WorldWideScience

Sample records for power networks identification

  1. Application of neural networks to connectional expert system for identification of transients in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub

    1991-01-01

    The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified

  2. Temporal neural network for the identification of nuclear power plant transients

    International Nuclear Information System (INIS)

    Uluyol, O.; Ragheb, M.

    1993-01-01

    In this paper a layered spatiotemporal neural network is proposed for the identification of nuclear power plant transients. The developed layered spatiotemporal network is inspired by the formal avalanche structure developed by S. Grossberg and offers advantages compared with the stationary pattern approach using the perceptron paradigm. Each layer in the network is trained to recognize a separate time-dependent accident scenario. Within each scenario, the temporal behavior of the relevant parameters such as pressurizer pressure, pressurizer water volume, cold and hot legs temperatures, vessel flow, and power, are considered. Numerical cases are considered where the proposed methodology is applied to two nuclear power plant anticipated transient scenarios: the Station Blackout and the Anticipated Transient without Scram transients in a pressurized water reactor . The transient signatures used were generated by modeling the accidents using RELAP5/MOD2, a best-estimate thermal-hydraulics numerical code. The ability of the proposed layered spatiotemporal network to operate at different noise levels is investigated. Its incorporation within an Insightful Algorithm and Anticipatory Systems context for identifying and in predicting the course of nuclear transients is discussed

  3. Identification of the actual state and entity availability forecasting in power engineering using neural-network technologies

    Science.gov (United States)

    Protalinsky, O. M.; Shcherbatov, I. A.; Stepanov, P. V.

    2017-11-01

    A growing number of severe accidents in RF call for the need to develop a system that could prevent emergency situations. In a number of cases accident rate is stipulated by careless inspections and neglects in developing repair programs. Across the country rates of accidents are growing because of a so-called “human factor”. In this regard, there has become urgent the problem of identification of the actual state of technological facilities in power engineering using data on engineering processes running and applying artificial intelligence methods. The present work comprises four model states of manufacturing equipment of engineering companies: defect, failure, preliminary situation, accident. Defect evaluation is carried out using both data from SCADA and ASEPCR and qualitative information (verbal assessments of experts in subject matter, photo- and video materials of surveys processed using pattern recognition methods in order to satisfy the requirements). Early identification of defects makes possible to predict the failure of manufacturing equipment using mathematical techniques of artificial neural network. In its turn, this helps to calculate predicted characteristics of reliability of engineering facilities using methods of reliability theory. Calculation of the given parameters provides the real-time estimation of remaining service life of manufacturing equipment for the whole operation period. The neural networks model allows evaluating possibility of failure of a piece of equipment consistent with types of actual defects and their previous reasons. The article presents the grounds for a choice of training and testing samples for the developed neural network, evaluates the adequacy of the neural networks model, and shows how the model can be used to forecast equipment failure. There have been carried out simulating experiments using a computer and retrospective samples of actual values for power engineering companies. The efficiency of the developed

  4. Identification of nonlinear dynamics in power plant components using neural networks

    International Nuclear Information System (INIS)

    Parlos, A.G.; Fernandez, B.; Tsai, W.K.

    1990-01-01

    Advances in digital computer technology have enabled widespread implementation of closed-loop digital control systems in a variety of industries. In some instances, however, the complexity of the plant and the uncertainty associated with the parameters involved in the mathematical modeling narrow the range of applicability of most systematic control system design methodologies. A multiyear project has been initiated to assess the feasibility of the artificial neural networks (ANNs) technology for computerized enhanced diagnostics and control of nuclear power plant components. At this stage of the project, a new methodology, based on backpropagation learning, has been developed for identifying the nonlinear dynamic systems from a set of input-output data known as the training set

  5. Recurrent-neural-network-based identification of a cascade hydraulic actuator for closed-loop automotive power transmission control

    International Nuclear Information System (INIS)

    You, Seung Han; Hahn, Jin Oh

    2012-01-01

    By virtue of its ease of operation compared with its conventional manual counterpart, automatic transmissions are commonly used as automotive power transmission control system in today's passenger cars. In accordance with this trend, research efforts on closed-loop automatic transmission controls have been extensively carried out to improve ride quality and fuel economy. State-of-the-art power transmission control algorithms may have limitations in performance because they rely on the steady-state characteristics of the hydraulic actuator rather than fully exploit its dynamic characteristics. Since the ultimate viability of closed-loop power transmission control is dominated by precise pressure control at the level of hydraulic actuator, closed-loop control can potentially attain superior efficacy in case the hydraulic actuator can be easily incorporated into model-based observer/controller design. In this paper, we propose to use a recurrent neural network (RNN) to establish a nonlinear empirical model of a cascade hydraulic actuator in a passenger car automatic transmission, which has potential to be easily incorporated in designing observers and controllers. Experimental analysis is performed to grasp key system characteristics, based on which a nonlinear system identification procedure is carried out. Extensive experimental validation of the established model suggests that it has superb one-step-ahead prediction capability over appropriate frequency range, making it an attractive approach for model-based observer/controller design applications in automotive systems

  6. Identification of exploration strategies for electric power distribution network using simulated annealing; Identificao de estrategias de exploracao de redes de distribuicao de energia electrica utilizando simulated annealing

    Energy Technology Data Exchange (ETDEWEB)

    Pereira, Jorge; Saraiva, J. Tome; Leao, Maria Teresa Ponce de [Instituto de Engenharia de Sistemas e Computadores (INESC), Porto (Portugal). E-mail: jpereira@inescn.pt; jsaraiva@inescn.pt; mleao@inescn.pt

    1999-07-01

    This paper presents a model for identification of optimum strategies for electric power distribution networks, considering the aim of minimizing the active power losses. This objective can be attained by modifying the transformer connections or modification of the condenser groups on duty. By the other side, specifications of voltage ranges for each bar and current intensity limits for the branches are admitted, in order to obtain a more realistic the used model. The paper describes the the simulated annealing in order to surpass the mentioned difficulties. The application of the method to the problem resolution allows the identification solutions based on exact models. The application is illustrated with the results obtained by using a IEEE test network and a network based on real distribution with 645 bars.

  7. Neural network of Gaussian radial basis functions applied to the problem of identification of nuclear accidents in a PWR nuclear power plant

    International Nuclear Information System (INIS)

    Gomes, Carla Regina; Canedo Medeiros, Jose Antonio Carlos

    2015-01-01

    Highlights: • It is presented a new method based on Artificial Neural Network (ANN) developed to deal with accident identification in PWR nuclear power plants. • Obtained results have shown the efficiency of the referred technique. • Results obtained with this method are as good as or even better to similar optimization tools available in the literature. - Abstract: The task of monitoring a nuclear power plant consists on determining, continuously and in real time, the state of the plant’s systems in such a way to give indications of abnormalities to the operators and enable them to recognize anomalies in system behavior. The monitoring is based on readings of a large number of meters and alarm indicators which are located in the main control room of the facility. On the occurrence of a transient or of an accident on the nuclear power plant, even the most experienced operators can be confronted with conflicting indications due to the interactions between the various components of the plant systems; since a disturbance of a system can cause disturbances on another plant system, thus the operator may not be able to distinguish what is cause and what is the effect. This cognitive overload, to which operators are submitted, causes a difficulty in understanding clearly the indication of an abnormality in its initial phase of development and in taking the appropriate and immediate corrective actions to face the system failure. With this in mind, computerized monitoring systems based on artificial intelligence that could help the operators to detect and diagnose these failures have been devised and have been the subject of research. Among the techniques that can be used in such development, radial basis functions (RBFs) neural networks play an important role due to the fact that they are able to provide good approximations to functions of a finite number of real variables. This paper aims to present an application of a neural network of Gaussian radial basis

  8. Autonomous power networks based power system

    International Nuclear Information System (INIS)

    Jokic, A.; Van den Bosch, P.P.J.

    2006-01-01

    This paper presented the concept of autonomous networks to cope with this increased complexity in power systems while enhancing market-based operation. The operation of future power systems will be more challenging and demanding than present systems because of increased uncertainties, less inertia in the system, replacement of centralized coordinating activities by decentralized parties and the reliance on dynamic markets for both power balancing and system reliability. An autonomous network includes the aggregation of networked producers and consumers in a relatively small area with respect to the overall system. The operation of an autonomous network is coordinated and controlled with one central unit acting as an interface between internal producers/consumers and the rest of the power system. In this study, the power balance problem and system reliability through provision of ancillary services was formulated as an optimization problem for the overall autonomous networks based power system. This paper described the simulation of an optimal autonomous network dispatching in day ahead markets, based on predicted spot prices for real power, and two ancillary services. It was concluded that large changes occur in a power systems structure and operation, most of them adding to the uncertainty and complexity of the system. The introduced concept of an autonomous power network-based power system was shown to be a realistic and consistent approach to formulate and operate a market-based dispatch of both power and ancillary services. 9 refs., 4 figs

  9. A Wireless Sensor Network with Enhanced Power Efficiency and Embedded Strain Cycle Identification for Fatigue Monitoring of Railway Bridges

    OpenAIRE

    Feltrin, Glauco; Popovic, Nemanja; Flouri, Kallirroi; Pietrzak, Piotr

    2016-01-01

    Wireless sensor networks have been shown to be a cost-effective monitoring tool for many applications on civil structures. Strain cycle monitoring for fatigue life assessment of railway bridges, however, is still a challenge since it is data intensive and requires a reliable operation for several weeks or months. In addition, sensing with electrical resistance strain gauges is expensive in terms of energy consumption. The induced reduction of battery lifetime of sensor nodes increases the mai...

  10. Parental Power and Adolescents' Parental Identification.

    Science.gov (United States)

    Acock, Alan C.; Yang, Wen Shan

    1984-01-01

    Combines McDonald's social power of parental identification with sex-linked models of parental identification to account for the identification of daughters (N=199) and sons (N=147) with their parents. Found that because of a halo effect, a gain in identification with one parent is not at the other parent's expense. (JAC)

  11. A Wireless Sensor Network with Enhanced Power Efficiency and Embedded Strain Cycle Identification for Fatigue Monitoring of Railway Bridges

    Directory of Open Access Journals (Sweden)

    Glauco Feltrin

    2016-01-01

    Full Text Available Wireless sensor networks have been shown to be a cost-effective monitoring tool for many applications on civil structures. Strain cycle monitoring for fatigue life assessment of railway bridges, however, is still a challenge since it is data intensive and requires a reliable operation for several weeks or months. In addition, sensing with electrical resistance strain gauges is expensive in terms of energy consumption. The induced reduction of battery lifetime of sensor nodes increases the maintenance costs and reduces the competitiveness of wireless sensor networks. To overcome this drawback, a signal conditioning hardware was designed that is able to significantly reduce the energy consumption. Furthermore, the communication overhead is reduced to a sustainable level by using an embedded data processing algorithm that extracts the strain cycles from the raw data. Finally, a simple software triggering mechanism that identifies events enabled the discrimination of useful measurements from idle data, thus increasing the efficiency of data processing. The wireless monitoring system was tested on a railway bridge for two weeks. The monitoring system demonstrated a good reliability and provided high quality data.

  12. Green Wireless Power Transfer Networks

    NARCIS (Netherlands)

    Liu, Q.; Golinnski, M.; Pawelczak, P.; Warnier, M.

    2016-01-01

    wireless power transfer network (WPTN) aims to support devices with cable-less energy on-demand. Unfortunately, wireless power transfer itself-especially through radio frequency radiation rectification-is fairly inefficient due to decaying power with distance, antenna polarization, etc.

  13. Entrepreneurial Idea Identification through Online Social Networks

    Science.gov (United States)

    Lang, Matthew C.

    2010-01-01

    The increasing use of social network websites may signal a change in the way the next generation of entrepreneurs identify entrepreneurial ideas. An important part of the entrepreneurship literature emphasizes how vital the use of social networks is to entrepreneurial idea identification, opportunity recognition, and ultimately new venture…

  14. Observability in electric power networks: identification critical measures methods; Observabilidade em redes de energia eletrica: metodos de identificacao de medidas criticas

    Energy Technology Data Exchange (ETDEWEB)

    London Junior, Joao Bosco Augusto

    1997-07-01

    One of the most important functions of the control and operation centers is to maintain service reliability in a electrical power system. In order to obtain a reliable operation of the power system, it is important to identify the critical measurements, and then to improve the measurement system using pseudo measurements. The goal of this work is to determine more efficient methods for critical measurement identification. A brief review of the some methods for observability analysis as well as two methodologies to identify critical measurements are presented. The first method has a combinatorial nature; the second one is supported by uni modular M matrix (incidence matrix of measurements for branches) and A matrix (incidence matrix of branch for nodes). The second method needs a combinatorial algorithm to be feasible, so that it becomes a slow method. Two new methods for critical measurements identification are presented in this work: the first one is based on the theory developed by Bretas (1996a), to analyse observability using graph paths; the second methods is supported y the Slutsker and Scudder (1987) theory, where identification is reached throughout the analysis of the jacobian matrix. (author)

  15. High voltage power network construction

    CERN Document Server

    Harker, Keith

    2018-01-01

    This book examines the key requirements, considerations, complexities and constraints relevant to the task of high voltage power network construction, from design, finance, contracts and project management to installation and commissioning, with the aim of providing an overview of the holistic end to end construction task in a single volume.

  16. Electronic Power Transformer for Power Distribution Networks

    Directory of Open Access Journals (Sweden)

    Ermuraсhi Iu.V.

    2017-12-01

    Full Text Available Reducing losses in electricity distribution networks is a current technical problem. This issue also has social and environmental aspects. As a promising solution one can examine the direct distribution from the medium voltage power network using new equipment based on the use of power electronics. The aim of the paper is to propose and argue an innovative technical solution for the realization of the Solid State Transformer (SST in order to decrease the number of energy transformation stages compared to the known solutions, simplifying the topology of the functional scheme with the reduction of production costs and the loss of energy in transformers used in electrical distribution networks. It is proposed the solution of simplifying the topology of the AC/AC electronic transformer by reducing the number of passive electronic components (resistors, inductors, capacitors and active (transistors. The inverter of the SST transformer ensures the switching mode of the transistors, using for this purpose the inductance of the magnetic leakage flux of the high frequency transformer. The robustness of the laboratory sample of the SST 10 / 0.22 kV transformer with the power of 20 kW was manufactured and tested. Testing of the laboratory sample confirmed the functionality of the proposed scheme and the possibility of switching of the transistors to at zero current (ZCS mode with the reduction of the energy losses. In the proposed converter a single high-frequency transformer with a simplified construction with two windings is used, which reduces its mass and the cost of making the transformer. The reduction in the manufacturing cost of the converter is also due to the decrease in the number of links between the functional elements.

  17. Neural networks and their application to nuclear power plant diagnosis

    International Nuclear Information System (INIS)

    Reifman, J.

    1997-01-01

    The authors present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal-hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for nuclear power plant operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-based diagnostic systems are identified. A total of thirty-four publications are reviewed

  18. Distribution network topology identification based on synchrophasor

    Directory of Open Access Journals (Sweden)

    Stefania Conti

    2018-03-01

    Full Text Available A distribution system upgrade moving towards Smart Grid implementation is necessary to face the proliferation of distributed generators and electric vehicles, in order to satisfy the increasing demand for high quality, efficient, secure, reliable energy supply. This perspective requires taking into account system vulnerability to cyber attacks. An effective attack could destroy stored information about network structure, historical data and so on. Countermeasures and network applications could be made impracticable since most of them are based on the knowledge of network topology. Usually, the location of each link between nodes in a network is known. Therefore, the methods used for topology identification determine if a link is open or closed. When no information on the location of the network links is available, these methods become totally unfeasible. This paper presents a method to identify the network topology using only nodal measures obtained by means of phasor measurement units.

  19. Identification of generalized state transfer matrix using neural networks

    International Nuclear Information System (INIS)

    Zhu Changchun

    2001-01-01

    The research is introduced on identification of generalized state transfer matrix of linear time-invariant (LTI) system by use of neural networks based on LM (Levenberg-Marquart) algorithm. Firstly, the generalized state transfer matrix is defined. The relationship between the identification of state transfer matrix of structural dynamics and the identification of the weight matrix of neural networks has been established in theory. A singular layer neural network is adopted to obtain the structural parameters as a powerful tool that has parallel distributed processing ability and the property of adaptation or learning. The constraint condition of weight matrix of the neural network is deduced so that the learning and training of the designed network can be more effective. The identified neural network can be used to simulate the structural response excited by any other signals. In order to cope with its further application in practical problems, some noise (5% and 10%) is expected to be present in the response measurements. Results from computer simulation studies show that this method is valid and feasible

  20. Crack identification by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Hwu, C.B.; Liang, Y.C. [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Inst. of Aeronaut. and Astronaut.

    1998-04-01

    In this paper, a most popular artificial neural network called the back propagation neural network (BPN) is employed to achieve an ideal on-line identification of the crack embedded in a composite plate. Different from the usual dynamic estimate, the parameters used for the present crack identification are the strains of static deformation. It is known that the crack effects are localized which may not be clearly reflected from the boundary information especially when the data is from static deformation only. To remedy this, we use data from multiple-loading modes in which the loading modes may include the opening, shearing and tearing modes. The results show that our method for crack identification is always stable and accurate no matter how far-away of the test data from its training set. (orig.) 8 refs.

  1. Parameter Identification by Bayes Decision and Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1994-01-01

    The problem of parameter identification by Bayes point estimation using neural networks is investigated.......The problem of parameter identification by Bayes point estimation using neural networks is investigated....

  2. Identification of fast power reactivity effect in nuclear power reactor

    International Nuclear Information System (INIS)

    Efanov, A.I.; Kaminskas, V.A.; Lavrukhin, V.S.; Rimidis, A.P.; Yanitskene, D.Yu.

    1987-01-01

    A nuclear power reactor is an object of control with distributed parameters, characteristics of which vary during operation time. At the same time the reactor as the object of control has internal feedback circuits, which are formed as a result of the effects of fuel parameters and a coolant (pressure, temperature, steam content) on the reactor breeding properties. The problem of internal feedback circuit identification in a nuclear power reactor is considered. Conditions for a point reactor identification are obtained and algorithms of parametric identification are constructed. Examples of identification of fast power reactivity effect for the RBMK-1000 reactor are given. Results of experimental testing have shown that the developed method of fast power reactivity effect identification permits according to the data of normal operation to construct adaptive models for the point nuclear reactor, designed for its behaviour prediction in stationary and transition operational conditions. Therefore, the models considered can be used for creating control systems of nuclear power reactor thermal capacity (of RBMK type reactor, in particular) which can be adapted to the change in the internal feedback circuit characteristics

  3. Maximal network reliability for a stochastic power transmission network

    International Nuclear Information System (INIS)

    Lin, Yi-Kuei; Yeh, Cheng-Ta

    2011-01-01

    Many studies regarded a power transmission network as a binary-state network and constructed it with several arcs and vertices to evaluate network reliability. In practice, the power transmission network should be stochastic because each arc (transmission line) combined with several physical lines is multistate. Network reliability is the probability that the network can transmit d units of electric power from a power plant (source) to a high voltage substation at a specific area (sink). This study focuses on searching for the optimal transmission line assignment to the power transmission network such that network reliability is maximized. A genetic algorithm based method integrating the minimal paths and the Recursive Sum of Disjoint Products is developed to solve this assignment problem. A real power transmission network is adopted to demonstrate the computational efficiency of the proposed method while comparing with the random solution generation approach.

  4. Network identification of hormonal regulation.

    Directory of Open Access Journals (Sweden)

    Daniel J Vis

    Full Text Available Relations among hormone serum concentrations are complex and depend on various factors, including gender, age, body mass index, diurnal rhythms and secretion stochastics. Therefore, endocrine deviations from healthy homeostasis are not easily detected or understood. A generic method is presented for detecting regulatory relations between hormones. This is demonstrated with a cohort of obese women, who underwent blood sampling at 10 minute intervals for 24-hours. The cohort was treated with bromocriptine in an attempt to clarify how hormone relations change by treatment. The detected regulatory relations are summarized in a network graph and treatment-induced changes in the relations are determined. The proposed method identifies many relations, including well-known ones. Ultimately, the method provides ways to improve the description and understanding of normal hormonal relations and deviations caused by disease or treatment.

  5. Distribution network fault section identification and fault location using artificial neural network

    DEFF Research Database (Denmark)

    Dashtdar, Masoud; Dashti, Rahman; Shaker, Hamid Reza

    2018-01-01

    In this paper, a method for fault location in power distribution network is presented. The proposed method uses artificial neural network. In order to train the neural network, a series of specific characteristic are extracted from the recorded fault signals in relay. These characteristics...... components of the sequences as well as three-phase signals could be obtained using statistics to extract the hidden features inside them and present them separately to train the neural network. Also, since the obtained inputs for the training of the neural network strongly depend on the fault angle, fault...... resistance, and fault location, the training data should be selected such that these differences are properly presented so that the neural network does not face any issues for identification. Therefore, selecting the signal processing function, data spectrum and subsequently, statistical parameters...

  6. 23 CFR 658.21 - Identification of National Network.

    Science.gov (United States)

    2010-04-01

    ... 23 Highways 1 2010-04-01 2010-04-01 false Identification of National Network. 658.21 Section 658... Identification of National Network. (a) To identify the National Network, a State may sign the routes or provide maps of lists of highways describing the National Network. (b) Exceptional local conditions on the...

  7. AC Power Local Network with Multiple Power Routers

    Directory of Open Access Journals (Sweden)

    Ryo Takahashi

    2013-12-01

    Full Text Available Controlling power flow and achieving appropriate matching between power sources and loads according to the quality of energy is expected to be one of the approaches to reduce wasted energy consumption. A power router, proposed recently, has the capability of realizing circuit switching in a power distribution network. This study focuses on the feasibility of an AC power routing network system composed of multiple power routers. To evaluate the feasibility, we experimentally confirm the circuit switching operation of the parallel and series configurations of the power routers, so that the network system can be designed by the combination of parallel and series configurations.

  8. Power consumption in multicore fibre networks

    DEFF Research Database (Denmark)

    Nooruzzaman, Md; Jain, Saurabh; Jung, Yongmin

    2017-01-01

    We study potential energy savings in MCF-based networks compared to SMF-based ones in a Pan-European network topology based on the power consumption of recently fabricated cladding-pumped multi-core optical fibre amplifiers....

  9. Empirical modeling of nuclear power plants using neural networks

    International Nuclear Information System (INIS)

    Parlos, A.G.; Atiya, A.; Chong, K.T.

    1991-01-01

    A summary of a procedure for nonlinear identification of process dynamics encountered in nuclear power plant components is presented in this paper using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the nonlinear structure for system identification. In the overall identification process, the feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of time-dependent system nonlinearities. The standard backpropagation learning algorithm is modified and is used to train the proposed hybrid network in a supervised manner. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The nonlinear response of a representative steam generator is predicted using a neural network and is compared to the response obtained from a sophisticated physical model during both high- and low-power operation. The transient responses compare well, though further research is warranted for training and testing of recurrent neural networks during more severe operational transients and accident scenarios

  10. Nonlinear identification of process dynamics using neural networks

    International Nuclear Information System (INIS)

    Parlos, A.G.; Atiya, A.F.; Chong, K.T.

    1992-01-01

    In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios

  11. Network-based identification of biomarkers coexpressed with multiple pathways.

    Science.gov (United States)

    Guo, Nancy Lan; Wan, Ying-Wooi

    2014-01-01

    Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.

  12. Port Hamiltonian modeling of Power Networks

    NARCIS (Netherlands)

    van Schaik, F.; van der Schaft, Abraham; Scherpen, Jacquelien M.A.; Zonetti, Daniele; Ortega, R

    2012-01-01

    In this talk a full nonlinear model for the power network in port–Hamiltonian framework is derived to study its stability properties. For this we use the modularity approach i.e., we first derive the models of individual components in power network as port-Hamiltonian systems and then we combine all

  13. Power consumption optimization strategy for wireless networks

    DEFF Research Database (Denmark)

    Cornean, Horia; Kumar, Sanjay; Marchetti, Nicola

    2011-01-01

    in order to reduce the total power consumption in a multi cellular network. We present an algorithm for power optimization under no interference and in presence of interference conditions, targeting to maximize the network capacity. The convergence of the algorithm is guaranteed if the interference...

  14. Dynamic Frequency Control in Power Networks

    OpenAIRE

    Zhao, Changhong; Mallada Garcia, Enrique; Low, Steven H.

    2016-01-01

    Node controllers in power distribution networks in accordance with embodiments of the invention enable dynamic frequency control. One embodiment includes a node controller comprising a network interface a processor; and a memory containing a frequency control application; and a plurality of node operating parameters describing the operating parameters of a node, where the node is selected from a group consisting of at least one generator node in a power distribution network wherein the proces...

  15. Parental Identification by the Adolescent: A Social Power Approach

    Science.gov (United States)

    McDonald, Gerald W.

    1977-01-01

    A social power theory of parental identification is presented, in contrast to sex-role theories of identification, which argues that the more parental power each parent is perceived to have, the higher the degree of adolescent identification with that parent. (Author)

  16. Power laws and fragility in flow networks.

    Science.gov (United States)

    Shore, Jesse; Chu, Catherine J; Bianchi, Matt T

    2013-01-01

    What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.

  17. Sensor network based vehicle classification and license plate identification system

    Energy Technology Data Exchange (ETDEWEB)

    Frigo, Janette Rose [Los Alamos National Laboratory; Brennan, Sean M [Los Alamos National Laboratory; Rosten, Edward J [Los Alamos National Laboratory; Raby, Eric Y [Los Alamos National Laboratory; Kulathumani, Vinod K [WEST VIRGINIA UNIV.

    2009-01-01

    Typically, for energy efficiency and scalability purposes, sensor networks have been used in the context of environmental and traffic monitoring applications in which operations at the sensor level are not computationally intensive. But increasingly, sensor network applications require data and compute intensive sensors such video cameras and microphones. In this paper, we describe the design and implementation of two such systems: a vehicle classifier based on acoustic signals and a license plate identification system using a camera. The systems are implemented in an energy-efficient manner to the extent possible using commercially available hardware, the Mica motes and the Stargate platform. Our experience in designing these systems leads us to consider an alternate more flexible, modular, low-power mote architecture that uses a combination of FPGAs, specialized embedded processing units and sensor data acquisition systems.

  18. [Terahertz Spectroscopic Identification with Deep Belief Network].

    Science.gov (United States)

    Ma, Shuai; Shen, Tao; Wang, Rui-qi; Lai, Hua; Yu, Zheng-tao

    2015-12-01

    Feature extraction and classification are the key issues of terahertz spectroscopy identification. Because many materials have no apparent absorption peaks in the terahertz band, it is difficult to extract theirs terahertz spectroscopy feature and identify. To this end, a novel of identify terahertz spectroscopy approach with Deep Belief Network (DBN) was studied in this paper, which combines the advantages of DBN and K-Nearest Neighbors (KNN) classifier. Firstly, cubic spline interpolation and S-G filter were used to normalize the eight kinds of substances (ATP, Acetylcholine Bromide, Bifenthrin, Buprofezin, Carbazole, Bleomycin, Buckminster and Cylotriphosphazene) terahertz transmission spectra in the range of 0.9-6 THz. Secondly, the DBN model was built by two restricted Boltzmann machine (RBM) and then trained layer by layer using unsupervised approach. Instead of using handmade features, the DBN was employed to learn suitable features automatically with raw input data. Finally, a KNN classifier was applied to identify the terahertz spectrum. Experimental results show that using the feature learned by DBN can identify the terahertz spectrum of different substances with the recognition rate of over 90%, which demonstrates that the proposed method can automatically extract the effective features of terahertz spectrum. Furthermore, this KNN classifier was compared with others (BP neural network, SOM neural network and RBF neural network). Comparisons showed that the recognition rate of KNN classifier is better than the other three classifiers. Using the approach that automatic extract terahertz spectrum features by DBN can greatly reduce the workload of feature extraction. This proposed method shows a promising future in the application of identifying the mass terahertz spectroscopy.

  19. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  20. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    1995-01-01

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  1. ISINA: INTEGRAL Source Identification Network Algorithm

    Science.gov (United States)

    Scaringi, S.; Bird, A. J.; Clark, D. J.; Dean, A. J.; Hill, A. B.; McBride, V. A.; Shaw, S. E.

    2008-11-01

    We give an overview of ISINA: INTEGRAL Source Identification Network Algorithm. This machine learning algorithm, using random forests, is applied to the IBIS/ISGRI data set in order to ease the production of unbiased future soft gamma-ray source catalogues. First, we introduce the data set and the problems encountered when dealing with images obtained using the coded mask technique. The initial step of source candidate searching is introduced and an initial candidate list is created. A description of the feature extraction on the initial candidate list is then performed together with feature merging for these candidates. Three training and testing sets are created in order to deal with the diverse time-scales encountered when dealing with the gamma-ray sky. Three independent random forests are built: one dealing with faint persistent source recognition, one dealing with strong persistent sources and a final one dealing with transients. For the latter, a new transient detection technique is introduced and described: the transient matrix. Finally the performance of the network is assessed and discussed using the testing set and some illustrative source examples. Based on observations with INTEGRAL, an ESA project with instruments and science data centre funded by ESA member states (especially the PI countries: Denmark, France, Germany, Italy, Spain), Czech Republic and Poland, and the participation of Russia and the USA. E-mail: simo@astro.soton.ac.uk

  2. Low-Power Wireless Sensor Network Infrastructures

    DEFF Research Database (Denmark)

    Hansen, Morten Tranberg

    Advancements in wireless communication and electronics improving form factor and hardware capabilities has expanded the applicability of wireless sensor networks. Despite these advancements, devices are still limited in terms of energy which creates the need for duty-cycling and low-power protocols...... peripherals need to by duty-cycled and the low-power wireless radios are severely influenced by the environmental effects causing bursty and unreliable wireless channels. This dissertation presents a communication stack providing services for low-power communication, secure communication, data collection......, and network management which enables construction of low-power wireless sensor network applications. More specifically, these services are designed with the extreme low-power scenarios of the SensoByg project in mind and are implemented as follows. First, low-power communication is implemented with Auto...

  3. Power Aware Dynamic Provisioning of HPC Networks

    Energy Technology Data Exchange (ETDEWEB)

    Groves, Taylor [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Grant, Ryan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-10-01

    Future exascale systems are under increased pressure to find power savings. The network, while it consumes a considerable amount of power is often left out of the picture when discussing total system power. Even when network power is being considered, the references are frequently a decade or older and rely on models that lack validation on modern inter- connects. In this work we explore how dynamic mechanisms of an Infiniband network save power and at what granularity we can engage these features. We explore this within the context of the host controller adapter (HCA) on the node and for the fabric, i.e. switches, using three different mechanisms of dynamic link width, frequency and disabling of links for QLogic and Mellanox systems. Our results show that while there is some potential for modest power savings, real world systems need to improved responsiveness to adjustments in order to fully leverage these savings. This page intentionally left blank.

  4. Investments in power networks and alternative measures

    International Nuclear Information System (INIS)

    2003-01-01

    Measures taken with respect to production and consumption are often alternatives to investments in the power networks. While decisions about production and consumption are taken in the market, the network operation is subject to monopoly regulation. In the central network, Statnett's commission is to invest on the basis of socioeconomic profitability. There is a need for much better coordination between network investments and other measures in the power system. The price signal from the market and general tariffs are not sufficient to realize optimal solutions, and there is a need for a ''visible hand'' that can contribute to the realization of the solutions that are the best in each individual situation. It is desirable to create processes and incentives that realize the best solutions, independently of dealing with network investments, local power production or other measures.

  5. Geometrical methods for power network analysis

    Energy Technology Data Exchange (ETDEWEB)

    Bellucci, Stefano; Tiwari, Bhupendra Nath [Istituto Nazioneale di Fisica Nucleare, Frascati, Rome (Italy). Lab. Nazionali di Frascati; Gupta, Neeraj [Indian Institute of Technology, Kanpur (India). Dept. of Electrical Engineering

    2013-02-01

    Uses advanced geometrical methods to analyse power networks. Provides a self-contained and tutorial introduction. Includes a fully worked-out example for the IEEE 5 bus system. This book is a short introduction to power system planning and operation using advanced geometrical methods. The approach is based on well-known insights and techniques developed in theoretical physics in the context of Riemannian manifolds. The proof of principle and robustness of this approach is examined in the context of the IEEE 5 bus system. This work addresses applied mathematicians, theoretical physicists and power engineers interested in novel mathematical approaches to power network theory.

  6. Behavioral Strategy: Strategic Consensus, Power and Networks

    NARCIS (Netherlands)

    M. Tarakci (Murat)

    2013-01-01

    textabstractOrganizations are embedded in a network of relationships and make sense of their business environment through the cognitive frames of their employees and executives who constantly experience battles for power. This dissertation integrates strategic management research with organizational

  7. A neural network model for non invasive subsurface stratigraphic identification

    International Nuclear Information System (INIS)

    Sullivan, John M. Jr.; Ludwig, Reinhold; Lai Qiang

    2000-01-01

    Ground-Penetrating Radar (GRP) is a powerful tool to examine the stratigraphy below ground surface for remote sensing. Increasingly GPR has also found applications in microwave NDE as an interrogation tool to assess dielectric layers. Unfortunately, GPR data is characterized by a high degree of uncertainty and natural physical ambiguity. Robust decomposition routines are sparse for this application. We have developed a hierarchical set of neural network modules which split the task of layer profiling into consecutive stages. Successful GPR profiling of the subsurface stratigraphy is of key importance for many remote sensing applications including microwave NDE. Neural network modules were designed to accomplish the two main processing goals of recognizing the 'subsurface pattern' followed by the identification of the depths of the subsurface layers like permafrost, groundwater table, and bedrock. We used an adaptive transform technique to transform raw GPR data into a small feature vector containing the most representative and discriminative features of the signal. This information formed the input for the neural network processing units. This strategy reduced the number of required training samples for the neural network by orders of magnitude. The entire processing system was trained using the adaptive transformed feature vector inputs and tested with real measured GPR data. The successful results of this system establishes the feasibility the feasibility of delineating subsurface layering nondestructively

  8. Decentralised electrical distribution network in power plants

    International Nuclear Information System (INIS)

    Mannila, P.; Lehtonen, M.

    2000-02-01

    A centralised network is a dominating network solution in today's power plants. In this study a centralised and a decentralised network were designed in order to compare them economically and technically. The emphasis of this study was on economical aspects, but also the most important technical aspects were included. The decentralised network requires less space and less cabling since there is no switchgear building and distribution transformers are placed close to the consumption in the field of a power plant. MV-motors and distribution transformers build up a ring. Less cabling and an absent switchgear building cause considerable savings. Component costs of both of the networks were estimated by using data from fulfilled power plant projects and turned out to be smaller for the decentralised network. Simulations for the decentralised network were done in order to find a way to carry out earth fault protection and location. It was found out that in high resistance earthed system the fault distance can be estimated by a relatively simple method. The decentralised network uses a field bus, which offers many new features to the automation system of a power plant. Diversified information can be collected from the protection devices in order to schedule only the needed maintenance duties at the right time. Through the field bus it is also possible to control remotely a power plant. The decentralised network is built up from ready-to-install modules. These modules are tested by the module manufacturer decreasing the need for field testing dramatically. The work contribution needed in the electrification and the management of a power plant project reduces also due the modules. During the lifetime of a power plant, maintenance is easier and more economical. (orig.)

  9. Statistical Power in Longitudinal Network Studies

    NARCIS (Netherlands)

    Stadtfeld, Christoph; Snijders, Tom A. B.; Steglich, Christian; van Duijn, Marijtje

    2018-01-01

    Longitudinal social network studies may easily suffer from a lack of statistical power. This is the case in particular for studies that simultaneously investigate change of network ties and change of nodal attributes. Such selection and influence studies have become increasingly popular due to the

  10. Identification of neutral biochemical network models from time series data

    Directory of Open Access Journals (Sweden)

    Maia Marco

    2009-05-01

    Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

  11. Identification of neutral biochemical network models from time series data.

    Science.gov (United States)

    Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S

    2009-05-05

    The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

  12. District heating and cooling systems for communities through power plant retrofit and distribution networks. Phase I. Identification and assessment. Final report

    Energy Technology Data Exchange (ETDEWEB)

    1979-09-01

    The study assesses the preliminary technical, economic, and institutional feasibility of district heating systems achieved by retrofitting existing utility power plants in three Wisconsin cities: Green Bay, Janesville/Beloit, and Madison. The overall approach consists of surveying the State of Wisconsin to identify all existing intermediate and base-loaded electric-generating facilities. Once identified, screening criteria are developed to narrow the list to the three most promising sites. For each of the three sites, an extensive market analysis is performed to identify and characterize thermal loads and survey potential users on their views and concerns regarding the concept. Parallel to this effort, each of the three sites is evaluated on its technical and institutional merits. The technical evaluation centers on identifying and evaluating utility plant retrofit schemes and distribution system alternatives to service the identified thermal market. The institutional analysis evaluates potential barriers such as environmental, distribution system right-of-way, and legal issues within the infrastructure of the state, city, and community. Finally, all previous aspects of the analysis are combined to determine the economic viability of each site. It is concluded that Green Bay is the most promising site.

  13. Reactive power management of power networks with wind generation

    CERN Document Server

    Amaris, Hortensia; Ortega, Carlos Alvarez

    2012-01-01

    As the energy sector shifts and changes to focus on renewable technologies, the optimization of wind power becomes a key practical issue. Reactive Power Management of Power Networks with Wind Generation brings into focus the development and application of advanced optimization techniques to the study, characterization, and assessment of voltage stability in power systems. Recent advances on reactive power management are reviewed with particular emphasis on the analysis and control of wind energy conversion systems and FACTS devices. Following an introduction, distinct chapters cover the 5 key

  14. Distributed control of deregulated electrical power networks

    NARCIS (Netherlands)

    Hermans, R.M.

    2012-01-01

    A prerequisite for reliable operation of electrical power networks is that supply and demand are balanced at all time, as efficient ways for storing large amounts of electrical energy are scarce. Balancing is challenging, however, due to the power system's dimensions and complexity, the low

  15. Wide-band segmented power distribution networks

    NARCIS (Netherlands)

    Tereshchenko, O.V.; Buesink, Frederik Johannes Karel; Leferink, Frank Bernardus Johannes

    2013-01-01

    This paper discusses a novel design of Power Distribution Network (PDN). By physical structuring of the power plane into repetitive symmetrical and asymmetrical segments of varying size, suppression of the propagation of unwanted noise throughout the PDN over a wide frequency range is achieved.

  16. An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

    OpenAIRE

    Wang, Weidong; Wang, Liqiang; Lu, Wei

    2016-01-01

    QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful techniqu...

  17. Efficiency of Finish power transmission network companies

    International Nuclear Information System (INIS)

    Anon.

    2001-01-01

    The Finnish Energy Market Authority has investigated the efficiency of power transmissions network companies. The results show that the intensification potential of the branch is 402 million FIM, corresponding to about 15% of the total costs of the branch and 7.3 % of the turnout. Energy Market Authority supervises the reasonableness of the power transmission prices, and it will use the results of the research in supervision. The research was carried out by the Quantitative Methods Research Group of Helsinki School of Economics. The main objective of the research was to create an efficiency estimation method for electric power distribution network business used for Finnish conditions. Data of the year 1998 was used as basic material in the research. Twenty-one of the 102 power distribution network operators was estimated to be totally efficient. Highest possible efficiency rate was 100, and the average of the efficiency rates of all the operators was 76.9, the minimum being 42.6

  18. Particle identification using artificial neural networks at BESIII

    International Nuclear Information System (INIS)

    Qin Gang; Lv Junguang; Bian Jianming; Chinese Academy of Sciences, Beijing

    2008-01-01

    A multilayered perceptrons' neural network technique has been applied in the particle identification at BESIII. The networks are trained in each sub-detector level. The NN output of sub-detectors can be sent to a sequential network or be constructed as PDFs for a likelihood. Good muon-ID, electron-ID and hadron-ID are obtained from the networks by using the simulated Monte Carlo samples. (authors)

  19. Neural networks and their potential application to nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    A network of artificial neurons, usually called an artificial neural network is a data processing system consisting of a number of highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks exhibit characteristics and capabilities not provided by any other technology. Neural networks may be designed so as to classify an input pattern as one of several predefined types or to create, as needed, categories or classes of system states which can be interpreted by a human operator. Neural networks have the ability to recognize patterns, even when the information comprising these patterns is noisy, sparse, or incomplete. Thus, systems of artificial neural networks show great promise for use in environments in which robust, fault-tolerant pattern recognition is necessary in a real-time mode, and in which the incoming data may be distorted or noisy. The application of neural networks, a rapidly evolving technology used extensively in defense applications, alone or in conjunction with other advanced technologies, to some of the problems of operating nuclear power plants has the potential to enhance the safety, reliability and operability of nuclear power plants. The potential applications of neural networking include, but are not limited to diagnosing specific abnormal conditions, identification of nonlinear dynamics and transients, detection of the change of mode of operation, control of temperature and pressure during start-up, signal validation, plant-wide monitoring using autoassociative neural networks, monitoring of check valves, modeling of the plant thermodynamics, emulation of core reload calculations, analysis of temporal sequences in NRC's ''licensee event reports,'' and monitoring of plant parameters

  20. Adaptive intelligent power systems: Active distribution networks

    International Nuclear Information System (INIS)

    McDonald, Jim

    2008-01-01

    Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems

  1. Some general rules governing huge power networks

    International Nuclear Information System (INIS)

    Clade, J.

    2010-01-01

    The very large networks operate on vast territories, on the scale not only of countries, but of continents. Their aim is twofold: transmitting electrical energy from the power plants - nuclear or thermal power plants, hydraulic, wind power plants etc. - to consumption areas (that is the transmission function); pooling the power plants, so as to operate at any time those which are the less expensive (interconnection of energy production) and to guarantee a safe continuous supply to the users (interconnection of power). However, the transmission of electrical energy is more costly than transmission, when possible, of primary energy sources, either fossil fuels or nuclear. When the sources are not chiefly hydraulic, it is pertinent to limit the transmission of electricity by siting the power plants as close as possible to the consumption areas. On the contrary, interconnection may allow significant savings in the way of power plant investments and fuel expenses. Therein is the main economical justification for very large electrical systems and networks, except in cases where distant hydraulic sources are operated. We must then think over large electrical networks mainly as tools for cooperation between power producers, aiming at an electrical supply to customers which is safe, continuous and as inexpensive as possible. (author)

  2. Power Aware Simulation Framework for Wireless Sensor Networks and Nodes

    Directory of Open Access Journals (Sweden)

    Daniel Weber

    2008-07-01

    Full Text Available The constrained resources of sensor nodes limit analytical techniques and cost-time factors limit test beds to study wireless sensor networks (WSNs. Consequently, simulation becomes an essential tool to evaluate such systems.We present the power aware wireless sensors (PAWiS simulation framework that supports design and simulation of wireless sensor networks and nodes. The framework emphasizes power consumption capturing and hence the identification of inefficiencies in various hardware and software modules of the systems. These modules include all layers of the communication system, the targeted class of application itself, the power supply and energy management, the central processing unit (CPU, and the sensor-actuator interface. The modular design makes it possible to simulate heterogeneous systems. PAWiS is an OMNeT++ based discrete event simulator written in C++. It captures the node internals (modules as well as the node surroundings (network, environment and provides specific features critical to WSNs like capturing power consumption at various levels of granularity, support for mobility, and environmental dynamics as well as the simulation of timing effects. A module library with standardized interfaces and a power analysis tool have been developed to support the design and analysis of simulation models. The performance of the PAWiS simulator is comparable with other simulation environments.

