without penalizing converter efficiency. A self-sustained oscillation control method is proposed to ensure soft switching under all operating conditions.

  12. Causality in Psychiatry: A Hybrid Symptom Network Construct Model

    Directory of Open Access Journals (Sweden)

    Gerald eYoung

    2015-11-01

    Full Text Available Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved that inform approaches to nosology, or classification, such as in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; American Psychiatric Association, 2013. However, network approaches to symptom interaction (i.e., symptoms are formative of the construct; e.g., McNally, Robinaugh, Wu, Wang, Deserno, & Borsboom, 2014, for PTSD (posttraumatic stress disorder are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth nonlinear dynamical systems theory (NLDST. The article applies the concept of emergent circular causality (Young, 2011 to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning and universal (e.g., causal processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.

  13. Hybrid wireless sensor network for rescue site monitoring after earthquake

    Science.gov (United States)

    Wang, Rui; Wang, Shuo; Tang, Chong; Zhao, Xiaoguang; Hu, Weijian; Tan, Min; Gao, Bowei

    2016-07-01

    This paper addresses the design of a low-cost, low-complexity, and rapidly deployable wireless sensor network (WSN) for rescue site monitoring after earthquakes. The system structure of the hybrid WSN is described. Specifically, the proposed hybrid WSN consists of two kinds of wireless nodes, i.e., the monitor node and the sensor node. Then the mechanism and the system configuration of the wireless nodes are detailed. A transmission control protocol (TCP)-based request-response scheme is proposed to allow several monitor nodes to communicate with the monitoring center. UDP-based image transmission algorithms with fast recovery have been developed to meet the requirements of in-time delivery of on-site monitor images. In addition, the monitor node contains a ZigBee module that used to communicate with the sensor nodes, which are designed with small dimensions to monitor the environment by sensing different physical properties in narrow spaces. By building a WSN using these wireless nodes, the monitoring center can display real-time monitor images of the monitoring area and visualize all collected sensor data on geographic information systems. In the end, field experiments were performed at the Training Base of Emergency Seismic Rescue Troops of China and the experimental results demonstrate the feasibility and effectiveness of the monitor system.

  14. Active Low Intrusion Hybrid Monitor for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Marlon Navia

    2015-09-01

    Full Text Available Several systems have been proposed to monitor wireless sensor networks (WSN. These systems may be active (causing a high degree of intrusion or passive (low observability inside the nodes. This paper presents the implementation of an active hybrid (hardware and software monitor with low intrusion. It is based on the addition to the sensor node of a monitor node (hardware part which, through a standard interface, is able to receive the monitoring information sent by a piece of software executed in the sensor node. The intrusion on time, code, and energy caused in the sensor nodes by the monitor is evaluated as a function of data size and the interface used. Then different interfaces, commonly available in sensor nodes, are evaluated: serial transmission (USART, serial peripheral interface (SPI, and parallel. The proposed hybrid monitor provides highly detailed information, barely disturbed by the measurement tool (interference, about the behavior of the WSN that may be used to evaluate many properties such as performance, dependability, security, etc. Monitor nodes are self-powered and may be removed after the monitoring campaign to be reused in other campaigns and/or WSNs. No other hardware-independent monitoring platforms with such low interference have been found in the literature.

  15. Causality in Psychiatry: A Hybrid Symptom Network Construct Model

    Science.gov (United States)

    Young, Gerald

    2015-01-01

    Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments. PMID:26635639

  16. A Hybrid Energy Sharing Framework for Green Cellular Networks

    KAUST Repository

    Farooq, Muhammad Junaid

    2016-12-09

    Cellular operators are increasingly turning towards renewable energy (RE) as an alternative to using traditional electricity in order to reduce operational expenditure and carbon footprint. Due to the randomness in both RE generation and mobile traffic at each base station (BS), a surplus or shortfall of energy may occur at any given time. To increase energy selfreliance and minimize the network’s energy cost, the operator needs to efficiently exploit the RE generated across all BSs. In this paper, a hybrid energy sharing framework for cellular network is proposed, where a combination of physical power lines and energy trading with other BSs using smart grid is used. Algorithms for physical power lines deployment between BSs, based on average and complete statistics of the net RE available, are developed. Afterwards, an energy management framework is formulated to optimally determine the quantities of electricity and RE to be procured and exchanged among BSs, respectively, while considering battery capacities and real-time energy pricing. Three cases are investigated where RE generation is unknown, perfectly known, and partially known ahead of time. Results investigate the time varying energy management of BSs and demonstrate considerable reduction in average energy cost thanks to the hybrid energy sharing scheme.

  17. Active Low Intrusion Hybrid Monitor for Wireless Sensor Networks.

    Science.gov (United States)

    Navia, Marlon; Campelo, Jose C; Bonastre, Alberto; Ors, Rafael; Capella, Juan V; Serrano, Juan J

    2015-09-18

    Several systems have been proposed to monitor wireless sensor networks (WSN). These systems may be active (causing a high degree of intrusion) or passive (low observability inside the nodes). This paper presents the implementation of an active hybrid (hardware and software) monitor with low intrusion. It is based on the addition to the sensor node of a monitor node (hardware part) which, through a standard interface, is able to receive the monitoring information sent by a piece of software executed in the sensor node. The intrusion on time, code, and energy caused in the sensor nodes by the monitor is evaluated as a function of data size and the interface used. Then different interfaces, commonly available in sensor nodes, are evaluated: serial transmission (USART), serial peripheral interface (SPI), and parallel. The proposed hybrid monitor provides highly detailed information, barely disturbed by the measurement tool (interference), about the behavior of the WSN that may be used to evaluate many properties such as performance, dependability, security, etc. Monitor nodes are self-powered and may be removed after the monitoring campaign to be reused in other campaigns and/or WSNs. No other hardware-independent monitoring platforms with such low interference have been found in the literature.

  18. SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

    Science.gov (United States)

    Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T

    2015-02-01

    Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks.

  19. A hybrid queuing strategy for network traffic on scale-free networks

    Science.gov (United States)

    Cai, Kai-Quan; Yu, Lu; Zhu, Yan-Bo

    2017-02-01

    In this paper, a hybrid queuing strategy (HQS) is proposed in traffic dynamics model on scale-free networks, where the delivery priority of packets in the queue is related to their distance to destination and the queue length of next jump. We compare the performance of the proposed HQS with that of the traditional first-in-first-out (FIFO) queuing strategy and the shortest-remaining-path-first (SRPF) queuing strategy proposed by Du et al. It is observed that the network traffic efficiency utilizing HQS with suitable value of parameter h can be further improved in the congestion state. Our work provides new insights for the understanding of the networked-traffic systems.

  20. Low-power hybrid wireless network for monitoring infant incubators.

    Science.gov (United States)

    Shin, D I; Shin, K H; Kim, I K; Park, K S; Lee, T S; Kim, S I; Lim, K S; Huh, S J

    2005-10-01

    We have created a pilot wireless network for the convenient monitoring of temperature and humidity of infant incubators. This system combines infrared and radio frequency (RF) communication in order to minimize the power consumption of slave devices, and we therefore call it a hybrid wireless network. The slave module installed in the infant incubator receives the calling signal from the host with an infrared receiver, and sends temperature and humidity data to the host with an RF transmitter. The power consumption of the host system is not critical, and hence it uses the maximum power of infrared transmission and continuously operating RF receiver. In our test implementation, we included four slave devices. The PC calls each slave device every second and then waits for 6 s, resulting in a total scan period of 10 s. Slave devices receive the calling signals and transmit three data values (temperature, moisture, and skin temperature); their power demand is 1 mW, and can run for about 1000 h on four AA-size nickel-hydride batteries.

  1. Modular and orthogonal synthesis of hybrid polymers and networks.

    Science.gov (United States)

    Liu, Shuang; Dicker, Kevin T; Jia, Xinqiao

    2015-03-28

    Biomaterials scientists strive to develop polymeric materials with distinct chemical make-up, complex molecular architectures, robust mechanical properties and defined biological functions by drawing inspirations from biological systems. Salient features of biological designs include (1) repetitive presentation of basic motifs; and (2) efficient integration of diverse building blocks. Thus, an appealing approach to biomaterials synthesis is to combine synthetic and natural building blocks in a modular fashion employing novel chemical methods. Over the past decade, orthogonal chemistries have become powerful enabling tools for the modular synthesis of advanced biomaterials. These reactions require building blocks with complementary functionalities, occur under mild conditions in the presence of biological molecules and living cells and proceed with high yield and exceptional selectivity. These chemistries have facilitated the construction of complex polymers and networks in a step-growth fashion, allowing facile modulation of materials properties by simple variations of the building blocks. In this review, we first summarize features of several types of orthogonal chemistries. We then discuss recent progress in the synthesis of step growth linear polymers, dendrimers and networks that find application in drug delivery, 3D cell culture and tissue engineering. Overall, orthogonal reactions and modulular synthesis have not only minimized the steps needed for the desired chemical transformations but also maximized the diversity and functionality of the final products. The modular nature of the design, combined with the potential synergistic effect of the hybrid system, will likely result in novel hydrogel matrices with robust structures and defined functions.

  2. Analysis methodology for flow-level evaluation of a hybrid mobile-sensor network

    NARCIS (Netherlands)

    Dimitrova, D.C.; Heijenk, G.; Braun, T.

    2011-01-01

    Our society uses a large diversity of co-existing wired and wireless networks in order to satisfy its communication needs. A cooper- ation between these networks can benefit performance, service availabil- ity and deployment ease, and leads to the emergence of hybrid networks. This position paper fo

  3. Analysis methodology for flow-level evaluation of a hybrid mobile-sensor network

    NARCIS (Netherlands)

    Dimitrova, D.C.; Heijenk, Gerhard J.; Braun, T.

    2012-01-01

    Our society uses a large diversity of co-existing wired and wireless networks in order to satisfy its communication needs. A cooper- ation between these networks can benefit performance, service availabil- ity and deployment ease, and leads to the emergence of hybrid networks. This position paper

  4. A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks.

    Science.gov (United States)

    Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin

    2016-01-01

    Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay.

  5. A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hao Wang

    2016-09-01

    Full Text Available Underwater Acoustic Sensor Networks (UASNs have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay.

  6. A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks

    Science.gov (United States)

    Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin

    2016-01-01

    Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay. PMID:27618044

  7. Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

    Science.gov (United States)

    Pan, Yongping; Yu, Haoyong

    2017-06-01

    This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

  8. NEURAL NETWORKS CONTROL OF THE HYBRID POWER UNIT BASED ON THE METHOD OF ADAPTIVE CRITICS

    Directory of Open Access Journals (Sweden)

    S. Serikov

    2012-01-01

    Full Text Available The formal statement of the optimization problem of hybrid vehicle power unit control is given. Its solving by neural networks method application on the basis of adaptive critic is considered.

  9. Seafloor classification using echo- waveforms: A method employing hybrid neural network architecture

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Mahale, V.; DeSouza, C.; Das, P.

    This letter presents seafloor classification study results of a hybrid artificial neural network architecture known as learning vector quantization. Single beam echo-sounding backscatter waveform data from three different seafloors of the western...

  10. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

    OpenAIRE

    Lukas Falat; Dusan Marcek; Maria Durisova

    2016-01-01

    This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the sug...

  11. Hybrid Method for Inverse Electromagnetic Coil Optimization Using Multi-transition and Hopfield Neural Networks

    OpenAIRE

    Yamamoto, Takeyoshi; Cingoski, Vlatko; Kaneda, Kazufumi; Yamashita, Hideo

    1996-01-01

    In this paper, a hybrid method for inverse optimization of electromagnetic coils utilizing the multi-transition neural network and the Hopfield neural network is proposed. Due to the discrete character of the neural network, an optimization problem is transformed into a discrete problem through the division of the entire coil area into elemental coils with constant current density. The minimization of the objective function is performed by the multi-transition neural network and the Hopfield ...

  12. Reduction of electromagnetic exposure using hybrid (DVB-H/UMTS networks

    Directory of Open Access Journals (Sweden)

    M. Schack

    2007-06-01

    Full Text Available The hybrid mobile communication network described in this paper consists of a point-to-point network (UMTS and a point-to-multipoint network (DVB-H. Using an additional DVB-H network increases the downlink capacity of the communications system. Another benefit of combining these two networks is an optimised transfer of data by collecting several user requests for a single response via the broadcast network DVB-H. It is analysed how the hybrid network structure influences the electromagnetic exposure. Therefore, realistic scenarios have been developed consisting of different user behaviour and different network structures. These scenarios provide building data for investigations of indoor coverage and realistic propagation of signals. In order to evaluate the grade of exposure, criteria have been defined. These criteria have been used for comparing a hybrid network with a single UMTS network in terms of electromagnetic exposure. The simulation results of the scenarios are shown for different network structures and network configurations.

  13. Low-Cost Hybrid ROADM Architectures for Scalable C/DWDM Metro Networks

    DEFF Research Database (Denmark)

    Nooruzzaman, Md; Halima, Elbiaze

    2016-01-01

    be introduced to merge CWDM and DWDM traffic at the optical layer. This ensures two advantages: reduced initial investment and scalability for deploying DWDM channels in the future. This article presents various ROADM architectures, and explores the novel optical node architecture of hybrid C/DWDM networks......CWDM networks have proven to be a promising first-step metro and access network architecture, offering a significant cost advantage over DWDM due to the lower cost of lasers and the filters used in CWDM modules. If demand grows beyond the capacity covered by CWDM channels, DWDM network elements can......, consisting of CWDM, hybrid C/DWDM, and junction nodes connecting two rings. Evaluation has shown that the hybrid ROADM architecture is superior to other conventional ROADM architectures in terms of scalability and the initial cost of optical nodes and networks....

  14. Nonlinear mechanics of hybrid polymer networks that mimic the complex mechanical environment of cells

    Science.gov (United States)

    Jaspers, Maarten; Vaessen, Sarah L.; van Schayik, Pim; Voerman, Dion; Rowan, Alan E.; Kouwer, Paul H. J.

    2017-05-01

    The mechanical properties of cells and the extracellular environment they reside in are governed by a complex interplay of biopolymers. These biopolymers, which possess a wide range of stiffnesses, self-assemble into fibrous composite networks such as the cytoskeleton and extracellular matrix. They interact with each other both physically and chemically to create a highly responsive and adaptive mechanical environment that stiffens when stressed or strained. Here we show that hybrid networks of a synthetic mimic of biological networks and either stiff, flexible and semi-flexible components, even very low concentrations of these added components, strongly affect the network stiffness and/or its strain-responsive character. The stiffness (persistence length) of the second network, its concentration and the interaction between the components are all parameters that can be used to tune the mechanics of the hybrids. The equivalence of these hybrids with biological composites is striking.

  15. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

  16. 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

    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...... 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...... model for a ∼1 mm(2) patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its...

  17. A harmonious unifying hybrid preferential model and its universal properties for complex dynamical networks

    Institute of Scientific and Technical Information of China (English)

    FANG JinQing; BI Qiao; LI Yong; LU XinBiao; LIU Qiang

    2007-01-01

    To describe the real world which is a harmonious unification world with both determinism and randomness, we propose a harmonious unifying hybrid preferential model (HUHPM) of a certain class of complex dynamical networks. HUHPM is governed only by the total hybrid ratio dlr according to the practical need. As some typical examples, the concepts and methods of the HUHPM are applied to the un-weighted BA model proposed by Barabási et al., the weighted BBV model proposed by Barat et al. and the weighted TDE model proposed by Wang et al. to get the so-called HUHPM-BA network, HUHPM-BBV network and HUHPM-TDE network.These HUHPM networks are investigated both analytically and numerically. It is found that the HUHPM reveals several universal properties, which more approach to the real-world networks for both un-weighted and weighted networks and have potential for applications.

  18. Hybrid Multilevel Monte Carlo Simulation of Stochastic Reaction Networks

    KAUST Repository

    Moraes, Alvaro

    2015-01-07

    Stochastic reaction networks (SRNs) is a class of continuous-time Markov chains intended to describe, from the kinetic point of view, the time-evolution of chemical systems in which molecules of different chemical species undergo a finite set of reaction channels. This talk is based on articles [4, 5, 6], where we are interested in the following problem: given a SRN, X, defined though its set of reaction channels, and its initial state, x0, estimate E (g(X(T))); that is, the expected value of a scalar observable, g, of the process, X, at a fixed time, T. This problem lead us to define a series of Monte Carlo estimators, M, such that, with high probability can produce values close to the quantity of interest, E (g(X(T))). More specifically, given a user-selected tolerance, TOL, and a small confidence level, η, find an estimator, M, based on approximate sampled paths of X, such that, P (|E (g(X(T))) − M| ≤ TOL) ≥ 1 − η; even more, we want to achieve this objective with near optimal computational work. We first introduce a hybrid path-simulation scheme based on the well-known stochastic simulation algorithm (SSA)[3] and the tau-leap method [2]. Then, we introduce a Multilevel Monte Carlo strategy that allows us to achieve a computational complexity of order O(T OL−2), this is the same computational complexity as in an exact method but with a smaller constant. We provide numerical examples to show our results.

  19. Integration of hybrid wireless networks in cloud services oriented enterprise information systems

    Science.gov (United States)

    Li, Shancang; Xu, Lida; Wang, Xinheng; Wang, Jue

    2012-05-01

    This article presents a hybrid wireless network integration scheme in cloud services-based enterprise information systems (EISs). With the emerging hybrid wireless networks and cloud computing technologies, it is necessary to develop a scheme that can seamlessly integrate these new technologies into existing EISs. By combining the hybrid wireless networks and computing in EIS, a new framework is proposed, which includes frontend layer, middle layer and backend layers connected to IP EISs. Based on a collaborative architecture, cloud services management framework and process diagram are presented. As a key feature, the proposed approach integrates access control functionalities within the hybrid framework that provide users with filtered views on available cloud services based on cloud service access requirements and user security credentials. In future work, we will implement the proposed framework over SwanMesh platform by integrating the UPnP standard into an enterprise information system.

  20. A New Type of Network Security Protocol Using Hybrid Encryption in Virtual Private Networking

    Directory of Open Access Journals (Sweden)

    E. Ramaraj

    2006-01-01

    Full Text Available Today wireless communications is acting as a major role in networks. Through year-end 2006, the employee's ability to install unmanaged access points will result is more than 50% of enterprises exposing sensitive information through the wireless virtual private networks (VPN. It enables you to send the data between two computers across a shared or public network in a manner that emulates the properties of a private link. The basic requirements for VPN are User Authentication, Address Management, Data Compression, Data Encryption and Key Management. The private links are established in VPN using Point-to-Point Tunneling Protocol (PPTP and Layer-Two-Tunneling Protocol (L2TP. These protocols are satisfies VPN requirements in five layers. In user authentication layer, multiple trusted authorities using Extensible Authentication Protocol (EAP do the authentication process. In fourth layer the data encryption part using RC4 called Microsoft-Point-to-Point Encryption (MPPE method. The aim of this paper, instead of multiple trusted authorities we focus single trusted authority using public key cryptography RSA in EAP and also we include AES-Rijndael stream cipher algorithm instead of RC4 for MPPE. We propose new type of hybrid encryption technique using AES-Rijndael for encryption and decryption and RSA used for key management.

  1. Hybrid scheduling mechanisms for Next-generation Passive Optical Networks based on network coding

    Science.gov (United States)

    Zhao, Jijun; Bai, Wei; Liu, Xin; Feng, Nan; Maier, Martin

    2014-10-01

    Network coding (NC) integrated into Passive Optical Networks (PONs) is regarded as a promising solution to achieve higher throughput and energy efficiency. To efficiently support multimedia traffic under this new transmission mode, novel NC-based hybrid scheduling mechanisms for Next-generation PONs (NG-PONs) including energy management, time slot management, resource allocation, and Quality-of-Service (QoS) scheduling are proposed in this paper. First, we design an energy-saving scheme that is based on Bidirectional Centric Scheduling (BCS) to reduce the energy consumption of both the Optical Line Terminal (OLT) and Optical Network Units (ONUs). Next, we propose an intra-ONU scheduling and an inter-ONU scheduling scheme, which takes NC into account to support service differentiation and QoS assurance. The presented simulation results show that BCS achieves higher energy efficiency under low traffic loads, clearly outperforming the alternative NC-based Upstream Centric Scheduling (UCS) scheme. Furthermore, BCS is shown to provide better QoS assurance.

  2. EXPONENTIAL STABILITY AND PERIODIC SOLUTION OF HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

    Institute of Scientific and Technical Information of China (English)

    谢惠琴; 王全义

    2004-01-01

    In this paper, we study the existence, uniqueness, and the global exponential stability of the periodic solution and equilibrium of hybrid bidirectional associative memory neural networks with discrete delays. By ingeniously importing real parameters di > 0 (i = 1, 2,..., n) which can be adjusted, making use of the Lyapunov functional method and some analysis techniques, some new sufficient conditions are established. Our results generalize and improve the related results in [9]. These conditions can be used both to design globally exponentially stable and periodical oscillatory hybrid bidirectional associative neural networks with discrete delays, and to enlarge the area of designing neural networks. Our work has important significance in related theory and its application.

  3. Optimizing performance of hybrid FSO/RF networks in realistic dynamic scenarios

    Science.gov (United States)

    Llorca, Jaime; Desai, Aniket; Baskaran, Eswaran; Milner, Stuart; Davis, Christopher

    2005-08-01

    Hybrid Free Space Optical (FSO) and Radio Frequency (RF) networks promise highly available wireless broadband connectivity and quality of service (QoS), particularly suitable for emerging network applications involving extremely high data rate transmissions such as high quality video-on-demand and real-time surveillance. FSO links are prone to atmospheric obscuration (fog, clouds, snow, etc) and are difficult to align over long distances due the use of narrow laser beams and the effect of atmospheric turbulence. These problems can be mitigated by using adjunct directional RF links, which provide backup connectivity. In this paper, methodologies for modeling and simulation of hybrid FSO/RF networks are described. Individual link propagation models are derived using scattering theory, as well as experimental measurements. MATLAB is used to generate realistic atmospheric obscuration scenarios, including moving cloud layers at different altitudes. These scenarios are then imported into a network simulator (OPNET) to emulate mobile hybrid FSO/RF networks. This framework allows accurate analysis of the effects of node mobility, atmospheric obscuration and traffic demands on network performance, and precise evaluation of topology reconfiguration algorithms as they react to dynamic changes in the network. Results show how topology reconfiguration algorithms, together with enhancements to TCP/IP protocols which reduce the network response time, enable the network to rapidly detect and act upon link state changes in highly dynamic environments, ensuring optimized network performance and availability.

  4. Cognitive and social information based PSO

    African Journals Online (AJOL)

    Dynamic chaos has been incorporated with hybrid PSO to improve the efficiency of the ... This technique very well avoids trapping of the swarm in ...... His research interest includes Computer Vision, Image Processing and Soft Computing.

  5. Single-Board-Computer-Based Traffic Generator for a Heterogeneous and Hybrid Smart Grid Communication Network

    OpenAIRE

    Do Nguyet Quang; Ong Hang See; Lai Lee Chee; Che Yung Xuen; Shashiteran A/L. Karuppiah

    2014-01-01

    In smart grid communication implementation, network traffic pattern is one of the main factors that affect the system’s performance. Examining different traffic patterns in smart grid is therefore crucial when analyzing the network performance. Due to the heterogeneous and hybrid nature of smart grid, the type of traffic distribution in the network is still unknown. The traffic that popularly used for simulation and analysis no longer reflects the real traffic in a multi-technology and bi-dir...

  6. Combining Quality of Service and Topology Control in Directional Hybrid Wireless Networks

    Science.gov (United States)

    2006-03-01

    concept of a global information grid (GIG) that will provide the network-centric environment necessary to achieve the goal of integrating traditional...together form high bandwidth capabilities for hybrid communication networks, suggest the potential for global connectivity while avoiding some of...included in the network and which links each commodity should flow on. The MILP can be solved using any linear solver application. We use XPRESS -MP

  7. High capacity fiber optic sensor networks using hybrid multiplexing techniques and their applications

    Science.gov (United States)

    Sun, Qizhen; Li, Xiaolei; Zhang, Manliang; Liu, Qi; Liu, Hai; Liu, Deming

    2013-12-01

    Fiber optic sensor network is the development trend of fiber senor technologies and industries. In this paper, I will discuss recent research progress on high capacity fiber sensor networks with hybrid multiplexing techniques and their applications in the fields of security monitoring, environment monitoring, Smart eHome, etc. Firstly, I will present the architecture of hybrid multiplexing sensor passive optical network (HSPON), and the key technologies for integrated access and intelligent management of massive fiber sensor units. Two typical hybrid WDM/TDM fiber sensor networks for perimeter intrusion monitor and cultural relics security are introduced. Secondly, we propose the concept of "Microstructure-Optical X Domin Refecltor (M-OXDR)" for fiber sensor network expansion. By fabricating smart micro-structures with the ability of multidimensional encoded and low insertion loss along the fiber, the fiber sensor network of simple structure and huge capacity more than one thousand could be achieved. Assisted by the WDM/TDM and WDM/FDM decoding methods respectively, we built the verification systems for long-haul and real-time temperature sensing. Finally, I will show the high capacity and flexible fiber sensor network with IPv6 protocol based hybrid fiber/wireless access. By developing the fiber optic sensor with embedded IPv6 protocol conversion module and IPv6 router, huge amounts of fiber optic sensor nodes can be uniquely addressed. Meanwhile, various sensing information could be integrated and accessed to the Next Generation Internet.

  8. Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks.

    Science.gov (United States)

    Wei, Yunkai; Ma, Xiaohui; Yang, Ning; Chen, Yijin

    2017-09-15

    Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs) are an inexorable trend for Wireless Sensor Networks (WSNs), including Wireless Rechargeable Sensor Network (WRSNs). However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS) algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN) controller's direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE) protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20-40% while ensuring feasible data delay.

  9. Spatially varying scatter compensation for chest radiographs using a hybrid Madaline artificial neural network

    Science.gov (United States)

    Lo, Joseph Y.; Baydush, Alan H.; Floyd, Carey E., Jr.

    1994-05-01

    We developed a hybrid artificial neural network for scatter compensation in digital portable chest radiographs. The network inputs an image region of interest (ROI), and outputs the scatter estimate at the ROI's center. We segmented each image into four regions by relative detected exposure, then trained a separate Adaline (adaptive linear element) or adaptive filter for each region. We produced a spatially varying hybrid Madaline (mulitple Adaline) by combining outputs from weight matrices of different sizes trained for different durations. The network was trained with 20 patient or 1280 examples, then evaluated with another 5 patients or 320 examples. Scatter estimation errors were not very different, ranging from the Adaline's 6.9 percent to the hybrid Madaline's 5.5 percent. Primary errors (more relevant to quantitative radiography techniques like dual energy imaging) were 43 percent for the Adaline, reduced to 27 percent for the Madaline, and further reduced to 19 percent for the hybrid Madaline. The trained weight matrices, which act like convolution filters, resembled the shape and magnitude of scatter point spread functions. All networks outperformed conventional convolution-subraction techniques using analytical kernels. With its spatially varying neural network model, the hybrid Madaline provided the most accurate and robust estimation of scatter and primary exposures.

  10. Hybrid SDN Architecture for Resource Consolidation in MPLS Networks

    DEFF Research Database (Denmark)

    Katov, Anton Nikolaev; Mihovska, Albena D.; Prasad, Neeli R.

    2015-01-01

    This paper proposes a methodology for resource consolidation towards minimizing the power consumption in a large network, with a substantial resource overprovisioning. The focus is on the operation of the core MPLS networks. The proposed approach is based on a software defined networking (SDN...

  11. Distinct regions of prefrontal cortex are associated with the controlled retrieval and selection of social information.

    Science.gov (United States)

    Satpute, Ajay B; Badre, David; Ochsner, Kevin N

    2014-05-01

    Research in social neuroscience has uncovered a social knowledge network that is particularly attuned to making social judgments. However, the processes that are being performed by both regions within this network and those outside of this network that are nevertheless engaged in the service of making a social judgment remain unclear. To help address this, we drew upon research in semantic memory, which suggests that making a semantic judgment engages 2 distinct control processes: A controlled retrieval process, which aids in bringing goal-relevant information to mind from long-term stores, and a selection process, which aids in selecting the information that is goal-relevant from the information retrieved. In a neuroimaging study, we investigated whether controlled retrieval and selection for social information engage distinct portions of both the social knowledge network and regions outside this network. Controlled retrieval for social information engaged an anterior ventrolateral portion of the prefrontal cortex, whereas selection engaged both the dorsomedial prefrontal cortex and temporoparietal junction within the social knowledge network. These results suggest that the social knowledge network may be more involved with the selection of social information than the controlled retrieval of it and incorporates lateral prefrontal regions in accessing memory for making social judgments.

  12. A study on the control of a hybrid MTDC system supplying a passive network

    DEFF Research Database (Denmark)

    Kotb, Omar; Ghandhari, Mehrdad; Eriksson, Robert

    2014-01-01

    A hybrid Multi-Terminal DC (MTDC) system can combine the benefits of both Line Commutated Converter (LCC) and Voltage Source Converter (VSC) technologies in the form of reduced losses and flexibility to connect to weak and passive grids. In this paper, an analysis of control strategies used...... in a hybrid MTDC system is presented. A case study of a four terminal hybrid MTDC system supplying a passive AC network was considered for simulation study. A control scheme based on voltage margin was developed to cope with the condition of main DC voltage controlling station tripping. Two various control...... scenarios for controlling the VSCs connected to the passive network were presented and compared. The system performance was studied through EMTP-RV simulations under different disturbances. The results show the ability of selected converter control modes and proposed control schemes to operate the hybrid...

  13. Protein modularity, cooperative binding, and hybrid regulatory states underlie transcriptional network diversification.

    Science.gov (United States)

    Baker, Christopher R; Booth, Lauren N; Sorrells, Trevor R; Johnson, Alexander D

    2012-09-28

    We examine how different transcriptional network structures can evolve from an ancestral network. By characterizing how the ancestral mode of gene regulation for genes specific to a-type cells in yeast species evolved from an activating paradigm to a repressing one, we show that regulatory protein modularity, conversion of one cis-regulatory sequence to another, distribution of binding energy among protein-protein and protein-DNA interactions, and exploitation of ancestral network features all contribute to the evolution of a novel regulatory mode. The formation of this derived mode of regulation did not disrupt the ancestral mode and thereby created a hybrid regulatory state where both means of transcription regulation (ancestral and derived) contribute to the conserved expression pattern of the network. Finally, we show how this hybrid regulatory state has resolved in different ways in different lineages to generate the diversity of regulatory network structures observed in modern species.

  14. Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models

    Science.gov (United States)

    Güreşen, Erkam; Kayakutlu, Gülgün

    Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).

  15. Adaptive Agent Model with Hybrid Routing Selection Strategy for Improving the Road-Network Congestion Problem

    Institute of Scientific and Technical Information of China (English)

    Bin Jiang; Chao Yang; Takao Terano

    2015-01-01

    This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road⁃network congestion problem. We focus on improving those severely congested links. Firstly, a multi⁃agent system is built, where each agent stands for a vehicle, and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent⁃based model captures the nonlinear feedback between vehicle routing behaviors and road⁃network congestion status. Secondly, a hybrid routing selection strategy is provided, which guides the vehicle routes adapting to the real⁃time road⁃network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution, by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road⁃network. Finally, we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And, the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom⁃up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.

  16. Hybrid Luminescent Films Obtained by Covalent Anchoring Terbium Complex to Silica-based Network

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    New monomer N-(4-carboxyphenyl)-NL-(propyltriethoxysilyl)urea (1) which acts as both a ligand for Tb3+ ion and a sol-gel precursor has been synthesized and characterized by 1H NMR, and MS. Hybrid luminescent thin films consisting of organoterbium covalently bonded to a silica-based network have been obtained in situ via a sol-gel approach. Strong line emission of Tb3+ ion was observed from the hybrid luminescent films under UV excitation.

  17. Hybrid Electric Vehicle Experimental Model with CAN Network Real Time Control

    Directory of Open Access Journals (Sweden)

    RATOI, M.

    2010-05-01

    Full Text Available In this paper an experimental model with a distributed control system of a hybrid electrical vehicle is presented. A communication CAN network of high speed (1 Mbps assures a distributed control of the all components. The modeling and the control of different operating regimes are realized on an experimental test-bench of a hybrid electrical vehicle. The experimental results concerning the variations of the mains variables (currents, torques, speeds are presented.

  18. A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2001-01-01

    In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

  19. Link reliability based hybrid routing for tactical mobile ad hoc network

    Institute of Scientific and Technical Information of China (English)

    Xie Xiaochuan; Wei Gang; Wu Keping; Wang Gang; Jia Shilou

    2008-01-01

    Tactical mobile ad hoc network (MANET) is a collection of mobile nodes forming a temporary network,without the aid of pre-established network infrastructure. The routing protocol has a crucial impact on the networkperformance in battlefields. Link reliability based hybrid routing (LRHR) is proposed, which is a novel hybrid routing protocol, for tactical MANET. Contrary to the traditional single path routing strategy, multiple paths are established between a pair of source-destination nodes. In the hybrid routing strategy, the rate of topological change provides a natural mechanism for switching dynamically between table-driven and on-demand routing. The simulation results indicate that the performances of the protocol in packet delivery ratio, routing overhead, and average end-to-end delay are better than the conventional routing protocol.

  20. A Hybrid Multicast-Unicast Infrastructure for Efficient Publish-Subscribe in Enterprise Networks

    CERN Document Server

    Bickson, Danny; Naaman, Nir; Tock, Yoav

    2009-01-01

    One of the main challenges in building a large scale publish-subscribe infrastructure in an enterprise network, is to provide the subscribers with the required information, while minimizing the consumed host and network resources. Typically, previous approaches utilize either IP multicast or point-to-point unicast for efficient dissemination of the information. In this work, we propose a novel hybrid framework, which is a combination of both multicast and unicast data dissemination. Our hybrid framework allows us to take the advantages of both multicast and unicast, while avoiding their drawbacks. We investigate several algorithms for computing the best mapping of publishers' transmissions into multicast and unicast transport. Using extensive simulations, we show that our hybrid framework reduces consumed host and network resources, outperforming traditional solutions. To insure the subscribers interests closely resemble those of real-world settings, our simulations are based on stock market data and on recor...

  1. A Location-Aware Vertical Handoff Algorithm for Hybrid Networks

    KAUST Repository

    Mehbodniya, Abolfazl

    2010-07-01

    One of the main objectives of wireless networking is to provide mobile users with a robust connection to different networks so that they can move freely between heterogeneous networks while running their computing applications with no interruption. Horizontal handoff, or generally speaking handoff, is a process which maintains a mobile user\\'s active connection as it moves within a wireless network, whereas vertical handoff (VHO) refers to handover between different types of networks or different network layers. Optimizing VHO process is an important issue, required to reduce network signalling and mobile device power consumption as well as to improve network quality of service (QoS) and grade of service (GoS). In this paper, a VHO algorithm in multitier (overlay) networks is proposed. This algorithm uses pattern recognition to estimate user\\'s position, and decides on the handoff based on this information. For the pattern recognition algorithm structure, the probabilistic neural network (PNN) which has considerable simplicity and efficiency over existing pattern classifiers is used. Further optimization is proposed to improve the performance of the PNN algorithm. Performance analysis and comparisons with the existing VHO algorithm are provided and demonstrate a significant improvement with the proposed algorithm. Furthermore, incorporating the proposed algorithm, a structure is proposed for VHO from the medium access control (MAC) layer point of view. © 2010 ACADEMY PUBLISHER.

  2. Design Hybrid Methods for Encoding Prior Knowledge in Feedforward Network with Application in Chemical Engineering

    Institute of Scientific and Technical Information of China (English)

    CHENChongwei; CHENDezhao

    2002-01-01

    Three-layer feedforward networks have been widely used in modeling chemical engineering processes and prior-knowledge-based methods have been introduced to improve their performances.In this paper,we propose the methodology of designing better prior-knowledge-based hybrid methods by combining the existing ones. Then according to this methodology,two hybrid methods,interpolation-optimization (IO) method and interpolation penalty-function (IPF) method,are designed as examples.Finally,both methods are applied to modeling two cases in chemical engineering to investigate their effectiveness.Simulation results show that the performances of the hybrid methods are better than those of their parents.

  3. Sulfonated polystyrene fiber network-induced hybrid proton exchange membranes.

    Science.gov (United States)

    Yao, Yingfang; Ji, Liwen; Lin, Zhan; Li, Ying; Alcoutlabi, Mataz; Hamouda, Hechmi; Zhang, Xiangwu

    2011-09-01

    A novel type of hybrid membrane was fabricated by incorporating sulfonated polystyrene (S-PS) electrospun fibers into Nafion for the application in proton exchange membrane fuel cells. With the introduction of S-PS fiber mats, a large amount of sulfonic acid groups in Nafion aggregated onto the interfaces between S-PS fibers and the ionomer matrix, forming continuous pathways for facile proton transport. The resultant hybrid membranes had higher proton conductivities than that of recast Nafion, and the conductivities were controlled by selectively adjusting the fiber diameters. Consequently, hybrid membranes fabricated by ionomers, such as Nafion, incorporated with ionic-conducting nanofibers established a promising strategy for the rational design of high-performance proton exchange membranes.

  4. Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms

    OpenAIRE

    Abraham, Ajith

    2004-01-01

    Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex s...

  5. Landmine detection and classification with complex-valued hybrid neural network using scattering parameters dataset.

    Science.gov (United States)

    Yang, Chih-Chung; Bose, N K

    2005-05-01

    Neural networks have been applied to landmine detection from data generated by different kinds of sensors. Real-valued neural networks have been used for detecting landmines from scattering parameters measured by ground penetrating radar (GPR) after disregarding phase information. This paper presents results using complex-valued neural networks, capable of phase-sensitive detection followed by classification. A two-layer hybrid neural network structure incorporating both supervised and unsupervised learning is proposed to detect and then classify the types of landmines. Tests are also reported on a benchmark data.

  6. Individual consistency and flexibility in human social information use.

    Science.gov (United States)

    Toelch, Ulf; Bruce, Matthew J; Newson, Lesley; Richerson, Peter J; Reader, Simon M

    2014-02-07

    Copying others appears to be a cost-effective way of obtaining adaptive information, particularly when flexibly employed. However, adult humans differ considerably in their propensity to use information from others, even when this 'social information' is beneficial, raising the possibility that stable individual differences constrain flexibility in social information use. We used two dissimilar decision-making computer games to investigate whether individuals flexibly adjusted their use of social information to current conditions or whether they valued social information similarly in both games. Participants also completed established personality questionnaires. We found that participants demonstrated considerable flexibility, adjusting social information use to current conditions. In particular, individuals employed a 'copy-when-uncertain' social learning strategy, supporting a core, but untested, assumption of influential theoretical models of cultural transmission. Moreover, participants adjusted the amount invested in their decision based on the perceived reliability of personally gathered information combined with the available social information. However, despite this strategic flexibility, participants also exhibited consistent individual differences in their propensities to use and value social information. Moreover, individuals who favoured social information self-reported as more collectivist than others. We discuss the implications of our results for social information use and cultural transmission.

  7. A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks.

    Science.gov (United States)

    Zhang, Qingguo; Fok, Mable P

    2017-01-09

    Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate's target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate's target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage-distance rate and the number of moved mobile sensors, when compare with other approaches.