  3. Research on artificial neural network applications for nuclear power plants

    International Nuclear Information System (INIS)

    Chang, Soon-Heung; Cheon, Se-Woo

    1992-01-01

    Artificial neural networks (ANNs) are an emerging computational technology which can significantly enhance a number of applications. These consist of many interconnected processing elements that exhibit human-like performance, i.e., learning, pattern recognition and associative memory skills. Several application studies on ANNs devoted to nuclear power plants have been carried out at the Korea Advanced Institute of Science and Technology since 1989. These studies include the feasibility of using ANNs for the following tasks: (1) thermal power prediction, (2) transient identification, (3) multiple alarm processing and diagnosis, (4) core thermal margin prediction, and (5) prediction of core parameters for fuel reloading. This paper introduces the back-propagation network (BPN) model which is the most commonly used algorithm, and summarizes each of the studies briefly. (author)

  4. RF Wireless Power Transfer: Regreening Future Networks

    OpenAIRE

    Tran, Ha-Vu; Kaddoum, Georges

    2017-01-01

    Green radio communication is an emerging topic since the overall footprint of information and communication technology (ICT) services is predicted to triple between 2007 and 2020. Given this research line, energy harvesting (EH) and wireless power transfer (WPT) networks can be evaluated as promising approaches. In this paper, an overview of recent trends for future green networks on the platforms of EH and WPT is provided. By rethinking the application of radio frequency (RF)-WPT, a new conc...

  5. Vibration analysis in nuclear power plant using neural networks

    International Nuclear Information System (INIS)

    Loskiewicz-Buczak, A.; Alguindigue, I.E.

    1993-01-01

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. This paper documents the authors' work on the design of a vibration monitoring methodology enhanced by neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to handle data which may be distorted or noisy. This paper describes three neural networks-based methods for the automation of some of the activities related to motion and vibration monitoring in engineering systems

  6. Boosted jet identification using particle candidates and deep neural networks

    CERN Document Server

    CMS Collaboration

    2017-01-01

    This note presents developments for the identification of hadronically decaying top quarks using deep neural networks in CMS. A new method that utilizes one dimensional convolutional neural networks based on jet constituent particles is proposed. Alternative methods using boosted decision trees based on jet observables are compared. The new method shows significant improvement in performance.

  7. FUZZY NEURAL NETWORK FOR OBJECT IDENTIFICATION ON INTEGRATED CIRCUIT LAYOUTS

    Directory of Open Access Journals (Sweden)

    A. A. Doudkin

    2015-01-01

    Full Text Available Fuzzy neural network model based on neocognitron is proposed to identify layout objects on images of topological layers of integrated circuits. Testing of the model on images of real chip layouts was showed a highеr degree of identification of the proposed neural network in comparison to base neocognitron.

  8. Electric power plants and networks. Elektrische Kraftwerke

    Energy Technology Data Exchange (ETDEWEB)

    Happoldt, H [Brown, Boveri und Cie A.G., Mannheim (Germany, F.R.). Abt. Centralen; Oeding, D [Brown, Boveri und Cie A.G., Mannheim (Germany, F.R.). Zentralbereich Forschung und Entwicklung

    1978-01-01

    This book is itended for enginers working in the planning, construction and operation of plants to generate and distribute electric power; it is also a valuable aid for students of power engineering. This new edition places more emphasis on the presentation and calculation of three-phase current networks with the aid of symmetric components. The equations used for calculation are adapted to VDE regulations as far as possible.

  9. Nuclear power plant transient identification using a neuro-fuzzy inference system

    International Nuclear Information System (INIS)

    Mol, Antonio Carlos de Abreu; Oliveira, Mauro Vitor de; Santos, Isaac Jose Antonio Luchetti dos; Carvalho, Paulo Victor Rodrigues de; Grecco, Claudio Henrique dos Santos; Auguto, Silas Cordeiro

    2005-01-01

    Transient identification in Nuclear Power Plant (NPP) is often a very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in nuclear power plants. The basis for the identification of a change in the system is that different system faults and anomalies lead to different patterns of evolution of the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments, that represents a specific type of event. In this work, an approach for the identification of transients is presented, aiming at helping the operator to make a decision relative to the procedure to be followed in situations of accidents/transients at nuclear power plants. In this way, a diagnostic strategy based on hierarchical use artificial neural networks (ANN) for a first level transient diagnose. After the ANN has done a preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. In order to validate the method, a Nuclear Power Plant transient identification problem, comprising postulated accidents, is proposed. Noisy data was used to evaluate the method robustness. The results obtained reveal the ability of the method in dealing with dynamic identification of transients and its reliability degree. (author)

  10. Topology Identification of General Dynamical Network with Distributed Time Delays

    International Nuclear Information System (INIS)

    Zhao-Yan, Wu; Xin-Chu, Fu

    2009-01-01

    General dynamical networks with distributed time delays are studied. The topology of the networks are viewed as unknown parameters, which need to be identified. Some auxiliary systems (also called the network estimators) are designed to achieve this goal. Both linear feedback control and adaptive strategy are applied in designing these network estimators. Based on linear matrix inequalities and the Lyapunov function method, the sufficient condition for the achievement of topology identification is obtained. This method can also better monitor the switching topology of dynamical networks. Illustrative examples are provided to show the effectiveness of this method. (general)

  11. Optimization-based topology identification of complex networks

    International Nuclear Information System (INIS)

    Tang Sheng-Xue; Chen Li; He Yi-Gang

    2011-01-01

    In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)

  12. Complexity of Resilient Power Distribution Networks

    International Nuclear Information System (INIS)

    May, Michael

    2008-01-01

    Power Systems in general and specifically the problems of communication, control, and coordination in human supervisory control of electric power transmission and distribution networks constitute a good case study for resilience engineering. Because of the high cost and high impact on society of transmission disturbances and blackouts and the vulnerability of power networks to terrorist attacks, Transmission Systems Operators (TSOs) are already focusing on organizational structures, procedures, and technical innovations that could improve the flexibility and robustness of power Systems and achieve the overall goal of providing secure power supply. For a number of reasons however the complexity of power Systems is increasing and new problems arise for human supervisory control and the ability of these Systems to implement fast recovery from disturbances. Around the world power Systems are currently being restructured to adapt to regional electricity markets and secure the availability, resilience and sustainability of electric power generation, transmission and distribution. This demands a reconsideration of the available decision support, the activity of human supervisory control of the highly automated processes involved and the procedures regulating it, as well as the role of the TSOs and the regional, national and international organizations set up to manage their activity. Unfortunately we can expect that human supervisory control of power Systems will become more complex in the near future for a number of reasons. The European Union for the Co-ordination of Transmission of Electricity (UCTE) has remarked that although the interconnected Systems of power transmission networks has been developed over the years with the main goal of providing secure power supply through common use of reserve capacities and the optimization of the use of energy resources, today's market dynamics imposing a high level of cross-border exchanges is 'out of the scope of the

  13. Neural network based electron identification in the ZEUS calorimeter

    International Nuclear Information System (INIS)

    Abramowicz, H.; Caldwell, A.; Sinkus, R.

    1995-01-01

    We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions characterized by the presence of a scattered electron in the final state. The performance of the algorithm is compared to an electron identification method based on a classical probabilistic approach. By means of a principle component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach. (orig.)

  14. Too much power to the networks

    Directory of Open Access Journals (Sweden)

    Alessandro Delfanti

    2009-10-01

    Full Text Available In his latest book titled “Communication power”, the famous sociologist of information society Manuel Castells focuses on the way in which power takes shape and acts in information societies, and the role of communication in defining, structuring, and changing it. From the rise of “mass self-communication” to the role of environmental movements and neuropolitics, the network is the key structure at play and the main lens used to analyse the transformations we are witnessing. To support his thesis Castells links media studies, power theory and brain science, but his insistence on networks puts in danger his ability to give to his readers a comprehensive and coherent interpretative framework.

  15. Mobility and power in networked European space

    DEFF Research Database (Denmark)

    Richardson, Tim; Jensen, Ole B.

    This paper seeks to contribute to debates about how urban, social and critical theory can conceptualise the socio-technologies of connection, resilience, mobility, and collapse in contemporary urban space. The paper offers a theoretical frame for conceptualising this New Urban Condition, focusing...... on themes of mobility, power, flow, network and scale. The analysis suggests the importance of close atention to the knowledge claims which are deployed in multi-level struggles to assert smooth futures in face of dysfunction....

  16. Power control in wireless sensor networks with variable interference

    NARCIS (Netherlands)

    Chincoli, M.; Syed, A.A.; Exarchakos, G.; Liotta, A.

    2016-01-01

    Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counterproductive

  17. Modeling and optimization of an electric power distribution network ...

    African Journals Online (AJOL)

    Modeling and optimization of an electric power distribution network planning system using ... of the network was modelled with non-linear mathematical expressions. ... given feasible locations, re-conductoring of existing feeders in the network, ...

  18. Wirelessly powered sensor networks and computational RFID

    CERN Document Server

    2013-01-01

    The Wireless Identification and Sensing Platform (WISP) is the first of a new class of RF-powered sensing and computing systems.  Rather than being powered by batteries, these sensor systems are powered by radio waves that are either deliberately broadcast or ambient.  Enabled by ongoing exponential improvements in the energy efficiency of microelectronics, RF-powered sensing and computing is rapidly moving along a trajectory from impossible (in the recent past), to feasible (today), toward practical and commonplace (in the near future). This book is a collection of key papers on RF-powered sensing and computing systems including the WISP.  Several of the papers grew out of the WISP Challenge, a program in which Intel Corporation donated WISPs to academic applicants who proposed compelling WISP-based projects.  The book also includes papers presented at the first WISP Summit, a workshop held in Berkeley, CA in association with the ACM Sensys conference, as well as other relevant papers. The book provides ...

  19. A modular structure to accident identification using neural networks

    International Nuclear Information System (INIS)

    Duque Estrada, Cassius Rodrigo

    2005-01-01

    This work uses the accident identification method based on Artificial Neural Networks (ANN) as basic blocks of a modular structure, allowing the inclusion of new accidents to be identified without modifying the ANN already trained. This structure comprises several modules for accident identification and one module for analysis. Each identification module follows the structure of the basic block. The identification modules are responsible for the recognition of an accident belonging to the specific set of events for which it were trained. The analysis module processes the output from the identification module to determine the system response. In order to test this structure it was proposed a transient identification problem comprising fifty accidents distributed in five identification modules. The results have demonstrated that the accident identification method used as basic block of a modular structure allows the inclusion of new sets of accidents, or variations of a same accident, without modifying the ANN already trained. For this, it is enough to include into the system an specific module for this new set of accidents. (author)

  20. Two-component network model in voice identification technologies

    Directory of Open Access Journals (Sweden)

    Edita K. Kuular

    2018-03-01

    Full Text Available Among the most important parameters of biometric systems with voice modalities that determine their effectiveness, along with reliability and noise immunity, a speed of identification and verification of a person has been accentuated. This parameter is especially sensitive while processing large-scale voice databases in real time regime. Many research studies in this area are aimed at developing new and improving existing algorithms for presentation and processing voice records to ensure high performance of voice biometric systems. Here, it seems promising to apply a modern approach, which is based on complex network platform for solving complex massive problems with a large number of elements and taking into account their interrelationships. Thus, there are known some works which while solving problems of analysis and recognition of faces from photographs, transform images into complex networks for their subsequent processing by standard techniques. One of the first applications of complex networks to sound series (musical and speech analysis are description of frequency characteristics by constructing network models - converting the series into networks. On the network ontology platform a previously proposed technique of audio information representation aimed on its automatic analysis and speaker recognition has been developed. This implies converting information into the form of associative semantic (cognitive network structure with amplitude and frequency components both. Two speaker exemplars have been recorded and transformed into pertinent networks with consequent comparison of their topological metrics. The set of topological metrics for each of network models (amplitude and frequency one is a vector, and together  those combine a matrix, as a digital "network" voiceprint. The proposed network approach, with its sensitivity to personal conditions-physiological, psychological, emotional, might be useful not only for person identification

  1. Neural network application for illicit substances identification

    International Nuclear Information System (INIS)

    Nunes, Wallace V.; Silva, Ademir X. da; Crispim, Verginia R.; Schirru, Roberto

    2000-01-01

    Thermal neutron activation analysis is based on neutron capture prompt gamma-ray analysis and has been used in wide variety of fields, for examples, for inspection of checked airline baggage and for detection of buried land mines. In all of these applications, the detected γ-ray intensities from the elements present are used to estimate their concentrations. A study about application using a trained neutral network is presented to determine the presence of illicit substances, such as explosives and drugs, carried out in the luggages. The illicit substances emit characteristic detected γ-ray which are the fingerprint of each isotope. The fingerprint data-base of the gamma-ray spectrum of substances is obtained via Monte Carlo N-Particle Transport code, MCNP, version 4B. It was possible to train the neural network to determine the presence of explosives and narcotics even hidden by several materials. (author)

  2. Comparing Different Fault Identification Algorithms in Distributed Power System

    Science.gov (United States)

    Alkaabi, Salim

    A power system is a huge complex system that delivers the electrical power from the generation units to the consumers. As the demand for electrical power increases, distributed power generation was introduced to the power system. Faults may occur in the power system at any time in different locations. These faults cause a huge damage to the system as they might lead to full failure of the power system. Using distributed generation in the power system made it even harder to identify the location of the faults in the system. The main objective of this work is to test the different fault location identification algorithms while tested on a power system with the different amount of power injected using distributed generators. As faults may lead the system to full failure, this is an important area for research. In this thesis different fault location identification algorithms have been tested and compared while the different amount of power is injected from distributed generators. The algorithms were tested on IEEE 34 node test feeder using MATLAB and the results were compared to find when these algorithms might fail and the reliability of these methods.

  3. Orientation identification of the power spectrum

    NARCIS (Netherlands)

    Rudnaya, M.; Mattheij, R.M.M.; Maubach, J.M.L.

    2010-01-01

    The image Fourier transform is widely used for defocus and astigmatism correction in electron microscopy. The shape of a power spectrum (the square of a modulus of image Fourier transform) is directly related to the three microscope’s controls, namely defocus and two-fold (two-parameter)

  4. Plant Species Identification by Bi-channel Deep Convolutional Networks

    Science.gov (United States)

    He, Guiqing; Xia, Zhaoqiang; Zhang, Qiqi; Zhang, Haixi; Fan, Jianping

    2018-04-01

    Plant species identification achieves much attention recently as it has potential application in the environmental protection and human life. Although deep learning techniques can be directly applied for plant species identification, it still needs to be designed for this specific task to obtain the state-of-art performance. In this paper, a bi-channel deep learning framework is developed for identifying plant species. In the framework, two different sub-networks are fine-tuned over their pretrained models respectively. And then a stacking layer is used to fuse the output of two different sub-networks. We construct a plant dataset of Orchidaceae family for algorithm evaluation. Our experimental results have demonstrated that our bi-channel deep network can achieve very competitive performance on accuracy rates compared to the existing deep learning algorithm.

  5. Transmission Power Control for Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Kuo-Hsien Hsia

    2017-02-01

    Full Text Available Wireless sensor networks can be widely applied for a security system or a smart home system. Since some of the wireless remote sensor nodes may be powered by energy storage devices such as batteries, it is a very important issue to transmit signals at lower power with the consideration of the communication effectiveness. In this paper, we will provide a fuzzy controller with two inputs and one output for received signal strength indicator (RSSI and link quality indicator (LQI to adjust transmission power suitably in order to maintaining a certain communication level with a reduced energy consumption. And we will divide the sampling period of a sensor node into four intervals so that the sensor node radio device does not in receiving or transmission status all the time. Hence the sensor node can adjust transmission power automatically and reduce sensor node power consumption. Experimental results show that the battery life can be extended to about 10 times for the designed sensor node comparing to a normal node.

  6. Applying a neuro-fuzzy approach for transient identification in a nuclear power plant

    International Nuclear Information System (INIS)

    Costa, Rafael G.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.; Carvalho, Paulo V.R.

    2009-01-01

    Transient identification in Nuclear Power Plant (NPP) is often a very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Several systems based on specialist systems, neural networks, and fuzzy logic have been developed for transient identification. In the work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A preliminary evaluation of the developed system was made at the Human-System Interface Laboratory (LABIHS). The obtained results show that the system can help the operators to take decisions during transients/accidents in the plant. (author)

  7. Structure Learning in Power Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-01-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  8. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    We study the problem of identifying and ranking the members of a community in a very large network with link analysis only, given a set of representatives of the community. We define the concept of a community justified by a formal analysis of a simple model of the evolution of a directed graph. ...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  9. Magnet power supply as a network object

    International Nuclear Information System (INIS)

    Cohen, S.; Stuewe, R.

    1991-01-01

    Magnet power supplies with embedded microprocessor controls are being installed in the beam-lines of the linear accelerator and proton storage ring at LAMPF. Using an RS422 link they communicate with the accelerator control system through a terminal server connected to the site-wide DECnet backbone. Each supply is, for all intents and purposes, a network object. The controller has a command set of over seventy-five three-character ASCII control and read-back instructions. Strategies for choosing the appropriate control protocol and the process of integrating these devices into a large accelerator control system will be presented. 7 refs., 2 figs., 1 tab

  10. Power converters for medium voltage networks

    CERN Document Server

    Islam, Md Rabiul; Zhu, Jianguo

    2014-01-01

    This book examines a number of topics, mainly in connection with advances in semiconductor devices and magnetic materials and developments in medium and large-scale renewable power plant technologies, grid integration techniques and new converter topologies, including advanced digital control systems for medium-voltage networks. The book's individual chapters provide an extensive compilation of fundamental theories and in-depth information on current research and development trends, while also exploring new approaches to overcoming some critical limitations of conventional grid integration te

  11. Identification of important nodes in directed biological networks: a network motif approach.

    Directory of Open Access Journals (Sweden)

    Pei Wang

    Full Text Available Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA, this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

  12. Network integration of distributed power generation

    Science.gov (United States)

    Dondi, Peter; Bayoumi, Deia; Haederli, Christoph; Julian, Danny; Suter, Marco

    The world-wide move to deregulation of the electricity and other energy markets, concerns about the environment, and advances in renewable and high efficiency technologies has led to major emphasis being placed on the use of small power generation units in a variety of forms. The paper reviews the position of distributed generation (DG, as these small units are called in comparison with central power plants) with respect to the installation and interconnection of such units with the classical grid infrastructure. In particular, the status of technical standards both in Europe and USA, possible ways to improve the interconnection situation, and also the need for decisions that provide a satisfactory position for the network operator (who remains responsible for the grid, its operation, maintenance and investment plans) are addressed.

  13. Electric Vehicle Integration into Modern Power Networks

    DEFF Research Database (Denmark)

    software tools to assess the impacts resulting from the electric vehicles deployment on the steady state and dynamic operation of electricity grids, identifies strategies to mitigate them and the possibility to support simultaneously large-scale integration of renewable energy sources. New business models......Electric Vehicle Integration into Modern Power Networks provides coverage of the challenges and opportunities posed by the progressive integration of electric drive vehicles. Starting with a thorough overview of the current electric vehicle and battery state-of-the-art, this work describes dynamic...... and control management architectures, as well as the communication infrastructure required to integrate electric vehicles as active demand are presented. Finally, regulatory issues of integrating electric vehicles into modern power systems are addressed. Inspired by two courses held under the EES...

  14. Electric Vehicle Integration into Modern Power Networks

    DEFF Research Database (Denmark)

    Electric Vehicle Integration into Modern Power Networks provides coverage of the challenges and opportunities posed by the progressive integration of electric drive vehicles. Starting with a thorough overview of the current electric vehicle and battery state-of-the-art, this work describes dynamic...... software tools to assess the impacts resulting from the electric vehicles deployment on the steady state and dynamic operation of electricity grids, identifies strategies to mitigate them and the possibility to support simultaneously large-scale integration of renewable energy sources. New business models...... and control management architectures, as well as the communication infrastructure required to integrate electric vehicles as active demand are presented. Finally, regulatory issues of integrating electric vehicles into modern power systems are addressed. Inspired by two courses held under the EES...

  15. A Novel Algorithm for Power Flow Transferring Identification Based on WAMS

    Directory of Open Access Journals (Sweden)

    Xu Yan

    2015-01-01

    Full Text Available After a faulted transmission line is removed, power flow on it will be transferred to other lines in the network. If those lines are heavily loaded beforehand, the transferred flow may cause the nonfault overload and the incorrect operation of far-ranging backup relays, which are considered as the key factors leading to cascading trips. In this paper, a novel algorithm for power flow transferring identification based on wide area measurement system (WAMS is proposed, through which the possible incorrect tripping of backup relays will be blocked in time. A new concept of Transferred Flow Characteristic Ratio (TFCR is presented and is applied to the identification criteria. Mathematical derivation of TFCR is carried out in detail by utilization of power system short circuit fault modeling. The feasibility and effectiveness of the proposed algorithm to prevent the malfunction of backup relays are demonstrated by a large number of simulations.

  16. A Heterogeneous Wireless Identification Network for the Localization of Animals Based on Stochastic Movements

    Directory of Open Access Journals (Sweden)

    Ivana Raos

    2009-05-01

    Full Text Available The improvement in the transmission range in wireless applications without the use of batteries remains a significant challenge in identification applications. In this paper, we describe a heterogeneous wireless identification network mostly powered by kinetic energy, which allows the localization of animals in open environments. The system relies on radio communications and a global positioning system. It is made up of primary and secondary nodes. Secondary nodes are kinetic-powered and take advantage of animal movements to activate the node and transmit a specific identifier, reducing the number of batteries of the system. Primary nodes are battery-powered and gather secondary-node transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. The system allows tracking based on contextual information obtained from statistical data.

  17. THE PROSPECTS OF DEVELOPMENT OF ELECTRIC POWER NETWORK IN GEORGIA

    International Nuclear Information System (INIS)

    Mshvidobadze, T.

    2007-01-01

    The possibility of application of one of the versions of development of the electric power network in Georgia is disscussed. The algorithm of grouping of the versions of power network development, which allows choosing the optimal network configuration under indefinite conditions, is offered. The experiments have demonstrated that the same optimal decision can be found by considerable reduction in the number of versions. (author)

  18. Network position and related power : how they affect and are affected by network management and outcomes

    NARCIS (Netherlands)

    Oukes, Tamara

    2018-01-01

    In network position and related power you learn more about how network position and related power affect and are affected by network management and outcomes. First, I expand our present understanding of how startups with a fragile network position manage business relationships by taking an

  19. A Gamma Memory Neural Network for System Identification

    Science.gov (United States)

    Motter, Mark A.; Principe, Jose C.

    1992-01-01

    A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

  20. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

    CERN Document Server

    Patan, Maciej

    2012-01-01

    Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...

  1. Neuro-diffuse algorithm for neutronic power identification of TRIGA Mark III reactor

    International Nuclear Information System (INIS)

    Rojas R, E.; Benitez R, J. S.; Segovia de los Rios, J. A.; Rivero G, T.

    2009-10-01

    In this work are presented the results of design and implementation of an algorithm based on diffuse logic systems and neural networks like method of neutronic power identification of TRIGA Mark III reactor. This algorithm uses the punctual kinetics equation as data generator of training, a cost function and a learning stage based on the descending gradient algorithm allow to optimize the parameters of membership functions of a diffuse system. Also, a series of criteria like part of the initial conditions of training algorithm are established. These criteria according to the carried out simulations show a quick convergence of neutronic power estimated from the first iterations. (Author)

  2. Reliability Analysis Techniques for Communication Networks in Nuclear Power Plant

    International Nuclear Information System (INIS)

    Lim, T. J.; Jang, S. C.; Kang, H. G.; Kim, M. C.; Eom, H. S.; Lee, H. J.

    2006-09-01

    The objectives of this project is to investigate and study existing reliability analysis techniques for communication networks in order to develop reliability analysis models for nuclear power plant's safety-critical networks. It is necessary to make a comprehensive survey of current methodologies for communication network reliability. Major outputs of this study are design characteristics of safety-critical communication networks, efficient algorithms for quantifying reliability of communication networks, and preliminary models for assessing reliability of safety-critical communication networks

  3. Effect of size heterogeneity on community identification in complex networks

    Energy Technology Data Exchange (ETDEWEB)

    Danon, L.; Diaz-Guilera, A.; Arenas, A.

    2008-01-01

    Identifying community structure can be a potent tool in the analysis and understanding of the structure of complex networks. Up to now, methods for evaluating the performance of identification algorithms use ad-hoc networks with communities of equal size. We show that inhomogeneities in community sizes can and do affect the performance of algorithms considerably, and propose an alternative method which takes these factors into account. Furthermore, we propose a simple modification of the algorithm proposed by Newman for community detection (Phys. Rev. E 69 066133) which treats communities of different sizes on an equal footing, and show that it outperforms the original algorithm while retaining its speed.

  4. Maintaining the power balance in an "empty network"

    NARCIS (Netherlands)

    Reza, M.; Dominguez, A.O.; Schavemaker, P.H.; Kling, W.L.

    2006-01-01

    This paper presents the concept of an empty network and shows how the power balance can be maintained in such a system. In this study, an empty network is defined as a power system in which no rotating mass is present; all generators are grid-connected via power electronic interfaces. One generator

  5. Social power and opinion formation in complex networks

    Science.gov (United States)

    Jalili, Mahdi

    2013-02-01

    In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts-Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given power to a random selection of nodes could not improve consensus properties. Introducing social power in Watts-Strogatz networks could not significantly change the consensus profile.

  6. On the identification of instabilities with neural networks on JET

    International Nuclear Information System (INIS)

    Murari, A.; Arena, P.; Buscarino, A.; Fortuna, L.; Iachello, M.

    2013-01-01

    JET plasmas are affected by various instabilities, which can be particularly dangerous in high performance discharges. An identification method, based on the use of advanced neural networks, called Recurrent Neural Networks (RNNs), has been applied to ELMs. The potential of the recurrent networks to identify the dynamics of the instabilities has been first tested using synthetic data. The networks have then been applied to JET experimental signals. An appropriate selection of the networks topology allows identifying quite well the time evolution of the edge temperature and of the magnetic fields, considered the best indicators of the ELMs. A quite limited number of periodic oscillations are used to train the networks, which then manage to follow quite well the dynamics of the instabilities, in a recurrent configuration on one of the inputs. The time evolution of the aforementioned signals, also during intervals not used in the training and never seen by the networks, are properly reproduced. A careful analysis of the various terms in the RNNs has the potential to give clear indications about the nature of these instabilities and their dynamical behaviour

  7. Discrete rate and variable power adaptation for underlay cognitive networks

    KAUST Repository

    Abdallah, Mohamed M.; Salem, Ahmed H.; Alouini, Mohamed-Slim; Qaraqe, Khalid A.

    2010-01-01

    We consider the problem of maximizing the average spectral efficiency of a secondary link in underlay cognitive networks. In particular, we consider the network setting whereby the secondary transmitter employs discrete rate and variable power

  8. Data identification for improving gene network inference using computational algebra.

    Science.gov (United States)

    Dimitrova, Elena; Stigler, Brandilyn

    2014-11-01

    Identification of models of gene regulatory networks is sensitive to the amount of data used as input. Considering the substantial costs in conducting experiments, it is of value to have an estimate of the amount of data required to infer the network structure. To minimize wasted resources, it is also beneficial to know which data are necessary to identify the network. Knowledge of the data and knowledge of the terms in polynomial models are often required a priori in model identification. In applications, it is unlikely that the structure of a polynomial model will be known, which may force data sets to be unnecessarily large in order to identify a model. Furthermore, none of the known results provides any strategy for constructing data sets to uniquely identify a model. We provide a specialization of an existing criterion for deciding when a set of data points identifies a minimal polynomial model when its monomial terms have been specified. Then, we relax the requirement of the knowledge of the monomials and present results for model identification given only the data. Finally, we present a method for constructing data sets that identify minimal polynomial models.

  9. Transmit Power Optimisation in Wireless Network

    Directory of Open Access Journals (Sweden)

    Besnik Terziu

    2011-09-01

    Full Text Available Transmit power optimisation in wireless networks based on beamforming have emerged as a promising technique to enhance the spectrum efficiency of present and future wireless communication systems. The aim of this study is to minimise the access point power consumption in cellular networks while maintaining a targeted quality of service (QoS for the mobile terminals. In this study, the targeted quality of service is delivered to a mobile station by providing a desired level of Signal to Interference and Noise Ratio (SINR. Base-stations are coordinated across multiple cells in a multi-antenna beamforming system. This study focuses on a multi-cell multi-antenna downlink scenario where each mobile user is equipped with a single antenna, but where multiple mobile users may be active simultaneously in each cell and are separated via spatial multiplexing using beamforming. The design criteria is to minimize the total weighted transmitted power across the base-stations subject to SINR constraints at the mobile users. The main contribution of this study is to define an iterative algorithm that is capable of finding the joint optimal beamformers for all basestations, based on a correlation-based channel model, the full-correlation model. Among all correlated channel models, the correlated channel model used in this study is the most accurate, giving the best performance in terms of power consumption. The environment here in this study is chosen to be Non-Light of- Sight (NLOS condition, where a signal from a wireless transmitter passes several obstructions before arriving at a wireless receiver. Moreover there are many scatterers local to the mobile, and multiple reflections can occur among them before energy arrives at the mobile. The proposed algorithm is based on uplink-downlink duality using the Lagrangian duality theory. Time-Division Duplex (TDD is chosen as the platform for this study since it has been adopted to the latest technologies in Fourth

  10. An efficient Neuro-Fuzzy approach to nuclear power plant transient identification

    Energy Technology Data Exchange (ETDEWEB)

    Gomes da Costa, Rafael [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Abreu Mol, Antonio Carlos de, E-mail: mol@ien.gov.br [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Instituto Nacional de C and T de Reatores Nucleares Inovadores (Brazil); Carvalho, Paulo Victor R. de, E-mail: paulov@ien.gov.br [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Lapa, Celso Marcelo Franklin, E-mail: lapa@ien.gov.br [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Instituto Nacional de C and T de Reatores Nucleares Inovadores (Brazil)

    2011-06-15

    Highlights: > We investigate a Neuro-Fuzzy modeling tool use for able transient identification. > The prelusive transient type identification is done by an artificial neural network. > After, the fuzzy-logic system analyzes the results emitting reliability degree of it. > The research support was made in a PWR simulator at the Brazilian Nuclear Engineering Institute. > The results show the potential to help operators' decisions in a nuclear power plant. - Abstract: Transient identification in nuclear power plants (NPP) is often a computational very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Recently, several works have been developed for transient identification. These works frequently present a non reliable response, using the 'don't know' as the system output. In this work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A validation of this identification system was made at the three loops Pressurized Water Reactor (PWR) simulator of the Human-System Interface Laboratory (LABIHS) of the Nuclear Engineering Institute

  11. An efficient Neuro-Fuzzy approach to nuclear power plant transient identification

    International Nuclear Information System (INIS)

    Gomes da Costa, Rafael; Abreu Mol, Antonio Carlos de; Carvalho, Paulo Victor R. de; Lapa, Celso Marcelo Franklin

    2011-01-01

    Highlights: → We investigate a Neuro-Fuzzy modeling tool use for able transient identification. → The prelusive transient type identification is done by an artificial neural network. → After, the fuzzy-logic system analyzes the results emitting reliability degree of it. → The research support was made in a PWR simulator at the Brazilian Nuclear Engineering Institute. → The results show the potential to help operators' decisions in a nuclear power plant. - Abstract: Transient identification in nuclear power plants (NPP) is often a computational very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Recently, several works have been developed for transient identification. These works frequently present a non reliable response, using the 'don't know' as the system output. In this work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A validation of this identification system was made at the three loops Pressurized Water Reactor (PWR) simulator of the Human-System Interface Laboratory (LABIHS) of the Nuclear Engineering Institute (IEN

  12. Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

    Science.gov (United States)

    Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

    2016-01-01

    Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845

  13. User Identification Framework in Social Network Services Environment

    Directory of Open Access Journals (Sweden)

    Brijesh BAKARIYA

    2014-01-01

    Full Text Available Social Network Service is a one of the service where people may communicate with one an-other; and may also exchange messages even of any type of audio or video communication. Social Network Service as name suggests a type of network. Such type of web application plays a dominant role in internet technology. In such type of online community, people may share their common interest. Facebook LinkedIn, orkut and many more are the Social Network Service and it is good medium of making link with people having unique or common interest and goals. But the problem of privacy protection is a big issue in today’s world. As social networking sites allows anonymous users to share information of other stuffs. Due to which cybercrime is also increasing to a rapid extent. In this article we preprocessed the web log data of Social Network Services and assemble that data on the basis of image file format like jpg, jpeg, gif, png, bmp etc. and also propose a framework for victim’s identification.

  14. Identification of influential users by neighbors in online social networks

    Science.gov (United States)

    Sheikhahmadi, Amir; Nematbakhsh, Mohammad Ali; Zareie, Ahmad

    2017-11-01

    Identification and ranking of influential users in social networks for the sake of news spreading and advertising has recently become an attractive field of research. Given the large number of users in social networks and also the various relations that exist among them, providing an effective method to identify influential users has been gradually considered as an essential factor. In most of the already-provided methods, those users who are located in an appropriate structural position of the network are regarded as influential users. These methods do not usually pay attention to the interactions among users, and also consider those relations as being binary in nature. This paper, therefore, proposes a new method to identify influential users in a social network by considering those interactions that exist among the users. Since users tend to act within the frame of communities, the network is initially divided into different communities. Then the amount of interaction among users is used as a parameter to set the weight of relations existing within the network. Afterward, by determining the neighbors' role for each user, a two-level method is proposed for both detecting users' influence and also ranking them. Simulation and experimental results on twitter data shows that those users who are selected by the proposed method, comparing to other existing ones, are distributed in a more appropriate distance. Moreover, the proposed method outperforms the other ones in terms of both the influential speed and capacity of the users it selects.

  15. Power-Hop: A Pervasive Observation for Real Complex Networks

    Science.gov (United States)

    2016-03-14

    e.g., power grid, the Internet and the web-graph), social (e.g., friendship networks — Facebook , Gowalla—and co- authorship networks ), urban (e.g...Mislove A., Cha M. and Gummadi K.P. On the evolution of user interaction in Facebook . In Proc. Workshop on Online Social Networks 2009. doi...scale-free distribution is pervasive and describes a large variety of networks , ranging from social and urban to technological and biological networks

  16. Characterizing and predicting the robustness of power-law networks

    International Nuclear Information System (INIS)

    LaRocca, Sarah; Guikema, Seth D.

    2015-01-01

    Power-law networks such as the Internet, terrorist cells, species relationships, and cellular metabolic interactions are susceptible to node failures, yet maintaining network connectivity is essential for network functionality. Disconnection of the network leads to fragmentation and, in some cases, collapse of the underlying system. However, the influences of the topology of networks on their ability to withstand node failures are poorly understood. Based on a study of the response of 2000 randomly-generated power-law networks to node failures, we find that networks with higher nodal degree and clustering coefficient, lower betweenness centrality, and lower variability in path length and clustering coefficient maintain their cohesion better during such events. We also find that network robustness, i.e., the ability to withstand node failures, can be accurately predicted a priori for power-law networks across many fields. These results provide a basis for designing new, more robust networks, improving the robustness of existing networks such as the Internet and cellular metabolic pathways, and efficiently degrading networks such as terrorist cells. - Highlights: • Examine relationship between network topology and robustness to failures. • Relationship is statistically significant for scale-free networks. • Use statistical models to estimate robustness to failures for real-world networks

  17. A fusion networking model for smart grid power distribution backbone communication network based on PTN

    Directory of Open Access Journals (Sweden)

    Wang Hao

    2016-01-01

    Full Text Available In current communication network for distribution in Chinese power grid systems, the fiber communication backbone network for distribution and TD-LTE power private wireless backhaul network of power grid are both bearing by the SDH optical transmission network, which also carries the communication network of transformer substation and main electric. As the data traffic of the distribution communication and TD-LTE power private wireless network grow rapidly in recent years, it will have a big impact with the SDH network’s bearing capacity which is mainly used for main electric communication in high security level. This paper presents a fusion networking model which use a multiple-layer PTN network as the unified bearing of the TD-LTE power private wireless backhaul network and fiber communication backbone network for distribution. Network dataflow analysis shows that this model can greatly reduce the capacity pressure of the traditional SDH network as well as ensure the reliability of the transmission of the communication network for distribution and TD-LTE power private wireless network.

  18. Power Control in Wireless Sensor Networks with Variable Interference

    Directory of Open Access Journals (Sweden)

    Michele Chincoli

    2016-01-01

    Full Text Available Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counterproductive effects to network performance. Yet, indiscriminate power boosting may detrimentally affect interference. We are interested in understanding the conditions under which coordinated power reduction may lead to better spectrum efficiency and interference mitigation and, thus, have beneficial effects on network performance. Through simulations, we analyze the performance of sensor nodes in an environment with variable interference. Then we study the relation between transmission power and communication efficiency, particularly in the context of Adaptive and Robust Topology (ART control, showing how appropriate power reduction can benefit both energy and spectrum efficiency. We also identify critical limitations in ART, discussing the potential of more cooperative power control approaches.

  19. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  20. Interference mitigation through adaptive power control in wireless sensor networks

    NARCIS (Netherlands)

    Chincoli, M.; Bacchiani, C.; Syed, Aly; Exarchakos, G.; Liotta, A.

    2016-01-01

    Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counter-productive

  1. Designing reliable wireless sensor network for nuclear power plant

    International Nuclear Information System (INIS)

    Fujiwara, Takeshi; Takahashi, Hiroyuki

    2007-01-01

    This study proposes an innovative method for the monitoring the nuclear power plant. In this field, false detection of the trouble, both 'false negative' and 'false positive' will become a serious problem. In the other hand, since nuclear power plant is such a complicated system, wireless is required for implementing into real field. Considering these backgrounds, we propose a new reliable health monitoring system for nuclear power plant. This is based on an idea, 'a network on a network', such as 'wireless global network' on 'local network with self-maintenance function.' (author)

  2. Characterizations of the Beta and the Degree Network Power Measure

    NARCIS (Netherlands)

    van den Brink, J.R.; Borm, P.; Hendrickx, R.; Owen, G.

    2008-01-01

    A symmetric network consists of a set of positions and a set of bilateral links between these positions. For every symmetric network we define a cooperative transferable utility game that measures the "power" of each coalition of positions in the network. Applying the Shapley value to this game

  3. Understanding the tariff. Access to the public power transportation network

    International Nuclear Information System (INIS)

    2002-01-01

    Since the European directive of December 19, 1996 about the common rules of the European power market, the eligible companies can chose their power supplier anywhere in Europe. The manager of the French power transportation network (RTE) supplies a network access to these companies according to a tariff fixed by the decree no. 2002-1014 from July 19, 2002. The aim of this document is to explain this tariff: tariffing principles ('mail-stamp' principle, voltage domain, subscribed output power tariffs, input power tariffs), tariffing elements (access to the grid, elements of output tariffs (subscribed power, overload, emergency tariffs, modifications etc..)), invoicing modalities, output tariffs, definitions. (J.S.)

  4. Power control algorithms for mobile ad hoc networks

    Directory of Open Access Journals (Sweden)

    Nuraj L. Pradhan

    2011-07-01

    We will also focus on an adaptive distributed power management (DISPOW algorithm as an example of the multi-parameter optimization approach which manages the transmit power of nodes in a wireless ad hoc network to preserve network connectivity and cooperatively reduce interference. We will show that the algorithm in a distributed manner builds a unique stable network topology tailored to its surrounding node density and propagation environment over random topologies in a dynamic mobile wireless channel.

  5. Load power device, system and method of load control and management employing load identification

    Science.gov (United States)

    Yang, Yi; Luebke, Charles John; Schoepf, Thomas J.

    2018-01-09

    A load power device includes a power input, at least one power output for at least one load, a plurality of sensors structured to sense voltage and current at the at least one power output, and a processor. The processor provides: (a) load identification based upon the sensed voltage and current, and (b) load control and management based upon the load identification.

  6. Village Building Identification Based on Ensemble Convolutional Neural Networks

    Science.gov (United States)

    Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

    2017-01-01

    In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

  7. Introduction to neural networks with electric power applications

    International Nuclear Information System (INIS)

    Wildberger, A.M.; Hickok, K.A.

    1990-01-01

    This is an introduction to the general field of neural networks with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in neural networks and to recognize those which might impact on electric power engineering. Beginning with a brief discussion of natural and artificial neurons, the characteristics of neural networks in general and how they learn, neural networks are compared with other modeling tools such as simulation and expert systems in order to provide guidance in selecting appropriate applications. In the power industry, possible applications include plant control, dispatching, and maintenance scheduling. In particular, neural networks are currently being investigated for enhancements to the Thermal Performance Advisor (TPA) which General Physics Corporation (GP) has developed to improve the efficiency of electric power generation

  8. The application of neural networks to flow regime identification

    International Nuclear Information System (INIS)

    Embrechts, M.; Yapo, T.C.; Lahey, R.T. Jr.

    1993-01-01

    This paper deals with the application of a Kohonen map for the identification of two-phase flow regimes where a mixture of gas and fluid flows through a horizontal tube. Depending on the relative flow velocities of the gas and the liquid phase, four distinct flow regimes can be identified: Wavy flow, plug flow, slug flow and annular flow. A schematic of these flow regimes is presented. The objective identification of two-phase flow regimes constitutes an important and challenging problem for the design of safe and reliable nuclear power plants. Previous attempts to classify these flow regimes are reviewed by Franca and Lahey. The authors describe how a Kohonen map can be applied to distinguish between flow regimes based on the Fourier power spectra and wavelet transforms of pressure drop fluctuations. The Fourier power spectra allowed the Kohonen map to identify the flow regimes successfully. In contrast, the Kohonen maps based on a wavelet transform could only distinguish between wavy and annular flows. An analysis of typical two-phase pressure drop data for an air/water mixture in a horizontal pipe is presented. Use of the wavelet transform and the Kohonen feature map are discussed

  9. Application of artificial neural networks to improve power transfer ...

    African Journals Online (AJOL)

    Application of artificial neural networks to improve power transfer capability through OLTC. ... International Journal of Engineering, Science and Technology ... Numerical results show that the setting of OLTC transformer in terms of the load model has a major effect on the maximum power transfer in power systems and the ...

  10. AS Migration and Optimization of the Power Integrated Data Network

    Science.gov (United States)

    Zhou, Junjie; Ke, Yue

    2018-03-01

    In the transformation process of data integration network, the impact on the business has always been the most important reference factor to measure the quality of network transformation. With the importance of the data network carrying business, we must put forward specific design proposals during the transformation, and conduct a large number of demonstration and practice to ensure that the transformation program meets the requirements of the enterprise data network. This paper mainly demonstrates the scheme of over-migrating point-to-point access equipment in the reconstruction project of power data comprehensive network to migrate the BGP autonomous domain to the specified domain defined in the industrial standard, and to smooth the intranet OSPF protocol Migration into ISIS agreement. Through the optimization design, eventually making electric power data network performance was improved on traffic forwarding, traffic forwarding path optimized, extensibility, get larger, lower risk of potential loop, the network stability was improved, and operational cost savings, etc.