  8. A new research tool for hybrid Bayesian networks using script language

    Science.gov (United States)

    Sun, Wei; Park, Cheol Young; Carvalho, Rommel

    2011-06-01

    While continuous variables become more and more inevitable in Bayesian networks for modeling real-life applications in complex systems, there are not much software tools to support it. Popular commercial Bayesian network tools such as Hugin, and Netica etc., are either expensive or have to discretize continuous variables. In addition, some free programs existing in the literature, commonly known as BNT, GeNie/SMILE, etc, have their own advantages and disadvantages respectively. In this paper, we introduce a newly developed Java tool for model construction and inference for hybrid Bayesian networks. Via the representation power of the script language, this tool can build the hybrid model automatically based on a well defined string that follows the specific grammars. Furthermore, it implements several inference algorithms capable to accommodate hybrid Bayesian networks, including Junction Tree algorithm (JT) for conditional linear Gaussian model (CLG), and Direct Message Passing (DMP) for general hybrid Bayesian networks with CLG structure. We believe this tool will be useful for researchers in the field.

  9. A Comparative Analysis for Hybrid Routing Protocol for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ms. Manisha M. Magdum

    2015-04-01

    Full Text Available Wireless Sensor Networks (WSNs consist of smallnodes with sensing, computation and wireless communicationscapabilities. These sensor networks interconnect a several othernodes when established in large and this opens up severaltechnical challenges and immense application possibilities.These wireless sensor networks communicate using multi-hopwireless communications, regular ad hoc routing techniquescannot be directly applied to sensor networks domain due tothe limited processing power and the finite power available toeach sensor nodes hence recent advances in wireless sensornetworks have developed many protocols depending on theapplication and network architecture and are specificallydesigned for sensor networks where energy awareness is anessential consideration. This paper presents routingprotocols for sensor networks and compares the routingprotocols that are presently of increasing importance. In this paper, we propose Hybrid Routing Protocol whichcombines the merits of proactive and reactive approach andovercome their demerits.

  10. TWDM-PON-AN optical backhaul solution for hybrid optical wireless networks

    Science.gov (United States)

    Naqshbandi, Fayiqa; Jha, Rakesh Kumar

    2016-10-01

    To improve the performance of broadband access networks Full Service Access Network selected Time and wavelength division multiplexed Passive Optical Network (TWDM-PON) as the primary solution for next-generation optical access (Next-Generation Passive Optical Networks 2 (NGPON2)). This paper reviews the recent progress in this access technology. Different possible solutions for the-next generation access are explained. Comparison of the different TWDM architectures experimentally demonstrated so far is made considering the large split, long reach and high capacity requirements of NGPON2. Major technical challenges in implementing the TWDM networks are discussed. Possible options for designing hybrid wireless-wireline architectures are explained taking care of the high bandwidth provided by the optical networks and high mobility of wireless networks. Also an integrated optical wireless architecture is suggested using TWDM-PON as an optical backhaul.

  11. Exponential synchronization of general chaotic delayed neural networks via hybrid feedback

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, and covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, recurrent multilayer perceptrons (RMLPs). By virtue of LyapunovKrasovskii stability theory and linear matrix inequality (LMI) technique, some exponential synchronization criteria are derived.Using the drive-response concept, hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria. Finally, detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.

  12. Networked Environments that Create Hybrid Spaces for Learning Science

    Science.gov (United States)

    Otrel-Cass, Kathrin; Khoo, Elaine; Cowie, Bronwen

    2014-01-01

    Networked learning environments that embed the essence of the Community of Inquiry (CoI) framework utilise pedagogies that encourage dialogic practices. This can be of significance for classroom teaching across all curriculum areas. In science education, networked environments are thought to support student investigations of scientific problems,…

  13. Scalable and Hybrid Radio Resource Management for Future Wireless Networks

    DEFF Research Database (Denmark)

    Mino, E.; Luo, Jijun; Tragos, E.

    2007-01-01

    The concept of ubiquitous and scalable system is applied in the IST WINNER II [1] project to deliver optimum performance for different deployment scenarios, from local area to wide area wireless networks. The integration in a unique radio system of a cellular and local area type networks supposes...

  14. Hybrid Modeling and Simulation of Automotive Supply Chain Network

    Directory of Open Access Journals (Sweden)

    Wen Wang

    2013-07-01

    Full Text Available According to the operation of automotive supply chain and the features of various simulation methods, we create and simulate a automotive supply chain network model with the core enterprise of two vehicle manufacturers, consisting of several parts suppliers, vehicle distributors and logistics service providers. On this basis of a conceptual model including the establishment of enterprise layer, business layer and operation layer, we establish a detailed model of the network system according to the network structure of automotive supply chain, the operation process and the internal business process of core enterprises; then we use System Dynamics (SD, Discrete Event Simulation (DES and Agent Based Modeling (ABM to describe the operating state of each node in the network model. We execute and analyze the simulation model of the whole network system described by Anylogic, using the results of the distributors’ inventory, inventory cost and customer’s satisfaction to prove the effectiveness of the model.

  15. HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NETWORK

    Directory of Open Access Journals (Sweden)

    Seyedeh Yasaman Rashida

    2013-06-01

    Full Text Available In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects.

  16. Hybrid Clustering-Classification Neural Network in the Medical Diagnostics of the Reactive Arthritis

    Directory of Open Access Journals (Sweden)

    Yevgeniy Bodyanskiy

    2016-08-01

    Full Text Available In the paper, the hybrid clustering-classification neural network is proposed. This network allows to increase a quality of information processing under the condition of overlapping classes due to the rational choice of learning rate parameter and introducing special procedure of fuzzy reasoning in the clustering-classification process, which occurs both with external learning signal ("supervised", and without one ("unsupervised". As similarity measure neighborhood function or membership one, cosine structures are used, which allow to provide a high flexibility due to self-learning-learning process and to provide some new useful properties. Many realized experiments have confirmed the efficiency of proposed hybrid clustering-classification neural network; also, this network was used for solving diagnostics task of reactive arthritis.

  17. Hybrid Control of Long-Endurance Aerial Robotic Vehicles for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Deok-Jin Lee

    2011-06-01

    Full Text Available This paper presents an effective hybrid control approach for building stable wireless sensor networks between heterogeneous unmanned vehicles using long‐ endurance aerial robotic vehicles. For optimal deployment of the aerial vehicles in communication networks, a gradient climbing based self‐estimating control algorithm is utilized to locate the aerial platforms to maintain maximum communication throughputs between distributed multiple nodes. The autonomous aerial robots, which function as communication relay nodes, extract and harvest thermal energy from the atmospheric environment to improve their flight endurance within specified communication coverage areas. The rapidly‐deployable sensor networks with the high‐endurance aerial vehicles can be used for various application areas including environment monitoring, surveillance, tracking, and decision‐making support. Flight test and simulation studies are conducted to evaluate the effectiveness of the proposed hybrid control technique for robust communication networks.

  18. Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System

    Directory of Open Access Journals (Sweden)

    ASTROV, I.

    2007-04-01

    Full Text Available This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.

  19. Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier

    Directory of Open Access Journals (Sweden)

    Wasi Haider Butt

    2014-01-01

    attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network.

  20. A Hybrid Networking Model for the Access Layer of the Communication Network for Distribution in Smart Grid

    Directory of Open Access Journals (Sweden)

    Wang Hao

    2016-01-01

    Full Text Available The access layer in the communication network for distribution is an important link in the automation of smart distribution power grid. In current access layer of communication network for distribution in Chinese power grid systems, several communication methods like optical fiber, mediumvoltage carrier communication, 1.8GHz TD-LTE power private wireless network, 230MHz TD-LTE power private wireless network, public wireless network are constructed concurrently and running simultaneously in an identical power supply area. This traditional networking model will cause repeated construction and operation and maintenance difficulties in the communication network of power grid. On the basis of giving a detailed analysis of the radio link budget of TD-LTE power private wireless network in two frequencies, this paper present a multi-communication methods hybrid networking model, which gives a clear boundary for different communication methods based on the isoline with equal signal strength of the TD-LTE power private wireless network and accomplish the optimization of communication resources for distribution.

  1. A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis

    Directory of Open Access Journals (Sweden)

    Juliana Yim

    2009-06-01

    Full Text Available This paper looks at the ability of a relatively new technique, hybrid ANN’s, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.

  2. A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis

    Directory of Open Access Journals (Sweden)

    Juliana Yim

    2005-01-01

    Full Text Available This paper looks at the ability of a relatively new technique, hybrid ANN's, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.

  3. Performance Analysis of Wireless Mobile Radio Hybrid(DS/FH) Spread Spectrum Network

    Institute of Scientific and Technical Information of China (English)

    余晓刚; 王华; 匡镜明

    2004-01-01

    By the flexible redefinition of frequency-occupation and frequency-collision event, the frequency-collision probability of hybrid(DS/FH) spread spectrum network is analyzed. This probability is based on the simultaneous transmission number threshold and is discussed in synchronous and asynchronous circumstances respectively. And then, the network throughput based on the packet correct reception probability is analyzed. Two models which have finite and infinite population respectively is discussed. At last, the numerical results are given.

  4. Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

    Directory of Open Access Journals (Sweden)

    Oliveira Rui

    2010-09-01

    Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.

  5. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

    2010-07-01

    Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  6. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Directory of Open Access Journals (Sweden)

    Fabio V. Goncalves, Helena M. Ramos, Luisa Fernanda R. Reis

    2010-01-01

    Full Text Available Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator – CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator – HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  7. Controllable wave propagation of hybrid dispersive medium with LC high-pass network (Conference Presentation)

    Science.gov (United States)

    Flores Parra, Edgar; Bergamini, Andrea E.; Ermanni, Paolo

    2017-04-01

    This work reports on the wave transmission characteristics of a hybrid one dimensional (1D) medium. The hybrid characteristic is the result of the coupling between a mechanical waveguide in the form of an elastic beam, and an electrical network. The network configuration investigated is an LC high-pass, consisting of a series of capacitors connected in series through grounded inductors. The capacitors correspond to a periodic array of piezoelectric patches that are bonded to the beam thus coupling the two waveguides. The coupling is characterized by a coincidence frequency/wavenumber corresponding to the intersection of the dispersion curves. At this coincidence frequency, the hybrid medium features attenuation of wave motion as a result of the energy transfer to the electrical network. This energy exchange is depicted in the dispersion by eigenvalue crossing, a particular case of eigenvalue veering. This paper presents the numerical investigations of the wave propagation in the considered medium, and validates the numerical findings with experimental evidence of the wave transmission characteristics. Moreover, the dispersion properties of the electrical network are further studied by varying the inductances thus exploiting the tunability of the periodic electrical domain, i.e: monoatomic and diatmomic unit cell configurations. The LC high-pass network offers several advantages over other configurations, from ease of implementation as the piezoelectric elements are not grounded, to a smaller inductance values to achieve attenuation at a given frequency. Such media could be interfaced with more complex electrical networks to create a new type of smart materials.

  8. Hybrid polymer networks as ultra low `k` dielectric layers

    Energy Technology Data Exchange (ETDEWEB)

    Lewicki, James; Worsley, Marcus A.

    2016-02-16

    According to one embodiment, a polymeric material includes at least one polydimethylsiloxane (PDMS) polymer, and at least one polyhedral oligomericsilsequioxane (POSS) molecule. According to another embodiment, a method includes providing at least one polydimethylsiloxane (PDMS) polymer, providing at least one polyhedral oligomericsilsequioxane (POSS) molecule, and coupling the at least one PDSM polymer to the at least one POSS molecule to form a hybrid polymeric material.

  9. Hybrid Spectral Efficient Cellular Network Deployment to Reduce RF Pollution

    National Research Council Canada - National Science Library

    Sumit Katiyar; R K Jain; N K Agrawal

    2012-01-01

    ... from these base stations may cause any health effect or hazard. Recently UP High Court in India ordered for removal of BTS towers from residential area, it has created panic among cellular communication network designers too...

  10. Networked Adaptive Interactive Hybrid Systems (NAIHS) for multiplatform engagement capability

    NARCIS (Netherlands)

    Kester, L.J.H.M.

    2008-01-01

    Advances in network technologies enable distributed systems, operating in complex physical environments, to coordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public

  11. Creating networking adaptive interactive hybrid systems : A methodic approach

    NARCIS (Netherlands)

    Kester, L.J.

    2011-01-01

    Advances in network technologies enable distributed systems, operating in complex physical environments, to coordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defense, crisis management, traffic management, public saf

  12. Hybrid modeling and empirical analysis of automobile supply chain network

    Science.gov (United States)

    Sun, Jun-yan; Tang, Jian-ming; Fu, Wei-ping; Wu, Bing-ying

    2017-05-01

    Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer-driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.

  13. Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

    Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

  14. Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2015-06-01

    Full Text Available Presenting a satisfactory and efficient training algorithm for artificial neural networks (ANN has been a challenging task in the supervised learning area. Particle swarm optimization (PSO is one of the most widely used algorithms due to its simplicity of implementation and fast convergence speed. On the other hand, Cuckoo Search (CS algorithm has been proven to have a good ability for finding the global optimum; however, it has a slow convergence rate. In this study, a hybrid algorithm based on PSO and CS is proposed to make use of the advantages of both PSO and CS algorithms. The proposed hybrid algorithm is employed as a new training method for feedforward neural networks (FNNs. To investigate the performance of the proposed algorithm, two benchmark problems are used and the results are compared with those obtained from FNNs trained by original PSO and CS algorithms. The experimental results show that the proposed hybrid algorithm outperforms both PSO and CS in training FNNs.

  15. ON MULTICAST TREE CONSTRUCTION IN IPV4-IPV6 HYBRID NETWORK

    Institute of Scientific and Technical Information of China (English)

    Zhang Chao; Zhang Yuan; Huang Yongfeng; Li Xing

    2010-01-01

    With the IPv4 addresses exhausting and IPv6 emerging,the Peer-to-Peer (P2P) overlay is becoming increasingly heterogeneous and complex: pure IPv4,dual stack and pure IPv6 hosts coexist,and the connectivity limitation between IPv4 and IPv6 hosts requires the overlay protocols to be fit for this hybrid situation. This paper sets out to answer the question of how to construct multicast tree on top of IPv4-IPv6 hybrid network. Our solution is a New Greedy Algorithm (NGA) which eliminates the problem of joining failure in the hybrid network and keeps the efficiency of greedy algorithm in tree construction. Simulation results show that our algorithm has excellent performance,which is very close to the optimal in many cases.

  16. Fuzzy Activation and Clustering of Nodes in a Hybrid Fibre Network Roll-out

    NARCIS (Netherlands)

    Kraak, J.J.; Phillipson, F.

    2015-01-01

    To design a Hybrid Fibre network, a selection of nodes is provided with active equipment and connected with fibre. If there is a need for a ring structure for high reliability, the activated nodes need to be clustered. In this paper a fuzzy method is proposed for this activation and clustering probl

  17. Hybrid professional learning networks for knowledge workers: educational theory inspiring new practices

    NARCIS (Netherlands)

    Bitter-Rijpkema, Marlies; Verjans, Steven

    2010-01-01

    Bitter-Rijpkema, M., & Verjans, S. (2010). Hybrid professional learning networks for knowledge workers: educational theory inspiring new practices. In L. Creanor, D. Hawkridge, K. Ng, & F. Rennie (Eds.), ALT-C 2010 - Conference Proceedings: "Into something rich and strange" - making sense of the

  18. Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

    2015-02-01

    This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

  19. Stochastic resonance enhancement of small-world neural networks by hybrid synapses and time delay

    Science.gov (United States)

    Yu, Haitao; Guo, Xinmeng; Wang, Jiang

    2017-01-01

    The synergistic effect of hybrid electrical-chemical synapses and information transmission delay on the stochastic response behavior in small-world neuronal networks is investigated. Numerical results show that, the stochastic response behavior can be regulated by moderate noise intensity to track the rhythm of subthreshold pacemaker, indicating the occurrence of stochastic resonance (SR) in the considered neural system. Inheriting the characteristics of two types of synapses-electrical and chemical ones, neural networks with hybrid electrical-chemical synapses are of great improvement in neuron communication. Particularly, chemical synapses are conducive to increase the network detectability by lowering the resonance noise intensity, while the information is better transmitted through the networks via electrical coupling. Moreover, time delay is able to enhance or destroy the periodic stochastic response behavior intermittently. In the time-delayed small-world neuronal networks, the introduction of electrical synapses can significantly improve the signal detection capability by widening the range of optimal noise intensity for the subthreshold signal, and the efficiency of SR is largely amplified in the case of pure chemical couplings. In addition, the stochastic response behavior is also profoundly influenced by the network topology. Increasing the rewiring probability in pure chemically coupled networks can always enhance the effect of SR, which is slightly influenced by information transmission delay. On the other hand, the capacity of information communication is robust to the network topology within the time-delayed neuronal systems including electrical couplings.

  20. A hybrid particle swarm optimization approach with neural network and set pair analysis for transmission network planning

    Institute of Scientific and Technical Information of China (English)

    刘吉成; 颜苏莉; 乞建勋

    2008-01-01

    Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.

  1. The probability of a gene tree topology within a phylogenetic network with applications to hybridization detection.

    Directory of Open Access Journals (Sweden)

    Yun Yu

    Full Text Available Gene tree topologies have proven a powerful data source for various tasks, including species tree inference and species delimitation. Consequently, methods for computing probabilities of gene trees within species trees have been developed and widely used in probabilistic inference frameworks. All these methods assume an underlying multispecies coalescent model. However, when reticulate evolutionary events such as hybridization occur, these methods are inadequate, as they do not account for such events. Methods that account for both hybridization and deep coalescence in computing the probability of a gene tree topology currently exist for very limited cases. However, no such methods exist for general cases, owing primarily to the fact that it is currently unknown how to compute the probability of a gene tree topology within the branches of a phylogenetic network. Here we present a novel method for computing the probability of gene tree topologies on phylogenetic networks and demonstrate its application to the inference of hybridization in the presence of incomplete lineage sorting. We reanalyze a Saccharomyces species data set for which multiple analyses had converged on a species tree candidate. Using our method, though, we show that an evolutionary hypothesis involving hybridization in this group has better support than one of strict divergence. A similar reanalysis on a group of three Drosophila species shows that the data is consistent with hybridization. Further, using extensive simulation studies, we demonstrate the power of gene tree topologies at obtaining accurate estimates of branch lengths and hybridization probabilities of a given phylogenetic network. Finally, we discuss identifiability issues with detecting hybridization, particularly in cases that involve extinction or incomplete sampling of taxa.

  2. Service and multimedia data transmission in IoT networks using hybrid communication devices

    Directory of Open Access Journals (Sweden)

    Saveliev Anton

    2017-01-01

    Full Text Available Employment of various protocols and technologies in IoT networks leads to the lack of module unification and increase in incompatible technical solutions. Modern IoT networks are not designed for streaming audio/video data, so their application field is limited. Also, modern IoT networks should have connection areas for devices transferring data to the Internet, and consider hardware and software specific characteristics of these devices. We offer one-size-fits-all solution for organization of IoT network, using hybrid modules. These devices provide flexibility, scalability, energy efficiency and multi-use of network for the transfer of various types of data. This approach takes into account software and hardware features of the devices used for data transmission in IoT networks, which helps to automate connecting the modules chosen by user.

  3. Hybrid computing using a neural network with dynamic external memory.

    Science.gov (United States)

    Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis

    2016-10-27

    Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

  4. Hybrid Neural Network Model of an Industrial Ethanol Fermentation Process Considering the Effect of Temperature

    Science.gov (United States)

    Mantovanelli, Ivana C. C.; Rivera, Elmer Ccopa; da Costa, Aline C.; Filho, Rubens Maciel

    In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.

  5. Small World Effects in a Harmonious Unifying Hybrid Preferential Model Networks

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Small world effects in the harmonious unifying hybrid preferential model (HUHPM) networks are studied both numerically and analytically. The idea and method of the HUHPM is applied to three typical examples of unweighted BA model, weighted BBV model, and the TDE model, so-called HUHPM-BA, HUHPM-BBV and HUHPM-TDE networks. Comparing the HUHPM with current typical models above, it is found that the HUHPM networks has the smallest average path length and the biggest average clustering coefficient. The results demonstrate that the HUHPM is more suitable not only for the un-weighted models but also for the weighted models.

  6. A novel hybrid-maximum neural network in stereo-matching process.

    Science.gov (United States)

    Laskowski, Lukasz

    2013-01-01

    In the present paper, the completely innovative architecture of artificial neural network based on Hopfield structure for solving a stereo-matching problem-hybrid neural network, consisting of the classical analog Hopfield neural network and the Maximum Neural Network-is described. The application of this kind of structure as a part of assistive device for visually impaired individuals is considered. The role of the analog Hopfield network is to find the attraction area of the global minimum, whereas Maximum Neural Network is finding accurate location of this minimum. The network presented here is characterized by an extremely high rate of work performance with the same accuracy as a classical Hopfield-like network, which makes it possible to use this kind of structure as a part of systems working in real time. The network considered here underwent experimental tests with the use of real stereo pictures as well as simulated stereo images. This enables error calculation and direct comparison with the classic analog Hopfield neural network as well as other networks proposed in the literature.

  7. PERFORMANCE EVALUATION OF DSDV IN HYBRID WIRELESS MESH NETWORK

    Directory of Open Access Journals (Sweden)

    V. Lakshmi Praba

    2011-09-01

    Full Text Available Wireless Mesh Network (WMN is a new wireless technology and it has the features of large area network coverage, Internet broadband access, self-healing, self-configuring and self-organizing. Routing is an important research issue in WMN. Many routing protocols are available in WMN. These protocols are divided into two categories proactive (Table Driven and reactive (On-demand protocols. This paper discusses the performance of proactive routing protocol Destination Sequenced Distance Vector (DSDV in WMN by considering the various performance metrics (packet delivery ratio, routing overhead and dropped packets by varying transmission rate and mesh client speed.

  8. Hybrid-ARQ in Multihop Networks with Opportunistic Relay Selection

    CERN Document Server

    Lo, Caleb K; Vishwanath, Sriram

    2007-01-01

    This paper develops a contention-based opportunistic feedback technique towards relay selection in a dense wireless network. This technique enables the forwarding of additional parity information from the selected relay to the destination. For a given network, the effects of varying key parameters such as the feedback probability are presented and discussed. A primary advantage of the proposed technique is that relay selection can be performed in a distributed way. Simulation results find its performance to closely match that of centralized schemes that utilize GPS information, unlike the proposed method. The proposed relay selection method is also found to achieve throughput gains over a point-to-point transmission strategy.

  9. A Game Theoretic Optimization Method for Energy Efficient Global Connectivity in Hybrid Wireless Sensor Networks.

    Science.gov (United States)

    Lee, JongHyup; Pak, Dohyun

    2016-08-29

    For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.

  10. A Game Theoretic Optimization Method for Energy Efficient Global Connectivity in Hybrid Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    JongHyup Lee

    2016-08-01

    Full Text Available For practical deployment of wireless sensor networks (WSN, WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.

  11. Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

    Science.gov (United States)

    Chiadamrong, N.; Piyathanavong, V.

    2017-04-01

    Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

  12. Hybrid Network Simulation for the ATLAS Trigger and Data Acquisition (TDAQ) System

    CERN Document Server

    Bonaventura, Matias Alejandro; The ATLAS collaboration; Castro, Rodrigo Daniel; Foguelman, Daniel Jacob

    2015-01-01

    The poster shows the ongoing research in the ATLAS TDAQ group in collaboration with the University of Buenos Aires in the area of hybrid data network simulations. he Data Network and Processing Cluster filters data in real-time, achieving a rejection factor in the order of 40000x and has real-time latency constrains. The dataflow between the processing units (TPUs) and Readout System (ROS) presents a “TCP Incast”-type network pathology which TCP cannot handle it efficiently. A credits system is in place which limits rate of queries and reduces latency. This large computer network, and the complex dataflow has been modelled and simulated using a PowerDEVS, a DEVS-based simulator. The simulation has been validated and used to produce what-if scenarios in the real network. Network Simulation with Hybrid Flows: Speedups and accuracy, combined • For intensive network traffic, Discrete Event simulation models (packet-level granularity) soon becomes prohibitive: Too high computing demands. • Fluid Flow simul...

  13. Single-Board-Computer-Based Traffic Generator for a Heterogeneous and Hybrid Smart Grid Communication Network

    Directory of Open Access Journals (Sweden)

    Do Nguyet Quang

    2014-02-01

    Full Text Available In smart grid communication implementation, network traffic pattern is one of the main factors that affect the system’s performance. Examining different traffic patterns in smart grid is therefore crucial when analyzing the network performance. Due to the heterogeneous and hybrid nature of smart grid, the type of traffic distribution in the network is still unknown. The traffic that popularly used for simulation and analysis no longer reflects the real traffic in a multi-technology and bi-directional communication system. Hence, in this study, a single-board computer is implemented as a traffic generator which can generate network traffic similar to those generated by various applications in the fully operational smart grid. By placing in a strategic and appropriate position, a collection of traffic generators allow network administrators to investigate and test the effect of heavy traffic on performance of smart grid communication system.

  14. Connectivity and Coverage in Hybrid Wireless Sensor Networks using Dynamic Random Geometric Graph Model

    Directory of Open Access Journals (Sweden)

    Jasmine Norman

    2011-10-01

    Full Text Available Random Geometric Graphs have been a very influential and well-studied model of large networks, such assensor networks, where the network nodes are represented by the vertices of the RGG, and the direct connectivity between nodes is represented by the edges. This assumes homogeneous wireless nodes with uniform transmission ranges. In real life, there exist heterogeneous wireless networks in which devices have dramatically different capabilities. The connectivity of a WSN is related to the positions of nodes, and those positions are heavily affected by the method of sensor deployment. As sensors may be spread in an arbitrary manner, one of the fundamental issues in a wireless sensor network is the coverage problem. In this paper, I study connectivity and coverage in hybrid WSN based on dynamic random geometric graph.

  15. HEAD: A Hybrid Mechanism to Enforce Node Cooperation in Mobile Ad Hoc Networks

    Institute of Scientific and Technical Information of China (English)

    QUO Jianli; LIU Hongwei; DONG Jian; YANG Xiaozong

    2007-01-01

    Mobile ad hoc networks rely on the cooperation of nodes for routing and forwarding. However, it may not be advantageous for individual nodes to cooperate. In order to make the mobile ad hoc network more robust, we propose a scheme called HEAD (a hybrid mechanism to enforce node cooperation in mobile ad hoc networks) to make the misbehavior unattractive. HEAD is an improvement to OCEAN (observation-based cooperation enforcement in ad hoc networks). It employs only first hand information and works on the top of DSR (dynamic source routing) protocol. By interacting with the DSR, HEAD can detect the misbehavior nodes in the packet forwarding process and isolate them in the route discovery process. In order to detect the misbehavior nodes quickly, HEAD introduces the warning message. In this paper, we also classify the misbehavior nodes into three types: malicious nodes, misleading nodes, and selfish nodes. They all can be detected by HEAD, and isolated from the network.

  16. Three-dimensional hybrid networks based on aspartic acid

    Indian Academy of Sciences (India)

    Anupama Ghosh; R A Sanguramath

    2008-01-01

    Three-dimensional achiral coordination polymers of the general formula M2(D, L-NHCH (COO)CH2COO)2.C4H4N2 where M = Ni and Co and pyrazine acts as the linker molecule have been prepared under hydrothermal conditions starting with [M(L-NHCH(COO)CH2COO).3H2O] possessing a helical chain structure. A three-dimensional hybrid compound of the formula Pb2.5[N{CH(COO)CH2COO}22H2O] has also been prepared hydrothermally starting with aspartic acid and Pb(NO3)2. In this lead compound, where a secondary amine formed by the dimerisation of aspartic acid acts as the ligand, there is two-dimensional inorganic connectivity and one-dimensional organic connectivity.

  17. Hybrid Neural Network and Support Vector Machine Method for Optimization

    Science.gov (United States)

    Rai, Man Mohan (Inventor)

    2007-01-01

    System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

  18. Research on Data Accurate Perceptual Social Information Network Evolution,Modeling and Burst Detection Based on Kernel Synergy%基于核协同数据精准感知的社会信息网络演化、建模及突发检测研究

    Institute of Scientific and Technical Information of China (English)

    刘畅

    2015-01-01

    目前,社会计算和面向网络化社会的研究在许多国家都被提升到了国家战略层次,成为了学术界的研究热点和前沿课题。社会信息网络(SIN)作为社会计算的重要研究内容也是我们应该关注的对象。内容包括数据精准感知型核协同SIN构建、多维协同感知型SIN演化分析、动态SIN建模和SIN上的突发检测进行了系统描述,不但可以为SIN研究提供理论支持,而且也可为涉及社会和谐发展的SIN上的突发检测应用提供新方法。%At present,the research on social computing and network oriented society, which has been elevated to the national strategic level in many countries,has become the focus and advanced subject of academic.Social information network (SIN) should be paid more attention to,as the object of important research contents of social computing. SIN,including the data accurate perception of nuclear burst detection,collaborative SIN build multidimensional cooperative sensing type SIN evolution analysis, the burst detection of dynamic SIN modeling and SIN of the system description, can not only provide theoretical support for SIN research, but also provide a new method for relates to the harmonious development of society on the SIN burst detection application.

  19. Hybrid Spectral Efficient Cellular Network Deployment to Reduce RF Pollution

    Science.gov (United States)

    Katiyar, Sumit; K. Jain, R.; K. Agrawal, N.

    2012-09-01

    As the mobile telecommunication systems are growing tremendously all over the world, the numbers of handheld and base stations are also rapidly growing and it became very popular to see these base stations distributed everywhere in the neighborhood and on roof tops which has caused a considerable amount of panic to the public in Palestine concerning wither the radiated electromagnetic fields from these base stations may cause any health effect or hazard. Recently UP High Court in India ordered for removal of BTS towers from residential area, it has created panic among cellular communication network designers too. Green cellular networks could be a solution for the above problem. This paper deals with green cellular networks with the help of multi-layer overlaid hierarchical structure (macro / micro / pico / femto cells). Macrocell for area coverage, micro for pedestrian and a slow moving traffic while pico for indoor use and femto for individual high capacity users. This could be the answer of the problem of energy conservation and enhancement of spectral density also.

  20. A Hybrid Constructive Algorithm for Single-Layer Feedforward Networks Learning.

    Science.gov (United States)

    Wu, Xing; Rózycki, Paweł; Wilamowski, Bogdan M

    2015-08-01

    Single-layer feedforward networks (SLFNs) have been proven to be a universal approximator when all the parameters are allowed to be adjustable. It is widely used in classification and regression problems. The SLFN learning involves two tasks: determining network size and training the parameters. Most current algorithms could not be satisfactory to both sides. Some algorithms focused on construction and only tuned part of the parameters, which may not be able to achieve a compact network. Other gradient-based optimization algorithms focused on parameters tuning while the network size has to be preset by the user. Therefore, trial-and-error approach has to be used to search the optimal network size. Because results of each trial cannot be reused in another trial, it costs much computation. In this paper, a hybrid constructive (HC)algorithm is proposed for SLFN learning, which can train all the parameters and determine the network size simultaneously. At first, by combining Levenberg-Marquardt algorithm and least-square method, a hybrid algorithm is presented for training SLFN with fixed network size. Then,with the hybrid algorithm, an incremental constructive scheme is proposed. A new randomly initialized neuron is added each time when the training entrapped into local minima. Because the training continued on previous results after adding new neurons, the proposed HC algorithm works efficiently. Several practical problems were given for comparison with other popular algorithms. The experimental results demonstrated that the HC algorithm worked more efficiently than those optimization methods with trial and error, and could achieve much more compact SLFN than those construction algorithms.

  1. The resilient hybrid fiber sensor network with self-healing function.

    Science.gov (United States)

    Xu, Shibo; Liu, Tiegen; Ge, Chunfeng; Chen, Qinnan; Zhang, Hongxia

    2015-03-01

    This paper presents a novel resilient fiber sensor network (FSN) with multi-ring architecture, which could interconnect various kinds of fiber sensors responsible for more than one measurands. We explain how the intelligent control system provides sensors with self-healing function meanwhile sensors are working properly, besides each fiber in FSN is under real-time monitoring. We explain the software process and emergency mechanism to respond failures or other circumstances. To improve the efficiency in the use of limited spectrum resources in some situations, we have two different structures to distribute the light sources rationally. Then, we propose a hybrid sensor working in FSN which is a combination of a distributed sensor and a FBG (Fiber Bragg Grating) array fused in a common fiber sensing temperature and vibrations simultaneously with neglectable crosstalk to each other. By making a failure to a working fiber in experiment, the feasibility and effectiveness of the network with a hybrid sensor has been demonstrated, hybrid sensors could not only work as designed but also survive from destructive failures with the help of resilient network and smart and quick self-healing actions. The network has improved the viability of the fiber sensors and diversity of measurands.

  2. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

    Directory of Open Access Journals (Sweden)

    Lukas Falat

    2016-01-01

    Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  3. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

    Science.gov (United States)

    Falat, Lukas; Marcek, Dusan; Durisova, Maria

    2016-01-01

    This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  4. Hybrid neural network model for the design of beam subjected to bending and shear

    Indian Academy of Sciences (India)

    H Sudarsana Rao; B Ramesh Babu

    2007-10-01

    There is no direct method for design of beams. In general the dimensions of the beam and reinforcement are initially assumed and then the interaction formula is used to verify the suitability of chosen dimensions. This approach necessitates few trials for coming up with an economical and safe design. This paper demonstrates the applicability of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) for the design of beams subjected to moment and shear. A hybrid neural network model which combines the features of feed forward neural networks and genetic algorithms has been developed for the design of beam subjected to moment and shear. The network has been trained with design data obtained from design experts in the field. The hybrid neural network model learned the design of beam in just 1000 training cycles. After successful learning, the model predicted the depth of the beam, area of steel, spacing of stirrups required for new problems with accuracy satisfying all design constraints. The various stages involved in the development of a genetic algorithm based neural network model are addressed at length in this paper.

  5. Cross Layer Analysis of P2MP Hybrid FSO/RF Network

    KAUST Repository

    Rakia, Tamer

    2017-02-22

    This paper presents and analyzes a point-tomultipoint (P2MP) network that uses a number of freespace optical (FSO) links for data transmission from the central node to the different remote nodes of the network. A common backup radio frequency (RF) link can be used by the central node for data transmission to any remote node in case any one of the FSO links fails. Each remote node is assigned a transmit buffer at the central node. Considering the transmission link from the central node to a tagged remote node, we study various performance metrics. Specifically,we study the throughput from the central node to the tagged node, the average transmit buffer size, the symbol queuing delay in the transmit buffer, the efficiency of the queuing system, the symbol loss probability, and the RF link utilization. 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.

  6. Thai Electoral Campaigning: Vote-Canvassing Networks and Hybrid Voting

    Directory of Open Access Journals (Sweden)

    Anyarat Chattharakul

    2010-01-01

    Full Text Available Based on evidence gathered through participant observation, this article illuminates the nature of vote-canvassing, previously a black box in Thai electoral studies. Offering a close-up study of the internal mechanisms of an individual Thai election campaign, this article reveals that vote-canvasser networks are underpinned by long-term dyadic relationships, both hierarchical and horizontal, between the candidate, vote-canvassers and voters. These networks continue to be the most important factor in winning elections. This article documents how candidates draw up an election campaign map and identify voters along residential lines to maximise their vote-canvassing strategy. The findings of this article challenge Anek’s 1996 concept of “two democracies”, which argues that rural voters are influenced by money, local leaders, political factions and corrupt politicians while more well-educated, urban, middle-class voters are more oriented toward the alternative policies offered by competing parties. The case study of Kom’s election campaign showed that the role of the much-vaunted middle-class voters is not decisive, even in suburban areas of Bangkok. While political marketing has grown in importance in Thai elections, it has not displaced traditional electoral practices. Thai society is, in fact, deeply fragmented and diverse – too complex to be divided in such a simplistic manner. This article suggests that rather than undergoing a linear transformation, political hybridisation is a key trend in Thai election campaigns.

  7. HRSSA - Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

    Science.gov (United States)

    Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong

    2016-07-01

    This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

  8. HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Marchetti, Luca, E-mail: marchetti@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); University of Trento, Department of Mathematics (Italy); Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy)

    2016-07-15

    This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

  9. A Hybrid Artificial Neural Network-based Scheduling Knowledge Acquisition Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Weida; WANG Wei; LIU Wenjian

    2006-01-01

    It is a key issue that constructing successful knowledge base to satisfy an efficient adaptive scheduling for the complex manufacturing system. Therefore, a hybrid artificial neural network (ANN)-based scheduling knowledge acquisition algorithm is presented in this paper. We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid optimization method, in which GA was introduced to present parallel search architecture and SA was introduced to increase escaping probability from local optima and ability to neighbor search. The hybrid method was utilized to resolve the optimal attributes subset of manufacturing system and determine the optimal topology and parameters of ANN under different scheduling objectives; ANN was used to evaluate the fitness of chromosome in the method and generate the scheduling knowledge after obtaining the optimal attributes subset, optimal ANN's topology and parameters. The experimental results demonstrate that the proposed algorithm produces significant performance improvements over other machine learning-based algorithms.

  10. Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs).

    Science.gov (United States)

    Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

    2014-12-01

    In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.

  11. Hybrid thiol-ene network nanocomposites based on multi(meth)acrylate POSS.

    Science.gov (United States)

    Li, Liguo; Liang, Rendong; Li, Yajie; Liu, Hongzhi; Feng, Shengyu

    2013-09-15

    First, multi(meth)acrylate functionalized POSS monomers were synthesized in this paper. Secondly, FTIR was used to evaluate the homopolymerization behaviors of multi(meth)acrylate POSS and their copolymerization behaviors in the thiol-ene reactions with octa(3-mercaptopropyl) POSS in the presence of photoinitiator. Results showed that the photopolymerization rate of multimethacrylate POSS was faster than that of multiacrylate POSS. The FTIR results also showed that the copolymerizations were dominant in the thiol-ene reactions with octa(3-mercaptopropyl) POSS, different from traditional (meth)acrylate-thiol system, in which homopolymerizations were predominant. Finally, the resulted hybrid networks based on POSS were characterized by XRD, FE-SEM, DSC, and TGA. The characterization results showed that hybrid networks based on POSS were homogeneous and exhibited high thermal stability.

  12. Alginate-polymethacrylate hybrid hydrogels with double ionic and covalent network for tissue engineering

    Science.gov (United States)

    Schizzi, I.; Utzeri, R.; Castellano, M.; Stagnaro, P.

    2016-05-01

    Hydrogels based on alginates are very promising candidates to realize scaffolds for tissue engineering. Indeed, alginate hydrogels are able to mimic the extracellular matrix (ECM) thus promoting in vitro and/or in vivo cell growth; moreover, their capability of giving rise to highly porous structures can specifically favor the osteochondral tissue regeneration. However, mechanical properties of polymeric hydrogels are often inadequate to endow the final constructs with the required characteristics of elasticity and toughness. Here alginate/polymethacrylate hybrid hydrogels, with a suitable porous structure and characterized by a double network, ionic (from alginate) and covalent (from polymethacrylate) were designed and realized. The mechanical performance of these hybrid materials resulted, as expected, improved due to the double interconnected network, where the alginate portion provides the appropriate micro-environment mimicking the ECM, whereas the polymethacrylate portion acts as a reinforce.