  11. Impedance-Source Networks for Electric Power Conversion Part II

    DEFF Research Database (Denmark)

    Siwakoti, Yam P.; Peng, Fang Zheng; Blaabjerg, Frede

    2015-01-01

    Impedance-source networks cover the entire spectrum of electric power conversion applications (dc-dc, dc-ac, ac-dc, ac-ac) controlled and modulated by different modulation strategies to generate the desired dc or ac voltage and current at the output. A comprehensive review of various impedance......-source-network-based power converters has been covered in a previous paper and main topologies were discussed from an application point of view. Now Part II provides a comprehensive review of the most popular control and modulation strategies for impedance-source network-based power converters/inverters. These methods...

  12. Disease candidate gene identification and prioritization using protein interaction networks

    Directory of Open Access Journals (Sweden)

    Aronow Bruce J

    2009-02-01

    Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

  13. Energy parameter estimation in solar powered wireless sensor networks

    KAUST Repository

    Mousa, Mustafa

    2014-02-24

    The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.

  14. Energy parameter estimation in solar powered wireless sensor networks

    KAUST Repository

    Mousa, Mustafa; Claudel, Christian G.

    2014-01-01

    The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.

  15. Dense power-law networks and simplicial complexes

    Science.gov (United States)

    Courtney, Owen T.; Bianconi, Ginestra

    2018-05-01

    There is increasing evidence that dense networks occur in on-line social networks, recommendation networks and in the brain. In addition to being dense, these networks are often also scale-free, i.e., their degree distributions follow P (k ) ∝k-γ with γ ∈(1 ,2 ] . Models of growing networks have been successfully employed to produce scale-free networks using preferential attachment, however these models can only produce sparse networks as the numbers of links and nodes being added at each time step is constant. Here we present a modeling framework which produces networks that are both dense and scale-free. The mechanism by which the networks grow in this model is based on the Pitman-Yor process. Variations on the model are able to produce undirected scale-free networks with exponent γ =2 or directed networks with power-law out-degree distribution with tunable exponent γ ∈(1 ,2 ) . We also extend the model to that of directed two-dimensional simplicial complexes. Simplicial complexes are generalization of networks that can encode the many body interactions between the parts of a complex system and as such are becoming increasingly popular to characterize different data sets ranging from social interacting systems to the brain. Our model produces dense directed simplicial complexes with power-law distribution of the generalized out-degrees of the nodes.

  16. Power generation using photovoltaic induction in an isolated power network

    International Nuclear Information System (INIS)

    Kalantar, M.; Jiang, J.

    2001-01-01

    Owing to increased emphasis on renewable resources, the development of suitable isolated power generators driven by energy sources, the development of suitable isolated power generators driven by energy sources such as photovoltaic, wind, small hydroelectric, biogas and etc. has recently assumed greater significance. A single phase capacitor self excited induction generator has emerged as a suitable candidate of isolated power sources. This paper presents performance analysis of a single phase self-excited induction generator driven by photovoltaic (P V) system for low power isolated stand-alone applications. A single phase induction machine can work as a self-excited induction generator when its rotor is driven at suitable speed by an photovoltaic powered do motor. Its excitation is provided by connecting a single phase capacitor bank at a stator terminals. Either to augment grid power or to get uninterrupted power during grid failure stand-alone low capacity ac generators are used. These are driven by photovoltaic, wind power or I C engines using kerosene, diesel, petrol or biogas as fuel. Self-excitation with capacitors at the stator terminals of the stator terminals of the induction machines is well demonstrated experimentally on a P V powered dc motor-induction machine set. The parameters and the excitation requirements of the induction machine run in self-excited induction generator mode are determined. The effects of variations in prime mover speed,terminal capacitance and load power factor on the machine terminal voltage are studied

  17. Reliable low-power wireless networks over unstable transmission power

    NARCIS (Netherlands)

    Kotian, Roshan; Exarchakos, Georgios; Liotta, Antonio

    2017-01-01

    Internet of Things promises large scale interconnected sensing and actuation capabilities in domains, areas, applications and activities never accessed before by Internet. Besides other technical barriers, wireless network node lifetime impedes its applicability. To reduce the energy cost incurred

  18. System identification and adaptive control theory and applications of the neurofuzzy and fuzzy cognitive network models

    CERN Document Server

    Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A

    2014-01-01

    Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented.  Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model  stems  from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...

  19. Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games

    Directory of Open Access Journals (Sweden)

    Emmanuel García

    2014-01-01

    Full Text Available This paper deals with the problem of identifying and filtering a class of continuous-time nonlinear dynamic games (nonlinear differential games subject to additive and undesired deterministic perturbations. Moreover, the mathematical model of this class is completely unknown with the exception of the control actions of each player, and even though the deterministic noises are known, their power (or their effect is not. Therefore, two differential neural networks are designed in order to obtain a feedback (perfect state information pattern for the mentioned class of games. In this way, the stability conditions for two state identification errors and for a filtering error are established, the upper bounds of these errors are obtained, and two new learning laws for each neural network are suggested. Finally, an illustrating example shows the applicability of this approach.

  20. Securing Gateways within Clustered Power Centric Network of Nodes

    Directory of Open Access Journals (Sweden)

    Qaisar Javaid

    2016-01-01

    Full Text Available Knowledge Networks are gaining momentum within cyber world. Knowledge leads to innovation and for this reason organizations focus on research and information gathering in order to gain and improve existing knowledge. This of information era, which is primarily based on world wide web technologies, enables significantly expanded networks of people to communicate and collaborate 'virtually' across teams, across entire organizations and across the world, anytime and anywhere. Innovations in computing and telecommunications have transformed the corporations from structured and manageable types to interwoven network of blurred boundaries such as; ad hoc networks and mobile wireless networks, etc. This study explores knowledge networks in Information Technology and security leaks that are found, as well as measures that are taken to counter this menace which is coming up with optimal Secure Clustered Power Centric node network. The paper concludes these measures, evaluating and integrating them to come up with a secured network design.

  1. Dynamic neural networks based on-line identification and control of high performance motor drives

    Science.gov (United States)

    Rubaai, Ahmed; Kotaru, Raj

    1995-01-01

    In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

  2. Vulnerability in the power network - a pre study

    International Nuclear Information System (INIS)

    Kjoelle, Gerd H.; Uhlen, Kjetil; Rolfseng, Lars; Stene, Birger

    2006-02-01

    Vulnerability in the power distribution network has been made a current topic because of various factors, from terror attacks to a strained power balance, large breakdowns in the power system in Europe and North America in recent time and an anticipated increase in climate-related challenges in the coming years; all related to the modern society's critical dependence on reliable power supply. Several questions are posed; whether there is a foundation to say that the vulnerability in the power network is increasing because of factors like cuts in staffing, reduced investments and increased exploitation of the capacity in the power systems, or increased average age on the air network. Different development features can indicate that the power network's ability to resist high stress is about to weaken. Examples of this is the slowly increasing trend in the number of non-reported interruptions, as well as an increase in the error frequency for power lines in the distribution network and for distribution transformers. In the pre-study there has not been found enough evidence to give a clear answer to whether the vulnerability in the power network is in fact on the rise. This is mainly due to the lack of good indicators, measuring methods and the foundation for documentation. Suggestions for methodology in order to identify unwanted incidents, estimate the probability and classify consequences for vulnerability analyses of power networks are presented. The methodology is concretised and exemplified in relation to a specific case: Power loss in the southern Norway affecting more than 250 000 people for 8-12 hours. Such a consequence is classified as critical. For four sub areas it has been exemplified which incidents may potentially cause such breaks. A summary is made of the most important challenges related to making vulnerability analyses of power networks. Comprised here are appropriate concepts, definitions, standards and measuring scales as well as data foundation

  3. Impedance-Source Networks for Electric Power Conversion Part I

    DEFF Research Database (Denmark)

    Siwakoti, Yam P.; Peng, Fang Zheng; Blaabjerg, Frede

    2015-01-01

    power chain, which may improve the reliability and performance of the power system. The first part of this paper provides a comprehensive review of the various impedance-source-networks-based power converters and discusses the main topologies from an application point of view. This review paper...... is the first of its kind with the aim of providing a “one-stop” information source and a selection guide on impedance-source networks for power conversion for researchers, designers, and application engineers. A comprehensive review of various modeling, control, and modulation techniques for the impedance...

  4. Lifetime Maximizing Adaptive Power Control in Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Sun, Fangting; Shayman, Mark

    2006-01-01

    ...: adaptive power control. They focus on the sensor networks that consist of a sink and a set of homogeneous wireless sensor nodes, which are randomly deployed according to a uniform distribution...

  5. Dynamic power control for wireless backbone mesh networks: a survey

    CSIR Research Space (South Africa)

    Olwal, TO

    2010-01-01

    Full Text Available points of failures, and robust against RF interference, obstacles or power outage. This is because WMRs forming wireless backbone mesh networks (WBMNs) are built on advanced physical technologies. Such nodes perform both accessing and forwarding...

  6. Wind power prediction based on genetic neural network

    Science.gov (United States)

    Zhang, Suhan

    2017-04-01

    The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

  7. Energy Harvesting in Heterogeneous Networks with Hybrid Powered Communication Systems

    KAUST Repository

    Alsharoa, Ahmad; Celik, Abdulkadir; Kamal, Ahmed E.

    2018-01-01

    In this paper, we investigate an energy efficient and energy harvesting (EH) system model in heterogeneous networks (HetNets) where all base stations (BSS) are equipped to harvest energy from renewable energy sources. We consider a hybrid power

  8. Effects of network node consolidation in optical access and aggregation networks on costs and power consumption

    Science.gov (United States)

    Lange, Christoph; Hülsermann, Ralf; Kosiankowski, Dirk; Geilhardt, Frank; Gladisch, Andreas

    2010-01-01

    The increasing demand for higher bit rates in access networks requires fiber deployment closer to the subscriber resulting in fiber-to-the-home (FTTH) access networks. Besides higher access bit rates optical access network infrastructure and related technologies enable the network operator to establish larger service areas resulting in a simplified network structure with a lower number of network nodes. By changing the network structure network operators want to benefit from a changed network cost structure by decreasing in short and mid term the upfront investments for network equipment due to concentration effects as well as by reducing the energy costs due to a higher energy efficiency of large network sites housing a high amount of network equipment. In long term also savings in operational expenditures (OpEx) due to the closing of central office (CO) sites are expected. In this paper different architectures for optical access networks basing on state-of-the-art technology are analyzed with respect to network installation costs and power consumption in the context of access node consolidation. Network planning and dimensioning results are calculated for a realistic network scenario of Germany. All node consolidation scenarios are compared against a gigabit capable passive optical network (GPON) based FTTH access network operated from the conventional CO sites. The results show that a moderate reduction of the number of access nodes may be beneficial since in that case the capital expenditures (CapEx) do not rise extraordinarily and savings in OpEx related to the access nodes are expected. The total power consumption does not change significantly with decreasing number of access nodes but clustering effects enable a more energyefficient network operation and optimized power purchase order quantities leading to benefits in energy costs.

  9. Predictive power control in wireless sensor networks

    NARCIS (Netherlands)

    Chincoli, M.; Syed, Aly; Mocanu, D.C.; Liotta, A.

    2016-01-01

    Communications in Wireless Sensor Networks (WSNs) are affected by dynamic environments, variable signal fluctuations and interference. Thus, prompt actions are necessary to achieve dependable communications and meet quality of service requirements. To this end, the reactive algorithms used in

  10. Communication Network Architectures Based on Ethernet Passive Optical Network for Offshore Wind Power Farms

    Directory of Open Access Journals (Sweden)

    Mohamed A. Ahmed

    2016-03-01

    Full Text Available Nowadays, with large-scale offshore wind power farms (WPFs becoming a reality, more efforts are needed to maintain a reliable communication network for WPF monitoring. Deployment topologies, redundancy, and network availability are the main items to enhance the communication reliability between wind turbines (WTs and control centers. Traditional communication networks for monitoring and control (i.e., supervisory control and data acquisition (SCADA systems using switched gigabit Ethernet will not be sufficient for the huge amount of data passing through the network. In this paper, the optical power budget, optical path loss, reliability, and network cost of the proposed Ethernet Passive Optical Network (EPON-based communication network for small-size offshore WPFs have been evaluated for five different network architectures. The proposed network model consists of an optical network unit device (ONU deployed on the WT side for collecting data from different internal networks. All ONUs from different WTs are connected to a central optical line terminal (OLT, placed in the control center. There are no active electronic elements used between the ONUs and the OLT, which reduces the costs and complexity of maintenance and deployment. As fiber access networks without any protection are characterized by poor reliability, three different protection schemes have been configured, explained, and discussed. Considering the cost of network components, the total implementation expense of different architectures with, or without, protection have been calculated and compared. The proposed network model can significantly contribute to the communication network architecture for next generation WPFs.

  11. Energy Efficient Distributed Fault Identification Algorithm in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Meenakshi Panda

    2014-01-01

    Full Text Available A distributed fault identification algorithm is proposed here to find both hard and soft faulty sensor nodes present in wireless sensor networks. The algorithm is distributed, self-detectable, and can detect the most common byzantine faults such as stuck at zero, stuck at one, and random data. In the proposed approach, each sensor node gathered the observed data from the neighbors and computed the mean to check whether faulty sensor node is present or not. If a node found the presence of faulty sensor node, then compares observed data with the data of the neighbors and predict probable fault status. The final fault status is determined by diffusing the fault information from the neighbors. The accuracy and completeness of the algorithm are verified with the help of statistical model of the sensors data. The performance is evaluated in terms of detection accuracy, false alarm rate, detection latency and message complexity.

  12. System Identification Using Multilayer Differential Neural Networks: A New Result

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2012-01-01

    Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

  13. Orientation selective neural network for cosmic muon identification

    International Nuclear Information System (INIS)

    Abramowicz, H.; Tel Aviv Univ.; Horn, D.; Naftaly, U.; Sahar-Pikielny, C.

    1997-01-01

    We discuss a novel method for identification of a linear pattern of pixels on a two-dimensional grid. Motivated by principles employed by the visual cortex, we construct orientation selective neurons in a neural network that performs this task. The method is then applied to a sample of data collected with the ZEUS detector at HERA in order to identify cosmic muons that leave a linear pattern of signals in the segmented uranium-scintillator calorimeter. A two dimensional representation of the relevant part of the detector is used. The algorithm performs well in the presence of noise and pixels with limited efficiency. Given its architecture, this system becomes a good candidate for fast pattern recognition in parallel processing devices. (orig.)

  14. Power to Detect Intervention Effects on Ensembles of Social Networks

    Science.gov (United States)

    Sweet, Tracy M.; Junker, Brian W.

    2016-01-01

    The hierarchical network model (HNM) is a framework introduced by Sweet, Thomas, and Junker for modeling interventions and other covariate effects on ensembles of social networks, such as what would be found in randomized controlled trials in education research. In this article, we develop calculations for the power to detect an intervention…

  15. Spectrum Reorganization and Bundling for Power Efficient Mobile Networks

    DEFF Research Database (Denmark)

    Micallef, Gilbert; Mogensen, Preben; Scheck, Hans-Otto

    2012-01-01

    are still required for supporting legacy devices and providing wider network coverage. In order to facilitate and reduce the cost of rolling out a new network, mobile operators often reuse existing sites. Radio frequency modules in base station sites house power amplifiers, which are designed to operate...

  16. A neural network model of lateralization during letter identification.

    Science.gov (United States)

    Shevtsova, N; Reggia, J A

    1999-03-01

    The causes of cerebral lateralization of cognitive and other functions are currently not well understood. To investigate one aspect of function lateralization, a bihemispheric neural network model for a simple visual identification task was developed that has two parallel interacting paths of information processing. The model is based on commonly accepted concepts concerning neural connectivity, activity dynamics, and synaptic plasticity. A combination of both unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules is used to train the model to identify a small set of letters presented as input stimuli in the left visual hemifield, in the central position, and in the right visual hemifield. Each visual hemifield projects onto the contralateral hemisphere, and the two hemispheres interact via a simulated corpus callosum. The contribution of each individual hemisphere to the process of input stimuli identification was studied for a variety of underlying asymmetries. The results indicate that multiple asymmetries may cause lateralization. Lateralization occurred toward the side having larger size, higher excitability, or higher learning rate parameters. It appeared more intensively with strong inhibitory callosal connections, supporting the hypothesis that the corpus callosum plays a functionally inhibitory role. The model demonstrates clearly the dependence of lateralization on different hemisphere parameters and suggests that computational models can be useful in better understanding the mechanisms underlying emergence of lateralization.

  17. 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....

  18. Neural Network with Local Memory for Nuclear Reactor Power Level Control

    International Nuclear Information System (INIS)

    Uluyol, Oender; Ragheb, Magdi; Tsoukalas, Lefteri

    2001-01-01

    A methodology is introduced for a neural network with local memory called a multilayered local output gamma feedback (LOGF) neural network within the paradigm of locally-recurrent globally-feedforward neural networks. It appears to be well-suited for the identification, prediction, and control tasks in highly dynamic systems; it allows for the presentation of different timescales through incorporation of a gamma memory. A learning algorithm based on the backpropagation-through-time approach is derived. The spatial and temporal weights of the network are iteratively optimized for a given problem using the derived learning algorithm. As a demonstration of the methodology, it is applied to the task of power level control of a nuclear reactor at different fuel cycle conditions. The results demonstrate that the LOGF neural network controller outperforms the classical as well as the state feedback-assisted classical controllers for reactor power level control by showing a better tracking of the demand power, improving the fuel and exit temperature responses, and by performing robustly in different fuel cycle and power level conditions

  19. Aspects concerning power distribution networks planning using artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Georgescu, Gh.; Gavrilas, M.; Cartina, Gh. [Gh. Asachi Technical Univ. of Iasi, Iasi (Romania)

    1997-12-31

    This paper presents the application of AI tools for the on-line identification of load structure in distribution networks. The authors have considered Artificial Neural Networks (ANN) which are known as valuable and fast tools for pattern identification or completion. This approach to the load model allows a more detailed analysis directed towards the optimization of system structure and working conditions. Traditional methods produce good results but raise the processing time problem, especially when applied to large systems. For such cases another approach appeal to the Genetic Algorithms, which are frequently referenced in the literature concerned with PDS (reconfiguration of open loop radial networks, optimal var-sources distribution, optimal selection of transformer tap position). (author)

  20. Advanced Power Converter for Universal and Flexible Power Management in Future Electricity Network

    DEFF Research Database (Denmark)

    Iov, Florin; Blaabjerg, Frede; Bassett, R.

    2007-01-01

    converters for grid connection of renewable sources will be needed. These power converters must be able to provide intelligent power management as well as ancillary services. This paper presents the overall structure and the control aspects of an advanced power converter for universal and flexible power......More "green" power provided by Distributed Generation will enter into the European electricity network in the near future. In order to control the power flow and to ensure proper and secure operation of this future grid, with an increased level of the renewable power, new power electronic...

  1. Horizontal two phase flow pattern identification by neural networks

    International Nuclear Information System (INIS)

    Crivelaro, Kelen Cristina Oliveira; Seleghim Junior, Paulo; Hervieu, Eric

    1999-01-01

    A multiphase fluid can flow according to several flow regimes. The problem associated with multiphase systems are basically related to the behavior of macroscopic parameters, such as pressure drop, thermal exchanges and so on, and their strong correlation to the flow regime. From the industrial applications point of view, the safety and longevity of equipment and systems can only be assured when they work according to the flow regimes for which they were designed to. This implies in the need to diagnose flow regimes in real time. The automatic diagnosis of flow regimes represents an objective of extreme importance, mainly for applications on nuclear and petrochemical industries. In this work, a neural network is used in association to a probe of direct visualization for the identification of a gas-liquid flow horizontal regimes, developed in an experimental circuit. More specifically, the signals produced by the probe are used to compose a qualitative image of the flow, which is promptly sent to the network for the recognition of the regimes. Results are presented for different transitions among the flow regimes, which demonstrate the extremely satisfactory performance of the diagnosis system. (author)

  2. Power quality enhancement of renewable energy source power network using SMES system

    International Nuclear Information System (INIS)

    Seo, H.R.; Kim, A.R.; Park, M.; Yu, I.K.

    2011-01-01

    Power quality enhancement of a renewable energy source power network is performed by a real-toroidal-type SMES coil. SMES unit charges and discharges the HTS coil to mitigate the fluctuation of PV system output power. The grid connected PV and SMES system has been modeled and simulated using power-hard-in-the-loop simulation. The PHILS results demonstrated the effectiveness of the SMES system for enhancing power quality. This paper deals with power quality enhancement of renewable energy source power network using SMES system and describes the operation characteristics of HTS SMES system using real-toroidal-type SMES coil for smoothening the fluctuation of large-scale renewable energy source such as photovoltaic (PV) power generation system. It generates maximum power of PV array under various weather conditions. SMES unit charges and discharges the HTS coil to mitigate the fluctuation of PV system output power. The SMES unit is controlled according to the PV array output and the utility power quality conditions. The grid connected PV and SMES system has been modeled and simulated using power-hard-in-the-loop simulation (PHILS). The PHILS results demonstrated the effectiveness of the SMES system for enhancing power quality in power network including large-scale renewable energy source, especially PV power generation system.

  3. Assessment of proactive transmission power control for wireless sensor networks

    NARCIS (Netherlands)

    kotian, Roshan; Exarchakos, Georgios; Liotta, Antonio

    2014-01-01

    In order to prolong lifetime of Wireless Sensor Networks (WSN), Transmission Power Control (TPC) techniques are employed. The existing TPC schemes adjust the transmission power mostly reacting to changes at link quality between communicating nodes. Proactive TPC has been proposed in the recent past

  4. Wind Power Plant Prediction by Using Neural Networks: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Z.; Gao, W.; Wan, Y. H.; Muljadi, E.

    2012-08-01

    This paper introduces a method of short-term wind power prediction for a wind power plant by training neural networks based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data.

  5. Autonomous Power Control MAC Protocol for Mobile Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    2006-01-01

    Full Text Available Battery energy limitation has become a performance bottleneck for mobile ad hoc networks. IEEE 802.11 has been adopted as the current standard MAC protocol for ad hoc networks. However, it was developed without considering energy efficiency. To solve this problem, many modifications on IEEE 802.11 to incorporate power control have been proposed in the literature. The main idea of these power control schemes is to use a maximum possible power level for transmitting RTS/CTS and the lowest acceptable power for sending DATA/ACK. However, these schemes may degrade network throughput and reduce the overall energy efficiency of the network. This paper proposes autonomous power control MAC protocol (APCMP, which allows mobile nodes dynamically adjusting power level for transmitting DATA/ACK according to the distances between the transmitter and its neighbors. In addition, the power level for transmitting RTS/CTS is also adjustable according to the power level for DATA/ACK packets. In this paper, the performance of APCMP protocol is evaluated by simulation and is compared with that of other protocols.

  6. Pilot Ionosonde Network for Identification of Traveling Ionospheric Disturbances

    Science.gov (United States)

    Reinisch, Bodo; Galkin, Ivan; Belehaki, Anna; Paznukhov, Vadym; Huang, Xueqin; Altadill, David; Buresova, Dalia; Mielich, Jens; Verhulst, Tobias; Stankov, Stanimir; Blanch, Estefania; Kouba, Daniel; Hamel, Ryan; Kozlov, Alexander; Tsagouri, Ioanna; Mouzakis, Angelos; Messerotti, Mauro; Parkinson, Murray; Ishii, Mamoru

    2018-03-01

    Traveling ionospheric disturbances (TIDs) are the ionospheric signatures of atmospheric gravity waves. Their identification and tracking is important because the TIDs affect all services that rely on predictable ionospheric radio wave propagation. Although various techniques have been proposed to measure TID characteristics, their real-time implementation still has several difficulties. In this contribution, we present a new technique, based on the analysis of oblique Digisonde-to-Digisonde "skymap" observations, to directly identify TIDs and specify the TID wave parameters based on the measurement of angle of arrival, Doppler frequency, and time of flight of ionospherically reflected high-frequency radio pulses. The technique has been implemented for the first time for the Network for TID Exploration project with data streaming from the network of European Digisonde DPS4D observatories. The performance is demonstrated during a period of moderate auroral activity, assessing its consistency with independent measurements such as data from auroral magnetometers and electron density perturbations from Digisondes and Global Navigation Satellite System stations. Given that the different types of measurements used for this assessment were not made at exactly the same time and location, and that there was insufficient coverage in the area between the atmospheric gravity wave sources and the measurement locations, we can only consider our interpretation as plausible and indicative for the reliability of the extracted TID characteristics. In the framework of the new TechTIDE project (European Commission H2020), a retrospective analysis of the Network for TID Exploration results in comparison with those extracted from Global Navigation Satellite System total electron content-based methodologies is currently being attempted, and the results will be the objective of a follow-up paper.

  7. Group handoff management in low power microcell-femtocell network

    Directory of Open Access Journals (Sweden)

    Debashis De

    2017-02-01

    Full Text Available This paper presents an analytical model of group based hand-off management based on bird flocking behavior. In the proposed scheme, a number of mobile devices form a group if these devices move together for a long time duration. Although call delivery or call generation are performed individually, hand-off is performed in a group. Dynamic group formation, group division and group merging methods are proposed in this paper. From the simulation results it is demonstrated that approximately 75%, 65% and 90% reduction in power, cost and latency consumption can be obtained respectively using group hand-off management. Thus the proposed scheme is referred as green, economic and fast hand-off strategy. In this paper instead of a macrocell network, a microcell-femtocell network is considered as the transmission power of a microcell or a femtocell base station is much less than a macrocell base station. Simulation results present that the microcell-femtocell network achieves approximately 25–55% and 35–55% reduction in power transmission, and 50–65% and 15–45% reduction in path loss than only a macrocell network and macrocell-femtocell network respectively. Thus microcell-femtocell network is a power-efficient network.

  8. Development of objective flow regime identification method using self-organizing neural network

    International Nuclear Information System (INIS)

    Lee, Jae Young; Kim, Nam Seok; Kwak, Nam Yee

    2004-01-01

    Two-phase flow shows various flow patterns according to the amount of the void and its relative velocity to the liquid flow. This variation directly affect the interfacial transfer which is the key factor for the design or analysis of the phase change systems. Especially the safety analysis of the nuclear power plant has been performed based on the numerical code furnished with the proper constitutive relations depending highly upon the flow regimes. Heavy efforts have been focused to identify the flow regime and at this moment we stand on relative very stable engineering background compare to the other research field. However, the issues related to objectiveness and transient flow regime are still open to study. Lee et al. and Ishii developed the method for the objective and instantaneous flow regime identification based on the neural network and new index of probability distribution of the flow regime which allows just one second observation for the flow regime identification. In the present paper, we developed the self-organized neural network for more objective approach to this problem. Kohonen's Self-Organizing Map (SOM) has been used for clustering, visualization, and abstraction. The SOM is trained through unsupervised competitive learning using a 'winner takes it all' policy. Therefore, its unsupervised training character delete the possible interference of the regime developer to the neural network training. After developing the computer code, we evaluate the performance of the code with the vertically upward two-phase flow in the pipes of 25.4 and 50.4 cmm I.D. Also, the sensitivity of the number of the clusters to the flow regime identification was made

  9. Exploring empowerment in settings: mapping distributions of network power.

    Science.gov (United States)

    Neal, Jennifer Watling

    2014-06-01

    This paper brings together two trends in the empowerment literature-understanding empowerment in settings and understanding empowerment as relational-by examining what makes settings empowering from a social network perspective. Specifically, extending Neal and Neal's (Am J Community Psychol 48(3/4):157-167, 2011) conception of network power, an empowering setting is defined as one in which (1) actors have existing relationships that allow for the exchange of resources and (2) the distribution of network power among actors in the setting is roughly equal. The paper includes a description of how researchers can examine distributions of network power in settings. Next, this process is illustrated in both an abstract example and using empirical data on early adolescents' peer relationships in urban classrooms. Finally, implications for theory, methods, and intervention related to understanding empowering settings are explored.

  10. The KKS power plant identification system. 3. ed.

    International Nuclear Information System (INIS)

    1988-01-01

    The previous first and second editions of the KKS, system for power plant identification, consisted of the following: introduction; instructions for application with a comparative presentation of the DIN/KKS systems and subject index; keys (functional key, equipment key, operating media key). The third edition now available incorporates the following revisions and additions: instructions for application refer exclusively to the KKS system; key updates; revised coordinating file for the equipment key and operating media key; a completely new section entitled 'Agreements for coordination of project activities', in an annex to the KKS instructions; comparison DIN/KKS adapted to new version of KKS instructions; the subject index of the 2nd edition has been extended by a keyword index referring to the explanations for application of the KKS system. (orig./HP) [de

  11. Low-Power Wireless Sensor Networks Protocols, Services and Applications

    CERN Document Server

    Suhonen, Jukka; Kaseva, Ville; Hämäläinen, Timo D; Hännikäinen, Marko

    2012-01-01

    Wireless sensor network (WSN) is an ad-hoc network technology comprising even thousands of autonomic and self-organizing nodes that combine environmental sensing, data processing, and wireless networking. The applications for sensor networks range from home and industrial environments to military uses. Unlike the traditional computer networks, a WSN is application-oriented and deployed for a specific task. WSNs are data centric, which means that messages are not send to individual nodes but to geographical locations or regions based on the data content. A WSN node is typically battery powered and characterized by extremely small size and low cost. As a result, the processing power, memory, and energy resources of an individual sensor node are limited. However, the feasibility of a WSN lies on the collaboration between the nodes. A reference WSN node comprises a Micro-Controller Unit (MCU) having few Million Instructions Per Second (MIPS) processing speed, tens of kilobytes program memory, few kilobytes data m...

  12. Use of neural networks to monitor power plant components

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.

    1992-01-01

    A new methodology is presented for nondestructive evaluation (NDE) of check valve performance and degradation. Artificial neural network (ANN) technology is utilized for processing frequency domain signatures of check valves operating in a nuclear power plant (NPP). Acoustic signatures obtained from different locations on a check valve are transformed from the time domain to the frequency domain and then used as input to a pretrained neural network. The neural network has been trained with data sets corresponding to normal operation, therefore establishing a basis for check valve satisfactory performance. Results obtained from the proposed methodology demonstrate the ability of neural networks to perform accurate and quick evaluations of check valve performance

  13. Synapse:neural network for predict power consumption: users guide

    Energy Technology Data Exchange (ETDEWEB)

    Muller, C; Mangeas, M; Perrot, N

    1994-08-01

    SYNAPSE is forecasting tool designed to predict power consumption in metropolitan France on the half hour time scale. Some characteristics distinguish this forecasting model from those which already exist. In particular, it is composed of numerous neural networks. The idea for using many neural networks arises from past tests. These tests showed us that a single neural network is not able to solve the problem correctly. From this result, we decided to perform unsupervised classification of the 24 consumption curves. From this classification, six classes appeared, linked with the weekdays: Mondays, Tuesdays, Wednesdays, Thursdays, Fridays, Saturdays, Sundays, holidays and bridge days. For each class and for each half hour, two multilayer perceptrons are built. The two of them forecast the power for one particular half hour, and for a day including one of the determined class. The input of these two network are different: the first one (short time forecasting) includes the powers for the most recent half hour and relative power of the previous day; the second (medium time forecasting) includes only the relative power of the previous day. A process connects the results of every networks and allows one to forecast more than one half-hour in advance. In this process, short time forecasting networks and medium time forecasting networks are used differently. The first kind of neural networks gives good results on the scale of one day. The second one gives good forecasts for the next predicted powers. In this note, the organization of the SYNAPSE program is detailed, and the user`s menu is described. This first version of synapse works and should allow the APC group to evaluate its utility. (authors). 6 refs., 2 appends.

  14. Braess's paradox in oscillator networks, desynchronization and power outage

    International Nuclear Information System (INIS)

    Witthaut, Dirk; Timme, Marc

    2012-01-01

    Robust synchronization is essential to ensure the stable operation of many complex networked systems such as electric power grids. Increasing energy demands and more strongly distributing power sources raise the question of where to add new connection lines to the already existing grid. Here we study how the addition of individual links impacts the emergence of synchrony in oscillator networks that model power grids on coarse scales. We reveal that adding new links may not only promote but also destroy synchrony and link this counter-intuitive phenomenon to Braess's paradox known for traffic networks. We analytically uncover its underlying mechanism in an elementary grid example, trace its origin to geometric frustration in phase oscillators, and show that it generically occurs across a wide range of systems. As an important consequence, upgrading the grid requires particular care when adding new connections because some may destabilize the synchronization of the grid—and thus induce power outages. (paper)

  15. Power networks in the heart of industrial civilization

    International Nuclear Information System (INIS)

    Bouneau, Ch.; Derdevet, M.; Percebois, J.

    2007-01-01

    Since more than a century, power networks have largely contributed to the economic development of our societies, to the improvement of our life conditions, and to the relations between communities. The 19. and 20. centuries have seen the multiplying and spreading of power networks over Europe, and at the beginning of the 21. century, it has become vital to improve and reinforce them. This book proposes to rediscover the main innovations of the 20. century and the men who participated to this energy revolution. It examines also the choices to be made in the months to come both in France and in Europe. The importance of a European energy policy, necessary for a development of power networks, is emphasized. At a time where each French end-user can chose his power supplier, this book allows to better understand the economic, social and political challenges of a real European solidarity. (J.S.)

  16. Power plant fault detection using artificial neural network

    Science.gov (United States)

    Thanakodi, Suresh; Nazar, Nazatul Shiema Moh; Joini, Nur Fazriana; Hidzir, Hidzrin Dayana Mohd; Awira, Mohammad Zulfikar Khairul

    2018-02-01

    The fault that commonly occurs in power plants is due to various factors that affect the system outage. There are many types of faults in power plants such as single line to ground fault, double line to ground fault, and line to line fault. The primary aim of this paper is to diagnose the fault in 14 buses power plants by using an Artificial Neural Network (ANN). The Multilayered Perceptron Network (MLP) that detection trained utilized the offline training methods such as Gradient Descent Backpropagation (GDBP), Levenberg-Marquardt (LM), and Bayesian Regularization (BR). The best method is used to build the Graphical User Interface (GUI). The modelling of 14 buses power plant, network training, and GUI used the MATLAB software.

  17. Research on spot power market equilibrium model considering the electric power network characteristics

    International Nuclear Information System (INIS)

    Wang, Chengmin; Jiang, Chuanwen; Chen, Qiming

    2007-01-01

    Equilibrium is the optimum operational condition for the power market by economics rule. A realistic spot power market cannot achieve the equilibrium condition due to network losses and congestions. The impact of the network losses and congestion on spot power market is analyzed in this paper in order to establish a new equilibrium model considering the network loss and transmission constraints. The OPF problem formulated according to the new equilibrium model is solved by means of the equal price principle. A case study on the IEEE-30-bus system is provided in order to prove the effectiveness of the proposed approach. (author)

  18. Modeling geomagnetic induced currents in Australian power networks

    Science.gov (United States)

    Marshall, R. A.; Kelly, A.; Van Der Walt, T.; Honecker, A.; Ong, C.; Mikkelsen, D.; Spierings, A.; Ivanovich, G.; Yoshikawa, A.

    2017-07-01

    Geomagnetic induced currents (GICs) have been considered an issue for high-latitude power networks for some decades. More recently, GICs have been observed and studied in power networks located in lower latitude regions. This paper presents the results of a model aimed at predicting and understanding the impact of geomagnetic storms on power networks in Australia, with particular focus on the Queensland and Tasmanian networks. The model incorporates a "geoelectric field" determined using a plane wave magnetic field incident on a uniform conducting Earth, and the network model developed by Lehtinen and Pirjola (1985). Model results for two intense geomagnetic storms of solar cycle 24 are compared with transformer neutral monitors at three locations within the Queensland network and one location within the Tasmanian network. The model is then used to assess the impacts of the superintense geomagnetic storm of 29-31 October 2003 on the flow of GICs within these networks. The model results show good correlation with the observations with coefficients ranging from 0.73 to 0.96 across the observing sites. For Queensland, modeled GIC magnitudes during the superstorm of 29-31 October 2003 exceed 40 A with the larger GICs occurring in the south-east section of the network. Modeled GICs in Tasmania for the same storm do not exceed 30 A. The larger distance spans and general east-west alignment of the southern section of the Queensland network, in conjunction with some relatively low branch resistance values, result in larger modeled GICs despite Queensland being a lower latitude network than Tasmania.

  19. Impact of network topology on synchrony of oscillatory power grids

    Energy Technology Data Exchange (ETDEWEB)

    Rohden, Martin; Sorge, Andreas; Witthaut, Dirk [Network Dynamics, Max Planck Institute for Dynamics and Self-Organization (MPIDS), 37077 Göttingen (Germany); Timme, Marc [Network Dynamics, Max Planck Institute for Dynamics and Self-Organization (MPIDS), 37077 Göttingen (Germany); Faculty of Physics, Georg August Universität Göttingen, Göttingen (Germany)

    2014-03-15

    Replacing conventional power sources by renewable sources in current power grids drastically alters their structure and functionality. In particular, power generation in the resulting grid will be far more decentralized, with a distinctly different topology. Here, we analyze the impact of grid topologies on spontaneous synchronization, considering regular, random, and small-world topologies and focusing on the influence of decentralization. We model the consumers and sources of the power grid as second order oscillators. First, we analyze the global dynamics of the simplest non-trivial (two-node) network that exhibit a synchronous (normal operation) state, a limit cycle (power outage), and coexistence of both. Second, we estimate stability thresholds for the collective dynamics of small network motifs, in particular, star-like networks and regular grid motifs. For larger networks, we numerically investigate decentralization scenarios finding that decentralization itself may support power grids in exhibiting a stable state for lower transmission line capacities. Decentralization may thus be beneficial for power grids, regardless of the details of their resulting topology. Regular grids show a specific sharper transition not found for random or small-world grids.

  20. Tritium-Powered Radiation Sensor Network

    Science.gov (United States)

    2015-09-01

    Photomultiplier Tube, Scintillator, Geiger counter, Zigbee, Wireless Network, Radiation detector, Dirty Bomb 16. SECURITY CLASSIFICATION OF: 17...operational lifetime of 150 years. Persistent sensing of the environment with vibration and radiation (electromagnetic [ EM ], acoustic, gamma, etc.) in...Transportation E-field electric field EH electron-hole EM electromagnetic GaAs gallium arsenide GPS global positioning system InGaP indium gallium

  1. Towards Self-Powered Wireless Sensor Networks

    OpenAIRE

    SPENZA, DORA

    2013-01-01

    Ubiquitous computing aims at creating smart environments in which computational and communication capabilities permeate the word at all scales, improving the human experience and quality of life in a totally unobtrusive yet completely reliable manner. According to this vision, an huge variety of smart devices and products (e.g., wireless sensor nodes, mobile phones, cameras, sensors, home appliances and industrial machines) are interconnected to realize a network of distributed agents that co...

  2. Power Minimization techniques for Networked Data Centers

    International Nuclear Information System (INIS)

    Low, Steven; Tang, Kevin

    2011-01-01

    Our objective is to develop a mathematical model to optimize energy consumption at multiple levels in networked data centers, and develop abstract algorithms to optimize not only individual servers, but also coordinate the energy consumption of clusters of servers within a data center and across geographically distributed data centers to minimize the overall energy cost and consumption of brown energy of an enterprise. In this project, we have formulated a variety of optimization models, some stochastic others deterministic, and have obtained a variety of qualitative results on the structural properties, robustness, and scalability of the optimal policies. We have also systematically derived from these models decentralized algorithms to optimize energy efficiency, analyzed their optimality and stability properties. Finally, we have conducted preliminary numerical simulations to illustrate the behavior of these algorithms. We draw the following conclusion. First, there is a substantial opportunity to minimize both the amount and the cost of electricity consumption in a network of datacenters, by exploiting the fact that traffic load, electricity cost, and availability of renewable generation fluctuate over time and across geographical locations. Judiciously matching these stochastic processes can optimize the tradeoff between brown energy consumption, electricity cost, and response time. Second, given the stochastic nature of these three processes, real-time dynamic feedback should form the core of any optimization strategy. The key is to develop decentralized algorithms that can be implemented at different parts of the network as simple, local algorithms that coordinate through asynchronous message passing.

  3. Future view of electric power information processing techniques. Architecture techniques for power supply communication network

    Energy Technology Data Exchange (ETDEWEB)

    Tanaka, Keisuke

    1988-06-20

    Present situations of a power supply communication are described, and the future trend of a power supply information network is reviewed. For the improvement of a transmission efficiency and quality and a cost benefit for the power supply communication, the introduction of digital networks has been promoted. As for a protection information network, since there is the difference between a required communication quality of system protection information and that of power supply operation information, the individual digital network configuration is expected, in addition, the increasing of image information transmission for monitoring is also estimated. As for a business information network, the construction of a broad-band switched network is expected with increasing of image transmission needs such as a television meeting. Furthermore, the expansion to a power supply ISDN which is possible to connect between a telephone, facsimile and data terminal, to exchange various media and to connect between networks is expected with higher communication services in the protection and business network. However, for its practical use, the standardization of various interfaces will become essential. (3 figs, 1 tab)

  4. SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATION NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    B. Jency Paulin

    2016-01-01

    Full Text Available Solar Energy is an important renewable and unlimited source of energy. Solar photovoltaic power forecasting, is an estimation of the expected power production, that help the grid operators to better manage the electric balance between power demand and supply. Neural network is a computational model that can predict new outcomes from past trends. The artificial neural network is used for photovoltaic plant energy forecasting. The output power for solar photovoltaic cell is predicted on hourly basis. In historical dataset collection process, two dataset was collected and used for analysis. The dataset was provided with three independent attributes and one dependent attributes. The implementation of Artificial Neural Network structure is done by Multilayer Perceptron (MLP and training procedure for neural network is done by error Back Propagation (BP. In order to train and test the neural network, the datasets are divided in the ratio 70:30. The accuracy of prediction can be done by using various error measurement criteria and the performance of neural network is to be noted.

  5. Educational commitment and social networking: The power of informal networks

    Science.gov (United States)

    Zwolak, Justyna P.; Zwolak, Michael; Brewe, Eric

    2018-06-01

    The lack of an engaging pedagogy and the highly competitive atmosphere in introductory science courses tend to discourage students from pursuing science, technology, engineering, and mathematics (STEM) majors. Once in a STEM field, academic and social integration has been long thought to be important for students' persistence. Yet, it is rarely investigated. In particular, the relative impact of in-class and out-of-class interactions remains an open issue. Here, we demonstrate that, surprisingly, for students whose grades fall in the "middle of the pack," the out-of-class network is the most significant predictor of persistence. To do so, we use logistic regression combined with Akaike's information criterion to assess in- and out-of-class networks, grades, and other factors. For students with grades at the very top (and bottom), final grade, unsurprisingly, is the best predictor of persistence—these students are likely already committed (or simply restricted from continuing) so they persist (or drop out). For intermediate grades, though, only out-of-class closeness—a measure of one's immersion in the network—helps predict persistence. This does not negate the need for in-class ties. However, it suggests that, in this cohort, only students that get past the convenient in-class interactions and start forming strong bonds outside of class are or become committed to their studies. Since many students are lost through attrition, our results suggest practical routes for increasing students' persistence in STEM majors.

  6. Rapid identification of sequences for orphan enzymes to power accurate protein annotation.

    Directory of Open Access Journals (Sweden)

    Kevin R Ramkissoon

    Full Text Available The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the "back catalog" of enzymology--"orphan enzymes," those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC database alone. In this study, we demonstrate how this orphan enzyme "back catalog" is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology's "back catalog" another powerful tool to drive accurate genome annotation.

  7. Rapid Identification of Sequences for Orphan Enzymes to Power Accurate Protein Annotation

    Science.gov (United States)

    Ojha, Sunil; Watson, Douglas S.; Bomar, Martha G.; Galande, Amit K.; Shearer, Alexander G.

    2013-01-01

    The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the “back catalog” of enzymology – “orphan enzymes,” those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC) database alone. In this study, we demonstrate how this orphan enzyme “back catalog” is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis) to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology’s “back catalog” another powerful tool to drive accurate genome annotation. PMID:24386392

  8. A Distributed Routing Scheme for Energy Management in Solar Powered Sensor Networks

    KAUST Repository

    Dehwah, Ahmad H.; Shamma, Jeff S.; Claudel, Christian G.

    2017-01-01

    Energy management is critical for solar-powered sensor networks. In this article, we consider data routing policies to optimize the energy in solar powered networks. Motivated by multipurpose sensor networks, the objective is to find the best

  9. Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks

    Directory of Open Access Journals (Sweden)

    M. Hadi Amini

    2018-01-01

    Full Text Available Electrified transportation and power systems are mutually coupled networks. In this paper, a novel framework is developed for interdependent power and transportation networks. Our approach constitutes solving an iterative least cost vehicle routing process, which utilizes the communication of electrified vehicles (EVs with competing charging stations, to exchange data such as electricity price, energy demand, and time of arrival. The EV routing problem is solved to minimize the total cost of travel using the Dijkstra algorithm with the input from EVs battery management system, electricity price from charging stations, powertrain component efficiencies and transportation network traffic conditions. Through the bidirectional communication of EVs with competing charging stations, EVs’ charging demand estimation is done much more accurately. Then the optimal power flow problem is solved for the power system, to find the locational marginal price at load buses where charging stations are connected. Finally, the electricity prices were communicated from the charging stations to the EVs, and the loop is closed. Locational electricity price acts as the shared parameter between the two optimization problems, i.e., optimal power flow and optimal routing problem. Electricity price depends on the power demand, which is affected by the charging of EVs. On the other hand, location of EV charging stations and their different pricing strategies might affect the routing decisions of the EVs. Our novel approach that combines the electrified transportation with power system operation, holds tremendous potential for solving electrified transportation issues and reducing energy costs. The effectiveness of the proposed approach is demonstrated using Shanghai transportation network and IEEE 9-bus test system. The results verify the cost-savings for both power system and transportation networks.

  10. Power, Avionics and Software Communication Network Architecture

    Science.gov (United States)

    Ivancic, William D.; Sands, Obed S.; Bakula, Casey J.; Oldham, Daniel R.; Wright, Ted; Bradish, Martin A.; Klebau, Joseph M.

    2014-01-01

    This document describes the communication architecture for the Power, Avionics and Software (PAS) 2.0 subsystem for the Advanced Extravehicular Mobile Unit (AEMU). The following systems are described in detail: Caution Warn- ing and Control System, Informatics, Storage, Video, Audio, Communication, and Monitoring Test and Validation. This document also provides some background as well as the purpose and goals of the PAS project at Glenn Research Center (GRC).

  11. Power Laws, Scale-Free Networks and Genome Biology

    CERN Document Server

    Koonin, Eugene V; Karev, Georgy P

    2006-01-01

    Power Laws, Scale-free Networks and Genome Biology deals with crucial aspects of the theoretical foundations of systems biology, namely power law distributions and scale-free networks which have emerged as the hallmarks of biological organization in the post-genomic era. The chapters in the book not only describe the interesting mathematical properties of biological networks but moves beyond phenomenology, toward models of evolution capable of explaining the emergence of these features. The collection of chapters, contributed by both physicists and biologists, strives to address the problems in this field in a rigorous but not excessively mathematical manner and to represent different viewpoints, which is crucial in this emerging discipline. Each chapter includes, in addition to technical descriptions of properties of biological networks and evolutionary models, a more general and accessible introduction to the respective problems. Most chapters emphasize the potential of theoretical systems biology for disco...

  12. Efficient Reactive Power Compensation Algorithm for Distribution Network

    Directory of Open Access Journals (Sweden)

    J. Jerome

    2017-12-01

    Full Text Available The use of automation and energy efficient equipment with electronic control would greatly improve industrial production.  These new devices are more sensitive to supply voltage deviation and the characteristics of the power system that was previously ignored are now very important. Hence the benefits of distribution automation have been widely acknowledged in recent years. This paper proposes an efficient load flow solution technique extended to find optimum location for reactive power compensation and network reconfiguration for planning and day-to-day operation of distribution networks.  This is required as a part of the distribution automation system (DAS for taking various control and operation decisions.  The method exploits the radial nature of the network and uses forward and backward propagation technique to calculate branch currents and node voltages.  The proposed method has been tested to analyze several practical distribution networks of various voltage levels and also having high R/X ratio.

  13. Mining Heterogeneous Information Networks by Exploring the Power of Links

    Science.gov (United States)

    Han, Jiawei

    Knowledge is power but for interrelated data, knowledge is often hidden in massive links in heterogeneous information networks. We explore the power of links at mining heterogeneous information networks with several interesting tasks, including link-based object distinction, veracity analysis, multidimensional online analytical processing of heterogeneous information networks, and rank-based clustering. Some recent results of our research that explore the crucial information hidden in links will be introduced, including (1) Distinct for object distinction analysis, (2) TruthFinder for veracity analysis, (3) Infonet-OLAP for online analytical processing of information networks, and (4) RankClus for integrated ranking-based clustering. We also discuss some of our on-going studies in this direction.

  14. Identification and evaluation of accident sequences in nuclear power reactors

    International Nuclear Information System (INIS)

    Amendola, A.; Capobianchi, S.; Mancini, G.; Olivi, L.; Volta, G.; Reina, G.

    1981-01-01

    Probabilistic analysis techniques are being more and more used for the evaluation of accident progression in nuclear power plants, especially after the issue of the Reactor Safety Study (Report WASH-1400). This study and subsequent discussions have indicated the necessity of better investigating some major items, namely: adequate data base for the probabilistic evaluations; completeness of the analysis with respect both to accident initiation and behaviour; adequate treatment of uncertainties on the physical and operational parameters governing the accident behaviour. Furthermore, recent occurrences have stressed the importance of the operational aspects of reactor safety, such as plant-specific identification of possible occurrences, their prompt recognition, on-line prediction of subsequent developments and actions to be taken. The paper reviews the contributions in progress at JRC-Ispra to all these aspects, and specifically reports on the following: (1) The set-up of a European Reliability Data System for the acquisition and organisation of operational data of LWRs in the European Community. (2) The development of more complete and realistic models of systems. This work includes multistate static models of components and systems with a view to automatic fault-tree construction and dynamic models for accident sequence identification. The dynamic modelling approach ESCS (Event Sequence and Consequences Spectrum), shown in detail with an example, represents a step forward with respect to event-tree technique and opens new possibilities in dealing with human factors and on-line diagnosis problems. (3) The development of RSM (Response Surface Methodology) for the analysis of uncertainty propagations in consequence and in probability of accident chains. (author)

  15. Energy-efficient power control for OFDMA cellular networks

    KAUST Repository

    Sboui, Lokman

    2016-12-24

    In this paper, we study the energy efficiency (EE) of orthogonal frequency-division multiple access (OFDMA) cellular networks. Our objective is to present a power allocation scheme that maximizes the EE of downlink communications. We propose a novel explicit expression of the optimal power allocation to each subcarrier. We also present the power control when the transmit power is limited by power budget constraint or/and minimal rate constraint and we highlight the occurrence of some transmission outage events depending on the constraints\\' parameters. In the numerical results, we show that our proposed power control improves the EE especially at high power budget regime and low minimal rate regime. In addition, we show that having a higher number of subcarriers enhances the OFDMA EE.

  16. Power-Aware Intrusion Detection in Mobile Ad Hoc Networks

    Science.gov (United States)

    Şen, Sevil; Clark, John A.; Tapiador, Juan E.

    Mobile ad hoc networks (MANETs) are a highly promising new form of networking. However they are more vulnerable to attacks than wired networks. In addition, conventional intrusion detection systems (IDS) are ineffective and inefficient for highly dynamic and resource-constrained environments. Achieving an effective operational MANET requires tradeoffs to be made between functional and non-functional criteria. In this paper we show how Genetic Programming (GP) together with a Multi-Objective Evolutionary Algorithm (MOEA) can be used to synthesise intrusion detection programs that make optimal tradeoffs between security criteria and the power they consume.

  17. Dynamic baseline detection method for power data network service

    Science.gov (United States)

    Chen, Wei

    2017-08-01

    This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.

  18. Nuclear power plant fault-diagnosis using artificial neural networks

    International Nuclear Information System (INIS)

    Kim, Keehoon; Aljundi, T.L.; Bartlett, E.B.

    1992-01-01

    Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant's training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses

  19. Neural networks and their potential application in nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    A neural network is a data processing system consisting of a number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Examples include reading Japanese Kanji characters and human handwriting, reading a typewritten manuscript aloud, compensating for alignment errors in robots, interpreting very noise signals (e.g., electroencephalograms), modeling complex systems that cannot be modeled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and describes research on the potential applications to nuclear power plants

  20. Power Consumption Evaluation of Distributed Computing Network Considering Traffic Locality

    Science.gov (United States)

    Ogawa, Yukio; Hasegawa, Go; Murata, Masayuki

    When computing resources are consolidated in a few huge data centers, a massive amount of data is transferred to each data center over a wide area network (WAN). This results in increased power consumption in the WAN. A distributed computing network (DCN), such as a content delivery network, can reduce the traffic from/to the data center, thereby decreasing the power consumed in the WAN. In this paper, we focus on the energy-saving aspect of the DCN and evaluate its effectiveness, especially considering traffic locality, i.e., the amount of traffic related to the geographical vicinity. We first formulate the problem of optimizing the DCN power consumption and describe the DCN in detail. Then, numerical evaluations show that, when there is strong traffic locality and the router has ideal energy proportionality, the system's power consumption is reduced to about 50% of the power consumed in the case where a DCN is not used; moreover, this advantage becomes even larger (up to about 30%) when the data center is located farthest from the center of the network topology.

  1. Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

    Science.gov (United States)

    Geng, Xiangyi; Lu, Shizeng; Jiang, Mingshun; Sui, Qingmei; Lv, Shanshan; Xiao, Hang; Jia, Yuxi; Jia, Lei

    2018-06-01

    A damage identification system of carbon fiber reinforced plastics (CFRP) structures is investigated using fiber Bragg grating (FBG) sensors and back propagation (BP) neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis (FEA). At last, the damage identification system is verified on a CFRP plate with 300 mm × 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

  2. Wireless Power Transfer and Data Collection in Wireless Sensor Networks

    OpenAIRE

    Li, Kai; Ni, Wei; Duan, Lingjie; Abolhasan, Mehran; Niu, Jianwei

    2017-01-01

    In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using Wireless Power Transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, while the other nodes waste their harvested energy, which is more than they need...

  3. Supply disruption cost for power network planning

    International Nuclear Information System (INIS)

    Kjoelle, G.H.

    1992-09-01

    A description is given of the method of approach to calculate the total annual socio-economic cost of power supply disruption and non-supplied energy, included the utilities' cost for planning. The total socio-economic supply disruption cost is the sum of the customers' disruption cost and the utilities' cost for failure and disruption. The mean weighted disruption cost for Norway for one hour disruption is NOK 19 per kWh. The customers' annual disruption cost is calculated with basis in the specific disruption cost referred to heavy load (January) and dimensioning maximum loads. The loads are reduced by factors taking into account the time variations of the failure frequency, duration, the loads and the disruption cost. 6 refs

  4. Network fault response of wind power plants in distribution systems during reverse power flows. Part II

    NARCIS (Netherlands)

    Boemer, J.C.; Gibescu, M.; vd Meijden, M.A.M.M.; Rawn, B.G.; Kling, W.L.

    2013-01-01

    Abstract—The ability of wind power park modules to control their response to transmission network faults allows for specification of new control features directed at stabilising the power system response during and after disturbances. However, the ‘effectiveness’ of these features in situations

  5. Self-Learning Power Control in Wireless Sensor Networks.

    Science.gov (United States)

    Chincoli, Michele; Liotta, Antonio

    2018-01-27

    Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.

  6. Modeling generator power plant portfolios and pollution taxes in electric power supply chain networks: a transportation network equilibrium transformation

    International Nuclear Information System (INIS)

    Kai Wu; Nagurney, A.; University of Massachusetts, Amherst, MA; Zugang Liu; Stranlund, J.K.

    2006-01-01

    Global climate change and fuel security risks have encouraged international and regional adoption of pollution/carbon taxes. A major portion of such policy interventions is directed at the electric power industry with taxes applied according to the type of fuel used by the power generators in their power plants. This paper proposes an electric power supply chain network model that captures the behavior of power generators faced with a portfolio of power plant options and subject to pollution taxes. We demonstrate that this general model can be reformulated as a transportation network equilibrium model with elastic demands and qualitatively analyzed and solved as such. The connections between these two different modeling schemas is done through finite-dimensional variational inequality theory. The numerical examples illustrate how changes in the pollution/carbon taxes affect the equilibrium electric power supply chain network production outputs, the transactions between the various decision-makers the demand market prices, as well as the total amount of carbon emissions generated. (author)

  7. Comparative analysis of the application of different Low Power Wide Area Network technologies in power grid

    Science.gov (United States)

    Wang, Hao; Sui, Hong; Liao, Xing; Li, Junhao

    2018-03-01

    Low Power Wide Area Network (LPWAN) technologies developed rapidly in recent years, but the application principle of different LPWAN technologies in power grid is still not clear. This paper gives a comparative analysis of two mainstream LPWAN technologies including NB-IoT and LoRa, and gives an application suggestion of these two LPWAN technologies, which can guide the planning and construction of LPWAN in power grid.

  8. More Opportunities than Wealth. A Network of Power and Frustration

    Energy Technology Data Exchange (ETDEWEB)

    Mahault, Benoit Alexandre [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Saxena, Avadh Behari [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Nisoli, Cristiano [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-08-17

    We introduce a minimal agent-based model to qualitatively conceptualize the allocation of limited wealth among more abundant opportunities. We study the interplay of power, satisfaction and frustration in the problem of wealth distribution, concentration, and inequality. This framework allows us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from, or lose wealth to, anybody else invariably leads to a complete polarization of the distribution of wealth vs. opportunity, only minimally ameliorated by disorder in a non-optimized society. The picture is however dramatically modified when hard constraints are imposed over agents, and they are forced to share wealth with neighbors on a network. We discuss the case of random networks and scale free networks. We then propose an out of equilibrium dynamics of the networks, based on a competition of power and frustration in the decision-making of agents that leads to network evolution. We show that the ratio of power and frustration controls different dynamical regimes separated by kinetic transition and characterized by drastically different values of the indices of equality.

  9. Feature-Augmented Neural Networks for Patient Note De-identification

    OpenAIRE

    Lee, Ji Young; Dernoncourt, Franck; Uzuner, Ozlem; Szolovits, Peter

    2016-01-01

    Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objective for a de-identification system is to have the highest possible recall. Recently, the first neural-network-based de-identification system has been proposed, yielding state-of-t...

  10. An Explication and Test of Communication Network Content and Multiplexity as Predictors of Organizational Identification.

    Science.gov (United States)

    Bullis, Connie; Bach, Betsy Wackernagel

    1991-01-01

    Examines the relationship between identification and communication using organizational identification (OI) as a theoretical framework for studying communication networks among incoming graduate students in three university departments of communication. Concludes that, irrespective of initial OI, stronger initial multiplexity predicts the growth…

  11. Network cost in transmission and distribution of electric power

    International Nuclear Information System (INIS)

    Lindahl, A.; Naeslund, B.; Oettinger-Biberg, C.; Olander, H.; Wuolikainen, T.; Fritz, P.

    1994-01-01

    This report is divided in two parts, where part 1 treats the charges on the regional nets with special emphasis on the net owners tariffs on a deregulated market. Part 2 describes the development of the network costs in electric power distribution for the period 1991-1993. 11 figs, 33 tabs

  12. Scalable power selection method for wireless mesh networks

    CSIR Research Space (South Africa)

    Olwal, TO

    2009-01-01

    Full Text Available This paper addresses the problem of a scalable dynamic power control (SDPC) for wireless mesh networks (WMNs) based on IEEE 802.11 standards. An SDPC model that accounts for architectural complexities witnessed in multiple radios and hops...

  13. In-node cognitive power control in Wireless Sensor Networks

    NARCIS (Netherlands)

    Chincoli, Michele; Liotta, Antonio

    2017-01-01

    Reliability, interoperability and efficiency are fundamental in Wireless Sensor Network deployment. Herein we look at how transmission power control may be used to reduce interference, which is particularly problematic in high-density conditions. We adopt a distributed approach where every node has

  14. Power and delay optimisation in multi-hop wireless networks

    KAUST Repository

    Xia, Li; Shihada, Basem

    2014-01-01

    in order to minimise the power consumption and the queueing delay of the whole network. With the assumptions of interference-free links and independently and identically distributed (i.i.d.) channel states, we formulate this problem using a semi

  15. Neutral networks and their application in nuclear power plants

    International Nuclear Information System (INIS)

    Zhao Fuyu; Li Tiejun; Liao Zhongyue

    1994-01-01

    The neutral theory has been applied to various fields and many achievements have been obtained in many aspects, and the theory has also applied to nuclear engineering. In this paper, a few patterns of neutral networks and application in nuclear power plant is surveyed so as to bring the researching direction to nuclear work's attention at home

  16. Extremal dependencies and rank correlations in power law networks

    NARCIS (Netherlands)

    Volkovich, Y.; Litvak, Nelli; Zwart, B.; Jie, Z.

    2009-01-01

    We analyze dependencies in complex networks characterized by power laws (Web sample, Wikipedia sample and a preferential attachment graph) using statistical techniques from the extreme value theory and the theory of multivariate regular variation. To the best of our knowledge, this is the first

  17. Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

    Science.gov (United States)

    Gao, Zhongke; Jin, Ningde

    2009-06-01

    The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

  18. Strategies for Power/Energy Saving in Distribution Networks

    Directory of Open Access Journals (Sweden)

    GRIGORAS, G.

    2010-05-01

    Full Text Available The power/energy losses reduction in distribution systems is an important issue during planning and operation, with important technical and economical implications. Thus, the energy losses minimization implies not only the technical improvement of the network, through its renewal with the introduction of the technological innovations in the equipment and circuit components as well as the optimal planning of the design and development of the network, but also requires the use of the methods and software tools to facilitate the operation process. The paper presents a strategy for power/energy saving which replacement of the 6 kV voltage level with 20 kV voltage level in correlation with the extent of using efficient transformers. In this line, different urban distribution networks were analyzed using fuzzy techniques.

  19. Economic assessment group on power transmission and distribution networks tariffs

    International Nuclear Information System (INIS)

    2000-06-01

    Facing the new law on the electric power market liberalization, the french government created an experts group to analyze solutions and assessment methods of the electrical networks costs and tariffs and to control their efficiency. This report presents the analysis and the conclusions of the group. It concerns the three main subjects: the regulation context, the tariffing of the electric power transmission and distribution (the cost and efficiency of the various options) and the tariffing of the electric power supply to the eligible consumers. The authors provide a guideline for a tariffing policy. (A.L.B.)

  20. Supporting Control Room Operators in Highly Automated Future Power Networks

    DEFF Research Database (Denmark)

    Chen, Minjiang; Catterson, Victoria; Syed, Mazheruddin

    2017-01-01

    Operating power systems is an extremely challenging task, not least because power systems have become highly interconnected, as well as the range of network issues that can occur. It is therefore a necessity to develop decision support systems and visualisation that can effectively support the hu...... the human operators for decisionmaking in the complex and dynamic environment of future highly automated power system. This paper aims to investigate the decision support functions associated with frequency deviation events for the proposed Web of Cells concept....

  1. Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

    Energy Technology Data Exchange (ETDEWEB)

    Syed A. Ahmad; Umesh Shukla; Ryan E. Irwin; Luiz A. DaSilva; Allen B. MacKenzie

    2011-03-01

    In this paper, we develop a unifying optimization formulation to describe the Dynamic Channel and Power Assignment (DCPA) problem and evaluation method for comparing DCPA algorithms. DCPA refers to the allocation of transmit power and frequency channels to links in a cognitive network so as to maximize the total number of feasible links while minimizing the aggregate transmit power. We apply our evaluation method to five algorithms representative of DCPA used in literature. This comparison illustrates the tradeoffs between control modes (centralized versus distributed) and channel/power assignment techniques. We estimate the complexity of each algorithm. Through simulations, we evaluate the effectiveness of the algorithms in achieving feasible link allocations in the network, as well as their power efficiency. Our results indicate that, when few channels are available, the effectiveness of all algorithms is comparable and thus the one with smallest complexity should be selected. The Least Interfering Channel and Iterative Power Assignment (LICIPA) algorithm does not require cross-link gain information, has the overall lowest run time, and highest feasibility ratio of all the distributed algorithms; however, this comes at a cost of higher average power per link.

  2. Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

    International Nuclear Information System (INIS)

    Husam Fayiz, Al Masri

    2017-01-01

    The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms. (paper)

  3. Potential applications of neural networks to nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    Application of neural networks to the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: diagnosing specific abnormal conditions, detection of the change of mode of operation, signal validation, monitoring of check valves, plant-wide monitoring using autoassociative neural networks, modeling of the plant thermodynamics, emulation of core reload calculations, monitoring of plant parameters, and analysis of plant vibrations. Each of these projects and its status are described briefly in this article. The objective of each of these projects is to enhance the safety and performance of nuclear plants through the use of neural networks

  4. Development of nuclear power plant diagnosis technique using neural networks

    International Nuclear Information System (INIS)

    Horiguchi, Masahiro; Fukawa, Naohiro; Nishimura, Kazuo

    1991-01-01

    A nuclear power plant diagnosis technique has been developed, called transient phenomena analysis, which employs neural network. The neural networks identify malfunctioning equipment by recognizing the pattern of main plant parameters, making it possible to locate the cause of an abnormality when a plant is in a transient state. In a case where some piece of equipment shows abnormal behavior, many plant parameters either directly or indirectly related to that equipment change simultaneously. When an abrupt change in a plant parameter is detected, changes in the 49 main plant parameters are classified into three types and a characteristic change pattern consisting of 49 data is defined. The neural networks then judge the cause of the abnormality from this pattern. This neural-network-based technique can recognize 100 patterns that are characterized by the causes of plant abnormality. (author)

  5. Development scheme of the public power transportation network

    International Nuclear Information System (INIS)

    2005-01-01

    Article 14 of the modified law from February 10, 2000 relative to the modernization and development of the electric utility foresees that the development scheme of the public power transportation network is regularly submitted to the approval of the ministry of energy after advice from the energy regulation commission. The development scheme identifies the areas of 'power fragility' with respect to the existing or future constraints susceptible to occur at the short- or medium-term on the French power grid. This document comprises the text of the law 2000-108 from February 10, 2000, and the complete development scheme with its appendixes (regulatory and administrative context relative to network projects, constraints relative to each administrative region). (J.S.)

  6. Development of Light Powered Sensor Networks for Thermal Comfort Measurement

    Directory of Open Access Journals (Sweden)

    Dasheng Lee

    2008-10-01

    Full Text Available Recent technological advances in wireless communications have enabled easy installation of sensor networks with air conditioning equipment control applications. However, the sensor node power supply, through either power lines or battery power, still presents obstacles to the distribution of the sensing systems. In this study, a novel sensor network, powered by the artificial light, was constructed to achieve wireless power transfer and wireless data communications for thermal comfort measurements. The sensing node integrates an IC-based temperature sensor, a radiation thermometer, a relative humidity sensor, a micro machined flow sensor and a microprocessor for predicting mean vote (PMV calculation. The 935 MHz band RF module was employed for the wireless data communication with a specific protocol based on a special energy beacon enabled mode capable of achieving zero power consumption during the inactive periods of the nodes. A 5W spotlight, with a dual axis tilt platform, can power the distributed nodes over a distance of up to 5 meters. A special algorithm, the maximum entropy method, was developed to estimate the sensing quantity of climate parameters if the communication module did not receive any response from the distributed nodes within a certain time limit. The light-powered sensor networks were able to gather indoor comfort-sensing index levels in good agreement with the comfort-sensing vote (CSV preferred by a human being and the experimental results within the environment suggested that the sensing system could be used in air conditioning systems to implement a comfort-optimal control strategy.

  7. Nuclear power plant status diagnostics using artificial neural networks

    International Nuclear Information System (INIS)

    Bartlett, E.B.; Uhrig, R.E.

    1991-01-01

    In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results

  8. Visiting Power Laws in Cyber-Physical Networking Systems

    Directory of Open Access Journals (Sweden)

    Ming Li

    2012-01-01

    Full Text Available Cyber-physical networking systems (CPNSs are made up of various physical systems that are heterogeneous in nature. Therefore, exploring universalities in CPNSs for either data or systems is desired in its fundamental theory. This paper is in the aspect of data, aiming at addressing that power laws may yet be a universality of data in CPNSs. The contributions of this paper are in triple folds. First, we provide a short tutorial about power laws. Then, we address the power laws related to some physical systems. Finally, we discuss that power-law-type data may be governed by stochastically differential equations of fractional order. As a side product, we present the point of view that the upper bound of data flow at large-time scaling and the small one also follows power laws.

  9. Hierarchical Communication Network Architectures for Offshore Wind Power Farms

    Directory of Open Access Journals (Sweden)

    Mohamed A. Ahmed

    2014-05-01

    Full Text Available Nowadays, large-scale wind power farms (WPFs bring new challenges for both electric systems and communication networks. Communication networks are an essential part of WPFs because they provide real-time control and monitoring of wind turbines from a remote location (local control center. However, different wind turbine applications have different requirements in terms of data volume, latency, bandwidth, QoS, etc. This paper proposes a hierarchical communication network architecture that consist of a turbine area network (TAN, farm area network (FAN, and control area network (CAN for offshore WPFs. The two types of offshore WPFs studied are small-scale WPFs close to the grid and medium-scale WPFs far from the grid. The wind turbines are modelled based on the logical nodes (LN concepts of the IEC 61400-25 standard. To keep pace with current developments in wind turbine technology, the network design takes into account the extension of the LNs for both the wind turbine foundation and meteorological measurements. The proposed hierarchical communication network is based on Switched Ethernet. Servers at the control center are used to store and process the data received from the WPF. The network architecture is modelled and evaluated via OPNET. We investigated the end-to-end (ETE delay for different WPF applications. The results are validated by comparing the amount of generated sensing data with that of received traffic at servers. The network performance is evaluated, analyzed and discussed in view of end-to-end (ETE delay for different link bandwidths.

  10. Identification of literary movements using complex networks to represent texts

    International Nuclear Information System (INIS)

    Amancio, Diego Raphael; Oliveira, Osvaldo N Jr; Fontoura Costa, Luciano da

    2012-01-01

    The use of statistical methods to analyze large databases of text has been useful in unveiling patterns of human behavior and establishing historical links between cultures and languages. In this study, we identified literary movements by treating books published from 1590 to 1922 as complex networks, whose metrics were analyzed with multivariate techniques to generate six clusters of books. The latter correspond to time periods coinciding with relevant literary movements over the last five centuries. The most important factor contributing to the distinctions between different literary styles was the average shortest path length, in particular the asymmetry of its distribution. Furthermore, over time there has emerged a trend toward larger average shortest path lengths, which is correlated with increased syntactic complexity, and a more uniform use of the words reflected in a smaller power-law coefficient for the distribution of word frequency. Changes in literary style were also found to be driven by opposition to earlier writing styles, as revealed by the analysis performed with geometrical concepts. The approaches adopted here are generic and may be extended to analyze a number of features of languages and cultures. (paper)

  11. Identification of crystalline structures using Moessbauer parameters and artificial neural network

    International Nuclear Information System (INIS)

    Salles, E.O.T.; Souza Junior, P.A. De; Garg, V.K.

    1995-01-01

    Moessbauer spectroscopy is a useful technique for characterizing the valences, electronic and magnetic states, coordination symmetric and site occupancies of Fe cations. The Moessbauer parameters of Isomer Shift (I.S.) and Quadrupole Splitting (Q.S.) are useful to distinguish paramagnetic ferrous and ferric ions in several substances, while the internal magnetic field provides information on the crystallinity. A correlation is being sought between Moessbauer parameters and several structure properties of some iron-containing minerals using Artificial Neural Networks (ANN). Distinct regions of crystalline structures are defined when any two parameters are plotted, but in several cases superposition of these regions leads to erroneous conclusions. We have tried to eliminate this difficulty by using convenient axes. These axes form n-dimensional vectors as input to our ANN. In recent years ANN has shown to be a powerful technique to solve problems as pattern recognition, optimization, preview ups and downs in stock market, automatic control and identification of a mineral from a Moessbauer spectrum of Moessbauer data bank. Using ANN we have been successful in identification of crystalline structures from plots of Moessbauer spectral parameters of I.S., Q.S., and structure using Moessbauer parameters of I.S., Q.S., and polyhedral volume of a coordination site are presented. (author) 28 refs.; 4 figs.; 2 tabs

  12. Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

    Science.gov (United States)

    Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

    2011-05-01

    A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

  13. Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

    Science.gov (United States)

    Kuznetsov, A. V.; Makaryants, G. M.

    2018-01-01

    There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

  14. Identification of Resting State Networks Involved in Executive Function.

    Science.gov (United States)

    Connolly, Joanna; McNulty, Jonathan P; Boran, Lorraine; Roche, Richard A P; Delany, David; Bokde, Arun L W

    2016-06-01

    The structural networks in the human brain are consistent across subjects, and this is reflected also in that functional networks across subjects are relatively consistent. These findings are not only present during performance of a goal oriented task but there are also consistent functional networks during resting state. It suggests that goal oriented activation patterns may be a function of component networks identified using resting state. The current study examines the relationship between resting state networks measured and patterns of neural activation elicited during a Stroop task. The association between the Stroop-activated networks and the resting state networks was quantified using spatial linear regression. In addition, we investigated if the degree of spatial association of resting state networks with the Stroop task may predict performance on the Stroop task. The results of this investigation demonstrated that the Stroop activated network can be decomposed into a number of resting state networks, which were primarily associated with attention, executive function, visual perception, and the default mode network. The close spatial correspondence between the functional organization of the resting brain and task-evoked patterns supports the relevance of resting state networks in cognitive function.

  15. Low Power Multi-Hop Networking Analysis in Intelligent Environments.

    Science.gov (United States)

    Etxaniz, Josu; Aranguren, Gerardo

    2017-05-19

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

  16. Comparison Of Power Quality Disturbances Classification Based On Neural Network

    Directory of Open Access Journals (Sweden)

    Nway Nway Kyaw Win

    2015-07-01

    Full Text Available Abstract Power quality disturbances PQDs result serious problems in the reliability safety and economy of power system network. In order to improve electric power quality events the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis MRA algorithm and feed forward neural network probabilistic and multilayer feed forward neural network based methodology for automatic classification of eight types of PQ signals flicker harmonics sag swell impulse fluctuation notch and oscillatory will be presented. The wavelet family Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The results show that the PNN can analyze different power disturbance types efficiently. Therefore it can be seen that PNN has better classification accuracy than MLFF.

  17. Curing critical links in oscillator networks as power flow models

    International Nuclear Information System (INIS)

    Rohden, Martin; Meyer-Ortmanns, Hildegard; Witthaut, Dirk; Timme, Marc

    2017-01-01

    Modern societies crucially depend on the robust supply with electric energy so that blackouts of power grids can have far reaching consequences. Typically, large scale blackouts take place after a cascade of failures: the failure of a single infrastructure component, such as a critical transmission line, results in several subsequent failures that spread across large parts of the network. Improving the robustness of a network to prevent such secondary failures is thus key for assuring a reliable power supply. In this article we analyze the nonlocal rerouting of power flows after transmission line failures for a simplified AC power grid model and compare different strategies to improve network robustness. We identify critical links in the grid and compute alternative pathways to quantify the grid’s redundant capacity and to find bottlenecks along the pathways. Different strategies are developed and tested to increase transmission capacities to restore stability with respect to transmission line failures. We show that local and nonlocal strategies typically perform alike: one can equally well cure critical links by providing backup capacities locally or by extending the capacities of bottleneck links at remote locations. (paper)

  18. Control of a hybrid compensator in a power network by an artificial neural network

    Directory of Open Access Journals (Sweden)

    I. S. Shaw

    1998-07-01

    Full Text Available Increased interest in the elimination of distortion in electrical power networks has led to the development of various compensator topologies. The increasing cost of electrical energy necessitates the cost-effective operation of any of these topologies. This paper considers the development of an artificial neural network based controller, trained by means of the backpropagation method, that ensures the cost-effective operation of the hybrid compensator consisting of various converters and filters.

  19. An aggregated approach to harmonic modelling of loads in power distribution networks

    Energy Technology Data Exchange (ETDEWEB)

    Moellerstedt, E.

    1998-06-01

    The use of power electronics have given possibilities for more sophisticated control of power networks. This creates new demands on power network modelling. The models must not only allow for efficient and accurate simulation, but also be suitable for analysis and control design. The Harmonic Norton Equivalent presented in this thesis addresses two problems that are central in control theory, namely model reduction and system identification. It is essential to have simple representations of large systems, and there must be a way to obtain these simple models experimentally, as detailed modelling most often is too complicated. The Harmonic Norton Equivalent has its roots in the method of harmonic balance. It is a frequency domain description of loads in electric networks and describes a linear relation between the current spectrum and the voltage spectrum. The linearization implies that aggregation of loads for model reduction is a straightforward, non-iterative procedure. The models can be obtained through analytical calculations, measurements or time domain simulations. A procedure for experimental estimation of model parameters is presented. The procedure is used to estimate the parameters of a dimmer model from measurements on a real dimmer. The obtained model shows a very good agreement with validation data 24 refs, 24 figs

  20. Topology identification of the complex networks with non-delayed and delayed coupling

    International Nuclear Information System (INIS)

    Guo Wanli; Chen Shihua; Sun Wen

    2009-01-01

    In practical situation, there exists many uncertain information in complex networks, such as the topological structures. So the topology identification is an important issue in the research of the complex networks. Based on LaSalle's invariance principle, in this Letter, an adaptive controlling method is proposed to identify the topology of a weighted general complex network model with non-delayed and delayed coupling. Finally, simulation results show that the method is effective.

  1. Identification and management of distributed data NGN, content-centric networks and the web

    CERN Document Server

    Bartolomeo, Giovanni

    2013-01-01

    Although several books and academic courses discuss data management and networking, few of them focus on the convergence of networking and software technologies for identifying, addressing, and managing distributed data. Focusing on this convergence, Identification and Management of Distributed Data: NGN, Content-Centric Networks and the Web collates and describes the various distributed data management technologies to help readers from various backgrounds understand the common aspects that govern distributed data management. With a focus on the primary problems in identifying, addressing, and

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

    Science.gov (United States)

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

    2015-03-02

    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.

  3. 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.

  4. The economic interplay between network and power market

    International Nuclear Information System (INIS)

    2001-01-01

    The transmission network and the production plants are both necessary for providing electricity to the consumers. The transmission network is a regulated natural monopoly. The regulations set clear framework conditions for the network activities and affect the incentives to invest. Decision about operations and investments in production and consumption, on the other hand, are made in the market. There is a considerable need for coordination of the activities within network and market in order for the whole to work well. In the short term much of the need for coordination can be taken care of by a sophisticated organization of the power market and via the transmission tariffs, such that the players in the market see price signals which lead them to exploit network and production plants in an optimal way. This is complicated, but theoretically possible. In the long term, measures in the network, production and consumption are alternatives, and in development projects one often has to chose one of the alternatives. So far, no good models exist for guaranteeing the best choice

  5. Reciprocity and the Emergence of Power Laws in Social Networks

    Science.gov (United States)

    Schnegg, Michael

    Research in network science has shown that many naturally occurring and technologically constructed networks are scale free, that means a power law degree distribution emerges from a growth model in which each new node attaches to the existing network with a probability proportional to its number of links (= degree). Little is known about whether the same principles of local attachment and global properties apply to societies as well. Empirical evidence from six ethnographic case studies shows that complex social networks have significantly lower scaling exponents γ ~ 1 than have been assumed in the past. Apparently humans do not only look for the most prominent players to play with. Moreover cooperation in humans is characterized through reciprocity, the tendency to give to those from whom one has received in the past. Both variables — reciprocity and the scaling exponent — are negatively correlated (r = -0.767, sig = 0.075). If we include this effect in simulations of growing networks, degree distributions emerge that are much closer to those empirically observed. While the proportion of nodes with small degrees decreases drastically as we introduce reciprocity, the scaling exponent is more robust and changes only when a relatively large proportion of attachment decisions follow this rule. If social networks are less scale free than previously assumed this has far reaching implications for policy makers, public health programs and marketing alike.

  6. Locality-Driven Parallel Static Analysis for Power Delivery Networks

    KAUST Repository

    Zeng, Zhiyu

    2011-06-01

    Large VLSI on-chip Power Delivery Networks (PDNs) are challenging to analyze due to the sheer network complexity. In this article, a novel parallel partitioning-based PDN analysis approach is presented. We use the boundary circuit responses of each partition to divide the full grid simulation problem into a set of independent subgrid simulation problems. Instead of solving exact boundary circuit responses, a more efficient scheme is proposed to provide near-exact approximation to the boundary circuit responses by exploiting the spatial locality of the flip-chip-type power grids. This scheme is also used in a block-based iterative error reduction process to achieve fast convergence. Detailed computational cost analysis and performance modeling is carried out to determine the optimal (or near-optimal) number of partitions for parallel implementation. Through the analysis of several large power grids, the proposed approach is shown to have excellent parallel efficiency, fast convergence, and favorable scalability. Our approach can solve a 16-million-node power grid in 18 seconds on an IBM p5-575 processing node with 16 Power5+ processors, which is 18.8X faster than a state-of-the-art direct solver. © 2011 ACM.

  7. Dynamic Power Tariff for Congestion Management in Distribution Networks

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei; Shahidehpour, Mohammad

    2018-01-01

    This paper proposes dynamic power tariff (DPT), a new concept for congestion management in distribution networks with high penetration of electric vehicles (EVs), and heat pumps (HPs). The DPT concept is proposed to overcome a drawback of the dynamic tariff (DT) method, i.e., DPT can replace...... the price sensitivity parameter in the DT method, which is relatively unrealistic in practice. Based on the control theory, a control model with two control loops, i.e., the power flow control and voltage control, is established to analyze the congestion management process by the DPT method. Furthermore...

  8. Large deviations and queueing networks: Methods for rate function identification

    OpenAIRE

    Atar, Rami; Dupuis, Paul

    1999-01-01

    This paper considers the problem of rate function identification for multidimensional queueing models with feedback. A set of techniques are introduced which allow this identification when the model possesses certain structural properties. The main tools used are representation formulas for exponential integrals, weak convergence methods, and the regularity properties of associated Skorokhod Problems. Two examples are treated as special cases of the general theory: the classical Jackson netwo...

  9. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  10. Power consumption analysis of operating systems for wireless sensor networks.

    Science.gov (United States)

    Lajara, Rafael; Pelegrí-Sebastiá, José; Perez Solano, Juan J

    2010-01-01

    In this paper four wireless sensor network operating systems are compared in terms of power consumption. The analysis takes into account the most common operating systems--TinyOS v1.0, TinyOS v2.0, Mantis and Contiki--running on Tmote Sky and MICAz devices. With the objective of ensuring a fair evaluation, a benchmark composed of four applications has been developed, covering the most typical tasks that a Wireless Sensor Network performs. The results show the instant and average current consumption of the devices during the execution of these applications. The experimental measurements provide a good insight into the power mode in which the device components are running at every moment, and they can be used to compare the performance of different operating systems executing the same tasks.

  11. Control strategies for power distribution networks with electric vehicles integration

    DEFF Research Database (Denmark)

    Hu, Junjie

    of electrical energy. A smart grid can also be dened as an electricity network that can intelligently integrate the actions of all users connected to it - generators, consumers and those that do both - in order to eciently deliver sustainable, economic and secure electricity supplies. This thesis focuses...... of the ii market. To build a complete solution for integration of EVs into the distribution network, a price coordinated hierarchical scheduling system is proposed which can well characterize the involved actors in the smart grid. With this system, we demonstrate that it is possible to schedule the charging......Demand side resources, like electric vehicles (EVs), can become integral parts of a smart grids because instead of just consuming power they are capable of providing valuable services to power systems. EVs can be used to balance the intermittent renewable energy resources such as wind and solar...

  12. Finding Minimum-Power Broadcast Trees for Wireless Networks

    Science.gov (United States)

    Arabshahi, Payman; Gray, Andrew; Das, Arindam; El-Sharkawi, Mohamed; Marks, Robert, II

    2004-01-01

    Some algorithms have been devised for use in a method of constructing tree graphs that represent connections among the nodes of a wireless communication network. These algorithms provide for determining the viability of any given candidate connection tree and for generating an initial set of viable trees that can be used in any of a variety of search algorithms (e.g., a genetic algorithm) to find a tree that enables the network to broadcast from a source node to all other nodes while consuming the minimum amount of total power. The method yields solutions better than those of a prior algorithm known as the broadcast incremental power algorithm, albeit at a slightly greater computational cost.

  13. Friend or Foe? Fake Profile Identification in Online Social Networks

    OpenAIRE

    Fire, Michael; Kagan, Dima; Elyashar, Aviad; Elovici, Yuval

    2013-01-01

    The amount of personal information unwillingly exposed by users on online social networks is staggering, as shown in recent research. Moreover, recent reports indicate that these networks are infested with tens of millions of fake users profiles, which may jeopardize the users' security and privacy. To identify fake users in such networks and to improve users' security and privacy, we developed the Social Privacy Protector software for Facebook. This software contains three protection layers,...

  14. Identification of illicit drugs by using SOM neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Liang Meiyan; Shen Jingling; Wang Guangqin [Beijing Key Lab for Terahertz Spectroscopy and Imaging, Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing 100037 (China)], E-mail: liangyan661982@163.com, E-mail: jinglingshen@gmail.com, E-mail: pywgq2004@163.com

    2008-07-07

    Absorption spectra of six illicit drugs were measured by using the terahertz time-domain spectroscopy technique in the range 0.2-2.6 THz and then clustered with self-organization feature map (SOM) artificial neural network. After the network training process, the spectra collected at another time were identified successfully by the well-trained SOM network. An effective distance was introduced as a quantitative criterion to decide which cluster the new spectra were affiliated with.