  13. A hybrid hopfield network-simulated annealing approach for frequency assignment in satellite communications systems.

    Science.gov (United States)

    Salcedo-Sanz, Sancho; Santiago-Mozos, Ricardo; Bousoño-Calzón, Carlos

    2004-04-01

    A hybrid Hopfield network-simulated annealing algorithm (HopSA) is presented for the frequency assignment problem (FAP) in satellite communications. The goal of this NP-complete problem is minimizing the cochannel interference between satellite communication systems by rearranging the frequency assignment, for the systems can accommodate the increasing demands. The HopSA algorithm consists of a fast digital Hopfield neural network which manages the problem constraints hybridized with a simulated annealing which improves the quality of the solutions obtained. We analyze the problem and its formulation, describing and discussing the HopSA algorithm and solving a set of benchmark problems. The results obtained are compared with other existing approaches in order to show the performance of the HopSA approach.

  14. Two-dimensional magnetic modeling of ferromagnetic materials by using a neural networks based hybrid approach

    Energy Technology Data Exchange (ETDEWEB)

    Cardelli, E.; Faba, A. [Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia (Italy); Laudani, A.; Lozito, G.M.; Riganti Fulginei, F.; Salvini, A. [Department of Engineering, Roma Tre University, Via V. Volterra 62, 00146 Rome (Italy)

    2016-04-01

    This paper presents a hybrid neural network approach to model magnetic hysteresis at macro-magnetic scale. That approach aims to be coupled together with numerical treatments of magnetic hysteresis such as FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, allowing a complete computer simulation with acceptable run times. The proposed Hybrid Neural System consists of four inputs representing the magnetic induction and magnetic field components at each time step and it is trained by 2D and scalar measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the Hybrid Neural System returns the predicted value of the field H at the same time step. Within the Hybrid Neural System, a suitably trained neural network is used for predicting the hysteretic behavior of the material to be modeled. Validations with experimental tests and simulations for symmetric, non-symmetric and minor loops are presented.

  15. A Hybrid Neural Network Prediction Model of Air Ticket Sales

    Directory of Open Access Journals (Sweden)

    Han-Chen Huang

    2013-11-01

    Full Text Available Air ticket sales revenue is an important source of revenue for travel agencies, and if future air ticket sales revenue can be accurately forecast, travel agencies will be able to advance procurement to achieve a sufficient amount of cost-effective tickets. Therefore, this study applied the Artificial Neural Network (ANN and Genetic Algorithms (GA to establish a prediction model of travel agency air ticket sales revenue. By verifying the empirical data, this study proved that the established prediction model has accurate prediction power, and MAPE (mean absolute percentage error is only 9.11%. The established model can provide business operators with reliable and efficient prediction data as a reference for operational decisions.

  16. Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2002-01-01

    As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

  17. A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Antonio A. F. Loureiro

    2009-09-01

    Full Text Available Routing is a basic function in wireless sensor networks (WSNs. For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption.

  18. Bosch automotive electrics and automotive electronics systems and components, networking and hybrid drive

    CERN Document Server

    2014-01-01

    The significance of electrical and electronic systems has increased considerably in the last few years and this trend is set to continue. The characteristics feature of innovative systems is the fact that they can work together in a network. This requires powerful bus systems that the electronic control units can use to exchange information. Networking and the various bus systems used in motor vehicles are the prominent new topic in the 5th edition of the "Automotive Electric, Automotive Electronics" technical manual. The existing chapters have also been updated, so that this new edition brings the reader up to date on the subjects of electrical and electronic systems in the motor vehicle. Content Electrical and electronical systems – Basic principles of networking - Examples of networked vehicles – Bus systems – Architecture of electronic systems – Mechatronics – Elektronics – Electronic control Units – Software – Sensors – Actuators – Hybrid drives – Vehicle electrical system – Start...

  19. A hybrid adaptive routing algorithm for event-driven wireless sensor networks.

    Science.gov (United States)

    Figueiredo, Carlos M S; Nakamura, Eduardo F; Loureiro, Antonio A F

    2009-01-01

    Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption.

  20. The Proposal Of Hybrid Intrusion Detection For Defence Of Sync Flood Attack In Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Ruchi Bhatnagar

    2012-05-01

    Full Text Available Data security is a huge responsibility for sensor network as there are various ways in which security can be breached, enabling hackers to access sensitive data. Threats to wireless sensor networks are numerous and potentially devastating. Security issues ranging from session hijacking to Denial of Service (DOS can plague a WSN. To aid in the defense and detection of these potential threats, WSN employ a security solution that includes an intrusion detection system (IDS. Different neural methods have been proposed in recent years for the development of intrusion detection system. In this paper, we surveyeddenial of service attacks that disseminate the WSN such a way that it temporarily paralyses a network and proposed a hybrid Intrusion Detection approach based on stream flow and session state transition analysis that monitor and analyze stream flow of data, identify abnormal network activity, detect policy violations against sync flood attack.

  1. Developing a Collaborative and Autonomous Training and Learning Environment for Hybrid Wireless Networks

    CERN Document Server

    Lobo, Jose Eduardo M; Brust, Matthias R; Rothkugel, Steffen; Adriano, Christian M

    2007-01-01

    With larger memory capacities and the ability to link into wireless networks, more and more students uses palmtop and handheld computers for learning activities. However, existing software for Web-based learning is not well-suited for such mobile devices, both due to constrained user interfaces as well as communication effort required. A new generation of applications for the learning domain that is explicitly designed to work on these kinds of small mobile devices has to be developed. For this purpose, we introduce CARLA, a cooperative learning system that is designed to act in hybrid wireless networks. As a cooperative environment, CARLA aims at disseminating teaching material, notes, and even components of itself through both fixed and mobile networks to interested nodes. Due to the mobility of nodes, CARLA deals with upcoming problems such as network partitions and synchronization of teaching material, resource dependencies, and time constraints.

  2. An Evolutionary Mobility Aware Multi-Objective Hybrid Routing Algorithm for Heterogeneous Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Kulkarni, Nandkumar P.; Prasad, Neeli R.; Prasad, Ramjee

    Researchers have faced numerous challenges while designing WSNs and protocols in many applications such as object tracking in military, detection of disastrous events, environment and health monitoring etc. Amongst all sustaining connectivity and capitalizing on the network lifetime is a serious...... deliberation. To tackle these two problems, Mobile Wireless Sensor Networks (MWSNs) is a better choice. In MWSN, Sensor nodes move freely to a target area without the need for any special infrastructure. Due to mobility, the routing process in MWSN has become more complicated as connections in the network can...... change dynamically. In this paper, the authors put forward an Evolutionary Mobility aware multi-objective hybrid Routing Protocol for heterogeneous wireless sensor networks (EMRP). EMRP uses two-level hierarchical clustering. EMRP selects the optimal path from source to sink using multiple metrics...

  3. A hybrid multiscale coarse-grained method for dynamics on complex networks

    CERN Document Server

    Shen, Chuansheng; Hou, Zhonghuai; Kurths, Jürgen

    2016-01-01

    Brute-force simulations for dynamics on very large networks are quite expensive. While phenomenological treatments may capture some macroscopic properties, they often ignore important microscopic details. Fortunately, one may be only interested in the property of local part and not in the whole network. Here, we propose a hybrid multiscale coarse-grained(HMCG) method which combines a fine Monte Carlo(MC) simulation on the part of nodes of interest with a more coarse Langevin dynamics on the rest part. We demonstrate the validity of our method by analyzing the equilibrium Ising model and the nonequilibrium susceptible-infected-susceptible model. It is found that HMCG not only works very well in reproducing the phase transitions and critical phenomena of the microscopic models, but also accelerates the evaluation of dynamics with significant computational savings compared to microscopic MC simulations directly for the whole networks. The proposed method is general and can be applied to a wide variety of network...

  4. Architectural and operational considerations emerging from hybrid RF-optical network loading simulations

    Science.gov (United States)

    Chen, Yijiang; Abraham, Douglas S.; Heckman, David P.; Kwok, Andrew; MacNeal, Bruce E.; Tran, Kristy; Wu, Janet P.

    2016-03-01

    A technology demonstration of free space optical communication at interplanetary distances is planned via one or more future NASA deep-space missions. Such demonstrations will "pave the way" for operational use of optical communications on future robotic/potential Human missions. Hence, the Deep Space Network architecture will need to evolve. Preliminary attempts to model the anticipated future mission set and simulate how well it loads onto assumed architectures with combinations of RF and optical apertures have been evaluated. This paper discusses the results of preliminary loading simulations for hybrid RF-optical network architectures and highlights key mission and ground infrastructure considerations that emerge.

  5. 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.

  6. A hybrid Genetic and Simulated Annealing Algorithm for Chordal Ring implementation in large-scale networks

    DEFF Research Database (Denmark)

    Riaz, M. Tahir; Gutierrez Lopez, Jose Manuel; Pedersen, Jens Myrup

    2011-01-01

    The paper presents a hybrid Genetic and Simulated Annealing algorithm for implementing Chordal Ring structure in optical backbone network. In recent years, topologies based on regular graph structures gained a lot of interest due to their good communication properties for physical topology...... of the networks. There have been many use of evolutionary algorithms to solve the problems which are in combinatory complexity nature, and extremely hard to solve by exact approaches. Both Genetic and Simulated annealing algorithms are similar in using controlled stochastic method to search the solution....... The paper combines the algorithms in order to analyze the impact of implementation performance....

  7. Hybrid Binary Exponential Back-Off Mechanism for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Arshad Mohammed

    2013-06-01

    Full Text Available Many mechanisms to improve the performance have been proposed in the IEEE 802.15.4 Wireless sensor networks area, due to its high influence in the modern day world. Most of them have improved the performance of the network compared to the standard CSMA/CA backoff method. But still there are improvements to almost every method proposed. In this paper, we have proposed a hybrid binary exponential backoff (HBEB, where we have used two mechanisms to effectively increase the performance, when there are moderate numbers of nodes. The performance analysis using markov chain analysis has been given in this paper along with simulation results for the proposed method.

  8. CLASSIFICATION OF NEURAL NETWORK FOR TECHNICAL CONDITION OF TURBOFAN ENGINES BASED ON HYBRID ALGORITHM

    Directory of Open Access Journals (Sweden)

    Valentin Potapov

    2016-12-01

    Full Text Available Purpose: This work presents a method of diagnosing the technical condition of turbofan engines using hybrid neural network algorithm based on software developed for the analysis of data obtained in the aircraft life. Methods: allows the engine diagnostics with deep recognition to the structural assembly in the presence of single structural damage components of the engine running and the multifaceted damage. Results: of the optimization of neural network structure to solve the problems of evaluating technical state of the bypass turbofan engine, when used with genetic algorithms.

  9. A Hybrid Time Synchronization Algorithm Based on Broadcast Sequencing for Wireless Sensor Networks

    Science.gov (United States)

    2014-09-01

    sequence per the flow charts detailed in Figures 43–45 located in Appendix A. The input 1 in Figure 12 is a recursive step from some of the...SYNCHRONIZATION ALGORITHM BASED ON BROADCAST SEQUENCING FOR WIRELESS SENSOR NETWORKS by Sung C. Park September 2014 Thesis Co-Advisors...REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE A HYBRID TIME SYNCHRONIZATION ALGORITHM BASED ON BROADCAST SEQUENCING FOR

  10. Hybrid Computation Model for Intelligent System Design by Synergism of Modified EFC with Neural Network

    OpenAIRE

    2015-01-01

    In recent past, it has been seen in many applications that synergism of computational intelligence techniques outperforms over an individual technique. This paper proposes a new hybrid computation model which is a novel synergism of modified evolutionary fuzzy clustering with associated neural networks. It consists of two modules: fuzzy distribution and neural classifier. In first module, mean patterns are distributed into the number of clusters based on the modified evolutionary fuzzy cluste...

  11. Acoustic characterization of seafloor sediment employing a hybrid method of neural network architecture and fuzzy algorithm

    Digital Repository Service at National Institute of Oceanography (India)

    De, C.; Chakraborty, B.

    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 6, NO. 4, OCTOBER 2009 743 Acoustic Characterization of Seafloor Sediment Employing a Hybrid Method of Neural Network Architecture and Fuzzy Algorithm Chanchal De and Bishwajit Chakraborty Abstract... backscatter data [11]–[13] and side-scan sonar images [14]–[16] have been demonstrated for seafloor classification. In this letter, seafloor sediment is characterized using an unsupervised architecture called Kohonen’s self-organizing Manuscript received...

  12. Using Hybrid Simulation/Analytical Queueing Networks to Capacitate USAF Air Mobility Command Passenger Terminals

    Science.gov (United States)

    2012-03-01

    airports’ terminal processing facilities again utilizing the AACC /IATA criteria. This method , however, received much criticism for various fundamental flaws...networks in the literature are limited, so a method for using DES to adjust for ar- rival time-dependency in QNA is developed. Second, beyond quality of...terminal in particular, using a hybrid of simulation and analytical methods . The challenge, then, is to determine the optimal capacity given estimated

  13. Hybrid passive-active modal networks for structural acoustic control (Conference Presentation)

    Science.gov (United States)

    Cunefare, Kenneth A.; Lossouarn, Boris; Collet, Manuel

    2017-04-01

    Distributions of piezoelectric patches bonded to structures provide a means to alter or control, through active or passive means, the dynamic response of the host structure. Numerous active control schemes for such composite structures have been explored. Alternatively, for certain structures, a passive electrical network may be implemented which presents an electrical analog of the modal response of the structure, effectively providing a multi-modal, distributed passive tuned mass modal damper capability. Numerous tuned-mass damper design concepts ("tunings") may be applied to such a passive network. Further, the distributed network analog, when coupled with active control concepts, permits a hybrid distributed passive-active modal control capability. This paper explores this hybrid distributed network control concept applied to a clamped rectangular plate. A unit-cell discrete representation of the plate leads to an electrical analog comprised of passive inductors, transformers and resistors. Addition of synthetic (or controlled) impedances at a limited set of points within the network permits dynamic adjustment of the frequency response of the system.

  14. Efficient MAC Protocol for Hybrid Wireless Network with Heterogeneous Sensor Nodes

    Directory of Open Access Journals (Sweden)

    Md. Nasre Alam

    2016-01-01

    Full Text Available Although several Directional Medium Access Control (DMAC protocols have been designed for use with homogeneous networks, it can take a substantial amount of time to change sensor nodes that are equipped with an omnidirectional antenna for sensor nodes with a directional antenna. Thus, we require a novel MAC protocol for use with an intermediate wireless network that consists of heterogeneous sensor nodes equipped with either an omnidirectional antenna or a directional antenna. The MAC protocols that have been designed for use in homogeneous networks are not suitable for use in a hybrid network due to deaf, hidden, and exposed nodes. Therefore, we propose a MAC protocol that exploits the characteristics of a directional antenna and can also work efficiently with omnidirectional nodes in a hybrid network. In order to address the deaf, hidden, and exposed node problems, we define RTS/CTS for the neighbor (RTSN/CTSN and Neighbor Information (NIP packets. The performance of the proposed MAC protocol is evaluated through a numerical analysis using a Markov model. In addition, the analytical results of the MAC protocol are verified through an OPNET simulation.

  15. Opportunistic Hybrid Transport Protocol (OHTP) for Cognitive Radio Ad Hoc Sensor Networks.

    Science.gov (United States)

    Bin Zikria, Yousaf; Nosheen, Summera; Ishmanov, Farruh; Kim, Sung Won

    2015-12-15

    The inefficient assignment of spectrum for different communications purposes, plus technology enhancements and ever-increasing usage of wireless technology is causing spectrum scarcity. To address this issue, one of the proposed solutions in the literature is to access the spectrum dynamically or opportunistically. Therefore, the concept of cognitive radio appeared, which opens up a new research paradigm. There is extensive research on the physical, medium access control and network layers. The impact of the transport layer on the performance of cognitive radio ad hoc sensor networks is still unknown/unexplored. The Internet's de facto transport protocol is not well suited to wireless networks because of its congestion control mechanism. We propose an opportunistic hybrid transport protocol for cognitive radio ad hoc sensor networks. We developed a new congestion control mechanism to differentiate true congestion from interruption loss. After such detection and differentiation, we propose methods to handle them opportunistically. There are several benefits to window- and rate-based protocols. To exploit the benefits of both in order to enhance overall system performance, we propose a hybrid transport protocol. We empirically calculate the optimal threshold value to switch between window- and rate-based mechanisms. We then compare our proposed transport protocol to Transmission Control Protocol (TCP)-friendly rate control, TCP-friendly rate control for cognitive radio, and TCP-friendly window-based control. We ran an extensive set of simulations in Network Simulator 2. The results indicate that the proposed transport protocol performs better than all the others.

  16. Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

    Directory of Open Access Journals (Sweden)

    Asmau M. Ahmed

    2017-07-01

    Full Text Available Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1 The mixing process should occur at macroscopic level and (2 Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model. Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

  17. HyberLoc: Providing Physical Layer Location Privacy in Hybrid Sensor Networks

    CERN Document Server

    El-Badry, Rania; Youssef, Moustafa

    2010-01-01

    In many hybrid wireless sensor networks' applications, sensor nodes are deployed in hostile environments where trusted and un-trusted nodes co-exist. In anchor-based hybrid networks, it becomes important to allow trusted nodes to gain full access to the location information transmitted in beacon frames while, at the same time, prevent un-trusted nodes from using this information. The main challenge is that un-trusted nodes can measure the physical signal transmitted from anchor nodes, even if these nodes encrypt their transmission. Using the measured signal strength, un-trusted nodes can still tri-laterate the location of anchor nodes. In this paper, we propose HyberLoc, an algorithm that provides anchor physical layer location privacy in anchor-based hybrid sensor networks. The idea is for anchor nodes to dynamically change their transmission power following a certain probability distribution, degrading the localization accuracy at un-trusted nodes while maintaining high localization accuracy at trusted node...

  18. Multifunctional hybrid networks based on self assembling peptide sequences

    Science.gov (United States)

    Sathaye, Sameer

    The overall aim of this dissertation is to achieve a comprehensive correlation between the molecular level changes in primary amino acid sequences of amphiphilic beta-hairpin peptides and their consequent solution-assembly properties and bulk network hydrogel behavior. This has been accomplished using two broad approaches. In the first approach, amino acid substitutions were made to peptide sequence MAX1 such that the hydrophobic surfaces of the folded beta-hairpins from the peptides demonstrate shape specificity in hydrophobic interactions with other beta-hairpins during the assembly process, thereby causing changes to the peptide nanostructure and bulk rheological properties of hydrogels formed from the peptides. Steric lock and key complementary hydrophobic interactions were designed to occur between two beta-hairpin molecules of a single molecule, LNK1 during beta-sheet fibrillar assembly of LNK1. Experimental results from circular dichroism, transmission electron microscopy and oscillatory rheology collectively indicate that the molecular design of the LNK1 peptide can be assigned the cause of the drastically different behavior of the networks relative to MAX1. The results indicate elimination or significant reduction of fibrillar branching due to steric complementarity in LNK1 that does not exist in MAX1, thus supporting the original hypothesis. As an extension of the designed steric lock and key complementarity between two beta-hairpin molecules of the same peptide molecule. LNK1, three new pairs of peptide molecules LP1-KP1, LP2-KP2 and LP3-KP3 that resemble complementary 'wedge' and 'trough' shapes when folded into beta-hairpins were designed and studied. All six peptides individually and when blended with their corresponding shape complement formed fibrillar nanostructures with non-uniform thickness values. Loose packing in the assembled structures was observed in all the new peptides as compared to the uniform tight packing in MAX1 by SANS analysis. This

  19. Algorithm for Hybrid Optical Fiber-Wireless Photonic Channel Allocation for Millimeter-waveband 5G Networks

    DEFF Research Database (Denmark)

    Gomez Gonzalvo, A.; Vegas Olmos, Juan José; Tafur Monroy, Idelfonso

    2014-01-01

    This paper presents a performance assessment of an algorithm for hybrid fiber-wireless photonic channel allocation in 5G using radio-over-fiber with active delivery. Simulations show reductions of network blocking probability in 98% of the tested cases......This paper presents a performance assessment of an algorithm for hybrid fiber-wireless photonic channel allocation in 5G using radio-over-fiber with active delivery. Simulations show reductions of network blocking probability in 98% of the tested cases...

  20. Hybrid optimization of dynamic deployment for networked fire control system

    Institute of Scientific and Technical Information of China (English)

    Chen Chen; Jie Chen; Bin Xin

    2013-01-01

    With applying the information technology to the military field, the advantages and importance of the networked combat are more and more obvious. In order to make ful use of limited battle-field resources and maximal y destroy enemy targets from arbitrary angle in a limited time, the research on firepower nodes dynamic deployment becomes a key problem of command and control. Con-sidering a variety of tactical indexes and actual constraints in air defense, a mathematical model is formulated to minimize the ene-my target penetration probability. Based on characteristics of the mathematical model and demands of the deployment problems, an assistance-based algorithm is put forward which combines the ar-tificial potential field (APF) method with a memetic algorithm. The APF method is employed to solve the constraint handling prob-lem and generate feasible solutions. The constrained optimization problem transforms into an optimization problem of APF para-meters adjustment, and the dimension of the problem is reduced greatly. The dynamic deployment is accomplished by generation and refinement of feasible solutions. The simulation results show that the proposed algorithm is effective and feasible in dynamic situation.

  1. Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

    Directory of Open Access Journals (Sweden)

    Antonino Laudani

    2015-01-01

    Full Text Available A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.

  2. An Adaptive Hybrid Multi-level Intelligent Intrusion Detection System for Network Security

    Directory of Open Access Journals (Sweden)

    P. Ananthi

    2014-04-01

    Full Text Available Intrusion Detection System (IDS plays a vital factor in providing security to the networks through detecting malicious activities. Due to the extensive advancements in the computer networking, IDS has become an active area of research to determine various types of attacks in the networks. A large number of intrusion detection approaches are available in the literature using several traditional statistical and data mining approaches. Data mining techniques in IDS observed to provide significant results. Data mining approaches for misuse and anomaly-based intrusion detection generally include supervised, unsupervised and outlier approaches. It is important that the efficiency and potential of IDS be updated based on the criteria of new attacks. This study proposes a novel Adaptive Hybrid Multi-level Intelligent IDS (AHMIIDS system which is the combined version of anomaly and misuse detection techniques. The anomaly detection is based on Bayesian Networks and then the misuse detection is performed using Adaptive Neuro Fuzzy Inference System (ANFIS. The outputs of both anomaly detection and misuse detection modules are applied to Decision Table Majority (DTM to perform the final decision making. A rule-base approach is used in this system. It is observed from the results that the proposed AHMIIDS performs better than other conventional hybrid IDS.

  3. Reconstruction of in-plane strain maps using hybrid dense sensor network composed of sensing skin

    Science.gov (United States)

    Downey, Austin; Laflamme, Simon; Ubertini, Filippo

    2016-12-01

    The authors have recently developed a soft-elastomeric capacitive (SEC)-based thin film sensor for monitoring strain on mesosurfaces. Arranged in a network configuration, the sensing system is analogous to a biological skin, where local strain can be monitored over a global area. Under plane stress conditions, the sensor output contains the additive measurement of the two principal strain components over the monitored surface. In applications where the evaluation of strain maps is useful, in structural health monitoring for instance, such signal must be decomposed into linear strain components along orthogonal directions. Previous work has led to an algorithm that enabled such decomposition by leveraging a dense sensor network configuration with the addition of assumed boundary conditions. Here, we significantly improve the algorithm’s accuracy by leveraging mature off-the-shelf solutions to create a hybrid dense sensor network (HDSN) to improve on the boundary condition assumptions. The system’s boundary conditions are enforced using unidirectional RSGs and assumed virtual sensors. Results from an extensive experimental investigation demonstrate the good performance of the proposed algorithm and its robustness with respect to sensors’ layout. Overall, the proposed algorithm is seen to effectively leverage the advantages of a hybrid dense network for application of the thin film sensor to reconstruct surface strain fields over large surfaces.

  4. A hybrid deep neural network and physically based distributed model for river stage prediction

    Science.gov (United States)

    hitokoto, Masayuki; sakuraba, Masaaki

    2016-04-01

    We developed the real-time river stage prediction model, using the hybrid deep neural network and physically based distributed model. As the basic model, 4 layer feed-forward artificial neural network (ANN) was used. As a network training method, the deep learning technique was applied. To optimize the network weight, the stochastic gradient descent method based on the back propagation method was used. As a pre-training method, the denoising autoencoder was used. Input of the ANN model is hourly change of water level and hourly rainfall, output data is water level of downstream station. In general, the desirable input of the ANN has strong correlation with the output. In conceptual hydrological model such as tank model and storage-function model, river discharge is governed by the catchment storage. Therefore, the change of the catchment storage, downstream discharge subtracted from rainfall, can be the potent input candidate of the ANN model instead of rainfall. From this point of view, the hybrid deep neural network and physically based distributed model was developed. The prediction procedure of the hybrid model is as follows; first, downstream discharge was calculated by the distributed model, and then estimates the hourly change of catchment storage form rainfall and calculated discharge as the input of the ANN model, and finally the ANN model was calculated. In the training phase, hourly change of catchment storage can be calculated by the observed rainfall and discharge data. The developed model was applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. The modeled catchment is 695 square km. For the training data, 5 water level gauging station and 14 rain-gauge station in the catchment was used. The training floods, superior 24 events, were selected during the period of 2005-2014. Prediction was made up to 6 hours, and 6 models were developed for each prediction time. To set the proper learning parameters and network

  5. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tao Ma

    2016-10-01

    Full Text Available The development of intrusion detection systems (IDS that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC and deep neural network (DNN algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN, support vector machine (SVM, random forest (RF and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

  6. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

    Science.gov (United States)

    Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

    2016-10-13

    The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

  7. Synchronization of stochastically hybrid coupled neural networks with coupling discrete and distributed time-varying delays

    Institute of Scientific and Technical Information of China (English)

    Tang Yang; Zhong Hui-Huang; Fang Jian-An

    2008-01-01

    A general model of linearly stochastically coupled identical connected neural networks with hybrid coupling is proposed,which is composed of constant coupling,coupling discrete time-varying delay and coupling distributed timevarying delay.All the coupling terms are subjected to stochastic disturbances described in terms of Brownian motion,which reflects a more realistic dynamical behaviour of coupled systems in practice.Based on a simple adaptive feedback controller and stochastic stability theory,several sufficient criteria are presented to ensure the synchronization of linearly stochastically coupled complex networks with coupling mixed time-varying delays.Finally,numerical simulatious illustrated by scale-free complex networks verify the effectiveness of the proposed controllers.

  8. MIMC: Middleware for Identifying & Mitigating Congestion Level in Hybrid Mobile Adhoc Network

    Directory of Open Access Journals (Sweden)

    P. G. Sunitha Hiremath

    2016-11-01

    Full Text Available Adoption of middleware system to solve the congestion problem in mobile ad-hoc network is few to find in the existing system. Research gap is found as existing congestion control mechanism in MANET doesn’t use middleware design and existing middleware system were never investigated for its applicability in congestion control over the mobile ad-hoc network. Therefore, we introduce a novel middleware system called as MIMC or Middleware for Identifying and Mitigating Congestion in Hybrid Mobile Adhoc Network. MIMC is also equipped with novel traf-fic modeling using rule-based control matrix that not only pro-vides a better scenario of congestion but also assists in decision making for routing, which the existing techniques fails. This paper discusses the algorithms, result discussion on multiple scenarios to show MIMC perform better congestion control as compared to existing techniques.

  9. Buffer Management and Hybrid Probability Choice Routing for Packet Delivery in Opportunistic Networks

    Directory of Open Access Journals (Sweden)

    Daru Pan

    2012-01-01

    Full Text Available Due to the features of long connection delays, frequent network partitions, and topology unsteadiness, the design of opportunistic networks faces the challenge of how to effectively deliver data based only on occasional encountering of nodes, where the conventional routing schemes do not work properly. This paper proposes a hybrid probability choice routing protocol with buffer management for opportunistic networks. A delivery probability function is set up based on continuous encounter duration time, which is used for selecting a better node to relay packets. By combining the buffer management utility and the delivery probability, a total utility is used to decide whether the packet should be kept in the buffer or be directly transmitted to the encountering node. Simulation results show that the proposed routing outperforms the existing one in terms of the delivery rate and the average delay.

  10. High Performance Hybrid Two Layer Router Architecture for FPGAs Using Network On Chip

    CERN Document Server

    Ezhumalai, P; Arun, C; Sakthivel, P; Sridharan, D

    2010-01-01

    Networks on Chip is a recent solution paradigm adopted to increase the performance of Multicore designs. The key idea is to interconnect various computation modules (IP cores) in a network fashion and transport packets simultaneously across them, thereby gaining performance. In addition to improving performance by having multiple packets in flight, NoCs also present a host of other advantages including scalability, power efficiency, and component reuse through modular design. This work focuses on design and development of high performance communication architectures for FPGAs using NoCs Once completely developed, the above methodology could be used to augment the current FPGA design flow for implementing multicore SoC applications. We design and implement an NoC framework for FPGAs, MultiClock OnChip Network for Reconfigurable Systems (MoCReS). We propose a novel microarchitecture for a hybrid two layer router that supports both packetswitched communications, across its local and directional ports, as well as...

  11. Behavioral Modeling of a C-Band Ring Hybrid Coupler Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    E. Demircioglu

    2010-12-01

    Full Text Available Artificial Neural Networks (ANNs gained importance on the RF microwave (MW design area and behavioral modeling of MW components in the past few decades. This paper presents a cost effective neural network (NN approach to overcome design, modeling and optimization problems of an 180deg ring hybrid coupler operating in C-Band. The proposed NN model is trained by data sets obtained from electromagnetic (EM simulators and neural test results are compared with simulator findings to determine the network accuracy. Moreover, necessary trade-offs are applied to improve the networks’ performance. Finally correlation factors, which are defined as comparison criteria between EM-simulator and proposed neural models, are calculated for each trade-off case.

  12. A Faster Routing Scheme for Stationary Wireless Sensor Networks - A Hybrid Approach

    CERN Document Server

    Norman, Jasmine; Roja, P Prapoorna; 10.5121/ijasuc.2010.1101

    2010-01-01

    A wireless sensor network consists of light-weight, low power, small size sensor nodes. Routing in wireless sensor networks is a demanding task. This demand has led to a number of routing protocols which efficiently utilize the limited resources available at the sensor nodes. Most of these protocols are either based on single hop routing or multi hop routing and typically find the minimum energy path without addressing other issues such as time delay in delivering a packet, load balancing, and redundancy of data. Response time is very critical in environment monitoring sensor networks where typically the sensors are stationary and transmit data to a base station or a sink node. In this paper a faster load balancing routing protocol based on location with a hybrid approach is proposed.

  13. Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

    Science.gov (United States)

    Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

    2017-08-01

    Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

  14. HOWRAN: An Hybrid Optical Wireless Radio Access Network for WiMAX Antennas Backhauling

    Science.gov (United States)

    Gagnaire, Maurice; Youssef, Tony

    In comparison to existing 3G or 3G+ wireless systems, fourth generation (4G), long-term evolution (LTE) or mobile Wimax are characterized by higher bit rates, highly fluctuant traffic matrices and higher antenna’s density. Current backhauling techniques federating radio antennas are not suited to these new characteristics. Several investigations are carried out for the design of new generation radio access networks (NG-RAN) in charge of concentrating radio cellular traffic from the base stations to the core network. In this paper, we propose an original approach based on an Hybrid Optical Wireless Radio Access Network (HOWRAN) exploiting the benefits of radio-over-fiber technologies and of recent advances in the field of optical devices and systems. As an illustration, we apply the HOWRAN concept to the backhauling of fixed or mobile WiMAX base stations. The two main innovative aspects of HOWRAN are depicted: its hardware architecture and its control plane.

  15. H-MAC: A Hybrid MAC Protocol for Wireless Sensor Networks

    CERN Document Server

    Mehta, S; 10.5121/ijcnc.2010.2208

    2010-01-01

    In this paper, we propose a hybrid medium access control protocol (H-MAC) for wireless sensor networks. It is based on the IEEE 802.11's power saving mechanism (PSM) and slotted aloha, and utilizes multiple slots dynamically to improve performance. Existing MAC protocols for sensor networks reduce energy consumptions by introducing variation in an active/sleep mechanism. But they may not provide energy efficiency in varying traffic conditions as well as they did not address Quality of Service (QoS) issues. H-MAC, the propose MAC protocol maintains energy efficiency as well as QoS issues like latency, throughput, and channel utilization. Our numerical results show that H-MAC has significant improvements in QoS parameters than the existing MAC protocols for sensor networks while consuming comparable amount of energy.

  16. Hybrid information retrieval policies based on cooperative cache in mobile P2P networks

    Institute of Scientific and Technical Information of China (English)

    Quanqing XU; Hengtao SHEN; Zaiben CHEN; Bin CUI; Xiaofang ZHOU; Yafei DAI

    2009-01-01

    The concept of Peer-to-Peer (P2P) has been in-troduced into mobile networks, which has led to the emer-gence of mobile P2P networks, and originated potential ap-plications in many fields. However, mobile P2P networks are subject to the limitations of transmission range, and highly dynamic and unpredictable network topology, giving rise to many new challenges for efficient information retrieval. In this paper, we propose an automatic and economical hybrid information retrieval approach based on cooperative cache. In this method, the region covered by a mobile P2P network is partitioned into subregions, each of which is identified by a unique ID and known to all peers. All the subregions then constitute a mobile Kademlia (MKad) network. The pro-posed hybrid retrieval approach aims to utilize the flooding-based and Distributed Hash Table (DHT)-based schemes in MKad for indexing and searching according to the designed utility functions. To further facilitate information retrieval, we present an effective cache update method by considering all relevant factors. At the same time, the combination of two different methods for cache update is also introduced. One of them is pull based on time stamp including two different pulls: an on-demand pull and a periodical pull, and the other is a push strategy using update records. Furthermore, we provide detailed mathematical analysis on the cache hit ratio of our approach. Simulation experiments in NS-2 showed that the proposed approach is more accurate and efficient than the existing methods.

  17. Wavelet neural networks initialization using hybridized clustering and harmony search algorithm: Application in epileptic seizure detection

    Science.gov (United States)

    Zainuddin, Zarita; Lai, Kee Huong; Ong, Pauline

    2013-04-01

    Artificial neural networks (ANNs) are powerful mathematical models that are used to solve complex real world problems. Wavelet neural networks (WNNs), which were developed based on the wavelet theory, are a variant of ANNs. During the training phase of WNNs, several parameters need to be initialized; including the type of wavelet activation functions, translation vectors, and dilation parameter. The conventional k-means and fuzzy c-means clustering algorithms have been used to select the translation vectors. However, the solution vectors might get trapped at local minima. In this regard, the evolutionary harmony search algorithm, which is capable of searching for near-optimum solution vectors, both locally and globally, is introduced to circumvent this problem. In this paper, the conventional k-means and fuzzy c-means clustering algorithms were hybridized with the metaheuristic harmony search algorithm. In addition to obtaining the estimation of the global minima accurately, these hybridized algorithms also offer more than one solution to a particular problem, since many possible solution vectors can be generated and stored in the harmony memory. To validate the robustness of the proposed WNNs, the real world problem of epileptic seizure detection was presented. The overall classification accuracy from the simulation showed that the hybridized metaheuristic algorithms outperformed the standard k-means and fuzzy c-means clustering algorithms.

  18. Submillimetre Network Formation by Light-induced Hybridization of Zeptomole-level DNA

    Science.gov (United States)

    Iida, Takuya; Nishimura, Yushi; Tamura, Mamoru; Nishida, Keisuke; Ito, Syoji; Tokonami, Shiho

    2016-12-01

    Macroscopic unique self-assembled structures are produced via double-stranded DNA formation (hybridization) as a specific binding essential in biological systems. However, a large amount of complementary DNA molecules are usually required to form an optically observable structure via natural hybridization, and the detection of small amounts of DNA less than femtomole requires complex and time-consuming procedures. Here, we demonstrate the laser-induced acceleration of hybridization between zeptomole-level DNA and DNA-modified nanoparticles (NPs), resulting in the assembly of a submillimetre network-like structure at the desired position with a dramatic spectral modulation within several minutes. The gradual enhancement of light-induced force and convection facilitated the two-dimensional network growth near the air-liquid interface with optical and fluidic symmetry breakdown. The simultaneous microscope observation and local spectroscopy revealed that the assembling process and spectral change are sensitive to the DNA sequence. Our findings establish innovative guiding principles for facile bottom-up production via various biomolecular recognition events.

  19. A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

    Science.gov (United States)

    Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

    2017-01-01

    A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.

  20. Resilient backhaul network design using hybrid radio/free-space optical technology

    KAUST Repository

    Douik, Ahmed

    2016-07-26

    The radio-frequency (RF) technology is a scalable solution for the backhaul planning. However, its performance is limited in terms of data rate and latency. Free Space Optical (FSO) backhaul, on the other hand, offers a higher data rate but is sensitive to weather conditions. To combine the advantages of RF and FSO backhauls, this paper proposes a cost-efficient backhaul network using the hybrid RF/FSO technology. To ensure a resilient backhaul, the paper imposes a given degree of redundancy by connecting each node through K link-disjoint paths so as to cope with potential link failures. Hence, the network planning problem considered in this paper is the one of minimizing the total deployment cost by choosing the appropriate link type, i.e., either hybrid RF/FSO or optical fiber (OF), between each couple of base-stations while guaranteeing K link-disjoint connections, a data rate target, and a reliability threshold. The paper solves the problem using graph theory techniques. It reformulates the problem as a maximum weight clique problem in the planning graph, under a specified realistic assumption about the cost of OF and hybrid RF/FSO links. Simulation results show the cost of the different planning and suggest that the proposed heuristic solution has a close-to-optimal performance for a significant gain in computation complexity. © 2016 IEEE.

  1. A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

    Science.gov (United States)

    Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

    2017-01-01

    A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model. PMID:28120889

  2. Hybrid partial least squares and neural network approach for short-term electrical load forecasting

    Institute of Scientific and Technical Information of China (English)

    Shukang YANG; Ming LU; Huifeng XUE

    2008-01-01

    Intelligent systems and methods such as the neural network (NN) are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares (PLS) and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.

  3. Performance Analysis of Hybrid Distribution in Human-Centric Multimedia Networking

    Institute of Scientific and Technical Information of China (English)

    HU Yuxiang; DONG Fang; LAN Julong

    2016-01-01

    With the booming of Human-centric mul-timedia networking (HMN), there are rising amount of human-made multimedia that needs to distribute to con-sumers with higher speed and efficiency. Hybrid distribu-tion of Client/Server (C/S) and Peer-to-Peer (P2P) have been successfully deployed on the Internet and the practi-cal benefits have been widely reported, while its theoretical performance remains unknown for mass data delivery un-fortunately. This paper presents an analytical and experi-mental study on the performance of accelerating large-scale hybrid distribution over the Internet. In particular, this pa-per focuses on the user behavior in HMN and establishes a user behavior model based on the Kermack-McKendrick model in epidemiology. Analytical expressions of average delay in HMN are then derived based on C/S, P2P and hy-brid distribution, respectively. Our simulation shows how to design and deploy a hybrid distribution system of HMN that helps to bridge the gap between system ultilization and quality of service, which provides direct guidance for practical system design.