  15. Identification of illicit drugs by using SOM neural networks

    International Nuclear Information System (INIS)

    Liang Meiyan; Shen Jingling; Wang Guangqin

    2008-01-01

    Absorption spectra of six illicit drugs were measured by using the terahertz time-domain spectroscopy technique in the range 0.2-2.6 THz and then clustered with self-organization feature map (SOM) artificial neural network. After the network training process, the spectra collected at another time were identified successfully by the well-trained SOM network. An effective distance was introduced as a quantitative criterion to decide which cluster the new spectra were affiliated with

  16. MyShake - Smartphone seismic network powered by citizen scientists

    Science.gov (United States)

    Kong, Q.; Allen, R. M.; Schreier, L.; Strauss, J. A.

    2017-12-01

    MyShake is a global smartphone seismic network that harnesses the power of crowdsourcing. It is driven by the citizen scientists that run MyShake on their personal smartphones. It has two components: an android application running on the smartphones to detect earthquake-like motion, and a network detection algorithm to aggregate results from multiple smartphones to confirm when an earthquake occurs. The MyShake application was released to the public on Feb 12th 2016. Within the first year, more than 250,000 people downloaded MyShake app around the world. There are more than 500 earthquakes recorded by the smartphones in this period, including events in Chile, Argentina, Mexico, Morocco, Greece, Nepal, New Zealand, Taiwan, Japan, and across North America. Currently, we are working on earthquake early warning with MyShake network and the shaking data provided by MyShake is a unique dataset that can be used for the research community.

  17. Price for the quality of the electric power network

    International Nuclear Information System (INIS)

    Baarsma, B.E.; Berkhout, P.H.G.; Hop, J.P.; Van Gemert, M.

    2004-01-01

    Power failures cause societal costs. Therefore, it is important that in the decision making process with regard to investments network managers take into account not only private costs and benefits, but also societal benefits of their investments. The benefits can be quantified by means of the so-called conjoint analysis and compared with the contingent valuation method (CVM). The article is followed by a reaction of employees of the Dutch Office of Energy Regulation (DTe) [nl

  18. On the Dual-Decomposition-Based Resource and Power Allocation with Sleeping Strategy for Heterogeneous Networks

    KAUST Repository

    Alsharoa, Ahmad M.; Ghazzai, Hakim; Yaacoub, Elias; Alouini, Mohamed-Slim

    2015-01-01

    In this paper, the problem of radio and power resource management in long term evolution heterogeneous networks (LTE HetNets) is investigated. The goal is to minimize the total power consumption of the network while satisfying the user quality

  19. Admission Control Threshold in Cellular Relay Networks with Power Adjustment

    Directory of Open Access Journals (Sweden)

    Lee Ki-Dong

    2009-01-01

    Full Text Available Abstract In the cellular network with relays, the mobile station can benefit from both coverage extension and capacity enhancement. However, the operation complexity increases as the number of relays grows up. Furthermore, in the cellular network with cooperative relays, it is even more complex because of an increased dimension of signal-to-noise ratios (SNRs formed in the cooperative wireless transmission links. In this paper, we propose a new method for admission capacity planning in a cellular network using a cooperative relaying mechanism called decode-and-forward. We mathematically formulate the dropping ratio using the randomness of "channel gain." With this, we formulate an admission threshold planning problem as a simple optimization problem, where we maximize the accommodation capacity (in number of connections subject to two types of constraints. (1 A constraint that the sum of the transmit powers of the source node and relay node is upper-bounded where both nodes can jointly adjust the transmit power. (2 A constraint that the dropping ratio is upper-bounded by a certain threshold value. The simplicity of the problem formulation facilitates its solution in real-time. We believe that the proposed planning method can provide an attractive guideline for dimensioning a cellular relay network with cooperative relays.

  20. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  1. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...

  2. Load power device and system for real-time execution of hierarchical load identification algorithms

    Science.gov (United States)

    Yang, Yi; Madane, Mayura Arun; Zambare, Prachi Suresh

    2017-11-14

    A load power device includes a power input; at least one power output for at least one load; and a plurality of sensors structured to sense voltage and current at the at least one power output. A processor is structured to provide real-time execution of: (a) a plurality of load identification algorithms, and (b) event detection and operating mode detection for the at least one load.

  3. Highly identified power-holders feel responsible: The interplay between social identification and social power within groups.

    Science.gov (United States)

    Scholl, Annika; Sassenberg, Kai; Ellemers, Naomi; Scheepers, Daan; de Wit, Frank

    2018-01-01

    Power relations affect dynamics within groups. Power-holders' decisions not only determine their personal outcomes, but also the outcomes of others in the group that they control. Yet, power-holders often tend to overlook this responsibility to take care of collective interests. The present research investigated how social identification - with the group to which both the powerful and the powerless belong - alters perceived responsibility among power-holders (and the powerless). Combining research on social power and social identity, we argue that power-holders perceive more responsibility than the powerless when strongly (rather than when weakly) identifying with the group. A study among leaders and an experiment supported this, highlighting that although power-holders are often primarily concerned about personal outcomes, they do feel responsible for considering others' interests when these others are included in the (social) self. © 2017 The British Psychological Society.

  4. Strategies for Power Line Communications Smart Metering Network Deployment

    Directory of Open Access Journals (Sweden)

    Alberto Sendin

    2014-04-01

    Full Text Available Smart Grids are becoming a reality all over the world. Nowadays, the research efforts for the introduction and deployment of these grids are mainly focused on the development of the field of Smart Metering. This emerging application requires the use of technologies to access the significant number of points of supply (PoS existing in the grid, covering the Low Voltage (LV segment with the lowest possible costs. Power Line Communications (PLC have been extensively used in electricity grids for a variety of purposes and, of late, have been the focus of renewed interest. PLC are really well suited for quick and inexpensive pervasive deployments. However, no LV grid is the same in any electricity company (utility, and the particularities of each grid evolution, architecture, circumstances and materials, makes it a challenge to deploy Smart Metering networks with PLC technologies, with the Smart Grid as an ultimate goal. This paper covers the evolution of Smart Metering networks, together with the evolution of PLC technologies until both worlds have converged to project PLC-enabled Smart Metering networks towards Smart Grid. This paper develops guidelines over a set of strategic aspects of PLC Smart Metering network deployment based on the knowledge gathered on real field; and introduces the future challenges of these networks in their evolution towards the Smart Grid.

  5. A port-Hamiltonian approach to power network modeling and analysis

    NARCIS (Netherlands)

    Fiaz, S.; Zonetti, D.; Ortega, R.; Scherpen, J.M.A.; van der Schaft, A.J.

    2013-01-01

    In this paper we present a systematic framework for modeling of power networks. The basic idea is to view the complete power network as a port-Hamiltonian system on a graph where edges correspond to components of the power network and nodes are buses. The interconnection constraints are given by the

  6. Autonomous target tracking of UAVs based on low-power neural network hardware

    Science.gov (United States)

    Yang, Wei; Jin, Zhanpeng; Thiem, Clare; Wysocki, Bryant; Shen, Dan; Chen, Genshe

    2014-05-01

    Detecting and identifying targets in unmanned aerial vehicle (UAV) images and videos have been challenging problems due to various types of image distortion. Moreover, the significantly high processing overhead of existing image/video processing techniques and the limited computing resources available on UAVs force most of the processing tasks to be performed by the ground control station (GCS) in an off-line manner. In order to achieve fast and autonomous target identification on UAVs, it is thus imperative to investigate novel processing paradigms that can fulfill the real-time processing requirements, while fitting the size, weight, and power (SWaP) constrained environment. In this paper, we present a new autonomous target identification approach on UAVs, leveraging the emerging neuromorphic hardware which is capable of massively parallel pattern recognition processing and demands only a limited level of power consumption. A proof-of-concept prototype was developed based on a micro-UAV platform (Parrot AR Drone) and the CogniMemTMneural network chip, for processing the video data acquired from a UAV camera on the y. The aim of this study was to demonstrate the feasibility and potential of incorporating emerging neuromorphic hardware into next-generation UAVs and their superior performance and power advantages towards the real-time, autonomous target tracking.

  7. Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks

    Science.gov (United States)

    Lyu, Bin; Yang, Zhen; Gui, Guan; Sari, Hikmet

    2017-01-01

    This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly divided into two parts corresponding to the data backscattering and transmission periods, respectively. The users first backscatter data to the information receiver in time division multiple access (TDMA) during the data backscattering period. When one user works in the BackCom mode, the other users harvest energy from the power station. During the data transmission period, two schemes, i.e., non-orthogonal multiple access (NOMA) and TDMA, are considered. To maximize the system throughput, the optimal time allocation policies are obtained. Simulation results demonstrate the superiority of the proposed model. PMID:28587171

  8. Energy Harvesting in Heterogeneous Networks with Hybrid Powered Communication Systems

    KAUST Repository

    Alsharoa, Ahmad

    2018-02-12

    In this paper, we investigate an energy efficient and energy harvesting (EH) system model in heterogeneous networks (HetNets) where all base stations (BSS) are equipped to harvest energy from renewable energy sources. We consider a hybrid power supply of green (renewable) and traditional micro-grid, such that traditional micro-grid is not exploited as long as the BSS can meet their power demands from harvested and stored green energy. Therefore, our goal is to minimize the networkwide energy consumption subject to users\\' certain quality of service and BSS\\' power consumption constraints. As a result of binary BS sleeping status and user-cell association variables, proposed is formulated as a binary linear programming (BLP) problem. A green communication algorithm based on binary particle swarm optimization is implemented to solve the problem with low complexity time.

  9. Research on Holographic Evaluation of Service Quality in Power Data Network

    Science.gov (United States)

    Wei, Chen; Jing, Tao; Ji, Yutong

    2018-01-01

    With the rapid development of power data network, the continuous development of the Power data application service system, more and more service systems are being put into operation. Following this, the higher requirements for network quality and service quality are raised, in the actual process for the network operation and maintenance. This paper describes the electricity network and data network services status. A holographic assessment model was presented to achieve a comprehensive intelligence assessment on the power data network and quality of service in the operation and maintenance on the power data network. This evaluation method avoids the problems caused by traditional means which performs a single assessment of network performance quality. This intelligent Evaluation method can improve the efficiency of network operation and maintenance guarantee the quality of real-time service in the power data network..

  10. Wireless Power Transfer for Distributed Estimation in Sensor Networks

    Science.gov (United States)

    Mai, Vien V.; Shin, Won-Yong; Ishibashi, Koji

    2017-04-01

    This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplify-and-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the non-convex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for colocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC.

  11. Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

    International Nuclear Information System (INIS)

    Canedo Medeiros, Jose Antonio Carlos; Schirru, Roberto

    2008-01-01

    In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results

  12. Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Canedo Medeiros, Jose Antonio Carlos [Universidade Federal do Rio de Janeiro, PEN/COPPE, UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: canedo@lmp.ufrj.br; Schirru, Roberto [Universidade Federal do Rio de Janeiro, PEN/COPPE, UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: schirru@lmp.ufrj.br

    2008-04-15

    In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results.

  13. Multimodal Neural Network for Overhead Person Re-identification

    DEFF Research Database (Denmark)

    Lejbølle, Aske Rasch; Nasrollahi, Kamal; Krogh, Benjamin

    2017-01-01

    Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify...

  14. Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Bo Fan

    2014-01-01

    Full Text Available Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Those influencing factors of specified parameter are analyzed, respectively, and various work states are covered to ensure the completeness of the training samples. Through signal preprocessing on samples and training dataset, different input parameters identifications with one network are compared and analyzed. The trained Elman neural network, applied in the identification model, is able to efficiently predict the rotor resistance in high accuracy. The simulation and experimental results show that the proposed method owns extensive adaptability and performs very well in its application to vector controlled induction motor. This identification method is able to enhance the performance of induction motor’s variable-frequency speed regulation.

  15. Supply curve bidding of electricity in constrained power networks

    International Nuclear Information System (INIS)

    Al-Agtash, Salem Y.

    2010-01-01

    This paper presents a Supply Curve Bidding (SCB) approach that complies with the notion of the Standard Market Design (SMD) in electricity markets. The approach considers the demand-side option and Locational Marginal Pricing (LMP) clearing. It iteratively alters Supply Function Equilibria (SFE) model solutions, then choosing the best bid based on market-clearing LMP and network conditions. It has been argued that SCB better benefits suppliers compared to fixed quantity-price bids. It provides more flexibility and better opportunity to achieving profitable outcomes over a range of demands. In addition, SCB fits two important criteria: simplifies evaluating electricity derivatives and captures smooth marginal cost characteristics that reflect actual production costs. The simultaneous inclusion of physical unit constraints and transmission security constraints will assure a feasible solution. An IEEE 24-bus system is used to illustrate perturbations of SCB in constrained power networks within the framework of SDM. By searching in the neighborhood of SFE model solutions, suppliers can obtain their best bid offers based on market-clearing LMP and network conditions. In this case, electricity producers can derive their best offering strategy both in the power exchange and the long-term contractual markets within a profitable, yet secure, electricity market. (author)

  16. Power and delay optimisation in multi-hop wireless networks

    KAUST Repository

    Xia, Li

    2014-02-05

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

  17. Supply curve bidding of electricity in constrained power networks

    Energy Technology Data Exchange (ETDEWEB)

    Al-Agtash, Salem Y. [Hijjawi Faculty of Engineering; Yarmouk University; Irbid 21163 (Jordan)

    2010-07-15

    This paper presents a Supply Curve Bidding (SCB) approach that complies with the notion of the Standard Market Design (SMD) in electricity markets. The approach considers the demand-side option and Locational Marginal Pricing (LMP) clearing. It iteratively alters Supply Function Equilibria (SFE) model solutions, then choosing the best bid based on market-clearing LMP and network conditions. It has been argued that SCB better benefits suppliers compared to fixed quantity-price bids. It provides more flexibility and better opportunity to achieving profitable outcomes over a range of demands. In addition, SCB fits two important criteria: simplifies evaluating electricity derivatives and captures smooth marginal cost characteristics that reflect actual production costs. The simultaneous inclusion of physical unit constraints and transmission security constraints will assure a feasible solution. An IEEE 24-bus system is used to illustrate perturbations of SCB in constrained power networks within the framework of SDM. By searching in the neighborhood of SFE model solutions, suppliers can obtain their best bid offers based on market-clearing LMP and network conditions. In this case, electricity producers can derive their best offering strategy both in the power exchange and the long-term contractual markets within a profitable, yet secure, electricity market. (author)

  18. Low-power cryptographic coprocessor for autonomous wireless sensor networks

    Science.gov (United States)

    Olszyna, Jakub; Winiecki, Wiesław

    2013-10-01

    The concept of autonomous wireless sensor networks involves energy harvesting, as well as effective management of system resources. Public-key cryptography (PKC) offers the advantage of elegant key agreement schemes with which a secret key can be securely established over unsecure channels. In addition to solving the key management problem, the other major application of PKC is digital signatures, with which non-repudiation of messages exchanges can be achieved. The motivation for studying low-power and area efficient modular arithmetic algorithms comes from enabling public-key security for low-power devices that can perform under constrained environment like autonomous wireless sensor networks. This paper presents a cryptographic coprocessor tailored to the autonomous wireless sensor networks constraints. Such hardware circuit is aimed to support the implementation of different public-key cryptosystems based on modular arithmetic in GF(p) and GF(2m). Key components of the coprocessor are described as GEZEL models and can be easily transformed to VHDL and implemented in hardware.

  19. Boosted decision trees as an alternative to artificial neural networks for particle identification

    International Nuclear Information System (INIS)

    Roe, Byron P.; Yang Haijun; Zhu Ji; Liu Yong; Stancu, Ion; McGregor, Gordon

    2005-01-01

    The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics

  20. 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....

  1. The NNSYSID Toolbox - A MATLAB Toolbox for System Identification with Neural Networks

    DEFF Research Database (Denmark)

    Nørgård, Peter Magnus; Ravn, Ole; Hansen, Lars Kai

    1996-01-01

    To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms for determi......To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms...

  2. Identification of serial number on bank card using recurrent neural network

    Science.gov (United States)

    Liu, Li; Huang, Linlin; Xue, Jian

    2018-04-01

    Identification of serial number on bank card has many applications. Due to the different number printing mode, complex background, distortion in shape, etc., it is quite challenging to achieve high identification accuracy. In this paper, we propose a method using Normalization-Cooperated Gradient Feature (NCGF) and Recurrent Neural Network (RNN) based on Long Short-Term Memory (LSTM) for serial number identification. The NCGF maps the gradient direction elements of original image to direction planes such that the RNN with direction planes as input can recognize numbers more accurately. Taking the advantages of NCGF and RNN, we get 90%digit string recognition accuracy.

  3. The power grid AGC frequency bias coefficient online identification method based on wide area information

    Science.gov (United States)

    Wang, Zian; Li, Shiguang; Yu, Ting

    2015-12-01

    This paper propose online identification method of regional frequency deviation coefficient based on the analysis of interconnected grid AGC adjustment response mechanism of regional frequency deviation coefficient and the generator online real-time operation state by measured data through PMU, analyze the optimization method of regional frequency deviation coefficient in case of the actual operation state of the power system and achieve a more accurate and efficient automatic generation control in power system. Verify the validity of the online identification method of regional frequency deviation coefficient by establishing the long-term frequency control simulation model of two-regional interconnected power system.

  4. Agent-based reactive power management of power distribution networks with distributed energy generation

    International Nuclear Information System (INIS)

    Rahman, M.S.; Mahmud, M.A.; Oo, A.M.T.; Pota, H.R.; Hossain, M.J.

    2016-01-01

    Highlights: • A coordinated multi-agent system is proposed for reactive power management. • A linear quadratic regulator with a proportional integral controller is designed. • Proposed multi-agent scheme provides accurate estimation and control of the system. • Voltage stability is improved with proper power management for different scenarios. • Results obtained from the proposed scheme is compared to the traditional approach. - Abstract: In this paper, a new agent-based distributed reactive power management scheme is proposed to improve the voltage stability of energy distribution systems with distributed generation units. Three types of agents – distribution system agent, estimator agent, and control agent are developed within the multi-agent framework. The agents simultaneously coordinated their activities through the online information and energy flow. The overall achievement of the proposed scheme depends on the coordination between two tasks – (i) estimation of reactive power using voltage variation formula and (ii) necessary control actions to provide the estimated reactive power to the distribution networks through the distributed static synchronous compensators. A linear quadratic regulator with a proportional integrator is designed for the control agent in order to control the reactive component of the current and the DC voltage of the compensators. The performance of the proposed scheme is tested on a 10-bus power distribution network under various scenarios. The effectiveness is validated by comparing the proposed approach to the conventional proportional integral control approach. It is found that, the agent-based scheme provides excellent robust performance under various operating conditions of the power distribution network.

  5. Wireless Power Transfer Protocols in Sensor Networks: Experiments and Simulations

    Directory of Open Access Journals (Sweden)

    Sotiris Nikoletseas

    2017-04-01

    Full Text Available Rapid technological advances in the domain of Wireless Power Transfer pave the way for novel methods for power management in systems of wireless devices, and recent research works have already started considering algorithmic solutions for tackling emerging problems. In this paper, we investigate the problem of efficient and balanced Wireless Power Transfer in Wireless Sensor Networks. We employ wireless chargers that replenish the energy of network nodes. We propose two protocols that configure the activity of the chargers. One protocol performs wireless charging focused on the charging efficiency, while the other aims at proper balance of the chargers’ residual energy. We conduct detailed experiments using real devices and we validate the experimental results via larger scale simulations. We observe that, in both the experimental evaluation and the evaluation through detailed simulations, both protocols achieve their main goals. The Charging Oriented protocol achieves good charging efficiency throughout the experiment, while the Energy Balancing protocol achieves a uniform distribution of energy within the chargers.

  6. Discrete rate and variable power adaptation for underlay cognitive networks

    KAUST Repository

    Abdallah, Mohamed M.

    2010-01-01

    We consider the problem of maximizing the average spectral efficiency of a secondary link in underlay cognitive networks. In particular, we consider the network setting whereby the secondary transmitter employs discrete rate and variable power adaptation under the constraints of maximum average transmit power and maximum average interference power allowed at the primary receiver due to the existence of an interference link between the secondary transmitter and the primary receiver. We first find the optimal discrete rates assuming a predetermined partitioning of the signal-to-noise ratio (SNR) of both the secondary and interference links. We then present an iterative algorithm for finding a suboptimal partitioning of the SNR of the interference link assuming a fixed partitioning of the SNR of secondary link selected for the case where no interference link exists. Our numerical results show that the average spectral efficiency attained by using the iterative algorithm is close to that achieved by the computationally extensive exhaustive search method for the case of Rayleigh fading channels. In addition, our simulations show that selecting the optimal partitioning of the SNR of the secondary link assuming no interference link exists still achieves the maximum average spectral efficiency for the case where the average interference constraint is considered. © 2010 IEEE.

  7. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  8. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  9. System Identification and Resonant Control of Thermoacoustic Engines for Robust Solar Power

    Directory of Open Access Journals (Sweden)

    Boe-Shong Hong

    2015-05-01

    Full Text Available It was found that thermoacoustic solar-power generators with resonant control are more powerful than passive ones. To continue the work, this paper focuses on the synthesis of robustly resonant controllers that guarantee single-mode resonance not only in steady states, but also in transient states when modelling uncertainties happen and working temperature temporally varies. Here the control synthesis is based on the loop shifting and the frequency-domain identification in advance thereof. Frequency-domain identification is performed to modify the mathematical modelling and to identify the most powerful mode, so that the DSP-based feedback controller can online pitch the engine to the most powerful resonant-frequency robustly and accurately. Moreover, this paper develops two control tools, the higher-order van-der-Pol oscillator and the principle of Dynamical Equilibrium, to assist in system identification and feedback synthesis, respectively.

  10. Reinforcement learning techniques for controlling resources in power networks

    Science.gov (United States)

    Kowli, Anupama Sunil

    As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

  11. Contribution of domain wall networks to the CMB power spectrum

    International Nuclear Information System (INIS)

    Lazanu, A.; Martins, C.J.A.P.; Shellard, E.P.S.

    2015-01-01

    We use three domain wall simulations from the radiation era to the late-time dark energy domination era based on the PRS algorithm to calculate the energy–momentum tensor components of domain wall networks in an expanding universe. Unequal time correlators in the radiation, matter and cosmological constant epochs are calculated using the scaling regime of each of the simulations. The CMB power spectrum of a network of domain walls is determined. The first ever quantitative constraint for the domain wall surface tension is obtained using a Markov chain Monte Carlo method; an energy scale of domain walls of 0.93 MeV, which is close but below the Zel'dovich bound, is determined

  12. Contribution of domain wall networks to the CMB power spectrum

    Energy Technology Data Exchange (ETDEWEB)

    Lazanu, A., E-mail: A.Lazanu@damtp.cam.ac.uk [Centre for Theoretical Cosmology, Department of Applied Mathematics and Theoretical Physics, Wilberforce Road, Cambridge CB3 0WA (United Kingdom); Martins, C.J.A.P., E-mail: Carlos.Martins@astro.up.pt [Centro de Astrofísica, Universidade do Porto, Rua das Estrelas, 4150-762 Porto (Portugal); Instituto de Astrofísica e Ciências do Espaço, CAUP, Rua das Estrelas, 4150-762 Porto (Portugal); Shellard, E.P.S., E-mail: E.P.S.Shellard@damtp.cam.ac.uk [Centre for Theoretical Cosmology, Department of Applied Mathematics and Theoretical Physics, Wilberforce Road, Cambridge CB3 0WA (United Kingdom)

    2015-07-30

    We use three domain wall simulations from the radiation era to the late-time dark energy domination era based on the PRS algorithm to calculate the energy–momentum tensor components of domain wall networks in an expanding universe. Unequal time correlators in the radiation, matter and cosmological constant epochs are calculated using the scaling regime of each of the simulations. The CMB power spectrum of a network of domain walls is determined. The first ever quantitative constraint for the domain wall surface tension is obtained using a Markov chain Monte Carlo method; an energy scale of domain walls of 0.93 MeV, which is close but below the Zel'dovich bound, is determined.

  13. Contribution of domain wall networks to the CMB power spectrum

    Directory of Open Access Journals (Sweden)

    A. Lazanu

    2015-07-01

    Full Text Available We use three domain wall simulations from the radiation era to the late-time dark energy domination era based on the PRS algorithm to calculate the energy–momentum tensor components of domain wall networks in an expanding universe. Unequal time correlators in the radiation, matter and cosmological constant epochs are calculated using the scaling regime of each of the simulations. The CMB power spectrum of a network of domain walls is determined. The first ever quantitative constraint for the domain wall surface tension is obtained using a Markov chain Monte Carlo method; an energy scale of domain walls of 0.93 MeV, which is close but below the Zel'dovich bound, is determined.

  14. Identification of control targets in Boolean molecular network models via computational algebra.

    Science.gov (United States)

    Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Laubenbacher, Reinhard

    2016-09-23

    Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg . This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks.

  15. Radioactivity nuclide identification based on BP and LM algorithm neural network

    International Nuclear Information System (INIS)

    Wang Jihong; Sun Jian; Wang Lianghou

    2012-01-01

    The paper provides the method which can identify radioactive nuclide based on the BP and LM algorithm neural network. Then, this paper compares the above-mentioned method with FR algorithm. Through the result of the Matlab simulation, the method of radioactivity nuclide identification based on the BP and LM algorithm neural network is superior to the FR algorithm. With the better effect and the higher accuracy, it will be the best choice. (authors)

  16. A network identity authentication system based on Fingerprint identification technology

    Science.gov (United States)

    Xia, Hong-Bin; Xu, Wen-Bo; Liu, Yuan

    2005-10-01

    Fingerprint verification is one of the most reliable personal identification methods. However, most of the automatic fingerprint identification system (AFIS) is not run via Internet/Intranet environment to meet today's increasing Electric commerce requirements. This paper describes the design and implementation of the archetype system of identity authentication based on fingerprint biometrics technology, and the system can run via Internet environment. And in our system the COM and ASP technology are used to integrate Fingerprint technology with Web database technology, The Fingerprint image preprocessing algorithms are programmed into COM, which deployed on the internet information server. The system's design and structure are proposed, and the key points are discussed. The prototype system of identity authentication based on Fingerprint have been successfully tested and evaluated on our university's distant education applications in an internet environment.

  17. MicroRadarNet: A network of weather micro radars for the identification of local high resolution precipitation patterns

    Science.gov (United States)

    Turso, S.; Paolella, S.; Gabella, M.; Perona, G.

    2013-01-01

    In this paper, MicroRadarNet, a novel micro radar network for continuous, unattended meteorological monitoring is presented. Key aspects and constraints are introduced. Specific design strategies are highlighted, leading to the technological implementations of this wireless, low-cost, low power consumption sensor network. Raw spatial and temporal datasets are processed on-board in real-time, featuring a consistent evaluation of the signals from the sensors and optimizing the data loads to be transmitted. Network servers perform the final post-elaboration steps on the data streams coming from each unit. Final network products are meteorological mappings of weather events, monitored with high spatial and temporal resolution, and lastly served to the end user through any Web browser. This networked approach is shown to imply a sensible reduction of the overall operational costs, including management and maintenance aspects, if compared to the traditional long range monitoring strategy. Adoption of the TITAN storm identification and nowcasting engine is also here evaluated for in-loop integration within the MicroRadarNet data processing chain. A brief description of the engine workflow is provided, to present preliminary feasibility results and performance estimates. The outcomes were not so predictable, taking into account relevant operational differences between a Western Alps micro radar scenario and the long range radar context in the Denver region of Colorado. Finally, positive results from a set of case studies are discussed, motivating further refinements and integration activities.

  18. Social networks : Effects on Identification, Performance and Satisfaction Effects on identification, performance and satisfaction

    NARCIS (Netherlands)

    Stormbroek-Burgers, van R.G.B.M.; Montfort, van K.; Sluis, van der E.C. (Lidewey)

    2011-01-01

    This study contributes to research on the impact of social networks on organizational outcomes in the context of the increasing number of professionals in the Netherlands. The aim of this study was to get insight into the characteristics of professionals’ social networks and to examine the effect of

  19. Particle Identification in Cherenkov Detectors using Convolutional Neural Networks

    CERN Document Server

    Theodore, Tomalty

    2016-01-01

    Cherenkov detectors are used for charged particle identification. When a charged particle moves through a medium faster than light can propagate in that medium, Cherenkov radiation is released in the shape of a cone in the direction of movement. The interior of the Cherenkov detector is instrumented with PMTs to detect this Cherenkov light. Particles, then, can be identified by the shapes of the images on the detector walls.

  20. White blood cells identification system based on convolutional deep neural learning networks.

    Science.gov (United States)

    Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

    2017-11-16

    White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

  1. An Adaptive Power Efficient Packet Scheduling Algorithm for Wimax Networks

    OpenAIRE

    R Murali Prasad; P. Satish Kumar

    2010-01-01

    Admission control schemes and scheduling algorithms are designed to offer QoS services in 802.16/802.16e networks and a number of studies have investigated these issues. But the channel condition and priority of traffic classes are very rarely considered in the existing scheduling algorithms. Although a number of energy saving mechanisms have been proposed for the IEEE 802.16e, to minimize the power consumption of IEEE 802.16e mobile stations with multiple real-time connections has not yet be...

  2. ONU power saving modes in next generation optical access networks: progress, efficiency and challenges.

    Science.gov (United States)

    Dixit, Abhishek; Lannoo, Bart; Colle, Didier; Pickavet, Mario; Demeester, Piet

    2012-12-10

    The optical network unit (ONU), installed at a customer's premises, accounts for about 60% of power in current fiber-to-the-home (FTTH) networks. We propose a power consumption model for the ONU and evaluate the ONU power consumption in various next generation optical access (NGOA) architectures. Further, we study the impact of the power savings of the ONU in various low power modes such as power shedding, doze and sleep.

  3. Real-time identification of vehicle motion-modes using neural networks

    Science.gov (United States)

    Wang, Lifu; Zhang, Nong; Du, Haiping

    2015-01-01

    A four-wheel ground vehicle has three body-dominated motion-modes, that is, bounce, roll, and pitch motion-modes. Real-time identification of these motion-modes can make vehicle suspensions, in particular, active suspensions, target on the dominant motion-mode and apply appropriate control strategies to improve its performance with less power consumption. Recently, a motion-mode energy method (MEM) was developed to identify the vehicle body motion-modes. However, this method requires the measurement of full vehicle states and road inputs, which are not always available in practice. This paper proposes an alternative approach to identify vehicle primary motion-modes with acceptable accuracy by employing neural networks (NNs). The effectiveness of the trained NNs is verified on a 10-DOF full-car model under various types of excitation inputs. The results confirm that the proposed method is effective in determining vehicle primary motion-modes with comparable accuracy to the MEM method. Experimental data is further used to validate the proposed method.

  4. Network Understanding of Herb Medicine via Rapid Identification of Ingredient-Target Interactions

    Science.gov (United States)

    Zhang, Hai-Ping; Pan, Jian-Bo; Zhang, Chi; Ji, Nan; Wang, Hao; Ji, Zhi-Liang

    2014-01-01

    Today, herb medicines have become the major source for discovery of novel agents in countermining diseases. However, many of them are largely under-explored in pharmacology due to the limitation of current experimental approaches. Therefore, we proposed a computational framework in this study for network understanding of herb pharmacology via rapid identification of putative ingredient-target interactions in human structural proteome level. A marketing anti-cancer herb medicine in China, Yadanzi (Brucea javanica), was chosen for mechanistic study. Total 7,119 ingredient-target interactions were identified for thirteen Yadanzi active ingredients. Among them, about 29.5% were estimated to have better binding affinity than their corresponding marketing drug-target interactions. Further Bioinformatics analyses suggest that simultaneous manipulation of multiple proteins in the MAPK signaling pathway and the phosphorylation process of anti-apoptosis may largely answer for Yadanzi against non-small cell lung cancers. In summary, our strategy provides an efficient however economic solution for systematic understanding of herbs' power.

  5. Identification of gene networks underlying dystocia in dairy cattle

    Science.gov (United States)

    Dystocia is a trait with a high impact in the dairy industry. Among its risk factors are calf weight, gestation length, breed and conformation. Biological networks have been proposed to capture the genetic architecture of complex traits, where GWAS show limitations. The objective of this study was t...

  6. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

  7. Inverse parameter identification for a branching 1 D arterial network

    CSIR Research Space (South Africa)

    Bogaers, Alfred EJ

    2012-07-01

    Full Text Available In this paper we investigate the invertability of a branching 1 D arterial blood flow network. We limit our investigation to a single bifurcating vessel, where the material properties, unloaded areas and variables characterizing the input and output...

  8. Substructure identification for shear structures: cross-power spectral density method

    International Nuclear Information System (INIS)

    Zhang, Dongyu; Johnson, Erik A

    2012-01-01

    In this paper, a substructure identification method for shear structures is proposed. A shear structure is divided into many small substructures; utilizing the dynamic equilibrium of a one-floor substructure, an inductive identification problem is formulated, using the cross-power spectral densities between structural floor accelerations and a reference response, to estimate the parameters of that one story. Repeating this procedure, all story parameters of the shear structure are identified from top to bottom recursively. An identification error analysis is performed for the proposed substructure method, revealing how uncertain factors (e.g. measurement noise) in the identification process affect the identification accuracy. According to the error analysis, a smart reference selection rule is designed to choose the optimal reference response that further enhances the identification accuracy. Moreover, based on the identification error analysis, explicit formulae are developed to calculate the variances of the parameter identification errors. A ten-story shear structure is used to illustrate the effectiveness of the proposed substructure method. The simulation results show that the method, combined with the reference selection rule, can very accurately identify structural parameters despite large measurement noise. Furthermore, the proposed formulae provide good predictions for the variances of the parameter identification errors, which are vital for providing accurate warnings of structural damage. (paper)

  9. Dynamically coated capillaries improve the identification power of capillary zone electrophoresis for basic drugs in toxicological analysis

    NARCIS (Netherlands)

    Boone, C.M; Jonkers, E.Z; Franke, J.P.; de Zeeuw, R.A; Ensing, K

    2001-01-01

    In systematic toxicological analysis (STA), analytical methods should have a high identification power. This can be suitably expressed by parameters such as mean list length (MLL) or discriminating power (DP). The reproducibility of a method has a great impact on its identification power, and should

  10. Parameter Identification for Nonlinear Circuit Models of Power BAW Resonator

    Directory of Open Access Journals (Sweden)

    CONSTANTINESCU, F.

    2011-02-01

    Full Text Available The large signal operation of the bulk acoustic wave (BAW resonators is characterized by the amplitude-frequency effect and the intermodulation effect. The measurement of these effects, together with that of the small signal frequency characteristic, are used in this paper for the parameter identification of the nonlinear circuit models developed previously by authors. As the resonator has been connected to the measurement bench by wire bonding, the parasitic elements of this connection have been taken into account, being estimated solving some electrical and magnetic field problems.

  11. Power, Status and Network Perceptions: The Effects of Network Bias on Organizational Outcomes

    Science.gov (United States)

    2012-09-01

    connectedness. This insight rests on joining two disparate streams of evidence. In one vein, psychologists examining how power affects social cognition have...posited to Distribution A: Approved for public release; distribution is unlimited. 30    facilitate many outcomes including prosocial behavior (Grant 2007...this cognitive social network research forward by connecting perceptions with one of the standard outcomes of organizational behavior research -- job

  12. People identification for domestic non-overlapping RGB-D camera networks

    NARCIS (Netherlands)

    Takac, B.; Rauterberg, G.W.M.; Català, A.; Chen, W.

    2015-01-01

    The ability to identify the specific person in a home camera network is very relevant for healthcare applications where humans need to be observed daily in their living environment. The appearance based people identification in a domestic environment has many similarities with the problem of

  13. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, M.; Stensballe, A.; Rasmussen, T.E.

    2004-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...

  14. A network identity authentication protocol of bank account system based on fingerprint identification and mixed encryption

    Science.gov (United States)

    Zhu, Lijuan; Liu, Jingao

    2013-07-01

    This paper describes a network identity authentication protocol of bank account system based on fingerprint identification and mixed encryption. This protocol can provide every bank user a safe and effective way to manage his own bank account, and also can effectively prevent the hacker attacks and bank clerk crime, so that it is absolute to guarantee the legitimate rights and interests of bank users.

  15. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, Majbrit; Stensballe, Allan; Rasmussen, Thomas E

    2011-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...

  16. Attention in Multimodal Neural Networks for Person Re-identification

    DEFF Research Database (Denmark)

    Lejbølle, Aske Rasch; Krogh, Benjamin; Nasrollahi, Kamal

    2018-01-01

    In spite of increasing interest from the research commu- nity, person re-identification remains an unsolved problem. Correctly deciding on a true match by comparing images of a person, captured by several cameras, requires extrac- tion of discriminative features to counter challenges...... such as changes in lighting, viewpoint and occlusion. Besides de- vising novel feature descriptors, the setup can be changed to capture persons from an overhead viewpoint rather than a horizontal. Furthermore, additional modalities can be considered that are not affected by similar environmental changes as RGB...

  17. A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

    Energy Technology Data Exchange (ETDEWEB)

    Acernese, F [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Barone, F [Istituto Nazionale di Fisica Nucleare, sez. Napoli, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Rosa, M de [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Rosa, R De [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Eleuteri, A [Istituto Nazionale di Fisica Nucleare, sez. Napoli, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Milano, L [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Tagliaferri, R [Dipartimento di Matematica ed Informatica, Universita di Salerno, via S Allende, I-84081 Baronissi (Salerno) (Italy)

    2002-06-21

    In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.

  18. A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

    International Nuclear Information System (INIS)

    Acernese, F; Barone, F; Rosa, M de; Rosa, R De; Eleuteri, A; Milano, L; Tagliaferri, R

    2002-01-01

    In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis

  19. A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

    CERN Document Server

    Acernese, F; Rosa, M D; Rosa, R D; Eleuteri, A; Milano, L; Tagliaferri, R

    2002-01-01

    In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.

  20. Robust synchronization of delayed neural networks based on adaptive control and parameters identification

    International Nuclear Information System (INIS)

    Zhou Jin; Chen Tianping; Xiang Lan

    2006-01-01

    This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique

  1. Supplying the power requirements to a sensor network using radio frequency power transfer.

    Science.gov (United States)

    Percy, Steven; Knight, Chris; Cooray, Francis; Smart, Ken

    2012-01-01

    Wireless power transmission is a method of supplying power to small electronic devices when there is no wired connection. One way to increase the range of these systems is to use a directional transmitting antenna, the problem with this approach is that power can only be transmitted through a narrow beam and directly forward, requiring the transmitter to always be aligned with the sensor node position. The work outlined in this article describes the design and testing of an autonomous radio frequency power transfer system that is capable of rotating the base transmitter to track the position of sensor nodes and transferring power to that sensor node. The system's base station monitors the node's energy levels and forms a charge queue to plan charging order and maintain energy levels of the nodes. Results show a radio frequency harvesting circuit with a measured S11 value of -31.5 dB and a conversion efficiency of 39.1%. Simulation and experimentation verified the level of power transfer and efficiency. The results of this work show a small network of three nodes with different storage types powered by a central base node.

  2. Supplying the Power Requirements to a Sensor Network Using Radio Frequency Power Transfer

    Directory of Open Access Journals (Sweden)

    Steven Percy

    2012-06-01

    Full Text Available Wireless power transmission is a method of supplying power to small electronic devices when there is no wired connection. One way to increase the range of these systems is to use a directional transmitting antenna, the problem with this approach is that power can only be transmitted through a narrow beam and directly forward, requiring the transmitter to always be aligned with the sensor node position. The work outlined in this article describes the design and testing of an autonomous radio frequency power transfer system that is capable of rotating the base transmitter to track the position of sensor nodes and transferring power to that sensor node. The system’s base station monitors the node’s energy levels and forms a charge queue to plan charging order and maintain energy levels of the nodes. Results show a radio frequency harvesting circuit with a measured S11 value of −31.5 dB and a conversion efficiency of 39.1%. Simulation and experimentation verified the level of power transfer and efficiency. The results of this work show a small network of three nodes with different storage types powered by a central base node.

  3. The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

    Energy Technology Data Exchange (ETDEWEB)

    Psihas, Fernanda [Indiana U.

    2017-11-22

    In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

  4. Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures.

    Science.gov (United States)

    Tibau, Elisenda; Valencia, Miguel; Soriano, Jordi

    2013-01-01

    Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

  5. Optimal power flow for distribution networks with distributed generation

    Directory of Open Access Journals (Sweden)

    Radosavljević Jordan

    2015-01-01

    Full Text Available This paper presents a genetic algorithm (GA based approach for the solution of the optimal power flow (OPF in distribution networks with distributed generation (DG units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators. [Projekat Ministarstva nauke Republike Srbije, br. TR33046

  6. Identification of Functional Information Subgraphs in Complex Networks

    International Nuclear Information System (INIS)

    Bettencourt, Luis M. A.; Gintautas, Vadas; Ham, Michael I.

    2008-01-01

    We present a general information theoretic approach for identifying functional subgraphs in complex networks. We show that the uncertainty in a variable can be written as a sum of information quantities, where each term is generated by successively conditioning mutual informations on new measured variables in a way analogous to a discrete differential calculus. The analogy to a Taylor series suggests efficient optimization algorithms for determining the state of a target variable in terms of functional groups of other nodes. We apply this methodology to electrophysiological recordings of cortical neuronal networks grown in vitro. Each cell's firing is generally explained by the activity of a few neurons. We identify these neuronal subgraphs in terms of their redundant or synergetic character and reconstruct neuronal circuits that account for the state of target cells

  7. Mutually cooperative epidemics on power-law networks

    Science.gov (United States)

    Cui, Peng-Bi; Colaiori, Francesca; Castellano, Claudio

    2017-08-01

    The spread of an infectious disease can, in some cases, promote the propagation of other pathogens favoring violent outbreaks, which cause a discontinuous transition to an endemic state. The topology of the contact network plays a crucial role in these cooperative dynamics. We consider a susceptible-infected-removed-type model with two mutually cooperative pathogens: An individual already infected with one disease has an increased probability of getting infected by the other. We present a heterogeneous mean-field theoretical approach to the coinfection dynamics on generic uncorrelated power-law degree-distributed networks and validate its results by means of numerical simulations. We show that, when the second moment of the degree distribution is finite, the epidemic transition is continuous for low cooperativity, while it is discontinuous when cooperativity is sufficiently high. For scale-free networks, i.e., topologies with diverging second moment, the transition is instead always continuous. In this way we clarify the effect of heterogeneity and system size on the nature of the transition, and we validate the physical interpretation about the origin of the discontinuity.