  4. AN EFFICIENT APPROACH FOR DVB-H HANDOVER IN DVB-H/UMTS HYBRID NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Liu Cong; Liao Jianxin; Zhu Xiaomin; Zhang Huiyuan; Ni Ping

    2008-01-01

    Handover in Digital Video Broadcasting for Handheids (DVB-H) aims to provide continuous mobile broadcasting services when a user is traveling through cell boundaries. A good handover control can improve the power efficiency and gain much better reception quality. This letter provides a novel approach for DVB-H handover based on DVB-H/Universal Mobile Telecommunications System (UMTS) hybrid network,which moves the main handover function from the terminals to the networks,so that it reduces the operation complexity of the terminals and increases the power saving. When the terminal can not receive the DVB-H signal in the transmission shadow areas or because of some other reasons,the UMTS networks may offer the same service to users to make the service continuous. As the UMTS networks have the topology of the DVB-H networks,by communicating with the terminals,the UMTS networks can help the terminals to predict the handover,and avoid unnecessary handover.

  5. Hybrid Evolutionary Approaches to Maximum Lifetime Routing and Energy Efficiency in Sensor Mesh Networks.

    Science.gov (United States)

    Rahat, Alma A M; Everson, Richard M; Fieldsend, Jonathan E

    2015-01-01

    Mesh network topologies are becoming increasingly popular in battery-powered wireless sensor networks, primarily because of the extension of network range. However, multihop mesh networks suffer from higher energy costs, and the routing strategy employed directly affects the lifetime of nodes with limited energy resources. Hence when planning routes there are trade-offs to be considered between individual and system-wide battery lifetimes. We present a multiobjective routing optimisation approach using hybrid evolutionary algorithms to approximate the optimal trade-off between the minimum lifetime and the average lifetime of nodes in the network. In order to accomplish this combinatorial optimisation rapidly, our approach prunes the search space using k-shortest path pruning and a graph reduction method that finds candidate routes promoting long minimum lifetimes. When arbitrarily many routes from a node to the base station are permitted, optimal routes may be found as the solution to a well-known linear program. We present an evolutionary algorithm that finds good routes when each node is allowed only a small number of paths to the base station. On a real network deployed in the Victoria & Albert Museum, London, these solutions, using only three paths per node, are able to achieve minimum lifetimes of over 99% of the optimum linear program solution's time to first sensor battery failure.

  6. Analysis of physical layer performance of hybrid optical-wireless access network

    Science.gov (United States)

    Shaddad, R. Q.; Mohammad, A. B.; Al-hetar, A. M.

    2011-09-01

    The hybrid optical-wireless access network (HOWAN) is a favorable architecture for next generation access network. It is an optimal combination of an optical backhaul and a wireless front-end for an efficient access network. In this paper, the HOWAN architecture is designed based on a wavelengths division multiplexing/time division multiplexing passive optical network (WDM/TDM PON) at the optical backhaul and a wireless fidelity (WiFi) technology at the wireless front-end. The HOWAN is proposed that can provide blanket coverage of broadband and flexible connection for end-users. Most of the existing works, based on performance evaluation are concerned on network layer aspects. This paper reports physical layer performance in terms of the bit error rate (BER), eye diagram, and signal-to-noise ratio (SNR) of the communication system. It accommodates 8 wavelength channels with 32 optical network unit/wireless access points (ONU/APs). It is demonstrated that downstream and upstream of 2 Gb/s can be achieved by optical backhaul for each wavelength channel along optical fiber length of 20 km and a data rate of 54 Mb/s per ONU/AP along a 50 m outdoor wireless link.

  7. A Fast Hybrid Algorithm of Global Optimization for Feedforward Neural Networks

    Institute of Scientific and Technical Information of China (English)

    JIANG Minghu; ZHANG Bo; ZHU Xiaoyan; JINAG Mingyan

    2001-01-01

    This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks (MLFNN).The effect of inexact line search on conjugacy was studied, based on which a generalized conjugate gradient method was proposed, showing global convergence for error backpagation of MLFNN. It overcomes the drawback of conventional BP and Polak-Ribieve conjugate gradient algorithms that maybe plunge into local minima. The hybrid algorithm's recognition rate is higher than that of Polak-Ribieve algorithm and convergence BP for test data, its training time is less than that of Fletcher-Reeves algorithm and far less than that of convergence BP, and it has a less complicated and stronger robustness to real speech data.

  8. Hybrid Method for the Navigation of Mobile Robot Using Fuzzy Logic and Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Zineb LAOUICI

    2014-11-01

    Full Text Available the aim of this paper is to present a strategy describing a hybrid approach for the navigation of a mobile robot in a partially known environment. The main idea is to combine between fuzzy logic approach suitable for the navigation in an unknown environment and spiking neural networks approach for solving the problem of navigation in a known environment. In the literature, many approaches exist for the navigation purpose, for solving separately the problem in both situations. Our idea is based on the fact that we consider a mixed environment, and try to exploit the known environment parts for improving the path and time of navigation between the starting point and the target. The Simulation results, which are shown on two simulated scenarios, indicate that the hybridization improves the performance of robot navigation with regard to path length and the time of navigation.

  9. Electron beam energy stabilization using a neural network hybrid controller at the Australian Synchrotron Linac.

    Energy Technology Data Exchange (ETDEWEB)

    Meier, E.; Morgan, M. J.; Biedron, S. G.; LeBlanc, G.; Wu, J. (OTD-ESE); (Monash Univ.); (Australian Synchrotron Project); (SLAC National Accelerator Lab.)

    2009-01-01

    This paper describes the implementation of a neural network hybrid controller for energy stabilization at the Australian Synchrotron Linac. The structure of the controller consists of a neural network (NNET) feed forward control, augmented by a conventional Proportional-Integral (PI) feedback controller to ensure stability of the system. The system is provided with past states of the machine in order to predict its future state, and therefore apply appropriate feed forward control. The NNET is able to cancel multiple frequency jitter in real-time. When it is not performing optimally due to jitter changes, the system can successfully be augmented by the PI controller to attenuate the remaining perturbations. With a view to control the energy and bunch length at the FERMI{at}Elettra Free Electron Laser (FEL), the present study considers a neural network hybrid feed forward-feedback type of control to rectify limitations related to feedback systems, such as poor response for high jitter frequencies or limited bandwidth, while ensuring robustness of control. The Australian Synchrotron Linac is equipped with a beam position monitor (BPM), that was provided by Sincrotrone Trieste from a former transport line thus allowing energy measurements and energy control experiments. The present study will consequently focus on correcting energy jitter induced by variations in klystron phase and voltage.

  10. Throughput and Energy Efficiency of a Cooperative Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks

    Directory of Open Access Journals (Sweden)

    Arindam Ghosh

    2013-11-01

    Full Text Available Due to its efficiency, reliability and better channel and resource utilization, cooperative transmission technologies have been attractive options in underwater as well as terrestrial sensor networks. Their performance can be further improved if merged with forward error correction (FEC techniques. In this paper, we propose and analyze a retransmission protocol named Cooperative-Hybrid Automatic Repeat reQuest (C-HARQ for underwater acoustic sensor networks, which exploits both the reliability of cooperative ARQ (CARQ and the efficiency of incremental redundancy-hybrid ARQ (IR-HARQ using rate-compatible punctured convolution (RCPC codes. Extensive Monte Carlo simulations are performed to investigate the performance of the protocol, in terms of both throughput and energy efficiency. The results clearly reveal the enhancement in performance achieved by the C-HARQ protocol, which outperforms both CARQ and conventional stop and wait ARQ (S&W ARQ. Further, using computer simulations, optimum values of various network parameters are estimated so as to extract the best performance out of the C-HARQ protocol.

  11. Hybrid Access Femtocells in Overlaid MIMO Cellular Networks with Transmit Selection under Poisson Field Interference

    KAUST Repository

    Abdel Nabi, Amr A

    2017-09-21

    This paper analyzes the performance of hybrid control-access schemes for small cells (such as femtocells) in the context of two-tier overlaid cellular networks. The proposed hybrid access schemes allow for sharing the same downlink resources between the small-cell network and the original macrocell network, and their mode of operations are characterized considering post-processed signal-to-interference-plus-noise ratios (SINRs) or pre-processed interference-aware operation. The work presents a detailed treatment of achieved performance of a desired user that benefits from MIMO arrays configuration through the use of transmit antenna selection (TAS) and maximal ratio combining (MRC) in the presence of Poisson field interference processes on spatial links. Furthermore, based on the interference awareness at the desired user, two TAS approaches are treated, which are the signal-to-noise (SNR)-based selection and SINR-based selection. The analysis is generalized to address the cases of highly-correlated and un-correlated aggregated interference on different transmit channels. In addition, the effect of delayed TAS due to imperfect feedback and the impact of arbitrary TAS processing are investigated. The analytical results are validated by simulations, to clarify some of the main outcomes herein.

  12. Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

    Directory of Open Access Journals (Sweden)

    Aydin Azizi

    2017-01-01

    Full Text Available Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI techniques. This effort has led the author to propose a novel AI algorithm, which has been named “hybrid AI optimization technique,” to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE and Ring Probabilistic Logic Neural Networks (RPLNN. The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS, and results have been compared with Genetic Algorithm (GA that demonstrates the feasibility of the proposed architecture successfully.

  13. A Dynamic Effective Fault Tolerance System in Robotic Manipulator using a Hybrid Neural Network based Controller

    Directory of Open Access Journals (Sweden)

    G. Jiji

    2014-04-01

    Full Text Available Robot manipulator play important role in the field of automobile industry, mainly it is used in gas welding application and manufacturing and assembling of motor parts. In complex trajectory, on each joint the speed of the robot manipulator is affected. For that reason, it is necessary to analyze the noise and vibration of robot's joints for predicting faults also improve the control precision of robotic manipulator. In this study we will propose a new fault detection system for Robot manipulator. The proposed hybrid fault detection system is designed based on fuzzy support vector machine and Artificial Neural Networks (ANNs. In this system the decouple joints are identified and corrected using fuzzy SVM, here non-linear signal are used for complete process and treatment, the Artificial Neural Networks (ANNs are used to detect the free-swinging and locked joint of the robot, two types of neural predictors are also employed in the proposed adaptive neural network structure. The simulation results of a hybrid controller demonstrate the feasibility and performance of the methodology.

  14. Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rupinder Singh

    2017-01-01

    Full Text Available In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack. For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.

  15. Joint Hybrid Backhaul and Access Links Design in Cloud-Radio Access Networks

    KAUST Repository

    Dhifallah, Oussama Najeeb

    2015-09-06

    The cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio systems. In this paper, we consider the downlink of a CRAN formed of one central processor (the cloud) and several base station (BS), where each BS is connected to the cloud via either a wireless or capacity-limited wireline backhaul link. The paper addresses the joint design of the hybrid backhaul links (i.e., designing the wireline and wireless backhaul connections from the cloud to the BSs) and the access links (i.e., determining the sparse beamforming solution from the BSs to the users). The paper formulates the hybrid backhaul and access link design problem by minimizing the total network power consumption. The paper solves the problem using a two-stage heuristic algorithm. At one stage, the sparse beamforming solution is found using a weighted mixed 11/12 norm minimization approach; the correlation matrix of the quantization noise of the wireline backhaul links is computed using the classical rate-distortion theory. At the second stage, the transmit powers of the wireless backhaul links are found by solving a power minimization problem subject to quality-of-service constraints, based on the principle of conservation of rate by utilizing the rates found in the first stage. Simulation results suggest that the performance of the proposed algorithm approaches the global optimum solution, especially at high signal-to-interference-plus-noise ratio (SINR).

  16. Throughput and energy efficiency of a cooperative hybrid ARQ protocol for underwater acoustic sensor networks.

    Science.gov (United States)

    Ghosh, Arindam; Lee, Jae-Won; Cho, Ho-Shin

    2013-11-08

    Due to its efficiency, reliability and better channel and resource utilization, cooperative transmission technologies have been attractive options in underwater as well as terrestrial sensor networks. Their performance can be further improved if merged with forward error correction (FEC) techniques. In this paper, we propose and analyze a retransmission protocol named Cooperative-Hybrid Automatic Repeat reQuest (C-HARQ) for underwater acoustic sensor networks, which exploits both the reliability of cooperative ARQ (CARQ) and the efficiency of incremental redundancy-hybrid ARQ (IR-HARQ) using rate-compatible punctured convolution (RCPC) codes. Extensive Monte Carlo simulations are performed to investigate the performance of the protocol, in terms of both throughput and energy efficiency. The results clearly reveal the enhancement in performance achieved by the C-HARQ protocol, which outperforms both CARQ and conventional stop and wait ARQ (S&W ARQ). Further, using computer simulations, optimum values of various network parameters are estimated so as to extract the best performance out of the C-HARQ protocol.

  17. Opportunistic Hybrid Transport Protocol (OHTP for Cognitive Radio Ad Hoc Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yousaf Bin Zikria

    2015-12-01

    Full Text Available The inefficient assignment of spectrum for different communications purposes, plus technology enhancements and ever-increasing usage of wireless technology is causing spectrum scarcity. To address this issue, one of the proposed solutions in the literature is to access the spectrum dynamically or opportunistically. Therefore, the concept of cognitive radio appeared, which opens up a new research paradigm. There is extensive research on the physical, medium access control and network layers. The impact of the transport layer on the performance of cognitive radio ad hoc sensor networks is still unknown/unexplored. The Internet’s de facto transport protocol is not well suited to wireless networks because of its congestion control mechanism. We propose an opportunistic hybrid transport protocol for cognitive radio ad hoc sensor networks. We developed a new congestion control mechanism to differentiate true congestion from interruption loss. After such detection and differentiation, we propose methods to handle them opportunistically. There are several benefits to window- and rate-based protocols. To exploit the benefits of both in order to enhance overall system performance, we propose a hybrid transport protocol. We empirically calculate the optimal threshold value to switch between window- and rate-based mechanisms. We then compare our proposed transport protocol to Transmission Control Protocol (TCP-friendly rate control, TCP-friendly rate control for cognitive radio, and TCP-friendly window-based control. We ran an extensive set of simulations in Network Simulator 2. The results indicate that the proposed transport protocol performs better than all the others.

  18. A HYBRID APPROACH FOR NODE CO-OPERATION BASED CLUSTERING IN MOBILE AD HOC NETWORKS

    Directory of Open Access Journals (Sweden)

    C. Sathiyakumar

    2013-01-01

    Full Text Available A Mobile Ad-Hoc Network (MANET is termed as a set of wireless nodes which could be built with infrastructure less environment where network services are afforded by the nodes themselves. In such a situation, if a node refuses to co-operate with other nodes, then it will lead to a considerable diminution in throughput and the network operation decreases to low optimum value. Mobile Ad hoc Networks (MANETs rely on the collaboration of nodes for packet routing ahead. Nevertheless, much of the existing work in MANETs imagines that mobile nodes (probably possessed by selfish users will pursue prearranged protocols without variation. Therefore, implementing the co-operation between the nodes turn out to be an significant issue. The previous work described a secured key model for ad hoc network with efficient node clustering based on reputation and ranking model. But the downside is that the co-operation with the nodes is less results in a communication error. To enhance the security in MANET, in this work, we present a hybrid approach, build a node co-operation among the nodes in MANET by evaluating the weightage of cooperativeness of each node in MANET. With the estimation of normal co-operative nodes, nodes are restructured on its own (self. Then clustering is made with the reorganized nodes to form a secured communication among the nodes in the MANET environment. The Simulation of the proposed Hybrid Approach for Node Cooperation based Clustering (HANCC work is done for varying topology, node size, attack type and intensity with different pause time settings and the performance evaluations are carried over in terms of node cooperativeness, clustering efficiency, communication overhead and compared with an existing secured key model. Compared to an existing secured key model, the proposed HANCC performance is 80-90% high.

  19. Using hybrid angle/distance information for distributed topology control in vehicular sensor networks.

    Science.gov (United States)

    Huang, Chao-Chi; Chiu, Yang-Hung; Wen, Chih-Yu

    2014-10-27

    In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs.

  20. Enzymatically cross-linked tilapia gelatin hydrogels: physical, chemical, and hybrid networks.

    Science.gov (United States)

    Bode, Franziska; da Silva, Marcelo Alves; Drake, Alex F; Ross-Murphy, Simon B; Dreiss, Cécile A

    2011-10-10

    This Article investigates different types of networks formed from tilapia fish gelatin (10% w/w) in the presence and absence of the enzymatic cross-linker microbial transglutaminase. The influence of the temperature protocol and cross-linker concentration (0-55 U mTGase/g gelatin) was examined in physical, chemical, and hybrid gels, where physical gels arise from the formation of triple helices that act as junction points when the gels are cooled below the gelation point. A combination of rheology and optical rotation was used to study the evolution of the storage modulus (G') over time and the number of triple helices formed for each type of gel. We attempted to separate the final storage modulus of the gels into its chemical and physical contributions to examine the existence or otherwise of synergism between the two types of networks. Our experiments show that the gel characteristics vary widely with the thermal protocol. The final storage modulus in chemical gels increased with enzyme concentration, possibly due to the preferential formation of closed loops at low cross-linker amount. In chemical-physical gels, where the physical network (helices) was formed consecutively to the covalent one, we found that below a critical enzyme concentration the more extensive the chemical network is (as measured by G'), the weaker the final gel is. The storage modulus attributed to the physical network decreased exponentially as a function of G' from the chemical network, but both networks were found to be purely additive. Helices were not thermally stabilized. The simultaneous formation of physical and chemical networks (physical-co-chemical) resulted in G' values higher than the individual networks formed under the same conditions. Two regimes were distinguished: at low enzyme concentration (10-20 U mTGase/g gelatin), the networks were formed in series, but the storage modulus from the chemical network was higher in the presence of helices (compared to pure chemical gels

  1. Cognitive and Social Information Processing of Children in Violent Families.

    Science.gov (United States)

    Rossman, B. B. Robbie; And Others

    While once thought to be oblivious to parental violence, child witnesses to parental violence are now considered to be at risk as victims of both chronic trauma and psychological maltreatment. The purpose of this study was to examine the relationships among childrens' parental violence history, cognitive skills, processing of social information,…

  2. Attachment and the Processing of Social Information in Adolescence

    Science.gov (United States)

    Dykas, Matthew J.; Cassidy, Jude

    2007-01-01

    A key proposition of attachment theory is that experience-based cognitive representations of attachment, often referred to as internal working models of attachment, influence the manner in which individuals process attachment-relevant social information (Bowlby, 1969/1982, 1973, 1980; Bretherton & Munholland, 1999; Main, Kaplan, & Cassidy, 1985).…

  3. Performance of the hybrid wireless mesh protocol for wireless mesh networks

    DEFF Research Database (Denmark)

    Boye, Magnus; Staalhagen, Lars

    2010-01-01

    . These challenges must first be overcome before satisfactory network stability and throughput can be achieved. This paper studies the performance of the Hybrid Wireless Mesh Protocol, the proposed routing protocol for the upcoming IEEE 802.11s standard. HWMP supports two modes of path selection: reactive...... and proactive. Two scenarios of different node density are considered for both path selection modes. The results presented in this paper are based on a simulation model of the HWMP specification in the IEEE 802.11s draft 4.0 implemented in OPNET Modeler....

  4. A hybrid medium access control for convergence of broadband wireless and wireline ATM networks

    DEFF Research Database (Denmark)

    Liu, Hong; Gliese, Ulrik Bo; Dittmann, Lars

    2000-01-01

    of contention, reservation and polling access techniques based on the dynamic TDMA system. Extensive simulation results using realistic data traffic sources, show that the proposed medium access scheme may provide QoS guarantees to different ATM traffic including the realistic MPEG video traces with low cell......In this paper, we propose a hybrid medium access control protocol for supporting broadband integrated services in the wireless ATM networks. The integrated services include CBR, VBR and ABR traffic varying from low bit-rate to very high bit-rate. The proposed protocol is an excellent compromise...

  5. Curing Kinetics of Hybrid Networks Composed of Benzoxazine and Multifunctional Novolac Epoxy

    Directory of Open Access Journals (Sweden)

    Wu Ke

    2015-01-01

    Full Text Available A novel hybrid network composed of benzoxazines (BZ and novolac epoxy resin (F-51 was prepared successfully. Thermal properties, curing kinetics, and decomposition process were studied using isothermal differential scanning calorimetry (DSC and thermogravimetric analysis (TGA in this paper. The reactive mechanism of F-51/BZ mixture system is different from the BZ homopolymers at low temperatures; two resin systems follow the autocatalytic model mainly at high temperatures. Thermogravimetric analysis indicates that F-51 can have no significant effect on thermal degradation temperatures and on increasing char yield.

  6. ANOMALY INTRUSION DETECTION DESIGN USING HYBRID OF UNSUPERVISED AND SUPERVISED NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    M. Bahrololum

    2009-07-01

    Full Text Available This paper proposed a new approach to design the system using a hybrid of misuse and anomalydetection for training of normal and attack packets respectively. The utilized method for attack training isthe combination of unsupervised and supervised Neural Network (NN for Intrusion Detection System. Bythe unsupervised NN based on Self Organizing Map (SOM, attacks will be classified into smallercategories considering their similar features, and then unsupervised NN based on Backpropagation willbe used for clustering. By misuse approach known packets would be identified fast and unknown attackswill be able to detect by this method.

  7. A Hybrid Model of a Genetic Regulatory Network in Mammalian Sclera

    Directory of Open Access Journals (Sweden)

    Qin Shu

    2013-08-01

    Full Text Available Myopia in human and animals is caused by the axial elongation of the eye and is closely linked to the thinning of the sclera which supports the eye tissue. This thinning has been correlated with the overproduction of matrix metalloproteinase (MMP-2, an enzyme that degrades the collagen structure of the sclera. In this short paper, we propose a descriptive model of a regulatory network with hysteresis, which seems necessary for creating oscillatory behavior in the hybrid model between MMP-2, MT1-MMP and TIMP-2. Numerical results provide insight on the type of equilibria present in the system.

  8. Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks

    Science.gov (United States)

    Jung, Young-Ho; Choi, Jihoon

    2017-01-01

    A new hybrid automatic repeat request (HARQ) scheme for multicast service for wireless sensor networks is proposed in this study. In the proposed algorithm, the HARQ operation is combined with an autonomous retransmission method that ensure a data packet is transmitted irrespective of whether or not the packet is successfully decoded at the receivers. The optimal number of autonomous retransmissions is determined to ensure maximum spectral efficiency, and a practical method that adjusts the number of autonomous retransmissions for realistic conditions is developed. Simulation results show that the proposed method achieves higher spectral efficiency than existing HARQ techniques. PMID:28245604

  9. Application of hybrid coded genetic algorithm in fuzzy neural network controller

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and bi nary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during the crossover operation and decimal encoding during the mutation operation, and the way of accepting new individuals by probability adopted, by which a new individual is accepted and its parent is discarded when its fitness is higher than that of its parent, and a new individual is accepted by probability when its fitness is lower than that of its parent. And concludes with calculations made with an example that these improvements enhance the speed of genetic algorithms to optimize the fuzzy neural network controller.

  10. Max-min energy-aware hybrid routing protocol for ad hoc networks

    Science.gov (United States)

    Wu, Shaochuan; Wang, Changhong; Bai, Xu

    2009-12-01

    Max-min energy-aware AODV and OLSR hybrid routing (MEAOHR) protocol aims at prolonging the lifetime of network for AOHR protocol. With a little modification to the AODV protocol part of AOHR protocol, MEAOHR protocol can provide minimal energy information of every routing to destination nodes and source nodes by RREQ packets and RREP packets respectively. In this way, destination nodes and source nodes can choose a routing respectively with the max-min energy value among all routings as the path for packet delivery. Simulation results and analysis prove that MEAOHR protocol can effectively provide longer network's lifetime and steadier end-to-end delay without any performance loss compared to AOHR protocol.

  11. Effects of Photovoltaic and Fuel Cell Hybrid System on Distribution Network Considering the Voltage Limits

    Directory of Open Access Journals (Sweden)

    ABYANEH, H. A.

    2010-11-01

    Full Text Available Development of distribution network and power consumption growth, increase voltage drop on the line impedance and therefore voltage drop in system buses. In some cases consumption is so high that voltage in some buses exceed from standard. In this paper, effect of the fuel cell and photovoltaic hybrid system on distribution network for solving expressed problem is studied. For determining the capacity of each distributed generation source, voltage limitation on the bus voltages under different conditions is considered. Simulation is done by using DIgSILENT software on the part of the 20 kV real life Sirjan distribution system. In this article, optimum location with regard to system and environmental conditions are studied in two different viewpoints.

  12. Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks

    Directory of Open Access Journals (Sweden)

    Bin Xu

    2015-01-01

    Full Text Available The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. Then, by adopting the best evolving strategy in HSCDA, a Multiobjective Community Detection Algorithm (MCDA based on kernel k-means (KKM and ratio cut (RC objective functions is proposed which efficiently make use of recommendation of strategy by statistical self-adaptive learning framework, thus assisting the process of community detection. Experimental results on artificial and real networks show that the proposed algorithms achieve a better performance compared with similar state-of-the-art approaches.

  13. Synchronization for an array of neural networks with hybrid coupling by a novel pinning control strategy.

    Science.gov (United States)

    Gong, Dawei; Lewis, Frank L; Wang, Liping; Xu, Ke

    2016-05-01

    In this paper, a novel pinning synchronization (synchronization with pinning control) scheme for an array of neural networks with hybrid coupling is investigated. The main contributions are as follows: (1) A novel pinning control strategy is proposed for the first time. Pinning control schemes are introduced as an array of column vector. The controllers are designed as simple linear systems, which are easy to be analyzed or tested. (2) Augmented Lyapunov-Krasovskii functional (LKF) is applied to introduce more relax variables, which can alleviate the requirements of the positive definiteness of the matrix. (3) Based on the appropriate LKF, by introducing some free weighting matrices, some novel synchronization criteria are derived. Furthermore, the proposed pinning control scheme described by column vector can also be expanded to almost all the other array of neural networks. Finally, numerical examples are provided to show the effectiveness of the proposed results.

  14. Hybrid heuristic and mathematical programming in oil pipelines networks: Use of immigrants

    Institute of Scientific and Technical Information of China (English)

    DE LA CRUZ J.M.; HERR(A)N-GONZ(A)LEZ A.; RISCO-MART(I)N J.L.; ANDR(E)S-TORO B.

    2005-01-01

    We solve the problem of petroleum products distribution through oil pipelines networks. This problem is modelled and solved using two techniques: A heuristic method like a multiobjective evolutionary algorithm and Mathematical Programming. In the multiobjective evolutionary algorithm, several objective functions are defined to express the goals of the solutions as well as the preferences among them. Some constraints are included as hard objective functions and some are evaluated through a repairing function to avoid infeasible solutions. In the Mathematical Programming approach the multiobjective optimization is solved using the Constraint Method in Mixed Integer Linear Programming. Some constraints of the mathematical model are nonlinear, so they are linearized. The results obtained with both methods for one concrete network are presented. They are compared with a hybrid solution, where we use the results obtained by Mathematical Programming as the seed of the evolutionary algorithm.

  15. Cutting force signal pattern recognition using hybrid neural network in end milling

    Institute of Scientific and Technical Information of China (English)

    Song-Tae SEONG; Ko-Tae JO; Young-Moon LEE

    2009-01-01

    Under certain cutting conditions in end milling, the signs of cutting forces change from positive to negative during a revolution of the tool. The change of force direction causes the cutting dynamics to be unstable which results in chatter vibration. Therefore, cutting force signal monitoring and classification are needed to determine the optimal cutting conditions and to improve the efficiency of cut. Artificial neural networks are powerful tools for solving highly complex and nonlinear problems. It can be divided into supervised and unsupervised learning machines based on the availability of a teacher. Hybrid neural network was introduced with both of functions of multilayer perceptron (MLP) trained with the back-propagation algorithm for monitoring and detecting abnormal state, and self organizing feature map (SOFM) for treating huge datum such as image processing and pattern recognition, for predicting and classifying cutting force signal patterns simultaneously. The validity of the results is verified with cutting experiments and simulation tests.

  16. A hybrid Evolutionary Functional Link Artificial Neural Network for Data mining and Classification

    Directory of Open Access Journals (Sweden)

    Faissal MILI

    2012-08-01

    Full Text Available This paper presents a specific structure of neural network as the functional link artificial neural network (FLANN. This technique has been employed for classification tasks of data mining. In fact, there are a few studies that used this tool for solving classification problems. In this present research, we propose a hybrid FLANN (HFLANN model, where the optimization process is performed using 3 known population based techniques such as genetic algorithms, particle swarm and differential evolution. This model will be empirically compared to FLANN based back-propagation algorithm and to others classifiers as decision tree, multilayer perceptron based back-propagation algorithm, radical basic function, support vector machine, and K-nearest Neighbor. Our results proved that the proposed model outperforms the other single model. (Abstract

  17. Hybrid fuzzy charged system search algorithm based state estimation in distribution networks

    Directory of Open Access Journals (Sweden)

    Sachidananda Prasad

    2017-06-01

    Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.

  18. Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

    Science.gov (United States)

    Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

    2015-10-01

    Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

  19. HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

    Directory of Open Access Journals (Sweden)

    Giane STUART

    1997-12-01

    Full Text Available This work presents the results of a Hybrid Neural Network (HNN technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.Neste trabalho são apresentados os resultados obtidos na modelagem da extração supercrítica de óleo essencial de alfavaca e alecrim usando uma rede híbrida neuronal. Utilizou-se uma rede híbrida na configuração em série para estimar os parâmetros do modelo fenomenológico empregado para descrever o processo de extração, o modelo de Sovová. Um pequeno conjunto de dados experimentais, para cada matriz vegetal, foi usado para gerar um conjunto estendido de dados, suficiente para a etapa de treinamento da rede. A validação da presente proposta foi efetuada através da comparação entre os resultados preditos e aqueles obtidos experimentalmente que não constaram do processo de treinamento da rede. Demonstra-se que a rede híbrida neuronal correlaciona e prediz satisfatoriamente os dados experimentais, mostrando-se portanto promissora no campo da modelagem do processo de extração supercrítica.

  20. A Software Framework for Rapid Application-Specific Hybrid Photonic Network-on-Chip Synthesis

    Directory of Open Access Journals (Sweden)

    Shirish Bahirat

    2016-05-01

    Full Text Available Network on Chip (NoC architectures have emerged in recent years as scalable communication fabrics to enable high bandwidth data transfers in chip multiprocessors (CMPs. These interconnection architectures still need to conquer many challenges, e.g., significant power consumption and high data transfer latencies. Hybrid electro-photonic NoCs have been recently proposed as a solution to mitigate some of these challenges. However, with increasing application complexity, hardware dependencies, and performance variability, optimization of hybrid photonic NoCs requires traversing a massive design space. To date, prior work on software tools for rapid automated NoC synthesis have mainly focused on electrical NoCs. In this article, we propose a novel suite of software tools for effectively synthesizing hybrid photonic NoCs. We formulate and solve the synthesis problem using four search-based optimization heuristics: (1 Ant Colony Optimization (ACO; (2 Particle Swarm Optimization (PSO; (3 Genetic Algorithm (GA; and (4 Simulated Annealing (SA. Our experimental results show significant promise for the ACO and PSO based heuristics. Our novel implementation of PSO achieves an average of 64% energy-delay product improvements over GA and 53% improvement over SA; while our novel ACO implementation achieves 107% energy-delay product improvements over GA and 62% improvement over SA.

  1. A hybrid analytical network process and fuzzy goal programming for supplier selection: A case study of auto part maker

    OpenAIRE

    Hesam Zande Hesami; Mohammad Ali Afshari; Seyed Ali Ayazi; Javad Siahkali Moradi

    2011-01-01

    The aim of this research is to present a hybrid model to select auto part suppliers. The proposed method of this paper uses factor analysis to find the most influencing factors on part maker selection and the results are validated using different statistical tests such as Cronbach's Alpha and Kaiser-Meyer.The hybrid model uses analytical network process to rank different part maker suppliers and fuzzy goal programming to choose the appropriate alternative among various choices. The implementa...

  2. An efficient hybrid protection scheme with shared/dedicated backup paths on elastic optical networks

    Directory of Open Access Journals (Sweden)

    Nogbou G. Anoh

    2017-02-01

    Full Text Available Fast recovery and minimum utilization of resources are the two main criteria for determining the protection scheme quality. We address the problem of providing a hybrid protection approach on elastic optical networks under contiguity and continuity of available spectrum constraints. Two main hypotheses are used in this paper for backup paths computation. In the first case, it is assumed that backup paths resources are dedicated. In the second case, the assumption is that backup paths resources are available shared resources. The objective of the study is to minimize spectrum utilization to reduce blocking probability on a network. For this purpose, an efficient survivable Hybrid Protection Lightpath (HybPL algorithm is proposed for providing shared or dedicated backup path protection based on the efficient energy calculation and resource availability. Traditional First-Fit and Best-Fit schemes are employed to search and assign the available spectrum resources. The simulation results show that HybPL presents better performance in terms of blocking probability, compared with the Minimum Resources Utilization Dedicated Protection (MRU-DP algorithm which offers better performance than the Dedicated Protection (DP algorithm.

  3. An energy efficient hybrid interference-resilient frame fragmentation for wireless sensor networks

    KAUST Repository

    Meer, Ammar M.

    2015-08-30

    Frame fragmentation into small blocks with dedicated error detection codes per block can reduce the unnecessary retransmission of the correctly received blocks. However, the optimal block size varies based on the wireless channel conditions. Further, blocks within a single frame may have different optimal sizes based on variations in interference patterns. This paper proposes a hybrid interference-resilient frame fragmentation (Hi-Frag) link-layer scheme for wireless sensor networks. It effectively addresses the challenges associated with dynamic partitioning of blocks while accounting for the observed error patterns. Hi-Frag is the first work to introduce an adaptive frame fragmentation scheme with hybrid block sizing, implemented and evaluated on a real WSN testbed. Hi-Frag shows substantial enhancements over fixed-size partial packet recovery protocols, achieving up to 2.5× improvement in throughput when the channel condition is noisy, while reducing network delays by up to 14% of the observed delay. On average, Hi-Frag shows 35% gain in throughput compared to static fragmentation approaches across all channel conditions used in our experiments. Also, Hi-Frag lowers the energy consumed per useful bit by 66% on average compared to conventional protocols, which increases the energy efficiency.

  4. Time-series analysis with a hybrid Box-Jenkins ARIMA and neural network model

    Institute of Scientific and Technical Information of China (English)

    Dilli R Aryal; WANG Yao-wu(王要武)

    2004-01-01

    Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades.More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model's unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

  5. Fiber sensor network with multipoint sensing using double-pass hybrid LPFG-FBG sensor configuration

    Science.gov (United States)

    Yong, Yun-Thung; Lee, Sheng-Chyan; Rahman, Faidz Abd

    2017-03-01

    This is a study on double-pass intensity-based hybrid Long Period Fiber Grating (LPFG)and Fiber Bragg Grating (FBG) sensor configuration where a fiber sensor network was constructed with multiple sensing capability. The sensing principle is based on interrogation of intensity changes of the reflected signal from an FBG caused by the LPFG spectral response to the surrounding perturbations. The sensor network developed was tested in monitoring diesel adulteration of up to a distance of 8 km. Kerosene concentration from 0% to 50% was added as adulterant into diesel. The sensitivity of the double-pass hybrid LPFG-FBG sensor over multiple points was>0.21 dB/% (for adulteration range of 0-30%) and >0.45 dB/% from 30% to 50% adulteration. It is found that the sensitivity can drop up to 35% when the fiber length increased from 0 km to 8 km (for the case of adulteration of 0-30%). With the multiple sensing capabilities, normalized FBG's reflected power can be demodulated at the same time for comparison of sensitivity performance across various fiber sensors.

  6. Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems

    Energy Technology Data Exchange (ETDEWEB)

    Kim, K.H. [Kangwon National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Park, J.K. [Seoul National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Hwang, K.J. [Univ. of Ulsan (Korea, Republic of). Dept. of Electrical Engineering; Kim, S.H. [Korea Electric Power Co., Seoul (Korea, Republic of). Power System Control Dept.

    1995-08-01

    In this paper, a hybrid model for short-term load forecast that integrates artificial neural networks and fuzzy expert systems is presented. The forecasted load is obtained by passing through two steps. In the first procedure, the artificial neural networks are trained with the load patterns corresponding to the forecasting hour, and the provisional forecasted load is obtained by the trained artificial neural networks. In the second procedure, the fuzzy expert systems modify the provisional forecasted load considering the possibility of load variation due to changes in temperature and the load behavior of holiday. In the test case of 1994 for implementation in short term load forecasting expert system of Korea Electric Power Corporation (KEPCO), the proposed hybrid model provided good forecasting accuracy of the mean absolute percentage errors below 1.3%. The comparison results with exponential smoothing method showed the efficiency and accuracy of the hybrid model.

  7. Hybrid RF and Digital Beamformer for Cellular Networks: Algorithms, Microwave Architectures, and Measurements

    Science.gov (United States)

    Venkateswaran, Vijay; Pivit, Florian; Guan, Lei

    2016-07-01

    Modern wireless communication networks, particularly cellular networks utilize multiple antennas to improve the capacity and signal coverage. In these systems, typically an active transceiver is connected to each antenna. However, this one-to-one mapping between transceivers and antennas will dramatically increase the cost and complexity of a large phased antenna array system. In this paper, firstly we propose a \\emph{partially adaptive} beamformer architecture where a reduced number of transceivers with a digital beamformer (DBF) is connected to an increased number of antennas through an RF beamforming network (RFBN). Then, based on the proposed architecture, we present a methodology to derive the minimum number of transceivers that are required for marco-cell and small-cell base stations, respectively. Subsequently, in order to achieve optimal beampatterns with given cellular standard requirements and RF operational constraints, we propose efficient algorithms to jointly design DBF and RFBN. Starting from the proposed algorithms, we specify generic microwave RFBNs for optimal marco-cell and small-cell networks. In order to verify the proposed approaches, we compare the performance of RFBN using simulations and anechoic chamber measurements. Experimental measurement results confirm the robustness and performance of the proposed hybrid DBF-RFBN concept eventually ensuring that theoretical multi-antenna capacity and coverage are achieved at a little incremental cost.

  8. Controlling chemical dosing for sulfide mitigation in sewer networks using a hybrid automata control strategy.

    Science.gov (United States)

    Liu, Yiqi; Ganigué, Ramon; Sharma, Keshab; Yuan, Zhiguo

    2013-01-01

    Chemicals such as magnesium hydroxide (Mg(OH)2) and iron salts are widely used to control sulfide-induced corrosion in sewer networks composed of interconnected sewer pipe lines and pumping stations. Chemical dosing control is usually non-automatic and based on experience, thus often resulting in sewage reaching the discharge point receiving inadequate or even no chemical dosing. Moreover, intermittent operation of pumping stations makes traditional control theory inadequate. A hybrid automata-based (HA-based) control method is proposed in this paper to coordinate sewage pumping station operations by considering their states, thereby ensuring suitable chemical concentrations in the network discharge. The performance of the proposed control method was validated through a simulation study of a real sewer network using real sewage flow data. The physical, chemical and biological processes were simulated using the well-established SeweX model. The results suggested that the HA-based control strategy significantly improved chemical dosing control performance and sulfide mitigation in sewer networks, compared to the current common practice.

  9. New Downlink Scheduling Framework for Hybrid Unicast and Multicast Traffic in WiMAX Networks

    Directory of Open Access Journals (Sweden)

    Rashid Karimi

    2012-10-01

    Full Text Available WiMAX networks based on IEEE 802.16 standard has expedited broadband wireless access surge in recent years. The traffic in these networks is identified in four types of class of service with different QoS requirements. Therefore, scheduling mechanism to manage these services in order to meet QoS requirements is a crucial fact and an important challenge. In this paper, for PMP mode of WiMAX networks, a two-level scheduling mechanism in MAC layer of Base Station (BS has been proposed. The proposed scheduling algorithm takes into account hybrid unicast and multicast downlink traffic including three classes of service: rtps, nrtps and BE. In the first level of this scheduling mechanism, we have used the scheduling algorithms WRR and FCFS to schedule the connections and in its second level, the PQ algorithm based on Aging method is used to manage and schedule the packets. The functionality of the proposed scheduling algorithm is compared with priority queuing (PQ algorithm. The resulting outcome of simulation shows that the proposed design has quite a better performance for Best Effort (BE service class. Furthermore the delay of the rtps class and total throughput of the network is increased noticeably

  10. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.

    Science.gov (United States)

    Ramadan Suleiman, Ahmed; Nehdi, Moncef L

    2017-02-07

    This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

  11. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Ahmed Ramadan Suleiman

    2017-02-01

    Full Text Available This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN. A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c, type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components on the self-healing performance in cement-based materials.

  12. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

    Science.gov (United States)

    Ramadan Suleiman, Ahmed; Nehdi, Moncef L.

    2017-01-01

    This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials. PMID:28772495

  13. Path-integral methods for analyzing the effects of fluctuations in stochastic hybrid neural networks.

    Science.gov (United States)

    Bressloff, Paul C

    2015-01-01

    We consider applications of path-integral methods to the analysis of a stochastic hybrid model representing a network of synaptically coupled spiking neuronal populations. The state of each local population is described in terms of two stochastic variables, a continuous synaptic variable and a discrete activity variable. The synaptic variables evolve according to piecewise-deterministic dynamics describing, at the population level, synapses driven by spiking activity. The dynamical equations for the synaptic currents are only valid between jumps in spiking activity, and the latter are described by a jump Markov process whose transition rates depend on the synaptic variables. We assume a separation of time scales between fast spiking dynamics with time constant [Formula: see text] and slower synaptic dynamics with time constant τ. This naturally introduces a small positive parameter [Formula: see text], which can be used to develop various asymptotic expansions of the corresponding path-integral representation of the stochastic dynamics. First, we derive a variational principle for maximum-likelihood paths of escape from a metastable state (large deviations in the small noise limit [Formula: see text]). We then show how the path integral provides an efficient method for obtaining a diffusion approximation of the hybrid system for small ϵ. The resulting Langevin equation can be used to analyze the effects of fluctuations within the basin of attraction of a metastable state, that is, ignoring the effects of large deviations. We illustrate this by using the Langevin approximation to analyze the effects of intrinsic noise on pattern formation in a spatially structured hybrid network. In particular, we show how noise enlarges the parameter regime over which patterns occur, in an analogous fashion to PDEs. Finally, we carry out a [Formula: see text]-loop expansion of the path integral, and use this to derive corrections to voltage-based mean-field equations, analogous

  14. ISART: A Generic Framework for Searching Books with Social Information

    Science.gov (United States)

    Cui, Xiao-Ping; Qu, Jiao; Geng, Bin; Zhou, Fang; Song, Li; Hao, Hong-Wei

    2016-01-01

    Effective book search has been discussed for decades and is still future-proof in areas as diverse as computer science, informatics, e-commerce and even culture and arts. A variety of social information contents (e.g, ratings, tags and reviews) emerge with the huge number of books on the Web, but how they are utilized for searching and finding books is seldom investigated. Here we develop an Integrated Search And Recommendation Technology (IsArt), which breaks new ground by providing a generic framework for searching books with rich social information. IsArt comprises a search engine to rank books with book contents and professional metadata, a Generalized Content-based Filtering model to thereafter rerank books with user-generated social contents, and a learning-to-rank technique to finally combine a wide range of diverse reranking results. Experiments show that this technology permits embedding social information to promote book search effectiveness, and IsArt, by making use of it, has the best performance on CLEF/INEX Social Book Search Evaluation datasets of all 4 years (from 2011 to 2014), compared with some other state-of-the-art methods. PMID:26863545

  15. ISART: A Generic Framework for Searching Books with Social Information.

    Science.gov (United States)

    Yin, Xu-Cheng; Zhang, Bo-Wen; Cui, Xiao-Ping; Qu, Jiao; Geng, Bin; Zhou, Fang; Song, Li; Hao, Hong-Wei

    2016-01-01

    Effective book search has been discussed for decades and is still future-proof in areas as diverse as computer science, informatics, e-commerce and even culture and arts. A variety of social information contents (e.g, ratings, tags and reviews) emerge with the huge number of books on the Web, but how they are utilized for searching and finding books is seldom investigated. Here we develop an Integrated Search And Recommendation Technology (IsArt), which breaks new ground by providing a generic framework for searching books with rich social information. IsArt comprises a search engine to rank books with book contents and professional metadata, a Generalized Content-based Filtering model to thereafter rerank books with user-generated social contents, and a learning-to-rank technique to finally combine a wide range of diverse reranking results. Experiments show that this technology permits embedding social information to promote book search effectiveness, and IsArt, by making use of it, has the best performance on CLEF/INEX Social Book Search Evaluation datasets of all 4 years (from 2011 to 2014), compared with some other state-of-the-art methods.

  16. Hybrid information privacy system: integration of chaotic neural network and RSA coding

    Science.gov (United States)

    Hsu, Ming-Kai; Willey, Jeff; Lee, Ting N.; Szu, Harold H.

    2005-03-01

    Electronic mails are adopted worldwide; most are easily hacked by hackers. In this paper, we purposed a free, fast and convenient hybrid privacy system to protect email communication. The privacy system is implemented by combining private security RSA algorithm with specific chaos neural network encryption process. The receiver can decrypt received email as long as it can reproduce the specified chaos neural network series, so called spatial-temporal keys. The chaotic typing and initial seed value of chaos neural network series, encrypted by the RSA algorithm, can reproduce spatial-temporal keys. The encrypted chaotic typing and initial seed value are hidden in watermark mixed nonlinearly with message media, wrapped with convolution error correction codes for wireless 3rd generation cellular phones. The message media can be an arbitrary image. The pattern noise has to be considered during transmission and it could affect/change the spatial-temporal keys. Since any change/modification on chaotic typing or initial seed value of chaos neural network series is not acceptable, the RSA codec system must be robust and fault-tolerant via wireless channel. The robust and fault-tolerant properties of chaos neural networks (CNN) were proved by a field theory of Associative Memory by Szu in 1997. The 1-D chaos generating nodes from the logistic map having arbitrarily negative slope a = p/q generating the N-shaped sigmoid was given first by Szu in 1992. In this paper, we simulated the robust and fault-tolerance properties of CNN under additive noise and pattern noise. We also implement a private version of RSA coding and chaos encryption process on messages.

  17. Financial Time Series Modelling with Hybrid Model Based on Customized RBF Neural Network Combined With Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Lukas Falat

    2014-01-01

    Full Text Available In this paper, authors apply feed-forward artificial neural network (ANN of RBF type into the process of modelling and forecasting the future value of USD/CAD time series. Authors test the customized version of the RBF and add the evolutionary approach into it. They also combine the standard algorithm for adapting weights in neural network with an unsupervised clustering algorithm called K-means. Finally, authors suggest the new hybrid model as a combination of a standard ANN and a moving average for error modeling that is used to enhance the outputs of the network using the error part of the original RBF. Using high-frequency data, they examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, authors perform the comparative out-of-sample analysis of the suggested hybrid model with statistical models and the standard neural network.

  18. An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate

    Directory of Open Access Journals (Sweden)

    Milad Jajarmizadeh

    2014-01-01

    Full Text Available Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear relationships using neural networks. In this study, a hybrid network presented as a feedforward modular neural network (FF-MNN has been developed to predict the daily rainfall-runoff of the Roodan watershed at the southern part of Iran. This FF-MNN has three layers—input, hidden, and output. The hidden layer has two types of neural expert or module. Hydrometeorological data of the catchment were collected for 21 years. Heuristic method was used to develop the MNN for exploring daily flow generalization. Two training algorithms, namely, backpropagation with momentum and Levenberg-Marquardt, were used. Sigmoid and linear transfer functions were employed to explore the network’s optimum behavior. Cross-validation and predictive uncertainty assessments were carried out to protect overtiring and overparameterization, respectively. Results showed that the FF-MNN could satisfactorily predict stream flow during testing period. The Nash-Sutcliff coefficient, coefficient of determination, and root mean square error obtained using MNN during training and test periods were 0.85, 0.85, and 39.4 and 0.57, 0.58, and 32.2, respectively. The predictive uncertainties for both periods were 0.39 and 0.44, respectively. Generally, the study showed that the FF-MNN can give promising prediction for rainfall-runoff relations.

  19. Hybrid swarm intelligence optimization approach for optimal data storage position identification in wireless sensor networks.

    Science.gov (United States)

    Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

    2015-01-01

    The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.

  20. iBole:A Hybrid Multi-Layer Architecture for Doctor Recommendation in Medical Social Networks

    Institute of Scientific and Technical Information of China (English)

    宫继兵; 王立; 孙胜涛; 彭思维

    2015-01-01

    In this paper, we try to systematically study how to perform doctor recommendation in medical social net-works (MSNs). Specifically, employing a real-world medical dataset as the source in our work, we propose iBole, a novel hybrid multi-layer architecture, to solve this problem. First, we mine doctor-patient relationships/ties via a time-constraint probability factor graph model (TPFG). Second, we extract network features for ranking nodes. Finally, we propose RWR-Model, a doctor recommendation model via the random walk with restart method. Our real-world experiments validate the effectiveness of the proposed methods. Experimental results show that we obtain good accuracy in mining doctor-patient relationships from the network, and the doctor recommendation performance is better than that of the baseline algorithms:traditional Ranking SVM (RSVM) and the individual doctor recommendation model (IDR-Model). The results of our RWR-Model are more reasonable and satisfactory than those of the baseline approaches.

  1. k-CONNECTED HYBRID RELAY NODE PLACEMENT IN WIRELESS SENSOR NETWORK FOR RESTORING CONNECTIVITY

    Directory of Open Access Journals (Sweden)

    Vijayvignesh Selvaraj

    2014-06-01

    Full Text Available Wireless Sensor Network (WSN consists of a number of sensor nodes for monitoring the environment. Scenario like floods, volcanic eruptions, earthquakes, tsunamis, avalanches, hailstorms and blizzards causes the sensor nodes to be damaged. In such worst case scenario, the deployed nodes in the monitoring area may split up into several segments. As a result sensor nodes in the network cannot communicate with each other due to partitions. Our algorithm investigates a strategy for restoring such kind of damage through either placement of Relay Nodes (RN’s or repositioning the existing nodes in the network. Unlike traditional schemes like minimum spanning tree, our proposed approach generates a different topology called as spider web. In this approach, both stationary and mobile relay nodes are used. Thus we are making our topology as a hybrid one. Though the numbers of relay nodes are increased, the robust connectivity and the balanced traffic load can be ensured. The validation of the proposed approach has been simulated and verified by QualNet Developer 5.0.2.

  2. Cost analysis of hybrid adaptive routing protocol for heterogeneous wireless sensor network

    Indian Academy of Sciences (India)

    NONITA SHARMA; AJAY K SHARMA

    2016-03-01

    This study aims to explore the impact of heterogeneity on a hybrid algorithm called Multi Adaptive Filter Algorithm by constructing series of experiments. Here, the simulations were made between ‘Total Energy Spent’ and ‘Number of Sources’ considering temporal correlation. The results were drawn from the trace information generated using ‘Monte Carlo’ simulation methods. After keen analysis, the results show that different levels of heterogeneity are best suited for correlated event detections. Moreover, based on the conclusions drawn,it can be safely inferred that n-level heterogeneity reduces the total energy spent close to 60%. Further, cost analysis recommends that adding progressive nodes preserves the cost factor in the bracket of 230–280$/Joule. Thenovel approach can immensely help the future solution providers to overcome the battery limitations of wireless sensor networks. This study provides insights into designing heterogeneous wireless sensor networks and aims atproviding the cost-benefit analysis that can be used in selecting the critical parameters of the network.

  3. Balancing energy consumption with hybrid clustering and routing strategy in wireless sensor networks.

    Science.gov (United States)

    Xu, Zhezhuang; Chen, Liquan; Liu, Ting; Cao, Lianyang; Chen, Cailian

    2015-10-20

    Multi-hop data collection in wireless sensor networks (WSNs) is a challenge issue due to the limited energy resource and transmission range of wireless sensors. The hybrid clustering and routing (HCR) strategy has provided an effective solution, which can generate a connected and efficient cluster-based topology for multi-hop data collection in WSNs. However, it suffers from imbalanced energy consumption, which results in the poor performance of the network lifetime. In this paper, we evaluate the energy consumption of HCR and discover an important result: the imbalanced energy consumption generally appears in gradient k = 1, i.e., the nodes that can communicate with the sink directly. Based on this observation, we propose a new protocol called HCR-1, which includes the adaptive relay selection and tunable cost functions to balance the energy consumption. The guideline of setting the parameters in HCR-1 is provided based on simulations. The analytical and numerical results prove that, with minor modification of the topology in Sensors 2015, 15 26584 gradient k = 1, the HCR-1 protocol effectively balances the energy consumption and prolongs the network lifetime.

  4. Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ranganathan Mohanasundaram

    2015-01-01

    Full Text Available The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.

  5. Effects of hybrid synapses on the vibrational resonance in small-world neuronal networks.

    Science.gov (United States)

    Yu, Haitao; Wang, Jiang; Sun, Jianbing; Yu, Haifeng

    2012-09-01

    We investigate the effect of vibrational resonance in small-world neuronal networks with hybrid chemical and electrical synapses. It is shown that, irrespective of the probability of chemical synapses, an optimal amplitude of high-frequency component of the signal can optimize the dynamical response of neuron populations to the low-frequency component, which encodes the information. This effect of vibrational resonance of neuronal systems depends extensively on the network structure and parameters, which determine the ability of neuronal networks to enhance the outreach of localized subthreshold low-frequency signal. In particular, chemical synaptic coupling is more efficient than the electrical coupling for the transmission of local input signal due to its selective coupling. Moreover, there exists an optimal small-world topology characterized by an optimal value of rewiring probability, warranting the largest peak value of the system response. Considering that two-frequency signals are ubiquity in brain dynamics, we expect the presented results could have important implications for signal processing in neuronal systems.

  6. Improved efficiency of hybrid organic photovoltaics by pulsed laser sintering of silver nanowire network transparent electrode.

    Science.gov (United States)

    Spechler, Joshua A; Nagamatsu, Ken A; Sturm, James C; Arnold, Craig B

    2015-05-20

    In this Research Article, we demonstrate pulsed laser processing of a silver nanowire network transparent conductor on top of an otherwise complete solar cell. The macroscopic pulsed laser irradiation serves to sinter nanowire-nanowire junctions on the nanoscale, leading to a much more conductive electrode. We fabricate hybrid silicon/organic heterojunction photovoltaic devices, which have ITO-free, solution processed, and laser processed transparent electrodes. Furthermore, devices which have high resistive losses show up to a 35% increase in power conversion efficiency after laser processing. We perform this study over a range of laser fluences, and a range of nanowire area coverage to investigate the sintering mechanism of nanowires inside of a device stack. The increase in device performance is modeled using a simple photovoltaic diode approach and compares favorably to the experimental data.

  7. Hybrid intelligent system for Sale Forecasting using Delphi and adaptive Fuzzy Back-Propagation Neural Networks

    Directory of Open Access Journals (Sweden)

    Attariuas Hicham

    2012-12-01

    Full Text Available ales forecasting is one of the most crucial issues addressed in business. Control and evaluation of future sales still seem concerned both researchers and policy makers and managers of companies. this research propose an intelligent hybrid sales forecasting system Delphi-FCBPN sales forecast based on Delphi Method, fuzzy clustering and Back-propagation (BP Neural Networks with adaptive learning rate. The proposed model is constructed to integrate expert judgments, using Delphi method, in enhancing the model of FCBPN. Winter’s Exponential Smoothing method will be utilized to take the trend effect into consideration. The data for this search come from an industrial company that manufactures packaging. Analyze of results show that the proposed model outperforms other three different forecasting models in MAPE and RMSE measures.

  8. Active semi-supervised learning method with hybrid deep belief networks.

    Science.gov (United States)

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

  9. A Hybrid Fresh Apple Export Volume Forecasting Model Based on Time Series and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Lihua Yang

    2015-04-01

    Full Text Available Export volume forecasting of fresh fruits is a complex task due to the large number of factors affecting the demand. In order to guide the fruit growers’ sales, decreasing the cultivating cost and increasing their incomes, a hybrid fresh apple export volume forecasting model is proposed. Using the actual data of fresh apple export volume, the Seasonal Decomposition (SD model of time series and Radial Basis Function (RBF model of artificial neural network are built. The predictive results are compared among the three forecasting model based on the criterion of Mean Absolute Percentage Error (MAPE. The result indicates that the proposed combined forecasting model is effective because it can improve the prediction accuracy of fresh apple export volumes.

  10. High-Capacity Hybrid Optical Fiber-Wireless Communications Links in Access Networks

    DEFF Research Database (Denmark)

    Pang, Xiaodan

    techniques with both coherent and incoherent optical sources are studied and demonstrated. Employments of advanced modulation formats including phase-shift keying (PSK), M-quadrature amplitude modulation (QAM) and orthogonal frequency-division multiplexing (OFDM) for high speed photonic-wireless transmission......Integration between fiber-optic and wireless communications systems in the "last mile" access networks is currently considered as a promising solution for both service providers and users, in terms of minimizing deployment cost, shortening upgrading period and increasing mobility and flexibility...... techniques. In conclusion, the results presented in the thesis show the feasibility of employing mm-wave signals, advanced modulation formats and spatial multiplexing technologies in next generation high capacity hybrid optical fiber-wireless access systems....

  11. A hybrid neural network system for prediction and recognition of promoter regions in human genome

    Institute of Scientific and Technical Information of China (English)

    CHEN Chuan-bo; LI Tao

    2005-01-01

    This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence,feeding these features as input for a hybrid neural network system (HNN) and then applies the HNN for prediction. It combines a novel promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm.Evaluation on Human chromosome 22 was ~66% in sensitivity and ~48% in specificity. Comparison with two other systems revealed that our method had superior sensitivity and specificity in predicting promoter regions. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at http://www.whtelecom.com/Prompredictor.htm.

  12. Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models

    Directory of Open Access Journals (Sweden)

    Abdelkrim Moussaoui

    2006-01-01

    Full Text Available The authors discuss the combination of an Artificial Neural Network (ANN with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capacity of the fitted ANN model to predict the unseen regions of data. As a result, test rolls obtained by the suggested hybrid model have shown high prediction quality comparatively to the usual empirical prediction models.

  13. Transit Network Design: a Hybrid Enhanced Artificial Bee Colony Approach and a Case Study

    Directory of Open Access Journals (Sweden)

    Y. Jiang

    2013-09-01

    Full Text Available A bus network design problem in a suburban area of Hong Kong is studied. The objective is to minimize the weighted sum of the number of transfers and the total travel time of passengers by restructuring bus routes and determining new frequencies. A mixed integer optimization model is developed and was solved by a Hybrid Enhanced Artificial Bee Colony algorithm (HEABC. A case study was conducted to investigate the effects of different design parameters, including the total number of bus routes available, the maximum route duration within the study area and the maximum allowable number of bus routes that originated from each terminal. The model and results are useful for improving bus service policies.

  14. Hybrid FPMS: A New Fairness Protocol Management Scheme for Community Wireless Mesh Networks

    CERN Document Server

    Widanapathirana, Chathuranga; Goi, Bok-Min

    2012-01-01

    Node cooperation during packet forwarding operations is critically important for fair resource utilization in Community Wireless Mesh Networks (CoWMNs). In a CoWMN, node cooperation is achieved by using fairness protocols specifically designed to detect and isolate malicious nodes, discourage unfair behavior, and encourage node participation in forwarding packets. In general, these protocols can be split into two groups: Incentive-based ones, which are managed centrally, and use credit allocation schemes. In contrast, reputation-based protocols that are decentralized, and rely on information exchange among neighboring nodes. Centrally managed protocols inevitably suffer from scalability problems. The decentralized, reputation-based protocols lacks in detection capability, suffer from false detections and error propagation compared to the centralized, incentive-based protocols. In this study, we present a new fairness protocol management scheme, called Hybrid FPMS that captures the superior detection capabilit...

  15. Hybrid Electromagnetism-Like Algorithm for Dynamic Supply Chain Network Design under Traffic Congestion and Uncertainty

    Directory of Open Access Journals (Sweden)

    Javid Jouzdani

    2016-01-01

    Full Text Available With the constantly increasing pressure of the competitive environment, supply chain (SC decision makers are forced to consider several aspects of business climate. More specifically, they should take into account the endogenous features (e.g., available means of transportation, and the variety of products and exogenous criteria (e.g., the environmental uncertainty, and transportation system conditions. In this paper, a mixed integer nonlinear programming (MINLP model for dynamic design of a supply chain network is proposed. In this model, multiple products and multiple transportation modes, the time value of money, traffic congestion, and both supply-side and demand-side uncertainties are considered. Due to the complexity of such models, conventional solution methods are not applicable; therefore, two hybrid Electromagnetism-Like Algorithms (EMA are designed and discussed for tackling the problem. The numerical results show the applicability of the proposed model and the capabilities of the solution approaches to the MINLP problem.

  16. A Hybrid P2P Overlay Network for Non-strictly Hierarchically Categorized Content

    Science.gov (United States)

    Wan, Yi; Asaka, Takuya; Takahashi, Tatsuro

    In P2P content distribution systems, there are many cases in which the content can be classified into hierarchically organized categories. In this paper, we propose a hybrid overlay network design suitable for such content called Pastry/NSHCC (Pastry for Non-Strictly Hierarchically Categorized Content). The semantic information of classification hierarchies of the content can be utilized regardless of whether they are in a strict tree structure or not. By doing so, the search scope can be restrained to any granularity, and the number of query messages also decreases while maintaining keyword searching availability. Through simulation, we showed that the proposed method provides better performance and lower overhead than unstructured overlays exploiting the same semantic information.

  17. Low-cost RAU with Optical Power Supply Used in a Hybrid RoF IEEE 802.11 Network

    Science.gov (United States)

    Kowalczyk, M.; Siuzdak, J.

    2014-09-01

    The paper presents design and implementation of a low-cost RAU (Remote Antenna Unit) device. It was designed to work in a hybrid Wi-Fi/optical network based on the IEEE 802.11b/g standard. An unique feature of the device is the possibility of optical power supply.

  18. Multigigabit W-Band (75–110 GHz) Bidirectional Hybrid Fiber-Wireless Systems in Access Networks

    DEFF Research Database (Denmark)

    Pang, Xiaodan; Lebedev, Alexander; Vegas Olmos, Juan José

    2014-01-01

    We experimentally demonstrate multigigabit capacity bidirectional hybrid fiber-wireless systems with RF carrier frequencies at the W-band (75-110 GHz) that enables the seamless convergence between wireless and fiber-optic data transmission systems in access networks. In this study, we evaluate...

  19. Large-scale hybrid Bayesian network for traffic load modeling from weigh-in-motion system data

    NARCIS (Netherlands)

    Morales-Nápoles, O.; Steenbergen, R.D.J.M.

    2014-01-01

    Traffic load plays an important role not only in the design of new bridges but also in the reliability assessment of existing structures. Weigh-in-motion systems are used to collect data to determine traffic loads. In this paper, the potential of hybrid nonparametric Bayesian networks (BNs) is

  20. Formation of hybrid gold nanoparticle network aggregates by specific host-guest interactions in a turbulent flow reactor

    NARCIS (Netherlands)

    Weinhart-Mejia, R.; Huskens, Jurriaan

    2014-01-01

    A multi-inlet vortex mixer (MIVM) was used to investigate the formation of hybrid gold nanoparticle network aggregates under highly turbulent flow conditions. To form aggregates, gold nanoparticles were functionalized with β-cyclodextrin (CD) and mixed with adamantyl (Ad)-terminated

  1. Formation of hybrid gold nanoparticle network aggregates by specific host-guest interactions in a turbulent flow reactor

    NARCIS (Netherlands)

    Mejia Ariza, Raquel; Huskens, Jurriaan

    2014-01-01

    A multi-inlet vortex mixer (MIVM) was used to investigate the formation of hybrid gold nanoparticle network aggregates under highly turbulent flow conditions. To form aggregates, gold nanoparticles were functionalized with β-cyclodextrin (CD) and mixed with adamantyl (Ad)-terminated poly(propyleneim

  2. Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

    Science.gov (United States)

    van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

    2016-01-01

    E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

  3. Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

    Directory of Open Access Journals (Sweden)

    Weide Li

    2017-01-01

    Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

  4. Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning

    Directory of Open Access Journals (Sweden)

    Liang Ding

    2007-11-01

    Full Text Available Wireless multimedia sensor networks (WMSN have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper proposes acollaborative semi-supervised classifier learning algorithm to achieve durative onlinelearning for support vector machine (SVM based robust target classification. The proposedalgorithm incrementally carries out the semi-supervised classifier learning process inhierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computingparadigm. For decreasing the energy consumption and improving the performance, somemetrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes,and a sensor node selection strategy is also proposed to reduce the impact of inevitablemissing detection and false detection. With the ant optimization routing, the learningprocess is implemented with the selected sensor nodes, which can decrease the energyconsumption. Experimental results demonstrate that the collaborative hybrid semi-supervised classifier learning algorithm can effectively implement target classification inhierarchical WMSN. It has outstanding performance in terms of energy efficiency and timecost, which verifies the effectiveness of the sensor nodes selection and ant optimizationrouting.

  5. Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and Teaching Learning Based Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jamal Salahaldeen Majeed Alneamy

    2014-01-01

    Full Text Available Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO algorithm and fuzzy wavelet neural network (FWNN for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI machine learning repository. The performance of the proposed method (TLBO_FWNN is estimated using K-fold cross validation based on mean square error (MSE, classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.

  6. QoS Supported IPTV Service Architecture over Hybrid-Tree-Based Explicit Routed Multicast Network

    Directory of Open Access Journals (Sweden)

    Chih-Chao Wen

    2012-01-01

    Full Text Available With the rapid advance in multimedia streaming and multicast transport technology, current IP multicast protocols, especially PIM-SM, become the major channel delivery mechanism for IPTV system over Internet. The goals for IPTV service are to provide two-way interactive services for viewers to select popular program channel with high quality for watching during fast channel surfing period. However, existing IP multicast protocol cannot meet above QoS requirements for IPTV applications between media server and subscribers. Therefore, we propose a cooperative scheme of hybrid-tree based on explicit routed multicast, called as HT-ERM to combine the advantages of shared tree and source tree for QoS-supported IPTV service. To increase network utilization, the constrained shortest path first (CSPF routing algorithm is designed for construction of hybrid tree to deliver the high-quality video stream over watching channel and standard quality over surfing channel. Furthermore, the Resource Reservation Protocol- Traffic Engineering (RSVP-TE is used as signaling mechanism to set up QoS path for multicast channel admission control. Our simulation results demonstrated that the proposed HT-ERM scheme outperforms other multicast QoS-based delivery scheme in terms of channel switching delay, resource utilization, and blocking ratio for IPTV service.

  7. Hybrid artificial neural network system for short-term load forecasting

    Directory of Open Access Journals (Sweden)

    Ilić Slobodan A.

    2012-01-01

    Full Text Available This paper presents a novel hybrid method for Short-Term Load Forecasting (STLF. The system comprises of two Artificial Neural Networks (ANN, assembled in a hierarchical order. The first ANN is a Multilayer Perceptron (MLP which functions as integrated load predictor (ILP for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor (HLP for a forecasting day. By using a separate ANN that predicts the integral of the load (ILP, additional information is presented to the actual forecasting ANN (HLP, while keeping its input space relatively small. This property enables online training and adaptation, as new data become available, because of the short training time. Different sizes of training sets have been tested, and the optimum of 30 day sliding time-window has been determined. The system has been verified on recorded data from Serbian electrical utility company. The results demonstrate better efficiency of the proposed method in comparison to non-hybrid methods because it produces better forecasts and yields smaller mean average percentage error (MAPE.

  8. HYBRID OF FUZZY CLUSTERING NEURAL NETWORK OVER NSL DATASET FOR INTRUSION DETECTION SYSTEM

    Directory of Open Access Journals (Sweden)

    Dahlia Asyiqin Ahmad Zainaddin

    2013-01-01

    Full Text Available Intrusion Detection System (IDS is one of the component that take part in the system defence, to identify abnormal activities happening in the computer system. Nowadays, IDS facing composite demands to defeat modern attack activities from damaging the computer systems. Anomaly-Based IDS examines ongoing traffic, activity, transactions and behavior in order to identify intrusions by detecting anomalies. These technique identifies activities which degenerates from the normal behaviours. In recent years, data mining approach for intrusion detection have been advised and used. The approach such as Genetic Algorithms , Support Vector Machines, Neural Networks as well as clustering has resulted in high accuracy and good detection rates but with moderate false alarm on novel attacks. Many researchers also have proposed hybrid data mining techniques. The previous resechers has intoduced the combination of Fuzzy Clustering and Artificial Neural Network. However, it was tested only on randomn selection of KDDCup 1999 dataset. In this study the framework experiment introduced, has been used over the NSL dataset to test the stability and reliability of the technique. The result of precision, recall and f-value rate is compared with previous experiment. Both dataset covers four types of main attacks, which are Derial of Services (DoS, User to Root (U2R, Remote to Local (R2L and Probe. Results had guarenteed that the hybrid approach performed better detection especially for low frequent over NSL datataset compared to original KDD dataset, due to the removal of redundancy and uncomplete elements in the original dataset. This electronic document is a “live” template. The various components of your paper [title, text, tables, figures and references] are already defined on the style sheet, as illustrated by the portions given in this document.

  9. Modeling and Analysis of Mesh Tree Hybrid Power/Ground Networks with Multiple Voltage Supply in Time Domain

    Institute of Scientific and Technical Information of China (English)

    Yi-Ci Cai; Jin Shi; Zu-Ying Luo; Xian-Long Hong

    2005-01-01

    This paper proposes a novel algorithm, which can be used to model and analyze mesh tree hybrid power/ground distribution networks with multiple voltage supply in time domain. Not only this algorithm enhances common method's ability on analysis of power/ground network with irregular topology, but also very high accuracy it keeps. The accuracy and stability of this algorithm is proved using strict math method in this paper. Also, the usage of both precondition technique based on Incomplete Choleskey Decomposition and fast variable elimination technique has improved the algorithm's efficiency a lot. Experimental results show that it can finish the analysis of power/ground network with enormous size within very short time. Also, this algorithm can be applied to analyze the clock network, bus network, and signal network without buffer under high working frequency because of the independence of the topology.

  10. Hierarchical hybrid control network design based on LON and master-slave RS-422/485 protocol

    Institute of Scientific and Technical Information of China (English)

    彭可; 陈际达; 陈岚

    2002-01-01

    Aiming at the weaknesses of LON bus, combining the coexistence of fieldbus and DCS (Distribu-ted Control Systems) in control networks, the authors introduce a hierarchical hybrid control network design based on LON and master-slave RS-422/485 protocol. This design adopts LON as the trunk, master-slave RS-422/485 control networks are connected to LON as special subnets by dedicated gateways. It is an implementation method for isomerous control network integration. Data management is ranked according to real-time requirements for different network data. The core components, such as control network nodes, router and gateway, are detailed in the paper. The design utilizes both communication advantage of LonWorks technology and the more powerful control ability of universal MCUs or PLCs, thus it greatly increases system response speed and performance-cost ratio.

  11. Transmission network-based energy and environmental assessment of plug-in hybrid electric vehicles

    Science.gov (United States)

    Valentine, Keenan; Acquaviva, Jonathan; Foster, E. J.; Zhang, K. Max

    2011-03-01

    The introduction of plug-in hybrid electric vehicles (PHEVs) is expected to have a significant impact on regional power systems and pollutant emissions. This paper analyzes the effects of various penetrations of PHEVs on the marginal fuel dispatch of coal, natural gas and oil, and on pollutant emissions of CO2, NOx, SO2 in the New York Metropolitan Area for two battery charging scenarios in a typical summer and winter day. A model of the AC transmission network of the Northeast Power Coordinating Council (NPCC) region with 693 generators is used to realistically incorporate network constraints into an economic dispatch model. A data-based transportation model of approximately 1 million commuters in NYMA is used to determine battery charging pattern. Results show that for all penetrations of PHEVs network-constrained economic dispatch of generation is significantly more realistic than unconstrained cases. Coal, natural gas and oil units are on the margin in the winter, and only natural gas and oil units are on the margin in the summer. Hourly changes in emissions from transportation and power production are dominated by vehicular activity with significant overall emissions reductions for CO2 and NOx, and a slight increase for SO2. Nighttime regulated charging produces less overall emissions than unregulated charging from when vehicles arrive home for the summer and vice versa for the winter. As PHEVs are poised to link the power and transportation sectors, data-based models combining network constraints and economic dispatch have been shown to improve understanding and facilitate control of this link.

  12. Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity

    Directory of Open Access Journals (Sweden)

    Michael Opoku Agyeman

    2017-04-01

    Full Text Available To meet the performance and scalability demands of the fast-paced technological growth towards exascale and big data processing with the performance bottleneck of conventional metal-based interconnects (wireline, alternative interconnect fabrics, such as inhomogeneous three-dimensional integrated network-on-chip (3D NoC and hybrid wired-wireless network-on-chip (WiNoC, have emanated as a cost-effective solution for emerging system-on-chip (SoC design. However, these interconnects trade off optimized performance for cost by restricting the number of area and power hungry 3D routers and wireless nodes. Moreover, the non-uniform distributed traffic in a chip multiprocessor (CMP demands an on-chip communication infrastructure that can avoid congestion under high traffic conditions while possessing minimal pipeline delay at low-load conditions. To this end, in this paper, we propose a low-latency adaptive router with a low-complexity single-cycle bypassing mechanism to alleviate the performance degradation due to the slow 2D routers in such emerging hybrid NoCs. The proposed router transmits a flit using dimension-ordered routing (DoR in the bypass datapath at low-loads. When the output port required for intra-dimension bypassing is not available, the packet is routed adaptively to avoid congestion. The router also has a simplified virtual channel allocation (VA scheme that yields a non-speculative low-latency pipeline. By combining the low-complexity bypassing technique with adaptive routing, the proposed router is able to balance the traffic in hybrid NoCs to achieve low-latency communication under various traffic loads. Simulation shows that the proposed router can reduce applications’ execution time by an average of 16.9% compared to low-latency routers, such as SWIFT. By reducing the latency between 2D routers (or wired nodes and 3D routers (or wireless nodes, the proposed router can improve the performance efficiency in terms of average

  13. Second Order Cone Programming (SOCP) Relaxation Based Optimal Power Flow with Hybrid VSC-HVDC Transmission and Active Distribution Networks

    DEFF Research Database (Denmark)

    Ding, Tao; Li, Cheng; Yang, Yongheng

    2017-01-01

    The detailed topology of renewable resource bases may have the impact on the optimal power flow of the VSC-HVDC transmission network. To address this issue, this paper develops an optimal power flow with the hybrid VSC-HVDC transmission and active distribution networks to optimally schedule...... the generation output and voltage regulation of both networks, which leads to a non-convex programming model. Furthermore, the non-convex power flow equations are based on the Second Order Cone Programming (SOCP) relaxation approach. Thus, the proposed model can be relaxed to a SOCP that can be tractably solved...

  14. Optimization of Hybrid Hub-and-Spoke Network Operation for Less-Than-Truckload Freight Transportation considering Incremental Quantity Discount

    Directory of Open Access Journals (Sweden)

    Weiya Chen

    2014-01-01

    Full Text Available This paper presents a mixed integer linear programming model (MILP for optimizing the hybrid hub-and-spoke network operation for a less-than-truckload transportation service. The model aims to minimize the total operation costs (transportation cost and transfer cost, given the determined demand matrix, truck load capacity, and uncapacitated road transportation. The model also incorporates an incremental quantity discount function to solve the reversal of the total cost and the total demand. The model is applied to a real case of a Chinese transportation company engaged in nationwide freight transportation. The numerical example shows that, with uncapacitated road transportation, the total costs and the total vehicle trips of the hybrid hub-and-spoke network operation are, respectively, 8.0% and 15.3% less than those of the pure hub-and-spoke network operation, and the assumed capacity constraints in an extension model result in more target costs on the hybrid hub-and-spoke network. The two models can be used to support the decision making in network operations by transportation and logistics companies.

  15. Hybrid Recovery Strategy Based on Random Terrain in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xiaoding Wang

    2017-01-01

    Full Text Available Providing successful data collection and aggregation is a primary goal for a broad spectrum of critical applications of wireless sensor networks. Unfortunately, the problem of connectivity loss, which may occur when a network suffers from natural disasters or human sabotages, may cause failure in data aggregation. To tackle this issue, plenty of strategies that deploy relay devices on target areas to restore connectivity have been devised. However, all of them assume that either the landforms of target areas are flat or there are sufficient relay devices. In real scenarios, such assumptions are not realistic. In this paper, we propose a hybrid recovery strategy based on random terrain (simply, HRSRT that takes both realistic terrain influences and quantitative limitations of relay devices into consideration. HRSRT is proved to accomplish the biconnectivity restoration and meanwhile minimize the energy cost for data collection and aggregation. In addition, both of complexity and approximation ratio of HRSRT are explored. The simulation results show that HRSRT performs well in terms of overall/maximum energy cost.

  16. A HYBRID GENETIC ALGORITHM-NEURAL NETWORK APPROACH FOR PRICING CORES AND REMANUFACTURED CORES

    Directory of Open Access Journals (Sweden)

    M. Seidi

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT:Sustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. Remanufacturing is an industrial process that makes used products reusable. One of the important aspects in both reverse logistics and remanufacturing is the pricing of returned and remanufactured products (called cores. In this paper, we focus on pricing the cores and remanufactured cores. First we present a mathematical model for this purpose. Since this model does not satisfy our requirements, we propose a simulation optimisation approach. This approach consists of a hybrid genetic algorithm based on a neural network employed as the fitness function. We use automata learning theory to obtain the learning rate required for training the neural network. Numerical results demonstrate that the optimal value of the acquisition price of cores and price of remanufactured cores is obtained by this approach.

    AFRIKAANSE OPSOMMING: Volhoubaarheid het ‘n belangrike saak geword in die meeste ekonomieë, wat verskeie maatskappye genoop het om produkherwinning en omgekeerde logistiek te onder oë te neem. Hervervaardiging is ‘n industriële proses wat gebruikte produkte weer bruikbaar maak. Een van die belangrike aspekte in beide omgekeerde logistiek en hervervaardiging is die prysbepaling van herwinne en hervervaardigde produkte. Hierdie artikel fokus op die prysbepalingsaspekte by wyse van ‘n wiskundige model.