  8. Role of centrality for the identification of influential spreaders in complex networks.

    Science.gov (United States)

    de Arruda, Guilherme Ferraz; Barbieri, André Luiz; Rodríguez, Pablo Martín; Rodrigues, Francisco A; Moreno, Yamir; Costa, Luciano da Fontoura

    2014-09-01

    The identification of the most influential spreaders in networks is important to control and understand the spreading capabilities of the system as well as to ensure an efficient information diffusion such as in rumorlike dynamics. Recent works have suggested that the identification of influential spreaders is not independent of the dynamics being studied. For instance, the key disease spreaders might not necessarily be so important when it comes to analyzing social contagion or rumor propagation. Additionally, it has been shown that different metrics (degree, coreness, etc.) might identify different influential nodes even for the same dynamical processes with diverse degrees of accuracy. In this paper, we investigate how nine centrality measures correlate with the disease and rumor spreading capabilities of the nodes in different synthetic and real-world (both spatial and nonspatial) networks. We also propose a generalization of the random walk accessibility as a new centrality measure and derive analytical expressions for the latter measure for simple network configurations. Our results show that for nonspatial networks, the k-core and degree centralities are the most correlated to epidemic spreading, whereas the average neighborhood degree, the closeness centrality, and accessibility are the most related to rumor dynamics. On the contrary, for spatial networks, the accessibility measure outperforms the rest of the centrality metrics in almost all cases regardless of the kind of dynamics considered. Therefore, an important consequence of our analysis is that previous studies performed in synthetic random networks cannot be generalized to the case of spatial networks.

  9. A study on methodologies for assessing safety critical network's risk impact on Nuclear Power Plant

    International Nuclear Information System (INIS)

    Lim, T. J.; Lee, H. J.; Park, S. K.; Seo, S. J.

    2006-08-01

    The objectives of this project is to investigate and study existing reliability analysis techniques for communication networks in order to develop reliability analysis models for Nuclear Power Plant's safety-critical networks. It is necessary to make a comprehensive survey of current methodologies for communication network reliability. Major outputs of the first year study are design characteristics of safety-critical communication networks, efficient algorithms for quantifying reliability of communication networks, and preliminary models for assessing reliability of safety-critical communication networks

  10. Network management for 900-1300 MW PWR power plant industrial network

    International Nuclear Information System (INIS)

    Schlosser, J.G.; Maillart, H.; Lopinto, Y.

    1993-07-01

    Communication networks are a vital element in industrial information system projects. Because of the complexity of these networks, which are heterogeneous in terms of hardware, protocols and communication software, the need is now clear to consider them as applications in and of themselves. For this reason, EDF communication networks are now covered by system requirement specifications with SPRINT, and by an acceptance policy which calls for conformance certificates and defines methods for interoperability tests. Network management completes the picture, and attempts to provide the resources with which to assess present network status and manage communication networks. For this purpose, EDF has selected the OSI standard, which is based on four key concepts: - the ''Manager - Agent'' structural model, - a service (CMISE) and a protocol (CMIP) which enable exchange of management information, - an object-oriented management data model, -functions for: configuration, anomaly, performance and alarm management. This document presents the results of a project to integrate into a single management platform communication systems which do not conform to the OSI management recommendations. There were three major phases to the project: - a review of the OSI management model, and most particularly of the information model advocated; - a review of the resources to be managed and a comparison of the information provided by the different agents. Specifications were proposed for the management object and attribute classes which can usefully be implemented; - integration of ARLIC. During development of a breadboard version based on a BULL ISM platform for management of the power plant local area network, one possible solution for integration of ARLIC was proposed, describing the ARLIC information modeling along the lines of the OSI model. This project showed that it is possible to use the OSI modeling to represent management data manipulated by the communication software used on

  11. An integer optimization algorithm for robust identification of non-linear gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Chemmangattuvalappil Nishanth

    2012-09-01

    Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

  12. Wind power, network congestion and hydro resource utilisation in the Norwegian power market

    International Nuclear Information System (INIS)

    Foersund, Finn; Singh, Balbir; Jensen, Trond; Larsen, Cato

    2005-01-01

    Capacity constraints in electricity networks can have important impacts on utilization of new renewable energy (RE) capacity and incumbent generation resources. Neglect of such impacts in development of RE resources can result in crowding-out of incumbent generation. This trade-off is particularly problematic if the incumbent generation also consists of renewable sources, such as hydropower in the Norwegian electricity system. This paper presents a numerical analysis of the current wind-power development plans in North Norway and their impacts on utilization of hydropower. Policy simulations in paper are conducted using a dynamic partial equilibrium model that is calibrated to reflect the structure of the Nordic power market. The paper draws conclusion and policy implications for integration of RE resources in the Norwegian power market. (Author)

  13. Adaptive control of a PWR core power using neural networks

    International Nuclear Information System (INIS)

    Arab-Alibeik, H.; Setayeshi, S.

    2005-01-01

    Reactor power control is important because of safety concerns and the call for regular and appropriate operation of nuclear power plants. It seems that the load-follow operation of these plants will be unavoidable in the future. Discrepancies between the real plant and the model used in controller design for load-follow operation encourage one to use auto-tuning and (or) adaptive techniques. Neural network technology shows great promise for addressing many problems in non-model-based adaptive control methods. Also, there has been a great attention to inverse control especially in the neural and fuzzy control context. Fortunately, online adaptation eliminates some limitations of inverse control and its shortcomings for real world applications. We use a neural adaptive inverse controller to control the power of a PWR reactor. The stability of the system and convergence of the controller parameters are guaranteed during online adaptation phase provided the controller is near the plant's real inverse after offline training period. The performance of the controller is verified using nonlinear simulations in diverse operating conditions

  14. Identification of Multiple-Mode Linear Models Based on Particle Swarm Optimizer with Cyclic Network Mechanism

    Directory of Open Access Journals (Sweden)

    Tae-Hyoung Kim

    2017-01-01

    Full Text Available This paper studies the metaheuristic optimizer-based direct identification of a multiple-mode system consisting of a finite set of linear regression representations of subsystems. To this end, the concept of a multiple-mode linear regression model is first introduced, and its identification issues are established. A method for reducing the identification problem for multiple-mode models to an optimization problem is also described in detail. Then, to overcome the difficulties that arise because the formulated optimization problem is inherently ill-conditioned and nonconvex, the cyclic-network-topology-based constrained particle swarm optimizer (CNT-CPSO is introduced, and a concrete procedure for the CNT-CPSO-based identification methodology is developed. This scheme requires no prior knowledge of the mode transitions between subsystems and, unlike some conventional methods, can handle a large amount of data without difficulty during the identification process. This is one of the distinguishing features of the proposed method. The paper also considers an extension of the CNT-CPSO-based identification scheme that makes it possible to simultaneously obtain both the optimal parameters of the multiple submodels and a certain decision parameter involved in the mode transition criteria. Finally, an experimental setup using a DC motor system is established to demonstrate the practical usability of the proposed metaheuristic optimizer-based identification scheme for developing a multiple-mode linear regression model.

  15. The computer simulation of the resonant network for the B-factory model power supply

    International Nuclear Information System (INIS)

    Zhou, W.; Endo, K.

    1993-07-01

    A high repetition model power supply and the resonant magnet network are simulated with the computer in order to check and improve the design of the power supply for the B-factory booster. We put our key point on a transient behavior of the power supply and the resonant magnet network. The results of the simulation are given. (author)

  16. Distributed routing algorithms to manage power flow in agent-based active distribution network

    NARCIS (Netherlands)

    Nguyen, H.P.; Kling, W.L.; Georgiadis, G.; Papatriantafilou, M.; Anh-Tuan, L.; Bertling, L.

    2010-01-01

    The current transition from passive to active electric distribution networks comes with problems and challenges on bi-directional power flow in the network and the uncertainty in the forecast of power generation from grid-connected renewable and distributed energy sources. The power flow management

  17. Real-time identification of residential appliance events based on power monitoring

    Science.gov (United States)

    Yang, Zhao; Zhu, Zhicheng; Wei, Zhiqiang; Yin, Bo; Wang, Xiuwei

    2018-03-01

    Energy monitoring for specific home appliances has been regarded as the pre-requisite for reducing residential energy consumption. To enhance the accuracy of identifying operation status of household appliances and to keep pace with the development of smart power grid, this paper puts forward the integration of electric current and power data on the basis of existing algorithm. If average power difference of several adjacent cycles varies from the baseline and goes beyond the pre-assigned threshold value, the event will be flagged. Based on MATLAB platform and domestic appliances simulations, the results of tested data and verified algorithm indicate that the power method has accomplished desired results of appliance identification.

  18. Neural Network Substorm Identification: Enabling TREx Sensor Web Modes

    Science.gov (United States)

    Chaddock, D.; Spanswick, E.; Arnason, K. M.; Donovan, E.; Liang, J.; Ahmad, S.; Jackel, B. J.

    2017-12-01

    Transition Region Explorer (TREx) is a ground-based sensor web of optical and radio instruments that is presently being deployed across central Canada. The project consists of an array of co-located blue-line, full-colour, and near-infrared all-sky imagers, imaging riometers, proton aurora spectrographs, and GNSS systems. A key goal of the TREx project is to create the world's first (artificial) intelligent sensor web for remote sensing space weather. The sensor web will autonomously control and coordinate instrument operations in real-time. To accomplish this, we will use real-time in-line analytics of TREx and other data to dynamically switch between operational modes. An operating mode could be, for example, to have a blue-line imager gather data at a one or two orders of magnitude higher cadence than it operates for its `baseline' mode. The software decision to increase the imaging cadence would be in response to an anticipated increase in auroral activity or other programmatic requirements. Our first test for TREx's sensor web technologies is to develop the capacity to autonomously alter the TREx operating mode prior to a substorm expansion phase onset. In this paper, we present our neural network analysis of historical optical and riometer data and our ability to predict an optical onset. We explore the preliminary insights into using a neural network to pick out trends and features which it deems are similar among substorms.

  19. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  20. Surrogate-assisted identification of influences of network construction on evolving weighted functional networks

    Science.gov (United States)

    Stahn, Kirsten; Lehnertz, Klaus

    2017-12-01

    We aim at identifying factors that may affect the characteristics of evolving weighted networks derived from empirical observations. To this end, we employ various chains of analysis that are often used in field studies for a data-driven derivation and characterization of such networks. As an example, we consider fully connected, weighted functional brain networks before, during, and after epileptic seizures that we derive from multichannel electroencephalographic data recorded from epilepsy patients. For these evolving networks, we estimate clustering coefficient and average shortest path length in a time-resolved manner. Lastly, we make use of surrogate concepts that we apply at various levels of the chain of analysis to assess to what extent network characteristics are dominated by properties of the electroencephalographic recordings and/or the evolving weighted networks, which may be accessible more easily. We observe that characteristics are differently affected by the unavoidable referencing of the electroencephalographic recording, by the time-series-analysis technique used to derive the properties of network links, and whether or not networks were normalized. Importantly, for the majority of analysis settings, we observe temporal evolutions of network characteristics to merely reflect the temporal evolutions of mean interaction strengths. Such a property of the data may be accessible more easily, which would render the weighted network approach—as used here—as an overly complicated description of simple aspects of the data.

  1. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Hazard Identification, Risk Assessment and Risk Control (HIRARC Accidents at Power Plant

    Directory of Open Access Journals (Sweden)

    Ahmad Asmalia Che

    2016-01-01

    Full Text Available Power plant had a reputation of being one of the most hazardous workplace environments. Workers in the power plant face many safety risks due to the nature of the job. Although power plants are safer nowadays since the industry has urged the employer to improve their employees’ safety, the employees still stumble upon many hazards thus accidents at workplace. The aim of the present study is to investigate work related accidents at power plants based on HIRARC (Hazard Identification, Risk Assessment and Risk Control process. The data were collected at two coal-fired power plant located in Malaysia. The finding of the study identified hazards and assess risk relate to accidents occurred at the power plants. The finding of the study suggested the possible control measures and corrective actions to reduce or eliminate the risk that can be used by power plant in preventing accidents from occurred

  3. The intelligent system for accident identification in nuclear power plant

    International Nuclear Information System (INIS)

    Hernandez, Jorge Luis.

    1998-01-01

    Accidental situations in NPP are of greet concern for operators, the facility, regulatory bodies and the environment. This work proposes a design of intelligent system aimed to assist the operator in the process of decision making when initiator events with higher relative contribution to the reactor core damage occur. The intelligent System uses the results of the pre operational Probabilistic Safety Assessment and the Thermal hydraulic Safety Analyses of the NPP Juragua as source for building its knowledge base. The nucleus of the system is presented as a design of an intelligent hybrid system from the combination of the artificial intelligence techniques: fussy logic and artificial neural networks. The system works with variables from the process of the firsts circuit, second circuit and the containment and it is presented as a model for the integration of safety analyses in the process of decision making by the operator when tackling with accidental situations

  4. Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network

    International Nuclear Information System (INIS)

    Bobin, C.; Bichler, O.; Lourenço, V.; Thiam, C.; Thévenin, M.

    2016-01-01

    Portal radiation monitors dedicated to the prevention of illegal traffic of nuclear materials at international borders need to deliver as fast as possible a radionuclide identification of a potential radiological threat. Spectrometry techniques applied to identify the radionuclides contributing to γ-emitter mixtures are usually performed using off-line spectrum analysis. As an alternative to these usual methods, a real-time processing based on an artificial neural network and Bayes’ rule is proposed for fast radionuclide identification. The validation of this real-time approach was carried out using γ-emitter spectra ( 241 Am, 133 Ba, 207 Bi, 60 Co, 137 Cs) obtained with a high-efficiency well-type NaI(Tl). The first tests showed that the proposed algorithm enables a fast identification of each γ-emitting radionuclide using the information given by the whole spectrum. Based on an iterative process, the on-line analysis only needs low-statistics spectra without energy calibration to identify the nature of a radiological threat. - Highlights: • A fast radionuclide identification algorithm applicable in spectroscopic portal monitors is presented. • The proposed algorithm combines a Bayesian sequential approach and a spiking neural network. • The algorithm was validated using the mixture of γ-emitter spectra provided by a well-type NaI(Tl) detector. • The radionuclide identification process is implemented using the whole γ-spectrum without energy calibration.

  5. Target identification using Zernike moments and neural networks

    Science.gov (United States)

    Azimi-Sadjadi, Mahmood R.; Jamshidi, Arta A.; Nevis, Andrew J.

    2001-10-01

    The development of an underwater target identification algorithm capable of identifying various types of underwater targets, such as mines, under different environmental conditions pose many technical problems. Some of the contributing factors are: targets have diverse sizes, shapes and reflectivity properties. Target emplacement environment is variable; targets may be proud or partially buried. Environmental properties vary significantly from one location to another. Bottom features such as sand, rocks, corals, and vegetation can conceal a target whether it is partially buried or proud. Competing clutter with responses that closely resemble those of the targets may lead to false positives. All the problems mentioned above contribute to overly difficult and challenging conditions that could lead to unreliable algorithm performance with existing methods. In this paper, we developed and tested a shape-dependent feature extraction scheme that provides features invariant to rotation, size scaling and translation; properties that are extremely useful for any target classification problem. The developed schemes were tested on an electro-optical imagery data set collected under different environmental conditions with variable background, range and target types. The electro-optic data set was collected using a Laser Line Scan (LLS) sensor by the Coastal Systems Station (CSS), located in Panama City, Florida. The performance of the developed scheme and its robustness to distortion, rotation, scaling and translation was also studied.

  6. Assessment on thermoelectric power factor in silicon nanowire networks

    Energy Technology Data Exchange (ETDEWEB)

    Lohn, Andrew J.; Kobayashi, Nobuhiko P. [Baskin School of Engineering, University of California Santa Cruz, CA (United States); Nanostructured Energy Conversion Technology and Research (NECTAR), Advanced Studies Laboratories, University of California Santa Cruz, NASA Ames Research Center, Moffett Field, CA (United States); Coleman, Elane; Tompa, Gary S. [Structured Materials Industries, Inc., Piscataway, NJ (United States)

    2012-01-15

    Thermoelectric devices based on three-dimensional networks of highly interconnected silicon nanowires were fabricated and the parameters that contribute to the power factor, namely the Seebeck coefficient and electrical conductivity were assessed. The large area (2 cm x 2 cm) devices were fabricated at low cost utilizing a highly scalable process involving silicon nanowires grown on steel substrates. Temperature dependence of the Seebeck coefficient was found to be weak over the range of 20-80 C at approximately -400 {mu}V/K for unintentionally doped devices and {+-}50 {mu}V/K for p-type and n-type devices, respectively. (Copyright copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  7. Optimization of power distribution networks; Optimierung von Energieverteilungsnetzen

    Energy Technology Data Exchange (ETDEWEB)

    Casteren, J. van [Digsilent GmbH (Netherlands); Chalmers Univ. of Technology, Goeteborg (Sweden)

    2000-03-01

    In a competition-oriented power market the optimization of distribution networks is more and more becoming a search for minimum investment and operating cost where all relevant cost factors should be taken into account. A so far neglected factor is the expectation of reliability-related cost. A new analytical calculation method permits flexible, realistic estimation of interruption costs to be expected. (orig.) [German] In einem wettbewerbsorientierten Strom-Markt wird die Optimierung der Verteilungsnetze mehr und mehr zu einer Suche nach den minimalen Investitions- und Betriebskosten, wobei moeglichst alle relevanten Kostenfaktoren beruecksichtigt werden muessen. Ein bisher vernachlaessigter Faktor ist dabei die Erwartung der zuverlaessigkeitsbedingten Kosten. Ein neues analytisches Berechnungsverfahren erlaubt nun flexibel eine realistische Abschaetzung der zu erwarteten Unterbrechungskosten. (orig.)

  8. Integrated Power Flow and Short Circuit Calculation Method for Distribution Network with Inverter Based Distributed Generation

    OpenAIRE

    Yang, Shan; Tong, Xiangqian

    2016-01-01

    Power flow calculation and short circuit calculation are the basis of theoretical research for distribution network with inverter based distributed generation. The similarity of equivalent model for inverter based distributed generation during normal and fault conditions of distribution network and the differences between power flow and short circuit calculation are analyzed in this paper. Then an integrated power flow and short circuit calculation method for distribution network with inverte...

  9. Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks.

    Science.gov (United States)

    Jia, Jie; Chen, Jian; Deng, Yansha; Wang, Xingwei; Aghvami, Abdol-Hamid

    2017-10-09

    The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms.

  10. Nuclear power plant maintenance optimisation SENUF network activity

    International Nuclear Information System (INIS)

    Ahlstrand, R.; Bieth, M.; Pla, P.; Rieg, C.; Trampus, P.

    2004-01-01

    During providing scientific and technical support to TACIS and PHARE nuclear safety programs a large amount of knowledge related to Russian design reactor systems has accumulated and led to creation of a new Network concerning Nuclear Safety in Central and Eastern Europe called ''Safety of Eastern European type Nuclear Facilities'' (SENUF). SENUF contributes to bring together all stakeholders of TACIS and PHARE: beneficiaries, end users, Eastern und Western nuclear industries, and thus, to favour fruitful technical exchanges and feedback of experience. At present the main focus of SENUF is the nuclear power plant maintenance as substantial element of plant operational safety as well as life management. A Working Group has been established on plant maintenance. One of its major tasks in 2004 is to prepare a status report on advanced strategies to optimise maintenance. Optimisation projects have an interface with the plant's overall life management program. Today, almost all plants involved in SENUF network have an explicit policy to extend their service life, thus, component ageing management, modernization and refurbishment actions became much more important. A database is also under development, which intends to help sharing the available knowledge and specific equipment and tools. (orig.)

  11. Nuclear power plant maintenance optimisation SENUF network activity

    Energy Technology Data Exchange (ETDEWEB)

    Ahlstrand, R.; Bieth, M.; Pla, P.; Rieg, C.; Trampus, P. [Inst. for Energy, EC DG Joint Research Centre, Petten (Netherlands)

    2004-07-01

    During providing scientific and technical support to TACIS and PHARE nuclear safety programs a large amount of knowledge related to Russian design reactor systems has accumulated and led to creation of a new Network concerning Nuclear Safety in Central and Eastern Europe called ''Safety of Eastern European type Nuclear Facilities'' (SENUF). SENUF contributes to bring together all stakeholders of TACIS and PHARE: beneficiaries, end users, Eastern und Western nuclear industries, and thus, to favour fruitful technical exchanges and feedback of experience. At present the main focus of SENUF is the nuclear power plant maintenance as substantial element of plant operational safety as well as life management. A Working Group has been established on plant maintenance. One of its major tasks in 2004 is to prepare a status report on advanced strategies to optimise maintenance. Optimisation projects have an interface with the plant's overall life management program. Today, almost all plants involved in SENUF network have an explicit policy to extend their service life, thus, component ageing management, modernization and refurbishment actions became much more important. A database is also under development, which intends to help sharing the available knowledge and specific equipment and tools. (orig.)

  12. Automatic failure identification of the nuclear power plant pellet fuel

    International Nuclear Information System (INIS)

    Oliveira, Adriano Fortunato de

    2010-01-01

    This paper proposed the development of an automatic technique for evaluating defects to help in the stage of fabrication of fuel elements. Was produced an intelligent image analysis for automatic recognition of defects in uranium pellets. Therefore, an Artificial Neural Network (ANN) was trained using segments of histograms of pellets, containing examples of both normal (no fault) and of defectives pellets (with major defects normally found). The images of the pellets were segmented into 11 shares. Histograms were made of these segments and trained the ANN. Besides automating the process, the system was able to obtain this classification accuracy of 98.33%. Although this percentage represents a significant advance ever in the quality control process, the use of more advanced techniques of photography and lighting will reduce it to insignificant levels with low cost. Technologically, the method developed, should it ever be implemented, will add substantial value in terms of process quality control and production outages in relation to domestic manufacturing of nuclear fuel. (author)

  13. A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

    Science.gov (United States)

    Hong, Xia

    2006-07-01

    In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

  14. Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

    International Nuclear Information System (INIS)

    Peng Yafu; Hsu, C.-F.

    2009-01-01

    This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

  15. Testing Situation Awareness Network for the Electrical Power Infrastructure

    Directory of Open Access Journals (Sweden)

    Rafał Leszczyna

    2016-09-01

    Full Text Available The contemporary electrical power infrastructure is exposed to new types of threats. The cause of such threats is related to the large number of new vulnerabilities and architectural weaknesses introduced by the extensive use of Information and communication Technologies (ICT in such complex critical systems. The power grid interconnection with the Internet exposes the grid to new types of attacks, such as Advanced Persistent Threats (APT or Distributed-Denial-ofService (DDoS attacks. When addressing this situation the usual cyber security technologies are prerequisite, but not sufficient. To counter evolved and highly sophisticated threats such as the APT or DDoS, state-of-the-art technologies including Security Incident and Event Management (SIEM systems, extended Intrusion Detection/Prevention Systems (IDS/IPS and Trusted Platform Modules (TPM are required. Developing and deploying extensive ICT infrastructure that supports wide situational awareness and allows precise command and control is also necessary. In this paper the results of testing the Situational Awareness Network (SAN designed for the energy sector are presented. The purpose of the tests was to validate the selection of SAN components and check their operational capability in a complex test environment. During the tests’ execution appropriate interaction between the components was verified.

  16. Channel Deviation-Based Power Control in Body Area Networks.

    Science.gov (United States)

    Van, Son Dinh; Cotton, Simon L; Smith, David B

    2018-05-01

    Internet enabled body area networks (BANs) will form a core part of future remote health monitoring and ambient assisted living technology. In BAN applications, due to the dynamic nature of human activity, the off-body BAN channel can be prone to deep fading caused by body shadowing and multipath fading. Using this knowledge, we present some novel practical adaptive power control protocols based on the channel deviation to simultaneously prolong the lifetime of wearable devices and reduce outage probability. The proposed schemes are both flexible and relatively simple to implement on hardware platforms with constrained resources making them inherently suitable for BAN applications. We present the key algorithm parameters used to dynamically respond to the channel variation. This allows the algorithms to achieve a better energy efficiency and signal reliability in everyday usage scenarios such as those in which a person undertakes many different activities (e.g., sitting, walking, standing, etc.). We also profile their performance against traditional, optimal, and other existing schemes for which it is demonstrated that not only does the outage probability reduce significantly, but the proposed algorithms also save up to average transmit power compared to the competing schemes.

  17. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    Science.gov (United States)

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  18. Flow Regime Identification of Co-Current Downward Two-Phase Flow With Neural Network Approach

    International Nuclear Information System (INIS)

    Hiroshi Goda; Seungjin Kim; Ye Mi; Finch, Joshua P.; Mamoru Ishii; Jennifer Uhle

    2002-01-01

    Flow regime identification for an adiabatic vertical co-current downward air-water two-phase flow in the 25.4 mm ID and the 50.8 mm ID round tubes was performed by employing an impedance void meter coupled with the neural network classification approach. This approach minimizes the subjective judgment in determining the flow regimes. The signals obtained by an impedance void meter were applied to train the self-organizing neural network to categorize these impedance signals into a certain number of groups. The characteristic parameters set into the neural network classification included the mean, standard deviation and skewness of impedance signals in the present experiment. The classification categories adopted in the present investigation were four widely accepted flow regimes, viz. bubbly, slug, churn-turbulent, and annular flows. These four flow regimes were recognized based upon the conventional flow visualization approach by a high-speed motion analyzer. The resulting flow regime maps classified by the neural network were compared with the results obtained through the flow visualization method, and consequently the efficiency of the neural network classification for flow regime identification was demonstrated. (authors)

  19. Individual Identification Using Functional Brain Fingerprint Detected by Recurrent Neural Network.

    Science.gov (United States)

    Chen, Shiyang; Hu, Xiaoping P

    2018-03-20

    Individual identification based on brain function has gained traction in literature. Investigating individual differences in brain function can provide additional insights into the brain. In this work, we introduce a recurrent neural network based model for identifying individuals based on only a short segment of resting state functional MRI data. In addition, we demonstrate how the global signal and differences in atlases affect the individual identifiability. Furthermore, we investigate neural network features that exhibit the uniqueness of each individual. The results indicate that our model is able to identify individuals based on neural features and provides additional information regarding brain dynamics.

  20. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  1. Topological derivatives of eigenvalues and neural networks in identification of imperfections

    International Nuclear Information System (INIS)

    Grzanek, M; Nowakowski, A; Sokolowski, J

    2008-01-01

    Numerical method for identification of imperfections is devised for elliptic spectral problems. The neural networks are employed for numerical solution. The topological derivatives of eigenvalues are used in the learning procedure of the neural networks. The topological derivatives of eigenvalues are determined by the methods of asymptotic analysis in singularly perturbed geometrical domains. The convergence of the numerical method in a probabilistic setting is analysed. The method is presented for the identification of small singular perturbations of the boundary of geometrical domain, however the framework is general and can be used for numerical solutions of inverse problems in the presence of small imperfections in the interior of the domain. Some numerical results are given for elliptic spectral problem in two spatial dimensions.

  2. Recognition of power quality events by using multiwavelet-based neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kaewarsa, Suriya; Attakitmongcol, Kitti; Kulworawanichpong, Thanatchai [School of Electrical Engineering, Suranaree University of Technology, 111 University Avenue, Muang District, Nakhon Ratchasima 30000 (Thailand)

    2008-05-15

    Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency. (author)

  3. Power Allocation and Outage Probability Analysis for SDN-based Radio Access Networks

    Science.gov (United States)

    Zhao, Yongxu; Chen, Yueyun; Mai, Zhiyuan

    2018-01-01

    In this paper, performance of Access network Architecture based SDN (Software Defined Network) is analyzed with respect to the power allocation issue. A power allocation scheme PSO-PA (Particle Swarm Optimization-power allocation) algorithm is proposed, the proposed scheme is subjected to constant total power with the objective of minimizing system outage probability. The entire access network resource configuration is controlled by the SDN controller, then it sends the optimized power distribution factor to the base station source node (SN) and the relay node (RN). Simulation results show that the proposed scheme reduces the system outage probability at a low complexity.

  4. Identification and simulation of the power quality problems using computer models

    International Nuclear Information System (INIS)

    Abro, M.R.; Memon, A.P.; Memon, Z.A.

    2005-01-01

    The Power Quality has become the main factor in our life. If this quality of power is being polluted over the Electrical Power Network, serious problems could arise within the modem social structure and its conveniences. The Nonlinear Characteristics of various office and Industrial equipment connected to the power grid could cause electrical disturbances to poor power quality. In many cases the electric power consumed is first converted to different form and such conversion process introduces harmonic pollution in the grid. These electrical disturbances could destroy certain sensitive equipment connected to the grid or in some cases could cause them to malfunction. In the huge power network identifying the source of such disturbance without causing interruption to the supply is a big problem. This paper attempts to study the power quality problem caused by typical loads using computer models paving the way to identify the source of the problem. PSB (Power System Blockset) Toolbox of MATLAB is used for this paper, which is designed to provide modem tool that rapidly and easily builds models and simulates the power system. The blockset uses the Simulink environment, allowing a model to be built using simple click and drag procedures. (author)

  5. Relay Protection Coordination for Photovoltaic Power Plant Connected on Distribution Network

    OpenAIRE

    Nikolovski, Srete; Papuga, Vanja; Knežević, Goran

    2014-01-01

    This paper presents a procedure and computation of relay protection coordination for a PV power plant connected to the distribution network. In recent years, the growing concern for environment preservation has caused expansion of photovoltaic PV power plants in distribution networks. Numerical computer simulation is an indispensable tool for studying photovoltaic (PV) systems protection coordination. In this paper, EasyPower computer program is used with the module Power Protector. Time-curr...

  6. On the distribution and mean of received power in stochastic cellular network

    OpenAIRE

    Cao, Fengming; Ganesh, Ayalvadi; Armour, Simon; Sooriyabandara, Mahesh

    2016-01-01

    This paper exploits the distribution and mean of received power for cellular network with stochastic network modeling to study the difference between the two cell association criteria, i.e. the strongest received power based cell association and the closest distance based cell association. Consequently we derive the analytical expression of the distribution and the mean of the nth strongest received power and the received power from the nth nearest base station and the derivations have been c...

  7. On joint power allocation and multipath routing in femto-relay networks

    OpenAIRE

    Hoteit , Sahar; Duhamel , Pierre; Lasaulce , Samson

    2016-01-01

    International audience; —Transmit power allocation techniques are very important to manage interference in small-cell networks. While available power allocation algorithms in the literature rely on a predefined routing protocol, we propose in this paper a power-efficient two-step algorithm that allows power allocation and routing to be performed jointly in femto-relay networks. First, we propose an interference-based partitioning method to cluster the femto-relays, then we adopt an iterative ...

  8. Power System Oscillation Modes Identifications: Guidelines for Applying TLS-ESPRIT Method

    Science.gov (United States)

    Gajjar, Gopal R.; Soman, Shreevardhan

    2013-05-01

    Fast measurements of power system quantities available through wide-area measurement systems enables direct observations for power system electromechanical oscillations. But the raw observations data need to be processed to obtain the quantitative measures required to make any inference regarding the power system state. A detailed discussion is presented for the theory behind the general problem of oscillatory mode indentification. This paper presents some results on oscillation mode identification applied to a wide-area frequency measurements system. Guidelines for selection of parametes for obtaining most reliable results from the applied method are provided. Finally, some results on real measurements are presented with our inference on them.

  9. Identification of initiating events using a master logic diagram in low-power and shutdown PSA for nuclear power plant

    International Nuclear Information System (INIS)

    Han, S. J.; Park, J. H.; Kim, T. W.; Ha, J. J.

    2003-01-01

    It is necessary to apply a formal technique instead of an empirical technique in the identification of initiating events for Low Power and ShutDown (LPSD) Probabilistic Safety Assessment (PSA) of Nuclear Power Plant (NPP). The present study focuses on the examination of Master Logic Diagram (MLD) technique as a formal technique in the identification of initiating events. The MLD technique is a deductive tool using top-down approach for the formal and logical indentification of initiating events. The present study modified the MLD used in the full power PSA considering the characteristics of LPSD operation. The modified MLD introduced a systematic formation in decomposition process of which the MLD for full power PSA lacked. The modified MLD was able to identify initiating events systematic and logical. However, the formal techniques including the MLD have a limitation for precisely identifying all of the initiating events. In order to overcome this limitation, it is necessary to combine it with an empirical technique. We expect that the modified MLD can be used in an upgrade of the current LPSD PSAs

  10. Identification of chaotic systems by neural network with hybrid learning algorithm

    International Nuclear Information System (INIS)

    Pan, S.-T.; Lai, C.-C.

    2008-01-01

    Based on the genetic algorithm (GA) and steepest descent method (SDM), this paper proposes a hybrid algorithm for the learning of neural networks to identify chaotic systems. The systems in question are the logistic map and the Duffing equation. Different identification schemes are used to identify both the logistic map and the Duffing equation, respectively. Simulation results show that our hybrid algorithm is more efficient than that of other methods

  11. Energy-efficient power control for OFDMA cellular networks

    KAUST Repository

    Sboui, Lokman; Rezki, Zouheir; Alouini, Mohamed-Slim

    2016-01-01

    explicit expression of the optimal power allocation to each subcarrier. We also present the power control when the transmit power is limited by power budget constraint or/and minimal rate constraint and we highlight the occurrence of some transmission

  12. Parameter identification of a BWR nuclear power plant model for use in optimal control

    International Nuclear Information System (INIS)

    Volf, K.

    1976-02-01

    The problem being considered is the modeling of a nuclear power plant for the development of an optimal control system of the plant. Current system identification concepts, combining input/output information with a-priori structural information are employed. Two of the known parameter identification methods i.e., a least squares method and a maximum likelihood technique, are studied as ways of parameter identification from measurement data. A low order state variable stochastic model of a BWR nuclear power plant is presented as an application of this approach. The model consists of a deterministic and a noise part. The deterministic part is formed by simplified modeling of the major plant dynamic phenomena. The moise part models the effects of input random disturbances to the deterministic part and additive measurement noise. Most of the model parameters are assumed to be initially unknown. They are identified using measurement data records. A detailed high order digital computer simulation is used to simulate plant dynamic behaviour since it is not conceivable for experimentation of this kind to be performed on the real nuclear power plant. The identification task consists in adapting the performance of the simple model to the data acquired from this plant simulation ensuring the applicability of the techniques to measurement data acquired directly from the plant. (orig.) [de

  13. Computer network for electric power control systems. Chubu denryoku (kabu) denryoku keito seigyoyo computer network

    Energy Technology Data Exchange (ETDEWEB)

    Tsuneizumi, T. (Chubu Electric Power Co. Inc., Nagoya (Japan)); Shimomura, S.; Miyamura, N. (Fuji Electric Co. Ltd., Tokyo (Japan))

    1992-06-03

    A computer network for electric power control system was developed that is applied with the open systems interconnection (OSI), an international standard for communications protocol. In structuring the OSI network, a direct session layer was accessed from the operation functions when high-speed small-capacity information is transmitted. File transfer, access and control having a function of collectively transferring large-capacity data were applied when low-speed large-capacity information is transmitted. A verification test for the realtime computer network (RCN) mounting regulation was conducted according to a verification model using a mini-computer, and a result that can satisfy practical performance was obtained. For application interface, kernel, health check and two-route transmission functions were provided as a connection control function, so were transmission verification function and late arrival abolishing function. In system mounting pattern, dualized communication server (CS) structure was adopted. A hardware structure may include a system to have the CS function contained in a host computer and a separate installation system. 5 figs., 6 tabs.

  14. THE EVALUATION OF THE EFFECT OF REACTIVE POWER COMPENSATION IN ELECTRIC POWER LOSSES IN ELECTRIC NETWORK OF RAILWAY JUNCTION

    OpenAIRE

    O. I. Bondar; I. L. Bondar

    2009-01-01

    In this work the generalized mathematical model of an electrical network of the electrified railway junction is proposed. An estimation of influence of static var compensators installation on electric power losses in a network is executed on the basis of given model.

  15. Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks

    Science.gov (United States)

    Dâmaso, Antônio; Maciel, Paulo

    2017-01-01

    Power consumption is a primary interest in Wireless Sensor Networks (WSNs), and a large number of strategies have been proposed to evaluate it. However, those approaches usually neither consider reliability issues nor the power consumption of applications executing in the network. A central concern is the lack of consolidated solutions that enable us to evaluate the power consumption of applications and the network stack also considering their reliabilities. To solve this problem, we introduce a fully automatic solution to design power consumption aware WSN applications and communication protocols. The solution presented in this paper comprises a methodology to evaluate the power consumption based on the integration of formal models, a set of power consumption and reliability models, a sensitivity analysis strategy to select WSN configurations and a toolbox named EDEN to fully support the proposed methodology. This solution allows accurately estimating the power consumption of WSN applications and the network stack in an automated way. PMID:29113078

  16. Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Antônio Dâmaso

    2017-11-01

    Full Text Available Power consumption is a primary interest in Wireless Sensor Networks (WSNs, and a large number of strategies have been proposed to evaluate it. However, those approaches usually neither consider reliability issues nor the power consumption of applications executing in the network. A central concern is the lack of consolidated solutions that enable us to evaluate the power consumption of applications and the network stack also considering their reliabilities. To solve this problem, we introduce a fully automatic solution to design power consumption aware WSN applications and communication protocols. The solution presented in this paper comprises a methodology to evaluate the power consumption based on the integration of formal models, a set of power consumption and reliability models, a sensitivity analysis strategy to select WSN configurations and a toolbox named EDEN to fully support the proposed methodology. This solution allows accurately estimating the power consumption of WSN applications and the network stack in an automated way.

  17. Statistical and optimization methods to expedite neural network training for transient identification

    International Nuclear Information System (INIS)

    Reifman, J.; Vitela, E.J.; Lee, J.C.

    1993-01-01

    Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network

  18. Identification of Pavement Distress Types and Pavement Condition Evaluation Based on Network Level Inspection for Jazan City Road Network

    Directory of Open Access Journals (Sweden)

    M Mubaraki

    2014-06-01

    Full Text Available The first step in establishing a pavement management system (PMS is road network identification. An important feature of a PMS is the ability to determine the current condition of a road network and predict its future condition. Pavement condition evaluation may involve structure, roughness, surface distress, and safety evaluation. In this study, a pavement distress condition rating procedure was used to achieve the objectives of this study. The main objectives of this study were to identify the common types of distress that exist on the Jazan road network (JRN, either on main roads or secondary roads, and to evaluate the pavement condition based on network level inspection. The study was conducted by collecting pavement distress types from 227 sample units on main roads and 500 sample units from secondary roads. Data were examined through analysis of common types of distress identified in both main and secondary roads. Through these data, pavement condition index (PCI for each sample unit was then calculated. Through these calculations, average PCIs for the main and secondary roads were determined. Results indicated that the most common pavement distress types on main roads were patching and utility cut patching, longitudinal and transverse cracking, polished aggregate, weathering and raveling, and alligator cracking. The most common pavement distress types on secondary roads were weathering and raveling, patching and utility cut patching, longitudinal and transverse cracking, potholes, and alligator cracking. The results also indicated that 65% of Jazan's main road network has an average pavement condition rating of very good while only 30% of Jazan's secondary roads network has an average pavement condition.

  19. Mentoring Support and Power: A Three Year Predictive Field Study on Protege Networking and Career Success

    Science.gov (United States)

    Blickle, Gerhard; Witzki, Alexander H.; Schneider, Paula B.

    2009-01-01

    Career success of early employees was analyzed from a power perspective and a developmental network perspective. In a predictive field study with 112 employees mentoring support and mentors' power were assessed in the first wave, employees' networking was assessed after two years, and career success (i.e. income and hierarchical position) and…

  20. Smart grids : combination of 'Virtual Power Plant'-concept and 'smart network'-design

    NARCIS (Netherlands)

    El Bakari, K.; Kling, W.L.

    2010-01-01

    The concept of virtual power plant (VPP) offers a solution to control and manage higher level of dispersed generation in nowadays passive distribution network. Under certain conditions the VPP is able to displace power and energy which implies more control on the energy flow in the networks. To

  1. Response of pressurized water reactor (PWR) to network power generation demands

    International Nuclear Information System (INIS)

    Schreiner, L.A.

    1991-01-01

    The flexibility of the PWR type reactor in terms of response to the variations of the network power demands, is demonstrated. The factors that affect the transitory flexibility and some design prospects that allow the reactor fits the requirements of the network power demands, are also discussed. (M.J.A.)

  2. Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network.

    Science.gov (United States)

    Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng

    2013-02-01

    This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

  3. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    Science.gov (United States)

    Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

  4. Linear Programming Approaches for Power Savings in Software-defined Networks

    NARCIS (Netherlands)

    Moghaddam, F.A.; Grosso, P.

    2016-01-01

    Software-defined networks have been proposed as a viable solution to decrease the power consumption of the networking component in data center networks. Still the question remains on which scheduling algorithms are most suited to achieve this goal. We propose 4 different linear programming

  5. Scenarios of power transmission networks; Cenarios de redes de transmissao de energia eletrica

    Energy Technology Data Exchange (ETDEWEB)

    Souza Fonseca, L.G. de; Savi, T C.O.; Morozowski Filho, M; Camargo, C C [ELETROSUL, Florianopolis, SC (Brazil)

    1985-12-31

    This work discusses the electrical network expansion considering long term horizons and a purpose of methodology for the establishment of transmission network scenarios. As part of the transmission scenario studies the network expansion problem is described by linearized power flow models and, for transmission system analysis and synthesis, the minimum effort criteria and interactive SINTRA program are used. 4 refs., 3 figs., 2 tabs.

  6. Impact of distributed generation units with power electronic converters on distribution network protection

    NARCIS (Netherlands)

    Morren, J.; Haan, de S.W.H.

    2008-01-01

    An increasing number of distributed generation units (DG units) are connected to the distribution network. These generators affect the operation and coordination of the distribution network protection. The influence from DG units that are coupled to the network with a power electronic converter

  7. Nanostructured Bulk Thermoelectric Generator for Efficient Power Harvesting for Self-powered Sensor Networks

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Yanliang [Idaho National Lab. (INL), Idaho Falls, ID (United States); Butt, Darryl [Idaho National Lab. (INL), Idaho Falls, ID (United States); Agarwal, Vivek [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2015-07-01

    The objective of this Nuclear Energy Enabling Technology research project is to develop high-efficiency and reliable thermoelectric generators for self-powered wireless sensors nodes utilizing thermal energy from nuclear plant or fuel cycle. The power harvesting technology has crosscutting significance to address critical technology gaps in monitoring nuclear plants and fuel cycle. The outcomes of the project will lead to significant advancement in sensors and instrumentation technology, reducing cost, improving monitoring reliability and therefore enhancing safety. The self-powered wireless sensor networks could support the long-term safe and economical operation of all the reactor designs and fuel cycle concepts, as well as spent fuel storage and many other nuclear science and engineering applications. The research is based on recent breakthroughs in high-performance nanostructured bulk (nanobulk) thermoelectric materials that enable high-efficiency direct heat-to-electricity conversion over a wide temperature range. The nanobulk thermoelectric materials that the research team at Boise State University and University of Houston has developed yield up to a 50% increase in the thermoelectric figure of merit, ZT, compared with state-of-the-art bulk counterparts. This report focuses on the selection of optimal thermoelectric materials for this project. The team has performed extensive study on two thermoelectric materials systems, i.e. the half-Heusler materials, and the Bismuth-Telluride materials. The report contains our recent research results on the fabrication, characterization and thermoelectric property measurements of these two materials.

  8. Economic benefits of broadened local area networks for electric power plants

    International Nuclear Information System (INIS)

    Holmes, T.