  17. A Hybrid System of Hierarchical Planning of Behaviour Selection Networks for Mobile Robot Control

    Directory of Open Access Journals (Sweden)

    Young-Seol Lee

    2014-04-01

    Full Text Available An office delivery robot receives a large amount of sensory data and there is uncertainty in its action outcomes. The robot should not only accomplish its goals using environmental information, but also consider various exceptions simultaneously. In this paper, we propose a hybrid system using hierarchical planning of modular behaviour selection networks to generate autonomous behaviour in the office delivery robot. Behaviour selection networks, one of the well-known behaviour-based methods suitable for goal-oriented tasks, are made up of several smaller behaviour modules. Planning is attached to the construct and adjust sequences of the modules by considering the sub-goals, the priority in each task and the user feedback. This helps the robot to quickly react in dynamic situations as well as achieve global goals efficiently. The proposed system is verified with both the Webot simulator and a Khepera II robot that runs in a real office environment carrying out delivery tasks. Experimental results have shown that a robot can achieve goals and generate module sequences successfully even in unpredictable situations. Additionally, the proposed planning method reduced the elapsed time during tasks by 17.5% since it adjusts the behaviour module sequences more effectively.

  18. WRHT: A Hybrid Technique for Detection of Wormhole Attack in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rupinder Singh

    2016-01-01

    Full Text Available Wormhole attack is a challenging security threat to wireless sensor networks which results in disrupting most of the routing protocols as this attack can be triggered in different modes. In this paper, WRHT, a wormhole resistant hybrid technique, is proposed, which can detect the presence of wormhole attack in a more optimistic manner than earlier techniques. WRHT is based on the concept of watchdog and Delphi schemes and ensures that the wormhole will not be left untreated in the sensor network. WRHT makes use of the dual wormhole detection mechanism of calculating probability factor time delay probability and packet loss probability of the established path in order to find the value of wormhole presence probability. The nodes in the path are given different ranking and subsequently colors according to their behavior. The most striking feature of WRHT consists of its capacity to defend against almost all categories of wormhole attacks without depending on any required additional hardware such as global positioning system, timing information or synchronized clocks, and traditional cryptographic schemes demanding high computational needs. The experimental results clearly indicate that the proposed technique has significant improvement over the existing wormhole attack detection techniques.

  19. Hybrid Power Forecasting Model for Photovoltaic Plants Based on Neural Network with Air Quality Index

    Directory of Open Access Journals (Sweden)

    Idris Khan

    2017-01-01

    Full Text Available High concentration of greenhouse gases in the atmosphere has increased dependency on photovoltaic (PV power, but its random nature poses a challenge for system operators to precisely predict and forecast PV power. The conventional forecasting methods were accurate for clean weather. But when the PV plants worked under heavy haze, the radiation is negatively impacted and thus reducing PV power; therefore, to deal with haze weather, Air Quality Index (AQI is introduced as a parameter to predict PV power. AQI, which is an indication of how polluted the air is, has been known to have a strong correlation with power generated by the PV panels. In this paper, a hybrid method based on the model of conventional back propagation (BP neural network for clear weather and BP AQI model for haze weather is used to forecast PV power with conventional parameters like temperature, wind speed, humidity, solar radiation, and an extra parameter of AQI as input. The results show that the proposed method has less error under haze condition as compared to conventional model of neural network.

  20. Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm.

    Science.gov (United States)

    Arabasadi, Zeinab; Alizadehsani, Roohallah; Roshanzamir, Mohamad; Moosaei, Hossein; Yarifard, Ali Asghar

    2017-04-01

    Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset.

  1. Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

    Science.gov (United States)

    Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.

    2017-09-01

    Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

  2. Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.

    Science.gov (United States)

    Kocaturk, Mehmet; Gulcur, Halil Ozcan; Canbeyli, Resit

    2015-01-01

    In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.

  3. A hybrid neural network structure for application to nondestructive TRU waste assay

    Energy Technology Data Exchange (ETDEWEB)

    Becker, G. [Idaho National Engineering Lab., Idaho Falls, ID (United States)

    1995-12-31

    The determination of transuranic (TRU) and associated radioactive material quantities entrained in waste forms is a necessary component. of waste characterization. Measurement performance requirements are specified in the National TRU Waste Characterization Program quality assurance plan for which compliance must be demonstrated prior to the transportation and disposition of wastes. With respect to this criterion, the existing TRU nondestructive waste assay (NDA) capability is inadequate for a significant fraction of the US Department of Energy (DOE) complex waste inventory. This is a result of the general application of safeguard-type measurement and calibration schemes to waste form configurations. Incompatibilities between such measurement methods and actual waste form configurations complicate regulation compliance demonstration processes and illustrate the need for an alternate measurement interpretation paradigm. Hence, it appears necessary to supplement or perhaps restructure the perceived solution and approach to the waste NDA problem. The first step is to understand the magnitude of the waste matrix/source attribute space associated with those waste form configurations in inventory and how this creates complexities and unknowns with respect to existing NDA methods. Once defined and/or bounded, a conceptual method must be developed that specifies the necessary tools and the framework in which the tools are used. A promising framework is a hybridized neural network structure. Discussed are some typical complications associated with conventional waste NDA techniques and how improvements can be obtained through the application of neural networks.

  4. A Hybrid Optimized Weighted Minimum Spanning Tree for the Shortest Intrapath Selection in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Matheswaran Saravanan

    2014-01-01

    Full Text Available Wireless sensor network (WSN consists of sensor nodes that need energy efficient routing techniques as they have limited battery power, computing, and storage resources. WSN routing protocols should enable reliable multihop communication with energy constraints. Clustering is an effective way to reduce overheads and when this is aided by effective resource allocation, it results in reduced energy consumption. In this work, a novel hybrid evolutionary algorithm called Bee Algorithm-Simulated Annealing Weighted Minimal Spanning Tree (BASA-WMST routing is proposed in which randomly deployed sensor nodes are split into the best possible number of independent clusters with cluster head and optimal route. The former gathers data from sensors belonging to the cluster, forwarding them to the sink. The shortest intrapath selection for the cluster is selected using Weighted Minimum Spanning Tree (WMST. The proposed algorithm computes the distance-based Minimum Spanning Tree (MST of the weighted graph for the multihop network. The weights are dynamically changed based on the energy level of each sensor during route selection and optimized using the proposed bee algorithm simulated annealing algorithm.

  5. A microarray gene expression data classification using hybrid back propagation neural network

    Directory of Open Access Journals (Sweden)

    Vimaladevi M.

    2014-01-01

    Full Text Available Classification of cancer establishes appropriate treatment and helps to decide the diagnosis. Cancer expands progressively from an alteration in a cell's genetic structure. This change (mutation results in cells with uncontrolled growth patterns. In cancer classification, the approach, Back propagation is sufficient and also it is a universal technique of training artificial neural networks. It is also called supervised learning method. It needs many dataset for input and output for making up the training set. The back propagation method may execute the function of collaborate multiple parties. In existing method, collaborative learning is limited and it considers only two parties. The proposed collaborative function can perform well and problems can be solved by utilizing the power of cloud computing. This technical note applies hybrid models of Back Propagation Neural networks (BPN and fast Genetic Algorithms (GA to estimate the feature selection in gene expression data. The proposed research work examines many feature selection algorithms which are “fragile”; that is, the superiority of their results varies broadly over data sets. By this research, it is suggested that this is due to higherorder interactions between features causing restricted minima in search space in which the algorithm becomes attentive. GAs may escape from such minima by chance, because works are highly stochastic. A neural net classifier with a genetic algorithm, using the GA to select features for classification by the neural net and incorporating the net as part of the objective function of the GA.

  6. Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks

    Directory of Open Access Journals (Sweden)

    Jose María Álvarez-Rodríguez

    2015-01-01

    Full Text Available The present paper introduces a hybrid technique to measure the expertise of users by analyzing their profiles and activities in social networks. Currently, both job seekers and talent hunters are looking for new and innovative techniques to filter jobs and candidates where candidates are trying to improve and make their profiles more attractive. In this sense, the Skillrank approach is based on the conjunction of existing and well-known information and expertise retrieval techniques that perfectly fit the existing web and social media environment to deliver an intelligent component to integrate the user context in the analysis of skills confidence. A major outcome of this approach is that it actually takes advantage of existing data and information available on the web to perform both a ranked list of experts in a field and a confidence value for every professional skill. Thus, expertise and experts can be detected, verified, and ranked using a suited trust metric. An experiment to validate the Skillrank technique based on precision and recall metrics is also presented using two different datasets: (1 ad hoc created using real data from a professional social network and (2 real data extracted from the LinkedIn API.

  7. Multi-Radio Mobile Device in Role of Hybrid Node Between WiFi and LTE networks

    Directory of Open Access Journals (Sweden)

    Pavel Masek

    2015-05-01

    Full Text Available With the ubiquitous wireless network coverage, Machine-Type Communications (MTC is emerging to enable data transfers using devices/sensors without need for human interaction. In this paper we, we introduce a comprehensive simulation scenario for modeling and analysis for heterogeneous MTC. We demonstrate the most expected scenario of MTC communication using the IEEE 802.11 standard for direct communication between sensors and for transmitting data between individual sensor and Machine-Type Communication Gateway (MTCG. The MTCG represents the hybrid node serving as bridge between two heterogeneous networks (WiFi and LTE. Following the idea of hybrid node, two active interfaces must be implemented on this node together with mechanism for handling the incoming traffic (from WiFi network to LTE network. As a simulation tool, the Network Simulator 3 (NS-3 with implemented LTE/EPC Network Simulator (LENA framework was used. The major contribution of this paper therefore lies in the implementation of logic for interconnection of two heterogeneous networks in simulation environment NS-3.

  8. An Efficient Multi-path Routing Algorithm Based on Hybrid Firefly Algorithm for Wireless Mesh Networks

    Directory of Open Access Journals (Sweden)

    K. Kumaravel

    2015-05-01

    Full Text Available Wireless Mesh Network (WMN uses the latest technology which helps in providing end users a high quality service referred to as the Internet’s “last mile”. Also considering WMN one of the most important technologies that are employed is multicast communication. Among the several issues routing which is significantly an important issue is addressed by every WMN technologies and this is done during the process of data transmission. The IEEE 802.11s Standard entails and sets procedures which need to be followed to facilitate interconnection and thus be able to devise an appropriate WMN. There has been introduction of several protocols by many authors which are mainly devised on the basis of machine learning and artificial intelligence. Multi-path routing may be considered as one such routing method which facilitates transmission of data over several paths, proving its capabilities as a useful strategy for achieving reliability in WMN. Though, multi-path routing in any manner cannot really guarantee deterministic transmission. As here there are multiple paths available for enabling data transmission from source to destination node. The algorithm that had been employed before in the studies conducted did not take in to consideration routing metrics which include energy aware metrics that are used for path selection during transferring of data. The following study proposes use of the hybrid multipath routing algorithm while taking in to consideration routing metrics which include energy, minimal loss for efficient path selection and transferring of data. Proposed algorithm here has two phases. In the first phase prim’s algorithm has been proposed so that in networks route discovery may be possible. For the second one the Hybrid firefly algorithm which is based on harmony search has been employed for selection of the most suitable and best through proper analysis of metrics which include energy awareness and minimal loss for every path that has

  9. Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.

    Science.gov (United States)

    Wang, K W; Deng, C; Li, J P; Zhang, Y Y; Li, X Y; Wu, M C

    2017-04-01

    Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

  10. Molecular Design: Network Architecture and Its Impact on the Organization and Mechanics of Peptide-Polyurea Hybrids.

    Science.gov (United States)

    Matolyak, Lindsay; Keum, Jong; Korley, LaShanda T J

    2016-12-12

    Nature has achieved controlled and tunable mechanics via hierarchical organization driven by physical and covalent interactions. Polymer-peptide hybrids have been designed to mimic natural materials utilizing these architectural strategies, obtaining diverse mechanical properties, stimuli responsiveness, and bioactivity. Here, utilizing a molecular design pathway, peptide-polyurea hybrid networks were synthesized to investigate the role of architecture and structural interplay on peptide hydrogen bonding, assembly, and mechanics. Networks formed from poly(β-benzyl-l-aspartate)-poly(dimethylsiloxane) copolymers covalently cross-linked with a triisocyanate yielded polyurea films with a globular-like morphology and parallel β-sheet secondary structures. The geometrical constraints imposed by the network led to an increase in peptide loading and ∼7x increase in Young's modulus while maintaining extensibility (∼160%). Thus, the interplay of physical and chemical bonds allowed for the modulation of resulting mechanical properties. This investigation provides a framework for the utilization of structural interplay and mechanical tuning in polymer-peptide hybrids, which offers a pathway for the design of future hybrid biomaterial systems.

  11. Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rajeev Kumar

    2016-01-01

    Full Text Available Currently, wireless sensor networks (WSNs are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i selection of optimal number of subregions and further subregion parts, (ii cluster head selection using ABC algorithm, and (iii efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS. The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.

  12. A hybrid analytical network process and fuzzy goal programming for supplier selection: A case study of auto part maker

    Directory of Open Access Journals (Sweden)

    Hesam Zande Hesami

    2011-10-01

    Full Text Available The aim of this research is to present a hybrid model to select auto part suppliers. The proposed method of this paper uses factor analysis to find the most influencing factors on part maker selection and the results are validated using different statistical tests such as Cronbach's Alpha and Kaiser-Meyer.The hybrid model uses analytical network process to rank different part maker suppliers and fuzzy goal programming to choose the appropriate alternative among various choices. The implementation of the proposed model of this paper is used for a case study of real-world problem and the results are discussed.

  13. Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic.

    Science.gov (United States)

    Khan, Faiz M; Schmitz, Ulf; Nikolov, Svetoslav; Engelmann, David; Pützer, Brigitte M; Wolkenhauer, Olaf; Vera, Julio

    2014-01-01

    A decade of successful results indicates that systems biology is the appropriate approach to investigate the regulation of complex biochemical networks involving transcriptional and post-transcriptional regulations. It becomes mandatory when dealing with highly interconnected biochemical networks, composed of hundreds of compounds, or when networks are enriched in non-linear motifs like feedback and feedforward loops. An emerging dilemma is to conciliate models of massive networks and the adequate description of non-linear dynamics in a suitable modeling framework. Boolean networks are an ideal representation of massive networks that are humble in terms of computational complexity and data demand. However, they are inappropriate when dealing with nested feedback/feedforward loops, structural motifs common in biochemical networks. On the other hand, models of ordinary differential equations (ODEs) cope well with these loops, but they require enormous amounts of quantitative data for a full characterization of the model. Here we propose hybrid models, composed of ODE and logical sub-modules, as a strategy to handle large scale, non-linear biochemical networks that include transcriptional and post-transcriptional regulations. We illustrate the construction of this kind of models using as example a regulatory network centered on E2F1, a transcription factor involved in cancer. The hybrid modeling approach proposed is a good compromise between quantitative/qualitative accuracy and scalability when considering large biochemical networks with a small highly interconnected core, and module of transcriptionally regulated genes that are not part of critical regulatory loops. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

  14. Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

    Science.gov (United States)

    Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

    2009-04-01

    θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

  15. Hybrid neural network model for simulating sorbitol synthesis by glucose-fructose oxidoreductase in Zymomonas mobilis CP4

    Directory of Open Access Journals (Sweden)

    Bravo S.

    2004-01-01

    Full Text Available A hybrid neural network model for simulating the process of enzymatic reduction of fructose to sorbitol process catalyzed by glucose-fructose oxidoreductase in Zymomonas mobilis CP4 is presented. Data used to derive and validate the model was obtained from experiments carried out under different conditions of pH, temperature and concentrations of both substrates (glucose and fructose involved in the reaction. Sonicated and lyophilized cells were used as source of the enzyme. The optimal pH for sorbitol synthesis at 30º C is 6.5. For a value of pH of 6, the optimal temperature is 35º C. The neural network in the model computes the value of the kinetic relationship. The hybrid neural network model is able to simulate changes in the substrates and product concentrations during sorbitol synthesis under pH and temperature conditions ranging between 5 and 7.5 and 25 and 40º C, respectively. Under these conditions the rate of sorbitol synthesis shows important differences. Values computed using the hybrid neural network model have an average error of 1.7·10-3 mole.

  16. Preparation and electrochemical performance of hyper-networked Li4Ti5O12/carbon hybrid nanofiber sheets for a battery-supercapacitor hybrid system.

    Science.gov (United States)

    Choi, Hong Soo; Kim, TaeHoon; Im, Ji Hyuk; Park, Chong Rae

    2011-10-07

    Hyper-networked Li(4)Ti(5)O(12)/carbon hybrid nanofiber sheets that contain both a faradaically rechargeable battery-type component, namely Li(4)Ti(5)O(12), and a non-faradaically rechargeable supercapacitor-type component, namely N-enriched carbon, are prepared by electrospinning and their dual function as a negative electrode of lithium-ion batteries (LIBs) and a capacitor is tested for a new class of hybrid energy storage (denoted BatCap). An aqueous solution composed of polyvinylpyrrolidone, lithium hydroxide, titanium(IV) bis(ammonium-lactato)dihydroxide and ammonium persulfate is electrospun to obtain hyper-networked nanofiber sheets. Next, the sheets are exposed to pyrrole monomer vapor to prepare the polypyrrole-coated nanofiber sheets (PPy-HNS). The hyper-networked Li(4)Ti(5)O(12)/N-enriched carbon hybrid nanofiber sheets (LTO/C-HNS) are then obtained by a stepwise heat treatment of the PPy-HNS. The LTO/C-HNS deliver a specific capacity of 135 mAh g(-1) at 4000 mA g(-1) as a negative electrode for LIBs. In addition, potentiodynamic experiments are performed using a full cell with activated carbon (AC) as the positive electrode and LTO/C-HNS as the negative electrode to estimate the capacitance properties. This new asymmetric electrode system exhibits a high energy density of 91 W kg(-1) and 22 W kg(-1) at power densities of 50 W kg(-1) and 4000 W kg(-1), respectively, which are superior to the values observed for the AC [symbol: see text] AC symmetric electrode system.

  17. Preparation and electrochemical performance of hyper-networked Li4Ti5O12/carbon hybrid nanofiber sheets for a battery-supercapacitor hybrid system

    Science.gov (United States)

    Choi, Hong Soo; Kim, TaeHoon; Im, Ji Hyuk; Park, Chong Rae

    2011-10-01

    Hyper-networked Li4Ti5O12/carbon hybrid nanofiber sheets that contain both a faradaically rechargeable battery-type component, namely Li4Ti5O12, and a non-faradaically rechargeable supercapacitor-type component, namely N-enriched carbon, are prepared by electrospinning and their dual function as a negative electrode of lithium-ion batteries (LIBs) and a capacitor is tested for a new class of hybrid energy storage (denoted BatCap). An aqueous solution composed of polyvinylpyrrolidone, lithium hydroxide, titanium(IV) bis(ammonium-lactato)dihydroxide and ammonium persulfate is electrospun to obtain hyper-networked nanofiber sheets. Next, the sheets are exposed to pyrrole monomer vapor to prepare the polypyrrole-coated nanofiber sheets (PPy-HNS). The hyper-networked Li4Ti5O12/N-enriched carbon hybrid nanofiber sheets (LTO/C-HNS) are then obtained by a stepwise heat treatment of the PPy-HNS. The LTO/C-HNS deliver a specific capacity of 135 mAh g - 1 at 4000 mA g - 1 as a negative electrode for LIBs. In addition, potentiodynamic experiments are performed using a full cell with activated carbon (AC) as the positive electrode and LTO/C-HNS as the negative electrode to estimate the capacitance properties. This new asymmetric electrode system exhibits a high energy density of 91 W kg - 1 and 22 W kg - 1 at power densities of 50 W kg - 1 and 4000 W kg - 1, respectively, which are superior to the values observed for the {AC} \\parallel {AC} symmetric electrode system.

  18. Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

    Directory of Open Access Journals (Sweden)

    You Zhu

    2016-05-01

    Full Text Available Based on logistic regression (LR and artificial neural network (ANN methods, we construct an LR model, an ANN model and three types of a two-stage hybrid model. The two-stage hybrid model is integrated by the LR and ANN approaches. We predict the credit risk of China’s small and medium-sized enterprises (SMEs for financial institutions (FIs in the supply chain financing (SCF by applying the above models. In the empirical analysis, the quarterly financial and non-financial data of 77 listed SMEs and 11 listed core enterprises (CEs in the period of 2012–2013 are chosen as the samples. The empirical results show that: (i the “negative signal” prediction accuracy ratio of the ANN model is better than that of LR model; (ii the two-stage hybrid model type I has a better performance of predicting “positive signals” than that of the ANN model; (iii the two-stage hybrid model type II has a stronger ability both in aspects of predicting “positive signals” and “negative signals” than that of the two-stage hybrid model type I; and (iv “negative signal” predictive power of the two-stage hybrid model type III is stronger than that of the two-stage hybrid model type II. In summary, the two-stage hybrid model III has the best classification capability to forecast SMEs credit risk in SCF, which can be a useful prediction tool for China’s FIs.

  19. Hybrid inversions of CO2 fluxes at regional scale applied to network design

    Science.gov (United States)

    Kountouris, Panagiotis; Gerbig, Christoph; -Thomas Koch, Frank

    2013-04-01

    Long term observations of atmospheric greenhouse gas measuring stations, located at representative regions over the continent, improve our understanding of greenhouse gas sources and sinks. These mixing ratio measurements can be linked to surface fluxes by atmospheric transport inversions. Within the upcoming years new stations are to be deployed, which requires decision making tools with respect to the location and the density of the network. We are developing a method to assess potential greenhouse gas observing networks in terms of their ability to recover specific target quantities. As target quantities we use CO2 fluxes aggregated to specific spatial and temporal scales. We introduce a high resolution inverse modeling framework, which attempts to combine advantages from pixel based inversions with those of a carbon cycle data assimilation system (CCDAS). The hybrid inversion system consists of the Lagrangian transport model STILT, the diagnostic biosphere model VPRM and a Bayesian inversion scheme. We aim to retrieve the spatiotemporal distribution of net ecosystem exchange (NEE) at a high spatial resolution (10 km x 10 km) by inverting for spatially and temporally varying scaling factors for gross ecosystem exchange (GEE) and respiration (R) rather than solving for the fluxes themselves. Thus the state space includes parameters for controlling photosynthesis and respiration, but unlike in a CCDAS it allows for spatial and temporal variations, which can be expressed as NEE(x,y,t) = λG(x,y,t) GEE(x,y,t) + λR(x,y,t) R(x,y,t) . We apply spatially and temporally correlated uncertainties by using error covariance matrices with non-zero off-diagonal elements. Synthetic experiments will test our system and select the optimal a priori error covariance by using different spatial and temporal correlation lengths on the error statistics of the a priori covariance and comparing the optimized fluxes against the 'known truth'. As 'known truth' we use independent fluxes

  20. A study of social information control affordances and gender difference in Facebook self-presentation.

    Science.gov (United States)

    Kuo, Feng-Yang; Tseng, Chih-Yi; Tseng, Fan-Chuan; Lin, Cathy S

    2013-09-01

    Affordances refer to how interface features of an IT artifact, perceived by its users in terms of their potentials for action, may predict the intensity of usage. This study investigates three social information affordances for expressive information control, privacy information control, and image information control in Facebook. The results show that the three affordances can significantly explain how Facebook's interface designs facilitate users' self-presentation activities. In addition, the findings reveal that males are more engaged in expressing information than females, while females are more involved in privacy control than males. A practical application of our study is to compare and contrast the level of affordances offered by various social network sites (SNS) like Facebook and Twitter, as well as differences in online self-presentations across cultures. Our approach can therefore be useful to investigate how SNS design features can be tailored to specific gender and culture needs.

  1. Development and Validation of the Social Information Processing Application: A Web-Based Measure of Social Information Processing Patterns in Elementary School-Age Boys

    Science.gov (United States)

    Kupersmidt, Janis B.; Stelter, Rebecca; Dodge, Kenneth A.

    2011-01-01

    The purpose of this study was to evaluate the psychometric properties of an audio computer-assisted self-interviewing Web-based software application called the Social Information Processing Application (SIP-AP) that was designed to assess social information processing skills in boys in RD through 5th grades. This study included a racially and…

  2. The Role of the Individual in the Social Information Process

    Directory of Open Access Journals (Sweden)

    Christian Fuchs

    2003-02-01

    Full Text Available Abstract: The aim of this paper is to point out which role the individual plays in the generation of information in social systems. First, it is argued that the individual is a social, self-conscious, creative, reflective, cultural, symbol- and language-using, active natural, producing, labouring, objective, corporeal, living, real, sensuous, visionary, imaginative, designing, co-operative being that makes its own history and can strive towards freedom and autonomy. Based on these assumptions the re-creation/self-organisation of social systems is described as a dialectic of actions and social structures and as a dialectic of individual information and social information. The individual enters economic, political and cultural relationships that result in the emergence and differentiation of social (i.e. economic, political and cultural information which enables and constrains individual actions and thinking. Individuals as actors in social systems are indispensable for social self-organisation.

  3. Guaranteed Cost Control for Exponential Synchronization of Cellular Neural Networks with Mixed Time-Varying Delays via Hybrid Feedback Control

    Directory of Open Access Journals (Sweden)

    T. Botmart

    2013-01-01

    Full Text Available The problem of guaranteed cost control for exponential synchronization of cellular neural networks with interval nondifferentiable and distributed time-varying delays via hybrid feedback control is considered. The interval time-varying delay function is not necessary to be differentiable. Based on the construction of improved Lyapunov-Krasovskii functionals is combined with Leibniz-Newton's formula and the technique of dealing with some integral terms. New delay-dependent sufficient conditions for the exponential synchronization of the error systems with memoryless hybrid feedback control are first established in terms of LMIs without introducing any free-weighting matrices. The optimal guaranteed cost control with linear error hybrid feedback is turned into the solvable problem of a set of LMIs. A numerical example is also given to illustrate the effectiveness of the proposed method.

  4. A Self-Consistent Scheme for Optical Response of large Hybrid Networks of Semiconductor Quantum Dots and Plasmonic Metal Nanoparticles

    Science.gov (United States)

    Barbiellini, Bernardo; Hayati, L.; Lane, C.; Bansil, A.; Mosallaei, H.

    We discuss a self-consistent scheme for treating the optical response of large, hybrid networks of semiconducting quantum dots (SQDs) and plasmonic metallic nanoparticles (MNPs). Our method is efficient and scalable and becomes exact in the limiting case of weakly interacting SQDs. The self-consistent equations obtained for the steady state are analogous to the Heisenberg equations of motion for the density matrix of a SQD placed in an effective electric field computed within the discrete dipole approximation (DDA). Illustrative applications of the theory to square and honeycomb SQD, MNP and hybrid SDQ/MNP lattices as well as SQD-MNP dimers are presented. Our results demonstrate that hybrid SQD-MNP lattices can provide flexible platforms for light manipulation with tunable resonant characteristics.

  5. Self-consistent scheme for optical response of large hybrid networks of semiconductor quantum dots and plasmonic metal nanoparticles

    Science.gov (United States)

    Hayati, L.; Lane, C.; Barbiellini, B.; Bansil, A.; Mosallaei, H.

    2016-06-01

    We discuss a self-consistent scheme for treating the optical response of large, hybrid networks of semiconducting quantum dots (SQDs) and plasmonic metallic nanoparticles (MNPs). Our method is efficient and scalable and becomes exact in the limiting case of weakly interacting SQDs. The self-consistent equations obtained for the steady state are analogous to the von Neumann equations of motion for the density matrix of a SQD placed in an effective electric field computed within the discrete dipole approximation. Illustrative applications of the theory to square and honeycomb SQD, MNP, and hybrid SDQ-MNP lattices as well as SQD-MNP dimers are presented. Our results demonstrate that hybrid SQD-MNP lattices can provide flexible platforms for light manipulation with tunable resonant characteristics.

  6. Optimal reactive power and voltage control in distribution networks with distributed generators by fuzzy adaptive hybrid particle swarm optimisation method

    DEFF Research Database (Denmark)

    Chen, Shuheng; Hu, Weihao; Su, Chi

    2015-01-01

    A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid PSO (FAHPSO) is proposed. The objective is to minimize comprehensive cost, consisting of power loss and operation cost of transformers...... and capacitors, and subject to constraints such as minimum and maximum reactive power limits of distributed generators, maximum deviation of bus voltages, maximum allowable daily switching operation number (MADSON). Particle swarm optimization (PSO) is used to solve the corresponding mixed integer non......-linear programming problem (MINLP) and the hybrid PSO method (HPSO), consisting of three PSO variants, is presented. In order to mitigate the local convergence problem, fuzzy adaptive inference is used to improve the searching process and the final fuzzy adaptive inference based hybrid PSO is proposed. The proposed...

  7. Hybrid metal-organic conductive network with plasmonic nanoparticles and fluorene (Conference Presentation)

    Science.gov (United States)

    Fontana, Laura; Fratoddi, Ilaria; Matassa, Roberto; Familiari, Giuseppe; Venditti, Iole; Batocchio, Chiara; Magnano, Elena; Nappini, Silvia; Leahu, Grigore; Belardini, Alessandro; Li Voti, Roberto; Sibilia, Concita

    2017-05-01

    For the development of new generation portable electronic devices, the realization of thin and flexible electrodes have a crucial role. Conductive organic systems can address this issue in different ways. Indeed, conductance in organic molecules were studied in different papers starting from seminal papers in last 70's [1] up to recent ones [2]. Among organic species, conduction and electronic characteristics of Fluorene derivatives were studied in different configurations [3,4]. Unfortunately, the conductance of organic materials is limited by charge transport mechanism [5]. Hybrid system with organic conductive compounds covalently linked with metal centres can lead to enhanced conductivity [6]. Here we synthesized gold and silver nanoparticles (AuNPs and AgNPs) stabilized with a fluorene thiolate derivative, namely 9,9-Didodecyl-2,7-bis(acetylthio)fluorene (FL). In the synthesis process the metal nanoparticles (MNPs) size results to be around 5 nm in diameter [7]. When deposited on a planar substrate, the hybrid compound form a regular network of MNPs separated each other by fluorene spacers covalently linked by thiol groups [8]. We deposited the network on substrate with two interdigitated electrodes in order to measure conductive properties (I-V characteristics). In I-V measurements it results to be that AgNPs based network is 200 times more conductive than AuNPs one. Selective oxidation of AgNPs network close to positive electrodes gives rise to a Schottky diode behavior in the I-V characteristic that could find potential applications in nano-electronics devices. The fluorescence and extinction spectra of FL-AgNPs and FL-AuNPs where characterised. References [1] C. K. Chiang, C. R. Fincher, Jr., Y. W. Park, A. J. Heeger, H. Shirakawa, E. J. Louis, S. C. Gau, and Alan G. MacDiarmid, Phys. Rev. Lett. 39, 1098 (1977). [2] Hylke B. Akkerman, Paul W. M. Blom, Dago M. de Leeuw and Bert de Boer, Nature 441, 69 (2006). [3] Rajendra Prasad Kalakodimi, Aletha M. Nowak

  8. Information networks and worker recruitment

    NARCIS (Netherlands)

    Schram, A.; Brandts, J.; Gërxhani, K.

    2007-01-01

    This paper studies experimentally how the existence of social information networks affects the ways in which firms recruit new personnel. Through such networks firms learn about prospective employees' performance in previous jobs. Assuming individualistic preferences social networks are predicted no

  9. DataBus-based hybrid routing approach for orbit access networks in lunar exploration

    Science.gov (United States)

    Zeng, Hui; Meng, Ke; Deng, Julia

    2012-06-01

    One of the major challenges for lunar exploration missions is how to achieve dynamic and robust routing. To reduce the development cost, it is desirable to leverage existing technologies, such as routing in mobile ad hoc networks (MANETs) and delay tolerant networks (DTN). However, these technologies are developed for the Earth environment and hence need further investigation for the lunar environment. To support robust access and dynamic mission operations, we propose a DataBus-based Hybrid Routing (DBHR) approach that combines MANET reactive routing protocol (such as AODV) and DTN-based bundle delivery. Our DBHR approach is designed for a tiered architecture where remote nodes communicate with upper-tier gateways through data carriers (DataBus) using short-range radio interfaces. Our scheme explores the (non)availability of the end-to-end path between two peers using MANET routing and provides diverse route options based upon different parameters. This interaction between hop-by-hop DTN technologies and end-to-end MANET protocol will result in a reliable and robust routing protocol for orbit access and improve the overall communication capabilities. To evaluate its performance, we implemented our proposed scheme on commercial-off-theshelf (COTS) routers with the custom OpenWRT and tailored IBR-DTN bundle protocol distribution. The on-demand service request and grant mechanisms are also developed in our implementation to allow certain DTN nodes to reserve the future access opportunities. Finally, we demonstrate the achieved capabilities and performance gains through experiments on a hardware test bed that consists of several COTS routers with our implementation.

  10. Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    Haisheng Song

    2013-01-01

    Full Text Available The back propagation neural network (BPNN algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

  11. ProjectiveSynchronization of Complex Dynamical Networks with Time-Varying Coupling Strength via Hybrid Feedback Control

    Institute of Scientific and Technical Information of China (English)

    郭晓永; 李俊民

    2011-01-01

    We introduce a hybrid feedback control scheme to design a controller for the projective synchronization of complex dynamical networks with unknown periodically time-varying parameters.A differential-difference mixed parametric learning law and an adaptive learning control law are constructed to ensure the asymptotic convergence of the error in the sense of square error norm.Moreover,numerical simulation results are used to verify the effectiveness of the proposed method.%We introduce a hybrid feedback control scheme to design a controller for the projective synchronization of complex dynamical networks with unknown periodically time-varying parameters. A differential-difference mixed parametric learning law and an adaptive learning control law are constructed to ensure the asymptotic convergence of the error in the sense of square error norm. Moreover, numerical simulation results are used to verify the effectiveness of the proposed method.

  12. Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

    Science.gov (United States)

    Yuan, Fei; Zhou, You; Wang, Meng; Yang, Jing; Wu, Kai; Lu, Changhong; Kong, Xiangyin; Cai, Yu-Dong

    2015-01-01

    Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer. PMID:26504486

  13. Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Zahid Farid

    2016-01-01

    Full Text Available In indoor environments, WiFi (RSS based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs. This model exploits machine learning, in particular Artificial Natural Network (ANN techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.

  14. Functional Carbon Nanotube/Mesoporous Carbon/MnO2 Hybrid Network for High-Performance Supercapacitors

    Directory of Open Access Journals (Sweden)

    Tao Tao

    2014-01-01

    Full Text Available A functional carbon nanotube/mesoporous carbon/MnO2 hybrid network has been developed successfully through a facile route. The resulting composites exhibited a high specific capacitance of 351 F/g at 1 A g−1, with intriguing charge/discharge rate performance and cycling stability due to a synergistic combination of large surface area and excellent electron-transport capabilities of MnO2 with the good conductivity of the carbon nanotube/mesoporous carbon networks. Such composite shows great potential to be used as electrodes for supercapacitors.

  15. Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

    Science.gov (United States)

    Furferi, Rocco; Governi, Lapo; Volpe, Yary

    2016-11-01

    Color matching of fabric blends is a key issue for the textile industry, mainly due to the rising need to create high-quality products for the fashion market. The process of mixing together differently colored fibers to match a desired color is usually performed by using some historical recipes, skillfully managed by company colorists. More often than desired, the first attempt in creating a blend is not satisfactory, thus requiring the experts to spend efforts in changing the recipe with a trial-and-error process. To confront this issue, a number of computer-based methods have been proposed in the last decades, roughly classified into theoretical and artificial neural network (ANN)-based approaches. Inspired by the above literature, the present paper provides a method for accurate estimation of spectrophotometric response of a textile blend composed of differently colored fibers made of different materials. In particular, the performance of the Kubelka-Munk (K-M) theory is enhanced by introducing an artificial intelligence approach to determine a more consistent value of the nonlinear function relationship between the blend and its components. Therefore, a hybrid K-M+ANN-based method capable of modeling the color mixing mechanism is devised to predict the reflectance values of a blend.

  16. An Integrated Hybrid Energy Harvester for Autonomous Wireless Sensor Network Nodes

    Directory of Open Access Journals (Sweden)

    Mukter Zaman

    2014-01-01

    Full Text Available Profiling environmental parameter using a large number of spatially distributed wireless sensor network (WSN NODEs is an extensive illustration of advanced modern technologies, but high power requirement for WSN NODEs limits the widespread deployment of these technologies. Currently, WSN NODEs are extensively powered up using batteries, but the battery has limitation of lifetime, power density, and environmental concerns. To overcome this issue, energy harvester (EH is developed and presented in this paper. Solar-based EH has been identified as the most viable source of energy to be harvested for autonomous WSN NODEs. Besides, a novel chemical-based EH is reported as the potential secondary source for harvesting energy because of its uninterrupted availability. By integrating both solar-based EH and chemical-based EH, a hybrid energy harvester (HEH is developed to power up WSN NODEs. Experimental results from the real-time deployment shows that, besides supporting the daily operation of WSN NODE and Router, the developed HEH is capable of producing a surplus of 971 mA·hr equivalent energy to be stored inside the storage for NODE and 528.24 mA·hr equivalent energy for Router, which is significantly enough for perpetual operation of autonomous WSN NODEs used in environmental parameter profiling.

  17. Polyaniline nanoparticle-carbon nanotube hybrid network vapour sensors with switchable chemo-electrical polarity

    Science.gov (United States)

    Lu, Jianbo; Park, Bong Jun; Kumar, Bijandra; Castro, Mickaël; Choi, Hyoung Jin; Feller, Jean-François

    2010-06-01

    Chemo-resistive sensors were prepared from monodisperse poly(aniline) nanoparticles (PaniNP) synthesized via oxidative dispersion polymerization. Poly(styrene sulfonic acid) (PSSA) was used as the stabilizer and dopant agent. PaniNP transducers were assembled by spraying layer by layer a solution containing different concentrations of PaniNP and multi-wall carbon nanotubes (MWNT) onto interdigitated electrodes. This process led to stable sensors with reproducible responses upon chemical cycling. Chemo-electrical properties of these sensors have been investigated in sequential flows of pure nitrogen and nitrogen saturated with a set of volatile organic compounds (VOC). Interestingly the sensing mode of PaniNP transducers (the NVC or PVC effect) can be switched simply by increasing PaniNP content or by the addition of only 0.5% of MWNT to reach a resistance lower than 150 Ω. Due to their original conducting architecture well imaged by atomic force microscopy (AFM), i.e. a double percolated conductive network, PaniNP-MWNT hybrids present both higher sensitivity and selectivity than other formulations, demonstrating a positive synergy. Mechanisms are proposed to describe the original chemo-electrical behaviours of PaniNP-based sensors and explain the origin of their selectivity and sensing principle. These features make them attractive to be integrated in e-noses.

  18. Polyaniline nanoparticle-carbon nanotube hybrid network vapour sensors with switchable chemo-electrical polarity

    Energy Technology Data Exchange (ETDEWEB)

    Lu Jianbo; Kumar, Bijandra; Castro, Mickael; Feller, Jean-Francois [Smart Plastics Group, European University of Brittany (UEB), LIMAT-B-UBS, Lorient 56321 (France); Park, Bong Jun; Choi, Hyoung Jin, E-mail: jean-francois.feller@univ-ubs.fr [Department of Polymer Science and Engineering, Inha University, Incheon 402-751 (Korea, Republic of)

    2010-06-25

    Chemo-resistive sensors were prepared from monodisperse poly(aniline) nanoparticles (PaniNP) synthesized via oxidative dispersion polymerization. Poly(styrene sulfonic acid) (PSSA) was used as the stabilizer and dopant agent. PaniNP transducers were assembled by spraying layer by layer a solution containing different concentrations of PaniNP and multi-wall carbon nanotubes (MWNT) onto interdigitated electrodes. This process led to stable sensors with reproducible responses upon chemical cycling. Chemo-electrical properties of these sensors have been investigated in sequential flows of pure nitrogen and nitrogen saturated with a set of volatile organic compounds (VOC). Interestingly the sensing mode of PaniNP transducers (the NVC or PVC effect) can be switched simply by increasing PaniNP content or by the addition of only 0.5% of MWNT to reach a resistance lower than 150 {Omega}. Due to their original conducting architecture well imaged by atomic force microscopy (AFM), i.e. a double percolated conductive network, PaniNP-MWNT hybrids present both higher sensitivity and selectivity than other formulations, demonstrating a positive synergy. Mechanisms are proposed to describe the original chemo-electrical behaviours of PaniNP-based sensors and explain the origin of their selectivity and sensing principle. These features make them attractive to be integrated in e-noses.

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

    Science.gov (United States)

    Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

    2014-09-01

    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.

  20. Development of hybrid polymer electrolyte membranes based on the semi-interpenetrating network concept

    Energy Technology Data Exchange (ETDEWEB)

    Colicchio, I.; Moeller, M. [Deutsches Wollforschungsinstitut an der RWTH Aachen, Pauwelsstrasse 8, 52056 Aachen (Germany); Lehrstuhl fuer Textilchemie und Makromolekulare Chemie der RWTH Aachen, Worringerweg 1, 52056 Aachen (Germany); Keul, H. [Lehrstuhl fuer Textilchemie und Makromolekulare Chemie der RWTH Aachen, Worringerweg 1, 52056 Aachen (Germany); Sanders, D.; Simon, U. [Institut fuer Anorganische Chemie der RWTH Aachen, Landoltweg 1, 52074 Aachen (Germany); Weirich, T.E. [Gemeinschaftslabor fuer Elektronenmikroskopie der RWTH Aachen, Ahornstrasse 55, 52074 Aachen (Germany)

    2006-07-15

    Hybrid inorganic/organic polymer electrolyte membranes for potential fuel cell applications are prepared by centrifugal casting from solutions of sulfonated polyetheretherketone (SPEEK) (DS 64%) and polyethoxysiloxane (PEOS) in dimethylacetamide, following the concept of a semi-interpenetrating network. The in situ transformation of PEOS into SiO{sub 2} occurs in a ''water free'' process. The morphology of the films obtained is controlled by the phase segregation process, determined by the rate of evaporation of the solvent and by the transformation of PEOS into SiO{sub 2}-particles. The latter process is influenced by the presence of a catalyst. Moreover, N-(3-triethoxysilylpropyl)-4,5-dihydroimidazole is added to the mixture to enhance the interaction between SPEEK and PEOS and to influence the membrane morphology. The size and size-distribution of the SiO{sub 2} particles formed in the organic matrix are examined by means of transmission electron microscopy. The TEM investigations show a strongly reduced particle size when N-(3-triethoxysilylpropyl)-4,5-dihydroimidazole is added to the mixture. Proton conductivity measurements are performed on the membranes by impedance spectroscopy in an open set-up that allows measurements along the longitudinal direction of the sample. All the samples show a plateau in impedance at medium frequencies that represents the proton conducting process. Nafion registered 115 is measured in the same set-up for comparison. (Abstract Copyright [2006], Wiley Periodicals, Inc.)

  1. A hybrid method for image Denoising based on Wavelet Thresholding and RBF network

    Directory of Open Access Journals (Sweden)

    Sandeep Dubey

    2012-06-01

    Full Text Available Digital image denoising is crucial part of image pre-processing. The application of denoising process in satellite image data and also in television broadcasting. Image data sets collected by image sensors are generally contaminated by noise. Imperfect instruments, problems with the data acquisition process, and interfering natural phenomena can all degrade the data of interest. Furthermore, noise can be introduced by transmission errors and compression. Thus, denoising is often a necessary and the first step to be taken before the images data is analyzed. In this paper we proposed a novel methodology for image denoising. Image denoising method based on wavelet transform and radial basis neural network and also used concept of soft thresholding. Wavelet transform decomposed image in to different layers, the decomposed layer differentiate by horizontal, vertical and diagonal. For the test of our hybrid method, we used noise image dataset. This data provided by UCI machine learning website. Our proposed method compare with traditional method and our base paper method and getting better comparative result.

  2. Combination of Hybrid Chaotic Encryption and LDPC for Secure Transmission of Images over Wireless Networks

    Directory of Open Access Journals (Sweden)

    Mona F. M. Mursi

    2014-11-01

    Full Text Available Robust and secure transmission strategy for high quality image through wireless networks is considered a great challenge. However, the majority of encrypted image transmission schemes don't consider well the effect of bit errors occurring during transmission. These errors are due to the factors that affect the information such as noise and multipath propagation. That should be handled by an efficient channel coding scheme. Our proposed scheme is based on combining hybrid chaotic encryption, which is based on two-dimensional chaotic maps which is utilized for data security, with an error correction technique based on the Low Density Parity Check (LDPC code. The LDPC is employed as channel coding for data communication in order to solve the problem of the channel’s limited bandwidth and improve throughput. Simulation results show that the proposed scheme achieves a high degree of robustness against channel impairments and wide varieties of attacks as wells as improved reliability of the wireless channel. In addition, LDPC is utilized for error correction in order to solve the limitations of wireless channels.

  3. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

    Directory of Open Access Journals (Sweden)

    Montri Inthachot

    2016-01-01

    Full Text Available This study investigated the use of Artificial Neural Network (ANN and Genetic Algorithm (GA for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

  4. Production of Engineered Fabrics Using Artificial Neural Network-Genetic Algorithm Hybrid Model

    Science.gov (United States)

    Mitra, Ashis; Majumdar, Prabal Kumar; Banerjee, Debamalya

    2015-10-01

    The process of fabric engineering which is generally practised in most of the textile mills is very complicated, repetitive, tedious and time consuming. To eliminate this trial and error approach, a new approach of fabric engineering has been attempted in this work. Data sets of construction parameters [comprising of ends per inch, picks per inch, warp count and weft count] and three fabric properties (namely drape coefficient, air permeability and thermal resistance) of 25 handloom cotton fabrics have been used. The weights and biases of three artificial neural network (ANN) models developed for the prediction of drape coefficient, air permeability and thermal resistance were used to formulate the fitness or objective function and constraints of the optimization problem. The optimization problem was solved using genetic algorithm (GA). In both the fabrics which were attempted for engineering, the target and simulated fabric properties were very close. The GA was able to search the optimum set of fabric construction parameters with reasonably good accuracy except in case of EPI. However, the overall result is encouraging and can be improved further by using larger data sets of handloom fabrics by hybrid ANN-GA model.

  5. Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Muhammad W. Khan

    2016-07-01

    Full Text Available Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of static nodes is developed based on hybrid measurements, namely angle of arrival and received signal strength data. An approach for localisation of sensor nodes is proposed as a weighted linear least squares algorithm. The unknown path-loss exponent associated with the received signal strength is estimated jointly with the coordinates of the sensor nodes via the generalised pattern search method. The algorithm’s performance validation is conducted both theoretically and by simulation. A theoretical mean square error expression is derived, followed by the derivation of the linear Cramer-Rao bound which serves as a benchmark for the proposed location estimators. Accurate results are demonstrated with 25%–30% improvement in estimation accuracy with a weighted linear least squares algorithm as compared to linear least squares solution.

  6. Fortification of Hybrid Intrusion Detection System Using Variants of Neural Networks and Support Vector Machines

    Directory of Open Access Journals (Sweden)

    A. M. Chandrashekhar

    2013-02-01

    Full Text Available Intrusion Detection Systems (IDS form a key part of system defence, where it identifies abnormalactivities happening in a computer system. In recent years different soft computing based techniques havebeen proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfecttechnology. This has provided an opportunity for data mining to make quite a lot of importantcontributions in the field of intrusion detection. In this paper we have proposed a new hybrid techniqueby utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzyand radial basis function(RBF SVM for fortification of the intrusion detection system. Theproposed technique has five major steps in which, first step is to perform the relevance analysis, and theninput data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that eachof the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster.Subsequently, a vector for SVM classification is formed and in the last step, classification using RBFSVMis performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 datasetand we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Ourtechnique could achieve better accuracy for all types of intrusions. The results of proposed technique arecompared with the other existing techniques. These comparisons proved the effectiveness of ourtechnique.

  7. Analysis of SDWDM Ring Network and Enhancement Using Different Hybrid Optical Amplifiers and Modulation Formats

    Science.gov (United States)

    Anand, Vineet; Sharma, Anurag

    2016-09-01

    In this paper, performance enhancement of super-dense wavelength division multiplexing (SDWDM) optical add-drop multiplexer optical ring network for six nodes, 50 wavelengths having channel spacing of 0.2 nm for 300 km unidirectional nonlinear fiber is successfully demonstrated. The performance of the designed system is enhanced by comparing different modulation formats (non-return to zero (NRZ), return to zero (RZ), soliton, chirped return to zero (CRZ), carrier-suppressed RZ (CSRZ)) and hybrid amplifiers (Erbium-doped fiber amplifier (EDFA)-EDFA, semiconductor optical amplifier (SOA)-SOA, SOA, EDFA, EDFA-SOA) on the basis of eye diagram and bit error rate (BER). It has been observed that CRZ modulation format and EDFA-SOA shows the best results. It has been reported that EDFA-SOA/CRZ modulation format can achieve BER as better as e-13, which gives best performance. The effect of channel spacing on SDWDM system and performance degradation due to crosstalk is also evaluated.

  8. Hybrid Control of Delay Induced Hopf Bifurcation of Dynamical Small-World Network

    Institute of Scientific and Technical Information of China (English)

    DING Dawei; ZHANG Xiaoyun; WANG Nian; LIANG Dong

    2017-01-01

    In this paper,we focus on the Hopf bifurcation control of a small-world network model with time-delay.With emphasis on the relationship between the Hopf bifurcation and the time-delay,we investigate the effect of time-delay by choosing it as the bifurcation parameter.By using tools from control and bifurcation theory,it is proved that there exists a critical value of time-delay for the stability of the model.When the time-delay passes through the critical value,the model loses its stability and a Hopf bifurcation occurs.To enhance the stability of the model,we propose an improved hybrid control strategy in which state feedback and parameter perturbation are used.Through linear stability analysis,we show that by adjusting the control parameter properly,the onset of Hopf bifurcation of the controlled model can be delayed or eliminated without changing the equilibrium point of the model.Finally,numerical simulations are given to verify the theoretical analysis.

  9. Multimodal Logistics Network Design over Planning Horizon through a Hybrid Meta-Heuristic Approach

    Science.gov (United States)

    Shimizu, Yoshiaki; Yamazaki, Yoshihiro; Wada, Takeshi

    Logistics has been acknowledged increasingly as a key issue of supply chain management to improve business efficiency under global competition and diversified customer demands. This study aims at improving a quality of strategic decision making associated with dynamic natures in logistics network optimization. Especially, noticing an importance to concern with a multimodal logistics under multiterms, we have extended a previous approach termed hybrid tabu search (HybTS). The attempt intends to deploy a strategic planning more concretely so that the strategic plan can link to an operational decision making. The idea refers to a smart extension of the HybTS to solve a dynamic mixed integer programming problem. It is a two-level iterative method composed of a sophisticated tabu search for the location problem at the upper level and a graph algorithm for the route selection at the lower level. To keep efficiency while coping with the resulting extremely large-scale problem, we invented a systematic procedure to transform the original linear program at the lower-level into a minimum cost flow problem solvable by the graph algorithm. Through numerical experiments, we verified the proposed method outperformed the commercial software. The results indicate the proposed approach can make the conventional strategic decision much more practical and is promising for real world applications.

  10. Evaluating water management strategies in watersheds by new hybrid Fuzzy Analytical Network Process (FANP) methods

    Science.gov (United States)

    RazaviToosi, S. L.; Samani, J. M. V.

    2016-03-01

    Watersheds are considered as hydrological units. Their other important aspects such as economic, social and environmental functions play crucial roles in sustainable development. The objective of this work is to develop methodologies to prioritize watersheds by considering different development strategies in environmental, social and economic sectors. This ranking could play a significant role in management to assign the most critical watersheds where by employing water management strategies, best condition changes are expected to be accomplished. Due to complex relations among different criteria, two new hybrid fuzzy ANP (Analytical Network Process) algorithms, fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and fuzzy max-min set methods are used to provide more flexible and accurate decision model. Five watersheds in Iran named Oroomeyeh, Atrak, Sefidrood, Namak and Zayandehrood are considered as alternatives. Based on long term development goals, 38 water management strategies are defined as subcriteria in 10 clusters. The main advantage of the proposed methods is its ability to overcome uncertainty. This task is accomplished by using fuzzy numbers in all steps of the algorithms. To validate the proposed method, the final results were compared with those obtained from the ANP algorithm and the Spearman rank correlation coefficient is applied to find the similarity in the different ranking methods. Finally, the sensitivity analysis was conducted to investigate the influence of cluster weights on the final ranking.

  11. Sol-gel network silica/modified montmorillonite clay hybrid nanocomposites for hydrophobic surface coatings.

    Science.gov (United States)

    Meera, Kamal Mohamed Seeni; Sankar, Rajavelu Murali; Murali, Adhigan; Jaisankar, Sellamuthu N; Mandal, Asit Baran

    2012-02-01

    Sol-gel silica/nanoclay composites were prepared through sol-gel polymerization technique using tetraethylorthosilicate precursor and montmorillonite (MMT) clay in aqueous media. In this study, both montmorillonite-K(+) and organically modified MMT (OMMT) clays were used. The prepared composites were coated on glass substrate by making 1 wt% solution in ethyltrichlorosilane. The incorporation of nanoclay does not alter the intensity of characteristic Si-O-Si peak of silica network. Thermogravimetric studies show that increasing clay content increased the degradation temperature of the composites. Differential scanning calorimetry (DSC) results of organically modified MMT nanoclay incorporated composite show a shift in the melting behavior up to 38°C. From DSC thermograms, we observed that the ΔH value decreased with increasing clay loading. X-ray diffraction patterns prove the presence of nanoclay in the composite and increase in the concentration of organically modified nanoclay from 3 to 5 wt% increases the intensity of the peak at 2θ=8° corresponds to OMMT. Morphology of the control silica gel composite was greatly influenced by the incorporation of OMMT. The presence of nanoclay changed the surface of control silica gel composite into cleaved surface with brittle in nature. Contact angle measurements were done for the coatings to study their surface behavior. These hybrid coatings on glass substrate may have applications for hydrophobic coatings on leather substrate.

  12. MULTI-OBJECTIVE OPTIMIZATION OF MULTIMEDIA PACKET SCHEDULING FOR AD HOC NETWORKS THROUGH HYBRIDIZED GENETIC ALGORITHM

    Directory of Open Access Journals (Sweden)

    R.Muthu Selvi

    2011-09-01

    Full Text Available This paper presents a new approach to optimize the schedule of the variable length multimedia packets inAd hoc networks using hybridized Genetic Algorithm (HGA.Existing algorithm called Virtual DeadlineScheduling (VDS attempts to guarantee m out of k job instances (consecutive packets in a real-timestream by their deadlines are serviced. VDS is capable of generating a feasible window constrainedschedule that utilizes 100% of resources. However, when VDS either services a packet or switches to a newrequest period, it must update the corresponding virtual deadline. Updating the service constraints is abottleneck for the algorithm which increases the time complexity. HGA overcomes the problem of updatingthe service constraints that leads to the increased time complexity. The packet length and the number ofpackets to be serviced are the two conflicting criteria which are affecting the throughput of scheduling.Using HGA, a trade off can be achieved between the packet length and the number of packets to beserviced. HGA produces an optimized schedule for the multimedia packets. Journals

  13. MULTI-OBJECTIVE OPTIMIZATION OF MULTIMEDIA PACKET SCHEDULING FOR AD HOC NETWORKS THROUGH HYBRIDIZED GENETIC ALGORITHM

    Directory of Open Access Journals (Sweden)

    R.Muthu Selvi

    2015-10-01

    Full Text Available This paper presents a new approach to optimize the schedule of the variable length multimedia packets in Ad hoc networks using hybridized Genetic Algorithm (HGA.Existing algorithm called Virtual Deadline Scheduling (VDS attempts to guarantee m out of k job instances (consecutive packets in a real-time stream by their deadlines are serviced. VDS is capable of generating a feasible window constrained schedule that utilizes 100% of resources. However, when VDS either services a packet or switches to a new request period, it must update the corresponding virtual deadline. Updating the service constraints is a bottleneck for the algorithm which increases the time complexity. HGA overcomes the problem of updating the service constraints that leads to the increased time complexity. The packet length and the number of packets to be serviced are the two conflicting criteria which are affecting the throughput of scheduling. Using HGA, a trade off can be achieved between the packet length and the number of packets to be serviced. HGA produces an optimized schedule for the multimedia packets. Journals.

  14. A Hybrid Network Model to Extract Key Criteria and Its Application for Brand Equity Evaluation

    Directory of Open Access Journals (Sweden)

    Chin-Yi Chen

    2012-01-01

    Full Text Available Making a decision implies that there are alternative choices to be considered, and a major challenge of decision-making is to identify the adequate criteria for program planning or problem evaluation. The decision-makers’ criteria consists of the characteristics or requirements each alternative must possess and the alternatives are rated on how well they possess each criterion. We often use criteria developed and used by different researchers and institutions, and these criteria have similar means and can be substituted for one another. Choosing from existing criteria offers a practical method to engineers hoping to derive a set of criteria for evaluating objects or programs. We have developed a hybrid model for extracting evaluation criteria which considers substitutions between the criteria. The model is developed based on Social Network Analysis and Maximum Mean De-Entropy algorithms. In this paper, the introduced methodology will also be applied to analyze the criteria for assessing brand equity as an application example. The proposed model demonstrates that it is useful in planning feasibility criteria and has applications in other evaluation-planning purposes.

  15. 3D Graphene-Foam-Reduced-Graphene-Oxide Hybrid Nested Hierarchical Networks for High-Performance Li-S Batteries.

    Science.gov (United States)

    Hu, Guangjian; Xu, Chuan; Sun, Zhenhua; Wang, Shaogang; Cheng, Hui-Ming; Li, Feng; Ren, Wencai

    2016-02-24

    A 3D graphene-foam-reduced-graphene-oxide hybrid nested hierarchical network is synthesized to achieve high sulfur loading and content simultaneously, which solves the "double low" issues of Li-S batteries. The obtained Li-S cathodes show a high areal capacity two times larger than that of commercial lithium-ion batteries, and a good cycling performance comparable to those at low sulfur loading.

  16. A robust cluster-based dynamic-super-node scheme for hybrid peer-to-peer network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Hybrid peer-to-peer (P2P) system can improve the performance of the entire system using super-peer. But it is difficult to measure a peer's capability exactly and ensure high reliability of the network. This paper proposes a scheme to solve these problems. Firstly, we present a hybrid P2P network in which the upper layer is Chord network and the lower layer is cluster. Then we provide a strategy to measure a peer's capability so that a cluster can be organized to be a sorting network in which peers are classified into three types: dynamic-super-node (DSN), backup-node (BN) and ordinary-node (ON). In a cluster, DSN and BNs are strongly connected. And based on this, we present an algorithm DSN flood min (DSNFM) to select DSN BN and maintain consensus of the cluster. Furthermore, we do a reliability analysis of the cluster based on churn rate of the network and gathered three rules of thumb from our simulations.

  17. Optimal Performance Monitoring of Hybrid Mid-Infrared Wavelength MIMO Free Space Optical and RF Wireless Networks in Fading Channels

    Science.gov (United States)

    Schmidt, Barnet Michael

    An optimal performance monitoring metric for a hybrid free space optical and radio-frequency (RF) wireless network, the Outage Capacity Objective Function, is analytically developed and studied. Current and traditional methods of performance monitoring of both optical and RF wireless networks are centered on measurement of physical layer parameters, the most common being signal-to-noise ratio, error rate, Q factor, and eye diagrams, occasionally combined with link-layer measurements such as data throughput, retransmission rate, and/or lost packet rate. Network management systems frequently attempt to predict or forestall network failures by observing degradations of these parameters and to attempt mitigation (such as offloading traffic, increasing transmitter power, reducing the data rate, or combinations thereof) prior to the failure. These methods are limited by the frequent low sensitivity of the physical layer parameters to the atmospheric optical conditions (measured by optical signal-to-noise ratio) and the radio frequency fading channel conditions (measured by signal-to-interference ratio). As a result of low sensitivity, measurements of this type frequently are unable to predict impending failures sufficiently in advance for the network management system to take corrective action prior to the failure. We derive and apply an optimal measure of hybrid network performance based on the outage capacity of the hybrid optical and RF channel, the outage capacity objective function. The objective function provides high sensitivity and reliable failure prediction, and considers both the effects of atmospheric optical impairments on the performance of the free space optical segment as well as the effect of RF channel impairments on the radio frequency segment. The radio frequency segment analysis considers the three most common RF channel fading statistics: Rayleigh, Ricean, and Nakagami-m. The novel application of information theory to the underlying physics of the

  18. Travelling in time with networks: Revealing present day hybridization versus ancestral polymorphism between two species of brown algae, Fucus vesiculosus and F. spiralis

    Directory of Open Access Journals (Sweden)

    Pearson Gareth A

    2011-01-01

    Full Text Available Abstract Background Hybridization or divergence between sympatric sister species provides a natural laboratory to study speciation processes. The shared polymorphism in sister species may either be ancestral or derive from hybridization, and the accuracy of analytic methods used thus far to derive convincing evidence for the occurrence of present day hybridization is largely debated. Results Here we propose the application of network analysis to test for the occurrence of present day hybridization between the two species of brown algae Fucus spiralis and F. vesiculosus. Individual-centered networks were analyzed on the basis of microsatellite genotypes from North Africa to the Pacific American coast, through the North Atlantic. Two genetic distances integrating different time steps were used, the Rozenfeld (RD; based on alleles divergence and the Shared Allele (SAD; based on alleles identity distances. A diagnostic level of genotype divergence and clustering of individuals from each species was obtained through RD while screening for exchanges through putative hybridization was facilitated using SAD. Intermediate individuals linking both clusters on the RD network were those sampled at the limits of the sympatric zone in Northwest Iberia. Conclusion These results suggesting rare hybridization were confirmed by simulation of hybrids and F2 with directed backcrosses. Comparison with the Bayesian method STRUCTURE confirmed the usefulness of both approaches and emphasized the reliability of network analysis to unravel and study hybridization

  19. Travelling in time with networks: Revealing present day hybridization versus ancestral polymorphism between two species of brown algae, Fucus vesiculosus and F. spiralis.

    Science.gov (United States)

    Moalic, Yann; Arnaud-Haond, Sophie; Perrin, Cécile; Pearson, Gareth A; Serrao, Ester A

    2011-01-31

    Hybridization or divergence between sympatric sister species provides a natural laboratory to study speciation processes. The shared polymorphism in sister species may either be ancestral or derive from hybridization, and the accuracy of analytic methods used thus far to derive convincing evidence for the occurrence of present day hybridization is largely debated. Here we propose the application of network analysis to test for the occurrence of present day hybridization between the two species of brown algae Fucus spiralis and F. vesiculosus. Individual-centered networks were analyzed on the basis of microsatellite genotypes from North Africa to the Pacific American coast, through the North Atlantic. Two genetic distances integrating different time steps were used, the Rozenfeld (RD; based on alleles divergence) and the Shared Allele (SAD; based on alleles identity) distances. A diagnostic level of genotype divergence and clustering of individuals from each species was obtained through RD while screening for exchanges through putative hybridization was facilitated using SAD. Intermediate individuals linking both clusters on the RD network were those sampled at the limits of the sympatric zone in Northwest Iberia. These results suggesting rare hybridization were confirmed by simulation of hybrids and F2 with directed backcrosses. Comparison with the Bayesian method STRUCTURE confirmed the usefulness of both approaches and emphasized the reliability of network analysis to unravel and study hybridization.

  20. Characterization of interdigitated electrode structures for water contaminant detection using a hybrid voltage divider and a vector network analyzer.

    Science.gov (United States)

    Rodríguez-Delgado, José Manuel; Rodríguez-Delgado, Melissa Marlene; Mendoza-Buenrostro, Christian; Dieck-Assad, Graciano; Omar Martínez-Chapa, Sergio

    2012-01-01

    Interdigitated capacitive electrode structures have been used to monitor or actuate over organic and electrochemical media in efforts to characterize biochemical properties. This article describes a method to perform a pre-characterization of interdigitated electrode structures using two methods: a hybrid voltage divider (HVD) and a vector network analyzer (VNA). Both methodologies develop some tests under two different conditions: free air and bi-distilled water media. Also, the HVD methodology is used for other two conditions: phosphate buffer with laccase (polyphenoloxidase; EC 1.10.3.2) and contaminated media composed by a mix of phosphate buffer and 3-ethylbenzothiazoline-6-sulfonic acid (ABTS). The purpose of this study is to develop and validate a characterization methodology using both, a hybrid voltage divider and VNA T-# network impedance models of the interdigitated capacitive electrode structure that will provide a shunt RC network of particular interest in detecting the amount of contamination existing in the water solution for the media conditions. This methodology should provide us with the best possible sensitivity in monitoring water contaminant media characteristics. The results show that both methods, the hybrid voltage divider and the VNA methodology, are feasible in determining impedance modeling parameters. These parameters can be used to develop electric interrogation procedures and devices such as dielectric characteristics to identify contaminant substances in water solutions.

  1. A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

    Science.gov (United States)

    Humphrey, Greer B.; Gibbs, Matthew S.; Dandy, Graeme C.; Maier, Holger R.

    2016-09-01

    Monthly streamflow forecasts are needed to support water resources decision making in the South East of South Australia, where baseflow represents a significant proportion of the total streamflow and soil moisture and groundwater are important predictors of runoff. To address this requirement, the utility of a hybrid monthly streamflow forecasting approach is explored, whereby simulated soil moisture from the GR4J conceptual rainfall-runoff model is used to represent initial catchment conditions in a Bayesian artificial neural network (ANN) statistical forecasting model. To assess the performance of this hybrid forecasting method, a comparison is undertaken of the relative performances of the Bayesian ANN, the GR4J conceptual model and the hybrid streamflow forecasting approach for producing 1-month ahead streamflow forecasts at three key locations in the South East of South Australia. Particular attention is paid to the quantification of uncertainty in each of the forecast models and the potential for reducing forecast uncertainty by using the hybrid approach is considered. Case study results suggest that the hybrid models developed in this study are able to take advantage of the complementary strengths of both the ANN models and the GR4J conceptual models. This was particularly the case when forecasting high flows, where the hybrid models were shown to outperform the two individual modelling approaches in terms of the accuracy of the median forecasts, as well as reliability and resolution of the forecast distributions. In addition, the forecast distributions generated by the hybrid models were up to 8 times more precise than those based on climatology; thus, providing a significant improvement on the information currently available to decision makers.

  2. An Energy-Aware Hybrid ARQ Scheme with Multi-ACKs for Data Sensing Wireless Sensor Networks.

    Science.gov (United States)

    Zhang, Jinhuan; Long, Jun

    2017-06-12

    Wireless sensor networks (WSNs) are one of the important supporting technologies of edge computing. In WSNs, reliable communications are essential for most applications due to the unreliability of wireless links. In addition, network lifetime is also an important performance metric and needs to be considered in many WSN studies. In the paper, an energy-aware hybrid Automatic Repeat-reQuest protocol (ARQ) scheme is proposed to ensure energy efficiency under the guarantee of network transmission reliability. In the scheme, the source node sends data packets continuously with the correct window size and it does not need to wait for the acknowledgement (ACK) confirmation for each data packet. When the destination receives K data packets, it will return multiple copies of one ACK for confirmation to avoid ACK packet loss. The energy consumption of each node in flat circle network applying the proposed scheme is statistical analyzed and the cases under which it is more energy efficiency than the original scheme is discussed. Moreover, how to select parameters of the scheme is addressed to extend the network lifetime under the constraint of the network reliability. In addition, the energy efficiency of the proposed schemes is evaluated. Simulation results are presented to demonstrate that a node energy consumption reduction could be gained and the network lifetime is prolonged.

  3. Three-dimensional Aerographite-GaN hybrid networks: Single step fabrication of porous and mechanically flexible materials for multifunctional applications

    Science.gov (United States)

    Schuchardt, Arnim; Braniste, Tudor; Mishra, Yogendra K.; Deng, Mao; Mecklenburg, Matthias; Stevens-Kalceff, Marion A.; Raevschi, Simion; Schulte, Karl; Kienle, Lorenz; Adelung, Rainer; Tiginyanu, Ion

    2015-03-01

    Three dimensional (3D) elastic hybrid networks built from interconnected nano- and microstructure building units, in the form of semiconducting-carbonaceous materials, are potential candidates for advanced technological applications. However, fabrication of these 3D hybrid networks by simple and versatile methods is a challenging task due to the involvement of complex and multiple synthesis processes. In this paper, we demonstrate the growth of Aerographite-GaN 3D hybrid networks using ultralight and extremely porous carbon based Aerographite material as templates by a single step hydride vapor phase epitaxy process. The GaN nano- and microstructures grow on the surface of Aerographite tubes and follow the network architecture of the Aerographite template without agglomeration. The synthesized 3D networks are integrated with the properties from both, i.e., nanoscale GaN structures and Aerographite in the form of flexible and semiconducting composites which could be exploited as next generation materials for electronic, photonic, and sensors applications.

  4. Metropolitian area network services comprised of virtual local area networks running over hybrid fiber-coax and asynchronous transfer mode technologies

    Science.gov (United States)

    Biedron, William S.

    1995-11-01

    Since 1990 there has been a rapid increase in the demand for communication services, especially local and wide area network (LAN/WAN) oriented services. With the introduction of the DFB laser transmitter, hybrid-fiber-coax (HFC) cable plant designs, ATM transport technologies and rf modems, new LAN/WAN services can now be defined and marketed to residential and business customers over existing cable TV systems. The term metropolitan area network (MAN) can be used to describe this overall network. This paper discusses the technical components needed to provision these services as well as provides some perspectives on integration issues. Architecture at the headend and in the backbone is discussed, as well as specific service definitions and the technology issues associated with each. The TCP/IP protocol is suggested as a primary protocol to be used throughout the MAN.

  5. A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes.

    Science.gov (United States)

    Chen, Cong; Zhang, Guohui; Tarefder, Rafiqul; Ma, Jianming; Wei, Heng; Guan, Hongzhi

    2015-07-01

    Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance.

  6. Designing and assessing a sustainable networked delivery (SND) system: hybrid business-to-consumer book delivery case study.

    Science.gov (United States)

    Kim, Junbeum; Xu, Ming; Kahhat, Ramzy; Allenby, Braden; Williams, Eric

    2009-01-01

    We attempted to design and assess an example of a sustainable networked delivery (SND) system: a hybrid business-to-consumer book delivery system. This system is intended to reduce costs, achieve significant reductions in energy consumption, and reduce environmental emissions of critical local pollutants and greenhouse gases. The energy consumption and concomitant emissions of this delivery system compared with existing alternative delivery systems were estimated. We found that regarding energy consumption, an emerging hybrid delivery system which is a sustainable networked delivery system (SND) would consume 47 and 7 times less than the traditional networked delivery system (TND) and e-commerce networked delivery system (END). Regarding concomitant emissions, in the case of CO2, the SND system produced 32 and 7 times fewer emissions than the TND and END systems. Also the SND system offer meaningful economic benefit such as the costs of delivery and packaging, to the online retailer, grocery, and consumer. Our research results show that the SND system has a lot of possibilities to save local transportation energy consumption and delivery costs, and reduce environmental emissions in delivery system.

  7. Hybrid forum or network? The social and political construction of an international 'technical consultation': male circumcision and HIV prevention.

    Science.gov (United States)

    Giami, Alain; Perrey, Christophe; Mendonça, André Luiz de Oliveira; de Camargo, Kenneth Rochel

    2015-01-01

    The technical consultation in Montreux, organised by World Health Organization and UNAIDS in 2007, recommended male circumcision as a method for preventing HIV transmission. This consultation came out of a long process of releasing reports and holding international and regional conferences, a process steered by an informal network. This network's relations with other parties is analysed along with its way of working and the exchanges during the technical consultation that led up to the formal adoption of a recommendation. Conducted in relation to the concepts of a 'hybrid forum' and 'network', this article shows that the decision was based on the formation and consolidation of a network of persons. They were active in all phases of this process, ranging from studies of the recommendation's efficacy, feasibility and acceptability to its adoption and implementation. In this sense, this consultation cannot be described as the constitution of a 'hybrid forum', which is characterised by its openness to a debate as well as a plurality of issues formulated by the actors and of resources used by them. On the contrary, little room was allowed for contradictory discussions, as if the decision had already been made before the Montreux consultation.

  8. Using practical and social information to influence flood adaptation behavior

    Science.gov (United States)

    Allaire, Maura C.

    2016-08-01

    As the prospect for more frequent and severe extreme weather events gains scientific support, many nations are evaluating mitigation and adaptation options. Insurance and home retrofits could reduce household welfare losses due to flood events. Yet even after disasters, households often fail to take risk mitigation actions. This paper presents the first randomized field experiment that tests the effect of information provision on household uptake of flood insurance and home retrofits. A sample of 364 flood-prone households in Bangkok was randomly split into treatment and control groups. The treatment group received practical details on home retrofits and flood insurance as well as social information regarding the insurance purchase decisions of peers. Results indicate that the information intervention increased insurance purchases by about five percentage points, while no effect was detected for home retrofits. This effect is nearly equal to the increase in uptake that the national insurance program in Thailand has achieved through all other means since its establishment in 2012. If scaled up to include all uninsured, flood-prone households in Bangkok, nearly 70,000 additional households could be insured. The results suggest that well-designed information interventions could increase uptake of flood insurance, without additional premium subsidies or mandates.

  9. Use of social information in seabirds: compass rafts indicate the heading of food patches.

    Directory of Open Access Journals (Sweden)

    Henri Weimerskirch

    Full Text Available Ward and Zahavi suggested in 1973 that colonies could serve as information centres, through a transfer of information on the location of food resources between unrelated individuals (Information Centre Hypothesis. Using GPS tracking and observations on group movements, we studied the search strategy and information transfer in two of the most colonial seabirds, Guanay cormorants (Phalacrocorax bougainvillii and Peruvian boobies (Sula variegata. Both species breed together and feed on the same prey. They do return to the same feeding zone from one trip to the next indicating high unpredictability in the location of food resources. We found that the Guanay cormorants use social information to select their bearing when departing the colony. They form a raft at the sea surface whose position is continuously adjusted to the bearing of the largest returning columns of cormorants. As such, the raft serves as a compass signal that gives an indication on the location of the food patches. Conversely, Peruvian boobies rely mainly on personal information based on memory to take heading at departure. They search for food patches solitarily or in small groups through network foraging by detecting the white plumage of congeners visible at long distance. Our results show that information transfer does occur and we propose a new mechanism of information transfer based on the use of rafts off colonies. The use of rafts for information transfer may be common in central place foraging colonial seabirds that exploit short lasting and/or unpredictably distributed food patches. Over the past decades Guanay cormorants have declined ten times whereas Peruvian boobies have remained relatively stable. We suggest that the decline of the cormorants could be related to reduced social information opportunities and that social behaviour and search strategies have the potential to play an important role in the population dynamics of colonial animals.

  10. Hybrid Deep Network and Polar Transformation Features for Static Hand Gesture Recognition in Depth Data

    Directory of Open Access Journals (Sweden)

    Vo Hoai Viet

    2016-05-01

    Full Text Available Static hand gesture recognition is an interesting and challenging problem in computer vision. It is considered a significant component of Human Computer Interaction and it has attracted many research efforts from the computer vision community in recent decades for its high potential applications, such as game interaction and sign language recognition. With the recent advent of the cost-effective Kinect, depth cameras have received a great deal of attention from researchers. It promoted interest within the vision and robotics community for its broad applications. In this paper, we propose the effective hand segmentation from the full depth image that is important step before extracting the features to represent for hand gesture. We also represent the novel hand descriptor explicitly encodes the shape and appearance information from depth maps that are significant characteristics for static hand gestures. We propose hand descriptor based on Polar Transformation coordinate is called Histogram of Polar Transformation (HPT in order to capture both shape and appearance. Beside a robust hand descriptor, a robust classification model also plays a very important role in the hand recognition model. In order to have a high performance in recognition rate, we propose hybrid model for classification based on Sparse Auto-encoder and Deep Neural Network. We demonstrate large improvements over the state-of-the-art methods on two challenging benchmark datasets are NTU Hand Digits and ASL Finger Spelling and achieve the overall accuracy as 97.7% and 84.58%, respectively. Our experiments show that the proposed method significantly outperforms state-of-the-art techniques.

  11. Hybrid feedback feedforward: An efficient design of adaptive neural network control.

    Science.gov (United States)

    Pan, Yongping; Liu, Yiqi; Xu, Bin; Yu, Haoyong

    2016-04-01

    This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost.

  12. A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels

    Directory of Open Access Journals (Sweden)

    Uttam Kumar

    2012-09-01

    Full Text Available Signals acquired by sensors in the real world are non-linear combinations, requiring non-linear mixture models to describe the resultant mixture spectra for the endmember’s (pure pixel’s distribution. This communication discusses inferring class fraction through a novel hybrid mixture model (HMM. HMM is a three-step process, where the endmembers are first derived from the images themselves using the N-FINDR algorithm. These endmembers are used by the linear mixture model (LMM in the second step that provides an abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual ground proportions are fed into neural network based multi-layer perceptron (MLP architecture as input to train the neurons. The neural output further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. HMM is first implemented and validated on simulated hyper spectral data of 200 bands and subsequently on real time MODIS data with a spatial resolution of 250 m. The results on computer simulated data show that the method gives acceptable results for unmixing pixels with an overall RMSE of 0.0089 ± 0.0022 with LMM and 0.0030 ± 0.0001 with the HMM when compared to actual class proportions. The unmixed MODIS images showed overall RMSE with HMM as 0.0191 ± 0.022 as compared to the LMM output considered alone that had an overall RMSE of 0.2005 ± 0.41, indicating that individual class abundances obtained from HMM are very close to the real observations.

  13. Simulation of Noise-Cancelling in the Cockpit of an Aircraft Using Two-Rate Hybrid Neural Network

    Directory of Open Access Journals (Sweden)

    ASTROV, I.

    2007-11-01

    Full Text Available This paper presents the two-rate hybrid neural network (TRHNN for processing of noisy signal. The received TRHNN consists of "fast" ADAptive LInear NEuron neural network (FADALINENN and "slow" radial basis neural network (SRBNN. The illustrative design example - noise-cancelling of noisy pilot's voice pattern - was carried out using the TRHNN. The received TRHNN has high speed of signal processing. This example demonstrates that the proposed TRHNN is capable not only to recognize the pilot's voice in the noisy voice pattern, but also to restore the pilot's voice. The simulation results with use the software package Simulink show the computing procedure and applicability of the TRHNNs for fast-acting signal processing and analysis in real-time flight conditions.