    1988-01-01

    The paper discusses economic benefits which influenced the choice of a broadband local area network for a power plant instead of an alternative multi-cable communication network. Broadband communication networks can offer significant economies over alternative technologies. One-time, cost avoidance savings and recurring annual savings are estimated to total $5.1 million in the first year. The cost/benefit analysis presented here can be used as a guide by other utilities to analyze communication networking alternatives. The paper also includes a discussion of local area network attributes relevant to the power plant installation

  9. Jimena: efficient computing and system state identification for genetic regulatory networks.

    Science.gov (United States)

    Karl, Stefan; Dandekar, Thomas

    2013-10-11

    Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones). Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.

  10. Identification of voltage stability condition of a power system using measurements of bus variables

    Directory of Open Access Journals (Sweden)

    Durlav Hazarika

    2014-12-01

    Full Text Available Several online methods were proposed for investigating the voltage stability condition of an interconnected power system using the measurements of voltage and current phasors at a bus. For this purpose, phasor measurement units (PMUs are used. A PMU is a device which measures the electrical waves on an electrical network, using a common time source (reference bus for synchronisation. This study proposes a method for online monitoring of voltage stability condition of a power system using measurements of bus variables namely – (i real power, (ii reactive power and (iii bus voltage magnitude at a bus. The measurements of real power, reactive power and bus voltage magnitude could be extracted/captured from a smart energy meter. The financial involvement for implementation of the proposed method would significantly lower compared with the PMU-based method.

  11. A Maximum Power Transfer Tracking Method for WPT Systems with Coupling Coefficient Identification Considering Two-Value Problem

    Directory of Open Access Journals (Sweden)

    Xin Dai

    2017-10-01

    Full Text Available Maximum power transfer tracking (MPTT is meant to track the maximum power point during the system operation of wireless power transfer (WPT systems. Traditionally, MPTT is achieved by impedance matching at the secondary side when the load resistance is varied. However, due to a loosely coupling characteristic, the variation of coupling coefficient will certainly affect the performance of impedance matching, therefore MPTT will fail accordingly. This paper presents an identification method of coupling coefficient for MPTT in WPT systems. Especially, the two-value issue during the identification is considered. The identification approach is easy to implement because it does not require additional circuit. Furthermore, MPTT is easy to realize because only two easily measured DC parameters are needed. The detailed identification procedure corresponding to the two-value issue and the maximum power transfer tracking process are presented, and both the simulation analysis and experimental results verified the identification method and MPTT.

  12. Neural network recognition of nuclear power plant transients

    International Nuclear Information System (INIS)

    Bartlett, E.B.; Danofsky, R.; Adams, J.; AlJundi, T.; Basu, A.; Dhanwada, C.; Kerr, J.; Kim, K.; Lanc, T.

    1993-01-01

    The objective of this report is to describe results obtained during the first year of funding that will lead to the development of an artificial neural network (ANN) fault - diagnostic system for the real - time classification of operational transients at nuclear power plants. The ultimate goal of this three-year project is to design, build, and test a prototype diagnostic adviser for use in the control room or technical support center at Duane Arnold Energy Center (DAEC); such a prototype could be integrated into the plant process computer or safety - parameter display system. The adviser could then warn and inform plant operators and engineers of plant component failures in a timely manner. This report describes the work accomplished in the first of three scheduled years for the project. Included herein is a summary of the first year's results as, well as individual descriptions of each of the major topics undertaken by the researchers. Also included are reprints of the articles written under this funding as well as those that were published during the funded period

  13. Governmentalizing Gramsci: Topologies of power and passive revolution in Cambodia’s garment production network

    OpenAIRE

    Arnold, D.; Hess, M.

    2017-01-01

    This article takes a fresh look at the multiple power relations between state, capital and labor in global production networks. Moving beyond debates about public vs. private governance, it brings together Antonio Gramsci’s concepts of hegemony and the integral state with Michel Foucault’s concepts of governmentality and the “dipositive” in order to analyze the power topologies that permeate global production networks. Using the Cambodian garment production network as example, we scrutinize t...

  14. Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis

    Science.gov (United States)

    Liang, B.; Iwnicki, S. D.; Zhao, Y.

    2013-08-01

    The power spectrum is defined as the square of the magnitude of the Fourier transform (FT) of a signal. The advantage of FT analysis is that it allows the decomposition of a signal into individual periodic frequency components and establishes the relative intensity of each component. It is the most commonly used signal processing technique today. If the same principle is applied for the detection of periodicity components in a Fourier spectrum, the process is called the cepstrum analysis. Cepstrum analysis is a very useful tool for detection families of harmonics with uniform spacing or the families of sidebands commonly found in gearbox, bearing and engine vibration fault spectra. Higher order spectra (HOS) (also known as polyspectra) consist of higher order moment of spectra which are able to detect non-linear interactions between frequency components. For HOS, the most commonly used is the bispectrum. The bispectrum is the third-order frequency domain measure, which contains information that standard power spectral analysis techniques cannot provide. It is well known that neural networks can represent complex non-linear relationships, and therefore they are extremely useful for fault identification and classification. This paper presents an application of power spectrum, cepstrum, bispectrum and neural network for fault pattern extraction of induction motors. The potential for using the power spectrum, cepstrum, bispectrum and neural network as a means for differentiating between healthy and faulty induction motor operation is examined. A series of experiments is done and the advantages and disadvantages between them are discussed. It has been found that a combination of power spectrum, cepstrum and bispectrum plus neural network analyses could be a very useful tool for condition monitoring and fault diagnosis of induction motors.

  15. Power factor improvement in three-phase networks with unbalanced inductive loads using the Roederstein ESTAmat RPR power factor controller

    Science.gov (United States)

    Diniş, C. M.; Cunţan, C. D.; Rob, R. O. S.; Popa, G. N.

    2018-01-01

    The paper presents the analysis of a power factor with capacitors banks, without series coils, used for improving power factor for a three-phase and single-phase inductive loads. In the experimental measurements, to improve the power factor, the Roederstein ESTAmat RPR power factor controller can command up to twelve capacitors banks, while experimenting using only six capacitors banks. Six delta capacitors banks with approximately equal reactive powers were used for experimentation. The experimental measurements were carried out with a three-phase power quality analyser which worked in three cases: a case without a controller with all capacitors banks permanently parallel connected with network, and two other cases with power factor controller (one with setting power factor at 0.92 and the other one at 1). When performing experiments with the power factor controller, a current transformer was used to measure the current on one phase (at a more charged or less loaded phase).

  16. Low power radio communication platform for wireless sensor network

    NARCIS (Netherlands)

    Dutta, R.; Bentum, Marinus Jan; van der Zee, Ronan A.R.; Kokkeler, Andre B.J.

    2009-01-01

    Wireless sensor networks are predicted to be the most versatile, popular and useful technology in the near future. A large number of applications are targeted which will hugely benefit from a network of tiny computers with few sensors, radio communication platform, intelligent networking and

  17. A lithology identification method for continental shale oil reservoir based on BP neural network

    Science.gov (United States)

    Han, Luo; Fuqiang, Lai; Zheng, Dong; Weixu, Xia

    2018-06-01

    The Dongying Depression and Jiyang Depression of the Bohai Bay Basin consist of continental sedimentary facies with a variable sedimentary environment and the shale layer system has a variety of lithologies and strong heterogeneity. It is difficult to accurately identify the lithologies with traditional lithology identification methods. The back propagation (BP) neural network was used to predict the lithology of continental shale oil reservoirs. Based on the rock slice identification, x-ray diffraction bulk rock mineral analysis, scanning electron microscope analysis, and the data of well logging and logging, the lithology was divided with carbonate, clay and felsic as end-member minerals. According to the core-electrical relationship, the frequency histogram was then used to calculate the logging response range of each lithology. The lithology-sensitive curves selected from 23 logging curves (GR, AC, CNL, DEN, etc) were chosen as the input variables. Finally, the BP neural network training model was established to predict the lithology. The lithology in the study area can be divided into four types: mudstone, lime mudstone, lime oil-mudstone, and lime argillaceous oil-shale. The logging responses of lithology were complicated and characterized by the low values of four indicators and medium values of two indicators. By comparing the number of hidden nodes and the number of training times, we found that the number of 15 hidden nodes and 1000 times of training yielded the best training results. The optimal neural network training model was established based on the above results. The lithology prediction results of BP neural network of well XX-1 showed that the accuracy rate was over 80%, indicating that the method was suitable for lithology identification of continental shale stratigraphy. The study provided the basis for the reservoir quality and oily evaluation of continental shale reservoirs and was of great significance to shale oil and gas exploration.

  18. Investigation of Hybrid Pseudo Bipolar HVDC Performances Supply Power to Passive AC Network

    Directory of Open Access Journals (Sweden)

    Kuan Li

    2014-07-01

    Full Text Available The traditional HVDC plays an important role in the development of power grid. But the traditional HVDC cannot supply power either to entirely passive AC network or to weak AC system. In fact, an entirely passive AC network can be effectively powered through VSC-HVDC. However, the cost of investment in VSC-HVDC is amazingly high due to the limitation of power electronics technology. Based on CSC and VSC, this paper proposes a method to build Hybrid HVDC, which makes the power supply to the passive AC network come true and, at the same time, lowers the investment cost. The effect of topology, steady mathematical model, startup characteristic, steady and transient characteristics in Hybrid HVDC system are systematically studied in this paper. The simulation result shows that Hybrid HVDC can supply power to the passive AC network with high stability. This study provides a theoretical basis for the further development of HVDC.

  19. Vital area identification software VIP for the physical protection of nuclear power plants

    International Nuclear Information System (INIS)

    Jung, Woo Sik; Park, Chang Kue; Yang, Joon Eon

    2004-01-01

    There are two major factors to be considered for the physical protection of nuclear power plants. They are a design basis threat (DBT) and the vital area identification (VAI). The DBT has been considered as 'the maximum credible threat.' The vital area is defined as 'an area inside a protected area containing equipment, systems or devices, or nuclear materials, the sabotage of which could directly or indirectly lead to unacceptable radiological consequences.' For the VAI of nuclear power plants, a software VIP (Vital area Identification Package based on PSA method) is being developed. The VIP is based on the current probabilistic safety assessment (PSA) techniques. The PSA method, including internal as well as external events, is known as the most complete and consistent method for identifying various accident sequences that might result in a core melt and radioactive material release to the environment. Thus, the VIP employs a fault tree analysis method in the PSA and utilizes the PSA results

  20. Identification of Successive ``Unobservable'' Cyber Data Attacks in Power Systems Through Matrix Decomposition

    Science.gov (United States)

    Gao, Pengzhi; Wang, Meng; Chow, Joe H.; Ghiocel, Scott G.; Fardanesh, Bruce; Stefopoulos, George; Razanousky, Michael P.

    2016-11-01

    This paper presents a new framework of identifying a series of cyber data attacks on power system synchrophasor measurements. We focus on detecting "unobservable" cyber data attacks that cannot be detected by any existing method that purely relies on measurements received at one time instant. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we formulate the identification problem of successive unobservable cyber attacks as a matrix decomposition problem of a low-rank matrix plus a transformed column-sparse matrix. We propose a convex-optimization-based method and provide its theoretical guarantee in the data identification. Numerical experiments on actual PMU data from the Central New York power system and synthetic data are conducted to verify the effectiveness of the proposed method.

  1. Coherency Identification of Generators Using a PAM Algorithm for Dynamic Reduction of Power Systems

    Directory of Open Access Journals (Sweden)

    Seung-Il Moon

    2012-11-01

    Full Text Available This paper presents a new coherency identification method for dynamic reduction of a power system. To achieve dynamic reduction, coherency-based equivalence techniques divide generators into groups according to coherency, and then aggregate them. In order to minimize the changes in the dynamic response of the reduced equivalent system, coherency identification of the generators should be clearly defined. The objective of the proposed coherency identification method is to determine the optimal coherent groups of generators with respect to the dynamic response, using the Partitioning Around Medoids (PAM algorithm. For this purpose, the coherency between generators is first evaluated from the dynamic simulation time response, and in the proposed method this result is then used to define a dissimilarity index. Based on the PAM algorithm, the coherent generator groups are then determined so that the sum of the index in each group is minimized. This approach ensures that the dynamic characteristics of the original system are preserved, by providing the optimized coherency identification. To validate the effectiveness of the technique, simulated cases with an IEEE 39-bus test system are evaluated using PSS/E. The proposed method is compared with an existing coherency identification method, which uses the K-means algorithm, and is found to provide a better estimate of the original system. 

  2. Joint Power Allocation and Beamforming in Amplify-and-Forward Relay Networks under Per-Node Power Constraint

    Directory of Open Access Journals (Sweden)

    Farzin Azami

    2017-01-01

    Full Text Available Two-way relay networks (TWRN have been intensively investigated over the past decade due to their ability to enhance the performance assessment of networks in terms of cellular coverage and spectral efficiency. Yet, power control in such systems is a nontrivial issue, particularly in multirelay networks where relays are deployed to ensure a required Quality of Service (QoS. In this paper, we envision to address this critical issue by minimizing the sum-power with respect to per-node power consumption and acceptable users’ rates. To tackle this, we employ a variable transformation to turn the fractional quadratically constrained quadratic problem (QCQP into semidefinite programming (SDP. This algorithm is also extended to a distributed format. Simulation results of deploying 10 relay stations reveal that the total power consumption will decrease to approximately 8 dBW for 6 bps/Hz sum-rate.

  3. Identification of seismically risk-sensitive systems and components in nuclear power plants: feasibility study

    International Nuclear Information System (INIS)

    Azarm, M.; Boccio, J.; Farahzad, P.

    1983-06-01

    An approach for the identification of risk-sensitive components in a nuclear power plant during and after a seismic event is described. Application of the methodology to two hypothetical power plants - a Boiling Water Reactor and a Pressurized Water Reactor - are presented and the results are given in tabular and graphical form. Conclusions drawn and lessons learned through the course of this study, based on the relative importance of various accident scenarios and sensitivity analyses, are discussed. In addition, the areas that may need further investigation are identified

  4. Identification of tipping elements of the Indian Summer Monsoon using climate network approach

    Science.gov (United States)

    Stolbova, Veronika; Surovyatkina, Elena; Kurths, Jurgen

    2015-04-01

    Spatial and temporal variability of the rainfall is a vital question for more than one billion of people inhabiting the Indian subcontinent. Indian Summer Monsoon (ISM) rainfall is crucial for India's economy, social welfare, and environment and large efforts are being put into predicting the Indian Summer Monsoon. For predictability of the ISM, it is crucial to identify tipping elements - regions over the Indian subcontinent which play a key role in the spatial organization of the Indian monsoon system. Here, we use climate network approach for identification of such tipping elements of the ISM. First, we build climate networks of the extreme rainfall, surface air temperature and pressure over the Indian subcontinent for pre-monsoon, monsoon and post-monsoon seasons. We construct network of extreme rainfall event using observational satellite data from 1998 to 2012 from the Tropical Rainfall Measuring Mission (TRMM 3B42V7) and reanalysis gridded daily rainfall data for a time period of 57 years (1951-2007) (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE). For the network of surface air temperature and pressure fields, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). Second, we filter out data by coarse-graining the network through network measures, and identify tipping regions of the ISM. Finally, we compare obtained results of the network analysis with surface wind fields and show that occurrence of the tipping elements is mostly caused by monsoonal wind circulation, migration of the Intertropical Convergence Zone (ITCZ) and Westerlies. We conclude that climate network approach enables to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to identify tipping regions of the ISM. Obtained tipping elements deserve a

  5. The impact of measurement errors in the identification of regulatory networks

    Directory of Open Access Journals (Sweden)

    Sato João R

    2009-12-01

    Full Text Available Abstract Background There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent and non-time series (independent data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models and dependent (autoregressive models data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error. The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

  6. Identification of hadronic tau decays at the ATLAS detector using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Duschinger, Dirk; Hanisch, Stefanie; Mader, Wolfgang; Madysa, Nico; Straessner, Arno [Institut fuer Kern- und Teilchenphysik, TU Dresden (Germany)

    2016-07-01

    One of the primary goals of the ATLAS experiment at the LHC is the search for physics beyond the Standard Model. The efficient identification of hadronically decaying tau leptons is crucial for this as they comprise the final states of several decay channels sensitive to new physics. (e. g. Higgs boson decays H → τ{sub had} τ{sub had}) The identification algorithm currently applied at ATLAS utilizes multi-variate methods and reconstructed particle properties to discriminate against QCD jets, which constitute an important background. This talk presents a new neural-network-based approach to hadronic tau decay identification and investigates its dependence on hyperparameters such as the network topology or number of training cycles. Ensembling is presented as a technique to improve classifier performance and robustness against overtraining. The resulting classifier is compared to the current approach based on Boosted Decision Trees. The study is based on 2012 data taken at the ATLAS detector at a center-of-mass energy of √(s)=8 TeV.

  7. Efficient Approach for Harmonic Resonance Identification of Large Wind Power Plants

    DEFF Research Database (Denmark)

    Ebrahimzadeh, Esmaeil; Blaabjerg, Frede; Wang, Xiongfei

    2016-01-01

    Unlike conventional power systems where the resonance frequencies are mainly determined by the passive components parameters, large Wind Power Plants (WPPs) may introduce additional harmonic resonances because of the interactions of the wideband control systems of power converters with each other...... and with passive components. This paper presents an efficient approach for identification of harmonic resonances in large WPPs containing power electronic converters, cable, transformer, capacitor banks, shunt reactors, etc. The proposed approach introduces a large WPP as a Multi-Input Multi-Output (MIMO) control...... system by considering the linearized models of the inner control loops of grid-side converters. Therefore, the resonance frequencies of the WPP resulting from passive components and the control loop interactions are identified based on the determinant of the transfer function matrix of the introduced...

  8. Weekly changes of power supplier - consequences for the network owner

    International Nuclear Information System (INIS)

    Graabak, Ingeborg

    1997-01-01

    In Norway, it is expected that owners of electric distribution networks will be required to make it possible for the customers to change supplier each week. This report examines what consequences such a requirement will have for the network owners. An inquiry among nine network owners shows that at present changing supplier implies a great deal of manual work on the part of the network owner since many do not have computer based tools adapted to handle the situation. If the number of weekly changes of suppliers does not increase beyond a few percent of the network owner's total number of customers over 1 to 3 years, the network owner can cope with the situation. However, if for some reason the increase becomes larger, many network owners will have great problems because they lack the necessary computer tools. 1 table

  9. Determination of power peak factor using control rods, ex-core detectors and neural networks

    International Nuclear Information System (INIS)

    Souza, Rose Mary Gomes do Prado

    2005-01-01

    This work presents a methodology based on the artificial neural network technique to predict in real time the power peak factor in a form that can be implemented in reactor protection systems. The neural network inputs were those available in the reactor protection systems, namely, the axial and quadrant power differences obtained from measured ex-core detector signals, and the position of control rods. The response of ex core detector signals was measured in experiments especially performed in the IPEN/MB-01 zero-power reactor. Several reactor states with different power density distribution were obtained by positioning the control rods in different configurations. The power distribution and its peak factor were calculated for each of these reactor states using the Citation code. The obtained results show that the power peak factor correlates well with the control rod position and the quadrant power difference, and with a lesser degree with the axial power differences. The data presented an inherent organisation and could be classified into different classes of power peak factor behaviour as a function of position of control rods, axial power difference and quadrant power difference. The RBF networks were able to identify classes and interpolate the power peak factor values. The relative error for the power peak factor estimation ranged from 0.19 % to 0.67 %, less than the one that was obtained performing a power density distribution map with in-core detectors. It was observed that the positions of control rods bear the detailed and localised information about the power density distribution, and that the axial and the quadrant power difference describe its global variations in the axial and radial directions. The results showed that the RBF and MLP networks produced similar results, and that a neural network correlation can be implemented in power reactor protection systems. (author)

  10. Identification of complex systems by artificial neural networks. Applications to mechanical frictions

    International Nuclear Information System (INIS)

    Dominguez, Manuel

    1998-01-01

    In the frame of complex systems modelization, we describe in this report the contribution of neural networks to mechanical friction modelization. This thesis is divided in three parts, each one corresponding to every stage of the realized work. The first part takes stock of the properties of neural networks by replacing them in the statistic frame of learning theory (particularly: non-linear and non-parametric regression models) and by showing the existing links with other more 'classic' techniques from automatics. We show then how identification models can be integrated in the neural networks description as a larger nonlinear model class. A methodology of neural networks use have been developed. We focused on validation techniques using correlation functions for non-linear systems, and on the use of regularization methods. The second part deals with the problematic of friction in mechanical systems. Particularly, we present the main current identified physical phenomena, which are integrated in advanced friction modelization. Characterization of these phenomena allows us to state a priori knowledge to be used in the identification stage. We expose some of the most well-known friction models: Dahl's model, Reset Integrator and Canuda's dynamical model, which are then used in simulation studies. The last part links the former one by illustrating a real-world application: an electric jack from SFIM-Industries, used in the Very Large Telescope (VLT) control scheme. This part begins with physical system presentation. The results are compared with more 'classic' methods. We finish using neural networks compensation scheme in closed-loop control. (author) [fr

  11. A Kalman-filter based approach to identification of time-varying gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Jie Xiong

    Full Text Available MOTIVATION: Conventional identification methods for gene regulatory networks (GRNs have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs. RESULTS: It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is proposed to recover the structure of time-varying GRNs. In the first step, the change points are detected by a Kalman-filter based method. The observed time series are divided into several segments using these detection results; and each time series segment belonging to two successive demarcating change points is associated with an individual static regulatory network. In the second step, conditional network structure identification methods are used to reconstruct the topology for each time interval. This two-step strategy efficiently decouples the change point detection problem and the topology inference problem. Simulation results show that the proposed strategy can detect the change points precisely and recover each individual topology structure effectively. Moreover, computation results with the developmental data of Drosophila Melanogaster show that the proposed change point detection procedure is also able to work effectively in real world applications and the change point estimation accuracy exceeds other existing approaches, which means the suggested strategy may also be helpful in solving actual GRN reconstruction problem.

  12. A neural network device for on-line particle identification in cosmic ray experiments

    International Nuclear Information System (INIS)

    Scrimaglio, R.; Finetti, N.; D'Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G.C.

    2004-01-01

    On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification

  13. Performance of wavelet analysis and neural networks for pathological voices identification

    Science.gov (United States)

    Salhi, Lotfi; Talbi, Mourad; Abid, Sabeur; Cherif, Adnane

    2011-09-01

    Within the medical environment, diverse techniques exist to assess the state of the voice of the patient. The inspection technique is inconvenient for a number of reasons, such as its high cost, the duration of the inspection, and above all, the fact that it is an invasive technique. This study focuses on a robust, rapid and accurate system for automatic identification of pathological voices. This system employs non-invasive, non-expensive and fully automated method based on hybrid approach: wavelet transform analysis and neural network classifier. First, we present the results obtained in our previous study while using classic feature parameters. These results allow visual identification of pathological voices. Second, quantified parameters drifting from the wavelet analysis are proposed to characterise the speech sample. On the other hand, a system of multilayer neural networks (MNNs) has been developed which carries out the automatic detection of pathological voices. The developed method was evaluated using voice database composed of recorded voice samples (continuous speech) from normophonic or dysphonic speakers. The dysphonic speakers were patients of a National Hospital 'RABTA' of Tunis Tunisia and a University Hospital in Brussels, Belgium. Experimental results indicate a success rate ranging between 75% and 98.61% for discrimination of normal and pathological voices using the proposed parameters and neural network classifier. We also compared the average classification rate based on the MNN, Gaussian mixture model and support vector machines.

  14. RBF Neural Network Approach for Identification and Control of DC Motors

    Directory of Open Access Journals (Sweden)

    EA Feilat

    2012-12-01

    Full Text Available In this paper, a neural network approach for the identification and control of a separately excited direct (DC motor (SEDCM driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN. The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

  15. Identification of conserved drought stress responsive gene-network across tissues and developmental stages in rice.

    Science.gov (United States)

    Smita, Shuchi; Katiyar, Amit; Pandey, Dev Mani; Chinnusamy, Viswanathan; Archak, Sunil; Bansal, Kailash Chander

    2013-01-01

    Identification of genes that are coexpressed across various tissues and environmental stresses is biologically interesting, since they may play coordinated role in similar biological processes. Genes with correlated expression patterns can be best identified by using coexpression network analysis of transcriptome data. In the present study, we analyzed the temporal-spatial coordination of gene expression in root, leaf and panicle of rice under drought stress and constructed network using WGCNA and Cytoscape. Total of 2199 differentially expressed genes (DEGs) were identified in at least three or more tissues, wherein 88 genes have coordinated expression profile among all the six tissues under drought stress. These 88 highly coordinated genes were further subjected to module identification in the coexpression network. Based on chief topological properties we identified 18 hub genes such as ABC transporter, ATP-binding protein, dehydrin, protein phosphatase 2C, LTPL153 - Protease inhibitor, phosphatidylethanolaminebinding protein, lactose permease-related, NADP-dependent malic enzyme, etc. Motif enrichment analysis showed the presence of ABRE cis-elements in the promoters of > 62% of the coordinately expressed genes. Our results suggest that drought stress mediated upregulated gene expression was coordinated through an ABA-dependent signaling pathway across tissues, at least for the subset of genes identified in this study, while down regulation appears to be regulated by tissue specific pathways in rice.

  16. Power variation and frequency regulation. Adaptation of PWR plant possibilities to the network needs

    International Nuclear Information System (INIS)

    Baboulin, J.P.; Burger, M.

    1980-01-01

    When the PWR are an important part of the power installed on a network, and that will be the case of the EDF network in the coming years, the participation of those plants to the power regulating becomes a necessity for the operating staff. This load regulating includes: daily variations of high amplitude; a permanent frequency - power regulating. The first part of the communication shows the network exploitation principles, and the resulting power variations concerning the existing nuclear power plants. Such transients are leading to stresses on fuel. The second part of the communication reports about the test program engaged by EDF in collaboration with the CEA and FRAMATOME, in order to study the fuel behaviour in real power conditions and power cycles, and that, just to the operational burn up of this fuel. (author)

  17. Identification of driving network of cellular differentiation from single sample time course gene expression data

    Science.gov (United States)

    Chen, Ye; Wolanyk, Nathaniel; Ilker, Tunc; Gao, Shouguo; Wang, Xujing

    Methods developed based on bifurcation theory have demonstrated their potential in driving network identification for complex human diseases, including the work by Chen, et al. Recently bifurcation theory has been successfully applied to model cellular differentiation. However, there one often faces a technical challenge in driving network prediction: time course cellular differentiation study often only contains one sample at each time point, while driving network prediction typically require multiple samples at each time point to infer the variation and interaction structures of candidate genes for the driving network. In this study, we investigate several methods to identify both the critical time point and the driving network through examination of how each time point affects the autocorrelation and phase locking. We apply these methods to a high-throughput sequencing (RNA-Seq) dataset of 42 subsets of thymocytes and mature peripheral T cells at multiple time points during their differentiation (GSE48138 from GEO). We compare the predicted driving genes with known transcription regulators of cellular differentiation. We will discuss the advantages and limitations of our proposed methods, as well as potential further improvements of our methods.

  18. Identification and network-enabled characterization of auxin response factor genes in Medicago truncatula

    Directory of Open Access Journals (Sweden)

    David J. Burks

    2016-12-01

    Full Text Available The Auxin Response Factor (ARF family of transcription factors is an important regulator of environmental response and symbiotic nodulation in the legume Medicago truncatula. While previous studies have identified members of this family, a recent spurt in gene expression data coupled with genome update and reannotation calls for a reassessment of the prevalence of ARF genes and their interaction networks in M. truncatula. We performed a comprehensive analysis of the M. truncatula genome and transcriptome that entailed search for novel ARF genes and the co-expression networks. Our investigation revealed 8 novel M. truncatula ARF (MtARF genes, of the total 22 identified, and uncovered novel gene co-expression networks as well. Furthermore, the topological clustering and single enrichment analysis of several network models revealed the roles of individual members of the MtARF family in nitrogen regulation, nodule initiation, and post-embryonic development through a specialized protein packaging and secretory pathway. In summary, this study not just shines new light on an important gene family, but also provides a guideline for identification of new members of gene families and their functional characterization through network analyses.

  19. Power consumption analysis of constant bit rate data transmission over 3G mobile wireless networks

    DEFF Research Database (Denmark)

    Wang, Le; Ukhanova, Ann; Belyaev, Evgeny

    2011-01-01

    This paper presents the analysis of the power consumption of data transmission with constant bit rate over 3G mobile wireless networks. Our work includes the description of the transition state machine in 3G networks, followed by the detailed energy consumption analysis and measurement results...... of the radio link power consumption. Based on these description and analysis, we propose power consumption model. The power model was evaluated on the smartphone Nokia N900, which follows a 3GPP Release 5 and 6 supporting HSDPA/HSPA data bearers. Further we propose method of parameters selection for 3GPP...... transition state machine that allows to decrease power consumption on the mobile device....

  20. Variable-Width Datapath for On-Chip Network Static Power Reduction

    Energy Technology Data Exchange (ETDEWEB)

    Michelogiannakis, George; Shalf, John

    2013-11-13

    With the tight power budgets in modern large-scale chips and the unpredictability of application traffic, on-chip network designers are faced with the dilemma of designing for worst- case bandwidth demands and incurring high static power overheads, or designing for an average traffic pattern and risk degrading performance. This paper proposes adaptive bandwidth networks (ABNs) which divide channels and switches into lanes such that the network provides just the bandwidth necessary in each hop. ABNs also activate input virtual channels (VCs) individually and take advantage of drowsy SRAM cells to eliminate false VC activations. In addition, ABNs readily apply to silicon defect tolerance with just the extra cost for detecting faults. For application traffic, ABNs reduce total power consumption by an average of 45percent with comparable performance compared to single-lane power-gated networks, and 33percent compared to multi-network designs.

  1. Low loss power splitter for antenna beam forming networks using probes in a waveguide

    OpenAIRE

    Dich, Mikael; Mortensen, Mette Dahl

    1994-01-01

    The design of a low loss one-to-four power splitter suitable for beam forming networks in antenna arrays is presented. The power splitter is constructed of a shorted waveguide in which five coaxial probes are inserted. Methods for the design of the power splitter are presented together with an experimental verification

  2. Low loss power splitter for antenna beam forming networks using probes in a waveguide

    DEFF Research Database (Denmark)

    Dich, Mikael; Mortensen, Mette Dahl

    1994-01-01

    The design of a low loss one-to-four power splitter suitable for beam forming networks in antenna arrays is presented. The power splitter is constructed of a shorted waveguide in which five coaxial probes are inserted. Methods for the design of the power splitter are presented together...

  3. Resting State EEG-based biometrics for individual identification using convolutional neural networks.

    Science.gov (United States)

    Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

    2015-08-01

    Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.

  4. NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

    Directory of Open Access Journals (Sweden)

    Min Peng

    2016-10-01

    Full Text Available Near-infrared (NIR face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN for NIR face recognition (specifically face identification in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.

  5. Intrusion detection techniques for plant-wide network in a nuclear power plant

    International Nuclear Information System (INIS)

    Rajasekhar, P.; Shrikhande, S.V.; Biswas, B.B.; Patil, R.K.

    2012-01-01

    Nuclear power plants have a lot of critical data to be sent to the operator workstations. A plant wide integrated communication network, with high throughput, determinism and redundancy, is required between the workstations and the field. Switched Ethernet network is a promising prospect for such an integrated communication network. But for such an integrated system, intrusion is a major issue. Hence the network should have an intrusion detection system to make the network data secure and enhance the network availability. Intrusion detection is the process of monitoring the events occurring in a network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of network security policies, acceptable user policies, or standard security practices. This paper states the various intrusion detection techniques and approaches which are applicable for analysis of a plant wide network. (author)

  6. Army Social Media: Harnessing the Power of Networked Communications

    Science.gov (United States)

    2011-09-01

    Social Networking : – Facebook – MySpace – Friendster 9/1/2011 Content sharing: -You Tube -Flickr -Vimeo -Photobucket Collaborating/ knowledge...Americans use social media tools and Web sites monthly Social networking is now the #1 activity on the web • Twitter: 54 Million users • Facebook ...anyone you don’t know on Facebook or social networking platforms -Don’t post deployment information, when you’re going on vacation or when

  7. Output power distributions of mobile radio base stations based on network measurements

    International Nuclear Information System (INIS)

    Colombi, D; Thors, B; Persson, T; Törnevik, C; Wirén, N; Larsson, L-E

    2013-01-01

    In this work output power distributions of mobile radio base stations have been analyzed for 2G and 3G telecommunication systems. The approach is based on measurements in selected networks using performance surveillance tools part of the network Operational Support System (OSS). For the 3G network considered, direct measurements of output power levels were possible, while for the 2G networks, output power levels were estimated from measurements of traffic volumes. Both voice and data services were included in the investigation. Measurements were conducted for large geographical areas, to ensure good overall statistics, as well as for smaller areas to investigate the impact of different environments. For high traffic hours, the 90th percentile of the averaged output power was found to be below 65% and 45% of the available output power for the 2G and 3G systems, respectively.

  8. Output power distributions of mobile radio base stations based on network measurements

    Science.gov (United States)

    Colombi, D.; Thors, B.; Persson, T.; Wirén, N.; Larsson, L.-E.; Törnevik, C.

    2013-04-01

    In this work output power distributions of mobile radio base stations have been analyzed for 2G and 3G telecommunication systems. The approach is based on measurements in selected networks using performance surveillance tools part of the network Operational Support System (OSS). For the 3G network considered, direct measurements of output power levels were possible, while for the 2G networks, output power levels were estimated from measurements of traffic volumes. Both voice and data services were included in the investigation. Measurements were conducted for large geographical areas, to ensure good overall statistics, as well as for smaller areas to investigate the impact of different environments. For high traffic hours, the 90th percentile of the averaged output power was found to be below 65% and 45% of the available output power for the 2G and 3G systems, respectively.

  9. Identification of human disease genes from interactome network using graphlet interaction.

    Directory of Open Access Journals (Sweden)

    Xiao-Dong Wang

    Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

  10. Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

    Science.gov (United States)

    Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

    2014-01-01

    Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

  11. DISTRIBUTION NETWORK RECONFIGURATION FOR POWER LOSS MINIMIZATION AND VOLTAGE PROFILE ENHANCEMENT USING ANT LION ALGORITHM

    Directory of Open Access Journals (Sweden)

    Maryam Shokouhi

    2017-06-01

    Full Text Available Distribution networks are designed as a ring and operated as a radial form. Therefore, the reconfiguration is a simple and cost-effective way to use existing facilities without the need for any new equipment in distribution networks to achieve various objectives such as: power loss reduction, feeder overload reduction, load balancing, voltage profile improvement, reducing the number of switching considering constraints that ultimately result in the power loss reduction. In this paper, a new method based on the Ant Lion algorithm (a modern meta-heuristic algorithm is provided for the reconfiguration of distribution networks. Considering the extension of the distribution networks and complexity of their communications networks, and the various parameters, using smart techniques is inevitable. The proposed approach is tested on the IEEE 33 & 69-bus radial standard distribution networks. The Evaluation of results in MATLAB software shows the effectiveness of the Ant Lion algorithm in the distribution network reconfiguration.

  12. Generalised power graph compression reveals dominant relationship patterns in complex networks.

    Science.gov (United States)

    Ahnert, Sebastian E

    2014-03-25

    We introduce a framework for the discovery of dominant relationship patterns in complex networks, by compressing the networks into power graphs with overlapping power nodes. When paired with enrichment analysis of node classification terms, the most compressible sets of edges provide a highly informative sketch of the dominant relationship patterns that define the network. In addition, this procedure also gives rise to a novel, link-based definition of overlapping node communities in which nodes are defined by their relationships with sets of other nodes, rather than through connections within the community. We show that this completely general approach can be applied to undirected, directed, and bipartite networks, yielding valuable insights into the large-scale structure of real-world networks, including social networks and food webs. Our approach therefore provides a novel way in which network architecture can be studied, defined and classified.

  13. A Distributed Routing Scheme for Energy Management in Solar Powered Sensor Networks

    KAUST Repository

    Dehwah, Ahmad H.

    2017-10-11

    Energy management is critical for solar-powered sensor networks. In this article, we consider data routing policies to optimize the energy in solar powered networks. Motivated by multipurpose sensor networks, the objective is to find the best network policy that maximizes the minimal energy among nodes in a sensor network, over a finite time horizon, given uncertain energy input forecasts. First, we derive the optimal policy in certain special cases using forward dynamic programming. We then introduce a greedy policy that is distributed and exhibits significantly lower complexity. When computationally feasible, we compare the performance of the optimal policy with the greedy policy. We also demonstrate the performance and computational complexity of the greedy policy over randomly simulated networks, and show that it yields results that are almost identical to the optimal policy, for greatly reduced worst-case computational costs and memory requirements. Finally, we demonstrate the implementation of the greedy policy on an experimental sensor network.

  14. Output power distributions of terminals in a 3G mobile communication network.

    Science.gov (United States)

    Persson, Tomas; Törnevik, Christer; Larsson, Lars-Eric; Lovén, Jan

    2012-05-01

    The objective of this study was to examine the distribution of the output power of mobile phones and other terminals connected to a 3G network in Sweden. It is well known that 3G terminals can operate with very low output power, particularly for voice calls. Measurements of terminal output power were conducted in the Swedish TeliaSonera 3G network in November 2008 by recording network statistics. In the analysis, discrimination was made between rural, suburban, urban, and dedicated indoor networks. In addition, information about terminal output power was possible to collect separately for voice and data traffic. Information from six different Radio Network Controllers (RNCs) was collected during at least 1 week. In total, more than 800000 h of voice calls were collected and in addition to that a substantial amount of data traffic. The average terminal output power for 3G voice calls was below 1 mW for any environment including rural, urban, and dedicated indoor networks. This is <1% of the maximum available output power. For data applications the average output power was about 6-8 dB higher than for voice calls. For rural areas the output power was about 2 dB higher, on average, than in urban areas. Copyright © 2011 Wiley Periodicals, Inc.

  15. Research on the effects of wind power grid to the distribution network of Henan province

    Science.gov (United States)

    Liu, Yunfeng; Zhang, Jian

    2018-04-01

    With the draining of traditional energy, all parts of nation implement policies to develop new energy to generate electricity under the favorable national policy. The wind has no pollution, Renewable and other advantages. It has become the most popular energy among the new energy power generation. The development of wind power in Henan province started relatively late, but the speed of the development is fast. The wind power of Henan province has broad development prospects. Wind power has the characteristics of volatility and randomness. The wind power access to power grids will cause much influence on the power stability and the power quality of distribution network, and some areas have appeared abandon the wind phenomenon. So the study of wind power access to power grids and find out improvement measures is very urgent. Energy storage has the properties of the space transfer energy can stabilize the operation of power grid and improve the power quality.

  16. Open-WiSe: a solar powered wireless sensor network platform.

    Science.gov (United States)

    González, Apolinar; Aquino, Raúl; Mata, Walter; Ochoa, Alberto; Saldaña, Pedro; Edwards, Arthur

    2012-01-01

    Because battery-powered nodes are required in wireless sensor networks and energy consumption represents an important design consideration, alternate energy sources are needed to provide more effective and optimal function. The main goal of this work is to present an energy harvesting wireless sensor network platform, the Open Wireless Sensor node (WiSe). The design and implementation of the solar powered wireless platform is described including the hardware architecture, firmware, and a POSIX Real-Time Kernel. A sleep and wake up strategy was implemented to prolong the lifetime of the wireless sensor network. This platform was developed as a tool for researchers investigating Wireless sensor network or system integrators.

  17. The application of PSA techniques to the vital area identification of nuclear power plants

    International Nuclear Information System (INIS)

    Ha, Jae Joo; Jung, Woo Sik; Park, Chang Kue

    2005-01-01

    This paper presents a Vital Area Identification (VAI) method based on the current Fault Tree Analysis (FTA) and Probabilistic Safety Assessment (PSA) techniques for the physical protection of nuclear power plants. A structured framework of a Top Event Prevention set Analysis (TEPA) application to the VAI of nuclear power plants is also delineated. One of the important processes for physical protection in a nuclear power plant is VIA that is a process for identifying areas containing nuclear materials, Structures, Systems or Components (SSCs) to be protected from sabotage, which could directly or indirectly lead to core damage and unacceptable radiological consequences. A software VIP (Vital area Identification Package based on the PSA method) is being developed by KAERI for the VAI of nuclear power plants. Furthermore, the KAERI fault tree solver FTREX (Fault Tree Reliability Evaluation eXpert) is specialized for the VIP to generate the candidates of the vital areas. FTREX can generate numerous MCSs for a huge fault tree with the lowest truncation limit and all possible prevention sets

  18. Evaluating the statistical power of DNA-based identification, exemplified by 'The missing grandchildren of Argentina'.

    Science.gov (United States)

    Kling, Daniel; Egeland, Thore; Piñero, Mariana Herrera; Vigeland, Magnus Dehli

    2017-11-01

    Methods and implementations of DNA-based identification are well established in several forensic contexts. However, assessing the statistical power of these methods has been largely overlooked, except in the simplest cases. In this paper we outline general methods for such power evaluation, and apply them to a large set of family reunification cases, where the objective is to decide whether a person of interest (POI) is identical to the missing person (MP) in a family, based on the DNA profile of the POI and available family members. As such, this application closely resembles database searching and disaster victim identification (DVI). If parents or children of the MP are available, they will typically provide sufficient statistical evidence to settle the case. However, if one must resort to more distant relatives, it is not a priori obvious that a reliable conclusion is likely to be reached. In these cases power evaluation can be highly valuable, for instance in the recruitment of additional family members. To assess the power in an identification case, we advocate the combined use of two statistics: the Probability of Exclusion, and the Probability of Exceedance. The former is the probability that the genotypes of a random, unrelated person are incompatible with the available family data. If this is close to 1, it is likely that a conclusion will be achieved regarding general relatedness, but not necessarily the specific relationship. To evaluate the ability to recognize a true match, we use simulations to estimate exceedance probabilities, i.e. the probability that the likelihood ratio will exceed a given threshold, assuming that the POI is indeed the MP. All simulations are done conditionally on available family data. Such conditional simulations have a long history in medical linkage analysis, but to our knowledge this is the first systematic forensic genetics application. Also, for forensic markers mutations cannot be ignored and therefore current models and

  19. Identification of Jets Containing b-Hadrons with Recurrent Neural Networks at the ATLAS Experiment

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    A novel b-jet identification algorithm is constructed with a Recurrent Neural Network (RNN) at the ATLAS Experiment. This talk presents the expected performance of the RNN based b-tagging in simulated $t \\bar t$ events. The RNN based b-tagging processes properties of tracks associated to jets which are represented in sequences. In contrast to traditional impact-parameter-based b-tagging algorithms which assume the tracks of jets are independent from each other, RNN based b-tagging can exploit the spatial and kinematic correlations of tracks which are initiated from the same b-hadrons. The neural network nature of the tagging algorithm also allows the flexibility of extending input features to include more track properties than can be effectively used in traditional algorithms.

  20. ATM: The Key To Harnessing the Power of Networked Multimedia.

    Science.gov (United States)

    Gross, Rod

    1996-01-01

    ATM (Asynchronous Transfer Mode) network technology handles the real-time continuous traffic flow necessary to support desktop multimedia applications. Describes network applications already used: desktop video collaboration, distance learning, and broadcasting video delivery. Examines the architecture of ATM technology, video delivery and sound…

  1. modeling and optimization of an electric power distribution network

    African Journals Online (AJOL)

    user

    EDNEPP) was solved by a mixed binary integer programming (MBIP) formulation of the network, where the steady-state operation of the network was modelled with non-linear mathematical expressions. The non-linear terms are linearized, using ...

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

    Directory of Open Access Journals (Sweden)

    Palukuru NAGENDRA

    2010-12-01

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

  3. Discovering functional interdependence relationship in PPI networks for protein complex identification.

    Science.gov (United States)

    Lam, Winnie W M; Chan, Keith C C

    2012-04-01

    Protein molecules interact with each other in protein complexes to perform many vital functions, and different computational techniques have been developed to identify protein complexes in protein-protein interaction (PPI) networks. These techniques are developed to search for subgraphs of high connectivity in PPI networks under the assumption that the proteins in a protein complex are highly interconnected. While these techniques have been shown to be quite effective, it is also possible that the matching rate between the protein complexes they discover and those that are previously determined experimentally be relatively low and the "false-alarm" rate can be relatively high. This is especially the case when the assumption of proteins in protein complexes being more highly interconnected be relatively invalid. To increase the matching rate and reduce the false-alarm rate, we have developed a technique that can work effectively without having to make this assumption. The name of the technique called protein complex identification by discovering functional interdependence (PCIFI) searches for protein complexes in PPI networks by taking into consideration both the functional interdependence relationship between protein molecules and the network topology of the network. The PCIFI works in several steps. The first step is to construct a multiple-function protein network graph by labeling each vertex with one or more of the molecular functions it performs. The second step is to filter out protein interactions between protein pairs that are not functionally interdependent of each other in the statistical sense. The third step is to make use of an information-theoretic measure to determine the strength of the functional interdependence between all remaining interacting protein pairs. Finally, the last step is to try to form protein complexes based on the measure of the strength of functional interdependence and the connectivity between proteins. For performance evaluation

  4. Geometry of power flows and convex-relaxed power flows in distribution networks with high penetration of renewables

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei; Zhao, Haoran

    2016-01-01

    Renewable energies are increasingly integrated in electric distribution networks and will cause severe overvoltage issues. Smart grid technologies make it possible to use coordinated control to mitigate the overvoltage issues and the optimal power flow (OPF) method is proven to be efficient...... in the applications such as curtailment management and reactive power control. Nonconvex nature of the OPF makes it difficult to solve and convex relaxation is a promising method to solve the OPF very efficiently. This paper investigates the geometry of the power flows and the convex-relaxed power flows when high...

  5. An Improved Harmony Search Algorithm for Power Distribution Network Planning

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-01-01

    Full Text Available Distribution network planning because of involving many variables and constraints is a multiobjective, discrete, nonlinear, and large-scale optimization problem. Harmony search (HS algorithm is a metaheuristic algorithm inspired by the improvisation process of music players. HS algorithm has several impressive advantages, such as easy implementation, less adjustable parameters, and quick convergence. But HS algorithm still has some defects such as premature convergence and slow convergence speed. According to the defects of the standard algorithm and characteristics of distribution network planning, an improved harmony search (IHS algorithm is proposed in this paper. We set up a mathematical model of distribution network structure planning, whose optimal objective function is to get the minimum annual cost and constraint conditions are overload and radial network. IHS algorithm is applied to solve the complex optimization mathematical model. The empirical results strongly indicate that IHS algorithm can effectively provide better results for solving the distribution network planning problem compared to other optimization algorithms.

  6. A pattern recognition approach based on DTW for automatic transient identification in nuclear power plants

    International Nuclear Information System (INIS)

    Galbally, Javier; Galbally, David

    2015-01-01

    Highlights: • Novel transient identification method for NPPs. • Low-complexity. • Low training data requirements. • High accuracy. • Fully reproducible protocol carried out on a real benchmark. - Abstract: Automatic identification of transients in nuclear power plants (NPPs) allows monitoring the fatigue damage accumulated by critical components during plant operation, and is therefore of great importance for ensuring that usage factors remain within the original design bases postulated by the plant designer. Although several schemes to address this important issue have been explored in the literature, there is still no definitive solution available. In the present work, a new method for automatic transient identification is proposed, based on the Dynamic Time Warping (DTW) algorithm, largely used in other related areas such as signature or speech recognition. The novel transient identification system is evaluated on real operational data following a rigorous pattern recognition protocol. Results show the high accuracy of the proposed approach, which is combined with other interesting features such as its low complexity and its very limited requirements of training data

  7. Modelling and developing a decision-making process of hazard zone identification in ship power plants

    International Nuclear Information System (INIS)

    Podsiadlo, Antoni; Tarelko, Wieslaw

    2006-01-01

    The most dangerous places in ships are their power plants. Particularly, they are very unsafe for operators carried out various necessary operation and maintenance activities. For this reason, ship machinery should be designed to ensure the maximum safety for its operators. It is a very difficult task. Therefore, it could not be solved by means of conventional design methods, which are used for design of uncomplicated technical equipment. One of the possible ways of solving this problem is to provide appropriate tools, which allow us to take the operator's safety into account during a design process, especially at its early stages. A computer-aided system supporting design of safe ship power plants could be such a tool. This paper deals with developing process of a prototype of the computer-aided system for hazard zone identification in ship power plants

  8. Modelling and developing a decision-making process of hazard zone identification in ship power plants

    Energy Technology Data Exchange (ETDEWEB)

    Podsiadlo, Antoni [Department of Engineering Sciences, Gdynia Maritime University, ul. Morska 83, 81-225 Gdynia (Poland)]. E-mail: topo@am.gdynia.pl; Tarelko, Wieslaw [Department of Engineering Sciences, Gdynia Maritime University, ul. Morska 83, 81-225 Gdynia (Poland)]. E-mail: tar@am.gdynia.pl

    2006-04-15

    The most dangerous places in ships are their power plants. Particularly, they are very unsafe for operators carried out various necessary operation and maintenance activities. For this reason, ship machinery should be designed to ensure the maximum safety for its operators. It is a very difficult task. Therefore, it could not be solved by means of conventional design methods, which are used for design of uncomplicated technical equipment. One of the possible ways of solving this problem is to provide appropriate tools, which allow us to take the operator's safety into account during a design process, especially at its early stages. A computer-aided system supporting design of safe ship power plants could be such a tool. This paper deals with developing process of a prototype of the computer-aided system for hazard zone identification in ship power plants.

  9. Weld defect identification in friction stir welding using power spectral density

    Science.gov (United States)

    Das, Bipul; Pal, Sukhomay; Bag, Swarup

    2018-04-01

    Power spectral density estimates are powerful in extraction of useful information retained in signal. In the current research work classical periodogram and Welch periodogram algorithms are used for the estimation of power spectral density for vertical force signal and transverse force signal acquired during friction stir welding process. The estimated spectral densities reveal notable insight in identification of defects in friction stir welded samples. It was observed that higher spectral density against each process signals is a key indication in identifying the presence of possible internal defects in the welded samples. The developed methodology can offer preliminary information regarding presence of internal defects in friction stir welded samples can be best accepted as first level of safeguard in monitoring the friction stir welding process.

  10. Development of Information Processing and the Network System for the Control and Management of Power Plant

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Eun Hee; Park, Doo Young; Woo, Joo Hee [Korea Electric Power Research Institute, Taejon (Korea, Republic of); Kwon, Wook Hyun; Park, Jeong Woo; Moon, Hong Joo; Moon, Sang Yong [Seoul National University, Seoul (Korea, Republic of)

    1997-12-31

    It is needed to supervise, control and manage the inter operation of the system that is connected together to achieve good operation and high performance of the power plant. Moreover, the interconnection of the power plant is indispensable and they must be managed together. At present the control management systems that are on operation at power plants are composed of various systems from different companies, and the power plants have their own structure, we have much difficulty in managing communication of the systems. So, this study suggests the standard specification of the communication network for power plants. We have developed the network hardware, the 7 layers UCA, the network application software, the gateway between 3 layers UCA and the 7 layers UCA. Finally, we have developed the interface to Infi`90 which is one of the most popularly used system for power plant control, so that PC can be used for the operation of Infi`90. (author). 82 refs., figs.

  11. Development of Information Processing and the Network System for the Control and Management of Power Plant

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Eun Hee; Park, Doo Young; Woo, Joo Hee [Korea Electric Power Research Institute, Taejon (Korea, Republic of); Kwon, Wook Hyun; Park, Jeong Woo; Moon, Hong Joo; Moon, Sang Yong [Seoul National University, Seoul (Korea, Republic of)

    1996-12-31

    It is needed to supervise, control and manage the inter operation of the system that is connected together to achieve good operation and high performance of the power plant. Moreover, the interconnection of the power plant is indispensable and they must be managed together. At present the control management systems that are on operation at power plants are composed of various systems from different companies, and the power plants have their own structure, we have much difficulty in managing communication of the systems. So, this study suggests the standard specification of the communication network for power plants. We have developed the network hardware, the 7 layers UCA, the network application software, the gateway between 3 layers UCA and the 7 layers UCA. Finally, we have developed the interface to Infi`90 which is one of the most popularly used system for power plant control, so that PC can be used for the operation of Infi`90. (author). 82 refs., figs.

  12. Energy-Efficient Optimal Power Allocation in Integrated Wireless Sensor and Cognitive Satellite Terrestrial Networks.

    Science.gov (United States)

    Shi, Shengchao; Li, Guangxia; An, Kang; Gao, Bin; Zheng, Gan

    2017-09-04

    This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach's method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint.

  13. Power consumption analysis of constant bit rate video transmission over 3G networks

    DEFF Research Database (Denmark)

    Ukhanova, Ann; Belyaev, Evgeny; Wang, Le

    2012-01-01

    This paper presents an analysis of the power consumption of video data transmission with constant bit rate over 3G mobile wireless networks. The work includes the description of the radio resource control transition state machine in 3G networks, followed by a detailed power consumption analysis...... and measurements of the radio link power consumption. Based on this description and analysis, we propose our power consumption model. The power model was evaluated on a smartphone Nokia N900, which follows 3GPP Release 5 and 6 supporting HSDPA/HSUPA data bearers. We also propose a method for parameter selection...... for the 3GPP transition state machine that allows to decrease power consumption on a mobile device taking signaling traffic, buffer size and latency restrictions into account. Furthermore, we discuss the gain in power consumption vs. PSNR for transmitted video and show the possibility of performing power...

  14. A novel power efficient location-based cooperative routing with transmission power-upper-limit for wireless sensor networks.

    Science.gov (United States)

    Shi, Juanfei; Calveras, Anna; Cheng, Ye; Liu, Kai

    2013-05-15

    The extensive usage of wireless sensor networks (WSNs) has led to the development of many power- and energy-efficient routing protocols. Cooperative routing in WSNs can improve performance in these types of networks. In this paper we discuss the existing proposals and we propose a routing algorithm for wireless sensor networks called Power Efficient Location-based Cooperative Routing with Transmission Power-upper-limit (PELCR-TP). The algorithm is based on the principle of minimum link power and aims to take advantage of nodes cooperation to make the link work well in WSNs with a low transmission power. In the proposed scheme, with a determined transmission power upper limit, nodes find the most appropriate next nodes and single-relay nodes with the proposed algorithm. Moreover, this proposal subtly avoids non-working nodes, because we add a Bad nodes Avoidance Strategy (BAS). Simulation results show that the proposed algorithm with BAS can significantly improve the performance in reducing the overall link power, enhancing the transmission success rate and decreasing the retransmission rate.

  15. A Novel Power Efficient Location-Based Cooperative Routing with Transmission Power-Upper-Limit for Wireless Sensor Networks

    Science.gov (United States)

    Shi, Juanfei; Calveras, Anna; Cheng, Ye; Liu, Kai

    2013-01-01

    The extensive usage of wireless sensor networks (WSNs) has led to the development of many power- and energy-efficient routing protocols. Cooperative routing in WSNs can improve performance in these types of networks. In this paper we discuss the existing proposals and we propose a routing algorithm for wireless sensor networks called Power Efficient Location-based Cooperative Routing with Transmission Power-upper-limit (PELCR-TP). The algorithm is based on the principle of minimum link power and aims to take advantage of nodes cooperation to make the link work well in WSNs with a low transmission power. In the proposed scheme, with a determined transmission power upper limit, nodes find the most appropriate next nodes and single-relay nodes with the proposed algorithm. Moreover, this proposal subtly avoids non-working nodes, because we add a Bad nodes Avoidance Strategy (BAS). Simulation results show that the proposed algorithm with BAS can significantly improve the performance in reducing the overall link power, enhancing the transmission success rate and decreasing the retransmission rate. PMID:23676625

  16. A study of electrical power network of renewable energies and water desalination research center using power quality phenomena and indices

    International Nuclear Information System (INIS)

    Segayer, Ali Mehemmed

    2008-08-01

    Renewable energies and water distillation research center (REWDRC) is a very strategic research facility and contains many important and critical industrial and electrical loads that must to be operated as a group to fulfill the requirements and the needs of the center in the operation of the main research facility of the center which a 10 MW reactor. Faults on the electrical or the industrial system can occur on many ways such as a malfunction in the questioned system, power quality related problem, or a failure of any of the loads (such as central ventilation or water circulation system or one of the substations) have a great diverse effect on the operation of the main research facility (reactor). In this research common problems due to power quality phenomena were studied, assessed through a assigning some power quality indices to the electrical network of the center so that the operational condition of the REWDRC electrical and industrial network could be evaluated. power quality indices (PQI) were assigned based on results of real time measurements at the points of common coupling of the network (PCC) and the initial power quality survey report. indices analysis was done using three methods which were the normalization method, method of comparing to the limit value and analysis of measurement data time function profile. As a result of this research a recommendation for safe operation against power quality disturbances was pointed out through a continuous monitoring of assigned power quality indices. (Author)

  17. Power-Aware Routing and Network Design with Bundled Links: Solutions and Analysis

    Directory of Open Access Journals (Sweden)

    Rosario G. Garroppo

    2013-01-01

    Full Text Available The paper deeply analyzes a novel network-wide power management problem, called Power-Aware Routing and Network Design with Bundled Links (PARND-BL, which is able to take into account both the relationship between the power consumption and the traffic throughput of the nodes and to power off both the chassis and even the single Physical Interface Card (PIC composing each link. The solutions of the PARND-BL model have been analyzed by taking into account different aspects associated with the actual applicability in real network scenarios: (i the time for obtaining the solution, (ii the deployed network topology and the resulting topology provided by the solution, (iii the power behavior of the network elements, (iv the traffic load, (v the QoS requirement, and (vi the number of paths to route each traffic demand. Among the most interesting and novel results, our analysis shows that the strategy of minimizing the number of powered-on network elements through the traffic consolidation does not always produce power savings, and the solution of this kind of problems, in some cases, can lead to spliting a single traffic demand into a high number of paths.

  18. Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

    Science.gov (United States)

    El-Nagar, Ahmad M

    2018-01-01

    In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Competition at the Wireless Sensor Network MAC Layer: Low Power Probing interfering with X-MAC

    International Nuclear Information System (INIS)

    Zacharias, Sven; Newe, Thomas

    2011-01-01

    Wireless Sensor Networks (WSNs) combine sensors with computer networks and enable very dense, in-situ and live measurements of data over a large area. Since this emerging technology has the potential to be embedded almost everywhere for numberless applications, interference between different networks can become a serious issue. For most WSNs, it is assumed today that the network medium access is non-competitive. On the basis of X-MAC interfered by Low Power Probing, this paper shows the danger and the effects of different sensor networks communicating on a single wireless channel of the 2.4 GHz band, which is used by the IEEE 802.15.4 standard.

  20. Competition at the Wireless Sensor Network MAC Layer: Low Power Probing interfering with X-MAC

    Energy Technology Data Exchange (ETDEWEB)

    Zacharias, Sven; Newe, Thomas, E-mail: Sven.Zacharias@ul.ie [University of Limerick (Ireland)

    2011-08-17

    Wireless Sensor Networks (WSNs) combine sensors with computer networks and enable very dense, in-situ and live measurements of data over a large area. Since this emerging technology has the potential to be embedded almost everywhere for numberless applications, interference between different networks can become a serious issue. For most WSNs, it is assumed today that the network medium access is non-competitive. On the basis of X-MAC interfered by Low Power Probing, this paper shows the danger and the effects of different sensor networks communicating on a single wireless channel of the 2.4 GHz band, which is used by the IEEE 802.15.4 standard.

  1. Competition at the Wireless Sensor Network MAC Layer: Low Power Probing interfering with X-MAC

    Science.gov (United States)

    Zacharias, Sven; Newe, Thomas

    2011-08-01

    Wireless Sensor Networks (WSNs) combine sensors with computer networks and enable very dense, in-situ and live measurements of data over a large area. Since this emerging technology has the potential to be embedded almost everywhere for numberless applications, interference between different networks can become a serious issue. For most WSNs, it is assumed today that the network medium access is non-competitive. On the basis of X-MAC interfered by Low Power Probing, this paper shows the danger and the effects of different sensor networks communicating on a single wireless channel of the 2.4 GHz band, which is used by the IEEE 802.15.4 standard.

  2. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-05-24

    Native Language Identification (NLI) is the task of predicting the native language of an author from their text written in a second language. The idea is to find writing habits that transfer from an author’s native language to their second language. Many approaches to this task have been studied, from simple word frequency analysis, to analyzing grammatical and spelling mistakes to find patterns and traits that are common between different authors of the same native language. This can be a very complex task, depending on the native language and the proficiency of the author’s second language. The most common approach that has seen very good results is based on the usage of n-gram features of words and characters. In this thesis, we attempt to extract lexical, grammatical, and semantic features from the sentences of non-native English essays using neural networks. The training and testing data was obtained from a large corpus of publicly available essays written by authors of several countries around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions of the neural networks, which were then used as feature inputs to a Support Vector Machine, making the final prediction. Results show that Long Short-Term Memory neural network can improve performance over a naive bag of words approach, but with a much smaller feature set. With more fine-tuning of neural network hyperparameters, these results will likely improve significantly.

  3. A novel maximum power point tracking method for PV systems using fuzzy cognitive networks (FCN)

    Energy Technology Data Exchange (ETDEWEB)

    Karlis, A.D. [Electrical Machines Laboratory, Department of Electrical & amp; Computer Engineering, Democritus University of Thrace, V. Sofias 12, 67100 Xanthi (Greece); Kottas, T.L.; Boutalis, Y.S. [Automatic Control Systems Laboratory, Department of Electrical & amp; Computer Engineering, Democritus University of Thrace, V. Sofias 12, 67100 Xanthi (Greece)

    2007-03-15

    Maximum power point trackers (MPPTs) play an important role in photovoltaic (PV) power systems because they maximize the power output from a PV system for a given set of conditions, and therefore maximize the array efficiency. This paper presents a novel MPPT method based on fuzzy cognitive networks (FCN). The new method gives a good maximum power operation of any PV array under different conditions such as changing insolation and temperature. The numerical results show the effectiveness of the proposed algorithm. (author)

  4. Using modular neural networks to monitor accident conditions in nuclear power plants

    International Nuclear Information System (INIS)

    Guo, Z.

    1992-01-01

    Nuclear power plants are very complex systems. The diagnoses of transients or accident conditions is very difficult because a large amount of information, which is often noisy, or intermittent, or even incomplete, need to be processed in real time. To demonstrate their potential application to nuclear power plants, neural networks axe used to monitor the accident scenarios simulated by the training simulator of TVA's Watts Bar Nuclear Power Plant. A self-organization network is used to compress original data to reduce the total number of training patterns. Different accident scenarios are closely related to different key parameters which distinguish one accident scenario from another. Therefore, the accident scenarios can be monitored by a set of small size neural networks, called modular networks, each one of which monitors only one assigned accident scenario, to obtain fast training and recall. Sensitivity analysis is applied to select proper input variables for modular networks

  5. A Power Planning Algorithm Based on RPL for AMI Wireless Sensor Networks.

    Science.gov (United States)

    Miguel, Marcio L F; Jamhour, Edgard; Pellenz, Marcelo E; Penna, Manoel C

    2017-03-25

    The advanced metering infrastructure (AMI) is an architecture for two-way communication between electric, gas and water meters and city utilities. The AMI network is a wireless sensor network that provides communication for metering devices in the neighborhood area of the smart grid. Recently, the applicability of a routing protocol for low-power and lossy networks (RPL) has been considered in AMI networks. Some studies in the literature have pointed out problems with RPL, including sub-optimal path selection and instability. In this paper, we defend the viewpoint that careful planning of the transmission power in wireless RPL networks can significantly reduce the pointed problems. This paper presents a method for planning the transmission power in order to assure that, after convergence, the size of the parent set of the RPL nodes is as close as possible to a predefined size. Another important feature is that all nodes in the parent set offer connectivity through links of similar quality.

  6. Feature Extraction Method for High Impedance Ground Fault Localization in Radial Power Distribution Networks

    DEFF Research Database (Denmark)

    Jensen, Kåre Jean; Munk, Steen M.; Sørensen, John Aasted

    1998-01-01

    A new approach to the localization of high impedance ground faults in compensated radial power distribution networks is presented. The total size of such networks is often very large and a major part of the monitoring of these is carried out manually. The increasing complexity of industrial...... of three phase voltages and currents. The method consists of a feature extractor, based on a grid description of the feeder by impulse responses, and a neural network for ground fault localization. The emphasis of this paper is the feature extractor, and the detection of the time instance of a ground fault...... processes and communication systems lead to demands for improved monitoring of power distribution networks so that the quality of power delivery can be kept at a controlled level. The ground fault localization method for each feeder in a network is based on the centralized frequency broadband measurement...

  7. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Institut Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D J; Pao, Yohhan [Case Western Reserve Univ., Cleveland, OH (United States)

    1991-10-01

    The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).

  8. A Power Balance Aware Wireless Charger Deployment Method for Complete Coverage in Wireless Rechargeable Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tu-Liang Lin

    2016-08-01

    Full Text Available Traditional sensor nodes are usually battery powered, and the limited battery power constrains the overall lifespan of the sensors. Recently, wireless power transmission technology has been applied in wireless sensor networks (WSNs to transmit wireless power from the chargers to the sensor nodes and solve the limited battery power problem. The combination of wireless sensors and wireless chargers forms a new type of network called wireless rechargeable sensor networks (WRSNs. In this research, we focus on how to effectively deploy chargers to maximize the lifespan of a network. In WSNs, the sensor nodes near the sink consume more power than nodes far away from the sink because of frequent data forwarding. This important power unbalanced factor has not been considered, however, in previous charger deployment research. In this research, a power balance aware deployment (PBAD method is proposed to address the power unbalance in WRSNs and to design the charger deployment with maximum charging efficiency. The proposed deployment method is effectively aware of the existence of the sink node that would cause unbalanced power consumption in WRSNs. The simulation results show that the proposed PBAD algorithm performs better than other deployment methods, and fewer chargers are deployed as a result.

  9. Network Condition Based Adaptive Control and its Application to Power Balancing in Electrical Grids

    DEFF Research Database (Denmark)

    Pedersen, Rasmus; Findrik, Mislav; Sloth, Christoffer

    2017-01-01

    To maintain a reliable and stable power grid there must be balance between consumption and production. To achieve power balance in a system with high penetration of distributed renewable resources and flexible assets, these individual system can be coordinated through a control unit to become part...... of the power balancing effort. Such control strategies require communication networks for exchange of control loop information. In this work, we show how a congested communication network can have a dramatic impact on the control performance of such a power balancing controller. To alleviate potential...

  10. An Integrated Design Environment to Evaluate Power/Performance Tradeoffs for Sensor Network Applications

    National Research Council Canada - National Science Library

    Bakshi, Amol

    2003-01-01

    Networks of inexpensive, low-power sensing nodes that can monitor the environment, perform limited processing on the samples, and detect events of interest in a collaborative fashion are fast becoming a reality...

  11. The Effect of Information Access Strategy on Power Consumption and Reliability in Wireless Sensor Network

    DEFF Research Database (Denmark)

    Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee

    2013-01-01

    This paper examines the effect of different information access strategies on power consumption and information reliability, considering the wireless sensor network as the source of information. Basically, the paper explores three different access strategies, namely; reactive, periodic and hybrid...

  12. Survey of wireless sensor network applications from a power utility’s distribution perspective

    CSIR Research Space (South Africa)

    Isaac, SJ

    2011-09-01

    Full Text Available provides an overview of the highlights from a comprehensive survey commissioned by Eskom regarding the feasibility of WSNs on the transmission and distribution network; the aim is to direct power utility investment and research and development effort...

  13. Optimal power allocation of a single transmitter-multiple receivers channel in a cognitive sensor network

    KAUST Repository

    Ayala Solares, Jose Roberto; Rezki, Zouheir; Alouini, Mohamed-Slim

    2012-01-01

    The optimal transmit power of a wireless sensor network with one transmitter and multiple receivers in a cognitive radio environment while satisfying independent peak, independent average, sum of peak and sum of average transmission rate constraints

  14. Joint sensor placement and power rating selection in energy harvesting wireless sensor networks

    KAUST Repository

    Bushnaq, Osama M.; Al-Naffouri, Tareq Y.; Chepuri, Sundeep Prabhakar; Leus, Geert

    2017-01-01

    In this paper, the focus is on optimal sensor placement and power rating selection for parameter estimation in wireless sensor networks (WSNs). We take into account the amount of energy harvested by the sensing nodes, communication link quality

  15. Lessons learned on solar powered wireless sensor network deployments in urban, desert environments

    KAUST Repository

    Dehwah, Ahmad H.; Mousa, Mustafa; Claudel, Christian G.

    2015-01-01

    The successful deployment of a large scale solar powered wireless sensor network in an urban, desert environment is a very complex task. Specific cities of such environments cause a variety of operational problems, ranging from hardware faults

  16. 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.

  17. Downlink power distributions for 2G and 3G mobile communication networks.

    Science.gov (United States)

    Colombi, Davide; Thors, Björn; Persson, Tomas; Wirén, Niklas; Larsson, Lars-Eric; Jonsson, Mikael; Törnevik, Christer

    2013-12-01

    Knowledge of realistic power levels is key when conducting accurate EMF exposure assessments. In this study, downlink output power distributions for radio base stations in 2G and 3G mobile communication networks have been assessed. The distributions were obtained from network measurement data collected from the Operations Support System, which normally is used for network monitoring and management. Significant amounts of data were gathered simultaneously for large sets of radio base stations covering wide geographical areas and different environments. The method was validated with in situ measurements. For the 3G network, the 90th percentile of the averaged output power during high traffic hours was found to be 43 % of the maximum available power. The corresponding number for 2G, with two or more transceivers installed, was 65 % or below.

  18. Down-link power distributions for 2G and 3G mobile communication networks

    International Nuclear Information System (INIS)

    Colombi, D.; Thors, B.; Persson, T.; Wiren, N.; Larsson, L. E.; Jonsson, M.; Toernevik, C.

    2013-01-01

    Knowledge of realistic power levels is key when conducting accurate EMF exposure assessments. In this study, down-link output power distributions for radio base stations in 2G and 3G mobile communication networks have been assessed. The distributions were obtained from network measurement data collected from the Operations Support System, which normally is used for network monitoring and management. Significant amounts of data were gathered simultaneously for large sets of radio base stations covering wide geographical areas and different environments. The method was validated with in situ measurements. For the 3G network, the 90. percentile of the averaged output power during high traffic hours was found to be 43 % of the maximum available power. The corresponding number for 2G, with two or more transceivers installed, was 65 % or below. (authors)

  19. An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks

    Science.gov (United States)

    Zheng, Jing; Lu, Jiren; Peng, Suping; Jiang, Tianqi

    2018-02-01

    The conventional arrival pick-up algorithms cannot avoid the manual modification of the parameters for the simultaneous identification of multiple events under different signal-to-noise ratios (SNRs). Therefore, in order to automatically obtain the arrivals of multiple events with high precision under different SNRs, in this study an algorithm was proposed which had the ability to pick up the arrival of microseismic or acoustic emission events based on deep recurrent neural networks. The arrival identification was performed using two important steps, which included a training phase and a testing phase. The training process was mathematically modelled by deep recurrent neural networks using Long Short-Term Memory architecture. During the testing phase, the learned weights were utilized to identify the arrivals through the microseismic/acoustic emission data sets. The data sets were obtained by rock physics experiments of the acoustic emission. In order to obtain the data sets under different SNRs, this study added random noise to the raw experiments' data sets. The results showed that the outcome of the proposed method was able to attain an above 80 per cent hit-rate at SNR 0 dB, and an approximately 70 per cent hit-rate at SNR -5 dB, with an absolute error in 10 sampling points. These results indicated that the proposed method had high selection precision and robustness.

  20. Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks.

    Science.gov (United States)

    Khan, Faisal Nadeem; Zhong, Kangping; Zhou, Xian; Al-Arashi, Waled Hussein; Yu, Changyuan; Lu, Chao; Lau, Alan Pak Tao

    2017-07-24

    We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

  1. Technical Survey on Applications of Wireless Sensor Networks in Nuclear Power Plants

    International Nuclear Information System (INIS)

    Jiang, Jin; Bari, Ataul; Chen, Dongyi; Hashemian, Hash M.

    2014-01-01

    Even though there is no general consensus on using wireless technologies in nuclear power plants, potential applications of wireless sensor networks within nuclear power plants (NPPs) has been investigated. The topics of interests include potential interaction of wireless sensor networks with the sensitive protection equipment, radiation damage of the electronics on board sensor nodes, optimal placement of relay nodes that collect and forward data in the network, and possible applications, such as radiation dose and level monitoring, and equipment condition monitoring. Several wireless sensor networks have been deployed on site of NPPs on a trial basis to perform these tasks. Different aspects of deployment of such wireless sensor networks in NPPs have also been examined. Industrial standards or guidelines for deployment of WSNs in NPPs are also been considered. This paper examines the state of the art of wireless sensor networks in NPPs

  2. Technical Survey on Applications of Wireless Sensor Networks in Nuclear Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Jin; Bari, Ataul [University of Western Ontario, Ontario (Canada); Chen, Dongyi [University of Electronic Science and Technology of China, Chengdu (China); Hashemian, Hash M. [AMS Technology Center, Knoxville (United States)

    2014-08-15

    Even though there is no general consensus on using wireless technologies in nuclear power plants, potential applications of wireless sensor networks within nuclear power plants (NPPs) has been investigated. The topics of interests include potential interaction of wireless sensor networks with the sensitive protection equipment, radiation damage of the electronics on board sensor nodes, optimal placement of relay nodes that collect and forward data in the network, and possible applications, such as radiation dose and level monitoring, and equipment condition monitoring. Several wireless sensor networks have been deployed on site of NPPs on a trial basis to perform these tasks. Different aspects of deployment of such wireless sensor networks in NPPs have also been examined. Industrial standards or guidelines for deployment of WSNs in NPPs are also been considered. This paper examines the state of the art of wireless sensor networks in NPPs.

  3. Nuclear power plant monitoring method by neural network and its application to actual nuclear reactor

    International Nuclear Information System (INIS)

    Nabeshima, Kunihiko; Suzuki, Katsuo; Shinohara, Yoshikuni; Tuerkcan, E.

    1995-11-01

    In this paper, the anomaly detection method for nuclear power plant monitoring and its program are described by using a neural network approach, which is based on the deviation between measured signals and output signals of neural network model. The neural network used in this study has three layered auto-associative network with 12 input/output, and backpropagation algorithm is adopted for learning. Furthermore, to obtain better dynamical model of the reactor plant, a new learning technique was developed in which the learning process of the present neural network is divided into initial and adaptive learning modes. The test results at the actual nuclear reactor shows that the neural network plant monitoring system is successfull in detecting in real-time the symptom of small anomaly over a wide power range including reactor start-up, shut-down and stationary operation. (author)

  4. Improvement of and Parameter Identification for the Bimodal Time-Varying Modified Kanai-Tajimi Power Spectral Model

    Directory of Open Access Journals (Sweden)

    Huiguo Chen

    2017-01-01

    Full Text Available Based on the Kanai-Tajimi power spectrum filtering method proposed by Du Xiuli et al., a genetic algorithm and a quadratic optimization identification technique are employed to improve the bimodal time-varying modified Kanai-Tajimi power spectral model and the parameter identification method proposed by Vlachos et al. Additionally, a method for modeling time-varying power spectrum parameters for ground motion is proposed. The 8244 Orion and Chi-Chi earthquake accelerograms are selected as examples for time-varying power spectral model parameter identification and ground motion simulations to verify the feasibility and effectiveness of the improved bimodal time-varying modified Kanai-Tajimi power spectral model. The results of this study provide important references for designing ground motion inputs for seismic analyses of major engineering structures.

  5. Identification of Hadronic Tau Lepton Decays at the ATLAS Detector Using Artificial Neural Networks

    CERN Document Server

    AUTHOR|(CDS)2093068; Zuber, Kai

    Tau leptons play an important role in a wide range of physics analyses at the LHC, such as the verification of the Standard Model at the TeV scale or the determination of Higgs boson properties. For the identification of hadronically decaying tau leptons with the ATLAS detector, a sophisticated, multi-variate algorithm is required. This is due to the high production cross section for QCD jets, the dominant background. Artificial neural networks (ANNs) have gained much attention in recent years by winning several pattern recognition contests. In this thesis, a survey of ANNs is given with a focus on developments of the past 20 years. Based on this work, a novel, ANN-based tau identification is presented which is competitive to the current BDT-based approach. The influence of various hyperparameters on the identification is studied and optimized. Both stability and performance are enhanced through formation of ANN ensembles. Additionally, a score-flattening algorithm is presented that is beneficial to physics a...

  6. Multivariate algorithms for initiating event detection and identification in nuclear power plants

    International Nuclear Information System (INIS)

    Wu, Shun-Chi; Chen, Kuang-You; Lin, Ting-Han; Chou, Hwai-Pwu

    2018-01-01

    Highlights: •Multivariate algorithms for NPP initiating event detection and identification. •Recordings from multiple sensors are simultaneously considered for detection. •Both spatial and temporal information is used for event identification. •Untrained event isolation avoids falsely relating an untrained event. •Efficacy of the algorithms is verified with data from the Maanshan NPP simulator. -- Abstract: To prevent escalation of an initiating event into a severe accident, promptly detecting its occurrence and precisely identifying its type are essential. In this study, several multivariate algorithms for initiating event detection and identification are proposed to help maintain safe operations of nuclear power plants (NPPs). By monitoring changes in the NPP sensing variables, an event is detected when the preset thresholds are exceeded. Unlike existing approaches, recordings from sensors of the same type are simultaneously considered for detection, and no subjective reasoning is involved in setting these thresholds. To facilitate efficient event identification, a spatiotemporal feature extractor is proposed. The extracted features consist of the temporal traits used by existing techniques and the spatial signature of an event. Through an F-score-based feature ranking, only those that are most discriminant in classifying the events under consideration will be retained for identification. Moreover, an untrained event isolation scheme is introduced to avoid relating an untrained event to those in the event dataset so that improper recovery actions can be prevented. Results from experiments containing data of 12 event classes and a total of 125 events generated using a Taiwan’s Maanshan NPP simulator are provided to illustrate the efficacy of the proposed algorithms.

  7. Integrated Multimedia Based Intelligent Group Decision Support System for Electrical Power Network

    OpenAIRE

    Ajay Kumar Saxena; S. 0. Bhatnagar; P. K Saxena

    2002-01-01

    Electrical Power Network in recent time requires an intelligent, virtual environment based decision process for the coordination of all its individual elements and the interrelated tasks. Its ultimate goal is to achieve maximum productivity and efficiency through the efficient and effective application of generation, transmission, distribution, pricing and regulatory systems. However, the complexity of electrical power network and the presence of conflicting multiple goals and objectives p...

  8. Verification of failover effects from distributed control system communication networks in digitalized nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Min, Moon Gi; Lee, Jae Ki; Lee, Kwang Hyun; Lee, Dong Il; Lim, Hee Taek [Korea Hydro and Nuclear Power Co., Ltd, Daejeon (Korea, Republic of)

    2017-08-15

    Distributed Control System (DCS) communication networks, which use Fast Ethernet with redundant networks for the transmission of information, have been installed in digitalized nuclear power plants. Normally, failover tests are performed to verify the reliability of redundant networks during design and manufacturing phases; however, systematic integrity tests of DCS networks cannot be fully performed during these phases because all relevant equipment is not installed completely during these two phases. In additions, practical verification tests are insufficient, and there is a need to test the actual failover function of DCS redundant networks in the target environment. The purpose of this study is to verify that the failover functions works correctly in certain abnormal conditions during installation and commissioning phase and identify the influence of network failover on the entire DCS. To quantify the effects of network failover in the DCS, the packets (Protocol Data Units) must be collected and resource usage of the system has to be monitored and analyzed. This study introduces the use of a new methodology for verification of DCS network failover during the installation and commissioning phases. This study is expected to provide insight into verification methodology and the failover effects from DCS redundant networks. It also provides test results of network performance from DCS network failover in digitalized domestic nuclear power plants (NPPs)

  9. Sink-to-Sink Coordination Framework Using RPL: Routing Protocol for Low Power and Lossy Networks

    Directory of Open Access Journals (Sweden)

    Meer M. Khan

    2016-01-01

    Full Text Available RPL (Routing Protocol for low power and Lossy networks is recommended by Internet Engineering Task Force (IETF for IPv6-based LLNs (Low Power and Lossy Networks. RPL uses a proactive routing approach and each node always maintains an active path to the sink node. Sink-to-sink coordination defines syntax and semantics for the exchange of any network defined parameters among sink nodes like network size, traffic load, mobility of a sink, and so forth. The coordination allows sink to learn about the network condition of neighboring sinks. As a result, sinks can make coordinated decision to increase/decrease their network size for optimizing over all network performance in terms of load sharing, increasing network lifetime, and lowering end-to-end latency of communication. Currently, RPL does not provide any coordination framework that can define message exchange between different sink nodes for enhancing the network performance. In this paper, a sink-to-sink coordination framework is proposed which utilizes the periodic route maintenance messages issued by RPL to exchange network status observed at a sink with its neighboring sinks. The proposed framework distributes network load among sink nodes for achieving higher throughputs and longer network’s life time.

  10. Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic.

    Science.gov (United States)

    Gu, Jinghua; Xuan, Jianhua; Riggins, Rebecca B; Chen, Li; Wang, Yue; Clarke, Robert

    2012-08-01

    Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive 'noise' in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context. In this article, we propose to establish a link between the quality of one target gene to represent its regulator and the uncertainty of its expression to represent other target genes. Specifically, an outlier sum statistic was used to measure the aggregated evidence for regulation events between target genes and their corresponding transcription factors. A Gibbs sampling method was then developed to estimate the marginal distribution of the outlier sum statistic, hence, to uncover underlying regulatory relationships. To evaluate the effectiveness of our proposed method, we compared its performance with that of an existing sampling-based method using both simulation data and yeast cell cycle data. The experimental results show that our method consistently outperforms the competing method in different settings of signal-to-noise ratio and network topology, indicating its robustness for biological applications. Finally, we applied our method to breast cancer cell line data and demonstrated its ability to extract biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. The Gibbs sampler MATLAB package is freely available at http://www.cbil.ece.vt.edu/software.htm. xuan@vt.edu Supplementary data are available at Bioinformatics online.

  11. A belief network approach for development of a nuclear power plant diagnosis system

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, I K; Kim, J T; Lee, D Y; Jung, C H; Kim, J Y; Lee, J S; Ham, C S [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1999-12-31

    Belief network (or Bayesian network) based on Bayes` rule in probabilistic theory can be applied to the reasoning of diagnostic system. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences. 6 refs., 3 figs. (Author)

  12. A belief network approach for development of a nuclear power plant diagnosis system

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, I. K.; Kim, J. T.; Lee, D. Y.; Jung, C. H.; Kim, J. Y.; Lee, J. S.; Ham, C. S. [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1998-12-31

    Belief network (or Bayesian network) based on Bayes` rule in probabilistic theory can be applied to the reasoning of diagnostic system. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences. 6 refs., 3 figs. (Author)

  13. Culture, agency and power: Theoretical reflections on informal economic networks and political process

    OpenAIRE

    Meagher, Kate

    2009-01-01

    Do network theory really offer a suitable concept for the theorization of informal processes of economic regulation and institutional change? This working paper challenges both essentialist and skeptical attitudes to networks through an examination of the positive and negative effects of network governance in contemporary societies in a range of regional contexts. The analysis focuses on three broad principles of non-state organization - culture, agency and power - and their role in shaping p...

  14. Research of PV Power Generation MPPT based on GABP Neural Network

    Science.gov (United States)

    Su, Yu; Lin, Xianfu

    2018-05-01

    Photovoltaic power generation has become the main research direction of new energy power generation. But high investment and low efficiency of photovoltaic industry arouse concern in some extent. So maximum power point tracking of photovoltaic power generation has been a popular study point. Due to slow response, oscillation at maximum power point and low precision, the algorithm based on genetic algorithm combined with BP neural network are designed detailedly in this paper. And the modeling and simulation are completed by use of MATLAB/SIMULINK. The results show that the algorithm is effective and the maximum power point can be tracked accurately and quickly.

  15. Power flow modelling in electric networks with renewable energy sources in large areas

    International Nuclear Information System (INIS)

    Buhawa, Z. M.; Dvorsky, E.

    2012-01-01

    In many worlds regions there is a great potential for utilizing home grid connected renewable power generating systems, with capacities of MW thousands. The optimal utilization of these sources is connected with power flow possibilities trough the power network in which they have to be connected. There is necessary to respect the long distances among the electric power sources with great outputs and power consumption and non even distribution of the power sources as well. The article gives the solution possibilities for Libya region under utilization of wind renewable sources in north in shore regions. (Authors)

  16. Real-Time Performance of a Self-Powered Environmental IoT Sensor Network System.

    Science.gov (United States)

    Wu, Fan; Rüdiger, Christoph; Yuce, Mehmet Rasit

    2017-02-01

    Wireless sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more lowpower sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting.

  17. Adiabatic superconducting cells for ultra-low-power artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrey E. Schegolev

    2016-10-01

    Full Text Available We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.

  18. High Frequency Resonance Damping of DFIG based Wind Power System under Weak Network

    DEFF Research Database (Denmark)

    Song, Yipeng; Wang, Xiongfei; Blaabjerg, Frede

    2017-01-01

    When operating in a micro or weak grid which has a relatively large network impedance, the Doubly Fed Induction Generator (DFIG) based wind power generation system is prone to suffer high frequency resonance due to the impedance interaction between DFIG system and the parallel compensated network...

  19. Optimal Power Flow for resistive DC Network : A Port-Hamiltonian approach

    NARCIS (Netherlands)

    Benedito, Ernest; del Puerto-Flores, D.; Doria-Cerezo, A.; Scherpen, Jacquelien M.A.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

    This paper studies the optimal power flow problem for resistive DC networks. The gradient method algorithm is written in a port-Hamiltonian form and the stability of the resulting dynamics is studied. Stability conditions are provided for general cyclic networks and a solution, when these conditions

  20. Network model and short circuit program for the Kennedy Space Center electric power distribution system

    Science.gov (United States)

    1976-01-01

    Assumptions made and techniques used in modeling the power network to the 480 volt level are discussed. Basic computational techniques used in the short circuit program are described along with a flow diagram of the program and operational procedures. Procedures for incorporating network changes are included in this user's manual.