  14. A hybrid MAC protocol design for energy-efficient very-high-throughput millimeter wave, wireless sensor communication networks

    Science.gov (United States)

    Jian, Wei; Estevez, Claudio; Chowdhury, Arshad; Jia, Zhensheng; Wang, Jianxin; Yu, Jianguo; Chang, Gee-Kung

    2010-12-01

    This paper presents an energy-efficient Medium Access Control (MAC) protocol for very-high-throughput millimeter-wave (mm-wave) wireless sensor communication networks (VHT-MSCNs) based on hybrid multiple access techniques of frequency division multiplexing access (FDMA) and time division multiplexing access (TDMA). An energy-efficient Superframe for wireless sensor communication network employing directional mm-wave wireless access technologies is proposed for systems that require very high throughput, such as high definition video signals, for sensing, processing, transmitting, and actuating functions. Energy consumption modeling for each network element and comparisons among various multi-access technologies in term of power and MAC layer operations are investigated for evaluating the energy-efficient improvement of proposed MAC protocol.

  15. Identifying Gene Regulatory Networks in Arabidopsis by In Silico Prediction, Yeast-1-Hybrid, and Inducible Gene Profiling Assays.

    Science.gov (United States)

    Sparks, Erin E; Benfey, Philip N

    2016-01-01

    A system-wide understanding of gene regulation will provide deep insights into plant development and physiology. In this chapter we describe a threefold approach to identify the gene regulatory networks in Arabidopsis thaliana that function in a specific tissue or biological process. Since no single method is sufficient to establish comprehensive and high-confidence gene regulatory networks, we focus on the integration of three approaches. First, we describe an in silico prediction method of transcription factor-DNA binding, then an in vivo assay of transcription factor-DNA binding by yeast-1-hybrid and lastly the identification of co-expression clusters by transcription factor induction in planta. Each of these methods provides a unique tool to advance our understanding of gene regulation, and together provide a robust model for the generation of gene regulatory networks.

  16. Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2013-01-01

    Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

  17. Further validation of artificial neural network-based emissions simulation models for conventional and hybrid electric vehicles.

    Science.gov (United States)

    Tóth-Nagy, Csaba; Conley, John J; Jarrett, Ronald P; Clark, Nigel N

    2006-07-01

    With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO2] and oxides of nitrogen [NO(x)]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO2 and NO(x) in the case of a class-8 truck but were more accurate as the truck weight increased.

  18. Hybrid ants-like search algorithms for P2P media streaming distribution in ad hoc networks

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Media streaming delivery in wireless ad hoc networks is challenging due to the stringent resource restrictions, potential high loss rate and the decentralized architecture. To support long and high-quality streams, one viable approach is that a media stream is partitioned into segments, and then the segments are replicated in a network and served in a peer-to-peer (P2P)fashion. However, the searching strategy for segments is one key problem with the approach. This paper proposes a hybrid ants-like search algorithm (HASA) for P2P media streaming distribution in ad hoc networks. It takes the advantages of random walks and ants-like algorithms for searching in unstructured P2P networks, such as low transmitting latency, less jitter times, and low unnecessary traffic. We quantify the performance of our scheme in terms of response time, jitter times, and network messages for media streaming distribution. Simulation results showed that it can effectively improve the search efficiency for P2P media streaming distribution in ad hoc networks.

  19. Energy efficient flexible hybrid wavelength division multiplexing-time division multiplexing passive optical network with pay as you grow deployment

    Science.gov (United States)

    Garg, Amit Kumar; Madavi, Amresh Ashok; Janyani, Vijay

    2017-02-01

    A flexible hybrid wavelength division multiplexing-time division multiplexing passive optical network architecture that allows dual rate signals to be sent at 1 and 10 Gbps to each optical networking unit depending upon the traffic load is proposed. The proposed design allows dynamic wavelength allocation with pay-as-you-grow deployment capability. This architecture is capable of providing up to 40 Gbps of equal data rates to all optical distribution networks (ODNs) and up to 70 Gbps of a asymmetrical data rate to the specific ODN. The proposed design handles broadcasting capability with simultaneous point-to-point transmission, which further reduces energy consumption. In this architecture, each module sends a wavelength to each ODN, thus making the architecture fully flexible; this flexibility allows network providers to use only required OLT components and switch off others. The design is also reliable to any module or TRx failure and provides services without any service disruption. Dynamic wavelength allocation and pay-as-you-grow deployment support network extensibility and bandwidth scalability to handle future generation access networks.

  20. Can video playback provide social information for foraging blue tits?

    Science.gov (United States)

    Hämäläinen, Liisa; Rowland, Hannah M; Mappes, Johanna; Thorogood, Rose

    2017-01-01

    Video playback is becoming a common method for manipulating social stimuli in experiments. Parid tits are one of the most commonly studied groups of wild birds. However, it is not yet clear if tits respond to video playback or how their behavioural responses should be measured. Behaviours may also differ depending on what they observe demonstrators encountering. Here we present blue tits (Cyanistes caeruleus) videos of demonstrators discovering palatable or aversive prey (injected with bitter-tasting Bitrex) from coloured feeding cups. First we quantify variation in demonstrators' responses to the prey items: aversive prey provoked high rates of beak wiping and head shaking. We then show that focal blue tits respond differently to the presence of a demonstrator on a video screen, depending on whether demonstrators discover palatable or aversive prey. Focal birds faced the video screen more during aversive prey presentations, and made more head turns. Regardless of prey type, focal birds also hopped more frequently during the presence of a demonstrator (compared to a control video of a different coloured feeding cup in an empty cage). Finally, we tested if demonstrators' behaviour affected focal birds' food preferences by giving individuals a choice to forage from the same cup as a demonstrator, or from the cup in the control video. We found that only half of the individuals made their choice in accordance to social information in the videos, i.e., their foraging choices were not different from random. Individuals that chose in accordance with a demonstrator, however, made their choice faster than individuals that chose an alternative cup. Together, our results suggest that video playback can provide social cues to blue tits, but individuals vary greatly in how they use this information in their foraging decisions.

  1. Can video playback provide social information for foraging blue tits?

    Directory of Open Access Journals (Sweden)

    Liisa Hämäläinen

    2017-03-01

    Full Text Available Video playback is becoming a common method for manipulating social stimuli in experiments. Parid tits are one of the most commonly studied groups of wild birds. However, it is not yet clear if tits respond to video playback or how their behavioural responses should be measured. Behaviours may also differ depending on what they observe demonstrators encountering. Here we present blue tits (Cyanistes caeruleus videos of demonstrators discovering palatable or aversive prey (injected with bitter-tasting Bitrex from coloured feeding cups. First we quantify variation in demonstrators’ responses to the prey items: aversive prey provoked high rates of beak wiping and head shaking. We then show that focal blue tits respond differently to the presence of a demonstrator on a video screen, depending on whether demonstrators discover palatable or aversive prey. Focal birds faced the video screen more during aversive prey presentations, and made more head turns. Regardless of prey type, focal birds also hopped more frequently during the presence of a demonstrator (compared to a control video of a different coloured feeding cup in an empty cage. Finally, we tested if demonstrators’ behaviour affected focal birds’ food preferences by giving individuals a choice to forage from the same cup as a demonstrator, or from the cup in the control video. We found that only half of the individuals made their choice in accordance to social information in the videos, i.e., their foraging choices were not different from random. Individuals that chose in accordance with a demonstrator, however, made their choice faster than individuals that chose an alternative cup. Together, our results suggest that video playback can provide social cues to blue tits, but individuals vary greatly in how they use this information in their foraging decisions.

  2. Hybrid Unifying Variable Supernetwork Model

    Institute of Scientific and Technical Information of China (English)

    LIU; Qiang; FANG; Jin-qing; LI; Yong

    2015-01-01

    In order to compare new phenomenon of topology change,evolution,hybrid ratio and network characteristics of unified hybrid network theoretical model with unified hybrid supernetwork model,this paper constructed unified hybrid variable supernetwork model(HUVSM).The first layer introduces a hybrid ratio dr,the

  3. Integration of V2H/V2G Hybrid System for Demand Response in Distribution Network

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yubo; Sheikh, Omar; Hu, Boyang; Chu, Chi-Cheng; Gadh, Rajit

    2014-11-03

    Integration of Electrical Vehicles (EVs) with power grid not only brings new challenges for load management, but also opportunities for distributed storage and generation in distribution network. With the introduction of Vehicle-to-Home (V2H) and Vehicle-to-Grid (V2G), EVs can help stabilize the operation of power grid. This paper proposed and implemented a hybrid V2H/V2G system with commercialized EVs, which is able to support both islanded AC/DC load and the power grid with one single platform. Standard industrial communication protocols are implemented for a seamless respond to remote Demand Respond (DR) signals. Simulation and implementation are carried out to validate the proposed design. Simulation and implementation results showed that the hybrid system is capable of support critical islanded DC/AC load and quickly respond to the remote DR signal for V2G within 1.5kW of power range.

  4. Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons.

    Science.gov (United States)

    Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

    2017-08-11

    This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.

  5. Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction

    Energy Technology Data Exchange (ETDEWEB)

    Salcedo-Sanz, Sancho; Perez-Bellido, Angel M.; Ortiz-Garcia, Emilio G.; Portilla-Figueras, Antonio [Department of Signal Theory and Communications, Universidad de Alcala, Madrid (Spain); Prieto, Luis [Wind Resource Department, Iberdrola Renovables, Madrid (Spain); Paredes, Daniel [Department of Physics of the Earth, Astronomy and Astrophysics II, Universidad Complutense de Madrid (Spain)

    2009-06-15

    This paper presents the hybridization of the fifth generation mesoscale model (MM5) with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at wind turbines in a wind park is an important parameter used to predict the total power production of the park. Our model for short-term wind speed forecast integrates a global numerical weather prediction model and observations at different heights (using atmospheric soundings) as initial and boundary conditions for the MM5 model. Then, the outputs of this model are processed using a neural network to obtain the wind speed forecast in specific points of the wind park. In the experiments carried out, we present some results of wind speed forecasting in a wind park located at the south-east of Spain. The results are encouraging, and show that our hybrid MM5-neural network approach is able to obtain good short-term predictions of wind speed at specific points. (author)

  6. Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma.

    Science.gov (United States)

    Glass, J O; Reddick, W E

    1998-11-01

    The evaluation of pediatric osteosarcoma has suffered from the lack of an accurate imaging measure of response. One major problem is that osteosarcoma do not shrink in response to chemotherapy; instead, viable tumor is replaced by necrotic tissue. Currently available techniques that use dynamic contrast-enhanced magnetic resonance imaging to quantitatively evaluate tumor response fail to assess the percentage of necrosis. At present, histopathologic evaluation of resected tissue is the only means of measuring the percentage of necrosis in treated osteosarcoma. The current study presents a non-invasive method to visualize necrotic and viable tumor and quantitatively assess the response of osteosarcoma. Our technique uses a hybrid neural network consisting of a Kohonen self-organizing map to segment dynamic contrast-enhanced magnetic resonance images and a multi-layer backpropagation neural network to classify the segmented images. Because the hybrid neural network is completely automated, our technique removes both inter- and intra-operator error. An analysis comparing the percentage of necrosis from our technique to the histopathologic analysis revealed a highly significant Spearman correlation coefficient of 0.617 with p < 0.001.

  7. Hybrid Scheduling/Signal-Level Coordination in the Downlink of Multi-Cloud Radio-Access Networks

    KAUST Repository

    Douik, Ahmed

    2016-03-28

    In the context of resource allocation in cloud- radio access networks, recent studies assume either signal-level or scheduling-level coordination. This paper, instead, considers a hybrid level of coordination for the scheduling problem in the downlink of a multi-cloud radio- access network, so as to benefit from both scheduling policies. Consider a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and therefore allows joint signal processing between them. Across the multiple clouds, however, only scheduling-level coordination is permitted, as it requires a lower level of backhaul communication. The frame structure of every BS is composed of various time/frequency blocks, called power- zones (PZs), and kept at fixed power level. The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled, at most, to a single cloud, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs\\' frame. The paper solves the problem using graph theory techniques by constructing the conflict graph. The scheduling problem is, then, shown to be equivalent to a maximum- weight independent set problem in the constructed graph, in which each vertex symbolizes an association of cloud, user, BS and PZ, with a weight representing the utility of that association. Simulation results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

  8. A Hybrid Combination Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Changhua Yao

    2014-01-01

    Full Text Available We propose a novel hybrid combination scheme in cooperative spectrum sensing (CSS, which utilizes the diversity of reporting channels to achieve better throughput performance. Secondary users (SUs with good reporting channel quality transmit quantized local observation statistics to fusion center (FC, while others report their local decisions. FC makes the final decision by carrying out hybrid combination. We derive the closed-form expressions of throughput and detection performance as a function of the number of SUs which report local observation statistics. The simulation and numerical results show that the hybrid combination scheme can achieve better throughput performance than hard combination scheme and soft combination scheme.

  9. SOM-based Hybrid Neural Network Model for Flood Inundation Extent Forecasting

    Science.gov (United States)

    Chang, Li-Chiu; Shen, Hung-Yu; Chang, Fi-John

    2014-05-01

    In recent years, the increasing frequency and severity of floods caused by climate change and/or land overuse has been reported both nationally and globally. Therefore, estimation of flood depths and extents may provide disaster information for alleviating risk and loss of life and property. The conventional inundation models commonly need a huge amount of computational time to carry out a high resolution spatial inundation map. Moreover, for implementing appropriate mitigation strategies of various flood conditions, different flood scenarios and the corresponding mitigation alternatives are required. Consequently, it is difficult to reach real-time forecast of the inundation extent by conventional inundation models. This study proposed a SOM-RNARX model, for on-line forecasting regional flood inundation depths and extents. The SOM-RNARX model is composed of SOM (Self-Organizing Map) and RNARX (recurrent configuration of nonlinear autoregressive with exogenous inputs). The SOM network categorizes various flood inundation maps of the study area to produce a meaningful regional flood topological map. The RNARX model is built to forecast the total flooded volume of the study area. To find the neuron with the closest total inundated volume to the forecasted total inundated volumes, the forecasted value is used to adjust the weights (inundated depths) of the closest neuron and obtain a regional flood inundation map. The proposed methodology was trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model in Yilan County, Taiwan. For comparison, the CHIM (clustering-based hybrid inundation model) model which was issued by Chang et al. (2010) was performed. The major difference between these two models is that CHIM classify flooding characteristics, and SOM-RNARX extracts the relationship between rainfall pattern and flooding spatial distribution. The results show that (1)two models can adequately provide on

  10. Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis–Menten and approximate kinetic equations

    DEFF Research Database (Denmark)

    Costa, Rafael S.; Machado, Daniel; Rocha, Isabel

    2010-01-01

    , represent nowadays the limiting factor in the construction of such models. In this study, we compare four alternative modeling approaches based on Michaelis–Menten kinetics for the bi-molecular reactions and different types of simplified rate equations for the remaining reactions (generalized mass action...... using the hybrid model composed of Michaelis–Menten and the approximate lin-log kinetics indicate that this is a possible suitable approach to model complex large-scale networks where the exact rate laws are unknown....

  11. Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china.

    Science.gov (United States)

    Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng

    2015-01-01

    Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

  12. 22 CFR 96.49 - Provision of medical and social information in incoming cases.

    Science.gov (United States)

    2010-04-01

    ... interests require a more expedited decision) to consider the needs of the child and their ability to meet... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Provision of medical and social information in... United States (incoming Cases) § 96.49 Provision of medical and social information in incoming cases. (a...

  13. Social Information Processing in Preschool Children: Relations to Sociodemographic Risk and Problem Behavior

    Science.gov (United States)

    Ziv, Yair; Sorongon, Alberto

    2011-01-01

    Using a multicomponent, process-oriented approach, the links between social information processing during the preschool years and (a) sociodemographic risk and (b) behavior problems in preschool were examined in a community sample of 196 children. Findings provided support for our initial hypotheses that aspects of social information processing in…

  14. Social Information Processing Patterns, Social Skills, and School Readiness in Preschool Children

    Science.gov (United States)

    Ziv, Yair

    2013-01-01

    The links among social information processing, social competence, and school readiness were examined in this short-term longitudinal study with a sample of 198 preschool children. Data on social information processing were obtained via child interview, data on child social competence were obtained via teacher report, and data on school readiness…

  15. 3D-macroporous hybrid scaffolds for tissue engineering: Network design and mathematical modeling of the degradation kinetics

    Energy Technology Data Exchange (ETDEWEB)

    Mansur, Herman S., E-mail: hmansur@demet.ufmg.br [Department of Metallurgical and Materials Engineering, Laboratory of Biomaterials and Tissue Engineering, Federal University of Minas Gerais (Brazil); Costa, Hermes S. [Department of Materials Engineering, CEFET-MG (Brazil); Mansur, Alexandra A.P.; Pereira, Marivalda [Department of Metallurgical and Materials Engineering, Laboratory of Biomaterials and Tissue Engineering, Federal University of Minas Gerais (Brazil)

    2012-04-01

    In the present study it is reported the synthesis, characterization and subsequent degradation performance of organic-inorganic hybrid systems chemically modified by bi-functional crosslinker (glutaraldehyde, GA). The hybrids were prepared by combining 70% poly (vinyl alcohol) and 30% bioactive glass (58SiO{sub 2}-33CaO-9P{sub 2}O{sub 5}, BaG) via sol-gel route using foaming-casting method producing different macroporous tri-dimensional scaffolds depending on the degree of network crosslinking. The in vitro degradation kinetics was evaluated by measuring the mass loss upon soaking into de-ionized water at 37 Degree-Sign C for up to 21 days and different mathematical models were tested. The PVA/BaG hybrids scaffolds properties 'as-synthesized' and after the degradation process were extensively characterized by Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), mechanical compressing tests and X-ray Micro-computed Tomography analysis ({mu}CT). The results have clearly shown the effectiveness of tailoring the PVA/BaG hybrids properties and degradation kinetics mechanisms by chemically engineering the structure at nano-order level using different concentrations of the crosslinker. Moreover, these hybrid crosslinked nanostructures have shown 3D hierarchical pore size with interconnected architecture within the range of 10-450 {mu}m for potential use in the field of bone regenerative medicine. Highlights: Black-Right-Pointing-Pointer Hybrid scaffolds 70% polyvinyl alcohol-30%/bioactive glass (58SiO{sub 2}-33CaO-9P{sub 2}O{sub 5}). Black-Right-Pointing-Pointer 3D-Macropore nanostructure engineered by covalent chemical crosslinker. Black-Right-Pointing-Pointer Pore size distribution and mechanical properties comparable to cancellous bone. Black-Right-Pointing-Pointer Analysis of degradation kinetics and mechanism using five mathematical models. Black-Right-Pointing-Pointer hybrid potentially appropriate for bone tissue

  16. Significant Storage on Sensor Storage Space, Energy Consumption and Better Security Based on Routing in Hybrid Sensor Networks

    Directory of Open Access Journals (Sweden)

    K.Nageswara rao

    2011-12-01

    Full Text Available WSNs are characterized by limited resources in term s of communication, computation and energy supply. A critical constraint on sensors networks is that s ensor nodes employ batteries. A second constraint i s that sensors will be deployed unattended and in large nu mbers, so that it will be difficult to change or re charge batteries in the sensors .The Energy Consumption in wireless sensor networks varies greatly based on t he protocols the sensors use and computations used to generate keys for communication among neighbor nodes. Previous research on sensor network security mainly considers homogeneous sensor networks, where all sensor nodes have the same capabilities. Research has shown that homogeneous ad hoc networks have poor performance and scalability. The many-to- one traffic pattern dominates in sensor networks, a nd hence a sensor may only communicate with a small po rtion of its neighbors. Key Management is a fundamental security operation. Most existing key m anagement schemes try to establish shared keys for all pairs of neighbor sensors, no matter whether these nodes communicate with each other or not, and this causes large overhead and more energy consumption a nd more storage requirement. In this paper, we adopt a Hybrid Sensor Network (HSN model for bette r performance and security. We propose a novel routing-driven key establishment scheme, which only establishes shared keys for neighbor sensors that communicate with each other. We utilize Elliptic Cu rve Cryptography in the design of an efficient key Establishment scheme for sensor nodes. The performa nce evaluation and security analysis show that our key Establishment scheme can provide better securit y with significant reductions on communication overhead, storage space and energy consumption than other key Establishment schemes.

  17. Hybrid metal-coordinate transient networks: using bio-inspired building blocks to engineer the mechanical properties of physical hydrogels

    Science.gov (United States)

    Grindy, Scott; Barrett, Devin; Messersmith, Phillip; Holten-Andersen, Niels

    2014-03-01

    Recently, metal-coordinate complex crosslinks have been suggested to contribute to the self-healing properties of mussel byssi. Two specific amino acid derivatives - 3,4 dihydroxy-L-phenylalanine (dopa) and histidine (his) - are known to form coordinate complexes with trivalent and divalent ions (respectively) in aqueous solutions. We show here that, by functionalizing poly(ethylene glycol) polymers with dopa and his we are (1) able to characterize the fundamental kinetics and energetics of each specific metal-ligand pair using small amplitude oscillatory shear rheology and (2) create hybrid networks using various mixtures of metals and ligands. From this information, we can design gels with specific target mechanical properties by tailoring the amounts and types of metal-ligand crosslinks present in the gel network, resulting in the ability to engineer the mechanical relaxation spectrum. This work provides basic understanding necessary to intelligently design materials which incorporate metal-ligand crosslinks in more complex architectures.

  18. Security Enhancement With Optimal QOS Using EAP-AKA In Hybrid Coupled 3G-WLAN Convergence Network

    CERN Document Server

    Shankar, R; Dananjayan, P; 10.5121/iju.2010.1303

    2010-01-01

    The third generation partnership project (3GPP) has addressed the feasibility of interworking and specified the interworking architecture and security architecture for third generation (3G)-wireless local area network (WLAN), it is developing, system architecture evolution (SAE)/ long term evolution (LTE) architecture, for the next generation mobile communication system. To provide a secure 3G-WLAN interworking in the SAE/LTE architecture, Extensible authentication protocol-authentication and key agreement (EAP-AKA) is used. However, EAP-AKA have several vulnerabilities. Therefore, this paper not only analyses the threats and attacks in 3G-WLAN interworking but also proposes a new authentication and key agreement protocol based on EAP-AKA. The proposed protocol combines elliptic curve Diffie-Hellman (ECDH) with symmetric key cryptosystem to overcome the vulnerabilities. The proposed protocol is used in hybrid coupled 3G-WLAN convergence network to analyse its efficiency in terms of QoS metrics, the results ob...

  19. Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits.

    Science.gov (United States)

    Guo, Xinjie; Merrikh-Bayat, Farnood; Gao, Ligang; Hoskins, Brian D; Alibart, Fabien; Linares-Barranco, Bernabe; Theogarajan, Luke; Teuscher, Christof; Strukov, Dmitri B

    2015-01-01

    The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2- x /Pt memristors and CMOS integrated circuit components.

  20. Highly sensitive piezo-resistive graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone composites with improved conductive network construction.

    Science.gov (United States)

    Zhao, Hang; Bai, Jinbo

    2015-05-13

    The constructions of internal conductive network are dependent on microstructures of conductive fillers, determining various electrical performances of composites. Here, we present the advanced graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone (GCHs/PDMS) composites with high piezo-resistive performance. GCH particles were synthesized by the catalyst chemical vapor deposition approach. The synthesized GCHs can be well dispersed in the matrix through the mechanical blending process. Due to the exfoliated GNP and aligned CNTs coupling structure, the flexible composite shows an ultralow percolation threshold (0.64 vol %) and high piezo-resistive sensitivity (gauge factor ∼ 10(3) and pressure sensitivity ∼ 0.6 kPa(-1)). Slight motions of finger can be detected and distinguished accurately using the composite film as a typical wearable sensor. These results indicate that designing the internal conductive network could be a reasonable strategy to improve the piezo-resistive performance of composites.

  1. Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks

    Science.gov (United States)

    Ray, Loye Lynn

    2014-01-01

    The need for detecting malicious behavior on a computer networks continued to be important to maintaining a safe and secure environment. The purpose of this study was to determine the relationship of multilayer feed forward neural network architecture to the ability of detecting abnormal behavior in networks. This involved building, training, and…

  2. Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks

    Science.gov (United States)

    Ray, Loye Lynn

    2014-01-01

    The need for detecting malicious behavior on a computer networks continued to be important to maintaining a safe and secure environment. The purpose of this study was to determine the relationship of multilayer feed forward neural network architecture to the ability of detecting abnormal behavior in networks. This involved building, training, and…

  3. Location-Recommendation-Aware Virtual Network Embedding in Energy-Efficient Optical-Wireless Hybrid Networks Supporting 5G Models

    National Research Council Canada - National Science Library

    Gong, Xiaoxue; Ning, Zhaolong; Guo, Lei; Wei, Xuetao; Song, Qingyang

    2016-01-01

    Given the possibility of ubiquitous 5G wireless access, location data bridge the gap between the physical world and digital online social networking services and also reflect user preferences and even...

  4. Multistep Wind Speed Forecasting Using a Novel Model Hybridizing Singular Spectrum Analysis, Modified Intelligent Optimization, and Rolling Elman Neural Network

    Directory of Open Access Journals (Sweden)

    Zhongshan Yang

    2016-01-01

    Full Text Available Wind speed high-accuracy forecasting, an important part of the electrical system monitoring and control, is of the essence to protect the safety of wind power utilization. However, the wind speed signals are always intermittent and intrinsic complexity; therefore, it is difficult to forecast them accurately. Many traditional wind speed forecasting studies have focused on single models, which leads to poor prediction accuracy. In this paper, a new hybrid model is proposed to overcome the shortcoming of single models by combining singular spectrum analysis, modified intelligent optimization, and the rolling Elman neural network. In this model, except for the multiple seasonal patterns used to reduce interferences from the original data, the rolling model is utilized to forecast the multistep wind speed. To verify the forecasting ability of the proposed hybrid model, 10 min and 60 min wind speed data from the province of Shandong, China, were proposed in this paper as the case study. Compared to the other models, the proposed hybrid model forecasts the wind speed with higher accuracy.

  5. A high performance neural network for solving nonlinear programming problems with hybrid constraints

    Science.gov (United States)

    Tao, Qing; Cao, Jinde; Xue, Meisheng; Qiao, Hong

    2001-09-01

    A continuous neural network is proposed in this Letter for solving optimization problems. It not only can solve nonlinear programming problems with the constraints of equality and inequality, but also has a higher performance. The main advantage of the network is that it is an extension of Newton's gradient method for constrained problems, the dynamic behavior of the network under special constraints and the convergence rate can be investigated. Furthermore, the proposed network is simpler than the existing networks even for solving positive definite quadratic programming problems. The network considered is constrained by a projection operator on a convex set. The advanced performance of the proposed network is demonstrated by means of simulation of several numerical examples.

  6. Conformal organic-inorganic hybrid network polymer thin films by molecular layer deposition using trimethylaluminum and glycidol.

    Science.gov (United States)

    Gong, Bo; Peng, Qing; Parsons, Gregory N

    2011-05-19

    Growing interest in nanoscale organic-inorganic hybrid network polymer materials is driving exploration of new bulk and thin film synthesis reaction mechanisms. Molecular layer deposition (MLD) is a vapor-phase deposition process, based on atomic layer deposition (ALD) which proceeds by exposing a surface to an alternating sequence of two or more reactant species, where each surface half-reaction goes to completion before the next reactant exposure. This work describes film growth using trimethyl aluminum and heterobifunctional glycidol at moderate temperatures (90-150 °C), producing a relatively stable organic-inorganic network polymer of the form (-Al-O-(C(4)H(8))-O-)(n). Film growth rate and in situ reaction analysis indicate that film growth does not initially follow a steady-state rate, but increases rapidly during early film growth. The mechanism is consistent with subsurface species transport and trapping, previously documented during MLD and ALD on polymers. A water exposure step after the TMA produces a more linear growth rate, likely by blocking TMA subsurface diffusion. Uniform and conformal films are formed on complex nonplanar substrates. Upon postdeposition annealing, films transform into microporous metal oxides with ∼5 Å pore size and surface area as high as ∼327 m(2)/g, and the resulting structures duplicate the shape of the original substrate. These hybrid films and porous materials could find uses in several research fields including gas separations and diffusion barriers, biomedical scaffolds, high surface area coatings, and others.

  7. A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network

    Directory of Open Access Journals (Sweden)

    Xiaowei Zhang

    2012-09-01

    Full Text Available Driving patterns exert an important influence on the fuel economy of vehicles, especially hybrid electric vehicles. This paper aims to build a method to identify driving patterns with enough accuracy and less sampling time compared than other driving pattern recognition algorithms. Firstly a driving pattern identifier based on a Learning Vector Quantization neural network is established to analyze six selected representative standard driving cycles. Micro-trip extraction and Principal Component Analysis methods are applied to ensure the magnitude and diversity of the training samples. Then via Matlab/Simulink, sample training simulation is conducted to determine the minimum neuron number of the Learning Vector Quantization neural network and, as a result, to help simplify the identifier model structure and reduce the data convergence time. Simulation results have proved the feasibility of this method, which decreases the sampling window length from about 250–300 s to 120 s with an acceptable accuracy. The driving pattern identifier is further used in an optimized co-simulation together with a parallel hybrid vehicle model and improves the fuel economy by about 8%.

  8. A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

    Science.gov (United States)

    Guarnaccia, Claudio; Quartieri, Joseph; Tepedino, Carmine

    2017-06-01

    The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to "learn" the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN on the residuals. This hybrid model will exploit interesting features and results, with a significant variation related to the number of steps forward in the prediction. It will be shown that the best results, in terms of prediction, are achieved predicting one step ahead in the future. Anyway, a 7 days prediction can be performed, with a slightly greater error, but offering a larger range of prediction, with respect to the single day ahead predictive model.

  9. Hybrid routing and spectrum assignment algorithms based on distance-adaptation combined coevolution and heuristics in elastic optical networks

    Science.gov (United States)

    Ding, Zhe; Xu, Zhanqi; Zeng, Xiaodong; Ma, Tao; Yang, Fan

    2014-04-01

    By adopting the orthogonal frequency division multiplexing technology, spectrum-sliced elastic optical path networks can offer flexible bandwidth to each connection request and utilize the spectrum resources efficiently. The routing and spectrum assignment (RSA) problems in SLICE networks are solved by using heuristic algorithms in most prior studies and addressed by intelligent algorithms in few investigations. The performance of RSA algorithms can be further improved if we could combine such two types of algorithms. Therefore, we propose three hybrid RSA algorithms: DACE-GMSF, DACE-GLPF, and DACE-GEMkPSF, which are the combination of the heuristic algorithm and coevolution based on distance-adaptive policy. In the proposed algorithms, we first groom the connection requests, then sort the connection requests by using the heuristic algorithm (most subcarriers first, longest path first, and extended most k paths' slots first), and finally search the approximately optimal solution with the coevolutionary policy. We present a model of the RSA problem by using integral linear programming, and key elements in the proposed algorithms are addressed in detail. Simulations under three topologies show that the proposed hybrid RSA algorithms can save spectrum resources efficiently.

  10. A Hybrid Neural Network and H-P Filter Model for Short-Term Vegetable Price Forecasting

    Directory of Open Access Journals (Sweden)

    Youzhu Li

    2014-01-01

    Full Text Available This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early-warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick-Prescott (H-P filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H-P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others.

  11. Determination of Efficiency of Hybrid Photovoltaic Thermal Air Collectors Using Artificial Neural Network Approach for Different PV Technology

    Directory of Open Access Journals (Sweden)

    G. N. Tiwari

    2012-01-01

    Full Text Available In this paper an attempt has been made to determine efficiency of semi transparent hybrid photovoltaic thermal double pass air collector for different PV technology and compare it with single pass air collector using artificial neural network (ANN technique for New Delhi weather station of India. The MATLAB 7.1 neural networks toolbox has been used for defining and training of ANN for determination of thermal, electrical, overall thermal and overall exergy efficiency of the system. The ANN model uses ambient air temperature, number of sunshine hours, number of clear days, temperature coefficient, cell efficiency, global and diffuse radiation as input parameters. The transfer function, neural network configuration and learning parameters have been selected based on highest convergence during training and testing of network. About 2000 sets of data from four weather stations (Bangalore, Mumbai, Srinagar and Jodhpur have been given as input for training and data of the fifth weather station (New Delhi has been used for testing purpose. It has been observed that the best transfer function for a given configuration is logsig. The feed forward back-propagation algorithm has been used in this analysis. Further the results of ANN model have been compared with analytical values on the basis of root mean square error.

  12. Scenarios and business models for mobile network operators utilizing the hybrid use concept of the UHF broadcasting spectrum

    Directory of Open Access Journals (Sweden)

    S. Yrjölä

    2016-09-01

    Full Text Available This paper explores and presents scenarios and business models for mobile network operators (MNOs in the novel hybrid use spectrum sharing concept of the Ultra High Frequency broadcasting spectrum (470-790 MHz used for Digital Terrestrial TV (DTT and Mobile Broadband (MBB. More flexible use of the band could lead to higher efficiency in delivering fast growing and converging MBB, media and TV content to meet changing consumer needs. On one hand, this could be beneficial for broadcasters (BC, e.g., by preserving the spectrum, by providing additional revenues, or by lowering cost of the spectrum and, on the other hand, for MNOs to gain faster access to new potentially lower cost, licensed, below 1GHz spectrum to cope with booming data traffic. As a collaborative benefit, the concept opens up new business opportunities for delivering TV and media content using MBB network with means to introduce this flexibly. This paper highlights the importance of developing sound business models for the new spectrum use concept, as they need to provide clear benefits to the key stakeholders to be adopted in real life. The paper applies a future and action oriented approach to the MBB using the concept to derive scenarios and business models for MNOs for accessing hybrid UHF bands. In order to address the convergence and transformation coming with the concept, business models are first developed for the current situation with separate exclusive spectrum bands. Novel business scenarios are then developed for the introduction of the new flexible hybrid UHF spectrum concept. The created business model indicates that the MNOs could benefit significantly from the new UHF bands, which would enable them to cope with increasing data traffic asymmetry, and to offer differentiation through personalized broadcasting and new media services. Moreover, it could significantly re-shape the business ecosystem around both the broadcasting and the mobile broadband by introducing

  13. A Novel Cluster-head Selection Algorithm Based on Hybrid Genetic Optimization for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Lejiang Guo

    2011-05-01

    Full Text Available Wireless Sensor Networks (WSN represent a new dimension in the field of network research. The cluster algorithm can significantly reduce the energy consumption of wireless sensor networks and prolong the network lifetime. This paper uses neuron to describe the WSN node and constructs neural network model for WSN. The neural network model includes three aspects: WSN node neuron model, WSN node control model and WSN node connection model. Through learning the framework of cluster algorithm for wireless sensor networks, this paper presents a weighted average of cluster-head selection algorithm based on an improved Genetic Optimization which makes the node weights directly related to the decision-making predictions. The Algorithm consists of two stages: single-parent evolution and population evolution. The initial population is formed in the stage of single-parent evolution by using gene pool, then the algorithm continues to the next further evolution process, finally the best solution will be generated and saved in the population. The simulation results illustrate that the new algorithm has the high convergence speed and good global searching capacity. It is to effectively balance the network energy consumption, improve the network life-cycle, ensure the communication quality and provide a certain theoretical foundation for the applications of the neural networks.

  14. A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.

    Science.gov (United States)

    Zhou, Qingping; Jiang, Haiyan; Wang, Jianzhou; Zhou, Jianling

    2014-10-15

    Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems.

  15. Design Hybrid method for intrusion detection using Ensemble cluster classification and SOM network

    OpenAIRE

    Deepak Rathore; Anurag Jain

    2012-01-01

    In current scenario of internet technology security is big challenge. Internet network threats by various cyber-attack and loss the system data and degrade the performance of host computer. In this sense intrusion detection are challenging field of research in concern of network security based on firewall and some rule based detection technique. In this paper we proposed an Ensemble Cluster Classification technique using som network for detection of mixed variable data generated by malicious ...

  16. A Communication Model for Adaptive Service Provisioning in Hybrid Wireless Networks

    CERN Document Server

    Brust, Matthias R

    2007-01-01

    Mobile entities with wireless links are able to form a mobile ad-hoc network. Such an infrastructureless network does not have to be administrated. However, self-organizing principles have to be applied to deal with upcoming problems, e.g. information dissemination. These kinds of problems are not easy to tackle, requiring complex algorithms. Moreover, the usefulness of pure ad-hoc networks is arguably limited. Hence, enthusiasm for mobile ad-hoc networks, which could eliminate the need for any fixed infrastructure, has been damped. The goal is to overcome the limitations of pure ad-hoc networks by augmenting them with instant Internet access, e.g. via integration of UMTS respectively GSM links. However, this raises multiple questions at the technical as well as the organizational level. Motivated by characteristics of small-world networks that describe an efficient network even without central or organized design, this paper proposes to combine mobile ad-hoc networks and infrastructured networks to form hybr...

  17. A multi-scale hybrid neural network retrieval model for dust storm detection, a study in Asia

    Science.gov (United States)

    Wong, Man Sing; Xiao, Fei; Nichol, Janet; Fung, Jimmy; Kim, Jhoon; Campbell, James; Chan, P. W.

    2015-05-01

    Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities.

  18. Investigations of the response of hybrid particle detectors for the Space Environmental Viewing and Analysis Network (SEVAN

    Directory of Open Access Journals (Sweden)

    A. Chilingarian

    2008-02-01

    Full Text Available A network of particle detectors located at middle to low latitudes known as SEVAN (Space Environmental Viewing and Analysis Network is being created in the framework of the International Heliophysical Year (IHY-2007. It aims to improve the fundamental research of the particle acceleration in the vicinity of the Sun and space environment conditions. The new type of particle detectors will simultaneously measure the changing fluxes of most species of secondary cosmic rays, thus turning into a powerful integrated device used for exploration of solar modulation effects. Ground-based detectors measure time series of secondary particles born in cascades originating in the atmosphere by nuclear interactions of protons and nuclei accelerated in the galaxy. During violent solar explosions, sometimes additional secondary particles are added to this "background" flux. The studies of the changing time series of secondary particles shed light on the high-energy particle acceleration mechanisms. The time series of intensities of high energy particles can also provide highly cost-effective information on the key characteristics of interplanetary disturbances. The recent results of the detection of the solar extreme events (2003–2005 by the monitors of the Aragats Space-Environmental Center (ASEC illustrate the wide possibilities provided by new particle detectors measuring neutron, electron and muon fluxes with inherent correlations. We present the results of the simulation studies revealing the characteristics of the SEVAN networks' basic measuring module. We illustrate the possibilities of the hybrid particle detector to measure neutral and charged fluxes of secondary CR, to estimate the efficiency and purity of detection; corresponding median energies of the primary proton flux, the ability to distinguish between neutron and proton initiated GLEs and some other important properties of hybrid particle detectors.

  19. Hybrid Multi-Layer Network Control for Emerging Cyber-Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Summerhill, Richard

    2009-08-14

    There were four basic task areas identified for the Hybrid-MLN project. They are: o Multi-Layer, Multi-Domain, Control Plane Architecture and Implementation, o Heterogeneous DataPlane Testing, o Simulation, o Project Publications, Reports, and Presentations.

  20. Hybrid Impedance Network-Based Converter With High Voltage Gain and No Commutation Problem

    DEFF Research Database (Denmark)

    Mostaan, Ali; N. Soltani, Mohsen; A. Gorji, Saman

    2016-01-01

    In this paper, a new hybrid converter based on Z-source DC/DC converter with common ground is introduced. The proposed converter can supply ac and dc loads simultaneously or individually (stand- alone ac or dc loads). Also, the commutation problem of its counterpart has been solved in this topolo...